From a29a91867d287ff9bcb705a48d8c81116894a9db Mon Sep 17 00:00:00 2001
From: Zou-Seay <81679448+Zou-Seay@users.noreply.github.com>
Date: Thu, 11 Jun 2026 16:28:00 +0800
Subject: [PATCH] Update project and configurations

---
 .gitignore                                    |    11 +
 ai_assistant_features.md                      |    55 +
 archive/test-ai.js                            |     2 +
 archive/test-ai.mjs                           |     9 +
 docs/DBUS_API.md                              |   473 +
 docs/architecture_overview.html               |   642 +
 docs/bert_integration_analysis.md             |   371 +
 docs/industrial_ai_interaction_plan.md        |  1332 +
 docs/nlu_integration_design.md                |   310 +
 docs/归档(包含调机流程).html                  |   469 +
 intelligent_cabin/.vscode/launch.json         |    44 +
 intelligent_cabin/README.md                   |   161 +
 intelligent_cabin/app/__init__.py             |     1 +
 .../app/__pycache__/__init__.cpython-311.pyc  |   Bin 0 -> 251 bytes
 .../app/__pycache__/__init__.cpython-312.pyc  |   Bin 0 -> 242 bytes
 .../app/__pycache__/__init__.cpython-313.pyc  |   Bin 0 -> 240 bytes
 .../app/__pycache__/main.cpython-311.pyc      |   Bin 0 -> 7932 bytes
 .../app/__pycache__/main.cpython-312.pyc      |   Bin 0 -> 7244 bytes
 .../app/__pycache__/main.cpython-313.pyc      |   Bin 0 -> 6862 bytes
 intelligent_cabin/app/core/__init__.py        |     1 +
 .../core/__pycache__/__init__.cpython-311.pyc |   Bin 0 -> 223 bytes
 .../core/__pycache__/__init__.cpython-312.pyc |   Bin 0 -> 214 bytes
 .../core/__pycache__/__init__.cpython-313.pyc |   Bin 0 -> 212 bytes
 .../__pycache__/bootstrap.cpython-311.pyc     |   Bin 0 -> 13919 bytes
 .../__pycache__/bootstrap.cpython-312.pyc     |   Bin 0 -> 14082 bytes
 .../__pycache__/bootstrap.cpython-313.pyc     |   Bin 0 -> 13458 bytes
 .../core/__pycache__/config.cpython-311.pyc   |   Bin 0 -> 4055 bytes
 .../core/__pycache__/config.cpython-312.pyc   |   Bin 0 -> 3808 bytes
 .../core/__pycache__/config.cpython-313.pyc   |   Bin 0 -> 3704 bytes
 intelligent_cabin/app/core/bootstrap.py       |   323 +
 intelligent_cabin/app/core/config.py          |    61 +
 .../data/bert_intent_eval_independent.jsonl   |    42 +
 ...t_intent_multilabel_eval_independent.jsonl |    37 +
 .../data/bert_intent_multilabel_train.jsonl   |    72 +
 .../app/data/bert_intent_test.jsonl           |    35 +
 .../app/data/bert_intent_train.jsonl          |   118 +
 intelligent_cabin/app/data/intents.json       |   352 +
 .../app/data/joint_nlu_eval.jsonl             |    10 +
 .../app/data/joint_nlu_eval_independent.jsonl |    43 +
 .../app/data/joint_nlu_multilabel_eval.jsonl  |    12 +
 .../app/data/joint_nlu_seed.jsonl             |    27 +
 intelligent_cabin/app/main.py                 |   151 +
 intelligent_cabin/app/plugins/__init__.py     |     1 +
 .../__pycache__/__init__.cpython-311.pyc      |   Bin 0 -> 237 bytes
 .../__pycache__/__init__.cpython-312.pyc      |   Bin 0 -> 228 bytes
 .../__pycache__/__init__.cpython-313.pyc      |   Bin 0 -> 226 bytes
 .../plugins/__pycache__/base.cpython-311.pyc  |   Bin 0 -> 1938 bytes
 .../plugins/__pycache__/base.cpython-312.pyc  |   Bin 0 -> 1673 bytes
 .../plugins/__pycache__/base.cpython-313.pyc  |   Bin 0 -> 1731 bytes
 .../plugins/__pycache__/mock.cpython-311.pyc  |   Bin 0 -> 15003 bytes
 .../plugins/__pycache__/mock.cpython-312.pyc  |   Bin 0 -> 13759 bytes
 .../plugins/__pycache__/mock.cpython-313.pyc  |   Bin 0 -> 13909 bytes
 intelligent_cabin/app/plugins/base.py         |    27 +
 intelligent_cabin/app/plugins/mock.py         |   216 +
 intelligent_cabin/app/schemas/__init__.py     |     1 +
 .../__pycache__/__init__.cpython-311.pyc      |   Bin 0 -> 238 bytes
 .../__pycache__/__init__.cpython-312.pyc      |   Bin 0 -> 229 bytes
 .../__pycache__/__init__.cpython-313.pyc      |   Bin 0 -> 227 bytes
 .../schemas/__pycache__/chat.cpython-311.pyc  |   Bin 0 -> 2658 bytes
 .../schemas/__pycache__/chat.cpython-312.pyc  |   Bin 0 -> 2398 bytes
 .../schemas/__pycache__/chat.cpython-313.pyc  |   Bin 0 -> 2301 bytes
 .../__pycache__/configuration.cpython-311.pyc |   Bin 0 -> 8184 bytes
 .../__pycache__/configuration.cpython-312.pyc |   Bin 0 -> 7793 bytes
 .../__pycache__/configuration.cpython-313.pyc |   Bin 0 -> 7028 bytes
 .../schemas/__pycache__/debug.cpython-311.pyc |   Bin 0 -> 2792 bytes
 .../schemas/__pycache__/debug.cpython-312.pyc |   Bin 0 -> 2297 bytes
 .../schemas/__pycache__/debug.cpython-313.pyc |   Bin 0 -> 2379 bytes
 .../schemas/__pycache__/demo.cpython-311.pyc  |   Bin 0 -> 1390 bytes
 .../schemas/__pycache__/demo.cpython-312.pyc  |   Bin 0 -> 1153 bytes
 .../schemas/__pycache__/demo.cpython-313.pyc  |   Bin 0 -> 1231 bytes
 .../__pycache__/intent.cpython-311.pyc        |   Bin 0 -> 1205 bytes
 .../__pycache__/intent.cpython-312.pyc        |   Bin 0 -> 988 bytes
 .../__pycache__/intent.cpython-313.pyc        |   Bin 0 -> 1020 bytes
 .../__pycache__/workflow.cpython-311.pyc      |   Bin 0 -> 2732 bytes
 .../__pycache__/workflow.cpython-312.pyc      |   Bin 0 -> 2269 bytes
 .../__pycache__/workflow.cpython-313.pyc      |   Bin 0 -> 2339 bytes
 intelligent_cabin/app/schemas/chat.py         |    46 +
 .../app/schemas/configuration.py              |   135 +
 intelligent_cabin/app/schemas/debug.py        |    42 +
 intelligent_cabin/app/schemas/demo.py         |    20 +
 intelligent_cabin/app/schemas/intent.py       |    16 +
 intelligent_cabin/app/schemas/workflow.py     |    38 +
 intelligent_cabin/app/services/__init__.py    |     1 +
 .../__pycache__/__init__.cpython-311.pyc      |   Bin 0 -> 262 bytes
 .../__pycache__/__init__.cpython-312.pyc      |   Bin 0 -> 253 bytes
 .../__pycache__/__init__.cpython-313.pyc      |   Bin 0 -> 251 bytes
 .../__pycache__/agent_service.cpython-311.pyc |   Bin 0 -> 66814 bytes
 .../__pycache__/agent_service.cpython-312.pyc |   Bin 0 -> 66986 bytes
 .../__pycache__/agent_service.cpython-313.pyc |   Bin 0 -> 63599 bytes
 .../__pycache__/classifier.cpython-311.pyc    |   Bin 0 -> 28453 bytes
 .../__pycache__/classifier.cpython-312.pyc    |   Bin 0 -> 24420 bytes
 .../__pycache__/classifier.cpython-313.pyc    |   Bin 0 -> 25034 bytes
 .../__pycache__/config_loader.cpython-311.pyc |   Bin 0 -> 10615 bytes
 .../__pycache__/config_loader.cpython-312.pyc |   Bin 0 -> 10705 bytes
 .../__pycache__/config_loader.cpython-313.pyc |   Bin 0 -> 9911 bytes
 .../__pycache__/dialog_act.cpython-311.pyc    |   Bin 0 -> 2343 bytes
 .../__pycache__/dialog_act.cpython-312.pyc    |   Bin 0 -> 2815 bytes
 .../__pycache__/dialog_act.cpython-313.pyc    |   Bin 0 -> 2180 bytes
 .../__pycache__/dialog_rules.cpython-311.pyc  |   Bin 0 -> 3558 bytes
 .../__pycache__/dialog_rules.cpython-312.pyc  |   Bin 0 -> 3155 bytes
 .../__pycache__/dialog_rules.cpython-313.pyc  |   Bin 0 -> 3233 bytes
 .../intent_registry.cpython-311.pyc           |   Bin 0 -> 3007 bytes
 .../intent_registry.cpython-312.pyc           |   Bin 0 -> 2418 bytes
 .../intent_registry.cpython-313.pyc           |   Bin 0 -> 2512 bytes
 .../__pycache__/joint_nlu.cpython-311.pyc     |   Bin 0 -> 23829 bytes
 .../__pycache__/joint_nlu.cpython-312.pyc     |   Bin 0 -> 22192 bytes
 .../__pycache__/joint_nlu.cpython-313.pyc     |   Bin 0 -> 21263 bytes
 .../__pycache__/knowledge_llm.cpython-312.pyc |   Bin 0 -> 10786 bytes
 .../knowledge_store.cpython-312.pyc           |   Bin 0 -> 7650 bytes
 .../multi_intent_detector.cpython-311.pyc     |   Bin 0 -> 12491 bytes
 .../multi_intent_detector.cpython-312.pyc     |   Bin 0 -> 10654 bytes
 .../multi_intent_detector.cpython-313.pyc     |   Bin 0 -> 10894 bytes
 .../__pycache__/planner.cpython-311.pyc       |   Bin 0 -> 64022 bytes
 .../__pycache__/planner.cpython-312.pyc       |   Bin 0 -> 55836 bytes
 .../__pycache__/planner.cpython-313.pyc       |   Bin 0 -> 57125 bytes
 .../response_policy.cpython-311.pyc           |   Bin 0 -> 19819 bytes
 .../response_policy.cpython-312.pyc           |   Bin 0 -> 17523 bytes
 .../response_policy.cpython-313.pyc           |   Bin 0 -> 18094 bytes
 .../rewrite_engine.cpython-311.pyc            |   Bin 0 -> 6055 bytes
 .../rewrite_engine.cpython-312.pyc            |   Bin 0 -> 5979 bytes
 .../rewrite_engine.cpython-313.pyc            |   Bin 0 -> 5607 bytes
 .../__pycache__/router.cpython-311.pyc        |   Bin 0 -> 20312 bytes
 .../__pycache__/router.cpython-312.pyc        |   Bin 0 -> 18031 bytes
 .../__pycache__/router.cpython-313.pyc        |   Bin 0 -> 18732 bytes
 .../__pycache__/session_store.cpython-311.pyc |   Bin 0 -> 7539 bytes
 .../__pycache__/session_store.cpython-312.pyc |   Bin 0 -> 6654 bytes
 .../__pycache__/session_store.cpython-313.pyc |   Bin 0 -> 6776 bytes
 .../__pycache__/social.cpython-311.pyc        |   Bin 0 -> 13847 bytes
 .../__pycache__/social.cpython-312.pyc        |   Bin 0 -> 12102 bytes
 .../__pycache__/social.cpython-313.pyc        |   Bin 0 -> 12360 bytes
 .../app/services/agent_service.py             |  1305 +
 intelligent_cabin/app/services/classifier.py  |   600 +
 .../app/services/config_loader.py             |   173 +
 intelligent_cabin/app/services/dialog_act.py  |    51 +
 .../app/services/dialog_rules.py              |    62 +
 .../app/services/intent_registry.py           |    29 +
 intelligent_cabin/app/services/joint_nlu.py   |   430 +
 .../app/services/knowledge_llm.py             |   238 +
 .../app/services/knowledge_store.py           |   152 +
 .../app/services/multi_intent_detector.py     |   219 +
 intelligent_cabin/app/services/planner.py     |  1347 +
 .../app/services/response_policy.py           |   299 +
 .../app/services/rewrite_engine.py            |   108 +
 intelligent_cabin/app/services/router.py      |   407 +
 .../app/services/session_store.py             |   125 +
 intelligent_cabin/app/services/social.py      |   250 +
 intelligent_cabin/archive/demo/demo.html      |  2254 ++
 .../archive/docs/current_system_flow.md       |  1722 ++
 intelligent_cabin/archive/docs/design.md      |  2104 ++
 .../archive/docs/solution_review.md           |   467 +
 .../patents/202510261979_CN120089140A.pdf     |   Bin 0 -> 1642936 bytes
 .../archive/patents/CN114299931B.pdf          |   Bin 0 -> 1047415 bytes
 .../archive/patents/CN115394300A.pdf          |   Bin 0 -> 747898 bytes
 .../texts/202510261979_CN120089140A.txt       |  1195 +
 .../archive/patents/texts/CN114299931B.txt    |  1095 +
 .../archive/patents/texts/CN115394300A.txt    |  1814 ++
 .../archive/reports/bert_local_test_report.md |    65 +
 .../reports/bert_local_test_result.json       |   426 +
 .../bert_multi_intent_independent_report.md   |    47 +
 .../bert_multi_intent_independent_result.json |  2582 ++
 .../reports/joint_nlu_independent_report.md   |    40 +
 .../reports/joint_nlu_independent_result.json |  1706 ++
 ...val_joint_bert_multi_intent_independent.py |    92 +
 .../archive/scripts/eval_joint_bert_nlu.py    |    59 +
 .../scripts/eval_joint_nlu_independent.py     |   275 +
 .../archive/scripts/eval_local_bert_intent.py |   231 +
 .../scripts/eval_local_bert_multi_intent.py   |   123 +
 ...val_local_bert_multi_intent_independent.py |   247 +
 .../archive/scripts/test_local_bert_intent.py |    97 +
 .../archive/scripts/train_joint_bert_nlu.py   |   500 +
 .../scripts/train_local_bert_intent.py        |   684 +
 .../scripts/train_local_bert_multi_intent.py  |   415 +
 .../archive/tests/test_agent_cloud_route.py   |   132 +
 intelligent_cabin/archive/tests/test_bert.py  |   235 +
 .../archive/tests/test_chat_stream.py         |   109 +
 .../archive/tests/test_config_loader.py       |    90 +
 ...st_dialog_continuation_and_multi_intent.py |   202 +
 .../tests/test_intent_coverage_and_stop.py    |   149 +
 .../tests/test_joint_nlu_integration.py       |    93 +
 .../tests/test_multi_intent_detector.py       |   144 +
 .../archive/tests/test_router_decisions.py    |   195 +
 .../archive/tests/test_social_chat.py         |   180 +
 .../archive/tests/test_workflow_templates.py  |   184 +
 intelligent_cabin/config/actions.yml          |    38 +
 intelligent_cabin/config/context_rewrite.yml  |    76 +
 intelligent_cabin/config/dialog_acts.yml      |    79 +
 intelligent_cabin/config/domain.yml           |    38 +
 intelligent_cabin/config/forms.yml            |    39 +
 .../config/knowledge/false_weld_alarm.md      |    82 +
 intelligent_cabin/config/responses.yml        |    30 +
 intelligent_cabin/config/rules.yml            |    43 +
 intelligent_cabin/config/voice_aliases.yml    |    95 +
 intelligent_cabin/config/workflows.yml        |    34 +
 .../models/local_bert_intent/config.json      |   108 +
 .../models/local_bert_intent/label_map.json   |    38 +
 .../local_bert_intent/special_tokens_map.json |    37 +
 .../models/local_bert_intent/tokenizer.json   | 21292 ++++++++++++++++
 .../local_bert_intent/tokenizer_config.json   |    56 +
 .../local_bert_intent/train_summary.json      |    10 +
 .../models/local_bert_intent/vocab.txt        | 21128 +++++++++++++++
 .../local_bert_multi_intent/config.json       |   104 +
 .../local_bert_multi_intent/label_map.json    |    36 +
 .../special_tokens_map.json                   |    37 +
 .../local_bert_multi_intent/tokenizer.json    | 21292 ++++++++++++++++
 .../tokenizer_config.json                     |    56 +
 .../train_summary.json                        |    56 +
 .../models/local_bert_multi_intent/vocab.txt  | 21128 +++++++++++++++
 .../joint_nlu_config.json                     |    57 +
 .../special_tokens_map.json                   |    37 +
 .../local_joint_bert_nlu/tokenizer.json       | 21292 ++++++++++++++++
 .../tokenizer_config.json                     |    56 +
 .../local_joint_bert_nlu/train_summary.json   |    17 +
 .../models/local_joint_bert_nlu/vocab.txt     | 21128 +++++++++++++++
 intelligent_cabin/pyproject.toml              |    24 +
 intelligent_cabin/requirements.txt            |     8 +
 package-lock.json                             |   844 +-
 package.json                                  |    10 +-
 scripts/generate-voice-aliases.mjs            |    98 +
 src/app/api/chat/route.ts                     |   138 +
 src/app/globals.css                           |    13 +-
 src/app/layout.tsx                            |    20 +-
 src/app/page.tsx                              |   656 +-
 src/app/practice/page.tsx                     |   209 +
 src/components/artifacts/ArtifactRenderer.tsx |   524 +
 .../artifacts/DieMaterialAdjust.tsx           |   235 +
 .../artifacts/DieStatusOverview.tsx           |   275 +
 .../artifacts/KnowledgeArtifact.tsx           |   220 +
 src/lib/artifacts/die-mock-data.ts            |    63 +
 src/lib/artifacts/factory.ts                  |   319 +
 src/lib/artifacts/mock.ts                     |   258 +
 src/lib/artifacts/reducer.ts                  |   169 +
 src/lib/artifacts/types.ts                    |   195 +
 src/lib/nlu/client.ts                         |    66 +
 src/lib/nlu/mock.ts                           |   205 +
 src/lib/nlu/pipeline.ts                       |   189 +
 src/lib/nlu/types.ts                          |   168 +
 src/lib/nlu/voice-aliases.gen.ts              |    90 +
 237 files changed, 164880 insertions(+), 90 deletions(-)
 create mode 100644 ai_assistant_features.md
 create mode 100644 archive/test-ai.js
 create mode 100644 archive/test-ai.mjs
 create mode 100644 docs/DBUS_API.md
 create mode 100644 docs/architecture_overview.html
 create mode 100644 docs/bert_integration_analysis.md
 create mode 100644 docs/industrial_ai_interaction_plan.md
 create mode 100644 docs/nlu_integration_design.md
 create mode 100644 docs/归档(包含调机流程).html
 create mode 100644 intelligent_cabin/.vscode/launch.json
 create mode 100644 intelligent_cabin/README.md
 create mode 100644 intelligent_cabin/app/__init__.py
 create mode 100644 intelligent_cabin/app/__pycache__/__init__.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/__pycache__/__init__.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/__pycache__/__init__.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/__pycache__/main.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/__pycache__/main.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/__pycache__/main.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/core/__init__.py
 create mode 100644 intelligent_cabin/app/core/__pycache__/__init__.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/core/__pycache__/__init__.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/core/__pycache__/__init__.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/core/__pycache__/bootstrap.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/core/__pycache__/bootstrap.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/core/__pycache__/bootstrap.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/core/__pycache__/config.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/core/__pycache__/config.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/core/__pycache__/config.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/core/bootstrap.py
 create mode 100644 intelligent_cabin/app/core/config.py
 create mode 100644 intelligent_cabin/app/data/bert_intent_eval_independent.jsonl
 create mode 100644 intelligent_cabin/app/data/bert_intent_multilabel_eval_independent.jsonl
 create mode 100644 intelligent_cabin/app/data/bert_intent_multilabel_train.jsonl
 create mode 100644 intelligent_cabin/app/data/bert_intent_test.jsonl
 create mode 100644 intelligent_cabin/app/data/bert_intent_train.jsonl
 create mode 100644 intelligent_cabin/app/data/intents.json
 create mode 100644 intelligent_cabin/app/data/joint_nlu_eval.jsonl
 create mode 100644 intelligent_cabin/app/data/joint_nlu_eval_independent.jsonl
 create mode 100644 intelligent_cabin/app/data/joint_nlu_multilabel_eval.jsonl
 create mode 100644 intelligent_cabin/app/data/joint_nlu_seed.jsonl
 create mode 100644 intelligent_cabin/app/main.py
 create mode 100644 intelligent_cabin/app/plugins/__init__.py
 create mode 100644 intelligent_cabin/app/plugins/__pycache__/__init__.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/plugins/__pycache__/__init__.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/plugins/__pycache__/__init__.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/plugins/__pycache__/base.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/plugins/__pycache__/base.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/plugins/__pycache__/base.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/plugins/__pycache__/mock.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/plugins/__pycache__/mock.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/plugins/__pycache__/mock.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/plugins/base.py
 create mode 100644 intelligent_cabin/app/plugins/mock.py
 create mode 100644 intelligent_cabin/app/schemas/__init__.py
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/__init__.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/__init__.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/__init__.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/chat.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/chat.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/chat.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/configuration.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/configuration.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/configuration.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/debug.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/debug.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/debug.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/demo.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/demo.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/demo.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/intent.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/intent.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/intent.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/workflow.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/workflow.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/schemas/__pycache__/workflow.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/schemas/chat.py
 create mode 100644 intelligent_cabin/app/schemas/configuration.py
 create mode 100644 intelligent_cabin/app/schemas/debug.py
 create mode 100644 intelligent_cabin/app/schemas/demo.py
 create mode 100644 intelligent_cabin/app/schemas/intent.py
 create mode 100644 intelligent_cabin/app/schemas/workflow.py
 create mode 100644 intelligent_cabin/app/services/__init__.py
 create mode 100644 intelligent_cabin/app/services/__pycache__/__init__.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/services/__pycache__/__init__.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/services/__pycache__/__init__.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/services/__pycache__/agent_service.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/services/__pycache__/agent_service.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/services/__pycache__/agent_service.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/services/__pycache__/classifier.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/services/__pycache__/classifier.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/services/__pycache__/classifier.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/services/__pycache__/config_loader.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/services/__pycache__/config_loader.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/services/__pycache__/config_loader.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/services/__pycache__/dialog_act.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/services/__pycache__/dialog_act.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/services/__pycache__/dialog_act.cpython-313.pyc
 create mode 100644 intelligent_cabin/app/services/__pycache__/dialog_rules.cpython-311.pyc
 create mode 100644 intelligent_cabin/app/services/__pycache__/dialog_rules.cpython-312.pyc
 create mode 100644 intelligent_cabin/app/services/__pycache__/dialog_rules.cpython-313.pyc
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diff --git a/.gitignore b/.gitignore
index 5ef6a52..952c3c4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -39,3 +39,14 @@ yarn-error.log*
 # typescript
 *.tsbuildinfo
 next-env.d.ts
+
+# models
+*.pt
+*.pth
+*.safetensors
+*.onnx
+*.bin
+*.h5
+*.ckpt
+*.tflite
+*.pb
diff --git a/ai_assistant_features.md b/ai_assistant_features.md
new file mode 100644
index 0000000..440ce00
--- /dev/null
+++ b/ai_assistant_features.md
@@ -0,0 +1,55 @@
+# AI 智能助理系统功能点与架构梳理
+
+## 一、 核心功能点清单
+
+### 1. 权限驱动的 AI 技能系统 (AI Skills & Query)
+* **联系人与群组权限管控**：支持配置 AI 对特定微信联系人和微信群组的读取和回复权限，确保隐私安全。
+* **历史聊天检索 Skill**：AI 具备调用本地工具的能力，可按需查询和检索授权范围内的历史聊天内容，作为精准回复和数据分析的上下文。
+
+### 2. 增强型三方协同对话 (Tripartite Collaborative Chat)
+* **本地 Web 交互终端**：提供用户与 AI 专属对话的本地可视化 Web 界面。
+* **全局上下文静默监听**：AI 能够静默监听“我”与“微信联系人”的实时聊天记录，确保 AI 在被唤醒或介入时，已经掌握完整的对话背景。
+* **灵活的消息代发与审批工作流**：
+  * **模式 A（人审机发）**：我起草或修改合同/文案 -> 发给 AI 检查、润色 -> AI 直接将最终版发送给客户。
+  * **模式 B（机审人发）**：AI 自动起草或修改合同/文案 -> 发给我 -> 我进行人工二次修改和确认 -> 由我发送给客户。
+
+### 3. AI 行为审计与溯源机制 (Audit & Traceability)
+* **全量行为事件日志**：详细记录 AI 的每一次工具调用（Skill 执行）、系统决策及状态流转。
+* **外部交互记录存档**：独立且完整地记录 AI 与所有外部微信联系人的对话明细。
+* **安全评估与熔断**：基于日志进行溯源，用于评估 AI 行为是否符合预期；支持在出现偏差时的人工介入和及时补救（如消息撤回、服务阻断等）。
+
+### 4. 记忆库人工维护 (Memory Base Management)
+* **Hermes Agent 集成**：采用 Hermes 架构理念来管理和沉淀 AI 智能体的状态与经验。
+* **人工介入知识库**：提供记忆库管理机制，允许人工查阅、修正、添加或删除 AI 的长短期记忆，确保 AI 的业务知识和认知能够准确演进。
+
+---
+
+## 二、 本地与远端架构切分方案评估与建议
+
+您目前的方案非常合理，既保证了本地数据处理的执行效率和操作隐私，又充分利用了云端大模型的算力、中心化管控和数据汇总能力。以下是针对该方案的详细拆解与优化建议：
+
+### 1. 本地服务 (Local) - 侧重于“感知、执行、交互”
+* **本地 Web 交互界面**：保障您日常操作的响应速度和最高的数据隐私。
+* **Skill 与工具执行引擎**：由于工具通常需要操作本地文件、调用本地微信客户端 RPA 接口或访问局域网资源，将其配置和执行放在本地是最佳且必须的实践。
+* **微信协议/客户端监听模块**：负责实时抓取微信聊天记录并发送消息，此模块强依赖本地环境。
+* **上下文聚合与组装**：在本地将抓取到的微信聊天记录、本地执行的 Skill 结果打包组装成 Prompt，再向上游远端发起请求。
+
+### 2. 远端服务 (Remote) - 侧重于“大脑、管控、资产”
+* **大模型 API 路由网关**：统一对接外部 LLM，隐藏真实的 API Key，便于统一计费和限流。
+* **AI 数字员工身份管理 (ID System)**：
+  * 统一定义和分配不同数字员工的 AI ID。
+  * 集中配置、版本化管理并下发不同 ID 对应的 **系统提示词 (System Prompt)**。
+* **中心化记忆库 (Hermes Agent Memory)**：
+  * **优势**：放在远端可以实现跨设备、跨终端的同步。未来在其他设备登录，AI 依然拥有同样的上下文和记忆。
+  * 提供统一的后台界面，供人工集中维护和调优记忆。
+* **行为审计中心 (Log Server)**：
+  * 接收来自本地异步上报的日志数据。
+  * 通过 AI ID 进行区分，提供数据可视化面板，方便集中进行溯源、统计、安全审查和模型能力评估。
+
+### 3. 架构切分建议与注意事项
+1. **私有化安全与本地独享模式 (Privacy & Local-exclusive AI)**：由于远端平台部署在局域网内，仅处理工作内容，无需进行额外的数据脱敏。若用户需要处理极度涉密数据或纯个人隐私数据，系统支持配置“本地独享 AI”模式：该模式下，AI 仅在本地运行和可见，不向远端平台上传任何聊天记录或行为日志，实现彻底的本地物理隔离。
+2. **弱网容灾机制**：本地应有简易的日志队列缓存。当局域网网络波动时，日志可先落盘本地，网络恢复后自动补传，保证审计数据的完整性。
+3. **指令与配置下发通道**：如果远端平台修改了 System Prompt 或人工更新了记忆库，需要有一个机制（如 WebSocket 长连接或本地定时轮询）让本地应用能够实时感知并更新，避免配置滞后。
+
+### 总结
+您设计的这套架构是典型的 **“云端大脑 + 边缘四肢（端云结合）”** 架构。远端统筹认知、记忆与合规管控，本地负责敏捷感知（监听微信）、行动（工具调用）和即时交互。整体方案逻辑清晰、健壮，非常有利于后续的持续迭代与多数字员工扩展。
diff --git a/archive/test-ai.js b/archive/test-ai.js
new file mode 100644
index 0000000..4f87180
--- /dev/null
+++ b/archive/test-ai.js
@@ -0,0 +1,2 @@
+const { streamText } = require('ai');
+console.log(Object.keys(streamText({})));
diff --git a/archive/test-ai.mjs b/archive/test-ai.mjs
new file mode 100644
index 0000000..911b518
--- /dev/null
+++ b/archive/test-ai.mjs
@@ -0,0 +1,9 @@
+import { streamText } from 'ai';
+const mockModel = {
+  provider: 'mock',
+  specificationVersion: 'v1',
+  defaultObjectGenerationMode: 'json',
+  doStream: async () => ({ stream: new ReadableStream(), rawCall: { rawPrompt: '', rawSettings: {} } })
+};
+const result = streamText({ model: mockModel, messages: [] });
+console.log(Object.keys(result).filter(k => k.includes('to')));
diff --git a/docs/DBUS_API.md b/docs/DBUS_API.md
new file mode 100644
index 0000000..bcbc416
--- /dev/null
+++ b/docs/DBUS_API.md
@@ -0,0 +1,473 @@
+# 线切割控制系统 DBus 接口说明文档
+
+## 基本信息
+
+| 项目 | 值 |
+|------|-----|
+| **服务名** | `com.wirecut.service` |
+| **对象路径** | `/com/wirecut/control` |
+| **接口名** | `com.wirecut.IControl` |
+| **总线类型** | System Bus（系统总线） |
+| **设计用途** | 供"小龙虾"等外部程序对接线切割控制系统 |
+
+---
+
+## 目录
+
+1. [运动控制接口](#1-运动控制接口)
+2. [加工参数接口](#2-加工参数接口)
+3. [状态查询接口](#3-状态查询接口)
+4. [NC 文件操作接口](#4-nc-文件操作接口)
+5. [工件坐标接口](#5-工件坐标接口)
+6. [放电设置接口](#6-放电设置接口)
+7. [信号（推送通知）](#7-信号推送通知)
+8. [状态码说明](#8-状态码说明)
+9. [命令行调用示例](#9-命令行调用示例)
+10. [注意事项](#10-注意事项)
+
+---
+
+## 1. 运动控制接口
+
+### startRun() - 启动加工
+- **描述**: 启动加工程序，等价于点击界面"运行"按钮
+- **参数**: 无
+- **返回**: 无
+
+```powershell
+# 启动加工
+qdbus com.wirecut.service /com/wirecut/control startRun
+```
+
+### stopRun() - 停止加工
+- **描述**: 停止当前加工，等价于点击"停止"按钮
+- **参数**: 无
+- **返回**: 无
+
+```bash
+qdbus com.wirecut.service /com/wirecut/control stopRun
+```
+
+### pauseRun() - 暂停加工
+- **描述**: 暂停加工（变频），等价于点击"变频暂停"
+- **参数**: 无
+- **返回**: 无
+
+```bash
+qdbus mock_qdbus.py com.wirecut.service /com/wirecut/control pauseRun
+```
+
+### homeAll() - 全轴回零
+- **描述**: 所有轴执行回零操作
+- **参数**: 无
+- **返回**: 无
+
+```bash
+qdbus com.wirecut.service /com/wirecut/control homeAll
+```
+
+### startKongZou() - 开始空走
+- **描述**: 启动空走模式（不放电测试）
+- **参数**: 无
+- **返回**: 无
+
+```bash
+qdbus com.wirecut.service /com/wirecut/control startKongZou
+```
+
+### stopKongZou() - 停止空走
+- **描述**: 停止空走模式
+- **参数**: 无
+- **返回**: 无
+
+```bash
+qdbus com.wirecut.service /com/wirecut/control stopKongZou
+```
+
+---
+
+## 2. 加工参数接口
+
+### setSpeed(speed: int) -> int - 设置加工速度
+- **描述**: 设置加工速度
+- **参数**:
+  - `speed`: 速度值，单位 mm/min，范围 1-9999
+- **返回**: 实际设置的速度值
+
+```bash
+qdbus com.wirecut.service /com/wirecut/control setSpeed 80
+```
+
+### getSpeed() -> int - 获取设定速度
+- **描述**: 获取当前设定的加工速度
+- **参数**: 无
+- **返回**: 设定速度值 (mm/min)
+
+```bash
+qdbus com.wirecut.service /com/wirecut/control getSpeed
+```
+
+### setVoltage(vol: int) - 设置放电电压
+- **描述**: 设置放电电压值
+- **参数**:
+  - `vol`: 电压值
+- **返回**: 无
+
+```bash
+qdbus com.wirecut.service /com/wirecut/control setVoltage 90
+```
+
+### setCurrent(cur: int) - 设置放电电流
+- **描述**: 设置放电电流值
+- **参数**:
+  - `cur`: 电流值
+- **返回**: 无
+
+```bash
+qdbus com.wirecut.service /com/wirecut/control setCurrent 5
+```
+
+---
+
+## 3. 状态查询接口
+
+### getStatus() -> QVariantMap - 获取完整状态
+- **描述**: 获取系统完整状态信息（字典格式）
+- **参数**: 无
+- **返回字段**:
+
+| 字段 | 类型 | 说明 |
+|------|------|------|
+| `running` | int | 运行状态：0=停止 1=运行中 2=暂停 |
+| `is_homed` | int | 是否已回零：0=否 1=是 |
+| `is_homing` | int | 是否正在回零：0=否 1=是 |
+| `pos_x` | double | X轴位置 (mm) |
+| `pos_y` | double | Y轴位置 (mm) |
+| `pos_z` | double | Z轴位置 (mm) |
+| `pos_u` | double | U轴位置 (mm) |
+| `pos_v` | double | V轴位置 (mm) |
+| `vol` | int | 当前电压值 |
+| `cur` | int | 当前电流值 |
+| `daohao` | int | 导号（加工段号） |
+| `speed` | int | 设定速度 (mm/min) |
+
+```bash
+qdbus com.wirecut.service /com/wirecut/control getStatus
+```
+
+### getAxisPos() -> QString - 获取各轴位置
+- **描述**: 轻量级接口，获取各轴当前位置
+- **参数**: 无
+- **返回**: `"x,y,z,u,v"` 格式字符串，保留3位小数，单位 mm
+
+```bash
+qdbus com.wirecut.service /com/wirecut/control getAxisPos
+# 返回示例: "10.500,20.321,0.000,0.123,0.000"
+```
+
+### isRunning() -> bool - 是否正在加工
+- **描述**: 查询是否处于加工状态
+- **参数**: 无
+- **返回**: true=运行中 false=已停止
+
+```bash
+qdbus com.wirecut.service /com/wirecut/control isRunning
+```
+
+### isHomed() -> bool - 是否已回零
+- **描述**: 查询是否已完成回零
+- **参数**: 无
+- **返回**: true=已回零 false=未回零
+
+```bash
+qdbus com.wirecut.service /com/wirecut/control isHomed
+```
+
+---
+
+## 4. NC 文件操作接口
+
+### loadNC(path: QString) -> bool - 加载 NC 文件
+- **描述**: 加载 NC 程序文件
+- **参数**:
+  - `path`: NC 文件的**绝对路径**
+- **返回**: true=成功 false=失败
+
+```bash
+qdbus com.wirecut.service /com/wirecut/control loadNC "/home/user/test.ngc"
+```
+
+### getCurrentNC() -> QString - 获取当前 NC 文件
+- **描述**: 获取当前已加载的 NC 文件路径
+- **参数**: 无
+- **返回**: NC 文件路径
+
+```bash
+qdbus com.wirecut.service /com/wirecut/control getCurrentNC
+```
+
+---
+
+## 5. 工件坐标接口
+
+### clearAxisOffset(axis: int) - 清除单轴工件坐标
+- **描述**: 清除指定轴的工件坐标偏移
+- **参数**:
+  - `axis`: 轴编号：0=X 1=Y 2=Z 3=U 4=V 5=C
+- **返回**: 无
+
+```bash
+# 清除 X 轴工件坐标
+qdbus com.wirecut.service /com/wirecut/control clearAxisOffset 0
+```
+
+### clearAllAxisOffset() - 清除所有轴工件坐标
+- **描述**: 清除全部6个轴的工件坐标偏移
+- **参数**: 无
+- **返回**: 无
+
+```bash
+qdbus com.wirecut.service /com/wirecut/control clearAllAxisOffset
+```
+
+---
+
+## 6. 放电设置接口
+
+### ShowDischargeSetting() - 打开放电设置界面
+- **描述**: 弹出放电设置窗口
+- **参数**: 无
+- **返回**: 无
+
+```bash
+qdbus com.wirecut.service /com/wirecut/control ShowDischargeSetting
+```
+
+### HideDischargeSetting() - 关闭放电设置界面
+- **描述**: 隐藏放电设置窗口
+- **参数**: 无
+- **返回**: 无
+
+```bash
+qdbus com.wirecut.service /com/wirecut/control HideDischargeSetting
+```
+
+### SetWorkpieceId(workpieceId: int) - 设置当前工件号
+- **描述**: 切换到指定工件编号
+- **参数**:
+  - `workpieceId`: 工件编号，范围 0-8
+- **返回**: 无
+
+```bash
+# 切换到工件3
+qdbus com.wirecut.service /com/wirecut/control SetWorkpieceId 3
+```
+
+### SetDischargePara(workpieceId, knifeId, paramType, value) - 设置放电参数
+- **描述**: 设置指定工件、指定刀号的放电参数
+- **参数**:
+  - `workpieceId`: 工件编号 (0-8)
+  - `knifeId`: 刀号 (1-11)
+  - `paramType`: 参数类型字符串：
+    - `"voltage"` / `"放电码"` - 放电码
+    - `"current"` / `"跟踪值"` - 跟踪值
+    - `"servo"` / `"速度"` - 速度上限
+  - `value`: 参数值
+- **返回**: 无
+
+```bash
+# 设置工件0，刀号1，放电码为80
+qdbus com.wirecut.service /com/wirecut/control SetDischargePara 0 1 voltage 80
+```
+
+### GetDischargePara(workpieceId, knifeId, paramType) -> int - 获取放电参数
+- **描述**: 获取指定工件、指定刀号的放电参数值
+- **参数**: 同 SetDischargePara
+- **返回**: 参数值
+
+```bash
+# 获取工件0，刀号1的放电码
+qdbus com.wirecut.service /com/wirecut/control GetDischargePara 0 1 voltage
+```
+
+### CopyToAllWorkpieces() - 复制到所有工件
+- **描述**: 将当前工件的放电参数复制到所有工件
+- **参数**: 无
+- **返回**: 无
+
+```bash
+qdbus com.wirecut.service /com/wirecut/control CopyToAllWorkpieces
+```
+
+---
+
+## 7. 信号（推送通知）
+
+DBus 服务会主动推送以下信号，外部程序可以监听：
+
+### runStateChanged(state: int) - 运行状态变化
+- **触发时机**: 运行状态改变时
+- **参数**:
+  - `state`: 0=停止 / 1=运行 / 2=暂停
+
+### alarmMessage(type: int, msg: QString) - 报警/提示
+- **触发时机**: 系统产生报警或提示时
+- **参数**:
+  - `type`: 0=提示 / 1=警告 / 2=错误
+  - `msg`: 消息内容
+
+### axisPosUpdated(x, y, z, u, v: double) - 轴位置刷新
+- **触发时机**: 每 500ms 周期推送一次
+- **参数**: 各轴当前位置 (mm)
+
+### machiningFinished() - 加工完成
+- **触发时机**: 加工程序执行完成时
+- **参数**: 无
+
+---
+
+## 8. 状态码说明
+
+### 运行状态码 (running 字段)
+
+| 值 | 状态 | 说明 |
+|----|------|------|
+| 0 | 停止 | 未运行或已停止 |
+| 1 | 运行中 | 正在加工 |
+| 2 | 暂停 | 已暂停（变频） |
+
+### 轴编号
+
+| 值 | 轴 | 说明 |
+|----|-----|------|
+| 0 | X | X轴 |
+| 1 | Y | Y轴 |
+| 2 | Z | Z轴 |
+| 3 | U | U轴 |
+| 4 | V | V轴 |
+| 5 | C | C轴 |
+
+---
+
+## 9. 命令行调用示例
+
+### 完整加工流程
+
+```bash
+# 1. 查看当前状态
+qdbus com.wirecut.service /com/wirecut/control getStatus
+
+# 2. 加载 NC 文件（必须绝对路径）
+qdbus com.wirecut.service /com/wirecut/control loadNC "/home/pi/sample.ngc"
+
+# 3. 设置加工速度
+qdbus com.wirecut.service /com/wirecut/control setSpeed 80
+
+# 4. 开始加工
+qdbus com.wirecut.service /com/wirecut/control startRun
+
+# 5. 实时查看轴位置
+qdbus com.wirecut.service /com/wirecut/control getAxisPos
+
+# 6. 暂停加工
+qdbus com.wirecut.service /com/wirecut/control pauseRun
+
+# 7. 停止加工
+qdbus com.wirecut.service /com/wirecut/control stopRun
+```
+
+### 空走测试流程
+
+```bash
+# 1. 回零
+qdbus com.wirecut.service /com/wirecut/control homeAll
+
+# 2. 开始空走（不放电）
+qdbus com.wirecut.service /com/wirecut/control startKongZou
+
+# 3. 停止空走
+qdbus com.wirecut.service /com/wirecut/control stopKongZou
+```
+
+### Python 调用示例（使用 dbus-python）
+
+```python
+import dbus
+
+# 连接系统总线
+bus = dbus.SystemBus()
+
+# 获取服务对象
+obj = bus.get_object('com.wirecut.service', '/com/wirecut/control')
+iface = dbus.Interface(obj, 'com.wirecut.IControl')
+
+# 调用方法
+status = iface.getStatus()
+print("运行状态:", status['running'])
+print("X轴位置:", status['pos_x'])
+
+# 加载 NC 文件
+iface.loadNC("/home/pi/test.ngc")
+
+# 启动加工
+iface.startRun()
+```
+
+---
+
+## 10. 注意事项
+
+### ⚠️ 重要提醒
+
+1. **CNC 软件必须运行**
+   - 所有 DBus 接口只有在 CNC 主程序启动后才能使用
+   - 软件未启动时调用会返回 DBus 错误
+
+2. **NC 文件路径必须为绝对路径**
+   - `loadNC()` 的 path 参数必须使用完整绝对路径
+   - 相对路径会导致加载失败
+
+3. **速度设置范围**
+   - 速度值范围：1-9999 mm/min
+   - 超出范围会被截断或忽略
+
+4. **System Bus 权限**
+   - 服务运行在 System Bus 上，可能需要 root 权限
+   - 普通用户调用可能需要配置 DBus 权限规则
+
+5. **线程安全**
+   - 所有接口调用都是异步队列执行（QueuedConnection）
+   - 调用后不会阻塞，实际执行由主界面事件循环处理
+
+6. **共享内存依赖**
+   - 部分参数（电压、电流、位置）依赖共享内存
+   - 共享内存未就绪时返回 0 或默认值
+
+---
+
+## 附录：DBus 权限配置
+
+如果普通用户无法调用，需要在 `/etc/dbus-1/system.d/` 下添加权限配置文件 `com.wirecut.conf`：
+
+```xml
+&lt;!DOCTYPE busconfig PUBLIC
+ "-//freedesktop//DTD D-BUS Bus Configuration 1.0//EN"
+ "http://www.freedesktop.org/standards/dbus/1.0/busconfig.dtd"&gt;
+&lt;busconfig&gt;
+  &lt;policy user="pi"&gt;
+    &lt;allow own="com.wirecut.service"/&gt;
+    &lt;allow send_destination="com.wirecut.service"/&gt;
+    &lt;allow receive_sender="com.wirecut.service"/&gt;
+  &lt;/policy&gt;
+  &lt;policy context="default"&gt;
+    &lt;allow send_destination="com.wirecut.service"/&gt;
+    &lt;allow receive_sender="com.wirecut.service"/&gt;
+  &lt;/policy&gt;
+&lt;/busconfig&gt;
+```
+
+---
+
+**文档版本**: v1.0  
+**生成日期**: 2026-05-20  
+**对应源码**: `/usr/share/runf/wirecutdbus.h` / `wirecutdbus.cpp`
diff --git a/docs/architecture_overview.html b/docs/architecture_overview.html
new file mode 100644
index 0000000..00b278a
--- /dev/null
+++ b/docs/architecture_overview.html
@@ -0,0 +1,642 @@
+<!doctype html>
+<html lang="zh-CN">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <title>工业 AI 交互画布：流程总览</title>
+    <style>
+      :root {
+        --bg: #f0f2f5;
+        --card: #fff;
+        --ink: #111827;
+        --muted: #667085;
+        --line: #d8dde6;
+        --soft: #f1f4f8;
+        --blue: #2563eb;
+        --green: #059669;
+        --amber: #d97706;
+        --violet: #7c3aed;
+        --dark: #111827;
+      }
+      * { box-sizing: border-box; margin: 0; padding: 0; }
+      body { background: var(--bg); color: var(--ink); font-family: "PingFang SC", "Microsoft YaHei", Arial, sans-serif; font-size: 14px; }
+      button { font: inherit; cursor: pointer; border: none; }
+
+      .page { max-width: 1500px; margin: 0 auto; padding: 24px 24px 60px; }
+
+      /* Hero */
+      .hero { display: flex; align-items: flex-start; justify-content: space-between; gap: 16px; margin-bottom: 18px; flex-wrap: wrap; }
+      h1 { font-size: 22px; font-weight: 800; }
+      .subtitle { color: var(--muted); font-size: 13px; margin-top: 5px; line-height: 1.6; }
+
+      /* Layout */
+      .main-grid { display: grid; grid-template-columns: 252px minmax(0, 1fr); gap: 16px; align-items: start; }
+
+      /* Left panel */
+      .l-panel { background: var(--card); border: 1px solid var(--line); border-radius: 12px; padding: 18px; }
+      .panel-title { font-size: 14px; font-weight: 700; margin-bottom: 12px; color: var(--ink); }
+      .stack { display: grid; gap: 8px; }
+      .layer { display: grid; grid-template-columns: 30px 1fr; gap: 10px; align-items: center; border: 1px solid var(--line); border-left: 4px solid var(--dark); border-radius: 7px; padding: 9px 12px; }
+      .layer[data-c="blue"]   { border-left-color: #2563eb; }
+      .layer[data-c="cyan"]   { border-left-color: #0891b2; }
+      .layer[data-c="violet"] { border-left-color: #7c3aed; }
+      .layer[data-c="green"]  { border-left-color: #059669; }
+      .layer[data-c="amber"]  { border-left-color: #d97706; }
+      .layer[data-c="dark"]   { border-left-color: #111827; }
+      .ln { display: flex; height: 24px; width: 24px; align-items: center; justify-content: center; border-radius: 50%; background: var(--soft); font-weight: 800; font-size: 12px; color: #334155; }
+      .lname { font-size: 12px; font-weight: 700; }
+      .ldesc { font-size: 11px; color: var(--muted); margin-top: 2px; line-height: 1.3; }
+
+      /* Right: flow panel */
+      .r-panel { background: var(--card); border: 1px solid var(--line); border-radius: 12px; overflow: hidden; }
+
+      /* Controls bar */
+      .controls { display: flex; align-items: center; justify-content: space-between; gap: 10px; padding: 14px 20px; border-bottom: 1px solid var(--line); background: #fafbfc; flex-wrap: wrap; }
+      .tg { display: inline-flex; gap: 3px; padding: 3px; border: 1px solid var(--line); border-radius: 9px; background: var(--card); }
+      .tbtn { background: transparent; color: #475467; padding: 8px 14px; border-radius: 6px; font-size: 13px; font-weight: 600; transition: all .15s; white-space: nowrap; }
+      .tbtn.active-flow { background: var(--dark); color: #fff; }
+      .tbtn.active-ux   { background: #4f46e5; color: #fff; }
+      .tbtn.active-tech { background: #0d9488; color: #fff; }
+
+      /* View indicator bar */
+      .view-bar { height: 4px; width: 100%; }
+      .view-bar.ux   { background: linear-gradient(90deg, #818cf8, #a78bfa); }
+      .view-bar.tech { background: linear-gradient(90deg, #0d9488, #059669); }
+
+      /* Flow body */
+      .flow-body { padding: 22px 26px 30px; overflow-x: auto; }
+      .flow-meta { margin-bottom: 18px; }
+      .flow-meta h2 { font-size: 17px; font-weight: 800; display: flex; align-items: center; gap: 8px; flex-wrap: wrap; }
+      .flow-meta p  { font-size: 13px; color: var(--muted); margin-top: 6px; line-height: 1.6; }
+      .badge { display: inline-flex; align-items: center; font-size: 11px; font-weight: 600; padding: 2px 10px; border-radius: 20px; }
+      .badge-a    { background: #dbeafe; color: #1d4ed8; }
+      .badge-b    { background: #d1fae5; color: #065f46; }
+      .badge-ux   { background: #ede9fe; color: #4c1d95; }
+      .badge-tech { background: #ccfbf1; color: #134e4a; }
+      .badge-plan { background: #fef3c7; color: #92400e; border: 1px solid #fcd34d; }
+      .plan-banner { margin: 0 0 16px; padding: 10px 16px; background: #fffbeb; border: 1px solid #fcd34d; border-radius: 8px; font-size: 12px; color: #78350f; line-height: 1.6; }
+
+      /* Legend */
+      .legend { display: flex; gap: 10px; flex-wrap: wrap; padding: 9px 14px; background: var(--soft); border-radius: 8px; margin-bottom: 18px; }
+      .lg { display: flex; align-items: center; gap: 5px; font-size: 11px; color: #475467; }
+      .lg-dot { width: 10px; height: 10px; border-radius: 2px; flex-shrink: 0; }
+
+      /* ═══ FLOWCHART BASE ═══ */
+      .fc { display: flex; flex-direction: column; align-items: center; min-width: 580px; }
+
+      /* Nodes */
+      .nd { border-radius: 8px; padding: 9px 16px; text-align: center; border: 1.5px solid var(--line); background: #fff; }
+      .nd .nt { font-size: 13px; font-weight: 700; line-height: 1.4; }
+      .nd .ns { font-size: 11px; color: var(--muted); margin-top: 3px; line-height: 1.35; }
+
+      /* Node type variants */
+      .nd-start    { background: var(--dark); border: none; border-radius: 22px; min-width: 180px; }
+      .nd-start .nt { color: #fff; font-size: 14px; }
+      .nd-start .ns { color: rgba(255,255,255,0.6); }
+
+      /* UX node types */
+      .nd-show   { background: #f5f3ff; border-color: #a78bfa; } /* 画面显示 */
+      .nd-show .nt { color: #4c1d95; }
+      .nd-do     { background: #ecfdf5; border-color: #34d399; } /* 用户操作 */
+      .nd-do .nt   { color: #065f46; }
+      .nd-end    { background: #f0fdf4; border-color: #86efac; } /* 结果/结束 */
+      .nd-end .nt  { color: #14532d; }
+      .nd-trig   { background: #fff7ed; border-color: #fdba74; } /* 触发条件/场景 */
+      .nd-trig .nt { color: #9a3412; }
+      .nd-tab    { background: #f0f9ff; border-color: #7dd3fc; border-style: dashed; }
+      .nd-tab .nt  { color: #0c4a6e; }
+
+      /* Tech node types */
+      .nd-route  { background: #fffbeb; border-color: #fbbf24; }
+      .nd-route .nt { color: #78350f; }
+      .nd-bert   { background: #f5f3ff; border-color: #c4b5fd; }
+      .nd-bert .nt  { color: #5b21b6; }
+      .nd-llm    { background: #fff7ed; border-color: #fdba74; }
+      .nd-llm .nt   { color: #9a3412; }
+      .nd-tool   { background: #dbeafe; border-color: #60a5fa; }
+      .nd-tool .nt  { color: #1e3a8a; }
+      .nd-kb     { background: #ecfdf5; border-color: #6ee7b7; }
+      .nd-kb .nt    { color: #064e3b; }
+      .nd-art    { background: #faf5ff; border-color: #c4b5fd; border-style: dashed; }
+      .nd-art .nt   { color: #5b21b6; }
+      .nd-snap   { background: #f0fdf4; border-color: #86efac; border-style: dashed; }
+      .nd-snap .nt  { color: #14532d; }
+      .nd-minor  { background: var(--soft); border-color: var(--line); }
+      .nd-minor .nt { color: #475467; }
+
+      /* Tags inside nodes */
+      .tag { display: inline-block; font-size: 9px; font-weight: 700; padding: 2px 6px; border-radius: 3px; margin: 3px 2px 0; }
+      .t-bert { background: #ede9fe; color: #5b21b6; }
+      .t-llm  { background: #fff7ed; color: #9a3412; }
+      .t-rule { background: #dcfce7; color: #166534; }
+      .t-tool { background: #dbeafe; color: #1e40af; }
+      .t-art  { background: #faf5ff; color: #5b21b6; }
+
+      /* Connectors */
+      .dn { width: 2px; background: #c9d0db; height: 20px; flex-shrink: 0; }
+      .dn.tall { height: 30px; }
+      .arr { width: 0; height: 0; border-left: 5px solid transparent; border-right: 5px solid transparent; border-top: 7px solid #b0b9c8; flex-shrink: 0; }
+
+      /* Branch spread link */
+      .slink { position: relative; width: 100%; height: 24px; flex-shrink: 0; }
+
+      /* Branch columns */
+      .brow { display: flex; width: 100%; align-items: flex-start; }
+      .bcol { display: flex; flex-direction: column; align-items: center; flex: 1; padding: 0 7px; min-width: 0; }
+
+      /* Branch labels */
+      .blbl { padding: 4px 11px; border-radius: 20px; font-size: 11px; font-weight: 700; white-space: nowrap; }
+      .bl-b  { background: #dbeafe; color: #1d4ed8; border: 1px solid #93c5fd; }
+      .bl-g  { background: #d1fae5; color: #065f46; border: 1px solid #6ee7b7; }
+      .bl-a  { background: #fef3c7; color: #92400e; border: 1px solid #fcd34d; }
+      .bl-v  { background: #ede9fe; color: #4c1d95; border: 1px solid #a78bfa; }
+      .bl-gr { background: var(--soft); color: #475467; border: 1px solid var(--line); }
+      .bl-hit  { background: #d1fae5; color: #065f46; border: 1px solid #6ee7b7; }
+      .bl-miss { background: #fef3c7; color: #92400e; border: 1px solid #fcd34d; }
+
+      /* Notes */
+      .fnote { font-size: 11px; color: #64748b; margin-top: 4px; text-align: center; line-height: 1.4; }
+      .fopt  { font-size: 11px; color: #6d28d9; margin-top: 4px; text-align: center; font-style: italic; }
+      .fwarn { font-size: 11px; color: #b91c1c; margin-top: 4px; text-align: center; }
+
+      /* Divider */
+      .fc-sep { width: 100%; border: none; border-top: 1px dashed var(--line); margin: 4px 0; }
+
+      @media (max-width: 1080px) { .main-grid { grid-template-columns: 1fr; } }
+    </style>
+  </head>
+  <body>
+    <main class="page">
+      <header class="hero">
+        <div>
+          <h1>工业 AI 交互画布 · 操作流程与技术链路</h1>
+          <p class="subtitle">选择视角，分别查看"用户界面交互路径"或"背后技术判断逻辑"。语音输入经过四阶段前置拦截后再进入 BERT NLU。</p>
+        </div>
+      </header>
+
+      <section class="main-grid">
+        <!-- Left: Architecture Layers -->
+        <aside class="l-panel">
+          <div class="panel-title">主链路层级</div>
+          <div class="stack">
+            <div class="layer" data-c="blue">
+              <div class="ln">1</div>
+              <div><div class="lname">输入层</div><div class="ldesc">文字 / 语音(ASR) / 点击</div></div>
+            </div>
+            <div class="layer" data-c="cyan">
+              <div class="ln">2</div>
+              <div><div class="lname">前置拦截层</div><div class="ldesc">停止词 → UI语音点击 → Slot填写 → BERT</div></div>
+            </div>
+            <div class="layer" data-c="violet">
+              <div class="ln">3</div>
+              <div><div class="lname">路由层</div><div class="ldesc">decision: execute / clarify / route_to_cloud</div></div>
+            </div>
+            <div class="layer" data-c="green">
+              <div class="ln">4</div>
+              <div><div class="lname">编排层</div><div class="ldesc">工具选择 / 知识检索 / Artifact 生成</div></div>
+            </div>
+            <div class="layer" data-c="amber">
+              <div class="ln">5</div>
+              <div><div class="lname">执行层</div><div class="ldesc">PLC / HMI / 知识库调用</div></div>
+            </div>
+            <div class="layer" data-c="dark">
+              <div class="ln">6</div>
+              <div><div class="lname">画布层</div><div class="ldesc">渲染 Artifact + 状态快照</div></div>
+            </div>
+          </div>
+        </aside>
+
+        <!-- Right: Flow Panel -->
+        <section class="r-panel">
+          <div class="controls">
+            <div class="tg" id="flowTabs">
+              <button class="tbtn active-flow" data-flow="A">大流程 A · 工控对话</button>
+              <button class="tbtn" data-flow="B">大流程 B · 调机流程 <span style="font-size:10px;opacity:.7;">🚧 下一版本</span></button>
+            </div>
+            <div class="tg" id="viewTabs">
+              <button class="tbtn active-ux" data-view="ux">👁 交互流程</button>
+              <button class="tbtn" data-view="tech">⚙️ 技术流程</button>
+            </div>
+          </div>
+          <div class="view-bar ux" id="viewBar"></div>
+          <div class="flow-body" id="flowBody"></div>
+        </section>
+      </section>
+    </main>
+
+    <script>
+      // ─── State ──────────────────────────────────────────────────────────────
+      const S = { flow: 'A', view: 'ux' };
+
+      // ─── Helpers ──────────────────────────────────────────────────────────────
+      const dn  = (h = 20) => `<div class="dn" style="height:${h}px"></div>`;
+      const arr = () => `<div class="arr"></div>`;
+      const da  = (h = 20) => dn(h) + arr();
+
+      function nd(cls, title, sub = '', tags = []) {
+        const ts = tags.map(t => `<span class="tag t-${t}">${t.toUpperCase()}</span>`).join('');
+        return `<div class="nd ${cls}"><div class="nt">${title}</div>${sub ? `<div class="ns">${sub}</div>` : ''}${ts ? `<div>${ts}</div>` : ''}</div>`;
+      }
+
+      function blbl(text, cls) {
+        return `<div class="blbl ${cls}">${text}</div>`;
+      }
+
+      // Horizontal branch spread link: n = 2 or 3
+      function slink(n) {
+        if (n === 3) {
+          return `<div class="slink">
+            <div style="position:absolute;top:0;left:16.67%;right:16.67%;height:2px;background:#c9d0db;"></div>
+            <div style="position:absolute;top:0;left:calc(16.67% - 1px);width:2px;height:24px;background:#c9d0db;"></div>
+            <div style="position:absolute;top:0;left:calc(50% - 1px);width:2px;height:24px;background:#c9d0db;"></div>
+            <div style="position:absolute;top:0;right:calc(16.67% - 1px);width:2px;height:24px;background:#c9d0db;"></div>
+          </div>`;
+        }
+        return `<div class="slink">
+          <div style="position:absolute;top:0;left:25%;right:25%;height:2px;background:#c9d0db;"></div>
+          <div style="position:absolute;top:0;left:calc(25% - 1px);width:2px;height:24px;background:#c9d0db;"></div>
+          <div style="position:absolute;top:0;right:calc(25% - 1px);width:2px;height:24px;background:#c9d0db;"></div>
+        </div>`;
+      }
+
+      // ─── Legends ─────────────────────────────────────────────────────────────
+      const legendUX = `<div class="legend">
+        <div class="lg"><div class="lg-dot" style="background:#f5f3ff;border:1px solid #a78bfa;"></div> 界面展示内容</div>
+        <div class="lg"><div class="lg-dot" style="background:#ecfdf5;border:1px solid #34d399;"></div> 用户可做的操作</div>
+        <div class="lg"><div class="lg-dot" style="background:#f0fdf4;border:1px solid #86efac;"></div> 最终结果/结束态</div>
+        <div class="lg"><div class="lg-dot" style="background:#f0f9ff;border:1px solid #7dd3fc;border-style:dashed;"></div> Tab 分页展示（不覆盖主卡）</div>
+      </div>`;
+
+      const legendTech = `<div class="legend">
+        <div class="lg"><div class="lg-dot" style="background:#fffbeb;border:1px solid #fbbf24;"></div> 路由判断节点</div>
+        <div class="lg"><div class="lg-dot" style="background:#f5f3ff;border:1px solid #c4b5fd;"></div> BERT / NLU</div>
+        <div class="lg"><div class="lg-dot" style="background:#fff7ed;border:1px solid #fdba74;"></div> LLM 推断</div>
+        <div class="lg"><div class="lg-dot" style="background:#dbeafe;border:1px solid #60a5fa;"></div> 工具调用</div>
+        <div class="lg"><div class="lg-dot" style="background:#ecfdf5;border:1px solid #6ee7b7;"></div> 知识库检索</div>
+        <div class="lg"><div class="lg-dot" style="background:#faf5ff;border:1px solid #c4b5fd;border-style:dashed;"></div> Artifact 生成</div>
+      </div>`;
+
+      // ═══════════════════════════════════════════════════════════════════════════
+      // FLOW A  ·  UX VIEW
+      // ═══════════════════════════════════════════════════════════════════════════
+      function renderA_UX() {
+        return `
+          <div class="flow-meta">
+            <h2>大流程 A · 普通对话 <span class="badge badge-a">无调机流程</span> <span class="badge badge-ux">👁 交互流程</span></h2>
+            <p>操作员从零开始发起一次输入。界面根据输入内容呈现三种不同的画布组件，操作员按提示进行确认或查阅。</p>
+          </div>
+          ${legendUX}
+          <div class="fc">
+
+            ${nd('nd-start', '👤 操作员输入文字或语音', '当前画布无激活流程')}
+            ${da()}
+
+            ${nd('nd-trig', '系统识别输入类型，分三种场景响应', '用户感知到的是画布出现了不同内容')}
+            ${slink(3)}
+
+            <div class="brow">
+
+              <!-- Branch 1 -->
+              <div class="bcol">
+                ${blbl('场景 1 · 操控设备', 'bl-b')}
+                ${da()}
+                ${nd('nd-show', '画布出现操控确认卡', '参数变更卡 或 设备动画卡')}
+                ${da()}
+                ${nd('nd-do', '操作员选择：确认或取消', '点击按钮 或 输入"确认/取消"')}
+                ${da()}
+                ${nd('nd-end', '画布显示执行结果', '成功 / 失败 / 重试')}
+              </div>
+
+              <!-- Branch 2 -->
+              <div class="bcol">
+                ${blbl('场景 2 · 询问设备问题', 'bl-g')}
+                ${da()}
+                ${nd('nd-show', '画布出现知识教学卡', 'SOP / 报警处理 / 说明书内容')}
+                ${da()}
+                ${nd('nd-do', '操作员查阅内容', '可展开详情、查看步骤')}
+                ${da()}
+                ${nd('nd-end', '内容展示完毕')}
+              </div>
+
+              <!-- Branch 3 -->
+              <div class="bcol">
+                ${blbl('场景 3 · 通用 / 无关', 'bl-gr')}
+                ${da()}
+                ${nd('nd-show', '对话框返回文字回答', '打招呼、天气、闲聊等内容')}
+                ${da()}
+                ${nd('nd-end', '回答完毕', '不影响工业主流程')}
+                <div class="fwarn">⚠ 非核心场景</div>
+              </div>
+
+            </div>
+          </div>
+        `;
+      }
+
+      // ═══════════════════════════════════════════════════════════════════════════
+      // FLOW A  ·  TECH VIEW
+      // ═══════════════════════════════════════════════════════════════════════════
+      function renderA_Tech() {
+        return `
+          <div class="flow-meta">
+            <h2>大流程 A · 普通对话 <span class="badge badge-a">工控对话</span> <span class="badge badge-tech">⚙️ 技术流程</span></h2>
+            <p>语音输入经过四阶段前置拦截（停止词→UI语音点击→Slot填写→BERT），只有前三阶段全部未命中的输入才进入 BERT NLU，再由 decision 字段驱动工具调用、知识检索或 LLM 作答。</p>
+          </div>
+          ${legendTech}
+          <div class="fc">
+
+            ${nd('nd-start', '语音 / 文字输入', '来自 ASR 转录文本 / 直接文字')}
+            ${da()}
+
+            ${nd('nd-route', '阶段 0 · 停止词检测', '命中 cancel 词表 → 直接生成 stop_action，流程终止', ['rule'])}
+            <div class="fnote">词表来自 voice_aliases.yml · cancel_words（静态构建）</div>
+            ${da()}
+
+            ${nd('nd-route', '阶段 1 · UI 可见元素语音点击匹配', '优先级：waiting_confirmation affirm/deny > 当前Artifact按钮 > 全局固定操作', ['rule'])}
+            ${slink(2)}
+
+            <div class="brow">
+              <div class="bcol">
+                ${blbl('命中 · 语音点击', 'bl-hit')}
+                ${da()}
+                ${nd('nd-tool', '生成 ActionEvent', 'actionId / artifactId / sourceText', ['rule'])}
+                ${da()}
+                ${nd('nd-snap', '画布状态机直接响应', '不调用 BERT，不产生新 Artifact')}
+              </div>
+              <div class="bcol">
+                ${blbl('未命中 · 继续', 'bl-miss')}
+                ${da()}
+                ${nd('nd-route', '阶段 1.5 · waiting_slot + inform 检测', 'session.status=waiting_slot AND 输入为数字/数值', ['rule'])}
+                ${slink(2)}
+                <div class="brow" style="width:100%;">
+                  <div class="bcol">
+                    ${blbl('命中 · 填槽', 'bl-hit')}
+                    ${da()}
+                    ${nd('nd-tool', 'fill_slots 接口', '直接补全当前 slot，不走 BERT', ['rule'])}
+                  </div>
+                  <div class="bcol">
+                    ${blbl('未命中 · 进入 BERT', 'bl-miss')}
+                    ${da()}
+                    ${nd('nd-bert', '阶段 2 · BERT NLU（intelligent_cabin）', 'POST /api/v1/agent/chat\n返回 intent_id + decision + slots', ['bert'])}
+                    <div class="fnote">inference 报错直接抛出，不降级</div>
+                  </div>
+                </div>
+              </div>
+            </div>
+
+            ${da(30)}
+            ${nd('nd-route', 'decision 字段路由', 'execute / clarify / route_to_cloud / reject', ['rule'])}
+            ${slink(3)}
+
+            <div class="brow">
+
+              <!-- Branch 1 -->
+              <div class="bcol">
+                ${blbl('execute · 设备控制域', 'bl-b')}
+                ${da()}
+                ${nd('nd-route', 'domain = machine_control\nconfidence_grade = high\nintent_id = wirecut_*')}
+                ${da()}
+                ${nd('nd-tool', '工业工具调用', 'DBus 写参 / 设备控制指令', ['tool'])}
+                ${da()}
+                ${nd('nd-art', '生成 Artifact', 'ParameterChangeArtifact\nDeviceActionArtifact', ['art'])}
+                ${da()}
+                ${nd('nd-snap', '画布渲染 + 等待 ActionEvent')}
+              </div>
+
+              <!-- Branch 2 -->
+              <div class="bcol">
+                ${blbl('route_to_cloud · 知识域', 'bl-g')}
+                ${da()}
+                ${nd('nd-route', 'domain = equipment_knowledge\n或 confidence 偏低')}
+                ${da()}
+                ${nd('nd-llm', 'LLM 语义兜底分析', '提取检索关键词', ['llm'])}
+                ${da()}
+                ${nd('nd-kb', '知识库检索', '说明书 / SOP / 报警手册', ['tool'])}
+                ${da()}
+                ${nd('nd-llm', 'LLM 组织检索结果', '生成教学结构', ['llm'])}
+                ${da()}
+                ${nd('nd-art', '生成 KnowledgeLessonArtifact', '', ['art'])}
+                ${da()}
+                ${nd('nd-snap', '画布渲染')}
+              </div>
+
+              <!-- Branch 3 -->
+              <div class="bcol">
+                ${blbl('reject · smalltalk / fallback', 'bl-gr')}
+                ${da()}
+                ${nd('nd-route', 'domain = smalltalk\n或无法匹配工业 domain')}
+                ${da()}
+                ${nd('nd-llm', 'LLM 直接作答', '', ['llm'])}
+                ${da()}
+                ${nd('nd-minor', '文字回复，不生成 Artifact')}
+                <div class="fnote">不写入 ArtifactStore</div>
+              </div>
+
+            </div>
+          </div>
+        `;
+      }
+
+
+      // ═══════════════════════════════════════════════════════════════════════════
+      // FLOW B  ·  UX VIEW  (🚧 下一版本计划中，暂未实现)
+      // ═══════════════════════════════════════════════════════════════════════════
+      function renderB_UX() {
+        return `
+          <div class="flow-meta">
+            <h2>大流程 B · 调机流程中 <span class="badge badge-b">Guided Procedure 激活</span> <span class="badge badge-ux">👁 交互流程</span> <span class="badge badge-plan">🚧 下一版本 · 计划中</span></h2>
+            <p>操作员确认进入调机向导后，调机流程卡占据画布主位。后续输入优先驱动当前步骤，其他内容以 Tab 或浮层展示，调机卡不被覆盖。</p>
+          </div>
+          <div class="plan-banner">⚠️ 此流程为下一版本设计规划，当前版本暂未实现。此图仅用于交互设计参考。</div>
+          ${legendUX}
+          <div class="fc">
+
+            ${nd('nd-show', '调机向导卡在画布主位展示', '步骤列表 · 当前步骤高亮 · 步骤指令可见')}
+            ${da()}
+
+            ${nd('nd-trig', '操作员输入或点击', '文字 / 语音 / 直接点击步骤按钮')}
+            ${slink(2)}
+
+            <div class="brow">
+
+              <!-- HIT -->
+              <div class="bcol">
+                ${blbl('✅ 场景 1 · 操作当前步骤', 'bl-hit')}
+                ${da()}
+                ${nd('nd-do', '输入"已完成/下一步/确认"或直接点击', '等同于用语音/文字替代手点击')}
+                ${da()}
+                ${nd('nd-show', '当前步骤标记完成，流程卡更新', '进入下一步 · 调机卡不离开主位')}
+                ${da()}
+                ${nd('nd-end', '所有步骤完成，调机流程收尾', '显示操作汇总')}
+              </div>
+
+              <!-- NOT HIT -->
+              <div class="bcol">
+                ${blbl('🔀 场景 2 · 其他内容输入', 'bl-miss')}
+                ${da()}
+                ${nd('nd-trig', '输入与当前步骤无关', '系统识别为新的操控/知识/通用内容')}
+                ${slink(3)}
+
+                <div class="brow" style="width:100%;">
+
+                  <div class="bcol">
+                    ${blbl('操控工具', 'bl-b')}
+                    ${da()}
+                    ${nd('nd-tab', 'Tab 出现工具执行结果', '调机 | 工具结果', [])}
+                    ${da()}
+                    ${nd('nd-do', '操作员查看后可确认')}
+                    <div class="fnote">调机卡保持主位</div>
+                  </div>
+
+                  <div class="bcol">
+                    ${blbl('知识查询', 'bl-g')}
+                    ${da()}
+                    ${nd('nd-tab', 'Tab 出现知识卡', '调机 | 知识参考', [])}
+                    ${da()}
+                    ${nd('nd-do', '操作员查阅后继续流程')}
+                    <div class="fnote">调机卡保持主位</div>
+                  </div>
+
+                  <div class="bcol">
+                    ${blbl('通用提问', 'bl-gr')}
+                    ${da()}
+                    ${nd('nd-show', '临时浮层文字回答', '不占画布主位')}
+                    ${da()}
+                    ${nd('nd-end', '回答完毕自动收起')}
+                    <div class="fnote">调机卡保持主位</div>
+                  </div>
+
+                </div>
+              </div>
+
+            </div>
+          </div>
+        `;
+      }
+
+      // ═══════════════════════════════════════════════════════════════════════════
+      // FLOW B  ·  TECH VIEW  (🚧 下一版本计划中，暂未实现)
+      // ═══════════════════════════════════════════════════════════════════════════
+      function renderB_Tech() {
+        return `
+          <div class="flow-meta">
+            <h2>大流程 B · 调机流程中 <span class="badge badge-b">Guided Procedure 激活</span> <span class="badge badge-tech">⚙️ 技术流程</span> <span class="badge badge-plan">🚧 下一版本 · 计划中</span></h2>
+            <p>GuidedProcedure Artifact 激活后，输入路由优先做 textAliases 规则匹配（类汽车语音点击），命中则直接更新状态机；未命中再走 BERT 新意图识别，结果以 Tab 渲染，不修改主 Artifact 实例。</p>
+          </div>
+          <div class="plan-banner">⚠️ 此流程为下一版本设计规划，当前版本暂未实现。此图仅用于技术设计参考。</div>
+          ${legendTech}
+          <div class="fc">
+
+            ${nd('nd-art', 'GuidedProcedure Artifact 激活', 'currentStepId · steps[] · actions[textAliases]')}
+            ${da()}
+
+            ${nd('nd-start', '文本输入', '来自文字 / ASR / 点击')}
+            ${da()}
+
+            ${nd('nd-route', '路由层：优先匹配当前步骤 textAliases', '规则匹配为主，BERT 置信度辅助确认', ['rule', 'bert'])}
+            ${slink(2)}
+
+            <div class="brow">
+
+              <!-- HIT -->
+              <div class="bcol">
+                ${blbl('命中 · textAliases 匹配成功', 'bl-hit')}
+                ${da()}
+                ${nd('nd-route', '生成 ArtifactActionEvent', 'actionId / source="text" / transcript', ['rule'])}
+                ${da()}
+                ${nd('nd-tool', '执行步骤对应工具动作', '写入测量値 / 推进步骤状态')}
+                ${da()}
+                ${nd('nd-bert', 'Reducer 更新 Artifact 状态', 'currentStepId → nextStepId\nstep.status → "completed"')}
+                ${da()}
+                ${nd('nd-snap', '快照保存（snapshotPolicy: persistent）', '步骤记录存档，支持复盘')}
+              </div>
+
+              <!-- NOT HIT -->
+              <div class="bcol">
+                ${blbl('未命中 · 进入新意图识别', 'bl-miss')}
+                ${da()}
+                ${nd('nd-bert', 'BERT NLU 识别新意图', '在当前调机上下文中重新分类 domain/intent', ['bert'])}
+                ${slink(3)}
+
+                <div class="brow" style="width:100%;">
+
+                  <div class="bcol">
+                    ${blbl('tool_call', 'bl-b')}
+                    ${da()}
+                    ${nd('nd-tool', '工具执行', '不修改主 Artifact', ['tool'])}
+                    ${da()}
+                    ${nd('nd-art', 'Tab 渲染结果', '主 Artifact 不受影响', ['art'])}
+                    <div class="fnote">ArtifactStore 主实例不变</div>
+                  </div>
+
+                  <div class="bcol">
+                    ${blbl('knowledge_query', 'bl-g')}
+                    ${da()}
+                    ${nd('nd-kb', '知识库检索\n+ LLM 组织', '', ['llm'])}
+                    ${da()}
+                    ${nd('nd-art', 'Tab 渲染知识卡', '主 Artifact 不受影响', ['art'])}
+                    <div class="fnote">ArtifactStore 主实例不变</div>
+                  </div>
+
+                  <div class="bcol">
+                    ${blbl('smalltalk', 'bl-gr')}
+                    ${da()}
+                    ${nd('nd-llm', 'LLM 直接作答', '', ['llm'])}
+                    ${da()}
+                    ${nd('nd-minor', '临时浮层，不写入\nArtifactStore')}
+                    <div class="fnote">主 Artifact 完全不受影响</div>
+                  </div>
+
+                </div>
+              </div>
+
+            </div>
+          </div>
+        `;
+      }
+
+      // ─── Render Dispatch ──────────────────────────────────────────────────────
+      const renderers = {
+        A: { ux: renderA_UX, tech: renderA_Tech },
+        B: { ux: renderB_UX, tech: renderB_Tech },
+      };
+
+      function render() {
+        const { flow, view } = S;
+
+        // Flow tabs
+        document.querySelectorAll('#flowTabs .tbtn').forEach(b => {
+          b.classList.toggle('active-flow', b.dataset.flow === flow);
+        });
+
+        // View tabs
+        document.querySelectorAll('#viewTabs .tbtn').forEach(b => {
+          b.classList.remove('active-ux', 'active-tech');
+          if (b.dataset.view === view) {
+            b.classList.add(view === 'ux' ? 'active-ux' : 'active-tech');
+          }
+        });
+
+        // Color bar
+        const bar = document.getElementById('viewBar');
+        bar.className = `view-bar ${view}`;
+
+        // Content
+        document.getElementById('flowBody').innerHTML = renderers[flow][view]();
+      }
+
+      // ─── Events ───────────────────────────────────────────────────────────────
+      document.getElementById('flowTabs').addEventListener('click', e => {
+        const btn = e.target.closest('.tbtn');
+        if (btn && btn.dataset.flow) { S.flow = btn.dataset.flow; render(); }
+      });
+
+      document.getElementById('viewTabs').addEventListener('click', e => {
+        const btn = e.target.closest('.tbtn');
+        if (btn && btn.dataset.view) { S.view = btn.dataset.view; render(); }
+      });
+
+      render();
+    </script>
+  </body>
+</html>
diff --git a/docs/bert_integration_analysis.md b/docs/bert_integration_analysis.md
new file mode 100644
index 0000000..73e53fa
--- /dev/null
+++ b/docs/bert_integration_analysis.md
@@ -0,0 +1,371 @@
+# intelligent_cabin BERT 接入 · 冲突分析与整合方案
+
+> 作者：AI 分析  
+> 日期：2026-05-26  
+> 状态：待你确认
+
+---
+
+## 背景速览
+
+| 侧 | 项目 | 技术栈 | 角色 |
+|---|---|---|---|
+| **Canvas（前端/编排侧）** | ai-canvas / Next.js | TypeScript + React | 画布渲染、Artifact 管理、LLM 编排、工业工具调用（DBus/PLC）|
+| **BERT 服务侧** | intelligent_cabin | Python / FastAPI | 本地 NLU：intent + slot，语音控制场景 |
+
+这两个项目目前是独立的，尚未对接。
+
+---
+
+## 一、冲突点（需要明确决策的地方）
+
+### 冲突 1：Domain 语义完全不匹配 ⚠️ 重要
+
+**问题**：`intelligent_cabin/config/domain.yml` 里的 intent 全部来自车机场景（导航、空调、音乐、车窗、车灯……），与 `DBUS_API.md` 描述的线切割工控场景（电压、电流、速度、NC 加工、空走、回零）**毫无重叠**。
+
+**后果**：如果直接把 BERT 服务接进来，意图识别的类别集合完全覆盖不了工控指令，识别结果几乎全是 unknown/reject。
+
+**需确认**：
+- [ ] 你们会用智能车机的这套预训练模型做迁移学习，还是重新训练工控专属的 BERT 模型？
+- [ ] 还是说这个 BERT 服务只是作为基础推理框架，`domain.yml` 需要被替换成工控版 domain？
+
+---
+
+### 冲突 2：置信度阈值体系 vs. Canvas 路由体系
+
+**Canvas 流程**（参考 `architecture_overview.html`）的技术视图里：
+```
+输入 → BERT NLU → confidence 判断 → [高置信+设备控制域] / [知识域/低置信] / [smalltalk]
+```
+
+**intelligent_cabin 的路由体系**（`router.py`）：
+```
+输入 → Classifier → FusionGrader → 
+  decision = execute / clarify / route_to_cloud / reject
+  阈值：execute_score=0.55, execute_margin=0.18, route_to_cloud=0.75
+```
+
+两套路由各有自己的 confidence 分级，**如果串联，会出现双重过滤**：
+- BERT 服务输出的 `score` 未必能直接映射到 Canvas 的 `confidence ≥ 阈值` 判断
+- Canvas 目前只依赖 BERT 输出的 `domain/intent/slot/confidence`，但 BERT 服务输出的是 `intent_id + score + decision`
+
+**需确认**：
+- [ ] Canvas 侧是否需要直接消费 `decision` 字段（execute/clarify/route_to_cloud/reject），还是只取 `intent_id + score`，Canvas 自己做二次路由？
+
+---
+
+### 冲突 3：`textAliases`（大流程 B）vs. BERT 意图识别的竞争
+
+**Canvas 大流程 B（调机流程中）的路由逻辑**：
+```
+输入 → 优先匹配 textAliases（如"完成"/"下一步"/"确认"）
+     → 未命中 → BERT NLU 重新识别
+```
+
+**intelligent_cabin** 里的 `DialogActEngine`（`dialog_act.py`）也在做相似的事情：
+```python
+affirm: ["确认", "好的", "继续", "可以", "确定"]
+cancel: ["取消", "算了", "不用了", "停止"]
+```
+
+**冲突**：
+- `textAliases` 是每个 Artifact Step 私有的，属于 Canvas 状态机层面的规则匹配
+- `DialogActEngine` 是系统级对话行为分类，优先级/触发顺序未对齐
+- 如果两套都跑，"确认"这个词可能被 `DialogActEngine` 吃掉后不再传给 Canvas 的步骤推进逻辑
+
+**整合建议**：
+- 大流程 B 中，textAliases 匹配应在 BERT 服务之前，Canvas 侧处理，不交给 BERT 服务
+- 把 `dialog_acts.yml` 的 `affirm/deny/cancel/modify` 从 BERT 服务中剥离，改为 Canvas 路由层的前置规则
+- 或者让 BERT 服务的 dialog_act 结果作为辅助 metadata 透传，Canvas 侧自行决策
+
+---
+
+### 冲突 4：slot 语义不兼容
+
+**BERT 服务的 slot 体系**（按车机场景设计）：
+- `temperature`：空调温度（16-30°C）
+- `destination`：导航目的地
+- `order_id`：订单号
+- `song`：歌曲名
+
+**线切割工控的 slot 需求**（从 `DBUS_API.md` 推导）：
+- `speed`：加工速度（mm/min，1-9999）
+- `voltage`：放电码/电压
+- `current`：跟踪值/电流
+- `workpiece_id`：工件编号（0-8）
+- `knife_id`：刀号（1-11）
+- `param_type`：参数类型（voltage/current/servo）
+- `nc_path`：NC 文件绝对路径
+- `axis`：轴编号（0-5，X/Y/Z/U/V/C）
+
+两套 slot 定义**完全不同**，需要重写 `domain.yml` 和 `rewrite_engine.py` 里的工控版本。
+
+---
+
+### 冲突 5：rewrite_engine 的上下文改写逻辑是车机特化的
+
+`rewrite_engine.py` 里写死了：
+```python
+_AC_CONTEXT_INTENTS = {"cabin_set_ac", "cabin_ac_on", "cabin_ac_off", ...}
+_AC_DEFAULT_TEMPERATURE = 24
+_AC_STEP = 2
+_AC_MIN_TEMPERATURE = 16
+_AC_MAX_TEMPERATURE = 30
+```
+
+这些都是车机空调的业务逻辑，工控场景需要换成类似：
+```python
+_PARAM_CONTEXT_INTENTS = {"wirecut_set_speed", "wirecut_set_voltage", "wirecut_set_current"}
+_SPEED_STEP = 5  # mm/min
+_VOLTAGE_STEP = 5  # 放电码
+```
+
+**但这恰恰也是最有价值可以复用的能力**：多轮相对调节（"再快一点"→ 改写为 "速度设为 85mm/min"）在工控场景同样刚需。
+
+---
+
+## 二、可以直接整合的能力（不需要改动或改动很小）
+
+### ✅ 整合点 1：FastAPI 服务直接作为 NLU 微服务接入
+
+Canvas（Next.js 侧）可以通过 HTTP 调用 `POST /api/v1/agent/chat` 或 `POST /api/v1/agent/fill-slots`，作为 Canvas 的 "感知语义层"。
+
+接入位置：Canvas 的"BERT NLU 意图识别"节点（技术流程视图第 2 层）
+
+```
+Canvas 输入 → HTTP POST → intelligent_cabin :8000/api/v1/agent/chat
+          ← {intent_id, score, slots, decision}
+Canvas 路由层根据 intent_id + score 继续分发
+```
+
+### ✅ 整合点 2：分级融合决策逻辑（router.py）可以直接复用
+
+`MultiStageIntentMatcher` 里的 `execute / clarify / route_to_cloud / reject` 四态决策逻辑，与 Canvas 流程中"高置信执行 / 知识域兜底 / smalltalk" 的三路分发思路高度一致，可以直接用。
+
+Canvas 只需要消费 BERT 服务返回的 `decision` 字段，然后：
+- `execute` → Canvas 走工具调用路径（DBus）
+- `clarify` → Canvas 展示澄清确认卡
+- `route_to_cloud` → Canvas 走 LLM 兜底路径
+- `reject` → Canvas 走 smalltalk / fallback
+
+### ✅ 整合点 3：会话状态管理（session_store.py）
+
+`SessionState` 里的 `context_memory` 和 `slots` 持久化机制，可以支持工控场景的多轮短句恢复（比如 "再快一点" → 记住上次速度值）。
+
+### ✅ 整合点 4：多命令拆分（planner.py）
+
+现有的 `sequence workflow` 和 `conditional workflow` 对工控多命令（"先回零，再开始加工"）直接可用。
+
+### ✅ 整合点 5：高风险确认机制（dialog_rules.py）
+
+`requires_confirmation` + `confirmation_required_risk_levels` 这套机制，完全可以映射到 Canvas 流程中"操控确认卡"的场景（Canvas 大流程 A 的场景 1），直接支持危险操作二次确认。
+
+---
+
+## 三、需要新建或重写的工控专属配置
+
+### 3.1 新建 `config/domain_wirecut.yml`
+
+需要把 DBUS_API.md 里的每个接口映射成 intent：
+
+| intent_id | 对应 DBus 方法 | 示例语句 | 需要 slot |
+|---|---|---|---|
+| `wirecut_start_run` | `startRun()` | "开始加工" / "启动" | 无 |
+| `wirecut_stop_run` | `stopRun()` | "停止加工" / "停机" | 无 |
+| `wirecut_pause_run` | `pauseRun()` | "暂停" / "变频暂停" | 无 |
+| `wirecut_home_all` | `homeAll()` | "全轴回零" / "回零" | 无 |
+| `wirecut_start_kongzou` | `startKongZou()` | "开始空走" | 无 |
+| `wirecut_stop_kongzou` | `stopKongZou()` | "停止空走" | 无 |
+| `wirecut_set_speed` | `setSpeed(speed)` | "速度调到80" / "加快一点" | `speed:int` |
+| `wirecut_set_voltage` | `setVoltage(vol)` | "电压设为90" | `voltage:int` |
+| `wirecut_set_current` | `setCurrent(cur)` | "电流设为5" | `current:int` |
+| `wirecut_get_status` | `getStatus()` | "查一下状态" / "当前状态" | 无 |
+| `wirecut_load_nc` | `loadNC(path)` | "加载NC文件" | `nc_path:str` |
+| `wirecut_set_discharge` | `SetDischargePara(...)` | "设置工件0刀1放电码80" | `workpiece_id, knife_id, param_type, value` |
+
+### 3.2 重写 `rewrite_engine.py` 里的工控上下文改写规则
+
+```python
+# 工控版示例
+_SPEED_CONTEXT_INTENTS = {"wirecut_set_speed"}
+_SPEED_STEP = 5          # mm/min 每步调节
+_SPEED_MIN = 1
+_SPEED_MAX = 9999
+
+_VOLTAGE_CONTEXT_INTENTS = {"wirecut_set_voltage"}
+_VOLTAGE_STEP = 5
+
+# 改写规则
+"再快一点"  → "速度设为 {last_speed + 5} mm/min"
+"再慢一点"  → "速度设为 {last_speed - 5} mm/min"
+"电压再高一点" → "电压设为 {last_voltage + 5}"
+```
+
+### 3.3 更新 `dialog_acts.yml` 适配工控确认场景
+
+当前 `dialog_acts.yml` 的 `affirm/deny/cancel` 词表可以保留，但需要补充工控场景的特化语句：
+
+```yaml
+# 工控场景补充
+affirm:
+  + "执行" / "开始" / "运行" / "启动"
+cancel:
+  + "停机" / "急停" / "中止"
+inform:
+  + 纯数字（速度值/电压值/电流值的slot填写）
+```
+
+---
+
+## 四、关于 dialog_act 和 rewrite_engine 能否通过配置文件驱动
+
+**直接回答：当前已经部分支持，但工控场景需要扩展。**
+
+### 4.1 dialog_act 配置化现状
+
+`dialog_act.py` 里的 `DialogActEngine` 是数据驱动的：
+```python
+@dataclass
+class DialogActEngine:
+    patterns: dict[str, tuple[str, ...]] = field(default_factory=...)
+```
+
+`config_loader.py` 里已经实现了从 `dialog_acts.yml` 加载：
+```python
+def _load_dialog_act_engine(self) -> DialogActEngine:
+    ...
+    return DialogActEngine(patterns={item.act_id: tuple(item.phrases) for item in parsed.acts})
+```
+
+**✅ 结论**：`dialog_acts.yml` 已经是配置文件，直接修改 `config/dialog_acts.yml` 就能修改 act 词表，**无需改代码**。
+
+**但是有个缺口**：当前 `dialog_acts.yml` 只支持 `字符串包含` 匹配（`phrase in normalized`），没有支持正则或数值范围匹配。工控场景里 "85" 这样的纯数字（作为速度 slot 的 `inform`）目前是靠 Python 代码里的 `if re.search(r"\d", normalized): return "inform"` 处理的，这个逻辑是硬编码在 `dialog_act.py` 里的，暂时没有配置化。
+
+**建议优化**（详见第五节）。
+
+### 4.2 rewrite_engine 配置化现状
+
+`rewrite_engine.py` 里的上下文改写逻辑**全部硬编码**，没有配置文件支持：
+
+```python
+_AC_CONTEXT_INTENTS = {"cabin_set_ac", ...}  # 硬编码
+_AC_DEFAULT_TEMPERATURE = 24                  # 硬编码
+_AC_STEP = 2                                  # 硬编码
+```
+
+**❌ 问题**：不同设备（线切割 / 激光切割 / 注塑机 / 其他）的参数名、步长、范围都不同，每台设备都要改代码是不合理的。
+
+**建议优化**（详见第五节）。
+
+---
+
+## 五、建议优化：将 rewrite_engine 和 dialog_act 配置化
+
+这是你关心的核心问题，以下是具体方案。
+
+### 5.1 新增 `config/context_rewrite.yml`
+
+```yaml
+# context_rewrite.yml
+param_contexts:
+  - intent_ids: ["wirecut_set_speed"]
+    slot_name: "speed"
+    unit: "mm/min"
+    step: 5
+    min_value: 1
+    max_value: 9999
+    default_value: 80
+    up_phrases: ["再快一点", "加快", "速度调高", "快一点"]
+    down_phrases: ["再慢一点", "减慢", "速度调低", "慢一点"]
+    rewrite_template: "速度设为 {value} mm/min"
+
+  - intent_ids: ["wirecut_set_voltage"]
+    slot_name: "voltage"
+    unit: ""
+    step: 5
+    min_value: 0
+    max_value: 200
+    default_value: 90
+    up_phrases: ["电压高一点", "电压调高"]
+    down_phrases: ["电压低一点", "电压调低"]
+    rewrite_template: "电压设为 {value}"
+
+  - intent_ids: ["wirecut_set_current"]
+    slot_name: "current"
+    unit: ""
+    step: 1
+    min_value: 0
+    max_value: 30
+    default_value: 5
+    up_phrases: ["电流大一点", "跟踪值高一点"]
+    down_phrases: ["电流小一点", "跟踪值低一点"]
+    rewrite_template: "电流设为 {value}"
+```
+
+然后修改 `rewrite_engine.py`，从 `context_rewrite.yml` 驱动逻辑，而不是硬编码。
+
+### 5.2 扩展 `config/dialog_acts.yml` 支持 inform 的数值捕获
+
+```yaml
+# dialog_acts.yml 新增字段
+acts:
+  - act_id: inform
+    phrases: []
+    # 新增：数值模式检测
+    numeric_patterns: ["\\d+"]   # 包含数字就认为是 inform
+```
+
+并在 `dialog_act.py` 里支持 `numeric_patterns` 字段，这样 "85" / "22度" 是否算 inform 就可以通过配置控制。
+
+### 5.3 `Settings`（config.py）里增加工控设备配置项
+
+`app/core/config.py` 里的 `Settings` 类目前通过环境变量驱动，建议增加工控配置路径：
+
+```python
+# 新增到 Settings 类
+context_rewrite_config_path: str = "config/context_rewrite.yml"
+device_domain_config_path: str = "config/domain_wirecut.yml"  # 可按设备切换
+```
+
+这样不同设备（线切割、激光机）只需要切换 `.env` 文件里的 `AGENT_DEVICE_DOMAIN_CONFIG_PATH` 就能换一套 domain + rewrite 配置，**不改代码**。
+
+---
+
+## 六、整合架构建议（整体链路）
+
+```
+语音输入
+    ↓ ASR（前端 / 设备侧）
+文本输入
+    ↓
+Canvas 路由层（Next.js / TypeScript）
+    ├── [大流程 B 激活] textAliases 匹配 → 直接推进 Artifact 步骤（不过 BERT）
+    └── [其他] HTTP → intelligent_cabin :8000
+                            ↓
+                     context rewrite（工控版配置化）
+                            ↓
+                     BERT NLU（工控 domain）
+                            ↓
+                     fusion decision
+                         ├── execute → Canvas 走 DBus 工具调用
+                         ├── clarify → Canvas 渲染 Artifact 确认卡
+                         ├── route_to_cloud → Canvas 走 LLM 兜底 + 知识库
+                         └── reject → Canvas smalltalk / fallback
+```
+
+---
+
+## 七、待你确认的清单
+
+| # | 问题 | 选项 |
+|---|---|---|
+| 1 | BERT 模型是否会重训成工控版，还是继续用车机模型迁移？ | A. 重训工控 BERT  B. 车机→工控迁移  C. 暂时用 mock/keyword |
+| 2 | Canvas 是消费 BERT 的 `decision` 字段，还是只取 `intent_id+score` 自己路由？ | A. 消费 decision  B. 自路由 |
+| 3 | 大流程 B 的 textAliases 匹配，确认在 Canvas 侧做，不经过 BERT 服务？ | A. 是  B. 也经过 BERT 但 BERT 优先级低 |
+| 4 | rewrite_engine 配置化的优先级？现在要做还是等工控 domain 确定后一起做？ | A. 现在就做  B. 等 domain 定了再做 |
+| 5 | dialog_acts.yml 的 `inform` 数值识别是否需要配置化？ | A. 配置化  B. 保持硬编码即可 |
+| 6 | 不同设备部署时，是一个 BERT 服务实例 + 不同 `.env` 配置，还是多个实例？ | A. 单实例多配置  B. 多实例 |
+
+---
+
+*本文档由 Antigravity 生成，请确认后继续推进实现。*
diff --git a/docs/industrial_ai_interaction_plan.md b/docs/industrial_ai_interaction_plan.md
new file mode 100644
index 0000000..5a11b7e
--- /dev/null
+++ b/docs/industrial_ai_interaction_plan.md
@@ -0,0 +1,1332 @@
+# 工业 AI 交互画布方案草案
+
+版本：v0.2  
+目标：把第一阶段产品交互、系统边界和技术路线收敛清楚。本文档默认采用推荐方案，不再把每个决策点作为待选项展开。
+
+## 1. 背景与目标
+
+这个项目不是一个“AI 聊天框”，而是一个面向工业生产现场的 **AI 交互画布**。AI 的回复不应该主要表现为长文字，而应该表现为漂亮、清晰、可交互的组件。
+
+核心目标有三类：
+
+1. **自然语言控制工业能力**  
+   用户先通过文字输入，后续再接入语音，要求 AI 调用工业软件、上位机软件、PLC/HMI 或其他工具能力，例如调参、开机、开灯、开泵、读取状态、执行生产动作。
+
+2. **自然语言查询设备知识**  
+   用户询问设备说明书、调机经验、故障处理、生产注意事项时，AI 不直接堆大段文字，而是生成像教学卡片一样的组件：视频、图片、步骤、重点、展开详情、专业说明、引用来源都分层显示。
+
+3. **引导式调机流程**  
+   AI 能带技师一步步完成调机。流程里既有 AI 自动调用工具完成的步骤，也有需要人工操作并反馈结果的步骤。第一阶段反馈来自鼠标点击和文字输入，后续再接入语音反馈。
+
+当前项目已经有一个很好的雏形：左侧聊天，右侧画布，根据 tool invocation 渲染不同组件。后续把它从“根据工具名写死渲染”升级为 **结构化 Artifact 协议 + React 组件注册表 + 状态机**。
+
+## 2. 产品定位
+
+### 2.1 不做什么
+
+- 不把这个系统做成完整工业软件 GUI 的替代品。
+- 不把 PLC Web HMI 的所有操作搬进 AI 界面。
+- 不让 LLM 随意生成前端代码并直接运行。
+- 第一阶段不实现权限体系、动作分级、ASR 和完整安全审计。
+
+### 2.2 要做什么
+
+- 做工业软件和 HMI 之外的 **AI 辅助交互层**。
+- 对关键动作提供清晰的可视化反馈、确认、进度、结果、失败原因和恢复建议。
+- 对知识查询提供“先简洁、后展开”的教学式体验。
+- 对调机任务提供流程引导、状态保存、点击/文字统一操作，语音操作作为后续扩展。
+
+## 3. 两类工业场景的交互边界
+
+### 3.1 上位机 GUI 场景
+
+设备本身已有上位机软件 GUI。AI 页面只做补充，不重复展示所有 GUI。
+
+适合展示：
+
+- AI 即将执行的动作。
+- 参数变更前后对比。
+- 当前动作的执行前提示。
+- 动作执行进度和结果。
+- 异常时的解释和下一步建议。
+- 调机步骤的引导卡片。
+
+不适合展示：
+
+- 完整工艺界面。
+- 所有实时数据点。
+- 原软件已有的复杂操作面板。
+
+### 3.2 PLC + Web HMI 场景
+
+PLC 是主控，另有 Web HMI 做实操显示。AI 画布重点是交互效果和辅助理解。
+
+适合展示：
+
+- “水泵已启动”“探照灯打开中”“产线准备中”等动画状态。
+- 当前 HMI 页面之外的解释性信息。
+- AI 对当前动作的总结。
+- 面向技师的流程提示、确认卡片、注意事项。
+
+不适合展示：
+
+- 替代 HMI 的核心控制界面。
+- 密集的实时仪表盘。
+- 需要毫秒级闭环控制的操作。
+
+## 4. 总体架构建议
+
+如果只想快速看宏观结构和下一步优化方向，优先看这份 HTML 总览：[`architecture_overview.html`](./architecture_overview.html)。
+
+第一阶段采用下面的分层：
+
+```mermaid
+flowchart LR
+  U["技师 / 操作员"] --> V["文字输入 / 鼠标点击"]
+  V --> R["意图路由器"]
+  R --> A["AI 编排层"]
+  A --> T["工业工具层"]
+  A --> K["知识库 / 说明书 / 视频图片"]
+  A --> S["Artifact 状态管理"]
+  S --> C["前端组件画布"]
+  C --> E["Action Event"]
+  E --> R
+  T --> S
+  K --> S
+```
+
+关键点：
+
+- **AI 编排层**：负责理解用户需求、选择工具、组织知识、决定展示什么组件。
+- **工业工具层**：由你后续实现，负责真正调用工业软件、PLC、HMI、设备接口。
+- **Artifact 状态管理**：保存当前组件、流程步骤、可点击动作、文本别名、状态快照。
+- **前端组件画布**：只渲染受控组件，不运行 LLM 生成的任意代码。
+- **意图路由器**：判断一句输入是在操作当前组件，还是在问新问题，还是要中断流程。
+
+后续语音接入时，ASR 只负责把语音变成文本；BERT NLU 可以放在意图路由器前后，用来提升当前组件操作、新问题、流程控制等意图分类的稳定性。第一阶段不实现 ASR 和 BERT NLU，只保证路由接口可以承接它们。
+
+### 4.1 宏观分层架构图
+
+后续接入语音识别、Python NLU、工业工具和可视化配置后，建议整体分成 7 层：
+
+```mermaid
+flowchart TB
+  subgraph L1["交互输入层"]
+    Mic["麦克风 / 语音流"]
+    TextInput["文字输入"]
+    Click["组件点击"]
+  end
+
+  subgraph L2["感知与语义层"]
+    ASR["ASR 服务\n语音转文本"]
+    Normalize["文本归一化\n单位/数字/同义词"]
+    NLU["Python NLU 服务\nDomain / Intent / Slot"]
+  end
+
+  subgraph L3["会话与路由层（TS）"]
+    Session["Session Context\n当前设备/当前流程/当前 Artifact"]
+    Router["Input Router\n当前组件优先 + NLU + LLM 兜底"]
+  end
+
+  subgraph L4["编排层（TS）"]
+    Orchestrator["AI / Tool Orchestrator\n选择工具、组织结果"]
+    UIInspect["inspect_ui_state\n让 LLM 读取画布摘要"]
+  end
+
+  subgraph L5["能力与配置层"]
+    ToolRegistry["Tool Registry\n工具目录 / 输入输出 Schema"]
+    VizRegistry["Visualization Registry\n工具到组件的绑定配置"]
+    Knowledge["Knowledge Registry\n说明书 / SOP / 视频 / 图片"]
+  end
+
+  subgraph L6["执行层"]
+    ToolGateway["Industrial Tool Gateway\n上位机 / PLC / HMI / Mock 工具"]
+    KnowledgeSearch["知识检索服务"]
+  end
+
+  subgraph L7["展示层"]
+    ArtifactStore["Artifact Store\n状态快照 / 流程实例"]
+    Canvas["React Artifact Canvas\n参数卡 / 动画卡 / 知识卡 / 流程卡"]
+  end
+
+  Mic --> ASR --> Normalize
+  TextInput --> Normalize
+  Click --> Router
+  Normalize --> Router
+  Router --> NLU
+  NLU --> Router
+  Session --> Router
+  Router --> Orchestrator
+  Orchestrator --> ToolRegistry
+  Orchestrator --> VizRegistry
+  Orchestrator --> Knowledge
+  Orchestrator --> ToolGateway
+  Orchestrator --> KnowledgeSearch
+  UIInspect --> ArtifactStore
+  ToolGateway --> ArtifactStore
+  KnowledgeSearch --> ArtifactStore
+  ArtifactStore --> Canvas
+  Canvas --> Click
+```
+
+各层职责：
+
+- **交互输入层**：接收语音、文字、点击，不判断业务含义。
+- **感知与语义层**：ASR 产出文本，Python NLU 产出结构化意图。
+- **会话与路由层**：决定一句话是在操作当前组件，还是开启新任务。
+- **编排层**：根据 NLU/LLM 结果选择工具，生成 artifact。
+- **能力与配置层**：维护工具目录、工具输入输出结构、可视化组件绑定。
+- **执行层**：真正调用工业软件、PLC、HMI、知识库。
+- **展示层**：渲染组件、保存流程状态、接收用户反馈。
+
+## 5. Artifact 组件协议
+
+### 5.1 推荐思路
+
+LLM 不直接返回 React/HTML，而是返回结构化组件数据。前端根据 `type` 从组件注册表中选择固定组件渲染。
+
+示例：
+
+```ts
+type InteractionArtifact = {
+  artifactId: string;
+  type:
+    | "parameter_change"
+    | "device_action"
+    | "production_start"
+    | "knowledge_lesson"
+    | "guided_procedure"
+    | "media_gallery"
+    | "alarm_explanation";
+  title: string;
+  status: "draft" | "waiting_confirmation" | "running" | "success" | "failed" | "paused";
+  props: Record<string, unknown>;
+  actions: ArtifactAction[];
+  llmContext: ArtifactLLMContext;
+  snapshotPolicy: "ephemeral" | "session" | "persistent";
+};
+
+type ArtifactAction = {
+  actionId: string;
+  label: string;
+  kind: "primary" | "secondary" | "danger" | "input" | "toggle" | "choice";
+  textAliases: string[];
+  voiceAliases?: string[];
+  requiresConfirmation?: boolean;
+  disabled?: boolean;
+};
+```
+
+这样做的好处：
+
+- UI 风格稳定，可控。
+- 工业执行边界更清晰。
+- 组件可以持续迭代，不依赖每次 LLM 生成。
+- 点击、文字指令和后续语音可以共用同一个 `actionId`。
+- LLM 可以只看到必要摘要，不必看到复杂 UI 细节。
+
+### 5.2 已确定的组件渲染路线
+
+第一阶段采用 **React 组件注册表 + JSON Artifact 协议**。
+
+具体做法：
+
+- 控制、调参、流程类交互全部使用固定 React 组件。
+- LLM 或后端编排层只产出结构化 JSON，不直接产出 JSX/HTML。
+- 前端根据 `artifact.type` 从组件注册表选择组件。
+- 知识内容中的长说明可以使用受限 Markdown，但外层仍由固定教学组件承载。
+- 暂不做动态远程组件，也不允许 LLM 生成任意 UI 代码。
+
+## 6. 工业控制类交互设计
+
+### 6.1 参数设置组件
+
+典型输入：
+
+- “把主轴转速调到 1800。”
+- “把水泵压力上限改成 0.8MPa。”
+- “温度补偿加 2 度。”
+
+组件应该展示：
+
+- 参数名称。
+- 当前值。
+- 目标值。
+- 单位。
+- 影响范围。
+- 是否需要确认。
+- 执行结果。
+- 可恢复/回滚信息。
+
+建议组件：`ParameterChangeCard`
+
+状态流：
+
+```mermaid
+stateDiagram-v2
+  [*] --> Preview: 解析参数变更
+  Preview --> WaitingConfirm: 展示旧值/新值
+  WaitingConfirm --> Running: 用户确认
+  WaitingConfirm --> Cancelled: 用户取消
+  Running --> Success: 工具执行成功
+  Running --> Failed: 工具执行失败
+  Failed --> WaitingConfirm: 修改后重试
+```
+
+交互细节：
+
+- 第一阶段统一采用“预览 -> 用户确认 -> 工具执行 -> 展示结果”的闭环。
+- 确认可以来自按钮点击，也可以来自文字输入，例如“确认修改”“执行”。
+- 后续接入语音时，语音确认也转换为同一个 `actionId`，不单独做一套逻辑。
+
+### 6.2 具象设备控制组件
+
+典型输入：
+
+- “打开探照灯。”
+- “启动水泵。”
+- “关闭除尘风机。”
+- “开始生产。”
+
+建议组件：
+
+- `DeviceActionCard`：打开灯、泵、阀、风机等单设备动作。
+- `ProductionStartCard`：开机生产、启动工艺、产线准备。
+- `MachineStatusAnimation`：动作动画，例如灯光亮起、水流动、泵叶旋转、设备进入运行态。
+
+展示内容：
+
+- 设备图标和动画。
+- 当前状态：关闭、准备中、启动中、运行、失败。
+- 执行动作。
+- 工具调用进度。
+- 执行前提示。
+- 执行后的状态确认。
+
+控制类动作不一定都需要长文字。界面可以用动画、状态徽标、颜色和短句表达结果。
+
+### 6.3 第一阶段控制执行边界
+
+第一阶段不实现权限体系、动作风险分级和高风险确认策略。控制类能力先按“可演示、可模拟、可替换真实工具”的方式设计。
+
+执行边界：
+
+- Mock 工具阶段：所有控制动作都可以完整演示 UI、状态流和失败处理。
+- 接真实工业工具后：工具层由你负责控制实际可执行范围，本项目只负责把意图、参数、确认和结果组织成清晰交互。
+- 所有控制类组件都保留“预览/确认/执行中/成功/失败”的状态结构，方便未来加权限、动作分级或审计。
+- `ProductionStartCard` 先作为交互样板，不在第一阶段实现复杂生产安全策略。
+
+## 7. 知识查询类交互设计
+
+### 7.1 展示原则
+
+知识问答不应该每次都显示大段文字。推荐采用“儿童教学式”的结构，但视觉和语言仍保持工业专业感：
+
+1. 先给一句结论。
+2. 展示核心图、视频或步骤。
+3. 给 3 到 5 个关键点。
+4. 专业细节折叠起来。
+5. 引用说明书或知识库来源。
+6. 用户需要时再展开。
+
+建议组件：`KnowledgeLessonArtifact`
+
+模块：
+
+- 顶部结论。
+- 设备/部件示意图。
+- 视频卡片。
+- 图片步骤卡。
+- “为什么这样做”解释区。
+- “专业详情”折叠区。
+- “继续调机”按钮。
+- “查看原文/来源”按钮。
+
+### 7.2 知识内容生成流程
+
+```mermaid
+flowchart TD
+  Q["用户问题"] --> R["检索说明书/知识库"]
+  R --> P["提取关键段落、图片、视频"]
+  P --> G["AI 组织教学结构"]
+  G --> A["生成 Knowledge Artifact"]
+  A --> UI["画布分层展示"]
+```
+
+建议每次知识回答都保留来源：
+
+- 文档名称。
+- 页码或章节。
+- 图片/视频 ID。
+- 知识库条目版本。
+- 生成时间。
+
+### 7.3 已确定的知识展示路线
+
+第一阶段采用 **固定教学外壳 + block 内容**。
+
+具体做法：
+
+- 外层固定为 `KnowledgeLessonArtifact`。
+- 内部使用 block 列表表达结论、步骤、图片、视频、注意事项、专业详情、引用来源。
+- 大段文字默认折叠，不直接铺满主界面。
+- block 协议先保持简单，后续可以自然演进成更完整的卡片编排器。
+
+示例 block：
+
+```ts
+type KnowledgeBlock =
+  | { type: "summary"; text: string }
+  | { type: "key_points"; items: string[] }
+  | { type: "steps"; steps: Array<{ title: string; body: string; media?: MediaRef[] }> }
+  | { type: "media"; media: MediaRef[] }
+  | { type: "details"; title: string; markdown: string; defaultOpen?: boolean }
+  | { type: "citations"; citations: KnowledgeCitation[] };
+```
+
+## 8. 引导式调机流程
+
+### 8.1 流程步骤类型
+
+调机流程建议抽象成 `GuidedProcedure`：
+
+```ts
+type ProcedureStep =
+  | {
+      type: "auto_tool";
+      title: string;
+      toolName: string;
+      args: Record<string, unknown>;
+    }
+  | {
+      type: "manual_action";
+      title: string;
+      instruction: string;
+      expectedFeedback: string[];
+      media?: MediaRef[];
+    }
+  | {
+      type: "measurement_input";
+      title: string;
+      field: string;
+      unit: string;
+      validRange?: [number, number];
+    }
+  | {
+      type: "decision_branch";
+      title: string;
+      choices: string[];
+    }
+  | {
+      type: "checklist_gate";
+      title: string;
+      checks: string[];
+    };
+```
+
+### 8.2 自动步骤与人工步骤
+
+自动步骤：
+
+- AI 调用工具读取状态。
+- AI 设置参数。
+- AI 触发设备动作。
+- AI 查询报警或日志。
+
+人工步骤：
+
+- 技师更换夹具。
+- 技师观察指示灯。
+- 技师测量间隙。
+- 技师确认工件位置。
+- 技师上传或选择现场照片。
+
+每一步都应该有明确状态：
+
+- 未开始。
+- 正在执行。
+- 等待人工反馈。
+- 已完成。
+- 失败。
+- 已跳过。
+- 已暂停。
+
+### 8.3 鼠标和文字输入统一操作
+
+所有按钮都应该映射为 `actionId`。鼠标点击和文字命令最终都生成同一种事件；后续接入语音后，语音识别结果也走同一个事件协议：
+
+```ts
+type ArtifactActionEvent = {
+  artifactId: string;
+  actionId: string;
+  source: "click" | "text" | "voice" | "system";
+  value?: unknown;
+  transcript?: string;
+  confidence?: number;
+  createdAt: string;
+};
+```
+
+示例：
+
+- 点击“已完成”按钮：`actionId = "step.complete"`
+- 文字输入“这一步完成了”：`actionId = "step.complete"`
+- 文字输入“压力是 0.72”：`actionId = "measurement.submit"`，`value = 0.72`
+
+这样前端、文字输入、未来语音和后端流程引擎不会各做一套逻辑。
+
+## 9. 输入路由策略：新问题还是操作当前组件
+
+这是本项目最关键的体验问题之一。
+
+### 9.1 推荐核心原则
+
+当存在正在进行的调机流程或控制确认时，系统进入 **前台任务模式**。这时用户输入优先解释为对当前组件的操作，而不是立刻开启新话题。
+
+可以把当前组件理解为持有一个“前台交互权”：
+
+- 当前组件有可执行动作。
+- 当前流程有等待反馈的步骤。
+- 用户没有明确输入“暂停当前流程”。
+
+在这种情况下，输入路由优先级如下：
+
+1. 全局流程指令：取消、暂停、继续、返回。
+2. 当前组件按钮或输入项：确认、取消、下一步、已完成、数值反馈。
+3. 当前流程相关问题：例如“这一步为什么要这样做？”
+4. 临时查询问题：例如“顺便查一下这个报警码。”
+5. 新任务或新流程。
+6. 闲聊。
+
+### 9.2 已确定的输入路由路线
+
+第一阶段采用 **规则优先 + LLM 兜底**。
+
+具体做法：
+
+- 当前组件按钮、确认、取消、下一步、已完成等短指令，用规则和 `textAliases` 直接匹配。
+- 数值输入用规则解析，例如“压力 0.72”“测量值是 3.1”。
+- 当前流程相关的解释性问题、新问题、复杂表达交给 LLM。
+- 后续接入 BERT NLU 时，把 BERT 输出作为意图路由器的一个信号源，不改变 Artifact Action Event 协议。
+
+### 9.3 防止闲聊打断调机流程
+
+推荐策略：
+
+- 调机流程进行中时，不允许普通新问题直接覆盖当前画布。
+- 新问题默认以“侧边临时回答”或“小浮层”展示，不替换主流程。
+- 如果新问题会打开新的大型组件，系统先问：“当前调机流程正在第 3 步，要暂停并打开新内容吗？”
+- 用户可以输入“暂停这个”“先问个问题”“继续刚才”。
+- 每个流程步骤都保存快照，随时可以恢复。
+
+### 9.4 组件状态快照
+
+建议保存：
+
+- 当前 artifact。
+- 当前流程 ID。
+- 当前步骤 ID。
+- 已完成步骤。
+- 已调用工具及结果。
+- 人工反馈记录。
+- 当前可用 action 列表。
+- 当前画布摘要。
+- 最近 N 条用户输入 transcript。
+
+状态快照用途：
+
+- 防止误打断。
+- 支持恢复流程。
+- 支持 LLM 理解当前界面。
+- 支持恢复和复盘。
+
+## 10. LLM 如何理解当前组件
+
+你提出的判断很对：工具调用后直接显示组件，而不是把完整组件都给 LLM 看，是合理的。但如果用户围绕界面内容提问，LLM 必须知道当前界面上有什么。
+
+### 10.1 推荐方案：组件提供 LLM 可读摘要
+
+每个组件除了 `props`，还提供一个 `llmContext`：
+
+```ts
+type ArtifactLLMContext = {
+  visibleSummary: string;
+  currentState: string;
+  activeStep?: string;
+  visibleFields: Array<{
+    label: string;
+    value: string;
+  }>;
+  availableActions: Array<{
+    actionId: string;
+    label: string;
+    textAliases: string[];
+    voiceAliases?: string[];
+  }>;
+  lastEvents: string[];
+};
+```
+
+LLM 不需要看完整 DOM 或截图，只需要看这个结构化摘要。
+
+### 10.2 已确定的 LLM 查看组件路线
+
+第一阶段采用 **极简上下文 + `inspect_ui_state` 工具**：
+
+- 每轮 prompt 默认带：当前是否有前台流程、当前步骤、是否等待用户反馈。
+- 当用户输入“这个按钮是什么意思”“为什么让我做这一步”“刚才显示的目标值是多少”时，LLM 调用 `inspect_ui_state` 获取详细组件状态。
+
+### 10.3 示例工具
+
+```ts
+inspect_ui_state({
+  detailLevel: "summary" | "actions" | "full",
+  artifactId?: string
+})
+```
+
+返回：
+
+```json
+{
+  "artifactId": "artifact_123",
+  "type": "guided_procedure",
+  "title": "主轴换型调机流程",
+  "status": "waiting_confirmation",
+  "currentStep": "第 3 步：确认夹具锁紧",
+  "visibleSummary": "画布正在等待技师确认夹具已经锁紧。",
+  "availableActions": [
+    { "actionId": "step.complete", "label": "已完成" },
+    { "actionId": "step.need_help", "label": "需要帮助" },
+    { "actionId": "procedure.pause", "label": "暂停流程" }
+  ]
+}
+```
+
+## 11. 工具调用与组件显示的关系
+
+### 11.1 推荐拆分
+
+一次工业工具调用应该拆成三类结果：
+
+```ts
+type ToolResponse = {
+  machineResult: unknown;
+  llmSummary: string;
+  artifact?: InteractionArtifact;
+  executionRecord: {
+    toolName: string;
+    args: unknown;
+    resultCode: string;
+    createdAt: string;
+  };
+};
+```
+
+- `machineResult`：给系统内部使用。
+- `llmSummary`：给 LLM 用，让它知道发生了什么。
+- `artifact`：给前端画布渲染。
+- `executionRecord`：保存最小执行记录，方便前端恢复状态和调试。
+
+这样可以避免把过多 UI 细节塞给 LLM，同时又能让 LLM 知道关键事实。
+
+### 11.2 已确定的工具结果展示路线
+
+第一阶段采用 **工具只执行，编排层生成 artifact**。
+
+具体做法：
+
+- 工业工具层只返回执行结果，不关心 UI。
+- AI 编排层或后端 Artifact Builder 根据工具结果生成 `InteractionArtifact`。
+- 前端只消费 artifact，不直接理解工业工具内部数据结构。
+- 这样后续替换 Mock 工具为真实工业工具时，UI 协议保持稳定。
+
+### 11.3 第二版：工具可视化组件分类与维护
+
+第二版需要增加一个“工具和可视化组件维护中心”，让工具能力和画布组件之间不再完全写死在代码里。这样新增工业工具时，可以配置它应该用哪种可视化方式呈现。
+
+工具可视化先分为三类：
+
+| 类型 | 用途 | 示例组件 | 适用工具 |
+|---|---|---|---|
+| 检索信息类型 | 展示查询、检索、知识库或设备资料结果 | `KnowledgeLessonArtifact`、`AlarmExplanationCard`、`MediaGalleryCard` | 查说明书、查报警码、查调机经验、查设备状态摘要 |
+| 图标动画类型 | 展示具象设备动作和执行状态 | `DeviceActionCard`、`ProductionStartCard`、`MachineStatusAnimation` | 开灯、开泵、开阀、启动风机、开机生产 |
+| 用户自定义类型 | 允许用户把某个工具绑定到自定义组件配置 | `CustomToolArtifact`、自定义 schema 驱动组件 | 特定设备动作、厂内专用流程、业务定制工具 |
+
+第二版建议增加 `ToolVisualizationConfig`：
+
+```ts
+type ToolVisualizationConfig = {
+  toolName: string;
+  displayName: string;
+  visualizationType: "retrieval_info" | "icon_animation" | "custom";
+  artifactType: InteractionArtifact["type"] | "custom_tool";
+  icon?: string;
+  animationPreset?: string;
+  propMapping: Array<{
+    from: string;
+    to: string;
+    transform?: "string" | "number" | "boolean" | "status" | "media_list";
+  }>;
+  defaultActions?: ArtifactAction[];
+  customSchema?: Record<string, unknown>;
+  enabled: boolean;
+};
+```
+
+维护中心需要支持：
+
+- 查看所有工具。
+- 给工具选择可视化类型。
+- 配置工具结果字段到 artifact props 的映射。
+- 配置按钮、文本别名和后续语音别名。
+- 配置图标、动画预设、状态文案。
+- 对用户自定义类型提供 schema 校验，避免组件收到不可渲染的数据。
+
+第二版不要求用户直接写 React 组件。更稳的方式是先提供“可配置组件模板”：
+
+- 信息卡模板。
+- 状态动画模板。
+- 步骤流程模板。
+- 参数对比模板。
+- 媒体资料模板。
+
+真正的远程自定义组件可以作为更后面的能力，因为它会带来版本、样式、执行边界和兼容性问题。
+
+## 12. 数据与状态设计
+
+### 12.1 核心对象
+
+建议至少有这些对象：
+
+- `ConversationSession`：一次对话会话。
+- `ArtifactSession`：当前画布状态。
+- `WorkflowInstance`：调机流程实例。
+- `ToolExecution`：每次工具调用记录。
+- `ActionEvent`：每次点击、文字、未来语音、系统事件。
+- `KnowledgeCitation`：知识来源。
+- `OperatorContext`：当前操作员、班组、设备。第一阶段不展开权限模型。
+
+### 12.2 状态保存级别
+
+| 级别 | 用途 | 示例 |
+|---|---|---|
+| 临时态 | 普通问答卡片 | 查一个概念 |
+| 会话态 | 当前任务内有效 | 当前参数修改确认 |
+| 持久态 | 可恢复、可复盘 | 调机流程、生产动作、工具调用 |
+
+调机流程和生产动作建议进入持久态。第一阶段先保证能恢复流程和复盘交互，不做完整审计系统。
+
+## 13. 输入与 NLU 接入边界
+
+### 13.0 汽车语音助手里的 Domain / Intent / Slot / 上下文状态
+
+汽车语音助手常用的 NLU 体系可以理解成四层语义结构：
+
+| 概念 | 含义 | 汽车例子 | 工业设备例子 |
+|---|---|---|---|
+| Domain | 业务领域，先判断用户在说哪类事情 | 空调、导航、车窗、音乐 | 参数设置、设备控制、报警知识、调机流程 |
+| Intent | 用户想完成的动作 | 打开空调、导航到公司、调高音量 | 设置参数、打开设备、查询报警、进入调机 |
+| Slot | 动作需要的参数 | 温度 24 度、目的地公司、音量 8 | 参数名主轴转速、目标值 1800、设备探照灯 |
+| Context State | 当前对话/界面/设备状态 | 正在导航、刚打开空调、当前温度 22 度 | 当前 Artifact、当前调机步骤、当前设备、等待确认 |
+
+例如汽车里一句话：“把空调调到 24 度”：
+
+```json
+{
+  "domain": "climate",
+  "intent": "set_temperature",
+  "slots": {
+    "temperature": 24,
+    "unit": "celsius"
+  },
+  "contextState": {
+    "vehicleArea": "front"
+  }
+}
+```
+
+工业设备里一句话：“把主轴转速调到 1800”：
+
+```json
+{
+  "domain": "machine_parameter",
+  "intent": "set_parameter",
+  "slots": {
+    "parameter": "主轴转速",
+    "value": 1800,
+    "unit": "rpm"
+  },
+  "contextState": {
+    "deviceId": "machine_01",
+    "activeArtifactId": "artifact_123"
+  }
+}
+```
+
+这里最容易混淆的是 **Intent 和 Tool**：
+
+- `intent` 是用户意图，例如 `set_parameter`。
+- `tool` 是系统能力，例如 `write_machine_parameter`。
+- 一个 intent 可能映射到多个 tool；同一个 tool 也可能被多个 intent 使用。
+
+所以 NLU 不应该直接决定 UI，也不应该强耦合具体 React 组件。NLU 的核心输出是“用户想做什么 + 参数是什么 + 置信度多少”。TS 编排层再根据工具目录和可视化配置决定调用哪个工具、展示哪个组件。
+
+### 13.1 第一阶段输入链路
+
+第一阶段只做文字输入和鼠标点击，不实现 ASR，也不实现 BERT NLU。为了后续接入语音和 BERT，当前架构要提前把“用户输入文本”和“组件动作事件”分开。
+
+```mermaid
+flowchart LR
+  I["文字输入"] --> N["归一化文本"]
+  B["按钮点击"] --> E["ActionEvent"]
+  N --> R["意图路由器"]
+  R --> C["当前组件动作"]
+  R --> L["LLM 新问题/复杂语义"]
+  C --> E["ActionEvent"]
+  L --> O["AI 编排层"]
+```
+
+### 13.2 后续语音和 BERT NLU 接入位置
+
+后续接入时建议链路是：
+
+```mermaid
+flowchart LR
+  M["麦克风"] --> A["ASR"]
+  A --> T["识别文本"]
+  T --> N["BERT NLU"]
+  N --> R["意图路由器"]
+  R --> E["ActionEvent / LLM 编排"]
+```
+
+这里的边界是：
+
+- ASR 只负责语音转文字。
+- BERT NLU 负责稳定识别短命令、当前组件动作、流程控制、数值输入等意图。
+- LLM 负责复杂问答、知识组织、流程解释和 artifact 生成。
+- 不管输入来自文字、ASR 还是 BERT，最终都尽量落到统一的 `ArtifactActionEvent`。
+
+本文档后续不展开 ASR 和 BERT NLU 的具体模型方案，只保留接口位置。
+
+### 13.3 推荐职责边界
+
+建议不要让 Python NLU 服务成为“工具和 UI 的唯一配置中心”。更清晰的边界是：
+
+| 模块 | 负责什么 | 不负责什么 |
+|---|---|---|
+| ASR 服务 | 音频转文本、置信度、分段、最终文本 | 不判断业务意图，不调工具 |
+| Python NLU 服务 | domain、intent、slot、置信度、候选工具建议 | 不调用工业工具，不决定最终 UI |
+| TS Input Router | 当前组件动作优先、规则匹配、调用 NLU、决定是否 LLM 兜底 | 不训练模型 |
+| Tool Registry | 工具列表、输入 Schema、输出 Schema、工具描述、示例 | 不做语义识别 |
+| Visualization Registry | 工具/intent 到 artifact 组件的绑定、字段映射、默认动作 | 不执行工具 |
+| Tool Orchestrator | 选择工具、调用工具、生成 artifact、维护状态 | 不做 ASR 和模型训练 |
+
+也就是说，NLU 服务可以暴露它“理解哪些 domain/intent/slot”，也可以返回候选工具名，但工具目录和可视化绑定建议由 TS 项目或单独配置中心维护。
+
+### 13.4 ASR 到 TS 的接口协议
+
+第一阶段可以先做非流式接口，后面再升级 WebSocket 流式识别。
+
+```http
+POST /asr/transcribe
+Content-Type: multipart/form-data
+```
+
+请求字段：
+
+| 字段 | 类型 | 说明 |
+|---|---|---|
+| audio | file | 音频文件或音频片段 |
+| sessionId | string | 当前会话 ID |
+| deviceId | string | 当前设备 ID |
+| language | string | 默认 `zh-CN` |
+| sampleRate | number | 采样率 |
+
+响应：
+
+```ts
+type ASRResult = {
+  requestId: string;
+  sessionId: string;
+  text: string;
+  normalizedText?: string;
+  confidence: number;
+  isFinal: boolean;
+  segments?: Array<{
+    text: string;
+    startMs: number;
+    endMs: number;
+    confidence: number;
+  }>;
+};
+```
+
+后续流式接口可以用 WebSocket：
+
+```txt
+Client -> ASR: audio_chunk
+ASR -> Client/TS: partial_transcript
+ASR -> Client/TS: final_transcript
+```
+
+但无论是否流式，进入 TS Input Router 的都应该是统一文本事件：
+
+```ts
+type InputTextEvent = {
+  eventId: string;
+  sessionId: string;
+  source: "text" | "asr";
+  text: string;
+  normalizedText?: string;
+  confidence?: number;
+  createdAt: string;
+};
+```
+
+### 13.5 TS 到 Python NLU 的接口协议
+
+核心解析接口：
+
+```http
+POST /nlu/parse
+Content-Type: application/json
+```
+
+请求：
+
+```ts
+type NLUParseRequest = {
+  requestId: string;
+  sessionId: string;
+  text: string;
+  normalizedText?: string;
+  locale: "zh-CN";
+  context: {
+    deviceId?: string;
+    activeArtifact?: {
+      artifactId: string;
+      type: string;
+      status: string;
+      availableActions: Array<{
+        actionId: string;
+        label: string;
+        textAliases: string[];
+      }>;
+    };
+    activeWorkflow?: {
+      workflowId: string;
+      currentStepId: string;
+      currentStepTitle: string;
+    };
+  };
+  toolCatalogVersion?: string;
+  nluSchemaVersion?: string;
+};
+```
+
+响应：
+
+```ts
+type NLUParseResponse = {
+  requestId: string;
+  modelVersion: string;
+  schemaVersion: string;
+  domain: string;
+  intent: string;
+  confidence: number;
+  slots: Record<
+    string,
+    {
+      value: string | number | boolean;
+      rawText?: string;
+      confidence?: number;
+      unit?: string;
+    }
+  >;
+  routeHint:
+    | "artifact_action"
+    | "tool_call"
+    | "knowledge_query"
+    | "workflow_control"
+    | "smalltalk"
+    | "fallback";
+  candidateTools?: Array<{
+    toolName: string;
+    confidence: number;
+    reason?: string;
+  }>;
+  clarification?: {
+    required: boolean;
+    question?: string;
+    missingSlots?: string[];
+  };
+};
+```
+
+示例：
+
+```json
+{
+  "requestId": "req_001",
+  "modelVersion": "bert-nlu-2026-05",
+  "schemaVersion": "nlu_schema_v1",
+  "domain": "machine_parameter",
+  "intent": "set_parameter",
+  "confidence": 0.94,
+  "slots": {
+    "parameter": { "value": "主轴转速", "rawText": "主轴转速", "confidence": 0.96 },
+    "value": { "value": 1800, "rawText": "1800", "confidence": 0.98, "unit": "rpm" }
+  },
+  "routeHint": "tool_call",
+  "candidateTools": [
+    { "toolName": "write_machine_parameter", "confidence": 0.91 }
+  ]
+}
+```
+
+### 13.6 NLU 元数据接口
+
+NLU 服务需要暴露自己支持的 domain、intent、slot，方便 TS 做校验、版本对齐和调试。
+
+```http
+GET /nlu/metadata
+```
+
+响应：
+
+```ts
+type NLUMetadata = {
+  serviceName: string;
+  modelVersion: string;
+  schemaVersion: string;
+  domains: Array<{
+    name: string;
+    description: string;
+    intents: Array<{
+      name: string;
+      description: string;
+      requiredSlots: string[];
+      optionalSlots: string[];
+      examples: string[];
+    }>;
+  }>;
+  slots: Array<{
+    name: string;
+    type: "string" | "number" | "boolean" | "enum";
+    values?: string[];
+    units?: string[];
+  }>;
+};
+```
+
+### 13.7 工具目录接口
+
+工具目录建议由 TS 项目或工业工具网关维护，NLU 可以读取它来训练/更新候选工具映射，但不要让 NLU 成为工具真实执行的源头。
+
+```http
+GET /tools/catalog
+```
+
+响应：
+
+```ts
+type ToolCatalog = {
+  version: string;
+  tools: Array<{
+    toolName: string;
+    displayName: string;
+    domain: string;
+    description: string;
+    inputSchema: Record<string, unknown>;
+    outputSchema: Record<string, unknown>;
+    examples: string[];
+    aliases: string[];
+    enabled: boolean;
+  }>;
+};
+```
+
+示例：
+
+```json
+{
+  "version": "tool_catalog_v1",
+  "tools": [
+    {
+      "toolName": "write_machine_parameter",
+      "displayName": "写入设备参数",
+      "domain": "machine_parameter",
+      "description": "把目标参数写入指定工业设备。",
+      "inputSchema": {
+        "type": "object",
+        "required": ["deviceId", "parameter", "value"],
+        "properties": {
+          "deviceId": { "type": "string" },
+          "parameter": { "type": "string" },
+          "value": { "type": ["number", "string"] },
+          "unit": { "type": "string" }
+        }
+      },
+      "outputSchema": {
+        "type": "object",
+        "properties": {
+          "previousValue": { "type": ["number", "string"] },
+          "targetValue": { "type": ["number", "string"] },
+          "status": { "type": "string" }
+        }
+      },
+      "examples": ["把主轴转速调到 1800", "温度补偿加 2 度"],
+      "aliases": ["调参数", "设置参数", "修改参数"],
+      "enabled": true
+    }
+  ]
+}
+```
+
+### 13.8 工具到可视化组件的绑定协议
+
+工具调用结果不应该直接等于 UI。第二版通过 Visualization Registry 维护工具和可视化组件的绑定。
+
+```http
+GET /visualizations/config
+PUT /visualizations/config/:bindingId
+```
+
+配置结构：
+
+```ts
+type ToolVisualizationBinding = {
+  bindingId: string;
+  target: {
+    type: "tool" | "intent";
+    name: string;
+  };
+  visualizationType: "retrieval_info" | "icon_animation" | "custom";
+  artifactType:
+    | "parameter_change"
+    | "device_action"
+    | "production_start"
+    | "knowledge_lesson"
+    | "guided_procedure"
+    | "custom_tool";
+  propMapping: Array<{
+    from: string;
+    to: string;
+    transform?: "string" | "number" | "boolean" | "status" | "media_list";
+  }>;
+  defaultActions: ArtifactAction[];
+  enabled: boolean;
+};
+```
+
+示例：参数写入工具绑定到参数对比卡：
+
+```json
+{
+  "bindingId": "viz_write_parameter",
+  "target": {
+    "type": "tool",
+    "name": "write_machine_parameter"
+  },
+  "visualizationType": "custom",
+  "artifactType": "parameter_change",
+  "propMapping": [
+    { "from": "tool.input.parameter", "to": "props.parameterName", "transform": "string" },
+    { "from": "tool.output.previousValue", "to": "props.currentValue" },
+    { "from": "tool.input.value", "to": "props.targetValue" },
+    { "from": "tool.input.unit", "to": "props.unit" }
+  ],
+  "defaultActions": [
+    {
+      "actionId": "artifact.confirm",
+      "label": "确认执行",
+      "kind": "primary",
+      "textAliases": ["确认", "执行", "确认执行"]
+    }
+  ],
+  "enabled": true
+}
+```
+
+### 13.9 一次完整链路
+
+```mermaid
+sequenceDiagram
+  participant UI as React 画布
+  participant ASR as ASR 服务
+  participant Router as TS Input Router
+  participant NLU as Python NLU
+  participant Tools as Tool Registry
+  participant Viz as Visualization Registry
+  participant Exec as 工业工具网关
+  participant Store as Artifact Store
+
+  UI->>ASR: 音频或语音片段
+  ASR-->>UI: transcript
+  UI->>Router: InputTextEvent
+  Router->>Store: inspect active artifact
+  alt 命中当前组件动作
+    Router->>Store: ArtifactActionEvent
+    Store-->>UI: 更新当前组件
+  else 需要语义识别
+    Router->>NLU: /nlu/parse(text + context)
+    NLU-->>Router: domain / intent / slots / candidateTools
+    Router->>Tools: 查询工具 schema
+    Router->>Exec: 调用工具
+    Exec-->>Router: tool result
+    Router->>Viz: 查询工具可视化绑定
+    Router->>Store: 生成并保存 artifact
+    Store-->>UI: 渲染交互组件
+  end
+```
+
+## 14. 第一阶段最小记录
+
+第一阶段先不实现权限、动作分级、高风险确认和完整审计系统。这里仅保留产品运行所需的最小记录能力，目的是支持状态恢复、问题排查和后续扩展。
+
+最小记录内容：
+
+- 当前会话 ID。
+- 当前 artifact 快照。
+- 当前流程步骤。
+- 用户输入 transcript。
+- 点击或文字触发的 `ActionEvent`。
+- Mock/真实工具调用名称、参数、结果码、时间。
+- 异常时展示明确的失败原因和恢复步骤。
+- 支持暂停、取消、恢复。
+
+后续如果要进入真实生产控制，再单独补权限、动作分级、二次确认、审计日志和合规要求。
+
+## 15. 前端组件库初始清单
+
+第一阶段建议做 8 个核心组件：
+
+| 组件 | 用途 |
+|---|---|
+| `ParameterChangeCard` | 参数前后对比、确认、执行结果 |
+| `DeviceActionCard` | 开灯、开泵、开阀、风机等具象设备动作 |
+| `ProductionStartCard` | 开机生产、启动前检查、启动进度 |
+| `KnowledgeLessonArtifact` | 说明书/知识库教学式回答 |
+| `GuidedProcedureArtifact` | 调机流程引导 |
+| `MeasurementInputCard` | 人工输入测量值，后续可支持语音填数 |
+| `MediaGalleryCard` | 视频、图片、说明资料展示 |
+| `AlarmExplanationCard` | 报警解释、原因、处理步骤 |
+
+## 16. 当前项目演进建议
+
+当前 `src/app/page.tsx` 里已经是左侧聊天 + 右侧画布。可以按这个路线演进：
+
+1. 把 `activeTool.toolName === "getWeather"` 这种判断抽成组件注册表。
+2. 定义 `InteractionArtifact` 类型。
+3. API 不再只返回工具结果，而是返回 `artifact`。
+4. 前端用 `artifact.type` 渲染组件。
+5. 所有组件按钮都发 `ArtifactActionEvent`。
+6. 加一个 `artifact store` 保存当前组件快照。
+7. 后端加 `inspect_ui_state` 工具，让 LLM 可按需读取当前组件摘要。
+8. 预留后续 BERT NLU/语音入口，把未来语音也转成 `ArtifactActionEvent`。
+
+## 17. 阶段计划
+
+### 阶段 1：交互画布原型
+
+目标：
+
+- 固定组件注册表。
+- Mock 工业工具。
+- 参数变更组件。
+- 设备动作动画组件。
+- 知识教学组件。
+
+验收：
+
+- 用户输入“把转速改到 1800”，右侧显示参数前后对比。
+- 用户确认后显示执行中、成功或失败。
+- 用户输入“打开探照灯”，右侧显示灯光动画和状态。
+- 用户问知识问题，右侧显示教学式卡片，而不是大段文本。
+
+### 阶段 2：流程引导
+
+目标：
+
+- 实现 `GuidedProcedureArtifact`。
+- 支持自动步骤和人工步骤。
+- 支持暂停、恢复、跳过、失败重试。
+- 支持文字反馈“已完成”“数值是 0.72”。
+
+验收：
+
+- 一个调机流程可以从第 1 步走到最后。
+- 中途问临时问题不会覆盖流程。
+- 可以输入“继续刚才”恢复流程。
+
+### 阶段 3：LLM 读取 UI 状态
+
+目标：
+
+- 实现 `llmContext`。
+- 实现 `inspect_ui_state`。
+- 用户围绕界面提问时，LLM 能知道当前组件和步骤。
+
+验收：
+
+- 用户问“刚才让我确认的目标值是多少”，AI 能答对。
+- 用户问“这个按钮是什么意思”，AI 能解释当前按钮。
+- 用户问“这一步为什么要做”，AI 能结合流程上下文回答。
+
+### 阶段 4：工业工具接入
+
+目标：
+
+- 接入你实现的工业软件能力。
+- 保持工具执行结果到 artifact 展示的稳定映射。
+- 保留最小执行记录和异常处理。
+
+验收：
+
+- 工具调用结果能稳定驱动组件状态变化。
+- 工具失败时组件能给出恢复建议。
+
+## 18. 已收敛的技术路线
+
+本阶段不再保留多个待选决策，统一采用以下路线：
+
+| 方向 | 已采用方案 |
+|---|---|
+| 组件渲染 | React 组件注册表 + JSON Artifact 协议 |
+| 知识展示 | 固定教学外壳 + block 内容 |
+| 输入路由 | 规则优先 + LLM 兜底 |
+| 流程状态 | 后端持久化 `WorkflowInstance`，原型阶段可先用会话态模拟 |
+| LLM 理解界面 | 每轮给极简上下文，需要时调用 `inspect_ui_state` |
+| 工具展示关系 | 工具只执行，编排层生成 artifact |
+| 第一阶段输入 | 文字输入 + 鼠标点击 |
+| 后续 NLU | 预留 BERT NLU 接口，但本阶段不实现 |
+| 语音/ASR | 预留接口，本阶段不实现 |
+| 权限/动作分级/高风险确认 | 本阶段不实现 |
+| 第二版工具可视化维护 | 增加工具和可视化组件维护中心，支持检索信息、图标动画、用户自定义三类 |
+
+## 19. 我建议的第一版 MVP 范围
+
+第一版不要做太大，建议只做这 5 条闭环：
+
+1. 参数修改：旧值/新值/确认/执行结果。
+2. 设备控制：打开探照灯或水泵的动画状态。
+3. 知识问答：说明书内容变成教学卡片。
+4. 调机流程：3 到 5 步，包含一个自动步骤和一个人工反馈步骤。
+5. 文字输入操作当前组件：确认、取消、下一步、已完成、数值输入。
+
+这 5 条跑通后，再扩展到真实工业软件和 PLC/HMI。
+
+## 20. 下一步建议
+
+建议按下面顺序进入实现：
+
+1. 先定义 `InteractionArtifact`、`ArtifactAction`、`ArtifactActionEvent` 类型。
+2. 建立组件注册表，把当前写死的 weather/stock 渲染替换成 artifact 渲染。
+3. 先实现两个控制类组件：`ParameterChangeCard` 和 `DeviceActionCard`。
+4. 再实现 `KnowledgeLessonArtifact`，支持 summary、key_points、steps、media、details、citations 这些 block。
+5. 再实现 `GuidedProcedureArtifact`，跑通 3 到 5 步调机流程。
+6. 加入 `inspect_ui_state`，让 LLM 能按需读取当前画布摘要。
+7. 最后预留 BERT NLU/ASR 接口，但不在第一阶段接入。
+
+完成后，这个项目就会从“聊天 + 单个工具卡片 demo”变成一个真正可扩展的工业 AI 交互画布原型。
diff --git a/docs/nlu_integration_design.md b/docs/nlu_integration_design.md
new file mode 100644
index 0000000..279cf0e
--- /dev/null
+++ b/docs/nlu_integration_design.md
@@ -0,0 +1,310 @@
+# NLU 接入设计方案
+
+> 状态：**已确认，进入实现阶段**
+> 关联文档：`bert_integration_analysis.md`、`architecture_overview.html`
+
+---
+
+## 第一部分：概念解释 — 两套术语怎么对应
+
+### 1.1 Canvas 设计里用的词
+
+在 `architecture_overview.html` 的技术流程视图里，BERT NLU 节点描述为：
+
+```
+BERT NLU 意图识别
+输出 domain / intent / slot / confidence 置信度
+```
+
+这是一套**面向业务语义**的描述，每个词的含义：
+
+| 词 | 含义 | 例子 |
+|---|---|---|
+| `domain` | 所属业务域，意图的分组 | `machine_control`、`equipment_knowledge`、`smalltalk` |
+| `intent` | 用户想做什么，域内的细分动作 | `wirecut_set_speed`、`wirecut_start_run`、`query_alarm` |
+| `slot` | 动作的具体参数，从句子里提取的关键值 | `speed=80`、`voltage=90`、`axis=X` |
+| `confidence` | 模型对这次识别结果的置信程度，0~1 | `0.94`（高）、`0.61`（中）、`0.32`（低）|
+
+Canvas 的路由逻辑就是：拿到这四个值之后，判断 `confidence ≥ 阈值 AND domain = machine_control` → 走工具调用路径。
+
+---
+
+### 1.2 intelligent_cabin NLU 服务里用的词
+
+`intelligent_cabin` 后端分两层输出：
+
+#### 层 A：JointBertNLU 的原始输出（`joint_nlu.py`）
+
+```python
+@dataclass
+class JointNluResult:
+    intent_id: str | None       # 识别出的意图 id，如 "wirecut_set_speed"
+    intent_score: float          # softmax 后的概率，就是置信度，0~1
+    candidates: list[JointCandidate]  # Top-K 候选意图及其概率
+    slots: dict[str, Any]        # 从句子里提取的 slot，如 {"speed": 80}
+    slot_items: list[JointSlot]  # slot 在原文中的精确位置和得分
+```
+
+这里的 `intent_id + intent_score + slots` 对应 Canvas 描述里的 `intent + confidence + slot`。
+`domain` 不是模型直接输出的，而是根据 `intent_id` 在 `domain.yml` 里查到的（`wirecut_set_speed` → domain `machine_control`）。
+
+#### 层 B：Router / FusionGrader 的决策输出（`router.py`）
+
+```python
+# MultiStageIntentMatcher._build_fusion_stage() 里
+decision = "execute" | "clarify" | "route_to_cloud" | "reject"
+```
+
+这是在原始 NLU 结果基础上做的**二次路由判断**，加入了：
+- 置信度是否够高（`score ≥ execute_score_threshold=0.55`）
+- 头两名候选的分差是否足够大（`margin ≥ execute_margin_threshold=0.18`）
+- 是否有多义性（ambiguous）
+
+它告诉上层"这个识别结果能不能直接执行"，而不只是"模型认为是哪个意图"。
+
+---
+
+### 1.3 两套词汇的完整映射关系
+
+```
+Canvas 的描述            ←→    intelligent_cabin 的实际字段
+─────────────────────────────────────────────────────────────
+domain                    ←→    intent_def.domain  (从 domain.yml 查)
+intent                    ←→    JointNluResult.intent_id
+slot                      ←→    JointNluResult.slots  (dict)
+confidence                ←→    JointNluResult.intent_score  (0~1)
+
+（以上是 NLU 层的概念对应）
+
+Canvas 的路由逻辑          ←→    Router 层的 decision 字段
+"高置信 + 设备控制域"      ←→    decision="execute" AND domain="machine_control"
+"知识域/低置信兜底"        ←→    decision="route_to_cloud" 或 domain="equipment_knowledge"
+"smalltalk"               ←→    decision="reject" 或 social_router 处理
+```
+
+> **关键点**：Canvas 当前用 Mock NLU（`src/lib/nlu/mock.ts`），它直接输出 `domain + intent + confidence + routeHint`。
+> 接入真实 NLU 后，两个项目**原生打通，当成一个项目**，不做兼容适配层，接口可以随时改。
+
+---
+
+## 第二部分：Canvas ↔ NLU 服务的统一路由方案
+
+### 2.1 两个项目合并为一个项目（已确认）
+
+`intelligent_cabin` 不作为独立服务做适配，而是直接作为 `ai-canvas` 的后端子模块。
+原来 `src/lib/nlu/mock.ts` 的格式**可以废弃**，不需要保持向后兼容。
+
+### 2.2 真实 NLU 服务的 HTTP 响应
+
+调用 `POST /api/v1/agent/chat` 后，服务返回 `ChatResponse`，与路由相关的核心字段：
+
+```json
+{
+  "session_id": "xxx",
+  "intent": "wirecut_set_speed",
+  "domain": "machine_control",
+  "decision": "execute",
+  "status": "completed",
+  "filled_slots": { "speed": 80 },
+  "routing_debug": {
+    "confidence_grade": "high",
+    "stages": [
+      { "stage": "classifier", "score": 0.87, "candidates": [...] },
+      {
+        "stage": "fusion",
+        "metadata": { "decision": "execute", "grade": "high",
+                      "classifier_signal": 0.87, "classifier_margin": 0.34 }
+      }
+    ]
+  }
+}
+```
+
+> `domain` 字段需要在 intelligent_cabin 的 `ChatResponse` schema 里加上（从 `IntentDefinition.domain` 填充），改动极小。
+
+### 2.3 Canvas 侧的 NluResult 类型（替换 mock.ts）
+
+```typescript
+// src/lib/nlu/types.ts  （新建，替换 mock.ts 里的类型）
+
+export type RouteHint =
+  | "tool_call"       // decision=execute + machine_control 域
+  | "knowledge_query" // decision=route_to_cloud 或 equipment_knowledge 域
+  | "smalltalk"       // decision=reject
+  | "fallback";
+
+export type NluResult = {
+  modelVersion: string;
+  domain: string;                                        // 后端直接返回
+  intent: string;                                        // intent_id
+  confidence: number;                                    // classifier stage score
+  slots: Record<string, string | number | boolean>;      // filled_slots
+  routeHint: RouteHint;
+  decisionGrade: "high" | "medium" | "low";
+  rawDecision: "execute" | "clarify" | "route_to_cloud" | "reject";
+};
+
+export function mapDecisionToRouteHint(
+  decision: string,
+  domain: string
+): RouteHint {
+  if (decision === "execute") {
+    if (domain === "machine_control") return "tool_call";
+    if (domain === "equipment_knowledge") return "knowledge_query";
+    return "tool_call";
+  }
+  if (decision === "route_to_cloud") return "knowledge_query";
+  if (decision === "reject") return "smalltalk";
+  return "fallback"; // clarify 等待补槽，暂用 fallback
+}
+```
+
+### 2.4 confidence 读取位置
+
+`routing_debug.stages` 里找 `stage === "classifier"` 的记录，取其 `score` 字段。
+这是 BERT 分类器 softmax 后的原始概率，等价于 Canvas 描述里的 `confidence`。
+
+---
+
+## 第三部分：语音处理前置拦截链路（已定稿）
+
+### 3.1 设计原则
+
+- **所有当前可见 UI 组件的按钮文本都参与语音匹配**，命中则直接触发点击事件，不调 BERT
+- 调机流程（GuidedProcedure）当前**不在实现范围**，相关 1c 拦截逻辑暂不涉及
+- BERT 报错**直接抛出**，不降级，不用 Mock 兜底
+
+### 3.2 四阶段处理链路
+
+```
+ASR 文本输入
+    │
+    ▼
+[阶段 0] 停止词检测                          ← 静态词表，构建时嵌入
+    ├── 命中 cancel_words → 生成 stop_action，链路终止
+    └── 未命中 ↓
+    │
+    ▼
+[阶段 1] UI 可见元素语音点击匹配              ← 纯前端规则，<1ms
+    ├── 1a. session.status=waiting_confirmation 时的 affirm/deny（最高优先级）
+    └── 1b. 当前可见 Artifact 按钮 text 匹配
+    │   命中任意 → 生成 ActionEvent，走 Canvas 状态机，链路终止
+    │
+    │ 全部未命中
+    ▼
+[阶段 1.5] waiting_slot + inform 检测
+    ├── session.status=waiting_slot && 输入为数字/数值类
+    └── 命中 → 调 fill_slots 接口，链路终止
+    │
+    ▼
+[阶段 2] BERT NLU（intelligent_cabin /api/v1/agent/chat）
+    ├── 报错 → 直接抛出，不降级
+    ├── decision=execute → 工具调用层（DBus）→ Artifact
+    ├── decision=clarify → 渲染补槽卡，等待 waiting_slot
+    ├── decision=route_to_cloud → LLM + 知识库 → KnowledgeLessonArtifact
+    └── decision=reject → LLM 直接作答，不写 ArtifactStore
+```
+
+### 3.3 阶段 1 内部优先级说明
+
+```typescript
+// 优先级从高到低（1c 调机 textAliases 暂不实现）
+const PRIORITY_ORDER = [
+  "waiting_confirmation_affirm_deny",  // 1a
+  "visible_artifact_button",           // 1b
+];
+```
+
+**为什么 1a 最高**：当高风险操作（如"开始加工"）弹出确认卡时，
+操作员说"确认"应当触发确认动作，而不是响应画布上同时存在的其他按钮。
+状态（`session.status`）决定优先级，而非文本本身。
+
+### 3.4 阶段 1 匹配实现（pipeline.ts 骨架）
+
+```typescript
+// src/lib/nlu/pipeline.ts
+
+import { AFFIRM_WORDS, CANCEL_WORDS } from "./voice-aliases.gen"; // 构建时生成
+
+type ActionEvent = {
+  type: "voice_click_event" | "slot_fill_event" | "stop_action";
+  actionId?: string;
+  artifactId?: string;
+  sourceText: string;
+};
+
+export async function processVoiceInput(
+  asrText: string,
+  session: CanvasSession
+): Promise<NluResult | ActionEvent> {
+
+  // 阶段 0：停止词
+  const norm = normalizeVoice(asrText);
+  if (CANCEL_WORDS.some(w => norm.includes(w))) {
+    return { type: "stop_action", sourceText: asrText };
+  }
+
+  // 阶段 1a：waiting_confirmation 状态的 affirm/deny
+  if (session.status === "waiting_confirmation") {
+    if (AFFIRM_WORDS.some(w => norm.includes(w))) {
+      return { type: "voice_click_event", actionId: "confirm", sourceText: asrText };
+    }
+    if (CANCEL_WORDS.some(w => norm.includes(w))) {
+      return { type: "voice_click_event", actionId: "cancel", sourceText: asrText };
+    }
+  }
+
+  // 阶段 1b：当前 Artifact 按钮匹配
+  const voiceClick = matchVoiceToAction(asrText, session.visibleActions);
+  if (voiceClick) {
+    return {
+      type: "voice_click_event",
+      actionId: voiceClick.actionId,
+      artifactId: voiceClick.artifactId,
+      sourceText: asrText,
+    };
+  }
+
+  // 阶段 1.5：waiting_slot + 数值输入
+  if (session.status === "waiting_slot" && isNumericInput(asrText)) {
+    return { type: "slot_fill_event", sourceText: asrText };
+  }
+
+  // 阶段 2：BERT NLU（报错直接抛出）
+  const response = await callNluService(asrText, session.sessionId);
+  return adaptNluResponse(response);
+}
+```
+
+### 3.5 voice_aliases 配置（已确认：静态构建）
+
+**词表位置**：`intelligent_cabin/config/voice_aliases.yml`（和 `dialog_acts.yml` 放在一起）
+
+```yaml
+# voice_aliases.yml
+affirm_words: ["确认", "好的", "执行", "是", "对", "继续", "好", "ok"]
+cancel_words: ["取消", "算了", "不要", "不用", "停止", "停"]
+
+# 工控设备别名（按 intent_id 分组，用于阶段 1b 的 Artifact voiceActions）
+intent_aliases:
+  wirecut_start_run:  ["开始", "启动", "加工", "跑起来"]
+  wirecut_stop_run:   ["停", "停机", "急停", "停止"]
+  wirecut_home_all:   ["回零", "归零", "回原点"]
+  wirecut_pause_run:  ["暂停", "变频暂停"]
+```
+
+**构建时生成**：构建脚本读取 yml → 生成 `src/lib/nlu/voice-aliases.gen.ts`，
+TypeScript 侧直接 import，不需要运行时 HTTP 请求。
+
+---
+
+## 第四部分：下一步实现计划
+
+| 步骤 | 位置 | 内容 |
+|---|---|---|
+| 1 | `intelligent_cabin` Python 侧 | `ChatResponse` schema 加 `domain` 字段，`agent_service.py` 填充 |
+| 2 | `intelligent_cabin/config/` | 创建 `voice_aliases.yml`，补充工控别名 |
+| 3 | `src/lib/nlu/` | 新建 `types.ts`，废弃 `mock.ts` 中旧类型 |
+| 4 | `src/lib/nlu/` | 新建 `pipeline.ts`，实现四阶段处理链路 |
+| 5 | `src/lib/artifacts/types.ts` | 各 Artifact 类型上加 `voiceActions` 字段 |
+| 6 | 构建配置 | 添加 yml → ts 生成脚本（`voice-aliases.gen.ts`） |
diff --git a/docs/归档(包含调机流程).html b/docs/归档(包含调机流程).html
new file mode 100644
index 0000000..8215ed7
--- /dev/null
+++ b/docs/归档(包含调机流程).html
@@ -0,0 +1,469 @@
+<!doctype html>
+<html lang="zh-CN">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <title>工业 AI 交互画布：流程总览</title>
+    <style>
+      :root {
+        --bg: #f0f2f5;
+        --card: #fff;
+        --ink: #111827;
+        --muted: #667085;
+        --line: #d8dde6;
+        --soft: #f1f4f8;
+        --blue: #2563eb;
+        --green: #059669;
+        --amber: #d97706;
+        --violet: #7c3aed;
+        --dark: #111827;
+      }
+      * { box-sizing: border-box; margin: 0; padding: 0; }
+      body { background: var(--bg); color: var(--ink); font-family: "PingFang SC", "Microsoft YaHei", Arial, sans-serif; font-size: 14px; }
+      button { font: inherit; cursor: pointer; border: none; }
+
+      .page { max-width: 1500px; margin: 0 auto; padding: 24px 24px 60px; }
+
+      /* Hero */
+      .hero { display: flex; align-items: flex-start; justify-content: space-between; gap: 16px; margin-bottom: 18px; flex-wrap: wrap; }
+      h1 { font-size: 22px; font-weight: 800; }
+      .subtitle { color: var(--muted); font-size: 13px; margin-top: 5px; line-height: 1.6; }
+
+      /* Layout */
+      .main-grid { display: grid; grid-template-columns: 252px minmax(0, 1fr); gap: 16px; align-items: start; }
+
+      /* Left panel */
+      .l-panel { background: var(--card); border: 1px solid var(--line); border-radius: 12px; padding: 18px; }
+      .panel-title { font-size: 14px; font-weight: 700; margin-bottom: 12px; color: var(--ink); }
+      .stack { display: grid; gap: 8px; }
+      .layer { display: grid; grid-template-columns: 30px 1fr; gap: 10px; align-items: center; border: 1px solid var(--line); border-left: 4px solid var(--dark); border-radius: 7px; padding: 9px 12px; }
+      .layer[data-c="blue"]   { border-left-color: #2563eb; }
+      .layer[data-c="cyan"]   { border-left-color: #0891b2; }
+      .layer[data-c="violet"] { border-left-color: #7c3aed; }
+      .layer[data-c="green"]  { border-left-color: #059669; }
+      .layer[data-c="amber"]  { border-left-color: #d97706; }
+      .layer[data-c="dark"]   { border-left-color: #111827; }
+      .ln { display: flex; height: 24px; width: 24px; align-items: center; justify-content: center; border-radius: 50%; background: var(--soft); font-weight: 800; font-size: 12px; color: #334155; }
+      .lname { font-size: 12px; font-weight: 700; }
+      .ldesc { font-size: 11px; color: var(--muted); margin-top: 2px; line-height: 1.3; }
+
+      /* Right: flow panel */
+      .r-panel { background: var(--card); border: 1px solid var(--line); border-radius: 12px; overflow: hidden; }
+
+      /* Controls bar */
+      .controls { display: flex; align-items: center; justify-content: space-between; gap: 10px; padding: 14px 20px; border-bottom: 1px solid var(--line); background: #fafbfc; flex-wrap: wrap; }
+      .tg { display: inline-flex; gap: 3px; padding: 3px; border: 1px solid var(--line); border-radius: 9px; background: var(--card); }
+      .tbtn { background: transparent; color: #475467; padding: 8px 14px; border-radius: 6px; font-size: 13px; font-weight: 600; transition: all .15s; white-space: nowrap; }
+      .tbtn.active-flow { background: var(--dark); color: #fff; }
+      .tbtn.active-ux   { background: #4f46e5; color: #fff; }
+      .tbtn.active-tech { background: #0d9488; color: #fff; }
+
+      /* View indicator bar */
+      .view-bar { height: 4px; width: 100%; }
+      .view-bar.ux   { background: linear-gradient(90deg, #818cf8, #a78bfa); }
+      .view-bar.tech { background: linear-gradient(90deg, #0d9488, #059669); }
+
+      /* Flow body */
+      .flow-body { padding: 22px 26px 30px; overflow-x: auto; }
+      .flow-meta { margin-bottom: 18px; }
+      .flow-meta h2 { font-size: 17px; font-weight: 800; display: flex; align-items: center; gap: 8px; flex-wrap: wrap; }
+      .flow-meta p  { font-size: 13px; color: var(--muted); margin-top: 6px; line-height: 1.6; }
+      .badge { display: inline-flex; align-items: center; font-size: 11px; font-weight: 600; padding: 2px 10px; border-radius: 20px; }
+      .badge-a    { background: #dbeafe; color: #1d4ed8; }
+      .badge-b    { background: #d1fae5; color: #065f46; }
+      .badge-ux   { background: #ede9fe; color: #4c1d95; }
+      .badge-tech { background: #ccfbf1; color: #134e4a; }
+
+      /* Legend */
+      .legend { display: flex; gap: 10px; flex-wrap: wrap; padding: 9px 14px; background: var(--soft); border-radius: 8px; margin-bottom: 18px; }
+      .lg { display: flex; align-items: center; gap: 5px; font-size: 11px; color: #475467; }
+      .lg-dot { width: 10px; height: 10px; border-radius: 2px; flex-shrink: 0; }
+
+      /* ═══ FLOWCHART BASE ═══ */
+      .fc { display: flex; flex-direction: column; align-items: center; min-width: 580px; }
+
+      /* Nodes */
+      .nd { border-radius: 8px; padding: 9px 16px; text-align: center; border: 1.5px solid var(--line); background: #fff; }
+      .nd .nt { font-size: 13px; font-weight: 700; line-height: 1.4; }
+      .nd .ns { font-size: 11px; color: var(--muted); margin-top: 3px; line-height: 1.35; }
+
+      /* Node type variants */
+      .nd-start    { background: var(--dark); border: none; border-radius: 22px; min-width: 180px; }
+      .nd-start .nt { color: #fff; font-size: 14px; }
+      .nd-start .ns { color: rgba(255,255,255,0.6); }
+
+      /* UX node types */
+      .nd-show   { background: #f5f3ff; border-color: #a78bfa; } /* 画面显示 */
+      .nd-show .nt { color: #4c1d95; }
+      .nd-do     { background: #ecfdf5; border-color: #34d399; } /* 用户操作 */
+      .nd-do .nt   { color: #065f46; }
+      .nd-end    { background: #f0fdf4; border-color: #86efac; } /* 结果/结束 */
+      .nd-end .nt  { color: #14532d; }
+      .nd-trig   { background: #fff7ed; border-color: #fdba74; } /* 触发条件/场景 */
+      .nd-trig .nt { color: #9a3412; }
+      .nd-tab    { background: #f0f9ff; border-color: #7dd3fc; border-style: dashed; }
+      .nd-tab .nt  { color: #0c4a6e; }
+
+      /* Tech node types */
+      .nd-route  { background: #fffbeb; border-color: #fbbf24; }
+      .nd-route .nt { color: #78350f; }
+      .nd-bert   { background: #f5f3ff; border-color: #c4b5fd; }
+      .nd-bert .nt  { color: #5b21b6; }
+      .nd-llm    { background: #fff7ed; border-color: #fdba74; }
+      .nd-llm .nt   { color: #9a3412; }
+      .nd-tool   { background: #dbeafe; border-color: #60a5fa; }
+      .nd-tool .nt  { color: #1e3a8a; }
+      .nd-kb     { background: #ecfdf5; border-color: #6ee7b7; }
+      .nd-kb .nt    { color: #064e3b; }
+      .nd-art    { background: #faf5ff; border-color: #c4b5fd; border-style: dashed; }
+      .nd-art .nt   { color: #5b21b6; }
+      .nd-snap   { background: #f0fdf4; border-color: #86efac; border-style: dashed; }
+      .nd-snap .nt  { color: #14532d; }
+      .nd-minor  { background: var(--soft); border-color: var(--line); }
+      .nd-minor .nt { color: #475467; }
+
+      /* Tags inside nodes */
+      .tag { display: inline-block; font-size: 9px; font-weight: 700; padding: 2px 6px; border-radius: 3px; margin: 3px 2px 0; }
+      .t-bert { background: #ede9fe; color: #5b21b6; }
+      .t-llm  { background: #fff7ed; color: #9a3412; }
+      .t-rule { background: #dcfce7; color: #166534; }
+      .t-tool { background: #dbeafe; color: #1e40af; }
+      .t-art  { background: #faf5ff; color: #5b21b6; }
+
+      /* Connectors */
+      .dn { width: 2px; background: #c9d0db; height: 20px; flex-shrink: 0; }
+      .dn.tall { height: 30px; }
+      .arr { width: 0; height: 0; border-left: 5px solid transparent; border-right: 5px solid transparent; border-top: 7px solid #b0b9c8; flex-shrink: 0; }
+
+      /* Branch spread link */
+      .slink { position: relative; width: 100%; height: 24px; flex-shrink: 0; }
+
+      /* Branch columns */
+      .brow { display: flex; width: 100%; align-items: flex-start; }
+      .bcol { display: flex; flex-direction: column; align-items: center; flex: 1; padding: 0 7px; min-width: 0; }
+
+      /* Branch labels */
+      .blbl { padding: 4px 11px; border-radius: 20px; font-size: 11px; font-weight: 700; white-space: nowrap; }
+      .bl-b  { background: #dbeafe; color: #1d4ed8; border: 1px solid #93c5fd; }
+      .bl-g  { background: #d1fae5; color: #065f46; border: 1px solid #6ee7b7; }
+      .bl-a  { background: #fef3c7; color: #92400e; border: 1px solid #fcd34d; }
+      .bl-v  { background: #ede9fe; color: #4c1d95; border: 1px solid #a78bfa; }
+      .bl-gr { background: var(--soft); color: #475467; border: 1px solid var(--line); }
+      .bl-hit  { background: #d1fae5; color: #065f46; border: 1px solid #6ee7b7; }
+      .bl-miss { background: #fef3c7; color: #92400e; border: 1px solid #fcd34d; }
+
+      /* Notes */
+      .fnote { font-size: 11px; color: #64748b; margin-top: 4px; text-align: center; line-height: 1.4; }
+      .fopt  { font-size: 11px; color: #6d28d9; margin-top: 4px; text-align: center; font-style: italic; }
+      .fwarn { font-size: 11px; color: #b91c1c; margin-top: 4px; text-align: center; }
+
+      /* Divider */
+      .fc-sep { width: 100%; border: none; border-top: 1px dashed var(--line); margin: 4px 0; }
+
+      @media (max-width: 1080px) { .main-grid { grid-template-columns: 1fr; } }
+    </style>
+  </head>
+  <body>
+    <main class="page">
+      <header class="hero">
+        <div>
+          <h1>工业 AI 交互画布 · 操作流程与技术链路</h1>
+          <p class="subtitle">选择视角，分别查看"用户界面交互路径"或"背后技术判断逻辑"。语音输入经过四阶段前置拦截后再进入 BERT NLU。</p>
+        </div>
+      </header>
+
+      <section class="main-grid">
+        <!-- Left: Architecture Layers -->
+        <aside class="l-panel">
+          <div class="panel-title">主链路层级</div>
+          <div class="stack">
+            <div class="layer" data-c="blue">
+              <div class="ln">1</div>
+              <div><div class="lname">输入层</div><div class="ldesc">文字 / 语音(ASR) / 点击</div></div>
+            </div>
+            <div class="layer" data-c="cyan">
+              <div class="ln">2</div>
+              <div><div class="lname">前置拦截层</div><div class="ldesc">停止词 → UI语音点击 → Slot填写 → BERT</div></div>
+            </div>
+            <div class="layer" data-c="violet">
+              <div class="ln">3</div>
+              <div><div class="lname">路由层</div><div class="ldesc">decision: execute / clarify / route_to_cloud</div></div>
+            </div>
+            <div class="layer" data-c="green">
+              <div class="ln">4</div>
+              <div><div class="lname">编排层</div><div class="ldesc">工具选择 / 知识检索 / Artifact 生成</div></div>
+            </div>
+            <div class="layer" data-c="amber">
+              <div class="ln">5</div>
+              <div><div class="lname">执行层</div><div class="ldesc">PLC / HMI / 知识库调用</div></div>
+            </div>
+            <div class="layer" data-c="dark">
+              <div class="ln">6</div>
+              <div><div class="lname">画布层</div><div class="ldesc">渲染 Artifact + 状态快照</div></div>
+            </div>
+          </div>
+        </aside>
+
+        <!-- Right: Flow Panel -->
+        <section class="r-panel">
+          <div class="controls">
+            <div class="tg" id="viewTabs">
+              <button class="tbtn active-ux" data-view="ux">👁 交互流程</button>
+              <button class="tbtn" data-view="tech">⚙️ 技术流程</button>
+            </div>
+          </div>
+          <div class="view-bar ux" id="viewBar"></div>
+          <div class="flow-body" id="flowBody"></div>
+        </section>
+      </section>
+    </main>
+
+    <script>
+      // ─── State ────────────────────────────────────────────────────────────────
+      const S = { view: 'ux' };
+
+      // ─── Helpers ──────────────────────────────────────────────────────────────
+      const dn  = (h = 20) => `<div class="dn" style="height:${h}px"></div>`;
+      const arr = () => `<div class="arr"></div>`;
+      const da  = (h = 20) => dn(h) + arr();
+
+      function nd(cls, title, sub = '', tags = []) {
+        const ts = tags.map(t => `<span class="tag t-${t}">${t.toUpperCase()}</span>`).join('');
+        return `<div class="nd ${cls}"><div class="nt">${title}</div>${sub ? `<div class="ns">${sub}</div>` : ''}${ts ? `<div>${ts}</div>` : ''}</div>`;
+      }
+
+      function blbl(text, cls) {
+        return `<div class="blbl ${cls}">${text}</div>`;
+      }
+
+      // Horizontal branch spread link: n = 2 or 3
+      function slink(n) {
+        if (n === 3) {
+          return `<div class="slink">
+            <div style="position:absolute;top:0;left:16.67%;right:16.67%;height:2px;background:#c9d0db;"></div>
+            <div style="position:absolute;top:0;left:calc(16.67% - 1px);width:2px;height:24px;background:#c9d0db;"></div>
+            <div style="position:absolute;top:0;left:calc(50% - 1px);width:2px;height:24px;background:#c9d0db;"></div>
+            <div style="position:absolute;top:0;right:calc(16.67% - 1px);width:2px;height:24px;background:#c9d0db;"></div>
+          </div>`;
+        }
+        return `<div class="slink">
+          <div style="position:absolute;top:0;left:25%;right:25%;height:2px;background:#c9d0db;"></div>
+          <div style="position:absolute;top:0;left:calc(25% - 1px);width:2px;height:24px;background:#c9d0db;"></div>
+          <div style="position:absolute;top:0;right:calc(25% - 1px);width:2px;height:24px;background:#c9d0db;"></div>
+        </div>`;
+      }
+
+      // ─── Legends ─────────────────────────────────────────────────────────────
+      const legendUX = `<div class="legend">
+        <div class="lg"><div class="lg-dot" style="background:#f5f3ff;border:1px solid #a78bfa;"></div> 界面展示内容</div>
+        <div class="lg"><div class="lg-dot" style="background:#ecfdf5;border:1px solid #34d399;"></div> 用户可做的操作</div>
+        <div class="lg"><div class="lg-dot" style="background:#f0fdf4;border:1px solid #86efac;"></div> 最终结果/结束态</div>
+        <div class="lg"><div class="lg-dot" style="background:#f0f9ff;border:1px solid #7dd3fc;border-style:dashed;"></div> Tab 分页展示（不覆盖主卡）</div>
+      </div>`;
+
+      const legendTech = `<div class="legend">
+        <div class="lg"><div class="lg-dot" style="background:#fffbeb;border:1px solid #fbbf24;"></div> 路由判断节点</div>
+        <div class="lg"><div class="lg-dot" style="background:#f5f3ff;border:1px solid #c4b5fd;"></div> BERT / NLU</div>
+        <div class="lg"><div class="lg-dot" style="background:#fff7ed;border:1px solid #fdba74;"></div> LLM 推断</div>
+        <div class="lg"><div class="lg-dot" style="background:#dbeafe;border:1px solid #60a5fa;"></div> 工具调用</div>
+        <div class="lg"><div class="lg-dot" style="background:#ecfdf5;border:1px solid #6ee7b7;"></div> 知识库检索</div>
+        <div class="lg"><div class="lg-dot" style="background:#faf5ff;border:1px solid #c4b5fd;border-style:dashed;"></div> Artifact 生成</div>
+      </div>`;
+
+      // ═══════════════════════════════════════════════════════════════════════════
+      // FLOW A  ·  UX VIEW
+      // ═══════════════════════════════════════════════════════════════════════════
+      function renderA_UX() {
+        return `
+          <div class="flow-meta">
+            <h2>大流程 A · 普通对话 <span class="badge badge-a">无调机流程</span> <span class="badge badge-ux">👁 交互流程</span></h2>
+            <p>操作员从零开始发起一次输入。界面根据输入内容呈现三种不同的画布组件，操作员按提示进行确认或查阅。</p>
+          </div>
+          ${legendUX}
+          <div class="fc">
+
+            ${nd('nd-start', '👤 操作员输入文字或语音', '当前画布无激活流程')}
+            ${da()}
+
+            ${nd('nd-trig', '系统识别输入类型，分三种场景响应', '用户感知到的是画布出现了不同内容')}
+            ${slink(3)}
+
+            <div class="brow">
+
+              <!-- Branch 1 -->
+              <div class="bcol">
+                ${blbl('场景 1 · 操控设备', 'bl-b')}
+                ${da()}
+                ${nd('nd-show', '画布出现操控确认卡', '参数变更卡 或 设备动画卡')}
+                ${da()}
+                ${nd('nd-do', '操作员选择：确认或取消', '点击按钮 或 输入"确认/取消"')}
+                ${da()}
+                ${nd('nd-end', '画布显示执行结果', '成功 / 失败 / 重试')}
+              </div>
+
+              <!-- Branch 2 -->
+              <div class="bcol">
+                ${blbl('场景 2 · 询问设备问题', 'bl-g')}
+                ${da()}
+                ${nd('nd-show', '画布出现知识教学卡', 'SOP / 报警处理 / 说明书内容')}
+                ${da()}
+                ${nd('nd-do', '操作员查阅内容', '可展开详情、查看步骤')}
+                ${da()}
+                ${nd('nd-end', '内容展示完毕')}
+              </div>
+
+              <!-- Branch 3 -->
+              <div class="bcol">
+                ${blbl('场景 3 · 通用 / 无关', 'bl-gr')}
+                ${da()}
+                ${nd('nd-show', '对话框返回文字回答', '打招呼、天气、闲聊等内容')}
+                ${da()}
+                ${nd('nd-end', '回答完毕', '不影响工业主流程')}
+                <div class="fwarn">⚠ 非核心场景</div>
+              </div>
+
+            </div>
+          </div>
+        `;
+      }
+
+      // ═══════════════════════════════════════════════════════════════════════════
+      // FLOW A  ·  TECH VIEW
+      // ═══════════════════════════════════════════════════════════════════════════
+      function renderA_Tech() {
+        return `
+          <div class="flow-meta">
+            <h2>大流程 A · 普通对话 <span class="badge badge-a">工控对话</span> <span class="badge badge-tech">⚙️ 技术流程</span></h2>
+            <p>语音输入经过四阶段前置拦截（停止词→UI语音点击→Slot填写→BERT），只有前三阶段全部未命中的输入才进入 BERT NLU，再由 decision 字段驱动工具调用、知识检索或 LLM 作答。</p>
+          </div>
+          ${legendTech}
+          <div class="fc">
+
+            ${nd('nd-start', '语音 / 文字输入', '来自 ASR 转录文本 / 直接文字')}
+            ${da()}
+
+            ${nd('nd-route', '阶段 0 · 停止词检测', '命中 cancel 词表 → 直接生成 stop_action，流程终止', ['rule'])}
+            <div class="fnote">词表来自 voice_aliases.yml · cancel_words（静态构建）</div>
+            ${da()}
+
+            ${nd('nd-route', '阶段 1 · UI 可见元素语音点击匹配', '优先级：waiting_confirmation affirm/deny > 当前Artifact按钮 > 全局固定操作', ['rule'])}
+            ${slink(2)}
+
+            <div class="brow">
+              <div class="bcol">
+                ${blbl('命中 · 语音点击', 'bl-hit')}
+                ${da()}
+                ${nd('nd-tool', '生成 ActionEvent', 'actionId / artifactId / sourceText', ['rule'])}
+                ${da()}
+                ${nd('nd-snap', '画布状态机直接响应', '不调用 BERT，不产生新 Artifact')}
+              </div>
+              <div class="bcol">
+                ${blbl('未命中 · 继续', 'bl-miss')}
+                ${da()}
+                ${nd('nd-route', '阶段 1.5 · waiting_slot + inform 检测', 'session.status=waiting_slot AND 输入为数字/数值', ['rule'])}
+                ${slink(2)}
+                <div class="brow" style="width:100%;">
+                  <div class="bcol">
+                    ${blbl('命中 · 填槽', 'bl-hit')}
+                    ${da()}
+                    ${nd('nd-tool', 'fill_slots 接口', '直接补全当前 slot，不走 BERT', ['rule'])}
+                  </div>
+                  <div class="bcol">
+                    ${blbl('未命中 · 进入 BERT', 'bl-miss')}
+                    ${da()}
+                    ${nd('nd-bert', '阶段 2 · BERT NLU（intelligent_cabin）', 'POST /api/v1/agent/chat\n返回 intent_id + decision + slots', ['bert'])}
+                    <div class="fnote">inference 报错直接抛出，不降级</div>
+                  </div>
+                </div>
+              </div>
+            </div>
+
+            ${da(30)}
+            ${nd('nd-route', 'decision 字段路由', 'execute / clarify / route_to_cloud / reject', ['rule'])}
+            ${slink(3)}
+
+            <div class="brow">
+
+              <!-- Branch 1 -->
+              <div class="bcol">
+                ${blbl('execute · 设备控制域', 'bl-b')}
+                ${da()}
+                ${nd('nd-route', 'domain = machine_control\nconfidence_grade = high\nintent_id = wirecut_*')}
+                ${da()}
+                ${nd('nd-tool', '工业工具调用', 'DBus 写参 / 设备控制指令', ['tool'])}
+                ${da()}
+                ${nd('nd-art', '生成 Artifact', 'ParameterChangeArtifact\nDeviceActionArtifact', ['art'])}
+                ${da()}
+                ${nd('nd-snap', '画布渲染 + 等待 ActionEvent')}
+              </div>
+
+              <!-- Branch 2 -->
+              <div class="bcol">
+                ${blbl('route_to_cloud · 知识域', 'bl-g')}
+                ${da()}
+                ${nd('nd-route', 'domain = equipment_knowledge\n或 confidence 偏低')}
+                ${da()}
+                ${nd('nd-llm', 'LLM 语义兜底分析', '提取检索关键词', ['llm'])}
+                ${da()}
+                ${nd('nd-kb', '知识库检索', '说明书 / SOP / 报警手册', ['tool'])}
+                ${da()}
+                ${nd('nd-llm', 'LLM 组织检索结果', '生成教学结构', ['llm'])}
+                ${da()}
+                ${nd('nd-art', '生成 KnowledgeLessonArtifact', '', ['art'])}
+                ${da()}
+                ${nd('nd-snap', '画布渲染')}
+              </div>
+
+              <!-- Branch 3 -->
+              <div class="bcol">
+                ${blbl('reject · smalltalk / fallback', 'bl-gr')}
+                ${da()}
+                ${nd('nd-route', 'domain = smalltalk\n或无法匹配工业 domain')}
+                ${da()}
+                ${nd('nd-llm', 'LLM 直接作答', '', ['llm'])}
+                ${da()}
+                ${nd('nd-minor', '文字回复，不生成 Artifact')}
+                <div class="fnote">不写入 ArtifactStore</div>
+              </div>
+
+            </div>
+          </div>
+        `;
+      }
+
+
+      // ─── Render Dispatch ──────────────────────────────────────────────────────
+      const renderers = {
+        ux: renderA_UX,
+        tech: renderA_Tech,
+      };
+
+      function render() {
+        const { view } = S;
+
+        // View tabs
+        document.querySelectorAll('#viewTabs .tbtn').forEach(b => {
+          b.classList.remove('active-ux', 'active-tech');
+          if (b.dataset.view === view) {
+            b.classList.add(view === 'ux' ? 'active-ux' : 'active-tech');
+          }
+        });
+
+        // Color bar
+        const bar = document.getElementById('viewBar');
+        bar.className = `view-bar ${view}`;
+
+        // Content
+        document.getElementById('flowBody').innerHTML = renderers[view]();
+      }
+
+      // ─── Events ───────────────────────────────────────────────────────────────
+      document.getElementById('viewTabs').addEventListener('click', e => {
+        const btn = e.target.closest('.tbtn');
+        if (btn && btn.dataset.view) { S.view = btn.dataset.view; render(); }
+      });
+
+      render();
+    </script>
+  </body>
+</html>
diff --git a/intelligent_cabin/.vscode/launch.json b/intelligent_cabin/.vscode/launch.json
new file mode 100644
index 0000000..61e6caa
--- /dev/null
+++ b/intelligent_cabin/.vscode/launch.json
@@ -0,0 +1,44 @@
+{
+  "version": "0.2.0",
+  "configurations": [
+    {
+      "name": "Debug FastAPI",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "uvicorn",
+      "python": "${workspaceFolder}/.venv/bin/python",
+      "cwd": "${workspaceFolder}",
+      "envFile": "${workspaceFolder}/.env",
+      "args": [
+        "app.main:app",
+        "--host",
+        "127.0.0.1",
+        "--port",
+        "8000",
+        "--reload"
+      ],
+      "jinja": true,
+      "justMyCode": false,
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Debug FastAPI Without Reload",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "uvicorn",
+      "python": "${workspaceFolder}/.venv/bin/python",
+      "cwd": "${workspaceFolder}",
+      "envFile": "${workspaceFolder}/.env",
+      "args": [
+        "app.main:app",
+        "--host",
+        "127.0.0.1",
+        "--port",
+        "8000"
+      ],
+      "jinja": true,
+      "justMyCode": false,
+      "console": "integratedTerminal"
+    }
+  ]
+}
\ No newline at end of file
diff --git a/intelligent_cabin/README.md b/intelligent_cabin/README.md
new file mode 100644
index 0000000..dde306d
--- /dev/null
+++ b/intelligent_cabin/README.md
@@ -0,0 +1,161 @@
+# Intelligent Cabin Agent
+
+## Quick Start
+
+1. Create and activate the Python 3.11 virtual environment:
+
+```bash
+uv venv .venv --python 3.11
+source .venv/bin/activate
+```
+2. Install dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+3. Start the service:
+
+```bash
+.venv/bin/uvicorn app.main:app --host 127.0.0.1 --port 8000
+```
+
+4. Open the API docs:
+
+```text
+http://127.0.0.1:8000/docs
+```
+
+Demo UI:
+
+```text
+http://127.0.0.1:8000/demo
+```
+
+Architecture and flow review:
+
+```text
+solution_review.md
+```
+
+The demo console supports:
+
+- local browser session history restore
+- runtime matcher/classifier/session backend switching
+- matcher routing debug panel with Top-K candidates
+- local `rewrite -> keyword/classifier/retrieval -> fusion` decision trace
+- direct display of classifier backend / raw label / fallback reason / raw candidate payload
+- workflow JSON visualization
+
+## Core APIs
+
+- `POST /api/v1/agent/chat`
+- `POST /api/v1/agent/fill-slots`
+- `GET /health`
+
+## Current Scope
+
+- Configurable session backend: memory / Redis
+- Config-driven intent registry
+- Router layer with pluggable matcher / extractor
+- Rule-based fast-path intent routing
+- Basic slot extraction
+- Plugin registry with mock handlers
+- Workflow response payloads
+
+## Runtime Config
+
+- `AGENT_SESSION_BACKEND=memory|redis`
+- `AGENT_REDIS_URL=redis://127.0.0.1:6379/0`
+- `AGENT_REDIS_KEY_PREFIX=agent:session`
+- `AGENT_SESSION_TTL_SECONDS=86400`
+- `AGENT_MATCHER_PIPELINE=keyword`
+- `AGENT_SLOT_EXTRACTOR_BACKEND=heuristic`
+- `AGENT_CLASSIFIER_BACKEND=mock`
+- `AGENT_CLASSIFIER_THRESHOLD=1.2`
+- `AGENT_CLASSIFIER_BERT_THRESHOLD=0.0`
+- `AGENT_CLASSIFIER_MODEL_PATH=/path/to/model`
+- `AGENT_CLASSIFIER_LABEL_MAP_PATH=/path/to/label_map.json`
+- `AGENT_CLASSIFIER_REMOTE_URL=http://127.0.0.1:9000/classify`
+- `AGENT_CLASSIFIER_REMOTE_TIMEOUT_SECONDS=3.0`
+- `AGENT_LOCAL_EXECUTE_THRESHOLD=1.65`
+- `AGENT_LOCAL_ROUTE_TO_CLOUD_THRESHOLD=0.75`
+- `AGENT_LOCAL_CLARIFY_MARGIN_THRESHOLD=0.12`
+- `AGENT_PLANNER_BACKEND=heuristic|dashscope`
+- `AGENT_PLANNER_BASE_URL=https://your-base-url/v1`
+- `AGENT_PLANNER_API_KEY=your-api-key`
+- `AGENT_PLANNER_MODEL_NAME=qwen3.5-plus`
+- `AGENT_PLANNER_TIMEOUT_SECONDS=6.0`
+
+Matcher pipeline examples:
+
+- `AGENT_MATCHER_PIPELINE=keyword`
+- `AGENT_MATCHER_PIPELINE=keyword,classifier,retrieval`
+- `AGENT_MATCHER_PIPELINE=keyword,retrieval`
+- `AGENT_MATCHER_PIPELINE=classifier`
+- `AGENT_MATCHER_PIPELINE=retrieval`
+
+Classifier backend examples:
+
+- `AGENT_CLASSIFIER_BACKEND=mock`
+- `AGENT_CLASSIFIER_BACKEND=bert`
+- `AGENT_CLASSIFIER_BACKEND=remote`
+- `AGENT_CLASSIFIER_TOP_K=3`
+
+For local BERT models:
+
+- install optional runtime deps such as `transformers` and a backend like `torch`
+- point `AGENT_CLASSIFIER_MODEL_PATH` to the local model directory
+- if the model outputs labels like `LABEL_0`, provide `AGENT_CLASSIFIER_LABEL_MAP_PATH`
+- use `AGENT_CLASSIFIER_BERT_THRESHOLD` instead of the mock threshold
+
+Example label map:
+
+```json
+{
+  "LABEL_0": "cs_query_order",
+  "LABEL_1": "cs_cancel_order",
+  "LABEL_2": "cabin_play_music"
+}
+```
+
+Remote classifier expected request payload:
+
+```json
+{
+  "text": "我的订单现在什么情况 A808001",
+  "top_k": 3,
+  "labels": ["cs_query_order", "cs_cancel_order", "cabin_play_music"]
+}
+```
+
+Remote classifier response payload:
+
+```json
+{
+  "intent_id": "cs_query_order",
+  "label": "LABEL_0",
+  "score": 0.982,
+  "model_name": "bert-remote-v1",
+  "candidates": [
+    {"label": "LABEL_0", "intent_id": "cs_query_order", "score": 0.982},
+    {"label": "LABEL_1", "intent_id": "cs_cancel_order", "score": 0.011},
+    {"label": "LABEL_7", "intent_id": "cs_query_logistics", "score": 0.007}
+  ]
+}
+```
+
+When `bert` or `remote` is unavailable or below threshold, the classifier falls back to `mock`, and the demo debug panel shows both the attempted backend and the fallback reason.
+
+Planner notes:
+
+- keep the planner key in environment variables, not in source code or front-end code
+- the planner uses `POST {base_url}/chat/completions`
+- for DashScope OpenAI-compatible endpoints, use the compatible `v1` base URL and set `AGENT_PLANNER_BACKEND=dashscope`
+- when cloud planning is unavailable, the service falls back to a local heuristic planner for multi-command splitting
+
+## Next Steps
+
+- Replace rule router with classifier + retrieval + LLM
+- Connect real business plugins
+- Add automated tests
diff --git a/intelligent_cabin/app/__init__.py b/intelligent_cabin/app/__init__.py
new file mode 100644
index 0000000..e9a035a
--- /dev/null
+++ b/intelligent_cabin/app/__init__.py
@@ -0,0 +1 @@
+"""Application package for the intelligent cabin agent service."""
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diff --git a/intelligent_cabin/app/core/__init__.py b/intelligent_cabin/app/core/__init__.py
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diff --git a/intelligent_cabin/app/core/bootstrap.py b/intelligent_cabin/app/core/bootstrap.py
new file mode 100644
index 0000000..4b1e51a
--- /dev/null
+++ b/intelligent_cabin/app/core/bootstrap.py
@@ -0,0 +1,323 @@
+from app.core.config import settings
+from app.plugins.base import PluginRegistry
+from app.plugins.mock import MockPluginExecutor
+from app.services.agent_service import AgentService
+from app.services.classifier import (
+    BertIntentClassifier,
+    IntentClassifier,
+    JointBertIntentClassifier,
+    MockIntentClassifier,
+    RemoteIntentClassifier,
+)
+from app.services.config_loader import ConfigLoader
+from app.services.intent_registry import IntentRegistry
+from app.services.joint_nlu import JointBertNLU
+from app.services.knowledge_llm import DashScopeKnowledgeLLM
+from app.services.knowledge_store import KnowledgeStore
+from app.services.multi_intent_detector import (
+    BertMultiIntentDetector,
+    JointBertMultiIntentDetector,
+    MultiIntentDetector,
+)
+from app.services.planner import (
+    CompositeWorkflowPlanner,
+    DashScopeWorkflowPlanner,
+    HeuristicWorkflowPlanner,
+    TemplateWorkflowPlanner,
+    WorkflowPlanner,
+)
+from app.services.response_policy import ResponsePolicy
+from app.services.rewrite_engine import ContextRewriteEngine
+from app.services.router import (
+    HeuristicSlotExtractor,
+    JointBertSlotExtractor,
+    IntentRouter,
+    Router,
+    build_matcher_pipeline,
+)
+from app.services.session_store import InMemorySessionStore, RedisSessionStore, SessionStore
+from app.services.social import DashScopeSocialResponder, SocialResponder, SocialRouter
+
+
+def build_session_store(session_backend: str | None = None) -> SessionStore:
+    backend = session_backend or settings.session_backend
+    if backend == "memory":
+        return InMemorySessionStore()
+    if backend == "redis":
+        return RedisSessionStore(
+            redis_url=settings.redis_url,
+            key_prefix=settings.redis_key_prefix,
+            ttl_seconds=settings.session_ttl_seconds,
+        )
+    raise ValueError(f"Unsupported session backend: {backend}")
+
+
+def build_router(
+    intent_registry: IntentRegistry,
+    matcher_pipeline: str | None = None,
+    classifier_backend: str | None = None,
+    classifier: IntentClassifier | None = None,
+    joint_nlu: JointBertNLU | None = None,
+) -> Router:
+    active_pipeline = matcher_pipeline or settings.matcher_pipeline
+    matcher_stages = [stage.strip() for stage in active_pipeline.split(",") if stage.strip()]
+    if not matcher_stages:
+        matcher_stages = ["classifier"]
+    if matcher_stages != ["classifier"]:
+        raise ValueError("Only classifier matcher pipeline is supported in bert-first mode")
+    if settings.slot_extractor_backend not in {"heuristic", "joint_bert"}:
+        raise ValueError(f"Unsupported slot extractor backend: {settings.slot_extractor_backend}")
+    classifier = classifier or build_classifier(
+        matcher_pipeline=active_pipeline,
+        classifier_backend=classifier_backend,
+        joint_nlu=joint_nlu,
+    )
+    if settings.slot_extractor_backend == "heuristic":
+        slot_extractor = HeuristicSlotExtractor()
+    else:
+        if joint_nlu is None:
+            raise ValueError("slot_extractor_backend=joint_bert requires a Joint NLU runtime")
+        slot_extractor = JointBertSlotExtractor(joint_nlu)
+    return IntentRouter(
+        matcher=build_matcher_pipeline(
+            intent_registry,
+            matcher_stages,
+            classifier=classifier,
+            route_to_cloud_threshold=settings.local_route_to_cloud_threshold,
+            clarify_margin_threshold=settings.local_clarify_margin_threshold,
+            classifier_execute_score_threshold=settings.local_classifier_execute_score_threshold,
+            classifier_execute_margin_threshold=settings.local_classifier_execute_margin_threshold,
+        ),
+        slot_extractor=slot_extractor,
+    )
+
+
+def build_classifier(
+    matcher_pipeline: str | None = None,
+    classifier_backend: str | None = None,
+    joint_nlu: JointBertNLU | None = None,
+) -> IntentClassifier | None:
+    active_pipeline = matcher_pipeline or settings.matcher_pipeline
+    active_backend = classifier_backend or settings.classifier_backend
+    if "classifier" not in active_pipeline:
+        return None
+    fallback = MockIntentClassifier(
+        threshold=settings.classifier_threshold,
+        top_k=settings.classifier_top_k,
+    )
+    if active_backend == "mock":
+        return fallback
+    if active_backend == "bert":
+        classifier = BertIntentClassifier(
+            model_path=settings.classifier_model_path,
+            threshold=settings.classifier_bert_threshold,
+            label_map_path=settings.classifier_label_map_path or None,
+            fallback=fallback,
+            top_k=settings.classifier_top_k,
+        )
+        if settings.classifier_warmup_enabled:
+            classifier.warmup(settings.classifier_warmup_text)
+        return classifier
+    if active_backend == "joint_bert":
+        runtime = joint_nlu or build_joint_nlu()
+        classifier = JointBertIntentClassifier(
+            nlu=runtime,
+            threshold=settings.joint_nlu_intent_threshold if settings.joint_nlu_intent_threshold > 0 else 0.0,
+            top_k=settings.joint_nlu_top_k,
+        )
+        if settings.classifier_warmup_enabled:
+            classifier.warmup(settings.classifier_warmup_text)
+        return classifier
+    if active_backend == "remote":
+        return RemoteIntentClassifier(
+            endpoint=settings.classifier_remote_url,
+            timeout_seconds=settings.classifier_remote_timeout_seconds,
+            threshold=settings.classifier_threshold,
+            fallback=fallback,
+            label_map_path=settings.classifier_label_map_path or None,
+            top_k=settings.classifier_top_k,
+        )
+    raise ValueError(f"Unsupported classifier backend: {active_backend}")
+
+
+def build_agent_service() -> AgentService:
+    return build_agent_service_with_runtime()
+
+
+def build_agent_service_with_runtime(
+    matcher_pipeline: str | None = None,
+    classifier_backend: str | None = None,
+    session_backend: str | None = None,
+) -> AgentService:
+    runtime_bundle = load_runtime_bundle()
+    intent_registry = runtime_bundle.intent_registry
+    active_classifier_backend = classifier_backend or settings.classifier_backend
+    needs_joint_nlu = active_classifier_backend == "joint_bert" or settings.slot_extractor_backend == "joint_bert"
+    joint_nlu = build_joint_nlu() if needs_joint_nlu else None
+    classifier = build_classifier(
+        matcher_pipeline=matcher_pipeline or settings.matcher_pipeline,
+        classifier_backend=active_classifier_backend,
+        joint_nlu=joint_nlu,
+    )
+    planner_clause_classifier = (
+        classifier
+        if settings.planner_clause_classifier_enabled and active_classifier_backend in {"bert", "remote", "joint_bert"}
+        else None
+    )
+    multi_intent_detector = build_multi_intent_detector(
+        classifier_backend=classifier_backend,
+        joint_nlu=joint_nlu,
+    )
+    plugin_registry = PluginRegistry()
+    MockPluginExecutor().register(plugin_registry)
+    return AgentService(
+        intent_registry=intent_registry,
+        router=build_router(
+            intent_registry,
+            matcher_pipeline=matcher_pipeline,
+            classifier_backend=active_classifier_backend,
+            classifier=classifier,
+            joint_nlu=joint_nlu,
+        ),
+        plugins=plugin_registry,
+        session_store=build_session_store(session_backend=session_backend),
+        rewrite_engine=runtime_bundle.rewrite_engine,
+        response_policy=ResponsePolicy(
+            templates=runtime_bundle.response_templates,
+            intent_hints=runtime_bundle.intent_hints,
+        ),
+        dialog_rules=runtime_bundle.dialog_rules,
+        dialog_act_engine=runtime_bundle.dialog_act_engine,
+        planner=build_planner(
+            runtime_bundle.workflow_templates,
+            clause_classifier=planner_clause_classifier,
+            multi_intent_detector=multi_intent_detector,
+            joint_nlu=joint_nlu,
+        ),
+        social_router=SocialRouter(),
+        social_responder=build_social_responder(),
+        knowledge_llm=build_knowledge_llm(),
+    )
+
+
+def build_intent_registry() -> IntentRegistry:
+    return load_runtime_bundle().intent_registry
+
+
+def load_runtime_bundle():
+    return ConfigLoader(
+        domain_path=settings.domain_config_path,
+        action_path=settings.action_config_path,
+        response_path=settings.response_config_path,
+        form_path=settings.form_config_path,
+        rule_path=settings.rule_config_path,
+        dialog_act_path=settings.dialog_act_config_path,
+        workflow_path=settings.workflow_config_path,
+        legacy_intent_path=settings.intent_config_path,
+        context_rewrite_path=settings.context_rewrite_config_path,
+    ).load()
+
+
+def build_joint_nlu() -> JointBertNLU:
+    runtime = JointBertNLU(
+        model_path=settings.joint_nlu_model_path,
+        intent_threshold=settings.joint_nlu_intent_threshold if settings.joint_nlu_intent_threshold > 0 else None,
+        top_k=settings.joint_nlu_top_k,
+    )
+    if settings.classifier_warmup_enabled:
+        runtime.warmup(settings.classifier_warmup_text)
+    return runtime
+
+
+def build_multi_intent_detector(
+    classifier_backend: str | None = None,
+    joint_nlu: JointBertNLU | None = None,
+) -> MultiIntentDetector | None:
+    active_backend = classifier_backend or settings.classifier_backend
+    if not settings.planner_multi_intent_detector_enabled:
+        return None
+    if active_backend not in {"bert", "joint_bert"}:
+        return None
+    if active_backend == "joint_bert":
+        runtime = joint_nlu or build_joint_nlu()
+        detector = JointBertMultiIntentDetector(
+            nlu=runtime,
+            threshold=settings.planner_multi_intent_detector_threshold if settings.planner_multi_intent_detector_threshold > 0 else None,
+            top_k=settings.planner_multi_intent_detector_top_k,
+            max_labels=settings.planner_multi_intent_detector_max_labels,
+        )
+        if settings.classifier_warmup_enabled:
+            detector.warmup(settings.classifier_warmup_text)
+        return detector
+    detector_model_path = settings.planner_multi_intent_detector_model_path or settings.classifier_model_path
+    detector = BertMultiIntentDetector(
+        model_path=detector_model_path,
+        threshold=settings.planner_multi_intent_detector_threshold,
+        top_k=settings.planner_multi_intent_detector_top_k,
+        max_labels=settings.planner_multi_intent_detector_max_labels,
+    )
+    if settings.classifier_warmup_enabled:
+        detector.warmup(settings.classifier_warmup_text)
+    return detector
+
+
+def build_planner(
+    workflow_templates=None,
+    clause_classifier: IntentClassifier | None = None,
+    multi_intent_detector: MultiIntentDetector | None = None,
+    joint_nlu: JointBertNLU | None = None,
+) -> WorkflowPlanner:
+    template_planner = TemplateWorkflowPlanner(
+        workflow_templates,
+        clause_classifier=clause_classifier,
+        multi_intent_detector=multi_intent_detector,
+        joint_nlu=joint_nlu,
+        classifier_weight=settings.planner_clause_classifier_weight,
+        model_only_threshold=settings.planner_clause_model_only_threshold,
+    )
+    local_first = CompositeWorkflowPlanner(
+        [
+            template_planner,
+            HeuristicWorkflowPlanner(
+                clause_classifier=clause_classifier,
+                multi_intent_detector=multi_intent_detector,
+                joint_nlu=joint_nlu,
+                classifier_weight=settings.planner_clause_classifier_weight,
+                model_only_threshold=settings.planner_clause_model_only_threshold,
+            ),
+        ]
+    )
+    if settings.planner_backend == "heuristic":
+        return local_first
+    if settings.planner_backend == "dashscope":
+        cloud_planner = DashScopeWorkflowPlanner(
+            base_url=settings.planner_base_url,
+            api_key=settings.planner_api_key,
+            model_name=settings.planner_model_name,
+            timeout_seconds=settings.planner_timeout_seconds,
+            fallback=local_first,
+            joint_nlu=joint_nlu,
+        )
+        return CompositeWorkflowPlanner([local_first, cloud_planner])
+    raise ValueError(f"Unsupported planner backend: {settings.planner_backend}")
+
+
+def build_social_responder() -> SocialResponder:
+    return DashScopeSocialResponder(
+        base_url=settings.planner_base_url,
+        api_key=settings.planner_api_key,
+        model_name=settings.planner_model_name,
+        timeout_seconds=settings.planner_timeout_seconds,
+    )
+
+
+def build_knowledge_llm() -> DashScopeKnowledgeLLM:
+    """构建知识库 LLM 问答器（与 planner 共用 DashScope 配置）。"""
+    store = KnowledgeStore(settings.knowledge_dir)
+    return DashScopeKnowledgeLLM(
+        base_url=settings.planner_base_url,
+        api_key=settings.planner_api_key,
+        model_name=settings.planner_model_name,
+        knowledge_store=store,
+        timeout_seconds=12.0,
+    )
diff --git a/intelligent_cabin/app/core/config.py b/intelligent_cabin/app/core/config.py
new file mode 100644
index 0000000..af2e433
--- /dev/null
+++ b/intelligent_cabin/app/core/config.py
@@ -0,0 +1,61 @@
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+
+class Settings(BaseSettings):
+    app_name: str = "Intelligent Cabin Agent"
+    app_env: str = "dev"
+    app_host: str = "0.0.0.0"
+    app_port: int = 8000
+    intent_config_path: str = "app/data/intents.json"
+    domain_config_path: str = "config/domain.yml"
+    action_config_path: str = "config/actions.yml"
+    response_config_path: str = "config/responses.yml"
+    form_config_path: str = "config/forms.yml"
+    rule_config_path: str = "config/rules.yml"
+    dialog_act_config_path: str = "config/dialog_acts.yml"
+    workflow_config_path: str = "config/workflows.yml"
+    # 本地上下文改写引擎配置（不同设备可切换不同 yml 文件）
+    context_rewrite_config_path: str = "config/context_rewrite.yml"
+    session_backend: str = "memory"
+    redis_url: str = "redis://127.0.0.1:6379/0"
+    redis_key_prefix: str = "agent:session"
+    session_ttl_seconds: int = 86400
+    matcher_pipeline: str = "classifier"
+    slot_extractor_backend: str = "joint_bert"
+    classifier_backend: str = "joint_bert"
+    classifier_threshold: float = 1.2
+    classifier_bert_threshold: float = 0.0
+    classifier_top_k: int = 3
+    classifier_model_path: str = ""
+    classifier_label_map_path: str = ""
+    classifier_warmup_enabled: bool = True
+    classifier_warmup_text: str = "打开车窗"
+    classifier_remote_url: str = ""
+    classifier_remote_timeout_seconds: float = 3.0
+    joint_nlu_model_path: str = "models/local_joint_bert_nlu"
+    joint_nlu_intent_threshold: float = 0.0
+    joint_nlu_top_k: int = 3
+    local_route_to_cloud_threshold: float = 0.75
+    local_clarify_margin_threshold: float = 0.12
+    local_classifier_execute_score_threshold: float = 0.55
+    local_classifier_execute_margin_threshold: float = 0.18
+    planner_backend: str = "heuristic"
+    planner_base_url: str = ""
+    planner_api_key: str = ""
+    planner_model_name: str = ""
+    planner_timeout_seconds: float = 6.0
+    planner_clause_classifier_enabled: bool = True
+    planner_clause_classifier_weight: float = 1.6
+    planner_clause_model_only_threshold: float = 0.62
+    planner_multi_intent_detector_enabled: bool = True
+    planner_multi_intent_detector_model_path: str = ""
+    planner_multi_intent_detector_threshold: float = 0.0
+    planner_multi_intent_detector_top_k: int = 8
+    planner_multi_intent_detector_max_labels: int = 4
+    # 本地知识库目录（存放 .md 格式知识文档）
+    knowledge_dir: str = "config/knowledge"
+
+    model_config = SettingsConfigDict(env_file=".env", env_prefix="AGENT_")
+
+
+settings = Settings()
diff --git a/intelligent_cabin/app/data/bert_intent_eval_independent.jsonl b/intelligent_cabin/app/data/bert_intent_eval_independent.jsonl
new file mode 100644
index 0000000..7ed55df
--- /dev/null
+++ b/intelligent_cabin/app/data/bert_intent_eval_independent.jsonl
@@ -0,0 +1,42 @@
+{"text":"空调先别吹了，关掉吧","expected_label":"cabin_ac_off","category":"business"}
+{"text":"车里有点闷，把冷气开起来","expected_label":"cabin_ac_on","category":"business"}
+{"text":"前挡有雾，赶紧除一下","expected_label":"cabin_defog_front_on","category":"business"}
+{"text":"后玻璃起雾了，开后挡除雾","expected_label":"cabin_defog_rear_on","category":"business"}
+{"text":"风太猛了，给我调小一档","expected_label":"cabin_fan_down","category":"business"}
+{"text":"出风再大一点","expected_label":"cabin_fan_up","category":"business"}
+{"text":"把灯熄了吧","expected_label":"cabin_lights_off","category":"business"}
+{"text":"天快黑了，把大灯打开","expected_label":"cabin_lights_on","category":"business"}
+{"text":"锁上所有车门","expected_label":"cabin_lock_doors","category":"business"}
+{"text":"两边后视镜收起来","expected_label":"cabin_mirror_fold","category":"business"}
+{"text":"把后视镜展开准备出发","expected_label":"cabin_mirror_unfold","category":"business"}
+{"text":"路线不用导了，结束导航","expected_label":"cabin_nav_cancel","category":"business"}
+{"text":"直接带我去龙阳路地铁站","expected_label":"cabin_nav_to","category":"business"}
+{"text":"这首不想听了，切到下一首","expected_label":"cabin_next_track","category":"business"}
+{"text":"音乐先停一下","expected_label":"cabin_pause_music","category":"business"}
+{"text":"放点适合夜里开车听的歌","expected_label":"cabin_play_music","category":"business"}
+{"text":"切回上一首","expected_label":"cabin_previous_track","category":"business"}
+{"text":"座椅加热可以关了","expected_label":"cabin_seat_heat_off","category":"business"}
+{"text":"主驾座椅加热打开","expected_label":"cabin_seat_heat_on","category":"business"}
+{"text":"把车内温度定在二十二度","expected_label":"cabin_set_ac","category":"business"}
+{"text":"天窗给我合上","expected_label":"cabin_sunroof_close","category":"business"}
+{"text":"把天窗翘起来透透气","expected_label":"cabin_sunroof_open","category":"business"}
+{"text":"车门解锁一下","expected_label":"cabin_unlock_doors","category":"business"}
+{"text":"声音太响了，压低点","expected_label":"cabin_volume_down","category":"business"}
+{"text":"音响直接静音","expected_label":"cabin_volume_mute","category":"business"}
+{"text":"把媒体音量往上加","expected_label":"cabin_volume_up","category":"business"}
+{"text":"把四个窗都关严","expected_label":"cabin_window_close","category":"business"}
+{"text":"左前窗打开一点","expected_label":"cabin_window_open","category":"business"}
+{"text":"雨停了，把雨刮停掉","expected_label":"cabin_wiper_off","category":"business"}
+{"text":"开始刮雨刷","expected_label":"cabin_wiper_on","category":"business"}
+{"text":"订单A551201别发了，撤单","expected_label":"cs_cancel_order","category":"business"}
+{"text":"A661202这个包裹送到哪了","expected_label":"cs_query_logistics","category":"business"}
+{"text":"帮我看看A771203这单处理进度","expected_label":"cs_query_order","category":"business"}
+{"text":"这个事情我要真人来跟进","expected_label":"cs_transfer_human","category":"business"}
+{"text":"你好呀","expected_label":"__social__","category":"social"}
+{"text":"早上好，今天心情不错","expected_label":"__social__","category":"social"}
+{"text":"你叫什么名字来着","expected_label":"__social__","category":"social"}
+{"text":"今天天气挺舒服的","expected_label":"__social__","category":"social"}
+{"text":"帮我点份炸鸡外卖","expected_label":"__out_of_scope__","category":"out_of_scope"}
+{"text":"给我订明晚的酒店","expected_label":"__out_of_scope__","category":"out_of_scope"}
+{"text":"人活着的意义是什么","expected_label":"__out_of_scope__","category":"out_of_scope"}
+{"text":"推荐一部悬疑电影","expected_label":"__out_of_scope__","category":"out_of_scope"}
diff --git a/intelligent_cabin/app/data/bert_intent_multilabel_eval_independent.jsonl b/intelligent_cabin/app/data/bert_intent_multilabel_eval_independent.jsonl
new file mode 100644
index 0000000..ac3cd32
--- /dev/null
+++ b/intelligent_cabin/app/data/bert_intent_multilabel_eval_independent.jsonl
@@ -0,0 +1,37 @@
+{"text":"车里闷，给我透个气，再放点轻松的歌","expected_intent_ids":["cabin_window_open","cabin_play_music"],"category":"cabin_parallel"}
+{"text":"先把空调开起来，顺手把窗户关好","expected_intent_ids":["cabin_ac_on","cabin_window_close"],"category":"cabin_parallel"}
+{"text":"带我去公司，路上播点民谣","expected_intent_ids":["cabin_nav_to","cabin_play_music"],"category":"cabin_parallel"}
+{"text":"有点热，把温度打到二十一度，再来点音乐","expected_intent_ids":["cabin_set_ac","cabin_play_music"],"category":"cabin_parallel"}
+{"text":"导航去虹桥站，然后把空调打开","expected_intent_ids":["cabin_nav_to","cabin_ac_on"],"category":"cabin_sequence"}
+{"text":"前挡看不清了，开除雾，风也加大一点","expected_intent_ids":["cabin_defog_front_on","cabin_fan_up"],"category":"cabin_parallel"}
+{"text":"后面玻璃有雾，先除雾，再把窗关上","expected_intent_ids":["cabin_defog_rear_on","cabin_window_close"],"category":"cabin_sequence"}
+{"text":"把空调开了，风别太小，再来首歌","expected_intent_ids":["cabin_ac_on","cabin_fan_up","cabin_play_music"],"category":"cabin_parallel"}
+{"text":"去浦东机场，车里凉一点，顺便放点歌","expected_intent_ids":["cabin_nav_to","cabin_set_ac","cabin_play_music"],"category":"cabin_parallel"}
+{"text":"先开一点窗，别那么闷，再把温度调低","expected_intent_ids":["cabin_window_open","cabin_set_ac"],"category":"cabin_parallel"}
+{"text":"把四个窗都关了，然后播点轻音乐","expected_intent_ids":["cabin_window_close","cabin_play_music"],"category":"cabin_sequence"}
+{"text":"把天窗打开透口气，再开空调","expected_intent_ids":["cabin_sunroof_open","cabin_ac_on"],"category":"cabin_parallel"}
+{"text":"开导航去徐家汇，顺便把风量调大","expected_intent_ids":["cabin_nav_to","cabin_fan_up"],"category":"cabin_parallel"}
+{"text":"音乐停一下，然后导航到公司","expected_intent_ids":["cabin_pause_music","cabin_nav_to"],"category":"cabin_sequence"}
+{"text":"锁车门，再把后视镜收起来","expected_intent_ids":["cabin_lock_doors","cabin_mirror_fold"],"category":"cabin_sequence"}
+{"text":"把车门解锁，再把镜子展开","expected_intent_ids":["cabin_unlock_doors","cabin_mirror_unfold"],"category":"cabin_sequence"}
+{"text":"路线别导了，音乐也停一下","expected_intent_ids":["cabin_nav_cancel","cabin_pause_music"],"category":"cabin_parallel"}
+{"text":"温度调到二十三度，风稍微小一点","expected_intent_ids":["cabin_set_ac","cabin_fan_down"],"category":"cabin_parallel"}
+{"text":"查下订单A812301，如果还没发货就取消掉","expected_intent_ids":["cs_query_order","cs_cancel_order"],"category":"cs_conditional"}
+{"text":"帮我看A812302物流，要是太慢就转人工","expected_intent_ids":["cs_query_logistics","cs_transfer_human"],"category":"cs_conditional"}
+{"text":"先查一下A812303这单进度，再帮我转人工客服","expected_intent_ids":["cs_query_order","cs_transfer_human"],"category":"cs_sequence"}
+{"text":"订单A812304先查下状态，再看看物流到了哪","expected_intent_ids":["cs_query_order","cs_query_logistics"],"category":"cs_sequence"}
+{"text":"我想先看看A812305有没有发货，没发的话直接撤单","expected_intent_ids":["cs_query_order","cs_cancel_order"],"category":"cs_conditional"}
+{"text":"把空调关掉","expected_intent_ids":["cabin_ac_off"],"category":"single_guard"}
+{"text":"帮我开一下前挡除雾","expected_intent_ids":["cabin_defog_front_on"],"category":"single_guard"}
+{"text":"风太大了，往小调一点","expected_intent_ids":["cabin_fan_down"],"category":"single_guard"}
+{"text":"给我导航到龙阳路","expected_intent_ids":["cabin_nav_to"],"category":"single_guard"}
+{"text":"来点轻音乐","expected_intent_ids":["cabin_play_music"],"category":"single_guard"}
+{"text":"把左前窗降一点","expected_intent_ids":["cabin_window_open"],"category":"single_guard"}
+{"text":"订单A812306不要了，直接取消","expected_intent_ids":["cs_cancel_order"],"category":"single_guard"}
+{"text":"A812307这个快递到哪了","expected_intent_ids":["cs_query_logistics"],"category":"single_guard"}
+{"text":"导航去公司，再把空调开开，歌也放起来","expected_intent_ids":["cabin_nav_to","cabin_ac_on","cabin_play_music"],"category":"cabin_parallel"}
+{"text":"把雨刮打开，顺便关下车窗","expected_intent_ids":["cabin_wiper_on","cabin_window_close"],"category":"cabin_parallel"}
+{"text":"雨停了，雨刮关掉，再把窗开一点","expected_intent_ids":["cabin_wiper_off","cabin_window_open"],"category":"cabin_sequence"}
+{"text":"把天窗合上，然后把音乐暂停","expected_intent_ids":["cabin_sunroof_close","cabin_pause_music"],"category":"cabin_sequence"}
+{"text":"先把音量调大，再切下一首","expected_intent_ids":["cabin_volume_up","cabin_next_track"],"category":"cabin_parallel"}
+{"text":"静音之后切回上一首","expected_intent_ids":["cabin_volume_mute","cabin_previous_track"],"category":"cabin_sequence"}
diff --git a/intelligent_cabin/app/data/bert_intent_multilabel_train.jsonl b/intelligent_cabin/app/data/bert_intent_multilabel_train.jsonl
new file mode 100644
index 0000000..423119c
--- /dev/null
+++ b/intelligent_cabin/app/data/bert_intent_multilabel_train.jsonl
@@ -0,0 +1,72 @@
+{"text": "打开车窗并播放音乐", "intent_ids": ["cabin_window_open", "cabin_play_music"]}
+{"text": "把空调打开然后开下车窗", "intent_ids": ["cabin_ac_on", "cabin_window_open"]}
+{"text": "导航去公司再来点轻音乐", "intent_ids": ["cabin_nav_to", "cabin_play_music"]}
+{"text": "把空调调到22度并播放周杰伦", "intent_ids": ["cabin_set_ac", "cabin_play_music"]}
+{"text": "打开空调顺便把车窗关上", "intent_ids": ["cabin_ac_on", "cabin_window_close"]}
+{"text": "导航去虹桥机场然后把空调调到21度", "intent_ids": ["cabin_nav_to", "cabin_set_ac"]}
+{"text": "开下窗，再来首歌", "intent_ids": ["cabin_window_open", "cabin_play_music"]}
+{"text": "空调开起来，风再大一点", "intent_ids": ["cabin_ac_on", "cabin_fan_up"]}
+{"text": "把空调调低一点，再把风量开大", "intent_ids": ["cabin_set_ac", "cabin_fan_up"]}
+{"text": "外面太吵了，关窗，然后放点轻音乐", "intent_ids": ["cabin_window_close", "cabin_play_music"]}
+{"text": "前挡起雾了，开除雾，再把风量调大", "intent_ids": ["cabin_defog_front_on", "cabin_fan_up"]}
+{"text": "后挡有雾，除一下，再把窗户关好", "intent_ids": ["cabin_defog_rear_on", "cabin_window_close"]}
+{"text": "把空调设到24度，顺便透透气", "intent_ids": ["cabin_set_ac", "cabin_window_open"]}
+{"text": "导航到公司并打开空调", "intent_ids": ["cabin_nav_to", "cabin_ac_on"]}
+{"text": "去徐家汇，车里太热了顺便降温", "intent_ids": ["cabin_nav_to", "cabin_set_ac"]}
+{"text": "来点歌，再把车窗打开一点", "intent_ids": ["cabin_play_music", "cabin_window_open"]}
+{"text": "把风量调小一点，然后放点音乐", "intent_ids": ["cabin_fan_down", "cabin_play_music"]}
+{"text": "开空调，关窗，播放轻音乐", "intent_ids": ["cabin_ac_on", "cabin_window_close", "cabin_play_music"]}
+{"text": "导航去最近的充电站，再开一点窗透气", "intent_ids": ["cabin_nav_to", "cabin_window_open"]}
+{"text": "把温度调到20度，关上车窗，再来一首夜曲", "intent_ids": ["cabin_set_ac", "cabin_window_close", "cabin_play_music"]}
+{"text": "查一下订单A700001，如果还没发货就取消", "intent_ids": ["cs_query_order", "cs_cancel_order"]}
+{"text": "帮我看下A700002这单物流，没到的话转人工", "intent_ids": ["cs_query_logistics", "cs_transfer_human"]}
+{"text": "查下订单A700003现在啥情况，然后帮我转人工", "intent_ids": ["cs_query_order", "cs_transfer_human"]}
+{"text": "先查订单A700004，再查物流进度", "intent_ids": ["cs_query_order", "cs_query_logistics"]}
+{"text": "打开空调并导航去公司再放点歌", "intent_ids": ["cabin_ac_on", "cabin_nav_to", "cabin_play_music"]}
+{"text": "帮我透透气，然后把温度调到21度", "intent_ids": ["cabin_window_open", "cabin_set_ac"]}
+{"text": "开前挡除雾，再把风开大一点", "intent_ids": ["cabin_defog_front_on", "cabin_fan_up"]}
+{"text": "后窗除雾后把车窗关上", "intent_ids": ["cabin_defog_rear_on", "cabin_window_close"]}
+{"text": "导航到浦东机场，空调开一下，来点民谣", "intent_ids": ["cabin_nav_to", "cabin_ac_on", "cabin_play_music"]}
+{"text": "把车里弄凉快点，顺便放点轻音乐", "intent_ids": ["cabin_set_ac", "cabin_play_music"]}
+{"text": "打开车窗和空调", "intent_ids": ["cabin_window_open", "cabin_ac_on"]}
+{"text": "开窗和开空调", "intent_ids": ["cabin_window_open", "cabin_ac_on"]}
+{"text": "把车窗打开，空调也打开", "intent_ids": ["cabin_window_open", "cabin_ac_on"]}
+{"text": "车里闷，给我透个气，再放点轻松的歌", "intent_ids": ["cabin_window_open", "cabin_play_music"]}
+{"text": "透透气，再来一首黄昏", "intent_ids": ["cabin_window_open", "cabin_play_music"]}
+{"text": "先把空调开起来，顺手把窗户关好", "intent_ids": ["cabin_ac_on", "cabin_window_close"]}
+{"text": "带我去公司，路上播点民谣", "intent_ids": ["cabin_nav_to", "cabin_play_music"]}
+{"text": "有点热，把温度打到二十一度，再来点音乐", "intent_ids": ["cabin_set_ac", "cabin_play_music"]}
+{"text": "导航去虹桥站，然后把空调打开", "intent_ids": ["cabin_nav_to", "cabin_ac_on"]}
+{"text": "前挡看不清了，开除雾，风也加大一点", "intent_ids": ["cabin_defog_front_on", "cabin_fan_up"]}
+{"text": "后面玻璃有雾，先除雾，再把窗关上", "intent_ids": ["cabin_defog_rear_on", "cabin_window_close"]}
+{"text": "把空调开了，风别太小，再来首歌", "intent_ids": ["cabin_ac_on", "cabin_fan_up", "cabin_play_music"]}
+{"text": "去浦东机场，车里凉一点，顺便放点歌", "intent_ids": ["cabin_nav_to", "cabin_set_ac", "cabin_play_music"]}
+{"text": "先开一点窗，别那么闷，再把温度调低", "intent_ids": ["cabin_window_open", "cabin_set_ac"]}
+{"text": "把四个窗都关了，然后播点轻音乐", "intent_ids": ["cabin_window_close", "cabin_play_music"]}
+{"text": "把天窗打开透口气，再开空调", "intent_ids": ["cabin_sunroof_open", "cabin_ac_on"]}
+{"text": "开导航去徐家汇，顺便把风量调大", "intent_ids": ["cabin_nav_to", "cabin_fan_up"]}
+{"text": "音乐停一下，然后导航到公司", "intent_ids": ["cabin_pause_music", "cabin_nav_to"]}
+{"text": "锁车门，再把后视镜收起来", "intent_ids": ["cabin_lock_doors", "cabin_mirror_fold"]}
+{"text": "把车门解锁，再把镜子展开", "intent_ids": ["cabin_unlock_doors", "cabin_mirror_unfold"]}
+{"text": "路线别导了，音乐也停一下", "intent_ids": ["cabin_nav_cancel", "cabin_pause_music"]}
+{"text": "温度调到二十三度，风稍微小一点", "intent_ids": ["cabin_set_ac", "cabin_fan_down"]}
+{"text": "查下订单A812301，如果还没发货就取消掉", "intent_ids": ["cs_query_order", "cs_cancel_order"]}
+{"text": "帮我看A812302物流，要是太慢就转人工", "intent_ids": ["cs_query_logistics", "cs_transfer_human"]}
+{"text": "先查一下A812303这单进度，再帮我转人工客服", "intent_ids": ["cs_query_order", "cs_transfer_human"]}
+{"text": "订单A812304先查下状态，再看看物流到了哪", "intent_ids": ["cs_query_order", "cs_query_logistics"]}
+{"text": "我想先看看A812305有没有发货，没发的话直接撤单", "intent_ids": ["cs_query_order", "cs_cancel_order"]}
+{"text": "导航去公司，再把空调开开，歌也放起来", "intent_ids": ["cabin_nav_to", "cabin_ac_on", "cabin_play_music"]}
+{"text": "把雨刮打开，顺便关下车窗", "intent_ids": ["cabin_wiper_on", "cabin_window_close"]}
+{"text": "雨停了，雨刮关掉，再把窗开一点", "intent_ids": ["cabin_wiper_off", "cabin_window_open"]}
+{"text": "把天窗合上，然后把音乐暂停", "intent_ids": ["cabin_sunroof_close", "cabin_pause_music"]}
+{"text": "先把音量调大，再切下一首", "intent_ids": ["cabin_volume_up", "cabin_next_track"]}
+{"text": "静音之后切回上一首", "intent_ids": ["cabin_volume_mute", "cabin_previous_track"]}
+{"text": "来点music", "intent_ids": ["cabin_play_music"]}
+{"text": "来点音乐，想听黄昏", "intent_ids": ["cabin_play_music"]}
+{"text": "放周杰伦的黄昏", "intent_ids": ["cabin_play_music"]}
+{"text": "来一首黄昏", "intent_ids": ["cabin_play_music"]}
+{"text": "给我放黄昏", "intent_ids": ["cabin_play_music"]}
+{"text": "开窗顺便放点歌", "intent_ids": ["cabin_window_open", "cabin_play_music"]}
+{"text": "导航到公司同时放点轻音乐", "intent_ids": ["cabin_nav_to", "cabin_play_music"]}
+{"text": "空调打开再把风量调大", "intent_ids": ["cabin_ac_on", "cabin_fan_up"]}
+{"text": "关窗再暂停音乐", "intent_ids": ["cabin_window_close", "cabin_pause_music"]}
diff --git a/intelligent_cabin/app/data/bert_intent_test.jsonl b/intelligent_cabin/app/data/bert_intent_test.jsonl
new file mode 100644
index 0000000..b231ed8
--- /dev/null
+++ b/intelligent_cabin/app/data/bert_intent_test.jsonl
@@ -0,0 +1,35 @@
+{"text":"查一下订单A700001现在什么状态","intent_id":"cs_query_order"}
+{"text":"我的订单A700002到哪一步了","intent_id":"cs_query_order"}
+{"text":"帮我看看A700003这个订单","intent_id":"cs_query_order"}
+{"text":"订单A700004现在处理到哪里","intent_id":"cs_query_order"}
+{"text":"确认下A700005订单状态","intent_id":"cs_query_order"}
+{"text":"帮我查A800001物流进度","intent_id":"cs_query_logistics"}
+{"text":"快递A800002到哪儿了","intent_id":"cs_query_logistics"}
+{"text":"看看A800003配送状态","intent_id":"cs_query_logistics"}
+{"text":"订单A800004物流更新了吗","intent_id":"cs_query_logistics"}
+{"text":"查询A800005的快递信息","intent_id":"cs_query_logistics"}
+{"text":"帮我取消A900001这个订单","intent_id":"cs_cancel_order"}
+{"text":"A900002别要了给我撤销","intent_id":"cs_cancel_order"}
+{"text":"把订单A900003取消掉","intent_id":"cs_cancel_order"}
+{"text":"我不要A900004了","intent_id":"cs_cancel_order"}
+{"text":"撤销一下A900005订单","intent_id":"cs_cancel_order"}
+{"text":"我要找人工客服处理","intent_id":"cs_transfer_human"}
+{"text":"现在转人工","intent_id":"cs_transfer_human"}
+{"text":"麻烦给我接人工服务","intent_id":"cs_transfer_human"}
+{"text":"帮我呼叫真人客服","intent_id":"cs_transfer_human"}
+{"text":"别机器人了我要人工","intent_id":"cs_transfer_human"}
+{"text":"导航到公司停车场","intent_id":"cabin_nav_to"}
+{"text":"带我去浦东机场T2","intent_id":"cabin_nav_to"}
+{"text":"去最近的服务区","intent_id":"cabin_nav_to"}
+{"text":"我要去徐家汇","intent_id":"cabin_nav_to"}
+{"text":"开导航去虹桥机场","intent_id":"cabin_nav_to"}
+{"text":"把空调设到22度","intent_id":"cabin_set_ac"}
+{"text":"车里温度调成24度","intent_id":"cabin_set_ac"}
+{"text":"冷气开到20度","intent_id":"cabin_set_ac"}
+{"text":"空调给我调低一点到21度","intent_id":"cabin_set_ac"}
+{"text":"温度改成23度","intent_id":"cabin_set_ac"}
+{"text":"播放一首轻音乐","intent_id":"cabin_play_music"}
+{"text":"来点周杰伦的歌","intent_id":"cabin_play_music"}
+{"text":"放一首夜曲","intent_id":"cabin_play_music"}
+{"text":"我想听摇滚","intent_id":"cabin_play_music"}
+{"text":"给我播点古典音乐","intent_id":"cabin_play_music"}
diff --git a/intelligent_cabin/app/data/bert_intent_train.jsonl b/intelligent_cabin/app/data/bert_intent_train.jsonl
new file mode 100644
index 0000000..7c981c1
--- /dev/null
+++ b/intelligent_cabin/app/data/bert_intent_train.jsonl
@@ -0,0 +1,118 @@
+{"text":"帮我查一下订单A123456","intent_id":"cs_query_order"}
+{"text":"查询订单A765432现在到哪一步了","intent_id":"cs_query_order"}
+{"text":"我的订单A998877是什么状态","intent_id":"cs_query_order"}
+{"text":"帮我看看订单A556677","intent_id":"cs_query_order"}
+{"text":"查下订单A112233","intent_id":"cs_query_order"}
+{"text":"订单A456789现在怎么样","intent_id":"cs_query_order"}
+{"text":"我想查订单A333444","intent_id":"cs_query_order"}
+{"text":"看看订单A909090进度","intent_id":"cs_query_order"}
+{"text":"订单A202501状态","intent_id":"cs_query_order"}
+{"text":"帮我确认一下订单A808001","intent_id":"cs_query_order"}
+{"text":"查物流A123456","intent_id":"cs_query_logistics"}
+{"text":"帮我看订单A765432物流","intent_id":"cs_query_logistics"}
+{"text":"快递A998877到哪了","intent_id":"cs_query_logistics"}
+{"text":"物流A556677现在什么情况","intent_id":"cs_query_logistics"}
+{"text":"查一下快递单A112233","intent_id":"cs_query_logistics"}
+{"text":"看看A456789物流信息","intent_id":"cs_query_logistics"}
+{"text":"订单A333444的快递到了吗","intent_id":"cs_query_logistics"}
+{"text":"帮我查查A909090配送进度","intent_id":"cs_query_logistics"}
+{"text":"物流单号A202501现在在哪里","intent_id":"cs_query_logistics"}
+{"text":"我的订单A808001物流到了哪","intent_id":"cs_query_logistics"}
+{"text":"帮我取消订单A123456","intent_id":"cs_cancel_order"}
+{"text":"取消一下A765432这个订单","intent_id":"cs_cancel_order"}
+{"text":"撤销订单A998877","intent_id":"cs_cancel_order"}
+{"text":"订单A556677我不要了","intent_id":"cs_cancel_order"}
+{"text":"把A112233这个订单取消掉","intent_id":"cs_cancel_order"}
+{"text":"我想退掉并取消A456789","intent_id":"cs_cancel_order"}
+{"text":"帮我撤回订单A333444","intent_id":"cs_cancel_order"}
+{"text":"A909090别发了直接取消","intent_id":"cs_cancel_order"}
+{"text":"取消订单号A202501","intent_id":"cs_cancel_order"}
+{"text":"把A808001撤销了","intent_id":"cs_cancel_order"}
+{"text":"帮我转人工客服","intent_id":"cs_transfer_human"}
+{"text":"我要人工服务","intent_id":"cs_transfer_human"}
+{"text":"接人工","intent_id":"cs_transfer_human"}
+{"text":"帮我联系人工客服","intent_id":"cs_transfer_human"}
+{"text":"转接人工处理","intent_id":"cs_transfer_human"}
+{"text":"这个问题我要找人工","intent_id":"cs_transfer_human"}
+{"text":"给我人工坐席","intent_id":"cs_transfer_human"}
+{"text":"不要机器人了转人工","intent_id":"cs_transfer_human"}
+{"text":"请给我人工客服","intent_id":"cs_transfer_human"}
+{"text":"帮我找真人客服","intent_id":"cs_transfer_human"}
+{"text":"导航去公司","intent_id":"cabin_nav_to"}
+{"text":"带我去机场","intent_id":"cabin_nav_to"}
+{"text":"去虹桥火车站","intent_id":"cabin_nav_to"}
+{"text":"导航到世纪大道","intent_id":"cabin_nav_to"}
+{"text":"帮我开车去陆家嘴","intent_id":"cabin_nav_to"}
+{"text":"去最近的充电站","intent_id":"cabin_nav_to"}
+{"text":"带我到南京东路","intent_id":"cabin_nav_to"}
+{"text":"导航去浦东机场","intent_id":"cabin_nav_to"}
+{"text":"去静安寺","intent_id":"cabin_nav_to"}
+{"text":"我要去公司园区","intent_id":"cabin_nav_to"}
+{"text":"把空调调到22度","intent_id":"cabin_set_ac"}
+{"text":"空调设成24度","intent_id":"cabin_set_ac"}
+{"text":"温度调低到20度","intent_id":"cabin_set_ac"}
+{"text":"车里空调开到23度","intent_id":"cabin_set_ac"}
+{"text":"帮我把车内温度设置为21度","intent_id":"cabin_set_ac"}
+{"text":"空调打到25度","intent_id":"cabin_set_ac"}
+{"text":"把冷气调成19度","intent_id":"cabin_set_ac"}
+{"text":"把空调温度改为26度","intent_id":"cabin_set_ac"}
+{"text":"车内设到18度","intent_id":"cabin_set_ac"}
+{"text":"温度给我设成22度","intent_id":"cabin_set_ac"}
+{"text":"播放轻音乐","intent_id":"cabin_play_music"}
+{"text":"来点歌","intent_id":"cabin_play_music"}
+{"text":"帮我放首歌","intent_id":"cabin_play_music"}
+{"text":"我想听周杰伦","intent_id":"cabin_play_music"}
+{"text":"来一首夜曲","intent_id":"cabin_play_music"}
+{"text":"放点摇滚","intent_id":"cabin_play_music"}
+{"text":"听一下古典音乐","intent_id":"cabin_play_music"}
+{"text":"给我播轻音乐","intent_id":"cabin_play_music"}
+{"text":"来点民谣","intent_id":"cabin_play_music"}
+{"text":"播放默认歌单","intent_id":"cabin_play_music"}
+{"text":"订单A310001现在受理了吗","intent_id":"cs_query_order"}
+{"text":"帮我看下A310002这单进展","intent_id":"cs_query_order"}
+{"text":"A310003这个订单处理到哪了","intent_id":"cs_query_order"}
+{"text":"查询一下订单A310004当前状态","intent_id":"cs_query_order"}
+{"text":"订单A310005现在啥情况","intent_id":"cs_query_order"}
+{"text":"帮我确认A310006订单有没有在处理","intent_id":"cs_query_order"}
+{"text":"A310007这笔订单最新进度是什么","intent_id":"cs_query_order"}
+{"text":"看看订单A310008有没有结果","intent_id":"cs_query_order"}
+{"text":"订单A320001物流到哪了","intent_id":"cs_query_logistics"}
+{"text":"帮我查A320002这单配送进度","intent_id":"cs_query_logistics"}
+{"text":"A320003快递现在运到哪里了","intent_id":"cs_query_logistics"}
+{"text":"看一下订单A320004有没有派件","intent_id":"cs_query_logistics"}
+{"text":"A320005物流轨迹更新了吗","intent_id":"cs_query_logistics"}
+{"text":"帮我追踪一下A320006运输状态","intent_id":"cs_query_logistics"}
+{"text":"订单A320007快件到哪一步了","intent_id":"cs_query_logistics"}
+{"text":"A320008这单现在送到哪儿了","intent_id":"cs_query_logistics"}
+{"text":"查一下A320009的派送信息","intent_id":"cs_query_logistics"}
+{"text":"A320010物流有没有新动态","intent_id":"cs_query_logistics"}
+{"text":"订单A330001不要了，帮我撤单","intent_id":"cs_cancel_order"}
+{"text":"A330002这单别发了直接取消","intent_id":"cs_cancel_order"}
+{"text":"把订单A330003停掉吧","intent_id":"cs_cancel_order"}
+{"text":"A330004这个订单我不想要了","intent_id":"cs_cancel_order"}
+{"text":"订单A330005给我撤回","intent_id":"cs_cancel_order"}
+{"text":"把A330006这笔订单关掉","intent_id":"cs_cancel_order"}
+{"text":"A330007先别发货了，取消掉","intent_id":"cs_cancel_order"}
+{"text":"帮我把订单A330008作废","intent_id":"cs_cancel_order"}
+{"text":"A330009这单不要了","intent_id":"cs_cancel_order"}
+{"text":"订单A330010撤单处理","intent_id":"cs_cancel_order"}
+{"text":"这个问题给我人工跟进","intent_id":"cs_transfer_human"}
+{"text":"安排真人客服接手","intent_id":"cs_transfer_human"}
+{"text":"机器人处理不了，转人工","intent_id":"cs_transfer_human"}
+{"text":"帮我叫个客服专员","intent_id":"cs_transfer_human"}
+{"text":"我要人工来处理这事","intent_id":"cs_transfer_human"}
+{"text":"规划路线去虹桥机场T2","intent_id":"cabin_nav_to"}
+{"text":"直接开去公司停车场","intent_id":"cabin_nav_to"}
+{"text":"给我导到最近的充电站","intent_id":"cabin_nav_to"}
+{"text":"徐家汇怎么走，导航一下","intent_id":"cabin_nav_to"}
+{"text":"出发去外滩","intent_id":"cabin_nav_to"}
+{"text":"把车内温度设为20度","intent_id":"cabin_set_ac"}
+{"text":"空调温度改到21度","intent_id":"cabin_set_ac"}
+{"text":"冷气帮我调到22度","intent_id":"cabin_set_ac"}
+{"text":"舱内调成23度","intent_id":"cabin_set_ac"}
+{"text":"给我把温度定在24度","intent_id":"cabin_set_ac"}
+{"text":"随机放点轻音乐","intent_id":"cabin_play_music"}
+{"text":"帮我播首晴天","intent_id":"cabin_play_music"}
+{"text":"来点适合开车听的民谣","intent_id":"cabin_play_music"}
+{"text":"打开音乐，放夜曲","intent_id":"cabin_play_music"}
+{"text":"给我放一些爵士","intent_id":"cabin_play_music"}
diff --git a/intelligent_cabin/app/data/intents.json b/intelligent_cabin/app/data/intents.json
new file mode 100644
index 0000000..5375e1f
--- /dev/null
+++ b/intelligent_cabin/app/data/intents.json
@@ -0,0 +1,352 @@
+[
+  {
+    "intent_id": "cs_query_order",
+    "plugin_id": "plugin.order.query",
+    "domain": "customer_service",
+    "risk_level": "low",
+    "required_slots": ["order_id"],
+    "ask_templates": {
+      "order_id": "请提供订单号。"
+    },
+    "keywords": ["查询订单", "查订单", "订单状态"],
+    "examples": ["帮我查一下订单", "我的订单到哪一步了", "查下订单状态"]
+  },
+  {
+    "intent_id": "cs_query_logistics",
+    "plugin_id": "plugin.logistics.query",
+    "domain": "customer_service",
+    "risk_level": "low",
+    "required_slots": ["order_id"],
+    "ask_templates": {
+      "order_id": "请提供订单号。"
+    },
+    "keywords": ["查物流", "物流", "快递"],
+    "examples": ["帮我查一下快递", "看看物流到哪了", "快递什么时候到"]
+  },
+  {
+    "intent_id": "cs_cancel_order",
+    "plugin_id": "plugin.order.cancel",
+    "domain": "customer_service",
+    "risk_level": "medium",
+    "required_slots": ["order_id"],
+    "ask_templates": {
+      "order_id": "请提供要取消的订单号。"
+    },
+    "keywords": ["取消订单", "撤销订单"],
+    "examples": ["帮我取消这个订单", "我不想要了取消吧", "撤销刚才的订单"]
+  },
+  {
+    "intent_id": "cs_transfer_human",
+    "plugin_id": "plugin.service.transfer_human",
+    "domain": "customer_service",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["转人工", "人工客服", "联系客服"],
+    "examples": ["我要人工客服", "帮我转人工", "联系客服"]
+  },
+  {
+    "intent_id": "cabin_nav_cancel",
+    "plugin_id": "plugin.cabin.navigation.cancel",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["取消导航", "结束导航", "停止导航"],
+    "examples": ["把导航关掉", "退出导航", "别导航了"]
+  },
+  {
+    "intent_id": "cabin_nav_to",
+    "plugin_id": "plugin.cabin.navigation",
+    "domain": "cabin",
+    "risk_level": "medium",
+    "required_slots": ["destination"],
+    "ask_templates": {
+      "destination": "请告诉我要导航去哪里。"
+    },
+    "keywords": ["导航去", "导航到", "带我去"],
+    "examples": ["导航去公司", "带我去机场", "导航到虹桥火车站"]
+  },
+  {
+    "intent_id": "cabin_ac_on",
+    "plugin_id": "plugin.cabin.ac.on",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["打开空调", "开启空调", "空调打开"],
+    "examples": ["把空调打开", "开空调", "启动空调"]
+  },
+  {
+    "intent_id": "cabin_ac_off",
+    "plugin_id": "plugin.cabin.ac.off",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["关闭空调", "关掉空调", "空调关闭"],
+    "examples": ["把空调关掉", "别吹空调了", "空调先关了"]
+  },
+  {
+    "intent_id": "cabin_set_ac",
+    "plugin_id": "plugin.cabin.ac_control",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": ["temperature"],
+    "ask_templates": {
+      "temperature": "请告诉我要设置多少度。"
+    },
+    "keywords": ["空调调到", "温度设成", "设成多少度"],
+    "examples": ["把空调调到22度", "温度设成24度", "空调调到20度"]
+  },
+  {
+    "intent_id": "cabin_fan_up",
+    "plugin_id": "plugin.cabin.fan.up",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["调大风量", "风量大一点", "风量调高"],
+    "examples": ["把风量调大一点", "空调风再大一点", "风量开大些"]
+  },
+  {
+    "intent_id": "cabin_fan_down",
+    "plugin_id": "plugin.cabin.fan.down",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["调小风量", "风量小一点", "风量调低"],
+    "examples": ["把风量调小一点", "空调风太大了", "风量关小些"]
+  },
+  {
+    "intent_id": "cabin_defog_front_on",
+    "plugin_id": "plugin.cabin.defog.front_on",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["打开前挡除雾", "前挡风除雾", "前窗除雾"],
+    "examples": ["帮我打开前挡除雾", "前挡风玻璃起雾了", "开一下前挡除雾"]
+  },
+  {
+    "intent_id": "cabin_defog_rear_on",
+    "plugin_id": "plugin.cabin.defog.rear_on",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["打开后挡除雾", "后挡风除雾", "后窗除雾"],
+    "examples": ["帮我打开后挡除雾", "后挡风玻璃起雾了", "开一下后挡除雾"]
+  },
+  {
+    "intent_id": "cabin_window_open",
+    "plugin_id": "plugin.cabin.window.open",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["打开车窗", "开车窗", "车窗打开"],
+    "examples": ["把车窗打开", "帮我开一下车窗", "打开一点车窗"]
+  },
+  {
+    "intent_id": "cabin_window_close",
+    "plugin_id": "plugin.cabin.window.close",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["关闭车窗", "关车窗", "车窗关上"],
+    "examples": ["把车窗关上", "帮我关一下车窗", "车窗全部关闭"]
+  },
+  {
+    "intent_id": "cabin_sunroof_open",
+    "plugin_id": "plugin.cabin.sunroof.open",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["打开天窗", "开天窗", "天窗打开"],
+    "examples": ["把天窗打开", "帮我开一下天窗", "天窗打开一点"]
+  },
+  {
+    "intent_id": "cabin_sunroof_close",
+    "plugin_id": "plugin.cabin.sunroof.close",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["关闭天窗", "关天窗", "天窗关上"],
+    "examples": ["把天窗关上", "帮我关一下天窗", "关闭全景天窗"]
+  },
+  {
+    "intent_id": "cabin_lock_doors",
+    "plugin_id": "plugin.cabin.doors.lock",
+    "domain": "cabin",
+    "risk_level": "medium",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["锁车门", "锁门", "车门锁上"],
+    "examples": ["帮我锁车", "把车门锁上", "全部车门上锁"]
+  },
+  {
+    "intent_id": "cabin_unlock_doors",
+    "plugin_id": "plugin.cabin.doors.unlock",
+    "domain": "cabin",
+    "risk_level": "medium",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["解锁车门", "开锁", "车门解锁"],
+    "examples": ["帮我解锁车门", "把车门打开锁", "全部车门解锁"]
+  },
+  {
+    "intent_id": "cabin_play_music",
+    "plugin_id": "plugin.cabin.music_play",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["播放音乐", "来点音乐", "放首歌"],
+    "examples": ["播放轻音乐", "来点歌", "帮我放首歌"]
+  },
+  {
+    "intent_id": "cabin_pause_music",
+    "plugin_id": "plugin.cabin.music.pause",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["暂停音乐", "暂停播放", "音乐暂停"],
+    "examples": ["把音乐暂停", "先别放了", "暂停当前歌曲"]
+  },
+  {
+    "intent_id": "cabin_next_track",
+    "plugin_id": "plugin.cabin.music.next",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["下一首", "切下一首", "换首歌"],
+    "examples": ["帮我切到下一首", "下一首歌", "换一首歌"]
+  },
+  {
+    "intent_id": "cabin_previous_track",
+    "plugin_id": "plugin.cabin.music.previous",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["上一首", "切上一首", "返回上一首"],
+    "examples": ["帮我切到上一首", "上一首歌", "返回刚才那首歌"]
+  },
+  {
+    "intent_id": "cabin_volume_up",
+    "plugin_id": "plugin.cabin.volume.up",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["调大音量", "音量大一点", "音量调高"],
+    "examples": ["把音量调大一点", "声音太小了", "音量开大些"]
+  },
+  {
+    "intent_id": "cabin_volume_down",
+    "plugin_id": "plugin.cabin.volume.down",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["调小音量", "音量小一点", "音量调低"],
+    "examples": ["把音量调小一点", "声音太大了", "音量关小些"]
+  },
+  {
+    "intent_id": "cabin_volume_mute",
+    "plugin_id": "plugin.cabin.volume.mute",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["静音", "关闭声音", "音量静音"],
+    "examples": ["把声音关掉", "先静音", "音响静音"]
+  },
+  {
+    "intent_id": "cabin_lights_on",
+    "plugin_id": "plugin.cabin.lights.on",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["打开车灯", "开灯", "车灯打开"],
+    "examples": ["把车灯打开", "帮我开一下灯", "打开大灯"]
+  },
+  {
+    "intent_id": "cabin_lights_off",
+    "plugin_id": "plugin.cabin.lights.off",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["关闭车灯", "关灯", "车灯关闭"],
+    "examples": ["把车灯关掉", "帮我关一下灯", "关闭大灯"]
+  },
+  {
+    "intent_id": "cabin_seat_heat_on",
+    "plugin_id": "plugin.cabin.seat_heat.on",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["打开座椅加热", "开座椅加热", "座椅加热打开"],
+    "examples": ["把座椅加热打开", "帮我开一下座椅加热", "打开主驾座椅加热"]
+  },
+  {
+    "intent_id": "cabin_seat_heat_off",
+    "plugin_id": "plugin.cabin.seat_heat.off",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["关闭座椅加热", "关座椅加热", "座椅加热关闭"],
+    "examples": ["把座椅加热关掉", "帮我关一下座椅加热", "关闭主驾座椅加热"]
+  },
+  {
+    "intent_id": "cabin_mirror_fold",
+    "plugin_id": "plugin.cabin.mirror.fold",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["折叠后视镜", "收起后视镜", "后视镜折叠"],
+    "examples": ["把后视镜折叠起来", "帮我收起后视镜", "折叠两侧后视镜"]
+  },
+  {
+    "intent_id": "cabin_mirror_unfold",
+    "plugin_id": "plugin.cabin.mirror.unfold",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["展开后视镜", "打开后视镜", "后视镜展开"],
+    "examples": ["把后视镜展开", "帮我打开后视镜", "展开两侧后视镜"]
+  },
+  {
+    "intent_id": "cabin_wiper_on",
+    "plugin_id": "plugin.cabin.wiper.on",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["打开雨刷", "开雨刷", "雨刷启动"],
+    "examples": ["把雨刷打开", "帮我开一下雨刷", "启动雨刮器"]
+  },
+  {
+    "intent_id": "cabin_wiper_off",
+    "plugin_id": "plugin.cabin.wiper.off",
+    "domain": "cabin",
+    "risk_level": "low",
+    "required_slots": [],
+    "ask_templates": {},
+    "keywords": ["关闭雨刷", "关雨刷", "雨刷停止"],
+    "examples": ["把雨刷关掉", "帮我关一下雨刷", "停止雨刮器"]
+  }
+]
diff --git a/intelligent_cabin/app/data/joint_nlu_eval.jsonl b/intelligent_cabin/app/data/joint_nlu_eval.jsonl
new file mode 100644
index 0000000..4eb202f
--- /dev/null
+++ b/intelligent_cabin/app/data/joint_nlu_eval.jsonl
@@ -0,0 +1,10 @@
+{"text":"把空调设到21度","intent_id":"cabin_set_ac","slots":[{"slot_name":"temperature","value":"21度","start":6,"end":9}]}
+{"text":"导航到虹桥机场","intent_id":"cabin_nav_to","slots":[{"slot_name":"destination","value":"虹桥机场","start":3,"end":7}]}
+{"text":"来首稻香","intent_id":"cabin_play_music","slots":[{"slot_name":"song","value":"稻香","start":2,"end":4}]}
+{"text":"放点摇滚","intent_id":"cabin_play_music","slots":[{"slot_name":"genre","value":"摇滚","start":2,"end":4}]}
+{"text":"查一下订单A700001现在什么状态","intent_id":"cs_query_order","slots":[{"slot_name":"order_id","value":"A700001","start":5,"end":12}]}
+{"text":"帮我查A900005物流进度","intent_id":"cs_query_logistics","slots":[{"slot_name":"order_id","value":"A900005","start":4,"end":11}]}
+{"text":"撤销订单A202501","intent_id":"cs_cancel_order","slots":[{"slot_name":"order_id","value":"A202501","start":4,"end":11}]}
+{"text":"把车窗打开","intent_id":"cabin_window_open","slots":[]}
+{"text":"把音乐暂停","intent_id":"cabin_pause_music","slots":[]}
+{"text":"帮我锁车","intent_id":"cabin_lock_doors","slots":[]}
diff --git a/intelligent_cabin/app/data/joint_nlu_eval_independent.jsonl b/intelligent_cabin/app/data/joint_nlu_eval_independent.jsonl
new file mode 100644
index 0000000..c519a9b
--- /dev/null
+++ b/intelligent_cabin/app/data/joint_nlu_eval_independent.jsonl
@@ -0,0 +1,43 @@
+{"text":"把空调调到22度","expected_intent_id":"cabin_set_ac","expected_slots":{"temperature":22},"category":"slot_temperature"}
+{"text":"空调给我调到20度","expected_intent_id":"cabin_set_ac","expected_slots":{"temperature":20},"category":"slot_temperature"}
+{"text":"车里温度设成24度","expected_intent_id":"cabin_set_ac","expected_slots":{"temperature":24},"category":"slot_temperature"}
+{"text":"把温度打到21度","expected_intent_id":"cabin_set_ac","expected_slots":{"temperature":21},"category":"slot_temperature"}
+{"text":"导航去公司停车场","expected_intent_id":"cabin_nav_to","expected_slots":{"destination":"公司停车场"},"category":"slot_destination"}
+{"text":"带我去浦东机场","expected_intent_id":"cabin_nav_to","expected_slots":{"destination":"浦东机场"},"category":"slot_destination"}
+{"text":"导航到南京东路","expected_intent_id":"cabin_nav_to","expected_slots":{"destination":"南京东路"},"category":"slot_destination"}
+{"text":"去虹桥机场","expected_intent_id":"cabin_nav_to","expected_slots":{"destination":"虹桥机场"},"category":"slot_destination"}
+{"text":"查一下订单A123456","expected_intent_id":"cs_query_order","expected_slots":{"order_id":"A123456"},"category":"slot_order"}
+{"text":"帮我看看订单A808001","expected_intent_id":"cs_query_order","expected_slots":{"order_id":"A808001"},"category":"slot_order"}
+{"text":"快递A998877到哪了","expected_intent_id":"cs_query_logistics","expected_slots":{"order_id":"A998877"},"category":"slot_order"}
+{"text":"取消订单A556677","expected_intent_id":"cs_cancel_order","expected_slots":{"order_id":"A556677"},"category":"slot_order"}
+{"text":"来一首青花瓷","expected_intent_id":"cabin_play_music","expected_slots":{"song":"青花瓷"},"category":"slot_music"}
+{"text":"播放夜的第七章","expected_intent_id":"cabin_play_music","expected_slots":{"song":"夜的第七章"},"category":"slot_music"}
+{"text":"来点爵士","expected_intent_id":"cabin_play_music","expected_slots":{"genre":"爵士"},"category":"slot_music"}
+{"text":"放点摇滚","expected_intent_id":"cabin_play_music","expected_slots":{"genre":"摇滚"},"category":"slot_music"}
+{"text":"给我播点民谣","expected_intent_id":"cabin_play_music","expected_slots":{"genre":"民谣"},"category":"slot_music"}
+{"text":"把车窗打开","expected_intent_id":"cabin_window_open","expected_slots":{},"category":"no_slot_control"}
+{"text":"把车窗关上","expected_intent_id":"cabin_window_close","expected_slots":{},"category":"no_slot_control"}
+{"text":"把天窗打开","expected_intent_id":"cabin_sunroof_open","expected_slots":{},"category":"no_slot_control"}
+{"text":"把天窗合上","expected_intent_id":"cabin_sunroof_close","expected_slots":{},"category":"no_slot_control"}
+{"text":"把空调打开","expected_intent_id":"cabin_ac_on","expected_slots":{},"category":"no_slot_control"}
+{"text":"把空调关掉","expected_intent_id":"cabin_ac_off","expected_slots":{},"category":"no_slot_control"}
+{"text":"把风量调大一点","expected_intent_id":"cabin_fan_up","expected_slots":{},"category":"no_slot_control"}
+{"text":"风太大了，往小调一点","expected_intent_id":"cabin_fan_down","expected_slots":{},"category":"no_slot_control"}
+{"text":"把音乐暂停","expected_intent_id":"cabin_pause_music","expected_slots":{},"category":"no_slot_control"}
+{"text":"帮我切到下一首","expected_intent_id":"cabin_next_track","expected_slots":{},"category":"no_slot_control"}
+{"text":"切回上一首","expected_intent_id":"cabin_previous_track","expected_slots":{},"category":"no_slot_control"}
+{"text":"先静音","expected_intent_id":"cabin_volume_mute","expected_slots":{},"category":"no_slot_control"}
+{"text":"把音量调大一点","expected_intent_id":"cabin_volume_up","expected_slots":{},"category":"no_slot_control"}
+{"text":"把音量调小一点","expected_intent_id":"cabin_volume_down","expected_slots":{},"category":"no_slot_control"}
+{"text":"把后视镜收起来","expected_intent_id":"cabin_mirror_fold","expected_slots":{},"category":"failure_replay"}
+{"text":"把镜子展开","expected_intent_id":"cabin_mirror_unfold","expected_slots":{},"category":"failure_replay"}
+{"text":"锁车门","expected_intent_id":"cabin_lock_doors","expected_slots":{},"category":"failure_replay"}
+{"text":"把车门解锁","expected_intent_id":"cabin_unlock_doors","expected_slots":{},"category":"failure_replay"}
+{"text":"路线别导了","expected_intent_id":"cabin_nav_cancel","expected_slots":{},"category":"failure_replay"}
+{"text":"音乐停一下","expected_intent_id":"cabin_pause_music","expected_slots":{},"category":"failure_replay"}
+{"text":"雨刮关掉","expected_intent_id":"cabin_wiper_off","expected_slots":{},"category":"failure_replay"}
+{"text":"把雨刮打开","expected_intent_id":"cabin_wiper_on","expected_slots":{},"category":"failure_replay"}
+{"text":"把左前窗降一点","expected_intent_id":"cabin_window_open","expected_slots":{},"category":"failure_replay"}
+{"text":"给我透个气","expected_intent_id":"cabin_window_open","expected_slots":{},"category":"failure_replay"}
+{"text":"风别太小","expected_intent_id":"cabin_fan_up","expected_slots":{},"category":"failure_replay"}
+{"text":"要是太慢就转人工","expected_intent_id":"cs_transfer_human","expected_slots":{},"category":"failure_replay"}
diff --git a/intelligent_cabin/app/data/joint_nlu_multilabel_eval.jsonl b/intelligent_cabin/app/data/joint_nlu_multilabel_eval.jsonl
new file mode 100644
index 0000000..935dead
--- /dev/null
+++ b/intelligent_cabin/app/data/joint_nlu_multilabel_eval.jsonl
@@ -0,0 +1,12 @@
+{"text":"打开车窗和空调","intent_ids":["cabin_window_open","cabin_ac_on"],"slots":[]}
+{"text":"导航去公司再放点轻音乐","intent_ids":["cabin_nav_to","cabin_play_music"],"slots":[{"slot_name":"destination","value":"公司","start":3,"end":5},{"slot_name":"genre","value":"轻音乐","start":8,"end":11}]}
+{"text":"把空调调到22度并播放夜曲","intent_ids":["cabin_set_ac","cabin_play_music"],"slots":[{"slot_name":"temperature","value":"22度","start":6,"end":9},{"slot_name":"song","value":"夜曲","start":12,"end":14}]}
+{"text":"开空调，关窗，再来点民谣","intent_ids":["cabin_ac_on","cabin_window_close","cabin_play_music"],"slots":[{"slot_name":"genre","value":"民谣","start":11,"end":13}]}
+{"text":"查一下订单A700001，如果还没发货就取消","intent_ids":["cs_query_order","cs_cancel_order"],"slots":[{"slot_name":"order_id","value":"A700001","start":5,"end":12}]}
+{"text":"查下A808001物流，没到就转人工","intent_ids":["cs_query_logistics","cs_transfer_human"],"slots":[{"slot_name":"order_id","value":"A808001","start":2,"end":9}]}
+{"text":"把风量调大一点，再把窗户打开","intent_ids":["cabin_fan_up","cabin_window_open"],"slots":[]}
+{"text":"透透气，再来一首黄昏","intent_ids":["cabin_window_open","cabin_play_music"],"slots":[{"slot_name":"song","value":"黄昏","start":9,"end":11}]}
+{"text":"导航到虹桥机场，空调也打开","intent_ids":["cabin_nav_to","cabin_ac_on"],"slots":[{"slot_name":"destination","value":"虹桥机场","start":3,"end":7}]}
+{"text":"后视镜收起来，再锁车门","intent_ids":["cabin_mirror_fold","cabin_lock_doors"],"slots":[]}
+{"text":"雨刮关掉并打开车窗","intent_ids":["cabin_wiper_off","cabin_window_open"],"slots":[]}
+{"text":"来点音乐，再把温度设成21度","intent_ids":["cabin_play_music","cabin_set_ac"],"slots":[{"slot_name":"temperature","value":"21度","start":12,"end":15}]}
diff --git a/intelligent_cabin/app/data/joint_nlu_seed.jsonl b/intelligent_cabin/app/data/joint_nlu_seed.jsonl
new file mode 100644
index 0000000..818e2a2
--- /dev/null
+++ b/intelligent_cabin/app/data/joint_nlu_seed.jsonl
@@ -0,0 +1,27 @@
+{"text":"把空调调到22度","intent_id":"cabin_set_ac","slots":[{"slot_name":"temperature","value":"22度","start":6,"end":9}]}
+{"text":"空调给我调到20度","intent_id":"cabin_set_ac","slots":[{"slot_name":"temperature","value":"20度","start":7,"end":10}]}
+{"text":"车里温度设成24度","intent_id":"cabin_set_ac","slots":[{"slot_name":"temperature","value":"24度","start":7,"end":10}]}
+{"text":"导航去公司停车场","intent_id":"cabin_nav_to","slots":[{"slot_name":"destination","value":"公司停车场","start":3,"end":8}]}
+{"text":"带我去浦东机场","intent_id":"cabin_nav_to","slots":[{"slot_name":"destination","value":"浦东机场","start":3,"end":7}]}
+{"text":"导航到南京东路","intent_id":"cabin_nav_to","slots":[{"slot_name":"destination","value":"南京东路","start":3,"end":7}]}
+{"text":"播放夜曲","intent_id":"cabin_play_music","slots":[{"slot_name":"song","value":"夜曲","start":2,"end":4}]}
+{"text":"来一首青花瓷","intent_id":"cabin_play_music","slots":[{"slot_name":"song","value":"青花瓷","start":3,"end":6}]}
+{"text":"放一首晴天","intent_id":"cabin_play_music","slots":[{"slot_name":"song","value":"晴天","start":3,"end":5}]}
+{"text":"来一首告白气球","intent_id":"cabin_play_music","slots":[{"slot_name":"song","value":"告白气球","start":3,"end":7}]}
+{"text":"播放稻香","intent_id":"cabin_play_music","slots":[{"slot_name":"song","value":"稻香","start":2,"end":4}]}
+{"text":"帮我播首夜的第七章","intent_id":"cabin_play_music","slots":[{"slot_name":"song","value":"夜的第七章","start":4,"end":9}]}
+{"text":"给我来一首晴天","intent_id":"cabin_play_music","slots":[{"slot_name":"song","value":"晴天","start":5,"end":7}]}
+{"text":"车里放首稻香","intent_id":"cabin_play_music","slots":[{"slot_name":"song","value":"稻香","start":4,"end":6}]}
+{"text":"播放轻音乐","intent_id":"cabin_play_music","slots":[{"slot_name":"genre","value":"轻音乐","start":2,"end":5}]}
+{"text":"来点爵士","intent_id":"cabin_play_music","slots":[{"slot_name":"genre","value":"爵士","start":2,"end":4}]}
+{"text":"给我播点民谣","intent_id":"cabin_play_music","slots":[{"slot_name":"genre","value":"民谣","start":4,"end":6}]}
+{"text":"随机放点流行","intent_id":"cabin_play_music","slots":[{"slot_name":"genre","value":"流行","start":4,"end":6}]}
+{"text":"来点古典音乐","intent_id":"cabin_play_music","slots":[{"slot_name":"genre","value":"古典","start":2,"end":4}]}
+{"text":"车里放点摇滚","intent_id":"cabin_play_music","slots":[{"slot_name":"genre","value":"摇滚","start":4,"end":6}]}
+{"text":"给我来点儿歌","intent_id":"cabin_play_music","slots":[{"slot_name":"genre","value":"儿歌","start":4,"end":6}]}
+{"text":"查一下订单A123456","intent_id":"cs_query_order","slots":[{"slot_name":"order_id","value":"A123456","start":5,"end":12}]}
+{"text":"帮我看看订单A808001","intent_id":"cs_query_order","slots":[{"slot_name":"order_id","value":"A808001","start":6,"end":13}]}
+{"text":"快递A998877到哪了","intent_id":"cs_query_logistics","slots":[{"slot_name":"order_id","value":"A998877","start":2,"end":9}]}
+{"text":"帮我查A202501物流","intent_id":"cs_query_logistics","slots":[{"slot_name":"order_id","value":"A202501","start":4,"end":11}]}
+{"text":"取消订单A556677","intent_id":"cs_cancel_order","slots":[{"slot_name":"order_id","value":"A556677","start":4,"end":11}]}
+{"text":"把A112233这个订单取消掉","intent_id":"cs_cancel_order","slots":[{"slot_name":"order_id","value":"A112233","start":1,"end":8}]}
diff --git a/intelligent_cabin/app/main.py b/intelligent_cabin/app/main.py
new file mode 100644
index 0000000..ba720e0
--- /dev/null
+++ b/intelligent_cabin/app/main.py
@@ -0,0 +1,151 @@
+import json
+from concurrent.futures import ThreadPoolExecutor, TimeoutError
+from pathlib import Path
+from uuid import uuid4
+
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import FileResponse, StreamingResponse
+
+
+from app.core.config import settings
+from app.core.bootstrap import build_agent_service_with_runtime, build_intent_registry
+from app.schemas.chat import ChatRequest, ChatResponse, FillSlotsRequest
+from app.schemas.demo import DemoRuntimeConfig, DemoRuntimeUpdateRequest
+
+app = FastAPI(title=settings.app_name)
+
+# CORS：允许 Canvas 前端跨域调用
+# 生产环境请将 allow_origins 替换为实际前端域名
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["http://localhost:3000", "http://127.0.0.1:3000"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+demo_html_path = Path(__file__).parent / "static" / "demo.html"
+chat_stream_executor = ThreadPoolExecutor(max_workers=8)
+
+runtime_config = DemoRuntimeConfig(
+    matcher_pipeline=settings.matcher_pipeline,
+    classifier_backend=settings.classifier_backend,
+    session_backend=settings.session_backend,
+    slot_extractor_backend=settings.slot_extractor_backend,
+    planner_backend=settings.planner_backend,
+    planner_model_name=settings.planner_model_name,
+)
+agent_service = build_agent_service_with_runtime(
+    matcher_pipeline=runtime_config.matcher_pipeline,
+    classifier_backend=runtime_config.classifier_backend,
+    session_backend=runtime_config.session_backend,
+)
+intent_registry = build_intent_registry()
+
+
+@app.get("/health")
+def health() -> dict[str, str]:
+    return {"status": "ok", "env": settings.app_env}
+
+
+@app.get("/")
+@app.get("/demo")
+def demo() -> FileResponse:
+    return FileResponse(demo_html_path)
+
+
+@app.post("/api/v1/agent/chat", response_model=ChatResponse)
+def chat(request: ChatRequest) -> ChatResponse:
+    return agent_service.handle_chat(request)
+
+
+@app.post("/api/v1/agent/chat-stream")
+def chat_stream(request: ChatRequest) -> StreamingResponse:
+    def stream():
+        future = chat_stream_executor.submit(agent_service.handle_chat, request)
+        try:
+            response = future.result(timeout=1.0)
+        except TimeoutError:
+            ack = {
+                "type": "ack",
+                "reply_text": "好的，正在处理中，请稍等一下。",
+                "status": "processing",
+                "trace_id": uuid4().hex,
+            }
+            yield json.dumps(ack, ensure_ascii=False) + "\n"
+            try:
+                response = future.result()
+            except Exception as exc:  # pragma: no cover - stream error fallback
+                payload = {
+                    "type": "error",
+                    "message": str(exc),
+                }
+                yield json.dumps(payload, ensure_ascii=False) + "\n"
+                return
+        except Exception as exc:  # pragma: no cover - stream error fallback
+            payload = {
+                "type": "error",
+                "message": str(exc),
+            }
+            yield json.dumps(payload, ensure_ascii=False) + "\n"
+            return
+
+        try:
+            payload = {
+                "type": "final",
+                "data": response.model_dump(mode="json"),
+            }
+            yield json.dumps(payload, ensure_ascii=False) + "\n"
+        except Exception as exc:  # pragma: no cover - stream error fallback
+            payload = {
+                "type": "error",
+                "message": str(exc),
+            }
+            yield json.dumps(payload, ensure_ascii=False) + "\n"
+
+    return StreamingResponse(stream(), media_type="application/x-ndjson")
+
+
+@app.post("/api/v1/agent/fill-slots", response_model=ChatResponse)
+def fill_slots(request: FillSlotsRequest) -> ChatResponse:
+    return agent_service.handle_fill_slots(request)
+
+
+@app.get("/api/v1/intents")
+def list_intents() -> list[dict[str, object]]:
+    return [intent.model_dump() for intent in intent_registry.list()]
+
+
+@app.get("/api/v1/demo/runtime", response_model=DemoRuntimeConfig)
+def get_demo_runtime() -> DemoRuntimeConfig:
+    return runtime_config
+
+
+@app.post("/api/v1/demo/runtime", response_model=DemoRuntimeConfig)
+def update_demo_runtime(request: DemoRuntimeUpdateRequest) -> DemoRuntimeConfig:
+    global agent_service, runtime_config
+
+    matcher_stages = [stage.strip() for stage in request.matcher_pipeline.split(",") if stage.strip()]
+    if matcher_stages != ["classifier"]:
+        raise HTTPException(status_code=400, detail="Only classifier matcher pipeline is supported in bert-first mode")
+
+    try:
+        next_service = build_agent_service_with_runtime(
+            matcher_pipeline=request.matcher_pipeline,
+            classifier_backend=request.classifier_backend,
+            session_backend=request.session_backend,
+        )
+    except Exception as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+    agent_service = next_service
+    runtime_config = DemoRuntimeConfig(
+        matcher_pipeline=request.matcher_pipeline,
+        classifier_backend=request.classifier_backend,
+        session_backend=request.session_backend,
+        slot_extractor_backend=settings.slot_extractor_backend,
+        planner_backend=settings.planner_backend,
+        planner_model_name=settings.planner_model_name,
+    )
+    return runtime_config
diff --git a/intelligent_cabin/app/plugins/__init__.py b/intelligent_cabin/app/plugins/__init__.py
new file mode 100644
index 0000000..87c9ecf
--- /dev/null
+++ b/intelligent_cabin/app/plugins/__init__.py
@@ -0,0 +1 @@
+"""Plugin adapters for the agent service."""
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diff --git a/intelligent_cabin/app/plugins/base.py b/intelligent_cabin/app/plugins/base.py
new file mode 100644
index 0000000..3cd4e82
--- /dev/null
+++ b/intelligent_cabin/app/plugins/base.py
@@ -0,0 +1,27 @@
+from __future__ import annotations
+
+from collections.abc import Callable
+from typing import Any
+
+PluginHandler = Callable[[dict[str, Any]], dict[str, Any]]
+
+
+class PluginRegistry:
+    def __init__(self) -> None:
+        self._handlers: dict[str, PluginHandler] = {}
+
+    def register(self, plugin_id: str, handler: PluginHandler) -> None:
+        self._handlers[plugin_id] = handler
+
+    def execute(self, plugin_id: str, slots: dict[str, Any]) -> dict[str, Any]:
+        handler = self._handlers.get(plugin_id)
+        if handler is None:
+            return {
+                "success": False,
+                "message": f"插件 {plugin_id} 未注册。",
+                "data": {"plugin_id": plugin_id, "slots": slots},
+            }
+        return handler(slots)
+
+    def registered_plugins(self) -> list[str]:
+        return sorted(self._handlers.keys())
diff --git a/intelligent_cabin/app/plugins/mock.py b/intelligent_cabin/app/plugins/mock.py
new file mode 100644
index 0000000..0118ad6
--- /dev/null
+++ b/intelligent_cabin/app/plugins/mock.py
@@ -0,0 +1,216 @@
+from __future__ import annotations
+
+from typing import Any
+
+from app.plugins.base import PluginRegistry
+
+
+class MockPluginExecutor:
+    def register(self, registry: PluginRegistry) -> PluginRegistry:
+        registry.register("plugin.order.query", self._query_order)
+        registry.register("plugin.logistics.query", self._query_logistics)
+        registry.register("plugin.order.cancel", self._cancel_order)
+        registry.register("plugin.service.transfer_human", self._transfer_human)
+        registry.register("plugin.cabin.navigation.cancel", self._navigate_cancel)
+        registry.register("plugin.cabin.navigation", self._navigate)
+        registry.register("plugin.cabin.ac.on", self._ac_on)
+        registry.register("plugin.cabin.ac.off", self._ac_off)
+        registry.register("plugin.cabin.ac_control", self._set_ac)
+        registry.register("plugin.cabin.fan.up", self._fan_up)
+        registry.register("plugin.cabin.fan.down", self._fan_down)
+        registry.register("plugin.cabin.defog.front_on", self._defog_front_on)
+        registry.register("plugin.cabin.defog.rear_on", self._defog_rear_on)
+        registry.register("plugin.cabin.window.open", self._window_open)
+        registry.register("plugin.cabin.window.close", self._window_close)
+        registry.register("plugin.cabin.sunroof.open", self._sunroof_open)
+        registry.register("plugin.cabin.sunroof.close", self._sunroof_close)
+        registry.register("plugin.cabin.doors.lock", self._lock_doors)
+        registry.register("plugin.cabin.doors.unlock", self._unlock_doors)
+        registry.register("plugin.cabin.music_play", self._play_music)
+        registry.register("plugin.cabin.music.pause", self._pause_music)
+        registry.register("plugin.cabin.music.next", self._next_track)
+        registry.register("plugin.cabin.music.previous", self._previous_track)
+        registry.register("plugin.cabin.volume.up", self._volume_up)
+        registry.register("plugin.cabin.volume.down", self._volume_down)
+        registry.register("plugin.cabin.volume.mute", self._volume_mute)
+        registry.register("plugin.cabin.lights.on", self._lights_on)
+        registry.register("plugin.cabin.lights.off", self._lights_off)
+        registry.register("plugin.cabin.seat_heat.on", self._seat_heat_on)
+        registry.register("plugin.cabin.seat_heat.off", self._seat_heat_off)
+        registry.register("plugin.cabin.mirror.fold", self._mirror_fold)
+        registry.register("plugin.cabin.mirror.unfold", self._mirror_unfold)
+        registry.register("plugin.cabin.wiper.on", self._wiper_on)
+        registry.register("plugin.cabin.wiper.off", self._wiper_off)
+        return registry
+
+    def _query_order(self, slots: dict[str, Any]) -> dict[str, Any]:
+        return {
+            "success": True,
+            "message": f"订单 {slots['order_id']} 当前待发货。",
+            "data": {"order_status": "pending_shipment"},
+        }
+
+    def _query_logistics(self, slots: dict[str, Any]) -> dict[str, Any]:
+        return {
+            "success": True,
+            "message": f"订单 {slots['order_id']} 最新物流状态为运输中。",
+            "data": {"logistics_status": "shipping"},
+        }
+
+    def _cancel_order(self, slots: dict[str, Any]) -> dict[str, Any]:
+        return {
+            "success": True,
+            "message": f"订单 {slots['order_id']} 已取消。",
+            "data": {"cancel_status": "cancelled"},
+        }
+
+    def _transfer_human(self, slots: dict[str, Any]) -> dict[str, Any]:
+        return {
+            "success": True,
+            "message": "已为你转接人工客服，请稍候。",
+            "data": {"queue_no": "A12", "reason": slots.get("reason", "用户请求")},
+        }
+
+    def _navigate(self, slots: dict[str, Any]) -> dict[str, Any]:
+        return {
+            "success": True,
+            "message": f"已开始导航到 {slots['destination']}。",
+            "data": {"route_id": "route_001", "destination": slots["destination"]},
+        }
+
+    def _navigate_cancel(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已结束当前导航。", {"navigation": "stopped"})
+
+    def _ac_on(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已打开空调。", {"power": "on"})
+
+    def _ac_off(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已关闭空调。", {"power": "off"})
+
+    def _set_ac(self, slots: dict[str, Any]) -> dict[str, Any]:
+        return {
+            "success": True,
+            "message": f"已将空调调到 {slots['temperature']} 度。",
+            "data": {"temperature": slots["temperature"]},
+        }
+
+    def _fan_up(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已调大风量。", {"fan": "up"})
+
+    def _fan_down(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已调小风量。", {"fan": "down"})
+
+    def _defog_front_on(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已打开前挡除雾。", {"defog": "front_on"})
+
+    def _defog_rear_on(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已打开后挡除雾。", {"defog": "rear_on"})
+
+    def _window_open(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已打开车窗。", {"window": "open"})
+
+    def _window_close(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已关闭车窗。", {"window": "close"})
+
+    def _sunroof_open(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已打开天窗。", {"sunroof": "open"})
+
+    def _sunroof_close(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已关闭天窗。", {"sunroof": "close"})
+
+    def _lock_doors(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已锁定车门。", {"doors": "locked"})
+
+    def _unlock_doors(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已解锁车门。", {"doors": "unlocked"})
+
+    def _play_music(self, slots: dict[str, Any]) -> dict[str, Any]:
+        target = slots.get("song") or slots.get("genre") or "默认歌单"
+        return {
+            "success": True,
+            "message": f"正在播放 {target}。",
+            "data": {"play_target": target},
+        }
+
+    def _pause_music(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已暂停播放。", {"music": "paused"})
+
+    def _next_track(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已切到下一首。", {"music": "next"})
+
+    def _previous_track(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已切到上一首。", {"music": "previous"})
+
+    def _volume_up(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已调大音量。", {"volume": "up"})
+
+    def _volume_down(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已调小音量。", {"volume": "down"})
+
+    def _volume_mute(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已静音。", {"volume": "mute"})
+
+    def _lights_on(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已打开车灯。", {"lights": "on"})
+
+    def _lights_off(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已关闭车灯。", {"lights": "off"})
+
+    def _seat_heat_on(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已打开座椅加热。", {"seat_heat": "on"})
+
+    def _seat_heat_off(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已关闭座椅加热。", {"seat_heat": "off"})
+
+    def _mirror_fold(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已折叠后视镜。", {"mirror": "folded"})
+
+    def _mirror_unfold(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已展开后视镜。", {"mirror": "unfolded"})
+
+    def _wiper_on(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已打开雨刷。", {"wiper": "on"})
+
+    def _wiper_off(self, slots: dict[str, Any]) -> dict[str, Any]:
+        _ = slots
+        return self._simple_action("好的，已关闭雨刷。", {"wiper": "off"})
+
+    def _fallback(self, slots: dict[str, Any]) -> dict[str, Any]:
+        return {
+            "success": True,
+            "message": "已接收请求，当前使用 mock 插件返回成功结果。",
+            "data": slots,
+        }
+
+    def _simple_action(self, message: str, data: dict[str, Any]) -> dict[str, Any]:
+        return {
+            "success": True,
+            "message": message,
+            "data": data,
+        }
diff --git a/intelligent_cabin/app/schemas/__init__.py b/intelligent_cabin/app/schemas/__init__.py
new file mode 100644
index 0000000..027bc04
--- /dev/null
+++ b/intelligent_cabin/app/schemas/__init__.py
@@ -0,0 +1 @@
+"""Pydantic schemas for the agent service."""
diff --git a/intelligent_cabin/app/schemas/__pycache__/__init__.cpython-311.pyc b/intelligent_cabin/app/schemas/__pycache__/__init__.cpython-311.pyc
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diff --git a/intelligent_cabin/app/schemas/chat.py b/intelligent_cabin/app/schemas/chat.py
new file mode 100644
index 0000000..9ac633a
--- /dev/null
+++ b/intelligent_cabin/app/schemas/chat.py
@@ -0,0 +1,46 @@
+from __future__ import annotations
+
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field
+
+from app.schemas.debug import RoutingDebug
+from app.schemas.workflow import Workflow
+
+
+class ChatRequest(BaseModel):
+    session_id: str
+    user_id: str
+    channel: str = "app"
+    input_text: str
+    input_type: Literal["text", "voice"] = "text"
+    metadata: dict[str, Any] = Field(default_factory=dict)
+
+
+class FillSlotsRequest(BaseModel):
+    session_id: str
+    user_id: str
+    input_text: str
+
+
+class ChatResponse(BaseModel):
+    session_id: str
+    reply_type: Literal["text", "ask_slot", "ask_confirmation", "workflow_result", "fallback", "clarify", "reject"] = "text"
+    reply_text: str
+    intent: str | None = None
+    domain: str | None = None
+    decision: str | None = None
+    decision_reason: str | None = None
+    status: str
+    pending_slots: list[str] = Field(default_factory=list)
+    filled_slots: dict[str, Any] = Field(default_factory=dict)
+    workflow: Workflow | None = None
+    routing_debug: RoutingDebug | None = None
+    first_response_latency_ms: float | None = None
+    total_latency_ms: float | None = None
+    processing_breakdown: dict[str, float] = Field(default_factory=dict)
+    trace_id: str
+    # ── 知识库查询结果（LLM function call 命中时填充）──────────────────────────
+    knowledge_doc_id: str | None = None     # 知识文档 ID（MD 文件名）
+    knowledge_doc_title: str | None = None  # 知识文档标题
+    knowledge_content: str | None = None    # 完整 MD 正文，供前端渲染知识卡片
diff --git a/intelligent_cabin/app/schemas/configuration.py b/intelligent_cabin/app/schemas/configuration.py
new file mode 100644
index 0000000..cf4b255
--- /dev/null
+++ b/intelligent_cabin/app/schemas/configuration.py
@@ -0,0 +1,135 @@
+from __future__ import annotations
+
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field
+
+from app.schemas.intent import IntentDefinition
+
+
+class ActionDefinition(BaseModel):
+    action_id: str
+    plugin_id: str
+    risk_level: Literal["low", "medium", "high"] = "low"
+    metadata: dict[str, Any] = Field(default_factory=dict)
+
+
+class DomainIntentDefinition(BaseModel):
+    intent_id: str
+    domain: str
+    action_id: str | None = None
+    plugin_id: str | None = None
+    risk_level: Literal["low", "medium", "high"] | None = None
+    label: str | None = None
+    required_slots: list[str] = Field(default_factory=list)
+    ask_templates: dict[str, str] = Field(default_factory=dict)
+    keywords: list[str] = Field(default_factory=list)
+    examples: list[str] = Field(default_factory=list)
+
+    def to_intent_definition(self, actions: dict[str, ActionDefinition]) -> IntentDefinition:
+        plugin_id = self.plugin_id
+        risk_level = self.risk_level
+        if self.action_id:
+            action = actions[self.action_id]
+            plugin_id = action.plugin_id
+            if risk_level is None:
+                risk_level = action.risk_level
+        if not plugin_id:
+            raise ValueError(f"intent {self.intent_id} is missing plugin_id/action_id mapping")
+        return IntentDefinition(
+            intent_id=self.intent_id,
+            plugin_id=plugin_id,
+            domain=self.domain,
+            risk_level=risk_level or "low",
+            required_slots=self.required_slots,
+            ask_templates=self.ask_templates,
+            keywords=self.keywords,
+            examples=self.examples,
+        )
+
+
+class DomainConfig(BaseModel):
+    intents: list[DomainIntentDefinition] = Field(default_factory=list)
+
+
+class ActionsConfig(BaseModel):
+    actions: list[ActionDefinition] = Field(default_factory=list)
+
+
+class ResponsesConfig(BaseModel):
+    templates: dict[str, str] = Field(default_factory=dict)
+
+
+class FormDefinition(BaseModel):
+    intent_id: str
+    required_slots: list[str] = Field(default_factory=list)
+    ask_templates: dict[str, str] = Field(default_factory=dict)
+
+
+class FormsConfig(BaseModel):
+    forms: list[FormDefinition] = Field(default_factory=list)
+
+
+class ConfirmationRuleConfig(BaseModel):
+    positive_tokens: list[str] = Field(default_factory=list)
+    negative_tokens: list[str] = Field(default_factory=list)
+    required_intents: list[str] = Field(default_factory=list)
+    required_risk_levels: list[Literal["low", "medium", "high"]] = Field(default_factory=list)
+
+
+class StopRuleConfig(BaseModel):
+    phrases: list[str] = Field(default_factory=list)
+
+
+class DialogRulesConfig(BaseModel):
+    stop: StopRuleConfig = Field(default_factory=StopRuleConfig)
+    confirmation: ConfirmationRuleConfig = Field(default_factory=ConfirmationRuleConfig)
+
+
+class DialogActPatternDefinition(BaseModel):
+    act_id: str
+    phrases: list[str] = Field(default_factory=list)
+    # 正则列表：文本匹配任意一个则命中该 act（用于数字类 inform）
+    numeric_patterns: list[str] = Field(default_factory=list)
+
+
+class DialogActsConfig(BaseModel):
+    acts: list[DialogActPatternDefinition] = Field(default_factory=list)
+
+
+# ── Context Rewrite Config ────────────────────────────────────────────────────
+
+class ParamContextDefinition(BaseModel):
+    """一类可相对调节参数的上下文改写规则。"""
+    intent_ids: list[str]
+    slot_name: str
+    unit: str = ""
+    step: int | float = 1
+    min_value: int | float = 0
+    max_value: int | float = 9999
+    default_value: int | float = 0
+    up_phrases: list[str] = Field(default_factory=list)
+    down_phrases: list[str] = Field(default_factory=list)
+    rewrite_template: str = "{value}"
+
+
+class ContextRewriteConfig(BaseModel):
+    param_contexts: list[ParamContextDefinition] = Field(default_factory=list)
+
+
+class WorkflowTemplateStepOverride(BaseModel):
+    depends_on: list[int] = Field(default_factory=list)
+    condition: dict[str, Any] = Field(default_factory=dict)
+    requires_confirmation: bool = False
+
+
+class WorkflowTemplateDefinition(BaseModel):
+    template_id: str
+    workflow_type: Literal["sequence", "conditional", "parallel"] = "sequence"
+    intent_sequence: list[str] = Field(default_factory=list)
+    step_overrides: list[WorkflowTemplateStepOverride] = Field(default_factory=list)
+    trigger_keywords: list[str] = Field(default_factory=list)
+
+
+class WorkflowTemplatesConfig(BaseModel):
+    templates: list[WorkflowTemplateDefinition] = Field(default_factory=list)
diff --git a/intelligent_cabin/app/schemas/debug.py b/intelligent_cabin/app/schemas/debug.py
new file mode 100644
index 0000000..d4c25ac
--- /dev/null
+++ b/intelligent_cabin/app/schemas/debug.py
@@ -0,0 +1,42 @@
+from __future__ import annotations
+
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+
+class IntentCandidate(BaseModel):
+    intent_id: str
+    score: float = 0.0
+    reason: str | None = None
+    model_name: str | None = None
+    raw_label: str | None = None
+    metadata: dict[str, Any] = Field(default_factory=dict)
+
+
+class MatcherStageDebug(BaseModel):
+    stage: str
+    accepted: bool = False
+    selected_intent: str | None = None
+    score: float = 0.0
+    elapsed_ms: float | None = None
+    reason: str | None = None
+    model_name: str | None = None
+    backend: str | None = None
+    fallback_used: bool = False
+    raw_label: str | None = None
+    error_message: str | None = None
+    metadata: dict[str, Any] = Field(default_factory=dict)
+    candidates: list[IntentCandidate] = Field(default_factory=list)
+
+
+class RoutingDebug(BaseModel):
+    selected_intent: str | None = None
+    matched_stage: str | None = None
+    decision: str = "reject"
+    decision_reason: str | None = None
+    confidence_grade: str | None = None
+    total_match_latency_ms: float | None = None
+    unknown_detected: bool = False
+    extracted_slots: dict[str, Any] = Field(default_factory=dict)
+    stages: list[MatcherStageDebug] = Field(default_factory=list)
diff --git a/intelligent_cabin/app/schemas/demo.py b/intelligent_cabin/app/schemas/demo.py
new file mode 100644
index 0000000..1ffccc3
--- /dev/null
+++ b/intelligent_cabin/app/schemas/demo.py
@@ -0,0 +1,20 @@
+from __future__ import annotations
+
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class DemoRuntimeConfig(BaseModel):
+    matcher_pipeline: Literal["classifier"]
+    classifier_backend: Literal["mock", "bert", "remote", "joint_bert"]
+    session_backend: Literal["memory", "redis"]
+    slot_extractor_backend: str
+    planner_backend: str
+    planner_model_name: str
+
+
+class DemoRuntimeUpdateRequest(BaseModel):
+    matcher_pipeline: Literal["classifier"] = Field(default="classifier")
+    classifier_backend: Literal["mock", "bert", "remote", "joint_bert"]
+    session_backend: Literal["memory", "redis"]
diff --git a/intelligent_cabin/app/schemas/intent.py b/intelligent_cabin/app/schemas/intent.py
new file mode 100644
index 0000000..ccf3cf5
--- /dev/null
+++ b/intelligent_cabin/app/schemas/intent.py
@@ -0,0 +1,16 @@
+from __future__ import annotations
+
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class IntentDefinition(BaseModel):
+    intent_id: str
+    plugin_id: str
+    domain: str
+    risk_level: Literal["low", "medium", "high"] = "low"
+    required_slots: list[str] = Field(default_factory=list)
+    ask_templates: dict[str, str] = Field(default_factory=dict)
+    keywords: list[str] = Field(default_factory=list)
+    examples: list[str] = Field(default_factory=list)
diff --git a/intelligent_cabin/app/schemas/workflow.py b/intelligent_cabin/app/schemas/workflow.py
new file mode 100644
index 0000000..6167e94
--- /dev/null
+++ b/intelligent_cabin/app/schemas/workflow.py
@@ -0,0 +1,38 @@
+from __future__ import annotations
+
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field
+
+
+class WorkflowStep(BaseModel):
+    step: int
+    step_id: str
+    intent_id: str
+    plugin_id: str
+    action: str
+    status: Literal["pending", "running", "completed", "failed", "skipped", "waiting_confirmation"] = "pending"
+    depends_on: list[str] = Field(default_factory=list)
+    slots: dict[str, Any] = Field(default_factory=dict)
+    condition: dict[str, Any] = Field(default_factory=dict)
+    requires_confirmation: bool = False
+    timeout_ms: int = 1500
+
+
+class MissingSlot(BaseModel):
+    slot_name: str
+    ask_template: str
+    priority: int = 1
+
+
+class Workflow(BaseModel):
+    workflow_id: str
+    workflow_type: Literal["single", "sequence", "conditional", "parallel"] = "single"
+    domain: str
+    intent_id: str
+    status: Literal["ready", "waiting_slot", "waiting_confirmation", "running", "completed", "failed"] = "ready"
+    risk_level: Literal["low", "medium", "high"] = "low"
+    slots: dict[str, Any] = Field(default_factory=dict)
+    missing_slots: list[MissingSlot] = Field(default_factory=list)
+    steps: list[WorkflowStep] = Field(default_factory=list)
+    meta: dict[str, Any] = Field(default_factory=dict)
diff --git a/intelligent_cabin/app/services/__init__.py b/intelligent_cabin/app/services/__init__.py
new file mode 100644
index 0000000..013d4a4
--- /dev/null
+++ b/intelligent_cabin/app/services/__init__.py
@@ -0,0 +1 @@
+"""Application services for orchestration and session management."""
diff --git a/intelligent_cabin/app/services/__pycache__/__init__.cpython-311.pyc b/intelligent_cabin/app/services/__pycache__/__init__.cpython-311.pyc
new file mode 100644
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diff --git a/intelligent_cabin/app/services/__pycache__/__init__.cpython-312.pyc b/intelligent_cabin/app/services/__pycache__/__init__.cpython-312.pyc
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diff --git a/intelligent_cabin/app/services/__pycache__/__init__.cpython-313.pyc b/intelligent_cabin/app/services/__pycache__/__init__.cpython-313.pyc
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diff --git a/intelligent_cabin/app/services/__pycache__/classifier.cpython-313.pyc b/intelligent_cabin/app/services/__pycache__/classifier.cpython-313.pyc
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diff --git a/intelligent_cabin/app/services/__pycache__/multi_intent_detector.cpython-312.pyc b/intelligent_cabin/app/services/__pycache__/multi_intent_detector.cpython-312.pyc
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diff --git a/intelligent_cabin/app/services/__pycache__/response_policy.cpython-311.pyc b/intelligent_cabin/app/services/__pycache__/response_policy.cpython-311.pyc
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diff --git a/intelligent_cabin/app/services/__pycache__/response_policy.cpython-312.pyc b/intelligent_cabin/app/services/__pycache__/response_policy.cpython-312.pyc
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diff --git a/intelligent_cabin/app/services/agent_service.py b/intelligent_cabin/app/services/agent_service.py
new file mode 100644
index 0000000..9696df5
--- /dev/null
+++ b/intelligent_cabin/app/services/agent_service.py
@@ -0,0 +1,1305 @@
+from __future__ import annotations
+
+import re
+from time import perf_counter
+from uuid import uuid4
+
+from app.plugins.base import PluginRegistry
+from app.schemas.chat import ChatRequest, ChatResponse, FillSlotsRequest
+from app.schemas.debug import IntentCandidate, MatcherStageDebug, RoutingDebug
+from app.schemas.workflow import MissingSlot, Workflow, WorkflowStep
+from app.services.dialog_act import DialogActEngine
+from app.services.dialog_rules import DialogRuleEngine
+from app.services.intent_registry import IntentRegistry
+from app.services.knowledge_llm import DashScopeKnowledgeLLM
+from app.services.planner import PlanningResult, WorkflowPlanner
+from app.services.response_policy import ResponsePolicy
+from app.services.rewrite_engine import ContextRewriteEngine, RewriteResult
+from app.services.router import Router
+from app.services.session_store import InMemorySessionStore, SessionState
+from app.services.social import SocialResponder, SocialRouter
+
+
+class AgentService:
+    _MUSIC_GENRE_HINTS = ("轻音乐", "摇滚", "古典", "民谣", "爵士", "流行", "儿歌")
+
+    def __init__(
+        self,
+        intent_registry: IntentRegistry,
+        router: Router,
+        plugins: PluginRegistry,
+        session_store: InMemorySessionStore | None = None,
+        rewrite_engine: ContextRewriteEngine | None = None,
+        response_policy: ResponsePolicy | None = None,
+        dialog_rules: DialogRuleEngine | None = None,
+        dialog_act_engine: DialogActEngine | None = None,
+        planner: WorkflowPlanner | None = None,
+        social_router: SocialRouter | None = None,
+        social_responder: SocialResponder | None = None,
+        knowledge_llm: DashScopeKnowledgeLLM | None = None,
+    ) -> None:
+        self.session_store = session_store or InMemorySessionStore()
+        self.intent_registry = intent_registry
+        self.router = router
+        self.plugins = plugins
+        self.rewrite_engine = rewrite_engine or ContextRewriteEngine()
+        self.response_policy = response_policy or ResponsePolicy()
+        self.dialog_rules = dialog_rules or DialogRuleEngine()
+        self.dialog_act_engine = dialog_act_engine or DialogActEngine()
+        self.planner = planner
+        self.social_router = social_router or SocialRouter()
+        self.social_responder = social_responder
+        self.knowledge_llm = knowledge_llm
+
+    def handle_chat(self, request: ChatRequest) -> ChatResponse:
+        started_at = perf_counter()
+        breakdown: dict[str, float] = {}
+
+        timer_started_at = perf_counter()
+        session = self.session_store.get_or_create(
+            session_id=request.session_id,
+            user_id=request.user_id,
+            channel=request.channel,
+        )
+        self._mark_timing(breakdown, "session_get_or_create_ms", timer_started_at)
+
+        timer_started_at = perf_counter()
+        self._update_dialog_act(session, request.input_text)
+        self._mark_timing(breakdown, "dialog_act_ms", timer_started_at)
+
+        timer_started_at = perf_counter()
+        stop_response = self._handle_stop_request(session, request.input_text)
+        self._mark_timing(breakdown, "stop_check_ms", timer_started_at)
+        if stop_response is not None:
+            first_response_at = perf_counter()
+            timer_started_at = perf_counter()
+            self._record_turn(session, request.input_text, stop_response.reply_text, RewriteResult(request.input_text, request.input_text))
+            self._mark_timing(breakdown, "record_turn_ms", timer_started_at)
+            timer_started_at = perf_counter()
+            self.session_store.save(session)
+            self._mark_timing(breakdown, "session_save_ms", timer_started_at)
+            return self._finalize_response(stop_response, started_at, breakdown, first_response_at)
+
+        if session.status == "waiting_confirmation":
+            confirmation = self._parse_confirmation_decision(request.input_text)
+            if confirmation is not None:
+                timer_started_at = perf_counter()
+                response = self._handle_confirmation(session, request.input_text)
+                self._mark_timing(breakdown, "confirmation_ms", timer_started_at)
+                first_response_at = perf_counter()
+                timer_started_at = perf_counter()
+                self._record_turn(session, request.input_text, response.reply_text, RewriteResult(request.input_text, request.input_text))
+                self._mark_timing(breakdown, "record_turn_ms", timer_started_at)
+                timer_started_at = perf_counter()
+                self.session_store.save(session)
+                self._mark_timing(breakdown, "session_save_ms", timer_started_at)
+                return self._finalize_response(response, started_at, breakdown, first_response_at)
+
+        timer_started_at = perf_counter()
+        continued_response = self._continue_active_task(session, request.input_text)
+        self._mark_timing(breakdown, "active_task_ms", timer_started_at)
+        if continued_response is not None:
+            first_response_at = perf_counter()
+            timer_started_at = perf_counter()
+            self._record_turn(session, request.input_text, continued_response.reply_text, RewriteResult(request.input_text, request.input_text))
+            self._mark_timing(breakdown, "record_turn_ms", timer_started_at)
+            timer_started_at = perf_counter()
+            self.session_store.save(session)
+            self._mark_timing(breakdown, "session_save_ms", timer_started_at)
+            return self._finalize_response(continued_response, started_at, breakdown, first_response_at)
+
+        timer_started_at = perf_counter()
+        social_response = self._handle_social_turn(session, request.input_text)
+        self._mark_timing(breakdown, "social_route_ms", timer_started_at)
+        if social_response is not None:
+            first_response_at = perf_counter()
+            timer_started_at = perf_counter()
+            self._record_turn(session, request.input_text, social_response.reply_text, RewriteResult(request.input_text, request.input_text))
+            self._mark_timing(breakdown, "record_turn_ms", timer_started_at)
+            timer_started_at = perf_counter()
+            self.session_store.save(session)
+            self._mark_timing(breakdown, "session_save_ms", timer_started_at)
+            return self._finalize_response(social_response, started_at, breakdown, first_response_at)
+
+        timer_started_at = perf_counter()
+        rewrite_result = self.rewrite_engine.rewrite(request.input_text, session)
+        rewrite_elapsed_ms = round((perf_counter() - timer_started_at) * 1000, 3)
+        breakdown["rewrite_ms"] = rewrite_elapsed_ms
+
+        timer_started_at = perf_counter()
+        route_result = self.router.route(rewrite_result.rewritten_text)
+        self._mark_timing(breakdown, "route_ms", timer_started_at)
+        self._attach_rewrite_debug(route_result.debug, rewrite_result, rewrite_elapsed_ms)
+
+        timer_started_at = perf_counter()
+        # ── BERT 信号不稳时，知识库优先检索（在 Planner 之前）──────────────────
+        # decision=route_to_cloud 表示 BERT 低置信，需发给 cloud planner
+        # 但如果本地 MD 知识库关键词已命中，直接走 knowledge_llm，跳过 cloud planner
+        if route_result.debug.decision == "route_to_cloud" and self.knowledge_llm is not None:
+            quick_hits = self.knowledge_llm._store.search(rewrite_result.rewritten_text, top_k=1)
+            if quick_hits and quick_hits[0].score >= 3.0:  # 至少命中一个 title 或多个 body 词
+                early_knowledge = self._try_knowledge_llm(
+                    session, request.input_text, route_result.debug, rewrite_result
+                )
+                if early_knowledge is not None:
+                    self._mark_timing(breakdown, "knowledge_llm_ms", timer_started_at)
+                    first_response_at = perf_counter()
+                    self._record_turn(session, request.input_text, early_knowledge.reply_text, rewrite_result)
+                    self.session_store.save(session)
+                    return self._finalize_response(early_knowledge, started_at, breakdown, first_response_at)
+        planning_result = self._plan_if_needed(session, rewrite_result.rewritten_text, route_result.debug)
+        self._mark_timing(breakdown, "planner_ms", timer_started_at)
+        if planning_result is not None and planning_result.accepted and planning_result.steps:
+            timer_started_at = perf_counter()
+            response = self._start_planned_workflow(session, planning_result, route_result.debug, request.input_text, rewrite_result)
+            self._mark_timing(breakdown, "response_build_ms", timer_started_at)
+            first_response_at = perf_counter()
+            timer_started_at = perf_counter()
+            self.session_store.save(session)
+            self._mark_timing(breakdown, "session_save_ms", timer_started_at)
+            return self._finalize_response(response, started_at, breakdown, first_response_at)
+
+        timer_started_at = perf_counter()
+        decision_response = self._handle_route_decision(session, route_result.debug, request.input_text, rewrite_result)
+        self._mark_timing(breakdown, "decision_response_ms", timer_started_at)
+        if decision_response is not None:
+            first_response_at = perf_counter()
+            timer_started_at = perf_counter()
+            self.session_store.save(session)
+            self._mark_timing(breakdown, "session_save_ms", timer_started_at)
+            return self._finalize_response(decision_response, started_at, breakdown, first_response_at)
+        intent = route_result.intent
+        if intent is None:
+            timer_started_at = perf_counter()
+            # ── BERT 未命中：尝试 LLM + knowledge_search function call 兆底 ──
+            knowledge_response = self._try_knowledge_llm(
+                session, request.input_text, route_result.debug, rewrite_result
+            )
+            if knowledge_response is not None:
+                self._mark_timing(breakdown, "knowledge_llm_ms", timer_started_at)
+                first_response_at = perf_counter()
+                self._record_turn(session, request.input_text, knowledge_response.reply_text, rewrite_result)
+                self.session_store.save(session)
+                return self._finalize_response(knowledge_response, started_at, breakdown, first_response_at)
+            response = self._fallback_response(session.session_id, route_result.debug)
+            self._mark_timing(breakdown, "response_build_ms", timer_started_at)
+            first_response_at = perf_counter()
+            timer_started_at = perf_counter()
+            self._record_turn(session, request.input_text, response.reply_text, rewrite_result)
+            self._mark_timing(breakdown, "record_turn_ms", timer_started_at)
+            timer_started_at = perf_counter()
+            self.session_store.save(session)
+            self._mark_timing(breakdown, "session_save_ms", timer_started_at)
+            return self._finalize_response(response, started_at, breakdown, first_response_at)
+
+        if session.current_intent != intent.intent_id:
+            session.pending_slots = []
+            session.slots = {}
+            session.workflow = None
+
+        session.current_intent = intent.intent_id
+        session.status = "understanding"
+        timer_started_at = perf_counter()
+        extracted_slots = self.router.extract_slots(rewrite_result.rewritten_text, intent)
+        self._mark_timing(breakdown, "slot_extract_ms", timer_started_at)
+        route_result.debug.extracted_slots = extracted_slots.copy()
+        session.routing_debug = route_result.debug.model_dump()
+        session.slots.update(extracted_slots)
+        self._update_context_memory(session, intent.intent_id, extracted_slots)
+
+        timer_started_at = perf_counter()
+        response = self._build_response_from_session(session)
+        self._mark_timing(breakdown, "response_build_ms", timer_started_at)
+        first_response_at = perf_counter()
+        timer_started_at = perf_counter()
+        self._record_turn(session, request.input_text, response.reply_text, rewrite_result)
+        self._mark_timing(breakdown, "record_turn_ms", timer_started_at)
+        timer_started_at = perf_counter()
+        self.session_store.save(session)
+        self._mark_timing(breakdown, "session_save_ms", timer_started_at)
+        return self._finalize_response(response, started_at, breakdown, first_response_at)
+
+    def _handle_route_decision(
+        self,
+        session: SessionState,
+        routing_debug: RoutingDebug,
+        user_text: str,
+        rewrite_result: RewriteResult,
+    ) -> ChatResponse | None:
+        if routing_debug.decision == "execute":
+            return None
+        if routing_debug.decision == "clarify":
+            response = ChatResponse(
+                session_id=session.session_id,
+                reply_type="clarify",
+                reply_text=self.response_policy.clarify(self._top_candidate_ids(routing_debug)),
+                intent=routing_debug.selected_intent,
+                domain=self._domain_for_intent(routing_debug.selected_intent),
+                decision=routing_debug.decision,
+                decision_reason=routing_debug.decision_reason,
+                status="clarify",
+
+                routing_debug=routing_debug,
+                trace_id=self._trace_id(),
+            )
+            self._record_turn(session, user_text, response.reply_text, rewrite_result)
+            return response
+        if routing_debug.decision == "reject":
+            response = ChatResponse(
+                session_id=session.session_id,
+                reply_type="reject",
+                reply_text=self.response_policy.reject(),
+                decision=routing_debug.decision,
+                decision_reason=routing_debug.decision_reason,
+                status="rejected",
+                routing_debug=routing_debug,
+                trace_id=self._trace_id(),
+            )
+            self._record_turn(session, user_text, response.reply_text, rewrite_result)
+            return response
+        if routing_debug.decision == "route_to_cloud":
+            candidate_ids = self._top_candidate_ids(routing_debug)
+            planner_stage = next((stage for stage in reversed(routing_debug.stages) if stage.stage == "planner"), None)
+            planner_reason = planner_stage.reason if planner_stage is not None else routing_debug.decision_reason
+            if self._planner_indicates_out_of_scope(planner_stage):
+                response = ChatResponse(
+                    session_id=session.session_id,
+                    reply_type="reject",
+                    reply_text=self.response_policy.reject(),
+                    intent=routing_debug.selected_intent,
+                    domain=self._domain_for_intent(routing_debug.selected_intent),
+                    decision="reject",
+                    decision_reason=planner_reason,
+
+                    status="rejected",
+                    routing_debug=routing_debug,
+                    trace_id=self._trace_id(),
+                )
+            elif candidate_ids:
+                response = ChatResponse(
+                    session_id=session.session_id,
+                    reply_type="clarify",
+                    reply_text=self.response_policy.clarify(candidate_ids),
+                    intent=routing_debug.selected_intent,
+                    domain=self._domain_for_intent(routing_debug.selected_intent),
+                    decision=routing_debug.decision,
+                    decision_reason=planner_reason,
+
+                    status="route_to_cloud",
+                    routing_debug=routing_debug,
+                    trace_id=self._trace_id(),
+                )
+            else:
+                response = ChatResponse(
+                    session_id=session.session_id,
+                    reply_type="fallback",
+                    reply_text=self.response_policy.fallback(),
+                    intent=routing_debug.selected_intent,
+                    domain=self._domain_for_intent(routing_debug.selected_intent),
+                    decision=routing_debug.decision,
+                    decision_reason=planner_reason,
+
+                    status="route_to_cloud",
+                    routing_debug=routing_debug,
+                    trace_id=self._trace_id(),
+                )
+            self._record_turn(session, user_text, response.reply_text, rewrite_result)
+            return response
+        return None
+
+    def handle_fill_slots(self, request: FillSlotsRequest) -> ChatResponse:
+        started_at = perf_counter()
+        breakdown: dict[str, float] = {}
+
+        timer_started_at = perf_counter()
+        session = self.session_store.get(request.session_id)
+        self._mark_timing(breakdown, "session_get_ms", timer_started_at)
+        if session is None or session.current_intent is None:
+            timer_started_at = perf_counter()
+            response = ChatResponse(
+                session_id=request.session_id,
+                reply_type="fallback",
+                reply_text=self.response_policy.fallback(),
+                status="fallback",
+                routing_debug=None,
+                trace_id=self._trace_id(),
+            )
+            self._mark_timing(breakdown, "response_build_ms", timer_started_at)
+            first_response_at = perf_counter()
+            if session is not None:
+                timer_started_at = perf_counter()
+                self._record_turn(session, request.input_text, response.reply_text, RewriteResult(request.input_text, request.input_text))
+                self._mark_timing(breakdown, "record_turn_ms", timer_started_at)
+                timer_started_at = perf_counter()
+                self.session_store.save(session)
+                self._mark_timing(breakdown, "session_save_ms", timer_started_at)
+            return self._finalize_response(response, started_at, breakdown, first_response_at)
+
+        timer_started_at = perf_counter()
+        self._update_dialog_act(session, request.input_text)
+        self._mark_timing(breakdown, "dialog_act_ms", timer_started_at)
+        timer_started_at = perf_counter()
+        stop_response = self._handle_stop_request(session, request.input_text)
+        self._mark_timing(breakdown, "stop_check_ms", timer_started_at)
+        if stop_response is not None:
+            first_response_at = perf_counter()
+            timer_started_at = perf_counter()
+            self._record_turn(session, request.input_text, stop_response.reply_text, RewriteResult(request.input_text, request.input_text))
+            self._mark_timing(breakdown, "record_turn_ms", timer_started_at)
+            timer_started_at = perf_counter()
+            self.session_store.save(session)
+            self._mark_timing(breakdown, "session_save_ms", timer_started_at)
+            return self._finalize_response(stop_response, started_at, breakdown, first_response_at)
+
+        if session.status == "waiting_confirmation":
+            if self._parse_confirmation_decision(request.input_text) is not None:
+                timer_started_at = perf_counter()
+                response = self._handle_confirmation(session, request.input_text)
+                self._mark_timing(breakdown, "confirmation_ms", timer_started_at)
+                first_response_at = perf_counter()
+                timer_started_at = perf_counter()
+                self._record_turn(session, request.input_text, response.reply_text, RewriteResult(request.input_text, request.input_text))
+                self._mark_timing(breakdown, "record_turn_ms", timer_started_at)
+                timer_started_at = perf_counter()
+                self.session_store.save(session)
+                self._mark_timing(breakdown, "session_save_ms", timer_started_at)
+                return self._finalize_response(response, started_at, breakdown, first_response_at)
+
+        timer_started_at = perf_counter()
+        continued_response = self._continue_active_task(session, request.input_text)
+        self._mark_timing(breakdown, "active_task_ms", timer_started_at)
+        if continued_response is not None:
+            first_response_at = perf_counter()
+            timer_started_at = perf_counter()
+            self._record_turn(session, request.input_text, continued_response.reply_text, RewriteResult(request.input_text, request.input_text))
+            self._mark_timing(breakdown, "record_turn_ms", timer_started_at)
+            timer_started_at = perf_counter()
+            self.session_store.save(session)
+            self._mark_timing(breakdown, "session_save_ms", timer_started_at)
+            return self._finalize_response(continued_response, started_at, breakdown, first_response_at)
+
+        timer_started_at = perf_counter()
+        social_response = self._handle_social_turn(session, request.input_text)
+        self._mark_timing(breakdown, "social_route_ms", timer_started_at)
+        if social_response is not None:
+            first_response_at = perf_counter()
+            timer_started_at = perf_counter()
+            self._record_turn(session, request.input_text, social_response.reply_text, RewriteResult(request.input_text, request.input_text))
+            self._mark_timing(breakdown, "record_turn_ms", timer_started_at)
+            timer_started_at = perf_counter()
+            self.session_store.save(session)
+            self._mark_timing(breakdown, "session_save_ms", timer_started_at)
+            return self._finalize_response(social_response, started_at, breakdown, first_response_at)
+
+        if session.status == "waiting_confirmation":
+            timer_started_at = perf_counter()
+            response = self._handle_confirmation(session, request.input_text)
+            self._mark_timing(breakdown, "confirmation_ms", timer_started_at)
+            first_response_at = perf_counter()
+            timer_started_at = perf_counter()
+            self._record_turn(session, request.input_text, response.reply_text, RewriteResult(request.input_text, request.input_text))
+            self._mark_timing(breakdown, "record_turn_ms", timer_started_at)
+            timer_started_at = perf_counter()
+            self.session_store.save(session)
+            self._mark_timing(breakdown, "session_save_ms", timer_started_at)
+            return self._finalize_response(response, started_at, breakdown, first_response_at)
+
+        existing_workflow = self._workflow_from_session(session)
+        if existing_workflow is not None and existing_workflow.workflow_type != "single":
+            intent = self.intent_registry.get(session.current_intent)
+            timer_started_at = perf_counter()
+            extracted_slots = self._extract_continuation_slots(session, intent, request.input_text)
+            self._mark_timing(breakdown, "slot_extract_ms", timer_started_at)
+            session.slots.update(extracted_slots)
+            self._update_context_memory(session, intent.intent_id, extracted_slots)
+            timer_started_at = perf_counter()
+            response = self._continue_planned_workflow(
+                session,
+                existing_workflow,
+                self._routing_debug_from_session(session),
+            )
+            self._mark_timing(breakdown, "response_build_ms", timer_started_at)
+            first_response_at = perf_counter()
+            timer_started_at = perf_counter()
+            self._record_turn(session, request.input_text, response.reply_text, RewriteResult(request.input_text, request.input_text))
+            self._mark_timing(breakdown, "record_turn_ms", timer_started_at)
+            timer_started_at = perf_counter()
+            self.session_store.save(session)
+            self._mark_timing(breakdown, "session_save_ms", timer_started_at)
+            return self._finalize_response(response, started_at, breakdown, first_response_at)
+
+        intent = self.intent_registry.get(session.current_intent)
+        timer_started_at = perf_counter()
+        extracted_slots = self._extract_continuation_slots(session, intent, request.input_text)
+        self._mark_timing(breakdown, "slot_extract_ms", timer_started_at)
+        session.slots.update(extracted_slots)
+        self._update_context_memory(session, intent.intent_id, extracted_slots)
+        if session.routing_debug is not None:
+            routing_debug = RoutingDebug.model_validate(session.routing_debug)
+            routing_debug.extracted_slots = session.slots.copy()
+            session.routing_debug = routing_debug.model_dump()
+        timer_started_at = perf_counter()
+        response = self._build_response_from_session(session)
+        self._mark_timing(breakdown, "response_build_ms", timer_started_at)
+        first_response_at = perf_counter()
+        timer_started_at = perf_counter()
+        self._record_turn(session, request.input_text, response.reply_text, RewriteResult(request.input_text, request.input_text))
+        self._mark_timing(breakdown, "record_turn_ms", timer_started_at)
+        timer_started_at = perf_counter()
+        self.session_store.save(session)
+        self._mark_timing(breakdown, "session_save_ms", timer_started_at)
+        return self._finalize_response(response, started_at, breakdown, first_response_at)
+
+    def _build_response_from_session(self, session: SessionState) -> ChatResponse:
+        assert session.current_intent is not None
+        intent = self.intent_registry.get(session.current_intent)
+        session.slots = self._normalize_slots_for_intent(intent.intent_id, session.slots)
+        pending_slots = self._pending_slots_for_intent(intent, session.slots)
+
+        if pending_slots:
+            session.pending_slots = pending_slots
+            session.status = "waiting_slot"
+            self.session_store.save(session)
+            first_slot = pending_slots[0]
+            workflow = self._build_workflow(session, pending_slots)
+            return ChatResponse(
+                session_id=session.session_id,
+                reply_type="ask_slot",
+                reply_text=self.response_policy.ask_for_slot(
+                    intent,
+                    first_slot,
+                    self._default_ask_template(intent, first_slot),
+                ),
+                intent=session.current_intent,
+                domain=self._domain_for_intent(session.current_intent),
+                status=session.status,
+                pending_slots=pending_slots,
+                filled_slots=session.slots,
+
+                workflow=workflow,
+                routing_debug=self._routing_debug_from_session(session),
+                trace_id=self._trace_id(),
+            )
+
+        plugin_result = self.plugins.execute(intent.plugin_id, session.slots)
+        session.pending_slots = []
+        session.status = "completed"
+        workflow = self._build_workflow(session, [])
+        workflow.status = "completed"
+        self.session_store.save(session)
+
+        return ChatResponse(
+            session_id=session.session_id,
+            reply_type="workflow_result",
+            reply_text=self.response_policy.workflow_result(intent, plugin_result),
+            intent=session.current_intent,
+            domain=self._domain_for_intent(session.current_intent),
+            status=session.status,
+            pending_slots=[],
+            filled_slots=session.slots,
+
+            workflow=workflow,
+            routing_debug=self._routing_debug_from_session(session),
+            trace_id=self._trace_id(),
+        )
+
+    def _build_workflow(self, session: SessionState, pending_slots: list[str]) -> Workflow:
+        assert session.current_intent is not None
+        intent = self.intent_registry.get(session.current_intent)
+        missing = [
+            MissingSlot(
+                slot_name=slot,
+                ask_template=self._default_ask_template(intent, slot),
+                priority=index + 1,
+            )
+            for index, slot in enumerate(pending_slots)
+        ]
+        step = WorkflowStep(
+            step=1,
+            step_id=f"step_{session.current_intent}_1",
+            intent_id=session.current_intent,
+            plugin_id=intent.plugin_id,
+            action=intent.plugin_id.split(".")[-1],
+            status="pending" if pending_slots else "completed",
+            slots=session.slots.copy(),
+        )
+        workflow = Workflow(
+            workflow_id=f"wf_{session.session_id}",
+            workflow_type="single",
+            domain=intent.domain,
+            intent_id=session.current_intent,
+            status="waiting_slot" if pending_slots else "ready",
+            risk_level=intent.risk_level,
+            slots=session.slots.copy(),
+            missing_slots=missing,
+            steps=[step],
+            meta={
+                "source": "rule_router",
+                "routing_debug": session.routing_debug or {},
+            },
+        )
+        session.workflow = workflow.model_dump()
+        return workflow
+
+    def _fallback_response(self, session_id: str, routing_debug: RoutingDebug | None = None) -> ChatResponse:
+        return ChatResponse(
+            session_id=session_id,
+            reply_type="fallback",
+            reply_text=self.response_policy.fallback(),
+            decision=routing_debug.decision if routing_debug is not None else None,
+            decision_reason=routing_debug.decision_reason if routing_debug is not None else None,
+            status="fallback",
+            routing_debug=routing_debug,
+            trace_id=self._trace_id(),
+        )
+
+    def _handle_stop_request(self, session: SessionState, text: str) -> ChatResponse | None:
+        if not self._is_stop_request(text) or not self._has_active_task(session):
+            return None
+        self._reset_active_task(session)
+        return ChatResponse(
+            session_id=session.session_id,
+            reply_type="text",
+            reply_text=self.response_policy.task_stopped(),
+            status="stopped",
+            pending_slots=[],
+            filled_slots={},
+            workflow=None,
+            routing_debug=None,
+            trace_id=self._trace_id(),
+        )
+
+    def _handle_social_turn(self, session: SessionState, text: str) -> ChatResponse | None:
+        social_route = self.social_router.route(text, session)
+        if social_route.category == "none":
+            return None
+        if self.social_responder is None:
+            reply_text = self.response_policy.open_social_fallback()
+            return self._social_response(
+                session=session,
+                reply_text=reply_text,
+                decision="open_social",
+                decision_reason="open social matched but no responder configured",
+            )
+        social_result = self.social_responder.reply(text, session)
+        reply_text = social_result.text.strip() or self.response_policy.open_social_fallback()
+        return self._social_response(
+            session=session,
+            reply_text=reply_text,
+            decision="open_social",
+            decision_reason=social_route.reason,
+        )
+
+    def _social_response(
+        self,
+        session: SessionState,
+        reply_text: str,
+        decision: str,
+        decision_reason: str,
+    ) -> ChatResponse:
+        session.context_memory["last_dialog_mode"] = decision
+        pending_hint = self.response_policy.pending_task_hint(
+            session.status,
+            session.pending_slots,
+            session.current_intent,
+        )
+        final_reply = self.response_policy.with_pending_hint(reply_text, pending_hint)
+        workflow = self._workflow_from_session(session)
+        status = session.status if session.status != "idle" else "social"
+        return ChatResponse(
+            session_id=session.session_id,
+            reply_type="text",
+            reply_text=final_reply,
+            intent=session.current_intent,
+            domain=self._domain_for_intent(session.current_intent),
+            decision=decision,
+            decision_reason=decision_reason,
+
+            status=status,
+            pending_slots=list(session.pending_slots),
+            filled_slots=session.slots.copy(),
+            workflow=workflow,
+            routing_debug=self._routing_debug_from_session(session),
+            trace_id=self._trace_id(),
+        )
+
+    def _routing_debug_from_session(self, session: SessionState) -> RoutingDebug | None:
+        if session.routing_debug is None:
+            return None
+        return RoutingDebug.model_validate(session.routing_debug)
+
+    def _trace_id(self) -> str:
+        return uuid4().hex
+
+    def _domain_for_intent(self, intent_id: str | None) -> str | None:
+        """Return the domain for a given intent_id, or None if unknown."""
+        if intent_id is None:
+            return None
+        try:
+            return self.intent_registry.get(intent_id).domain
+        except KeyError:
+            return None
+
+    def _planner_indicates_out_of_scope(self, planner_stage: MatcherStageDebug | None) -> bool:
+        if planner_stage is None or planner_stage.accepted:
+            return False
+        reason = str(planner_stage.reason or "").strip().lower()
+        if not reason:
+            return False
+        out_of_scope_signals = (
+            "no matching intent",
+            "there is no matching intent",
+            "not in the catalog",
+            "intent catalog only contains",
+            "provided intent catalog only contains",
+            "out of scope",
+            "third-party app action",
+            "outside known local capabilities",
+            "没有匹配意图",
+            "不在意图目录",
+            "不在目录中",
+            "超出能力范围",
+            "能力之外",
+        )
+        return any(signal in reason for signal in out_of_scope_signals)
+
+    def _mark_timing(self, breakdown: dict[str, float], name: str, started_at: float) -> None:
+        breakdown[name] = round((perf_counter() - started_at) * 1000, 3)
+
+    def _finalize_response(
+        self,
+        response: ChatResponse,
+        started_at: float,
+        breakdown: dict[str, float],
+        first_response_at: float | None = None,
+    ) -> ChatResponse:
+        first_ready_at = first_response_at or perf_counter()
+        response.first_response_latency_ms = round((first_ready_at - started_at) * 1000, 3)
+        response.total_latency_ms = round((perf_counter() - started_at) * 1000, 3)
+        response.processing_breakdown = dict(breakdown)
+        if response.routing_debug is not None:
+            if response.routing_debug.total_match_latency_ms is not None:
+                response.processing_breakdown.setdefault(
+                    "match_pipeline_ms",
+                    response.routing_debug.total_match_latency_ms,
+                )
+        return response
+
+    def _top_candidate_ids(self, routing_debug: RoutingDebug, limit: int = 3) -> list[str]:
+        if not routing_debug.stages:
+            return []
+        fusion_stage = next((stage for stage in reversed(routing_debug.stages) if stage.stage == "fusion"), None)
+        if fusion_stage is None:
+            return []
+        return [candidate.intent_id for candidate in fusion_stage.candidates[:limit]]
+
+    def _plan_if_needed(
+        self,
+        session: SessionState,
+        text: str,
+        routing_debug: RoutingDebug,
+    ) -> PlanningResult | None:
+        if self.planner is None:
+            return None
+        if not self._should_use_planner(text, routing_debug):
+            return None
+        planner_started_at = perf_counter()
+        result = self.planner.plan(
+            text,
+            self.intent_registry.list(),
+            context={
+                "current_intent": session.current_intent,
+                "slots": session.slots,
+                "context_memory": session.context_memory,
+            },
+        )
+        planner_elapsed_ms = round((perf_counter() - planner_started_at) * 1000, 3)
+        self._attach_planner_debug(routing_debug, result, planner_elapsed_ms)
+        return result
+
+    def _should_use_planner(self, text: str, routing_debug: RoutingDebug) -> bool:
+        fusion_stage = routing_debug.stages[-1] if routing_debug.stages else None
+        has_complex_pattern = any(token in text for token in ("然后", "并且", "同时", "如果", "若", "先", "后", "顺便", "以及"))
+        if has_complex_pattern:
+            return True
+        if self._looks_like_parallel_request(text, fusion_stage):
+            return True
+        if text.count("，") >= 1 or text.count(",") >= 1 or text.count("；") >= 1:
+            return True
+        if fusion_stage is not None and fusion_stage.stage == "fusion" and not fusion_stage.accepted:
+            return True
+        return False
+
+    def _attach_planner_debug(
+        self,
+        routing_debug: RoutingDebug,
+        planning_result: PlanningResult,
+        elapsed_ms: float | None = None,
+    ) -> None:
+        metadata = {
+            "workflow_type": planning_result.workflow_type,
+            "raw_response": planning_result.raw_response,
+        }
+        metadata.update(planning_result.metadata)
+        routing_debug.stages.append(
+            MatcherStageDebug(
+                stage="planner",
+                accepted=planning_result.accepted,
+                selected_intent=planning_result.steps[0].intent_id if planning_result.steps else None,
+                score=float(len(planning_result.steps)),
+                elapsed_ms=elapsed_ms,
+                reason=planning_result.reason,
+                model_name=planning_result.model_name,
+                backend=planning_result.backend,
+                error_message=planning_result.error_message,
+                metadata=metadata,
+                candidates=[
+                    IntentCandidate(
+                        intent_id=step.intent_id,
+                        score=1.0,
+                        reason=step.reason,
+                        model_name=planning_result.model_name,
+                        metadata=planning_result.metadata.get("normalized_steps", [{}])[index]
+                        if index < len(planning_result.metadata.get("normalized_steps", []))
+                        else {"slots": step.slots},
+                    )
+                    for index, step in enumerate(planning_result.steps[:5])
+                ],
+            )
+        )
+
+    def _start_planned_workflow(
+        self,
+        session: SessionState,
+        planning_result: PlanningResult,
+        routing_debug: RoutingDebug,
+        user_text: str,
+        rewrite_result: RewriteResult,
+    ) -> ChatResponse:
+        workflow = self._build_planned_workflow(session, planning_result)
+        session.workflow = workflow.model_dump()
+        session.routing_debug = routing_debug.model_dump()
+        response = self._continue_planned_workflow(session, workflow, routing_debug)
+        self._record_turn(session, user_text, response.reply_text, rewrite_result)
+        return response
+
+    def _build_planned_workflow(self, session: SessionState, planning_result: PlanningResult) -> Workflow:
+        steps: list[WorkflowStep] = []
+        first_intent = planning_result.steps[0].intent_id
+        domain = self.intent_registry.get(first_intent).domain
+        risk_level = self.intent_registry.get(first_intent).risk_level
+        step_id_map = {index: f"step_{session.session_id}_{index}" for index in range(1, len(planning_result.steps) + 1)}
+        for index, planned_step in enumerate(planning_result.steps, start=1):
+            intent = self.intent_registry.get(planned_step.intent_id)
+            steps.append(
+                WorkflowStep(
+                    step=index,
+                    step_id=step_id_map[index],
+                    intent_id=planned_step.intent_id,
+                    plugin_id=intent.plugin_id,
+                    action=intent.plugin_id.split(".")[-1],
+                    status="pending",
+                    depends_on=[
+                        step_id_map[dep_index]
+                        for dep_index in planned_step.depends_on
+                        if dep_index in step_id_map
+                    ],
+                    slots=dict(planned_step.slots),
+                    condition=dict(planned_step.condition),
+                    requires_confirmation=planned_step.requires_confirmation or self._requires_confirmation(intent),
+                )
+            )
+        return Workflow(
+            workflow_id=f"wf_{session.session_id}",
+            workflow_type=planning_result.workflow_type if planning_result.workflow_type in {"single", "sequence", "conditional", "parallel"} else "sequence",
+            domain=domain,
+            intent_id=first_intent,
+            status="ready",
+            risk_level=risk_level,
+            slots=session.slots.copy(),
+            missing_slots=[],
+            steps=steps,
+            meta={
+                "source": planning_result.backend,
+                "planner_model": planning_result.model_name,
+                "planner_reason": planning_result.reason,
+                "planner_debug": planning_result.metadata,
+                "step_results": {},
+                "confirmed_steps": [],
+            },
+        )
+
+    def _continue_planned_workflow(
+        self,
+        session: SessionState,
+        workflow: Workflow,
+        routing_debug: RoutingDebug | None,
+        carry_messages: list[str] | None = None,
+    ) -> ChatResponse:
+        step_messages: list[str] = list(carry_messages or [])
+        step_results = workflow.meta.setdefault("step_results", {})
+        confirmed_steps = workflow.meta.setdefault("confirmed_steps", [])
+        for step in workflow.steps:
+            if step.status in {"completed", "skipped"}:
+                continue
+            if step.depends_on and not all(self._is_step_completed(workflow, dependency) for dependency in step.depends_on):
+                continue
+            intent = self.intent_registry.get(step.intent_id)
+            merged_slots = session.slots.copy()
+            merged_slots.update(step.slots)
+            merged_slots = self._normalize_slots_for_intent(intent.intent_id, merged_slots)
+            step.slots = merged_slots.copy()
+            session.current_intent = intent.intent_id
+            missing_slots = self._pending_slots_for_intent(intent, merged_slots)
+            if missing_slots:
+                workflow.status = "waiting_slot"
+                session.pending_slots = missing_slots
+                session.status = "waiting_slot"
+                workflow.missing_slots = [
+                    MissingSlot(
+                        slot_name=slot,
+                        ask_template=self._default_ask_template(intent, slot),
+                        priority=index + 1,
+                    )
+                    for index, slot in enumerate(missing_slots)
+                ]
+                session.workflow = workflow.model_dump()
+                return ChatResponse(
+                    session_id=session.session_id,
+                    reply_type="ask_slot",
+                    reply_text=self.response_policy.ask_for_slot(
+                        intent,
+                        missing_slots[0],
+                        self._default_ask_template(intent, missing_slots[0]),
+                    ),
+                    intent=intent.intent_id,
+                    domain=intent.domain,
+                    status=session.status,
+                    pending_slots=missing_slots,
+
+                    filled_slots=merged_slots,
+                    workflow=workflow,
+                    routing_debug=routing_debug,
+                    trace_id=self._trace_id(),
+                )
+
+            condition_state = self._evaluate_step_condition(step, workflow)
+            if condition_state is False:
+                step.status = "skipped"
+                reason = step.condition.get("description") if step.condition else None
+                step_messages.append(self.response_policy.step_skipped(intent, reason))
+                continue
+
+            if step.requires_confirmation and step.step_id not in confirmed_steps:
+                step.status = "waiting_confirmation"
+                workflow.status = "waiting_confirmation"
+                session.pending_slots = ["confirmation"]
+                session.status = "waiting_confirmation"
+                workflow.meta["pending_confirmation"] = {
+                    "step_id": step.step_id,
+                    "intent_id": intent.intent_id,
+                    "detail": step.condition.get("description") if step.condition else None,
+                }
+                session.workflow = workflow.model_dump()
+                return ChatResponse(
+                    session_id=session.session_id,
+                    reply_type="ask_confirmation",
+                    reply_text=self.response_policy.ask_for_confirmation(
+                        intent,
+                        str(workflow.meta["pending_confirmation"].get("detail") or "").strip() or None,
+                    ),
+                    intent=intent.intent_id,
+                    domain=intent.domain,
+                    status=session.status,
+                    pending_slots=["confirmation"],
+
+                    filled_slots=merged_slots,
+                    workflow=workflow,
+                    routing_debug=routing_debug,
+                    trace_id=self._trace_id(),
+                )
+
+            plugin_result = self.plugins.execute(intent.plugin_id, merged_slots)
+            step.status = "completed"
+            step_results[step.step_id] = plugin_result
+            session.slots.update(merged_slots)
+            self._update_context_memory(session, intent.intent_id, merged_slots)
+            step_messages.append(self.response_policy.workflow_result(intent, plugin_result))
+
+        workflow.status = "completed"
+        workflow.missing_slots = []
+        session.pending_slots = []
+        session.status = "completed"
+        session.current_intent = workflow.intent_id
+        session.workflow = workflow.model_dump()
+        return ChatResponse(
+            session_id=session.session_id,
+            reply_type="workflow_result",
+            reply_text=self.response_policy.workflow_summary(step_messages),
+            intent=workflow.intent_id,
+            domain=self._domain_for_intent(workflow.intent_id),
+            status=session.status,
+            pending_slots=[],
+
+            filled_slots=session.slots,
+            workflow=workflow,
+            routing_debug=routing_debug,
+            trace_id=self._trace_id(),
+        )
+
+    def _requires_confirmation(self, intent) -> bool:
+        return self.dialog_rules.requires_confirmation(intent.intent_id, intent.risk_level)
+
+    def _is_step_completed(self, workflow: Workflow, step_id: str) -> bool:
+        dependency = next((item for item in workflow.steps if item.step_id == step_id), None)
+        return dependency is not None and dependency.status == "completed"
+
+    def _evaluate_step_condition(self, step: WorkflowStep, workflow: Workflow) -> bool | None:
+        if not step.condition:
+            return None
+        source_step_id = self._resolve_condition_source_step(step, workflow)
+        if source_step_id is None:
+            return None
+        step_results = workflow.meta.get("step_results", {})
+        source_result = step_results.get(source_step_id, {})
+        if not isinstance(source_result, dict):
+            return None
+        field_name = str(step.condition.get("field", "")).strip()
+        operator = str(step.condition.get("operator", "equals")).strip() or "equals"
+        expected = step.condition.get("value")
+        actual = source_result.get("data", {}).get(field_name)
+        if operator == "equals":
+            return actual == expected
+        if operator == "not_equals":
+            return actual != expected
+        if operator == "in" and isinstance(expected, list):
+            return actual in expected
+        return None
+
+    def _resolve_condition_source_step(self, step: WorkflowStep, workflow: Workflow) -> str | None:
+        source_step = step.condition.get("source_step")
+        try:
+            if source_step is not None:
+                source_index = int(source_step)
+                matched = next((item for item in workflow.steps if item.step == source_index), None)
+                if matched is not None:
+                    return matched.step_id
+        except (TypeError, ValueError):
+            return None
+        if step.depends_on:
+            return step.depends_on[0]
+        return None
+
+    def _handle_confirmation(self, session: SessionState, text: str) -> ChatResponse:
+        workflow = self._workflow_from_session(session)
+        routing_debug = self._routing_debug_from_session(session)
+        if workflow is None:
+            return self._fallback_response(session.session_id, routing_debug)
+
+        pending = workflow.meta.get("pending_confirmation", {})
+        step_id = str(pending.get("step_id", "")).strip()
+        if not step_id:
+            return self._fallback_response(session.session_id, routing_debug)
+
+        decision = self._parse_confirmation_decision(text)
+        step = next((item for item in workflow.steps if item.step_id == step_id), None)
+        if step is None:
+            return self._fallback_response(session.session_id, routing_debug)
+        intent = self.intent_registry.get(step.intent_id)
+
+        if decision is None:
+            return ChatResponse(
+                session_id=session.session_id,
+                reply_type="ask_confirmation",
+                reply_text=self.response_policy.confirm_retry(),
+                intent=intent.intent_id,
+                domain=intent.domain,
+                status=session.status,
+                pending_slots=["confirmation"],
+
+                filled_slots=session.slots,
+                workflow=workflow,
+                routing_debug=routing_debug,
+                trace_id=self._trace_id(),
+            )
+
+        workflow.meta.pop("pending_confirmation", None)
+        if decision is False:
+            step.status = "skipped"
+            session.pending_slots = []
+            session.status = "running"
+            skipped_reason = self.response_policy.confirm_cancelled()
+            step_messages = [skipped_reason]
+            response = self._continue_planned_workflow(session, workflow, routing_debug, carry_messages=step_messages)
+            return response
+
+        confirmed_steps = workflow.meta.setdefault("confirmed_steps", [])
+        if step_id not in confirmed_steps:
+            confirmed_steps.append(step_id)
+        session.pending_slots = []
+        session.status = "running"
+        return self._continue_planned_workflow(session, workflow, routing_debug)
+
+    def _workflow_from_session(self, session: SessionState) -> Workflow | None:
+        if session.workflow is None:
+            return None
+        return Workflow.model_validate(session.workflow)
+
+    def _attach_rewrite_debug(
+        self,
+        routing_debug: RoutingDebug,
+        rewrite_result: RewriteResult,
+        elapsed_ms: float | None = None,
+    ) -> None:
+        routing_debug.stages.insert(
+            0,
+            MatcherStageDebug(
+                stage="rewrite",
+                accepted=rewrite_result.applied,
+                score=1.0 if rewrite_result.applied else 0.0,
+                elapsed_ms=elapsed_ms,
+                reason=rewrite_result.reason or "no rewrite needed",
+                model_name="context-rewrite",
+                metadata={
+                    "original_text": rewrite_result.original_text,
+                    "rewritten_text": rewrite_result.rewritten_text,
+                    **rewrite_result.metadata,
+                },
+            ),
+        )
+
+    def _update_context_memory(
+        self,
+        session: SessionState,
+        intent_id: str,
+        extracted_slots: dict[str, object],
+    ) -> None:
+        session.context_memory["last_intent"] = intent_id
+        if "temperature" in extracted_slots:
+            session.context_memory["last_temperature"] = extracted_slots["temperature"]
+        if "destination" in extracted_slots:
+            session.context_memory["last_destination"] = extracted_slots["destination"]
+        if "song" in extracted_slots:
+            session.context_memory["last_song"] = extracted_slots["song"]
+        if "genre" in extracted_slots:
+            session.context_memory["last_genre"] = extracted_slots["genre"]
+
+    def _normalize_slots_for_intent(self, intent_id: str, slots: dict[str, object]) -> dict[str, object]:
+        normalized = dict(slots)
+        if intent_id == "cabin_set_ac":
+            temperature = self._normalize_temperature_value(normalized.get("temperature"))
+            if temperature is not None:
+                normalized["temperature"] = temperature
+            else:
+                normalized.pop("temperature", None)
+        if intent_id == "cabin_play_music":
+            media_query = str(normalized.get("media_query") or "").strip()
+            if media_query and "song" not in normalized and "genre" not in normalized:
+                if any(genre in media_query for genre in self._MUSIC_GENRE_HINTS):
+                    matched = next((genre for genre in self._MUSIC_GENRE_HINTS if genre in media_query), None)
+                    if matched:
+                        normalized["genre"] = matched
+                else:
+                    normalized["song"] = media_query
+            normalized.pop("media_query", None)
+        return normalized
+
+    def _continue_active_task(self, session: SessionState, text: str) -> ChatResponse | None:
+        if session.current_intent is None:
+            return None
+        if session.status not in {"waiting_slot", "waiting_confirmation"}:
+            return None
+        if session.status == "waiting_confirmation":
+            return None
+        intent = self.intent_registry.get(session.current_intent)
+        extracted_slots = self._extract_continuation_slots(session, intent, text)
+        if not extracted_slots:
+            return None
+        session.slots.update(extracted_slots)
+        self._update_context_memory(session, intent.intent_id, extracted_slots)
+        if session.routing_debug is not None:
+            routing_debug = RoutingDebug.model_validate(session.routing_debug)
+            routing_debug.extracted_slots = session.slots.copy()
+            session.routing_debug = routing_debug.model_dump()
+        existing_workflow = self._workflow_from_session(session)
+        if existing_workflow is not None and existing_workflow.workflow_type != "single":
+            return self._continue_planned_workflow(
+                session,
+                existing_workflow,
+                self._routing_debug_from_session(session),
+            )
+        return self._build_response_from_session(session)
+
+    def _extract_continuation_slots(
+        self,
+        session: SessionState,
+        intent,
+        text: str,
+    ) -> dict[str, object]:
+        extracted = self.router.extract_slots(text, intent)
+        if extracted:
+            return extracted
+        normalized = text.strip()
+        if not normalized:
+            return {}
+        candidate_texts = self._continuation_candidate_texts(intent.intent_id, normalized, session.pending_slots)
+        merged: dict[str, object] = {}
+        for candidate_text in candidate_texts:
+            candidate_slots = self.router.extract_slots(candidate_text, intent)
+            if candidate_slots:
+                merged.update(candidate_slots)
+                break
+        if merged:
+            return merged
+        if intent.intent_id == "cabin_play_music" and self._requires_music_query(session.pending_slots):
+            compact = normalized.strip("，。,.!?！？ ")
+            if not compact:
+                return {}
+            if self.dialog_act_engine.detect(compact) in {"thanks", "greeting", "goodbye"}:
+                return {}
+            matched_genre = next((genre for genre in self._MUSIC_GENRE_HINTS if genre in compact), None)
+            if matched_genre is not None:
+                return {"genre": matched_genre}
+            if len(compact) <= 20:
+                return {"song": compact}
+        return {}
+
+    def _continuation_candidate_texts(
+        self,
+        intent_id: str,
+        text: str,
+        pending_slots: list[str],
+    ) -> list[str]:
+        pending = list(pending_slots)
+        if intent_id == "cabin_play_music" and self._requires_music_query(pending):
+            return [f"播放{text}", f"来一首{text}", f"来点{text}"]
+        if intent_id == "cabin_nav_to" and "destination" in pending:
+            return [f"导航去{text}", f"带我去{text}"]
+        if intent_id == "cabin_set_ac" and "temperature" in pending:
+            return [f"把空调调到{text}", f"温度设成{text}"]
+        if intent_id in {"cs_query_order", "cs_query_logistics", "cs_cancel_order"} and "order_id" in pending:
+            prefix = {
+                "cs_query_order": "查一下订单",
+                "cs_query_logistics": "查一下物流",
+                "cs_cancel_order": "取消订单",
+            }.get(intent_id, "")
+            return [f"{prefix}{text}"] if prefix else [text]
+        return []
+
+    def _pending_slots_for_intent(self, intent, slots: dict[str, object]) -> list[str]:
+        pending_slots = [slot for slot in intent.required_slots if slot not in slots]
+        if intent.intent_id == "cabin_play_music" and not self._has_music_query(slots):
+            pending_slots.append("media_query")
+        deduped: list[str] = []
+        for slot in pending_slots:
+            if slot not in deduped:
+                deduped.append(slot)
+        return deduped
+
+    def _default_ask_template(self, intent, slot_name: str) -> str:
+        if slot_name in intent.ask_templates:
+            return str(intent.ask_templates[slot_name])
+        if slot_name == "media_query":
+            return "想听什么风格或者具体的歌名？"
+        return "请补充一个关键信息。"
+
+    def _has_music_query(self, slots: dict[str, object]) -> bool:
+        return bool(str(slots.get("song") or "").strip() or str(slots.get("genre") or "").strip())
+
+    def _requires_music_query(self, pending_slots: list[str]) -> bool:
+        return "media_query" in pending_slots or not pending_slots
+
+    def _looks_like_parallel_request(self, text: str, fusion_stage: MatcherStageDebug | None) -> bool:
+        _ = fusion_stage
+        compact = re.sub(r"\s+", "", text)
+        return bool(re.search(r"(和|跟|以及|外加).+", compact) and len(compact) >= 5)
+
+    def _normalize_temperature_value(self, raw_value: object) -> int | None:
+        if isinstance(raw_value, bool):
+            return None
+        if isinstance(raw_value, (int, float)):
+            value = int(raw_value)
+        elif isinstance(raw_value, str):
+            match = re.search(r"-?\d+", raw_value)
+            if not match:
+                return None
+            value = int(match.group(0))
+        else:
+            return None
+        return max(16, min(30, value))
+
+    def _record_turn(
+        self,
+        session: SessionState,
+        user_text: str,
+        agent_text: str,
+        rewrite_result: RewriteResult,
+    ) -> None:
+        session.last_user_text = user_text
+        session.last_agent_text = agent_text
+        session.context_memory["last_raw_user_text"] = user_text
+        session.context_memory["last_rewritten_text"] = rewrite_result.rewritten_text
+
+    def _update_dialog_act(self, session: SessionState, text: str) -> None:
+        session.context_memory["last_dialog_act"] = self.dialog_act_engine.detect(text)
+
+    def _has_active_task(self, session: SessionState) -> bool:
+        if session.status in {"waiting_slot", "waiting_confirmation", "running", "understanding"}:
+            return True
+        workflow = self._workflow_from_session(session)
+        if workflow is not None and workflow.status not in {"completed", "failed"}:
+            return True
+        return False
+
+    def _is_stop_request(self, text: str) -> bool:
+        return self.dialog_rules.is_stop_request(text)
+
+    def _reset_active_task(self, session: SessionState) -> None:
+        session.status = "idle"
+        session.current_intent = None
+        session.pending_slots = []
+        session.slots = {}
+        session.workflow = None
+        session.routing_debug = None
+
+    def _parse_confirmation_decision(self, text: str) -> bool | None:
+        return self.dialog_rules.parse_confirmation_decision(text)
+
+    def _try_knowledge_llm(
+        self,
+        session: SessionState,
+        user_text: str,
+        routing_debug: RoutingDebug,
+        rewrite_result: RewriteResult,
+    ) -> ChatResponse | None:
+        """BERT 未命中时尝试 LLM + knowledge_search function call。
+        有结果返回 ChatResponse（含 knowledge_content），无结果返回 None。
+        """
+        if self.knowledge_llm is None:
+            return None
+
+        result = self.knowledge_llm.reply(user_text)
+
+        # LLM 调用失败或本地兜底但没有文档 → 退回原始 fallback
+        if result.backend == "local-fallback" and result.doc_id is None:
+            return None
+
+        return ChatResponse(
+            session_id=session.session_id,
+            reply_type="text",
+            reply_text=result.reply_text,
+            intent=None,
+            domain="knowledge",
+            decision="knowledge_llm",
+            decision_reason=(
+                f"BERT 未命中，LLM 通过 knowledge_search 命中文档：{result.doc_id}"
+                if result.doc_id
+                else "BERT 未命中，LLM 自主回答"
+            ),
+            status="completed",
+            knowledge_doc_id=result.doc_id,
+            knowledge_doc_title=result.doc_title,
+            knowledge_content=result.doc_content,
+            routing_debug=routing_debug,
+            trace_id=self._trace_id(),
+        )
diff --git a/intelligent_cabin/app/services/classifier.py b/intelligent_cabin/app/services/classifier.py
new file mode 100644
index 0000000..eabb4de
--- /dev/null
+++ b/intelligent_cabin/app/services/classifier.py
@@ -0,0 +1,600 @@
+from __future__ import annotations
+
+import importlib
+import json
+import re
+from dataclasses import dataclass
+from pathlib import Path
+from time import perf_counter
+from typing import Any, Protocol
+from urllib import error, request
+
+from app.schemas.intent import IntentDefinition
+from app.services.joint_nlu import JointBertNLU
+
+
+@dataclass
+class ClassificationResult:
+    intent: IntentDefinition | None
+    score: float = 0.0
+    model_name: str = "mock-classifier"
+    candidates: list[tuple[IntentDefinition, float]] | None = None
+    backend_name: str | None = None
+    used_fallback: bool = False
+    fallback_reason: str | None = None
+    error_message: str | None = None
+    raw_label: str | None = None
+    raw_candidates: list[dict[str, Any]] | None = None
+
+
+class IntentClassifier(Protocol):
+    def predict(self, text: str, intents: list[IntentDefinition]) -> ClassificationResult:
+        ...
+
+
+class MockIntentClassifier:
+    """A local classifier stub that mimics a BERT-style scoring interface."""
+
+    def __init__(self, threshold: float = 1.2, top_k: int = 3) -> None:
+        self._threshold = threshold
+        self._top_k = top_k
+
+    def predict(self, text: str, intents: list[IntentDefinition]) -> ClassificationResult:
+        query_tokens = self._tokenize(text)
+        if not query_tokens:
+            return ClassificationResult(
+                intent=None,
+                score=0.0,
+                backend_name="mock-classifier",
+                raw_candidates=[],
+            )
+
+        scored_intents: list[tuple[IntentDefinition, float]] = []
+        for intent in intents:
+            score = self._score_intent(query_tokens, intent)
+            scored_intents.append((intent, score))
+
+        scored_intents.sort(key=lambda item: item[1], reverse=True)
+        best_intent: IntentDefinition | None = scored_intents[0][0] if scored_intents else None
+        best_score = scored_intents[0][1] if scored_intents else 0.0
+        top_candidates = [(intent, score) for intent, score in scored_intents[: self._top_k] if score > 0]
+        raw_candidates = [
+            {
+                "label": intent.intent_id,
+                "intent_id": intent.intent_id,
+                "score": score,
+            }
+            for intent, score in top_candidates
+        ]
+
+        if best_score < self._threshold:
+            return ClassificationResult(
+                intent=None,
+                score=best_score,
+                candidates=top_candidates,
+                backend_name="mock-classifier",
+                fallback_reason="below threshold",
+                raw_candidates=raw_candidates,
+            )
+        return ClassificationResult(
+            intent=best_intent,
+            score=best_score,
+            candidates=top_candidates,
+            backend_name="mock-classifier",
+            raw_label=best_intent.intent_id if best_intent is not None else None,
+            raw_candidates=raw_candidates,
+        )
+
+    def _score_intent(self, query_tokens: set[str], intent: IntentDefinition) -> float:
+        score = 0.0
+        for keyword in intent.keywords:
+            keyword_tokens = self._tokenize(keyword)
+            overlap = len(query_tokens & keyword_tokens)
+            if overlap:
+                score += overlap * 1.4
+
+        for example in intent.examples:
+            example_tokens = self._tokenize(example)
+            overlap = len(query_tokens & example_tokens)
+            if not example_tokens:
+                continue
+            coverage = overlap / len(example_tokens)
+            score = max(score, overlap + coverage)
+
+        if intent.domain == "customer_service" and any(token in query_tokens for token in {"订单", "物流", "快递"}):
+            score += 0.2
+        if intent.domain == "cabin" and any(token in query_tokens for token in {"导航", "空调", "音乐", "歌曲"}):
+            score += 0.2
+        return score
+
+    def _tokenize(self, text: str) -> set[str]:
+        cleaned = re.sub(r"[，。！？、\s]+", " ", text.strip().lower())
+        tokens = {token for token in cleaned.split(" ") if token}
+        compact = cleaned.replace(" ", "")
+        for size in (2, 3, 4):
+            for index in range(0, max(len(compact) - size + 1, 0)):
+                tokens.add(compact[index : index + size])
+        return tokens
+
+
+class BertIntentClassifier:
+    """
+    A pluggable local classifier interface for future BERT/Transformer models.
+
+    Expected model behavior:
+    - input: user text
+    - output labels: intent_id strings, or numeric indices mapped through a label file
+    """
+
+    def __init__(
+        self,
+        model_path: str,
+        threshold: float = 0.5,
+        label_map_path: str | None = None,
+        fallback: IntentClassifier | None = None,
+        top_k: int = 3,
+    ) -> None:
+        self._model_path = model_path
+        self._threshold = threshold
+        self._label_map_path = label_map_path
+        self._fallback = fallback
+        self._top_k = top_k
+        self._pipeline = None
+        self._label_map = self._load_label_map(label_map_path)
+        self._warmed_up = False
+        self._warmup_elapsed_ms: float | None = None
+        self._warmup_error_message: str | None = None
+
+    def predict(self, text: str, intents: list[IntentDefinition]) -> ClassificationResult:
+        pipeline = self._get_pipeline()
+        if pipeline is None:
+            return self._predict_with_fallback(
+                text,
+                intents,
+                attempted_backend="bert-local",
+                fallback_reason="bert model is unavailable",
+                error_message=self._pipeline_error_message(),
+            )
+
+        try:
+            raw_output = pipeline(text, truncation=True, top_k=self._top_k)
+        except Exception as exc:
+            return self._predict_with_fallback(
+                text,
+                intents,
+                attempted_backend="bert-local",
+                fallback_reason="bert inference failed",
+                error_message=str(exc),
+            )
+
+        normalized = self._normalize_pipeline_candidates(raw_output)
+        if not normalized:
+            return self._predict_with_fallback(
+                text,
+                intents,
+                attempted_backend="bert-local",
+                fallback_reason="bert returned empty result",
+                raw_candidates=[],
+            )
+
+        resolved = self._resolve_candidates(normalized, intents)
+        top_candidate = resolved["top_candidate"]
+        if top_candidate["intent"] is None:
+            return self._predict_with_fallback(
+                text,
+                intents,
+                attempted_backend="bert-local",
+                fallback_reason="bert label is not mapped to a known intent",
+                score=top_candidate["score"],
+                raw_label=top_candidate["label"],
+                raw_candidates=normalized,
+            )
+        if top_candidate["score"] < self._threshold:
+            return self._predict_with_fallback(
+                text,
+                intents,
+                attempted_backend="bert-local",
+                fallback_reason="bert score is below threshold",
+                score=top_candidate["score"],
+                raw_label=top_candidate["label"],
+                raw_candidates=normalized,
+            )
+
+        return ClassificationResult(
+            intent=top_candidate["intent"],
+            score=top_candidate["score"],
+            model_name="bert-local",
+            candidates=resolved["known_candidates"],
+            backend_name="bert-local",
+            raw_label=top_candidate["label"],
+            raw_candidates=normalized,
+        )
+
+    def _get_pipeline(self):
+        if self._pipeline is not None:
+            return self._pipeline
+        if not self._model_path or not Path(self._model_path).exists():
+            return None
+        try:
+            transformers = importlib.import_module("transformers")
+        except ImportError:
+            return None
+        self._pipeline = transformers.pipeline(
+            "text-classification",
+            model=self._model_path,
+            tokenizer=self._model_path,
+        )
+        return self._pipeline
+
+    def warmup(self, sample_text: str = "打开车窗") -> bool:
+        if self._warmed_up:
+            return True
+        started_at = perf_counter()
+        pipeline = self._get_pipeline()
+        if pipeline is None:
+            self._warmup_error_message = self._pipeline_error_message()
+            self._warmup_elapsed_ms = round((perf_counter() - started_at) * 1000, 3)
+            return False
+        try:
+            warmup_texts = [sample_text.strip() or "打开车窗", f"请帮我{sample_text.strip() or '打开车窗'}"]
+            for text in dict.fromkeys(warmup_texts):
+                pipeline(text, truncation=True, top_k=self._top_k)
+        except Exception as exc:
+            self._warmup_error_message = str(exc)
+            self._warmup_elapsed_ms = round((perf_counter() - started_at) * 1000, 3)
+            return False
+        self._warmup_error_message = None
+        self._warmup_elapsed_ms = round((perf_counter() - started_at) * 1000, 3)
+        self._warmed_up = True
+        return True
+
+    def _pipeline_error_message(self) -> str:
+        if not self._model_path:
+            return "AGENT_CLASSIFIER_MODEL_PATH is empty"
+        if not Path(self._model_path).exists():
+            return f"model path not found: {self._model_path}"
+        try:
+            importlib.import_module("transformers")
+        except ImportError:
+            return "transformers is not installed"
+        return "pipeline init failed"
+
+    def _load_label_map(self, label_map_path: str | None) -> dict[str, str]:
+        if not label_map_path:
+            return {}
+        path = Path(label_map_path)
+        if not path.exists():
+            return {}
+        data = json.loads(path.read_text(encoding="utf-8"))
+        return {str(key): str(value) for key, value in data.items()}
+
+    def _resolve_label(self, label: str) -> str | None:
+        if label in self._label_map:
+            return self._label_map[label]
+        return label or None
+
+    def _normalize_pipeline_candidates(self, raw_output: Any) -> list[dict[str, Any]]:
+        items = raw_output
+        if isinstance(items, list) and items and isinstance(items[0], list):
+            items = items[0]
+        if not isinstance(items, list):
+            items = [items]
+        normalized: list[dict[str, Any]] = []
+        for item in items:
+            if not isinstance(item, dict):
+                continue
+            label = str(item.get("label", ""))
+            score = float(item.get("score", 0.0))
+            normalized.append(
+                {
+                    "label": label,
+                    "intent_id": self._resolve_label(label),
+                    "score": score,
+                }
+            )
+        return normalized[: self._top_k]
+
+    def _resolve_candidates(
+        self,
+        normalized_candidates: list[dict[str, Any]],
+        intents: list[IntentDefinition],
+    ) -> dict[str, Any]:
+        intent_map = {intent.intent_id: intent for intent in intents}
+        known_candidates: list[tuple[IntentDefinition, float]] = []
+        resolved_items: list[dict[str, Any]] = []
+        for item in normalized_candidates:
+            intent = intent_map.get(str(item.get("intent_id") or ""))
+            if intent is not None:
+                known_candidates.append((intent, float(item.get("score", 0.0))))
+            resolved_items.append(
+                {
+                    "intent": intent,
+                    "label": str(item.get("label", "")),
+                    "score": float(item.get("score", 0.0)),
+                }
+            )
+        return {
+            "known_candidates": known_candidates,
+            "top_candidate": resolved_items[0],
+        }
+
+    def _predict_with_fallback(
+        self,
+        text: str,
+        intents: list[IntentDefinition],
+        attempted_backend: str,
+        fallback_reason: str,
+        score: float = 0.0,
+        raw_label: str | None = None,
+        raw_candidates: list[dict[str, Any]] | None = None,
+        error_message: str | None = None,
+    ) -> ClassificationResult:
+        if self._fallback is None:
+            return ClassificationResult(
+                intent=None,
+                score=score,
+                model_name=attempted_backend,
+                backend_name=attempted_backend,
+                used_fallback=False,
+                fallback_reason=fallback_reason,
+                error_message=error_message,
+                raw_label=raw_label,
+                raw_candidates=raw_candidates or [],
+            )
+        fallback_result = self._fallback.predict(text, intents)
+        return ClassificationResult(
+            intent=fallback_result.intent,
+            score=fallback_result.score,
+            model_name=fallback_result.model_name,
+            candidates=fallback_result.candidates,
+            backend_name=attempted_backend,
+            used_fallback=True,
+            fallback_reason=fallback_reason,
+            error_message=error_message,
+            raw_label=raw_label,
+            raw_candidates=raw_candidates or fallback_result.raw_candidates or [],
+        )
+
+
+class JointBertIntentClassifier:
+    def __init__(
+        self,
+        nlu: JointBertNLU,
+        threshold: float = 0.35,
+        top_k: int = 3,
+    ) -> None:
+        self._nlu = nlu
+        self._threshold = threshold
+        self._top_k = top_k
+
+    def warmup(self, sample_text: str = "打开车窗") -> bool:
+        return self._nlu.warmup(sample_text)
+
+    def predict(self, text: str, intents: list[IntentDefinition]) -> ClassificationResult:
+        result = self._nlu.predict(text, intents)
+        raw_candidates = [
+            {
+                "label": item.intent_id,
+                "intent_id": item.intent_id,
+                "score": item.score,
+            }
+            for item in result.candidates[: self._top_k]
+        ]
+        known_candidates = [
+            (intent, item.score)
+            for item in result.candidates[: self._top_k]
+            for intent in intents
+            if intent.intent_id == item.intent_id
+        ]
+        selected_intent = next((intent for intent in intents if intent.intent_id == result.intent_id), None)
+        if selected_intent is None or result.intent_score < self._threshold:
+            return ClassificationResult(
+                intent=None,
+                score=result.intent_score,
+                model_name=result.model_name,
+                candidates=known_candidates,
+                backend_name=result.backend_name,
+                fallback_reason=result.error_message or "joint bert score is below threshold or no intent selected",
+                error_message=result.error_message,
+                raw_label=result.intent_id,
+                raw_candidates=raw_candidates,
+            )
+        return ClassificationResult(
+            intent=selected_intent,
+            score=result.intent_score,
+            model_name=result.model_name,
+            candidates=known_candidates,
+            backend_name=result.backend_name,
+            raw_label=result.intent_id,
+            raw_candidates=raw_candidates,
+        )
+
+
+class RemoteIntentClassifier:
+    """
+    A remote classifier client.
+
+    Expected response payload:
+    {
+      "intent_id": "cs_query_order",
+      "score": 0.98,
+      "model_name": "bert-remote"
+    }
+    """
+
+    def __init__(
+        self,
+        endpoint: str,
+        timeout_seconds: float = 3.0,
+        threshold: float = 0.5,
+        fallback: IntentClassifier | None = None,
+        label_map_path: str | None = None,
+        top_k: int = 3,
+    ) -> None:
+        self._endpoint = endpoint
+        self._timeout_seconds = timeout_seconds
+        self._threshold = threshold
+        self._fallback = fallback
+        self._label_map = self._load_label_map(label_map_path)
+        self._top_k = top_k
+
+    def predict(self, text: str, intents: list[IntentDefinition]) -> ClassificationResult:
+        if not self._endpoint:
+            return self._predict_with_fallback(
+                text,
+                intents,
+                attempted_backend="remote-classifier",
+                fallback_reason="remote endpoint is not configured",
+                error_message="AGENT_CLASSIFIER_REMOTE_URL is empty",
+            )
+
+        payload = json.dumps(
+            {
+                "text": text,
+                "top_k": self._top_k,
+                "labels": [intent.intent_id for intent in intents],
+            }
+        ).encode("utf-8")
+        req = request.Request(
+            self._endpoint,
+            data=payload,
+            headers={"Content-Type": "application/json"},
+            method="POST",
+        )
+        try:
+            with request.urlopen(req, timeout=self._timeout_seconds) as response:
+                data = json.loads(response.read().decode("utf-8"))
+        except (error.URLError, TimeoutError, ValueError) as exc:
+            return self._predict_with_fallback(
+                text,
+                intents,
+                attempted_backend="remote-classifier",
+                fallback_reason="remote inference failed",
+                error_message=str(exc),
+            )
+
+        model_name = str(data.get("model_name", "remote-classifier"))
+        normalized = self._normalize_remote_candidates(data)
+        if not normalized:
+            return self._predict_with_fallback(
+                text,
+                intents,
+                attempted_backend=model_name,
+                fallback_reason="remote response has no candidates",
+                raw_candidates=[],
+            )
+
+        intent_map = {intent.intent_id: intent for intent in intents}
+        known_candidates = [
+            (intent_map[item["intent_id"]], item["score"])
+            for item in normalized
+            if item["intent_id"] in intent_map
+        ]
+        top_candidate = normalized[0]
+        selected_intent = intent_map.get(top_candidate["intent_id"])
+        if selected_intent is None:
+            return self._predict_with_fallback(
+                text,
+                intents,
+                attempted_backend=model_name,
+                fallback_reason="remote top label is not mapped to a known intent",
+                score=top_candidate["score"],
+                raw_label=top_candidate["label"],
+                raw_candidates=normalized,
+            )
+        if top_candidate["score"] < self._threshold:
+            return self._predict_with_fallback(
+                text,
+                intents,
+                attempted_backend=model_name,
+                fallback_reason="remote score is below threshold",
+                score=top_candidate["score"],
+                raw_label=top_candidate["label"],
+                raw_candidates=normalized,
+            )
+        return ClassificationResult(
+            intent=selected_intent,
+            score=top_candidate["score"],
+            model_name=model_name,
+            candidates=known_candidates,
+            backend_name=model_name,
+            raw_label=top_candidate["label"],
+            raw_candidates=normalized,
+        )
+
+    def _load_label_map(self, label_map_path: str | None) -> dict[str, str]:
+        if not label_map_path:
+            return {}
+        path = Path(label_map_path)
+        if not path.exists():
+            return {}
+        data = json.loads(path.read_text(encoding="utf-8"))
+        return {str(key): str(value) for key, value in data.items()}
+
+    def _resolve_label(self, label: str) -> str | None:
+        if label in self._label_map:
+            return self._label_map[label]
+        return label or None
+
+    def _normalize_remote_candidates(self, data: dict[str, Any]) -> list[dict[str, Any]]:
+        raw_candidates = data.get("candidates") or data.get("predictions") or []
+        if not raw_candidates and data.get("intent_id"):
+            raw_candidates = [
+                {
+                    "intent_id": data.get("intent_id"),
+                    "label": data.get("label") or data.get("intent_id"),
+                    "score": data.get("score", 0.0),
+                }
+            ]
+
+        normalized: list[dict[str, Any]] = []
+        for item in raw_candidates:
+            if not isinstance(item, dict):
+                continue
+            label = str(item.get("label") or item.get("intent_id") or "")
+            intent_id = str(item.get("intent_id") or self._resolve_label(label) or "")
+            normalized.append(
+                {
+                    "label": label,
+                    "intent_id": intent_id,
+                    "score": float(item.get("score", 0.0)),
+                }
+            )
+        return normalized[: self._top_k]
+
+    def _predict_with_fallback(
+        self,
+        text: str,
+        intents: list[IntentDefinition],
+        attempted_backend: str,
+        fallback_reason: str,
+        score: float = 0.0,
+        raw_label: str | None = None,
+        raw_candidates: list[dict[str, Any]] | None = None,
+        error_message: str | None = None,
+    ) -> ClassificationResult:
+        if self._fallback is None:
+            return ClassificationResult(
+                intent=None,
+                score=score,
+                model_name=attempted_backend,
+                backend_name=attempted_backend,
+                used_fallback=False,
+                fallback_reason=fallback_reason,
+                error_message=error_message,
+                raw_label=raw_label,
+                raw_candidates=raw_candidates or [],
+            )
+        fallback_result = self._fallback.predict(text, intents)
+        return ClassificationResult(
+            intent=fallback_result.intent,
+            score=fallback_result.score,
+            model_name=fallback_result.model_name,
+            candidates=fallback_result.candidates,
+            backend_name=attempted_backend,
+            used_fallback=True,
+            fallback_reason=fallback_reason,
+            error_message=error_message,
+            raw_label=raw_label,
+            raw_candidates=raw_candidates or fallback_result.raw_candidates or [],
+        )
diff --git a/intelligent_cabin/app/services/config_loader.py b/intelligent_cabin/app/services/config_loader.py
new file mode 100644
index 0000000..bc63932
--- /dev/null
+++ b/intelligent_cabin/app/services/config_loader.py
@@ -0,0 +1,173 @@
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+from app.schemas.configuration import (
+    ActionsConfig,
+    ContextRewriteConfig,
+    DialogActsConfig,
+    DialogRulesConfig,
+    DomainConfig,
+    FormsConfig,
+    ResponsesConfig,
+    WorkflowTemplatesConfig,
+)
+from app.services.dialog_act import DialogActEngine
+from app.services.dialog_rules import DialogRuleEngine
+from app.services.intent_registry import IntentRegistry
+from app.services.rewrite_engine import ContextRewriteEngine
+
+
+@dataclass
+class RuntimeConfigBundle:
+    intent_registry: IntentRegistry
+    response_templates: dict[str, str]
+    intent_hints: dict[str, str]
+    dialog_rules: DialogRuleEngine
+    dialog_act_engine: DialogActEngine
+    workflow_templates: WorkflowTemplatesConfig
+    rewrite_engine: ContextRewriteEngine = field(default_factory=ContextRewriteEngine)
+
+
+class ConfigLoader:
+    def __init__(
+        self,
+        domain_path: str,
+        action_path: str,
+        response_path: str,
+        form_path: str | None = None,
+        rule_path: str | None = None,
+        dialog_act_path: str | None = None,
+        workflow_path: str | None = None,
+        legacy_intent_path: str | None = None,
+        context_rewrite_path: str | None = None,
+    ) -> None:
+        self._domain_path = Path(domain_path)
+        self._action_path = Path(action_path)
+        self._response_path = Path(response_path)
+        self._form_path = Path(form_path) if form_path else None
+        self._rule_path = Path(rule_path) if rule_path else None
+        self._dialog_act_path = Path(dialog_act_path) if dialog_act_path else None
+        self._workflow_path = Path(workflow_path) if workflow_path else None
+        self._legacy_intent_path = Path(legacy_intent_path) if legacy_intent_path else None
+        self._context_rewrite_path = Path(context_rewrite_path) if context_rewrite_path else None
+
+    def load(self) -> RuntimeConfigBundle:
+        if self._domain_path.exists() and self._action_path.exists():
+            return self._load_from_config_files()
+        if self._legacy_intent_path is not None and self._legacy_intent_path.exists():
+            return RuntimeConfigBundle(
+                intent_registry=IntentRegistry.from_json(str(self._legacy_intent_path)),
+                response_templates=self._load_response_templates(),
+                intent_hints={},
+                dialog_rules=self._load_dialog_rules(),
+                dialog_act_engine=self._load_dialog_act_engine(),
+                workflow_templates=self._load_workflow_templates(),
+                rewrite_engine=self._load_rewrite_engine(),
+            )
+        raise FileNotFoundError(
+            "no runtime config found, expected config/*.yml or legacy intent json"
+        )
+
+    def _load_from_config_files(self) -> RuntimeConfigBundle:
+        domain = DomainConfig.model_validate(self._read_structured_file(self._domain_path))
+        actions = ActionsConfig.model_validate(self._read_structured_file(self._action_path))
+        forms = self._load_forms()
+        action_map = {item.action_id: item for item in actions.actions}
+        form_map = {item.intent_id: item for item in forms.forms}
+        intents = []
+        for item in domain.intents:
+            form = form_map.get(item.intent_id)
+            if form is not None:
+                item = item.model_copy(
+                    update={
+                        "required_slots": form.required_slots,
+                        "ask_templates": form.ask_templates,
+                    }
+                )
+            intents.append(item.to_intent_definition(action_map))
+        intent_hints = {
+            item.intent_id: item.label.strip()
+            for item in domain.intents
+            if item.label and item.label.strip()
+        }
+        return RuntimeConfigBundle(
+            intent_registry=IntentRegistry(intents),
+            response_templates=self._load_response_templates(),
+            intent_hints=intent_hints,
+            dialog_rules=self._load_dialog_rules(),
+            dialog_act_engine=self._load_dialog_act_engine(),
+            workflow_templates=self._load_workflow_templates(),
+            rewrite_engine=self._load_rewrite_engine(),
+        )
+
+    def _load_response_templates(self) -> dict[str, str]:
+        if not self._response_path.exists():
+            return {}
+        raw = self._read_structured_file(self._response_path)
+        parsed = ResponsesConfig.model_validate(raw)
+        return parsed.templates
+
+    def _load_forms(self) -> FormsConfig:
+        if self._form_path is None or not self._form_path.exists():
+            return FormsConfig()
+        raw = self._read_structured_file(self._form_path)
+        return FormsConfig.model_validate(raw)
+
+    def _load_dialog_rules(self) -> DialogRuleEngine:
+        if self._rule_path is None or not self._rule_path.exists():
+            return DialogRuleEngine()
+        raw = self._read_structured_file(self._rule_path)
+        parsed = DialogRulesConfig.model_validate(raw)
+        return DialogRuleEngine(
+            stop_phrases=tuple(parsed.stop.phrases) or DialogRuleEngine.stop_phrases,
+            positive_confirmation_tokens=tuple(parsed.confirmation.positive_tokens)
+            or DialogRuleEngine.positive_confirmation_tokens,
+            negative_confirmation_tokens=tuple(parsed.confirmation.negative_tokens)
+            or DialogRuleEngine.negative_confirmation_tokens,
+            confirmation_required_intents=tuple(parsed.confirmation.required_intents)
+            or DialogRuleEngine.confirmation_required_intents,
+            confirmation_required_risk_levels=tuple(parsed.confirmation.required_risk_levels)
+            or DialogRuleEngine.confirmation_required_risk_levels,
+            metadata={"source": str(self._rule_path)},
+        )
+
+    def _load_dialog_act_engine(self) -> DialogActEngine:
+        if self._dialog_act_path is None or not self._dialog_act_path.exists():
+            return DialogActEngine()
+        raw = self._read_structured_file(self._dialog_act_path)
+        parsed = DialogActsConfig.model_validate(raw)
+        return DialogActEngine(
+            patterns={
+                item.act_id: tuple(item.phrases)
+                for item in parsed.acts
+            },
+            numeric_patterns={
+                item.act_id: tuple(item.numeric_patterns)
+                for item in parsed.acts
+                if item.numeric_patterns
+            },
+        )
+
+    def _load_rewrite_engine(self) -> ContextRewriteEngine:
+        if self._context_rewrite_path is None or not self._context_rewrite_path.exists():
+            return ContextRewriteEngine()
+        raw = self._read_structured_file(self._context_rewrite_path)
+        config = ContextRewriteConfig.model_validate(raw)
+        return ContextRewriteEngine(config=config)
+
+    def _load_workflow_templates(self) -> WorkflowTemplatesConfig:
+        if self._workflow_path is None or not self._workflow_path.exists():
+            return WorkflowTemplatesConfig()
+        raw = self._read_structured_file(self._workflow_path)
+        return WorkflowTemplatesConfig.model_validate(raw)
+
+    def _read_structured_file(self, path: Path) -> dict[str, Any]:
+        if path.suffix.lower() == ".json":
+            return json.loads(path.read_text(encoding="utf-8"))
+        return yaml.safe_load(path.read_text(encoding="utf-8")) or {}
diff --git a/intelligent_cabin/app/services/dialog_act.py b/intelligent_cabin/app/services/dialog_act.py
new file mode 100644
index 0000000..4cf7377
--- /dev/null
+++ b/intelligent_cabin/app/services/dialog_act.py
@@ -0,0 +1,51 @@
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass, field
+
+
+@dataclass
+class DialogActEngine:
+    """
+    基于配置词表的对话行为检测器。
+    - patterns          : act_id → 触发词组 tuple，逐词包含匹配
+    - numeric_patterns  : act_id → 正则 tuple，全文正则匹配（用于数字类 inform）
+
+    词表和正则均从 config/dialog_acts.yml 加载，不同设备部署时修改配置文件即可，无需改代码。
+    """
+
+    patterns: dict[str, tuple[str, ...]] = field(
+        default_factory=lambda: {
+            "affirm": ("确认", "好的", "继续", "可以", "确定"),
+            "deny": ("不要", "不行", "否", "不"),
+            "cancel": ("取消", "算了", "不用了", "停止"),
+            "modify": ("改成", "换成", "再低一点", "再高一点", "调大", "调小"),
+            "chitchat": ("你好", "谢谢", "再见", "天气", "真不错"),
+            "request": ("帮我", "打开", "关闭", "导航", "播放", "查询", "查"),
+            "inform": (),
+        }
+    )
+    # act_id → 正则表达式 tuple（全文 search，任意命中即触发）
+    numeric_patterns: dict[str, tuple[str, ...]] = field(
+        default_factory=lambda: {
+            "inform": (r"\d+",),
+        }
+    )
+
+    def detect(self, text: str) -> str:
+        normalized = re.sub(r"\s+", "", text.strip().lower())
+        if not normalized:
+            return "unknown"
+
+        # 1. 词表包含匹配（保持原有优先级顺序）
+        for act_id, phrases in self.patterns.items():
+            if any(phrase and phrase in normalized for phrase in phrases):
+                return act_id
+
+        # 2. 正则匹配（主要用于 inform 的数字检测）
+        for act_id, regexes in self.numeric_patterns.items():
+            for pattern in regexes:
+                if re.search(pattern, normalized):
+                    return act_id
+
+        return "unknown"
diff --git a/intelligent_cabin/app/services/dialog_rules.py b/intelligent_cabin/app/services/dialog_rules.py
new file mode 100644
index 0000000..3e59bfd
--- /dev/null
+++ b/intelligent_cabin/app/services/dialog_rules.py
@@ -0,0 +1,62 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+
+@dataclass
+class DialogRuleEngine:
+    stop_phrases: tuple[str, ...] = (
+        "不用了",
+        "算了",
+        "先不要了",
+        "先这样吧",
+        "停一下",
+        "停止",
+        "停止当前任务",
+        "结束这次操作",
+        "别弄了",
+        "不需要了",
+    )
+    positive_confirmation_tokens: tuple[str, ...] = (
+        "确认",
+        "好的",
+        "是",
+        "继续",
+        "可以",
+        "确定",
+        "yes",
+        "ok",
+    )
+    negative_confirmation_tokens: tuple[str, ...] = (
+        "取消",
+        "不用",
+        "不要",
+        "否",
+        "no",
+        "算了",
+        "停止",
+    )
+    confirmation_required_intents: tuple[str, ...] = ("cs_cancel_order",)
+    confirmation_required_risk_levels: tuple[str, ...] = ("high",)
+    metadata: dict[str, object] = field(default_factory=dict)
+
+    def is_stop_request(self, text: str) -> bool:
+        normalized = text.strip().lower().replace(" ", "")
+        if not normalized:
+            return False
+        return any(phrase in normalized for phrase in self.stop_phrases)
+
+    def parse_confirmation_decision(self, text: str) -> bool | None:
+        normalized = text.strip().lower()
+        if not normalized:
+            return None
+        if any(token == normalized or token in normalized for token in self.negative_confirmation_tokens):
+            return False
+        if any(token == normalized or token in normalized for token in self.positive_confirmation_tokens):
+            return True
+        return None
+
+    def requires_confirmation(self, intent_id: str, risk_level: str) -> bool:
+        if intent_id in self.confirmation_required_intents:
+            return True
+        return risk_level in self.confirmation_required_risk_levels
diff --git a/intelligent_cabin/app/services/intent_registry.py b/intelligent_cabin/app/services/intent_registry.py
new file mode 100644
index 0000000..f095135
--- /dev/null
+++ b/intelligent_cabin/app/services/intent_registry.py
@@ -0,0 +1,29 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+from app.schemas.intent import IntentDefinition
+
+
+class IntentRegistry:
+    def __init__(self, intents: list[IntentDefinition]) -> None:
+        self._intents = {intent.intent_id: intent for intent in intents}
+
+    @classmethod
+    def from_json(cls, file_path: str) -> "IntentRegistry":
+        data = json.loads(Path(file_path).read_text(encoding="utf-8"))
+        intents = [IntentDefinition.model_validate(item) for item in data]
+        return cls(intents)
+
+    def get(self, intent_id: str) -> IntentDefinition:
+        return self._intents[intent_id]
+
+    def list(self) -> list[IntentDefinition]:
+        return list(self._intents.values())
+
+    def match(self, text: str) -> IntentDefinition | None:
+        for intent in self._intents.values():
+            if any(keyword in text for keyword in intent.keywords):
+                return intent
+        return None
diff --git a/intelligent_cabin/app/services/joint_nlu.py b/intelligent_cabin/app/services/joint_nlu.py
new file mode 100644
index 0000000..4f6e260
--- /dev/null
+++ b/intelligent_cabin/app/services/joint_nlu.py
@@ -0,0 +1,430 @@
+from __future__ import annotations
+
+import json
+from collections import OrderedDict
+from dataclasses import dataclass, field
+from pathlib import Path
+from time import perf_counter
+from typing import Any
+
+import torch
+from transformers import AutoConfig, AutoModel, AutoTokenizer
+
+from app.schemas.intent import IntentDefinition
+
+
+OPTIONAL_SLOT_NAMES_BY_INTENT: dict[str, set[str]] = {
+    "cabin_play_music": {"song", "genre"},
+}
+
+BLOCKED_INTENT_LABELS = {"__social__", "__out_of_scope__"}
+
+
+def allowed_slot_names(intent_id: str, required_slots: list[str] | None = None) -> set[str]:
+    required = set(required_slots or [])
+    return required | OPTIONAL_SLOT_NAMES_BY_INTENT.get(intent_id, set())
+
+
+@dataclass
+class JointSlot:
+    slot_name: str
+    value: str
+    start: int
+    end: int
+    score: float = 0.0
+
+
+@dataclass
+class JointCandidate:
+    intent_id: str
+    score: float
+
+
+@dataclass
+class JointNluResult:
+    intent_id: str | None = None
+    intent_score: float = 0.0
+    candidates: list[JointCandidate] = field(default_factory=list)
+    multi_intent_candidates: list[JointCandidate] = field(default_factory=list)
+    slots: dict[str, Any] = field(default_factory=dict)
+    slot_items: list[JointSlot] = field(default_factory=list)
+    model_name: str = "joint-bert-local"
+    backend_name: str = "joint-bert-local"
+    error_message: str | None = None
+
+
+class JointBertForNLU(torch.nn.Module):
+    def __init__(
+        self,
+        base_model_name: str,
+        num_intents: int,
+        num_slot_labels: int,
+        encoder_config_path: str | Path | None = None,
+    ) -> None:
+        super().__init__()
+        if encoder_config_path is not None:
+            encoder_config = AutoConfig.from_pretrained(encoder_config_path, local_files_only=True)
+            self.encoder = AutoModel.from_config(encoder_config)
+        else:
+            self.encoder = AutoModel.from_pretrained(base_model_name)
+        hidden_size = int(self.encoder.config.hidden_size)
+        dropout_prob = float(getattr(self.encoder.config, "hidden_dropout_prob", 0.1))
+        self.dropout = torch.nn.Dropout(dropout_prob)
+        self.intent_classifier = torch.nn.Linear(hidden_size, num_intents)
+        self.slot_classifier = torch.nn.Linear(hidden_size, num_slot_labels)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        token_type_ids: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        encoder_kwargs = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+        }
+        if token_type_ids is not None:
+            encoder_kwargs["token_type_ids"] = token_type_ids
+        outputs = self.encoder(**encoder_kwargs)
+        sequence_output = self.dropout(outputs.last_hidden_state)
+        pooled_output = self.dropout(sequence_output[:, 0])
+        intent_logits = self.intent_classifier(pooled_output)
+        slot_logits = self.slot_classifier(sequence_output)
+        return intent_logits, slot_logits
+
+
+class JointBertNLU:
+    def __init__(
+        self,
+        model_path: str,
+        intent_threshold: float | None = None,
+        multi_intent_threshold: float | None = None,
+        top_k: int = 5,
+        max_multi_intents: int = 4,
+        max_cache_size: int = 8,
+    ) -> None:
+        self._model_path = Path(model_path)
+        self._intent_threshold = intent_threshold
+        self._multi_intent_threshold = multi_intent_threshold
+        self._top_k = top_k
+        self._max_multi_intents = max_multi_intents
+        self._max_cache_size = max_cache_size
+        self._runtime: tuple[AutoTokenizer, JointBertForNLU, dict[str, Any], torch.device] | None = None
+        self._warmup_elapsed_ms: float | None = None
+        self._warmup_error_message: str | None = None
+        self._warmed_up = False
+        self._cache: OrderedDict[str, dict[str, Any]] = OrderedDict()
+
+    def warmup(self, sample_text: str = "把空调调到22度") -> bool:
+        started_at = perf_counter()
+        try:
+            self._predict_raw(sample_text)
+        except Exception as exc:
+            self._warmup_error_message = str(exc)
+            self._warmup_elapsed_ms = round((perf_counter() - started_at) * 1000, 3)
+            return False
+        self._warmup_error_message = None
+        self._warmup_elapsed_ms = round((perf_counter() - started_at) * 1000, 3)
+        self._warmed_up = True
+        return True
+
+    def predict(self, text: str, intents: list[IntentDefinition]) -> JointNluResult:
+        try:
+            raw_result = self._predict_raw(text)
+        except Exception as exc:
+            return JointNluResult(error_message=str(exc))
+        candidates = self._filter_known_candidates(raw_result["candidates"], intents, limit=self._top_k)
+        multi_candidates = self.predict_multi_intents(text, intents)
+        top_candidate = candidates[0] if candidates else None
+        if top_candidate is None or top_candidate.score < self._resolved_intent_threshold():
+            return JointNluResult(
+                intent_id=None,
+                intent_score=top_candidate.score if top_candidate is not None else 0.0,
+                candidates=candidates,
+                multi_intent_candidates=multi_candidates,
+                slots={},
+                slot_items=[],
+            )
+        intent_def = next((intent for intent in intents if intent.intent_id == top_candidate.intent_id), None)
+        if intent_def is None:
+            return JointNluResult(
+                intent_id=None,
+                intent_score=top_candidate.score,
+                candidates=candidates,
+                multi_intent_candidates=multi_candidates,
+                slots={},
+                slot_items=[],
+            )
+        slot_items = self._filter_slot_items(raw_result["slot_items"], intent_def.intent_id, intent_def.required_slots)
+        return JointNluResult(
+            intent_id=top_candidate.intent_id,
+            intent_score=top_candidate.score,
+            candidates=candidates,
+            multi_intent_candidates=multi_candidates,
+            slots=self._slot_items_to_dict(slot_items),
+            slot_items=slot_items,
+        )
+
+    def predict_multi_intents(
+        self,
+        text: str,
+        intents: list[IntentDefinition],
+        threshold: float | None = None,
+        max_labels: int | None = None,
+        top_k: int | None = None,
+    ) -> list[JointCandidate]:
+        try:
+            raw_result = self._predict_raw(text)
+        except Exception:
+            return []
+        threshold = self._multi_intent_threshold if threshold is None else threshold
+        if threshold is None:
+            threshold = self._resolved_multi_intent_threshold()
+        max_labels = self._max_multi_intents if max_labels is None else max_labels
+        ranked = self._filter_known_candidates(raw_result["candidates"], intents, limit=top_k or self._top_k)
+        selected: list[JointCandidate] = []
+        for item in ranked:
+            if item.score < threshold:
+                continue
+            selected.append(item)
+            if len(selected) >= max_labels:
+                break
+        return selected
+
+    def extract_slots(self, text: str, intent: IntentDefinition) -> dict[str, Any]:
+        try:
+            raw_result = self._predict_raw(text)
+        except Exception:
+            return {}
+        slot_items = self._filter_slot_items(raw_result["slot_items"], intent.intent_id, intent.required_slots)
+        return self._slot_items_to_dict(slot_items)
+
+    def extract_slots_by_intent_id(
+        self,
+        text: str,
+        intent_id: str,
+        required_slots: list[str] | None = None,
+    ) -> dict[str, Any]:
+        try:
+            raw_result = self._predict_raw(text)
+        except Exception:
+            return {}
+        slot_items = self._filter_slot_items(raw_result["slot_items"], intent_id, required_slots or [])
+        return self._slot_items_to_dict(slot_items)
+
+    def _filter_known_candidates(
+        self,
+        candidates: list[JointCandidate],
+        intents: list[IntentDefinition],
+        limit: int | None = None,
+    ) -> list[JointCandidate]:
+        known_intents = {intent.intent_id for intent in intents}
+        filtered = [
+            item
+            for item in candidates
+            if item.intent_id in known_intents and item.intent_id not in BLOCKED_INTENT_LABELS
+        ]
+        return filtered[:limit] if limit is not None else filtered
+
+    def _slot_items_to_dict(self, slot_items: list[JointSlot]) -> dict[str, Any]:
+        slots: dict[str, Any] = {}
+        for item in slot_items:
+            if item.slot_name == "temperature":
+                digits = "".join(ch for ch in item.value if ch.isdigit())
+                if digits:
+                    slots[item.slot_name] = int(digits)
+                    continue
+            slots[item.slot_name] = item.value
+        return slots
+
+    def _filter_slot_items(
+        self,
+        slot_items: list[JointSlot],
+        intent_id: str,
+        required_slots: list[str],
+    ) -> list[JointSlot]:
+        allowed = allowed_slot_names(intent_id, required_slots)
+        if not allowed:
+            return []
+        filtered = [item for item in slot_items if item.slot_name in allowed]
+        deduped: list[JointSlot] = []
+        seen: set[tuple[str, int, int]] = set()
+        for item in filtered:
+            key = (item.slot_name, item.start, item.end)
+            if key in seen:
+                continue
+            seen.add(key)
+            deduped.append(item)
+        return deduped
+
+    def _predict_raw(self, text: str) -> dict[str, Any]:
+        normalized = (text or "").strip()
+        if not normalized:
+            return {"candidates": [], "slot_items": []}
+        if normalized in self._cache:
+            cached = self._cache.pop(normalized)
+            self._cache[normalized] = cached
+            return cached
+        tokenizer, model, metadata, device = self._load_runtime()
+        encoded = tokenizer(
+            normalized,
+            truncation=True,
+            max_length=int(metadata.get("max_length", 64)),
+            return_offsets_mapping=True,
+            return_tensors="pt",
+        )
+        offset_mapping = encoded.pop("offset_mapping")[0].tolist()
+        encoded = {key: value.to(device) for key, value in encoded.items()}
+        model.eval()
+        with torch.no_grad():
+            intent_logits, slot_logits = model(**encoded)
+            slot_probs = torch.softmax(slot_logits, dim=-1)[0].detach().cpu()
+            slot_ids = torch.argmax(slot_probs, dim=-1).tolist()
+        intent_probs = self._intent_probabilities(intent_logits.detach().cpu()[0], metadata)
+        intent_labels = metadata.get("intent_labels", [])
+        slot_labels = metadata.get("slot_labels", [])
+        candidates = [
+            JointCandidate(intent_id=str(intent_labels[index]), score=float(score))
+            for index, score in sorted(
+                list(enumerate(intent_probs)),
+                key=lambda item: item[1],
+                reverse=True,
+            )
+        ]
+        slot_items = self._decode_slot_items(
+            text=normalized,
+            offset_mapping=offset_mapping,
+            slot_ids=slot_ids,
+            slot_probs=slot_probs,
+            slot_labels=slot_labels,
+        )
+        result = {
+            "candidates": candidates,
+            "slot_items": slot_items,
+        }
+        self._cache[normalized] = result
+        while len(self._cache) > self._max_cache_size:
+            self._cache.popitem(last=False)
+        return result
+
+    def _intent_probabilities(self, intent_logits: torch.Tensor, metadata: dict[str, Any]) -> list[float]:
+        task_type = str(metadata.get("intent_task", "single_label")).strip() or "single_label"
+        if task_type == "multi_label":
+            return torch.sigmoid(intent_logits).tolist()
+        return torch.softmax(intent_logits, dim=-1).tolist()
+
+    def _decode_slot_items(
+        self,
+        text: str,
+        offset_mapping: list[list[int]],
+        slot_ids: list[int],
+        slot_probs: torch.Tensor,
+        slot_labels: list[str],
+    ) -> list[JointSlot]:
+        items: list[JointSlot] = []
+        current_name: str | None = None
+        current_start: int | None = None
+        current_end: int | None = None
+        current_scores: list[float] = []
+
+        def flush() -> None:
+            nonlocal current_name, current_start, current_end, current_scores
+            if current_name is None or current_start is None or current_end is None or current_start >= current_end:
+                current_name = None
+                current_start = None
+                current_end = None
+                current_scores = []
+                return
+            value = text[current_start:current_end].strip()
+            if value:
+                items.append(
+                    JointSlot(
+                        slot_name=current_name,
+                        value=value,
+                        start=current_start,
+                        end=current_end,
+                        score=round(sum(current_scores) / max(len(current_scores), 1), 4),
+                    )
+                )
+            current_name = None
+            current_start = None
+            current_end = None
+            current_scores = []
+
+        for index, label_id in enumerate(slot_ids):
+            if index >= len(offset_mapping):
+                break
+            start, end = offset_mapping[index]
+            if end <= start:
+                flush()
+                continue
+            label = str(slot_labels[label_id]) if label_id < len(slot_labels) else "O"
+            token_score = float(slot_probs[index][label_id].item())
+            if label == "O":
+                flush()
+                continue
+            prefix, _, name = label.partition("-")
+            if prefix == "B" or current_name != name:
+                flush()
+                current_name = name
+                current_start = start
+                current_end = end
+                current_scores = [token_score]
+                continue
+            current_end = end
+            current_scores.append(token_score)
+        flush()
+        return items
+
+    def _load_runtime(self) -> tuple[AutoTokenizer, JointBertForNLU, dict[str, Any], torch.device]:
+        if self._runtime is not None:
+            return self._runtime
+        if not self._model_path.exists():
+            raise FileNotFoundError(f"joint nlu model path not found: {self._model_path}")
+        metadata_path = self._model_path / "joint_nlu_config.json"
+        state_dict_path = self._model_path / "model_state.pt"
+        if not metadata_path.exists():
+            raise FileNotFoundError(f"joint nlu config missing: {metadata_path}")
+        if not state_dict_path.exists():
+            raise FileNotFoundError(f"joint nlu model state missing: {state_dict_path}")
+        metadata = json.loads(metadata_path.read_text(encoding="utf-8"))
+        tokenizer = AutoTokenizer.from_pretrained(self._model_path)
+        model = JointBertForNLU(
+            base_model_name=str(metadata["base_model_name"]),
+            num_intents=len(metadata["intent_labels"]),
+            num_slot_labels=len(metadata["slot_labels"]),
+            encoder_config_path=self._resolve_encoder_config_path(metadata),
+        )
+        state_dict = torch.load(state_dict_path, map_location="cpu")
+        model.load_state_dict(state_dict)
+        device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
+        model.to(device)
+        self._runtime = (tokenizer, model, metadata, device)
+        return self._runtime
+
+    def _resolve_encoder_config_path(self, metadata: dict[str, Any]) -> Path | None:
+        local_config = self._model_path / "config.json"
+        if local_config.exists():
+            return self._model_path
+
+        base_model_path = Path(str(metadata.get("base_model_name", "")))
+        if base_model_path.exists() and (base_model_path / "config.json").exists():
+            return base_model_path
+
+        for candidate_name in ("local_bert_intent", "local_bert_multi_intent"):
+            candidate_path = self._model_path.parent / candidate_name
+            if (candidate_path / "config.json").exists():
+                return candidate_path
+        return None
+
+    def _resolved_intent_threshold(self) -> float:
+        if self._intent_threshold is not None:
+            return self._intent_threshold
+        metadata = self._runtime[2] if self._runtime is not None else {}
+        return float(metadata.get("intent_threshold", 0.35))
+
+    def _resolved_multi_intent_threshold(self) -> float:
+        if self._multi_intent_threshold is not None:
+            return self._multi_intent_threshold
+        metadata = self._runtime[2] if self._runtime is not None else {}
+        return float(metadata.get("multi_intent_threshold", metadata.get("intent_threshold", 0.45)))
diff --git a/intelligent_cabin/app/services/knowledge_llm.py b/intelligent_cabin/app/services/knowledge_llm.py
new file mode 100644
index 0000000..581e397
--- /dev/null
+++ b/intelligent_cabin/app/services/knowledge_llm.py
@@ -0,0 +1,238 @@
+"""
+app/services/knowledge_llm.py
+
+当 BERT NLU 未命中时，使用 LLM + knowledge_search function call 查询本地知识库。
+
+流程：
+  1. 构建 tools=[knowledge_search] 发给 LLM
+  2. 若 LLM 返回 tool_calls → 执行 KnowledgeStore.search() → 拼结果再发一次 LLM
+  3. LLM 生成最终回复 reply_text + knowledge_doc_id（可选）
+
+返回 KnowledgeReplyResult，包含：
+  - reply_text: 简短自然语言摘要
+  - doc_id / doc_content: 命中的知识文档（供前端渲染 KnowledgeArtifact）
+"""
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field
+from typing import Any
+from urllib import error, request
+
+from app.services.knowledge_store import KnowledgeDoc, KnowledgeStore
+
+
+@dataclass
+class KnowledgeReplyResult:
+    reply_text: str
+    backend: str
+    model_name: str
+    doc_id: str | None = None
+    doc_content: str | None = None        # 原始 MD 内容，前端渲染用
+    doc_title: str | None = None
+    error_message: str | None = None
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+
+# ── LLM 工具定义（OpenAI function calling 格式，DashScope 兼容）────────────────
+_KNOWLEDGE_SEARCH_TOOL: dict[str, Any] = {
+    "type": "function",
+    "function": {
+        "name": "knowledge_search",
+        "description": (
+            "搜索本地设备知识库，获取焊管机/弯管机产线相关的故障排查、操作规程等知识。"
+            "当用户问到设备故障、报警处理、操作方法、工艺参数时请调用此工具。"
+        ),
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "query": {
+                    "type": "string",
+                    "description": "搜索关键词，如'虚焊报警'、'激光扫描仪操作'、'弯管模具调节'等",
+                }
+            },
+            "required": ["query"],
+        },
+    },
+}
+
+_SYSTEM_PROMPT = """\
+你是焊管机产线智能助手，负责回答操作工人关于设备故障、工艺调节、仪器使用的问题。
+你有一个工具 knowledge_search 可以查询本地设备知识库，遇到设备类问题时请先调用它。
+回答时语言简洁、口语化，先给出结论，再说步骤，总长度不超过 100 字。
+如果工具返回了相关知识，请基于知识内容回答，不要编造。
+如果没有找到相关知识，诚实告知"暂未找到相关资料，建议联系技术支持"。
+"""
+
+
+class DashScopeKnowledgeLLM:
+    """使用 DashScope（OpenAI 兼容 API）+ function calling 的知识库问答器。"""
+
+    def __init__(
+        self,
+        base_url: str,
+        api_key: str,
+        model_name: str,
+        knowledge_store: KnowledgeStore,
+        timeout_seconds: float = 12.0,
+        max_tool_rounds: int = 2,
+    ) -> None:
+        self._base_url = base_url.rstrip("/")
+        self._api_key = api_key
+        self._model_name = model_name
+        self._store = knowledge_store
+        self._timeout = timeout_seconds
+        self._max_tool_rounds = max_tool_rounds
+
+    # ── 主入口 ─────────────────────────────────────────────────────────────────
+
+    def reply(self, user_text: str) -> KnowledgeReplyResult:
+        """完整 function-call 对话流（最多 max_tool_rounds 轮工具调用）。"""
+        if not self._base_url or not self._api_key or not self._model_name:
+            return self._local_fallback(user_text, "LLM not configured")
+
+        messages: list[dict[str, Any]] = [
+            {"role": "system", "content": _SYSTEM_PROMPT},
+            {"role": "user", "content": user_text},
+        ]
+
+        found_doc: KnowledgeDoc | None = None
+
+        for _round in range(self._max_tool_rounds):
+            raw = self._chat(messages, tools=[_KNOWLEDGE_SEARCH_TOOL])
+            if raw is None:
+                return self._local_fallback(user_text, "LLM request failed")
+
+            choice = self._first_choice(raw)
+            if choice is None:
+                return self._local_fallback(user_text, "empty choices")
+
+            finish_reason = choice.get("finish_reason", "")
+            message = choice.get("message", {})
+
+            # ── 工具调用分支 ─────────────────────────────────────────────────
+            if finish_reason == "tool_calls" or message.get("tool_calls"):
+                tool_calls = message.get("tool_calls", [])
+                messages.append({"role": "assistant", **message})
+
+                for tc in tool_calls:
+                    fn_name = tc.get("function", {}).get("name", "")
+                    fn_args_raw = tc.get("function", {}).get("arguments", "{}")
+                    tc_id = tc.get("id", "call_0")
+
+                    if fn_name == "knowledge_search":
+                        try:
+                            fn_args = json.loads(fn_args_raw)
+                        except json.JSONDecodeError:
+                            fn_args = {"query": user_text}
+
+                        query = fn_args.get("query", user_text)
+                        tool_result, found_doc = self._run_knowledge_search(query)
+                    else:
+                        tool_result = f"Unknown tool: {fn_name}"
+
+                    messages.append({
+                        "role": "tool",
+                        "tool_call_id": tc_id,
+                        "content": tool_result,
+                    })
+                # 继续下一轮 LLM 调用
+                continue
+
+            # ── 正常文本回复 ─────────────────────────────────────────────────
+            content = self._extract_content(message)
+            if not content:
+                return self._local_fallback(user_text, "empty content")
+
+            return KnowledgeReplyResult(
+                reply_text=content,
+                backend="dashscope",
+                model_name=self._model_name,
+                doc_id=found_doc.doc_id if found_doc else None,
+                doc_content=found_doc.content if found_doc else None,
+                doc_title=found_doc.title if found_doc else None,
+            )
+
+        # 超出工具调用轮数，直接本地兜底
+        return self._local_fallback(user_text, "max tool rounds exceeded")
+
+    # ── 内部工具执行 ───────────────────────────────────────────────────────────
+
+    def _run_knowledge_search(self, query: str) -> tuple[str, KnowledgeDoc | None]:
+        """执行本地知识库搜索，返回 (tool_result_str, best_doc)。"""
+        results = self._store.search(query, top_k=2)
+        if not results:
+            return "未找到相关知识文档。", None
+
+        best = results[0]
+        # 给 LLM 的 tool result：文档标题 + 正文（截断到 800 字节）
+        excerpt = best.doc.content[:800]
+        tool_text = (
+            f"[知识库检索结果]\n"
+            f"文档：{best.doc.title}\n"
+            f"命中关键词：{', '.join(best.matched_keywords)}\n\n"
+            f"{excerpt}"
+        )
+        return tool_text, best.doc
+
+    # ── HTTP 调用 ──────────────────────────────────────────────────────────────
+
+    def _chat(
+        self,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]] | None = None,
+    ) -> dict[str, Any] | None:
+        payload: dict[str, Any] = {
+            "model": self._model_name,
+            "temperature": 0.3,
+            "enable_thinking": False,
+            "max_tokens": 300,
+            "messages": messages,
+        }
+        if tools:
+            payload["tools"] = tools
+            payload["tool_choice"] = "auto"
+
+        req = request.Request(
+            self._endpoint(),
+            data=json.dumps(payload, ensure_ascii=False).encode("utf-8"),
+            headers={
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {self._api_key}",
+            },
+            method="POST",
+        )
+        try:
+            with request.urlopen(req, timeout=self._timeout) as resp:
+                return json.loads(resp.read().decode("utf-8"))
+        except (error.URLError, TimeoutError, ValueError, OSError):
+            return None
+
+    def _endpoint(self) -> str:
+        if self._base_url.endswith("/chat/completions"):
+            return self._base_url
+        return f"{self._base_url}/chat/completions"
+
+    def _first_choice(self, payload: dict[str, Any]) -> dict[str, Any] | None:
+        choices = payload.get("choices")
+        if not isinstance(choices, list) or not choices:
+            return None
+        return choices[0]
+
+    def _extract_content(self, message: dict[str, Any]) -> str:
+        content = message.get("content", "")
+        if isinstance(content, list):
+            return "".join(
+                str(item.get("text", "")).strip()
+                for item in content
+                if isinstance(item, dict) and item.get("type") == "text"
+            ).strip()
+        return str(content).strip()
+
+    def _local_fallback(self, _user_text: str, reason: str) -> KnowledgeReplyResult:
+        return KnowledgeReplyResult(
+            reply_text="暂未找到相关资料，建议联系技术支持或查阅设备手册。",
+            backend="local-fallback",
+            model_name="knowledge-fallback",
+            error_message=reason,
+        )
diff --git a/intelligent_cabin/app/services/knowledge_store.py b/intelligent_cabin/app/services/knowledge_store.py
new file mode 100644
index 0000000..182514c
--- /dev/null
+++ b/intelligent_cabin/app/services/knowledge_store.py
@@ -0,0 +1,152 @@
+"""
+app/services/knowledge_store.py
+
+本地 Markdown 知识库加载与关键词检索。
+- 所有 .md 文件存放在 config/knowledge/ 目录
+- 基于关键词打分，支持多文档排序返回
+"""
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass, field
+from pathlib import Path
+
+
+@dataclass
+class KnowledgeDoc:
+    """一篇知识文档的元数据与正文。"""
+
+    doc_id: str                    # 文件名（不含扩展名）
+    title: str                     # MD 首行 # 标题，无则用文件名
+    content: str                   # 完整原始 Markdown 内容
+    keywords: list[str] = field(default_factory=list)  # 从正文抽取的高频词
+
+
+@dataclass
+class SearchResult:
+    doc: KnowledgeDoc
+    score: float
+    matched_keywords: list[str]
+
+
+class KnowledgeStore:
+    """从 config/knowledge/*.md 加载知识库，提供关键词检索。"""
+
+    def __init__(self, knowledge_dir: str | Path) -> None:
+        self._dir = Path(knowledge_dir)
+        self._docs: dict[str, KnowledgeDoc] = {}
+        self._load()
+
+    # ── 公开 API ───────────────────────────────────────────────────────────────
+
+    def search(self, query: str, top_k: int = 3) -> list[SearchResult]:
+        """根据 query 检索最相关的知识文档，返回最多 top_k 条。"""
+        query_tokens = self._tokenize(query)
+        if not query_tokens:
+            return []
+
+        results: list[SearchResult] = []
+        for doc in self._docs.values():
+            score, matched = self._score(doc, query_tokens)
+            if score > 0:
+                results.append(SearchResult(doc=doc, score=score, matched_keywords=matched))
+
+        results.sort(key=lambda r: r.score, reverse=True)
+        return results[:top_k]
+
+    def get(self, doc_id: str) -> KnowledgeDoc | None:
+        return self._docs.get(doc_id)
+
+    def all_doc_ids(self) -> list[str]:
+        return list(self._docs.keys())
+
+    def reload(self) -> None:
+        """热重载知识库（添加新 MD 文件后调用）。"""
+        self._docs.clear()
+        self._load()
+
+    # ── 内部逻辑 ───────────────────────────────────────────────────────────────
+
+    def _load(self) -> None:
+        if not self._dir.exists():
+            return
+        for md_path in sorted(self._dir.glob("*.md")):
+            doc = self._parse_md(md_path)
+            self._docs[doc.doc_id] = doc
+
+    def _parse_md(self, path: Path) -> KnowledgeDoc:
+        content = path.read_text(encoding="utf-8")
+        doc_id = path.stem
+
+        # 提取第一个 # 标题作为文档标题
+        title_match = re.search(r"^#+\s+(.+)", content, re.MULTILINE)
+        title = title_match.group(1).strip() if title_match else doc_id
+
+        # 提取关键词：去标点后的中文词段（2~6字）
+        keywords = self._extract_keywords(content)
+        return KnowledgeDoc(doc_id=doc_id, title=title, content=content, keywords=keywords)
+
+    def _extract_keywords(self, content: str) -> list[str]:
+        """提取 MD 正文中的中文词段作为候选关键词。"""
+        # 去掉 Markdown 语法符号
+        text = re.sub(r"[#`*_>|~\[\]()!]", " ", content)
+        text = re.sub(r"https?://\S+", " ", text)
+        # 中文词段（2-6 个汉字）
+        words = re.findall(r"[\u4e00-\u9fff]{2,6}", text)
+        # 去重，保留顺序
+        seen: set[str] = set()
+        unique: list[str] = []
+        for w in words:
+            if w not in seen:
+                seen.add(w)
+                unique.append(w)
+        return unique
+
+    def _tokenize(self, text: str) -> list[str]:
+        """将 query 分割成候选检索词。
+
+        策略：
+        1. 提取所有连续中文字段（2字以上）作为候选
+        2. 在连续中文字段上做滑动窗口（2-5字），覆盖子串匹配
+        避免整句 '虚焊报警怎么办' 作为单一 token 无法匹配 '虚焊报警'
+        """
+        # 提取所有连续中文片段
+        chinese_chunks = re.findall(r"[\u4e00-\u9fff]+", text)
+        tokens: list[str] = []
+        for chunk in chinese_chunks:
+            # 滑动窗口：长度 2 到 min(5, len(chunk))
+            for size in range(2, min(6, len(chunk) + 1)):
+                for start in range(len(chunk) - size + 1):
+                    tokens.append(chunk[start : start + size])
+            # 整体 chunk 也加入（用于长词精确匹配）
+            if len(chunk) > 1:
+                tokens.append(chunk)
+        # 去重保序
+        seen: set[str] = set()
+        unique: list[str] = []
+        for t in tokens:
+            if t not in seen:
+                seen.add(t)
+                unique.append(t)
+        return unique
+
+    def _score(self, doc: KnowledgeDoc, query_tokens: list[str]) -> tuple[float, list[str]]:
+        """给文档打分：命中 title 得 3 分，命中 content 得 1 分（上限 5）。"""
+        score = 0.0
+        matched: list[str] = []
+        seen: set[str] = set()
+        content_lower = doc.content.lower()
+        title_lower = doc.title.lower()
+
+        for token in query_tokens:
+            token_lower = token.lower()
+            if token_lower in seen:
+                continue
+            in_title = token_lower in title_lower
+            in_content = token_lower in content_lower
+            if in_title or in_content:
+                seen.add(token_lower)
+                matched.append(token)
+                score += 3.0 if in_title else 1.0
+
+        return min(score, 15.0), matched  # 上限 15，避免极端高分
diff --git a/intelligent_cabin/app/services/multi_intent_detector.py b/intelligent_cabin/app/services/multi_intent_detector.py
new file mode 100644
index 0000000..852bb43
--- /dev/null
+++ b/intelligent_cabin/app/services/multi_intent_detector.py
@@ -0,0 +1,219 @@
+from __future__ import annotations
+
+import importlib
+from dataclasses import dataclass, field
+from pathlib import Path
+from time import perf_counter
+from typing import Any, Protocol
+
+from app.schemas.intent import IntentDefinition
+from app.services.joint_nlu import JointBertNLU
+
+
+@dataclass
+class MultiIntentCandidate:
+    intent_id: str
+    score: float
+    label: str | None = None
+
+
+@dataclass
+class MultiIntentDetectionResult:
+    detected: bool = False
+    candidates: list[MultiIntentCandidate] = field(default_factory=list)
+    reason: str | None = None
+    backend_name: str = "none"
+    error_message: str | None = None
+    raw_scores: list[dict[str, Any]] = field(default_factory=list)
+
+
+class MultiIntentDetector(Protocol):
+    def detect(self, text: str, intents: list[IntentDefinition]) -> MultiIntentDetectionResult:
+        ...
+
+
+class JointBertMultiIntentDetector:
+    """
+    A multi-intent detector backed by the same Joint BERT runtime as single-intent and slot extraction.
+    """
+
+    def __init__(
+        self,
+        nlu: JointBertNLU,
+        threshold: float | None = None,
+        top_k: int = 8,
+        max_labels: int = 4,
+    ) -> None:
+        self._nlu = nlu
+        self._threshold = threshold
+        self._top_k = top_k
+        self._max_labels = max_labels
+
+    def detect(self, text: str, intents: list[IntentDefinition]) -> MultiIntentDetectionResult:
+        candidates = self._nlu.predict_multi_intents(
+            text,
+            intents,
+            threshold=self._threshold,
+            max_labels=self._max_labels,
+            top_k=self._top_k,
+        )
+        return MultiIntentDetectionResult(
+            detected=len(candidates) >= 2,
+            candidates=[
+                MultiIntentCandidate(intent_id=item.intent_id, score=item.score, label=item.intent_id)
+                for item in candidates
+            ],
+            reason=f"joint bert multi-label candidates={len(candidates)} threshold={self._threshold}",
+            backend_name="joint-bert-multi-label",
+            raw_scores=[
+                {"intent_id": item.intent_id, "label": item.intent_id, "score": float(item.score)}
+                for item in candidates
+            ],
+        )
+
+    def warmup(self, sample_text: str = "打开空调并打开车窗") -> bool:
+        return self._nlu.warmup(sample_text)
+
+
+class BertMultiIntentDetector:
+    """
+    A stage-2 multi-intent detector backed by a dedicated multi-label BERT head.
+    """
+
+    def __init__(
+        self,
+        model_path: str,
+        threshold: float = 0.45,
+        top_k: int = 8,
+        max_labels: int = 4,
+        blocked_labels: set[str] | None = None,
+    ) -> None:
+        self._model_path = model_path
+        self._threshold = threshold
+        self._top_k = top_k
+        self._max_labels = max_labels
+        self._blocked_labels = blocked_labels or {"__social__", "__out_of_scope__"}
+        self._tokenizer = None
+        self._model = None
+        self._torch = None
+        self._error_message: str | None = None
+        self._warmed_up = False
+        self._warmup_elapsed_ms: float | None = None
+
+    def detect(self, text: str, intents: list[IntentDefinition]) -> MultiIntentDetectionResult:
+        runtime = self._get_runtime()
+        if runtime is None:
+            return MultiIntentDetectionResult(
+                detected=False,
+                reason="multi-label detector unavailable",
+                backend_name="bert-multi-label",
+                error_message=self._error_message,
+            )
+        torch, tokenizer, model = runtime
+        intent_map = {intent.intent_id: intent for intent in intents}
+        try:
+            encoded = tokenizer(
+                text,
+                truncation=True,
+                padding=False,
+                return_tensors="pt",
+            )
+            model.eval()
+            with torch.no_grad():
+                logits = model(**encoded).logits.squeeze(0)
+            probs = torch.sigmoid(logits).detach().cpu().tolist()
+        except Exception as exc:
+            return MultiIntentDetectionResult(
+                detected=False,
+                reason="multi-label forward failed",
+                backend_name="bert-multi-label",
+                error_message=str(exc),
+            )
+
+        id2label = getattr(model.config, "id2label", {}) or {}
+        raw_scores: list[dict[str, Any]] = []
+        for index, score in enumerate(probs):
+            label = str(id2label.get(index, f"LABEL_{index}"))
+            raw_scores.append(
+                {
+                    "label": label,
+                    "intent_id": label,
+                    "score": float(score),
+                }
+            )
+        raw_scores.sort(key=lambda item: item["score"], reverse=True)
+        raw_top = raw_scores[: self._top_k]
+
+        candidates: list[MultiIntentCandidate] = []
+        for item in raw_top:
+            intent_id = str(item.get("intent_id") or "")
+            if intent_id in self._blocked_labels:
+                continue
+            if intent_id not in intent_map:
+                continue
+            score = float(item.get("score", 0.0))
+            if score < self._threshold:
+                continue
+            candidates.append(
+                MultiIntentCandidate(
+                    intent_id=intent_id,
+                    score=score,
+                    label=str(item.get("label") or intent_id),
+                )
+            )
+            if len(candidates) >= self._max_labels:
+                break
+
+        return MultiIntentDetectionResult(
+            detected=len(candidates) >= 2,
+            candidates=candidates,
+            reason=f"bert multi-label candidates={len(candidates)} threshold={self._threshold}",
+            backend_name="bert-multi-label",
+            raw_scores=raw_top,
+        )
+
+    def warmup(self, sample_text: str = "打开空调并打开车窗") -> bool:
+        if self._warmed_up:
+            return True
+        started_at = perf_counter()
+        runtime = self._get_runtime()
+        if runtime is None:
+            self._warmup_elapsed_ms = round((perf_counter() - started_at) * 1000, 3)
+            return False
+        torch, tokenizer, model = runtime
+        try:
+            encoded = tokenizer(sample_text, truncation=True, padding=False, return_tensors="pt")
+            model.eval()
+            with torch.no_grad():
+                _ = model(**encoded).logits
+        except Exception as exc:
+            self._error_message = str(exc)
+            self._warmup_elapsed_ms = round((perf_counter() - started_at) * 1000, 3)
+            return False
+        self._warmed_up = True
+        self._warmup_elapsed_ms = round((perf_counter() - started_at) * 1000, 3)
+        return True
+
+    def _get_runtime(self):
+        if self._tokenizer is not None and self._model is not None and self._torch is not None:
+            return self._torch, self._tokenizer, self._model
+        if not self._model_path or not Path(self._model_path).exists():
+            self._error_message = "multi-intent model path is empty or missing"
+            return None
+        try:
+            transformers = importlib.import_module("transformers")
+            torch = importlib.import_module("torch")
+        except ImportError as exc:
+            self._error_message = str(exc)
+            return None
+        try:
+            self._tokenizer = transformers.AutoTokenizer.from_pretrained(self._model_path)
+            self._model = transformers.AutoModelForSequenceClassification.from_pretrained(self._model_path)
+            self._torch = torch
+        except Exception as exc:
+            self._error_message = str(exc)
+            return None
+        return self._torch, self._tokenizer, self._model
+
+
+SigmoidBertMultiIntentDetector = BertMultiIntentDetector
diff --git a/intelligent_cabin/app/services/planner.py b/intelligent_cabin/app/services/planner.py
new file mode 100644
index 0000000..f8205fe
--- /dev/null
+++ b/intelligent_cabin/app/services/planner.py
@@ -0,0 +1,1347 @@
+from __future__ import annotations
+
+import json
+import re
+from dataclasses import dataclass, field
+from typing import Any, Protocol
+from urllib import error, request
+
+from app.services.classifier import ClassificationResult, IntentClassifier
+from app.services.joint_nlu import JointBertNLU
+from app.services.multi_intent_detector import MultiIntentDetectionResult, MultiIntentDetector
+from app.schemas.configuration import WorkflowTemplateDefinition, WorkflowTemplatesConfig
+from app.schemas.intent import IntentDefinition
+
+
+@dataclass
+class PlannedStep:
+    intent_id: str
+    slots: dict[str, Any] = field(default_factory=dict)
+    depends_on: list[int] = field(default_factory=list)
+    condition: dict[str, Any] = field(default_factory=dict)
+    requires_confirmation: bool = False
+    reason: str | None = None
+
+
+@dataclass
+class PlanningResult:
+    accepted: bool
+    workflow_type: str = "single"
+    steps: list[PlannedStep] = field(default_factory=list)
+    model_name: str = "heuristic-planner"
+    backend: str = "local"
+    reason: str | None = None
+    error_message: str | None = None
+    raw_response: str | None = None
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class ClauseParseResult:
+    clause_text: str
+    selected_intent_id: str | None = None
+    score: float = 0.0
+    slots: dict[str, Any] = field(default_factory=dict)
+    reason: str | None = None
+    candidates: list[dict[str, Any]] = field(default_factory=list)
+
+
+@dataclass
+class MultiIntentParseResult:
+    clauses: list[ClauseParseResult] = field(default_factory=list)
+    workflow_type: str = "single"
+    detected: bool = False
+    reason: str | None = None
+    detector: MultiIntentDetectionResult | None = None
+
+    @property
+    def matched_ids(self) -> list[str]:
+        return [clause.selected_intent_id for clause in self.clauses if clause.selected_intent_id]
+
+    @property
+    def matched_clauses(self) -> list[ClauseParseResult]:
+        return [clause for clause in self.clauses if clause.selected_intent_id]
+
+
+class WorkflowPlanner(Protocol):
+    def plan(
+        self,
+        text: str,
+        intents: list[IntentDefinition],
+        context: dict[str, Any] | None = None,
+    ) -> PlanningResult:
+        ...
+
+
+class TemplateWorkflowPlanner:
+    def __init__(
+        self,
+        templates: WorkflowTemplatesConfig | None = None,
+        clause_classifier: IntentClassifier | None = None,
+        multi_intent_detector: MultiIntentDetector | None = None,
+        joint_nlu: JointBertNLU | None = None,
+        classifier_weight: float = 1.6,
+        model_only_threshold: float = 0.62,
+    ) -> None:
+        self._templates = templates or WorkflowTemplatesConfig()
+        self._clause_classifier = clause_classifier
+        self._multi_intent_detector = multi_intent_detector
+        self._joint_nlu = joint_nlu
+        self._classifier_weight = classifier_weight
+        self._model_only_threshold = model_only_threshold
+
+    def plan(
+        self,
+        text: str,
+        intents: list[IntentDefinition],
+        context: dict[str, Any] | None = None,
+    ) -> PlanningResult:
+        _ = context
+        if not self._templates.templates:
+            return PlanningResult(
+                accepted=False,
+                model_name="template-planner",
+                backend="local-template",
+                reason="no workflow templates configured",
+            )
+        parse_result = _analyze_multi_intent(
+            text,
+            intents,
+            clause_classifier=self._clause_classifier,
+            multi_intent_detector=self._multi_intent_detector,
+            joint_nlu=self._joint_nlu,
+            classifier_weight=self._classifier_weight,
+            model_only_threshold=self._model_only_threshold,
+        )
+        matched_ids = parse_result.matched_ids
+        if len(matched_ids) < 2:
+            return PlanningResult(
+                accepted=False,
+                model_name="template-planner",
+                backend="local-template",
+                reason="not enough matched clauses for workflow template",
+                metadata={
+                    "matched_intents": matched_ids,
+                    "input_clauses": [item.clause_text for item in parse_result.clauses],
+                    "clause_analysis": _serialize_clause_analysis(parse_result),
+                    "multi_intent_detected": parse_result.detected,
+                    "multi_intent_detector": _serialize_multi_intent_detector(parse_result.detector),
+                },
+            )
+
+        for template in self._templates.templates:
+            if self._matches_template(text, matched_ids, template):
+                steps = self._build_steps(template, parse_result)
+                return PlanningResult(
+                    accepted=True,
+                    workflow_type=template.workflow_type,
+                    steps=steps,
+                    model_name="template-planner",
+                    backend="local-template",
+                    reason=f"matched workflow template: {template.template_id}",
+                    metadata={
+                        "template_id": template.template_id,
+                        "input_clauses": [item.clause_text for item in parse_result.clauses],
+                        "matched_intents": matched_ids,
+                        "clause_analysis": _serialize_clause_analysis(parse_result),
+                        "multi_intent_detected": parse_result.detected,
+                        "multi_intent_detector": _serialize_multi_intent_detector(parse_result.detector),
+                        "normalized_steps": [
+                            {
+                                "intent_id": step.intent_id,
+                                "slots": step.slots,
+                                "depends_on": step.depends_on,
+                                "condition": step.condition,
+                                "requires_confirmation": step.requires_confirmation,
+                            }
+                            for step in steps
+                        ],
+                    },
+                )
+
+        return PlanningResult(
+            accepted=False,
+            model_name="template-planner",
+            backend="local-template",
+            reason="no workflow template matched the current clause intents",
+            metadata={
+                "matched_intents": matched_ids,
+                "input_clauses": [item.clause_text for item in parse_result.clauses],
+                "clause_analysis": _serialize_clause_analysis(parse_result),
+                "multi_intent_detected": parse_result.detected,
+                "multi_intent_detector": _serialize_multi_intent_detector(parse_result.detector),
+            },
+        )
+
+    def _matches_template(self, text: str, matched_ids: list[str], template: WorkflowTemplateDefinition) -> bool:
+        if template.trigger_keywords and not all(keyword in text for keyword in template.trigger_keywords):
+            return False
+        return matched_ids[: len(template.intent_sequence)] == template.intent_sequence
+
+    def _build_steps(
+        self,
+        template: WorkflowTemplateDefinition,
+        parse_result: MultiIntentParseResult,
+    ) -> list[PlannedStep]:
+        steps: list[PlannedStep] = []
+        matched_clauses = parse_result.matched_clauses
+        for index, intent_id in enumerate(template.intent_sequence):
+            clause_result = matched_clauses[index] if index < len(matched_clauses) else None
+            clause_text = clause_result.clause_text if clause_result is not None else ""
+            override = template.step_overrides[index] if index < len(template.step_overrides) else None
+            steps.append(
+                PlannedStep(
+                    intent_id=intent_id,
+                    slots=_extract_slots_for_intent(
+                        clause_text or " ".join(item.clause_text for item in parse_result.clauses),
+                        intent_id,
+                        joint_nlu=self._joint_nlu,
+                    ),
+                    depends_on=list(override.depends_on) if override is not None else [],
+                    condition=dict(override.condition) if override is not None else {},
+                    requires_confirmation=bool(override.requires_confirmation) if override is not None else False,
+                    reason=f"workflow template matched clause {index + 1}: {clause_result.selected_intent_id if clause_result is not None else intent_id}",
+                )
+            )
+        return steps
+
+
+class CompositeWorkflowPlanner:
+    def __init__(self, planners: list[WorkflowPlanner]) -> None:
+        self._planners = planners
+
+    def plan(
+        self,
+        text: str,
+        intents: list[IntentDefinition],
+        context: dict[str, Any] | None = None,
+    ) -> PlanningResult:
+        last_result = PlanningResult(accepted=False, reason="no planner configured")
+        for planner in self._planners:
+            result = planner.plan(text, intents, context)
+            last_result = result
+            if result.accepted:
+                return result
+        return last_result
+
+
+class HeuristicWorkflowPlanner:
+    def __init__(
+        self,
+        clause_classifier: IntentClassifier | None = None,
+        multi_intent_detector: MultiIntentDetector | None = None,
+        joint_nlu: JointBertNLU | None = None,
+        classifier_weight: float = 1.6,
+        model_only_threshold: float = 0.62,
+    ) -> None:
+        self._clause_classifier = clause_classifier
+        self._multi_intent_detector = multi_intent_detector
+        self._joint_nlu = joint_nlu
+        self._classifier_weight = classifier_weight
+        self._model_only_threshold = model_only_threshold
+
+    def plan(
+        self,
+        text: str,
+        intents: list[IntentDefinition],
+        context: dict[str, Any] | None = None,
+    ) -> PlanningResult:
+        _ = context
+        parse_result = _analyze_multi_intent(
+            text,
+            intents,
+            clause_classifier=self._clause_classifier,
+            multi_intent_detector=self._multi_intent_detector,
+            joint_nlu=self._joint_nlu,
+            classifier_weight=self._classifier_weight,
+            model_only_threshold=self._model_only_threshold,
+        )
+        if not parse_result.detected and not _has_complex_pattern(text):
+            return PlanningResult(
+                accepted=False,
+                model_name="heuristic-planner",
+                backend="local-heuristic",
+                reason="single command or no explicit planning pattern detected",
+                metadata={
+                    "input_clauses": [item.clause_text for item in parse_result.clauses],
+                    "clause_analysis": _serialize_clause_analysis(parse_result),
+                    "multi_intent_detected": parse_result.detected,
+                    "multi_intent_detector": _serialize_multi_intent_detector(parse_result.detector),
+                },
+            )
+        steps: list[PlannedStep] = []
+        for clause in parse_result.matched_clauses:
+            if clause.selected_intent_id is None:
+                continue
+            steps.append(
+                PlannedStep(
+                    intent_id=clause.selected_intent_id,
+                    slots=clause.slots.copy(),
+                    reason=clause.reason or f"heuristic parse for clause: {clause.clause_text}",
+                )
+            )
+        workflow_type = parse_result.workflow_type
+        if workflow_type == "conditional":
+            steps = _apply_conditional_hints(text, steps)
+        if len(steps) >= 2:
+            return PlanningResult(
+                accepted=True,
+                workflow_type=workflow_type,
+                steps=steps,
+                model_name="heuristic-planner",
+                backend="local-heuristic",
+                reason=parse_result.reason or "heuristic planner split the request into multiple executable clauses",
+                metadata={
+                    "input_clauses": [item.clause_text for item in parse_result.clauses],
+                    "clause_analysis": _serialize_clause_analysis(parse_result),
+                    "multi_intent_detected": parse_result.detected,
+                    "multi_intent_detector": _serialize_multi_intent_detector(parse_result.detector),
+                    "normalized_steps": [
+                        {
+                            "intent_id": step.intent_id,
+                            "slots": step.slots,
+                            "depends_on": step.depends_on,
+                            "condition": step.condition,
+                            "requires_confirmation": step.requires_confirmation,
+                            "reason": step.reason,
+                        }
+                        for step in steps
+                    ],
+                },
+            )
+        return PlanningResult(
+            accepted=False,
+            workflow_type=workflow_type,
+            steps=steps,
+            model_name="heuristic-planner",
+            backend="local-heuristic",
+            reason="planning detected but local heuristic could not produce enough executable steps",
+            metadata={
+                "input_clauses": [item.clause_text for item in parse_result.clauses],
+                "clause_analysis": _serialize_clause_analysis(parse_result),
+                "multi_intent_detected": parse_result.detected,
+                "multi_intent_detector": _serialize_multi_intent_detector(parse_result.detector),
+            },
+        )
+
+class DashScopeWorkflowPlanner:
+    def __init__(
+        self,
+        base_url: str,
+        api_key: str,
+        model_name: str,
+        timeout_seconds: float = 6.0,
+        fallback: WorkflowPlanner | None = None,
+        joint_nlu: JointBertNLU | None = None,
+    ) -> None:
+        self._base_url = base_url.rstrip("/")
+        self._api_key = api_key
+        self._model_name = model_name
+        self._timeout_seconds = timeout_seconds
+        self._fallback = fallback or HeuristicWorkflowPlanner(joint_nlu=joint_nlu)
+        self._joint_nlu = joint_nlu
+
+    def plan(
+        self,
+        text: str,
+        intents: list[IntentDefinition],
+        context: dict[str, Any] | None = None,
+    ) -> PlanningResult:
+        if not self._base_url or not self._api_key or not self._model_name:
+            return self._fallback_result(
+                text,
+                intents,
+                context,
+                reason="planner is not configured",
+                error_message="set AGENT_PLANNER_BASE_URL / AGENT_PLANNER_API_KEY / AGENT_PLANNER_MODEL_NAME",
+            )
+
+        payload = {
+            "model": self._model_name,
+            "temperature": 0.1,
+            "enable_thinking": False,
+            "max_tokens": 320,
+            "messages": [
+                {
+                    "role": "system",
+                    "content": self._system_prompt(intents),
+                },
+                {
+                    "role": "user",
+                    "content": json.dumps(
+                        {
+                            "text": text,
+                            "clauses": _split_clauses(text),
+                            "context": context or {},
+                        },
+                        ensure_ascii=False,
+                    ),
+                },
+            ],
+        }
+        req = request.Request(
+            self._endpoint(),
+            data=json.dumps(payload).encode("utf-8"),
+            headers={
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {self._api_key}",
+            },
+            method="POST",
+        )
+        try:
+            with request.urlopen(req, timeout=self._timeout_seconds) as response:
+                data = json.loads(response.read().decode("utf-8"))
+        except (error.URLError, TimeoutError, ValueError) as exc:
+            return self._fallback_result(
+                text,
+                intents,
+                context,
+                reason="cloud planner request failed",
+                error_message=str(exc),
+            )
+
+        content = self._extract_content(data)
+        if not content:
+            return self._fallback_result(
+                text,
+                intents,
+                context,
+                reason="cloud planner returned empty content",
+                raw_response=json.dumps(data, ensure_ascii=False),
+            )
+
+        try:
+            plan_data = self._parse_json_content(content)
+        except ValueError as exc:
+            return self._fallback_result(
+                text,
+                intents,
+                context,
+                reason="cloud planner returned invalid json",
+                error_message=str(exc),
+                raw_response=content,
+            )
+
+        steps, normalization_meta = self._normalize_steps(plan_data, text, intents)
+        accepted = bool(plan_data.get("accepted", bool(steps)))
+        workflow_type = str(plan_data.get("workflow_type", "single" if len(steps) <= 1 else "sequence"))
+        return PlanningResult(
+            accepted=accepted,
+            workflow_type=workflow_type,
+            steps=steps,
+            model_name=self._model_name,
+            backend="dashscope",
+            reason=str(plan_data.get("reason", "")).strip() or "cloud planner parsed successfully",
+            raw_response=content,
+            metadata={
+                "input_clauses": _split_clauses(text),
+                "parsed_plan": plan_data,
+                "normalized_steps": normalization_meta,
+            },
+        )
+
+    def _endpoint(self) -> str:
+        if self._base_url.endswith("/chat/completions"):
+            return self._base_url
+        return f"{self._base_url}/chat/completions"
+
+    def _system_prompt(self, intents: list[IntentDefinition]) -> str:
+        intent_catalog = [
+            {
+                "intent_id": intent.intent_id,
+                "required_slots": intent.required_slots,
+                "keywords": intent.keywords[:4],
+            }
+            for intent in intents
+        ]
+        return (
+            "Plan user requests into executable steps for a cabin/service assistant. "
+            "Return strict JSON only, no markdown. "
+            "Use only intent_id values from the catalog. "
+            "If the request is a single clear command, return one step. "
+            "If it contains multiple commands, return sequence steps. "
+            "Fill slots whenever the value is explicit in the user text. "
+            "Examples: order_id like A123456, destination like 公司/机场, temperature as integer, music may use song or genre such as 轻音乐. "
+            "Each step should only contain slots relevant to that intent. "
+            "For conditional requests, mark workflow_type as conditional and attach depends_on plus condition. "
+            "Use requires_confirmation=true for risky actions such as canceling an order. "
+            'Schema: {"accepted":true,"workflow_type":"single|sequence|conditional","reason":"brief","steps":[{"intent_id":"...","slots":{},"depends_on":[1],"condition":{"field":"order_status","operator":"equals","value":"pending_shipment","description":"only cancel if not shipped"},"requires_confirmation":true,"reason":"brief"}]}. '
+            f"Catalog={json.dumps(intent_catalog, ensure_ascii=False, separators=(',', ':'))}"
+        )
+
+    def _extract_content(self, data: dict[str, Any]) -> str:
+        choices = data.get("choices")
+        if not isinstance(choices, list) or not choices:
+            return ""
+        message = choices[0].get("message", {})
+        content = message.get("content", "")
+        if isinstance(content, str):
+            return content.strip()
+        if isinstance(content, list):
+            text_parts = []
+            for item in content:
+                if isinstance(item, dict) and item.get("type") == "text":
+                    text_parts.append(str(item.get("text", "")))
+            return "".join(text_parts).strip()
+        return ""
+
+    def _parse_json_content(self, content: str) -> dict[str, Any]:
+        cleaned = content.strip()
+        if cleaned.startswith("```"):
+            cleaned = cleaned.strip("`")
+            if cleaned.startswith("json"):
+                cleaned = cleaned[4:].strip()
+        start = cleaned.find("{")
+        end = cleaned.rfind("}")
+        if start < 0 or end < 0 or end <= start:
+            raise ValueError("planner content does not contain a JSON object")
+        return json.loads(cleaned[start : end + 1])
+
+    def _normalize_steps(
+        self,
+        plan_data: dict[str, Any],
+        text: str,
+        intents: list[IntentDefinition],
+    ) -> tuple[list[PlannedStep], list[dict[str, Any]]]:
+        intent_map = {intent.intent_id: intent for intent in intents}
+        clauses = _split_clauses(text)
+        raw_steps = plan_data.get("steps", [])
+        steps: list[PlannedStep] = []
+        normalization_meta: list[dict[str, Any]] = []
+        if not isinstance(raw_steps, list):
+            return steps, normalization_meta
+
+        for index, raw_step in enumerate(raw_steps):
+            if not isinstance(raw_step, dict):
+                continue
+            intent_id = str(raw_step.get("intent_id", "")).strip()
+            if not intent_id or intent_id not in intent_map:
+                continue
+            cloud_slots = raw_step.get("slots", {})
+            if not isinstance(cloud_slots, dict):
+                cloud_slots = {}
+            clause_text = clauses[index] if index < len(clauses) else text
+            clause_slots = _extract_slots_for_intent(clause_text, intent_id, joint_nlu=self._joint_nlu)
+            full_text_slots = _extract_slots_for_intent(text, intent_id, joint_nlu=self._joint_nlu)
+            normalized_slots = self._merge_slots(intent_id, cloud_slots, clause_slots, full_text_slots)
+            steps.append(
+                PlannedStep(
+                    intent_id=intent_id,
+                    slots=normalized_slots,
+                    depends_on=self._normalize_depends_on(raw_step.get("depends_on")),
+                    condition=self._normalize_condition(intent_id, raw_step.get("condition")),
+                    requires_confirmation=bool(raw_step.get("requires_confirmation", False)),
+                    reason=str(raw_step.get("reason", "")).strip() or None,
+                )
+            )
+            normalization_meta.append(
+                {
+                    "intent_id": intent_id,
+                    "clause_text": clause_text,
+                    "cloud_slots": cloud_slots,
+                    "clause_slots": clause_slots,
+                    "full_text_slots": full_text_slots,
+                    "normalized_slots": normalized_slots,
+                    "depends_on": steps[-1].depends_on,
+                    "condition": steps[-1].condition,
+                    "requires_confirmation": steps[-1].requires_confirmation,
+                }
+            )
+        inferred_type = str(plan_data.get("workflow_type", ""))
+        if inferred_type == "conditional" or _has_conditional_pattern(text):
+            steps = _apply_conditional_hints(text, steps)
+            for index, step in enumerate(steps):
+                if index < len(normalization_meta):
+                    normalization_meta[index]["depends_on"] = step.depends_on
+                    normalization_meta[index]["condition"] = step.condition
+                    normalization_meta[index]["requires_confirmation"] = step.requires_confirmation
+        return steps, normalization_meta
+
+    def _merge_slots(
+        self,
+        intent_id: str,
+        cloud_slots: dict[str, Any],
+        clause_slots: dict[str, Any],
+        full_text_slots: dict[str, Any],
+    ) -> dict[str, Any]:
+        merged: dict[str, Any] = {}
+        for source in (full_text_slots, clause_slots, cloud_slots):
+            for key, value in source.items():
+                if value in ("", None, []):
+                    continue
+                merged[key] = value
+        allowed_slot_keys = {
+            "cs_query_order": {"order_id"},
+            "cs_query_logistics": {"order_id"},
+            "cs_cancel_order": {"order_id"},
+            "cabin_nav_to": {"destination"},
+            "cabin_set_ac": {"temperature"},
+            "cabin_play_music": {"song", "genre"},
+        }.get(intent_id)
+        if allowed_slot_keys is None:
+            return merged
+        return {key: value for key, value in merged.items() if key in allowed_slot_keys}
+
+    def _normalize_depends_on(self, raw_value: Any) -> list[int]:
+        if not isinstance(raw_value, list):
+            return []
+        result: list[int] = []
+        for item in raw_value:
+            try:
+                index = int(item)
+            except (TypeError, ValueError):
+                continue
+            if index > 0:
+                result.append(index)
+        return result
+
+    def _normalize_condition(self, intent_id: str, raw_value: Any) -> dict[str, Any]:
+        if not isinstance(raw_value, dict):
+            return {}
+        normalized: dict[str, Any] = {}
+        for key in ("field", "operator", "value", "description"):
+            if key in raw_value and raw_value[key] not in ("", None):
+                normalized[key] = raw_value[key]
+        source_step = raw_value.get("source_step")
+        try:
+            if source_step is not None and int(source_step) > 0:
+                normalized["source_step"] = int(source_step)
+        except (TypeError, ValueError):
+            pass
+        if (
+            intent_id == "cs_cancel_order"
+            and normalized.get("field") == "order_status"
+            and normalized.get("value") == "pending_shipment"
+        ):
+            normalized["description"] = "仅在订单未发货时取消"
+        return normalized
+
+    def _fallback_result(
+        self,
+        text: str,
+        intents: list[IntentDefinition],
+        context: dict[str, Any] | None,
+        reason: str,
+        error_message: str | None = None,
+        raw_response: str | None = None,
+    ) -> PlanningResult:
+        fallback = self._fallback.plan(text, intents, context)
+        fallback.reason = reason
+        fallback.error_message = error_message
+        fallback.raw_response = raw_response
+        fallback.backend = "dashscope-fallback"
+        return fallback
+
+
+def _extract_slots_for_intent(
+    text: str,
+    intent_id: str,
+    joint_nlu: JointBertNLU | None = None,
+) -> dict[str, Any]:
+    if joint_nlu is not None:
+        predicted = joint_nlu.extract_slots_by_intent_id(text, intent_id)
+        if predicted:
+            return predicted
+    slots: dict[str, Any] = {}
+    cleaned_text = text.strip()
+    if not cleaned_text:
+        return slots
+
+    order_id_match = re.search(r"\b[A-Za-z]\d{5,}\b", cleaned_text)
+    if order_id_match:
+        slots["order_id"] = order_id_match.group(0)
+
+    temperature_match = re.search(r"(\d{2})\s*度", cleaned_text)
+    if temperature_match:
+        slots["temperature"] = int(temperature_match.group(1))
+
+    if intent_id == "cabin_nav_to":
+        destination = _extract_destination(cleaned_text)
+        if destination:
+            slots["destination"] = destination
+
+    if intent_id == "cabin_play_music":
+        music_slots = _extract_music_slots(cleaned_text)
+        slots.update(music_slots)
+
+    return slots
+
+
+def _extract_destination(text: str) -> str | None:
+    for pattern in (
+        r"导航去(?P<destination>.+)",
+        r"导航到(?P<destination>.+)",
+        r"带我去(?P<destination>.+)",
+        r"去(?P<destination>.+)",
+        r"到(?P<destination>.+)",
+    ):
+        match = re.search(pattern, text)
+        if match:
+            destination = re.split(r"(?:然后|并且|同时|再|,|，|；|;)", match.group("destination"), maxsplit=1)[0]
+            destination = destination.strip(" ，。")
+            if destination:
+                return destination
+    return None
+
+
+def _extract_music_slots(text: str) -> dict[str, Any]:
+    slots: dict[str, Any] = {}
+    genre_keywords = ("轻音乐", "摇滚", "古典", "民谣", "爵士", "流行", "儿歌")
+    for genre in genre_keywords:
+        if genre in text:
+            slots["genre"] = genre
+            break
+
+    for trigger in ("播放", "来点", "放点", "听", "来首", "来一首"):
+        if trigger in text:
+            raw_target = text.split(trigger, maxsplit=1)[-1]
+            raw_target = re.split(r"(?:然后|并且|同时|再|,|，|；|;)", raw_target, maxsplit=1)[0].strip(" ，。")
+            matched_genre = next((genre for genre in genre_keywords if genre in raw_target), None)
+            if matched_genre:
+                slots["genre"] = matched_genre
+                break
+            target = raw_target.strip(" 的一首首个歌曲音乐吧呀啊，。")
+            if target and target not in {"歌", "音乐"} and len(target) > 1:
+                slots["song"] = target
+            break
+
+    if "song" in slots and slots["song"] in genre_keywords:
+        slots["genre"] = slots.pop("song")
+    return slots
+
+
+def _analyze_multi_intent(
+    text: str,
+    intents: list[IntentDefinition],
+    clause_classifier: IntentClassifier | None = None,
+    multi_intent_detector: MultiIntentDetector | None = None,
+    joint_nlu: JointBertNLU | None = None,
+    classifier_weight: float = 1.6,
+    model_only_threshold: float = 0.62,
+) -> MultiIntentParseResult:
+    detector_result = multi_intent_detector.detect(text, intents) if multi_intent_detector is not None else None
+    detector_prior = _build_detector_prior(detector_result)
+    clauses = _split_clauses(text)
+    parsed_clauses: list[ClauseParseResult] = []
+    for clause in clauses:
+        parsed_clauses.extend(
+            _parse_clause_candidates(
+                clause,
+                intents,
+                full_text=text,
+                clause_classifier=clause_classifier,
+                detector_prior=detector_prior,
+                joint_nlu=joint_nlu,
+                classifier_weight=classifier_weight,
+                model_only_threshold=model_only_threshold,
+            )
+        )
+    matched_count = sum(1 for item in parsed_clauses if item.selected_intent_id is not None)
+    has_multi_connector = len(clauses) >= 2 or _has_complex_pattern(text)
+    workflow_type = "conditional" if _has_conditional_pattern(text) else "sequence"
+    detected = matched_count >= 2 and has_multi_connector
+    if matched_count < 2:
+        workflow_type = "single"
+    reason = (
+        f"detected {matched_count} executable clauses from multi-intent utterance"
+        if detected
+        else "did not detect multiple executable clauses"
+    )
+    return MultiIntentParseResult(
+        clauses=parsed_clauses,
+        workflow_type=workflow_type,
+        detected=detected,
+        reason=reason,
+        detector=detector_result,
+    )
+
+
+def _parse_clause_candidates(
+    clause: str,
+    intents: list[IntentDefinition],
+    full_text: str | None = None,
+    clause_classifier: IntentClassifier | None = None,
+    detector_prior: dict[str, float] | None = None,
+    joint_nlu: JointBertNLU | None = None,
+    classifier_weight: float = 1.6,
+    model_only_threshold: float = 0.62,
+) -> list[ClauseParseResult]:
+    cleaned_clause = clause.strip()
+    if not cleaned_clause:
+        return [ClauseParseResult(clause_text=clause)]
+    scored: list[tuple[IntentDefinition, float, list[str], dict[str, Any]]] = []
+    for intent in intents:
+        score, reasons, signal_meta = _score_intent_for_clause(cleaned_clause, intent, full_text=full_text)
+        if score > 0:
+            scored.append((intent, score, reasons, signal_meta))
+    scored = _fuse_clause_scores(
+        clause=cleaned_clause,
+        intents=intents,
+        heuristic_scored=scored,
+        classifier_result=_predict_clause_with_classifier(cleaned_clause, intents, clause_classifier),
+        detector_prior=detector_prior or {},
+        classifier_weight=classifier_weight,
+        model_only_threshold=model_only_threshold,
+    )
+    candidates = [
+        {
+            "intent_id": intent.intent_id,
+            "score": round(score, 4),
+            "reason": "; ".join(reasons[:3]) if reasons else "heuristic clause score",
+            "heuristic_score": round(float(signal_meta.get("heuristic_score", score)), 4),
+            "model_score": round(float(signal_meta.get("model_score", 0.0)), 4),
+        }
+        for intent, score, reasons, signal_meta in scored[:5]
+    ]
+    if not scored or scored[0][1] < 1.1:
+        return [
+            ClauseParseResult(
+                clause_text=cleaned_clause,
+                candidates=candidates,
+                reason="no clause candidate scored above multi-intent threshold",
+            )
+        ]
+
+    parallel_candidates = _collect_parallel_clause_candidates(cleaned_clause, scored)
+    if len(parallel_candidates) >= 2:
+        return [
+            ClauseParseResult(
+                clause_text=cleaned_clause,
+                selected_intent_id=intent.intent_id,
+                score=score,
+                slots=_extract_slots_for_intent(cleaned_clause, intent.intent_id, joint_nlu=joint_nlu),
+                reason="; ".join(reasons[:4]) if reasons else "heuristic clause match",
+                candidates=candidates,
+            )
+            for intent, score, reasons, _signal_meta in parallel_candidates
+        ]
+
+    intent, score, reasons, _signal_meta = scored[0]
+    return [
+        ClauseParseResult(
+            clause_text=cleaned_clause,
+            selected_intent_id=intent.intent_id,
+            score=score,
+            slots=_extract_slots_for_intent(cleaned_clause, intent.intent_id, joint_nlu=joint_nlu),
+            reason="; ".join(reasons[:4]) if reasons else "heuristic clause match",
+            candidates=candidates,
+        )
+    ]
+
+
+def _score_intent_for_clause(
+    clause: str,
+    intent: IntentDefinition,
+    full_text: str | None = None,
+) -> tuple[float, list[str], dict[str, Any]]:
+    score = 0.0
+    reasons: list[str] = []
+    signal_meta = _collect_intent_signal_meta(clause, intent, full_text=full_text)
+    for keyword in intent.keywords:
+        if keyword and keyword in clause:
+            score += 2.0
+            reasons.append(f"keyword:{keyword}")
+    for example in intent.examples:
+        if example and example in clause:
+            score += 1.2
+            reasons.append(f"example:{example}")
+    action_hits = signal_meta["action_hits"]
+    object_hits = signal_meta["object_hits"]
+    qualifier_hits = signal_meta["qualifier_hits"]
+    if action_hits:
+        score += 0.8
+        reasons.append(f"action:{action_hits[0]}")
+    if object_hits:
+        score += 0.8
+        reasons.append(f"object:{object_hits[0]}")
+    if action_hits and object_hits:
+        score += 0.9
+        reasons.append("action_object_pair")
+    if qualifier_hits:
+        score += 0.5
+        reasons.append(f"qualifier:{qualifier_hits[0]}")
+    shared_context_hits = signal_meta["shared_context_hits"]
+    if shared_context_hits and (action_hits or object_hits):
+        score += 0.45
+        reasons.append(f"context:{shared_context_hits[0]}")
+    if intent.intent_id == "cabin_set_ac" and re.search(r"\d{2}\s*度", clause):
+        score += 1.1
+        reasons.append("explicit_temperature")
+        signal_meta["explicit_slot"] = True
+    if intent.intent_id in {"cs_query_order", "cs_query_logistics", "cs_cancel_order"} and re.search(r"\b[A-Za-z]\d{5,}\b", clause):
+        score += 0.7
+        reasons.append("explicit_order_id")
+        signal_meta["explicit_slot"] = True
+    return score, reasons, signal_meta
+
+
+def _collect_intent_signal_meta(
+    clause: str,
+    intent: IntentDefinition,
+    full_text: str | None = None,
+) -> dict[str, Any]:
+    hint_config = _INTENT_HINTS.get(intent.intent_id, {})
+    action_hits = [token for token in hint_config.get("actions", ()) if token in clause]
+    object_hits = [token for token in hint_config.get("objects", ()) if token in clause]
+    qualifier_hits = [token for token in hint_config.get("qualifiers", ()) if token in clause]
+    shared_context_hits: list[str] = []
+    if full_text and full_text != clause:
+        shared_context_hits = [
+            token
+            for token in hint_config.get("shared_context", ())
+            if token in full_text and token not in clause
+        ]
+    return {
+        "action_hits": action_hits,
+        "object_hits": object_hits,
+        "qualifier_hits": qualifier_hits,
+        "shared_context_hits": shared_context_hits,
+        "explicit_slot": False,
+        "family": str(hint_config.get("family") or intent.intent_id),
+    }
+
+
+def _predict_clause_with_classifier(
+    clause: str,
+    intents: list[IntentDefinition],
+    clause_classifier: IntentClassifier | None,
+) -> ClassificationResult | None:
+    if clause_classifier is None:
+        return None
+    try:
+        return clause_classifier.predict(clause, intents)
+    except Exception:
+        return None
+
+
+def _fuse_clause_scores(
+    clause: str,
+    intents: list[IntentDefinition],
+    heuristic_scored: list[tuple[IntentDefinition, float, list[str], dict[str, Any]]],
+    classifier_result: ClassificationResult | None,
+    detector_prior: dict[str, float],
+    classifier_weight: float,
+    model_only_threshold: float,
+) -> list[tuple[IntentDefinition, float, list[str], dict[str, Any]]]:
+    intent_map = {intent.intent_id: intent for intent in intents}
+    classifier_candidates = _extract_classifier_candidates(classifier_result, intent_map)
+    merged: dict[str, dict[str, Any]] = {}
+    for intent, score, reasons, signal_meta in heuristic_scored:
+        merged[intent.intent_id] = {
+            "intent": intent,
+            "heuristic_score": score,
+            "model_score": 0.0,
+            "reasons": list(reasons),
+            "signal_meta": dict(signal_meta),
+        }
+
+    for item in classifier_candidates:
+        intent = item["intent"]
+        if intent is None:
+            continue
+        bucket = merged.setdefault(
+            intent.intent_id,
+            {
+                "intent": intent,
+                "heuristic_score": 0.0,
+                "model_score": 0.0,
+                "reasons": [],
+                "signal_meta": _collect_intent_signal_meta(clause, intent),
+            },
+        )
+        bucket["model_score"] = max(bucket["model_score"], item["score"])
+        bucket["reasons"].append(f"bert:{item['score']:.3f}")
+
+    fused: list[tuple[IntentDefinition, float, list[str], dict[str, Any]]] = []
+    for item in merged.values():
+        heuristic_score = float(item["heuristic_score"])
+        model_score = float(item["model_score"])
+        detector_score = float(detector_prior.get(item["intent"].intent_id, 0.0))
+        if heuristic_score <= 0 and not _allow_model_bootstrap(
+            clause,
+            model_score,
+            classifier_candidates,
+            model_only_threshold,
+        ):
+            continue
+        fused_score = heuristic_score + model_score * classifier_weight
+        if detector_score > 0:
+            fused_score += detector_score * 0.55
+            item["reasons"].append(f"multi:{detector_score:.3f}")
+        if heuristic_score > 0 and model_score > 0:
+            fused_score += 0.25
+            item["reasons"].append("heuristic_bert_agree")
+        signal_meta = dict(item["signal_meta"])
+        signal_meta["heuristic_score"] = heuristic_score
+        signal_meta["model_score"] = model_score
+        signal_meta["detector_score"] = detector_score
+        if heuristic_score <= 0 and model_score >= model_only_threshold:
+            item["reasons"].append("bert_bootstrap")
+        fused.append((item["intent"], fused_score, item["reasons"], signal_meta))
+    fused.sort(key=lambda row: row[1], reverse=True)
+    return fused
+
+
+def _build_detector_prior(detector_result: MultiIntentDetectionResult | None) -> dict[str, float]:
+    if detector_result is None:
+        return {}
+    prior: dict[str, float] = {}
+    for candidate in detector_result.candidates:
+        if candidate.score <= 0:
+            continue
+        prior[candidate.intent_id] = candidate.score
+    return prior
+
+
+def _extract_classifier_candidates(
+    classifier_result: ClassificationResult | None,
+    intent_map: dict[str, IntentDefinition],
+) -> list[dict[str, Any]]:
+    if classifier_result is None:
+        return []
+    normalized: list[dict[str, Any]] = []
+    for item in classifier_result.raw_candidates or []:
+        if not isinstance(item, dict):
+            continue
+        intent_id = str(item.get("intent_id") or "")
+        intent = intent_map.get(intent_id)
+        if intent is None:
+            continue
+        normalized.append(
+            {
+                "intent": intent,
+                "intent_id": intent_id,
+                "score": float(item.get("score", 0.0)),
+                "label": str(item.get("label") or intent_id),
+            }
+        )
+    if normalized:
+        return normalized
+    if classifier_result.candidates:
+        return [
+            {
+                "intent": intent,
+                "intent_id": intent.intent_id,
+                "score": float(score),
+                "label": intent.intent_id,
+            }
+            for intent, score in classifier_result.candidates
+        ]
+    return []
+
+
+def _allow_model_bootstrap(
+    clause: str,
+    model_score: float,
+    classifier_candidates: list[dict[str, Any]],
+    model_only_threshold: float,
+) -> bool:
+    if model_score < model_only_threshold:
+        return False
+    compact_clause = re.sub(r"\s+", "", clause)
+    if len(compact_clause) < 4 and not re.search(r"\d{2}\s*度|\b[A-Za-z]\d{5,}\b", clause):
+        return False
+    runner_up_score = 0.0
+    if len(classifier_candidates) >= 2:
+        runner_up_score = float(classifier_candidates[1].get("score", 0.0))
+    return model_score - runner_up_score >= 0.18
+
+
+def _collect_parallel_clause_candidates(
+    clause: str,
+    scored: list[tuple[IntentDefinition, float, list[str], dict[str, Any]]],
+) -> list[tuple[IntentDefinition, float, list[str], dict[str, Any]]]:
+    if not _looks_like_parallel_clause(clause):
+        return []
+    selected: list[tuple[IntentDefinition, float, list[str], dict[str, Any]]] = []
+    used_families: set[str] = set()
+    strong_candidates = [item for item in scored if item[1] >= 1.35]
+    for intent, score, reasons, signal_meta in strong_candidates:
+        family = str(signal_meta.get("family") or intent.intent_id)
+        if family in used_families:
+            continue
+        has_strong_anchor = bool(signal_meta["object_hits"] or signal_meta["explicit_slot"] or any(reason.startswith("keyword:") for reason in reasons))
+        if not has_strong_anchor:
+            continue
+        selected.append((intent, score, reasons, signal_meta))
+        used_families.add(family)
+        if len(selected) >= 4:
+            break
+    if len(selected) < 2:
+        return []
+    selected.sort(key=lambda item: (_signal_position(clause, item[3]), -item[1]))
+    return selected
+
+
+def _signal_position(clause: str, signal_meta: dict[str, Any]) -> int:
+    candidates = []
+    for token in list(signal_meta.get("object_hits") or []) + list(signal_meta.get("action_hits") or []):
+        if not token:
+            continue
+        position = clause.find(token)
+        if position >= 0:
+            candidates.append(position)
+    if not candidates:
+        return len(clause) + 1
+    return min(candidates)
+
+
+def _split_clauses(text: str) -> list[str]:
+    clauses = re_split(text)
+    return [item for item in clauses if item]
+
+
+def _has_complex_pattern(text: str) -> bool:
+    patterns = ("然后", "再", "并且", "并", "同时", "如果", "若", "先", "后", "顺便", "接着", "以及", "和")
+    return any(token in text for token in patterns)
+
+
+def _has_conditional_pattern(text: str) -> bool:
+    return any(token in text for token in ("如果", "若", "还没", "未发货", "没发货"))
+
+
+def _apply_conditional_hints(text: str, steps: list[PlannedStep]) -> list[PlannedStep]:
+    if len(steps) < 2:
+        return steps
+    has_unshipped_condition = any(token in text for token in ("还没发货", "未发货", "没发货"))
+    query_index = next((index for index, step in enumerate(steps) if step.intent_id == "cs_query_order"), None)
+    cancel_index = next((index for index, step in enumerate(steps) if step.intent_id == "cs_cancel_order"), None)
+    if has_unshipped_condition and query_index is not None and cancel_index is not None and cancel_index > query_index:
+        cancel_step = steps[cancel_index]
+        if not cancel_step.depends_on:
+            cancel_step.depends_on = [query_index + 1]
+        if not cancel_step.condition:
+            cancel_step.condition = {
+                "source_step": query_index + 1,
+                "field": "order_status",
+                "operator": "equals",
+                "value": "pending_shipment",
+                "description": "仅在订单未发货时取消",
+            }
+        cancel_step.requires_confirmation = True
+    return steps
+
+
+def re_split(text: str) -> list[str]:
+    chunks = re.split(r"(?:然后|并且|同时|再|顺便|接着|并|,|，|；|;)", text)
+    return [chunk.strip() for chunk in chunks if chunk.strip()]
+
+
+def _looks_like_parallel_clause(text: str) -> bool:
+    return any(token in text for token in ("和", "以及", "并", "同时", "还有"))
+
+
+def _serialize_clause_analysis(parse_result: MultiIntentParseResult) -> list[dict[str, Any]]:
+    return [
+        {
+            "clause_text": clause.clause_text,
+            "selected_intent_id": clause.selected_intent_id,
+            "score": round(clause.score, 4),
+            "slots": clause.slots,
+            "reason": clause.reason,
+            "candidates": clause.candidates,
+        }
+        for clause in parse_result.clauses
+    ]
+
+
+def _serialize_multi_intent_detector(
+    detector_result: MultiIntentDetectionResult | None,
+) -> dict[str, Any] | None:
+    if detector_result is None:
+        return None
+    return {
+        "detected": detector_result.detected,
+        "reason": detector_result.reason,
+        "backend_name": detector_result.backend_name,
+        "error_message": detector_result.error_message,
+        "candidates": [
+            {
+                "intent_id": candidate.intent_id,
+                "score": round(candidate.score, 4),
+                "label": candidate.label,
+            }
+            for candidate in detector_result.candidates
+        ],
+        "raw_scores": [
+            {
+                "intent_id": str(item.get("intent_id") or ""),
+                "label": str(item.get("label") or ""),
+                "score": round(float(item.get("score", 0.0)), 4),
+            }
+            for item in detector_result.raw_scores[:8]
+        ],
+    }
+
+
+_INTENT_HINTS: dict[str, dict[str, Any]] = {
+    "cabin_ac_on": {
+        "family": "ac_power",
+        "actions": ("打开", "开启", "启动", "开"),
+        "objects": ("空调", "ac", "冷气", "冷风"),
+    },
+    "cabin_ac_off": {
+        "family": "ac_power",
+        "actions": ("关闭", "关掉", "关", "停掉"),
+        "objects": ("空调", "ac", "冷气", "冷风"),
+    },
+    "cabin_set_ac": {
+        "family": "ac_temperature",
+        "actions": ("调到", "设到", "设成", "调低", "调高", "升高", "降低"),
+        "objects": ("空调", "温度", "度", "冷气"),
+        "qualifiers": ("冷一点", "热一点"),
+    },
+    "cabin_fan_up": {
+        "family": "fan_speed",
+        "actions": ("调大", "调高", "加大"),
+        "objects": ("风量", "风"),
+    },
+    "cabin_fan_down": {
+        "family": "fan_speed",
+        "actions": ("调小", "调低", "减小"),
+        "objects": ("风量", "风"),
+    },
+    "cabin_defog_front_on": {
+        "family": "defog_front",
+        "actions": ("打开", "开启", "开", "除", "除雾"),
+        "objects": ("前挡", "前挡风", "前窗", "前玻璃"),
+    },
+    "cabin_defog_rear_on": {
+        "family": "defog_rear",
+        "actions": ("打开", "开启", "开", "除", "除雾"),
+        "objects": ("后挡", "后挡风", "后窗", "后玻璃"),
+    },
+    "cabin_window_open": {
+        "family": "window",
+        "actions": ("打开", "开", "升起来"),
+        "objects": ("车窗", "窗户", "窗"),
+    },
+    "cabin_window_close": {
+        "family": "window",
+        "actions": ("关闭", "关上", "关掉", "降下来", "降下", "关"),
+        "objects": ("车窗", "窗户", "窗"),
+    },
+    "cabin_sunroof_open": {
+        "family": "sunroof",
+        "actions": ("打开", "开", "翘起", "翘起来"),
+        "objects": ("天窗",),
+    },
+    "cabin_sunroof_close": {
+        "family": "sunroof",
+        "actions": ("关闭", "关上", "合上", "关掉", "合起来"),
+        "objects": ("天窗",),
+    },
+    "cabin_lock_doors": {
+        "family": "door_lock",
+        "actions": ("锁上", "锁住", "上锁", "锁"),
+        "objects": ("车门", "门", "车锁"),
+    },
+    "cabin_unlock_doors": {
+        "family": "door_lock",
+        "actions": ("解锁", "开锁"),
+        "objects": ("车门", "门", "车锁"),
+    },
+    "cabin_nav_to": {
+        "family": "nav",
+        "actions": ("导航", "去", "到", "带我去"),
+        "objects": ("公司", "机场", "家", "目的地"),
+    },
+    "cabin_nav_cancel": {
+        "family": "nav",
+        "actions": ("取消", "结束", "停止", "退出", "关掉"),
+        "objects": ("导航", "路线"),
+    },
+    "cabin_play_music": {
+        "family": "music_playback",
+        "actions": ("播放", "来点", "放点", "听", "来首", "来一首"),
+        "objects": ("音乐", "歌", "轻音乐", "摇滚", "流行"),
+    },
+    "cabin_pause_music": {
+        "family": "music_playback",
+        "actions": ("暂停", "停掉", "停止"),
+        "objects": ("音乐", "歌", "播放"),
+    },
+    "cabin_next_track": {
+        "family": "track_switch",
+        "actions": ("下一首", "切到下一首", "切歌", "换一首"),
+        "objects": ("歌", "歌曲", "音乐"),
+    },
+    "cabin_previous_track": {
+        "family": "track_switch",
+        "actions": ("上一首", "切回上一首", "回到上一首"),
+        "objects": ("歌", "歌曲", "音乐"),
+    },
+    "cabin_volume_up": {
+        "family": "volume",
+        "actions": ("调大", "加大", "开大", "调高"),
+        "objects": ("音量", "声音", "媒体音量"),
+    },
+    "cabin_volume_down": {
+        "family": "volume",
+        "actions": ("调小", "压低", "关小", "调低"),
+        "objects": ("音量", "声音", "媒体音量"),
+    },
+    "cabin_volume_mute": {
+        "family": "volume",
+        "actions": ("静音", "关掉", "关闭"),
+        "objects": ("音量", "声音", "音响"),
+    },
+    "cabin_lights_on": {
+        "family": "lights",
+        "actions": ("打开", "开", "点亮"),
+        "objects": ("灯", "车灯", "大灯"),
+    },
+    "cabin_lights_off": {
+        "family": "lights",
+        "actions": ("关闭", "关掉", "关", "熄了"),
+        "objects": ("灯", "车灯", "大灯"),
+    },
+    "cabin_seat_heat_on": {
+        "family": "seat_heat",
+        "actions": ("打开", "开启", "开"),
+        "objects": ("座椅加热",),
+    },
+    "cabin_seat_heat_off": {
+        "family": "seat_heat",
+        "actions": ("关闭", "关掉", "关"),
+        "objects": ("座椅加热",),
+    },
+    "cabin_mirror_fold": {
+        "family": "mirror",
+        "actions": ("折叠", "收起"),
+        "objects": ("后视镜",),
+    },
+    "cabin_mirror_unfold": {
+        "family": "mirror",
+        "actions": ("展开", "打开"),
+        "objects": ("后视镜",),
+    },
+    "cabin_wiper_on": {
+        "family": "wiper",
+        "actions": ("打开", "启动", "开始"),
+        "objects": ("雨刷", "雨刮", "雨刮器"),
+    },
+    "cabin_wiper_off": {
+        "family": "wiper",
+        "actions": ("关闭", "停掉", "停止"),
+        "objects": ("雨刷", "雨刮", "雨刮器"),
+    },
+    "cs_query_order": {
+        "family": "order_query",
+        "actions": ("查", "查询", "看看"),
+        "objects": ("订单",),
+        "shared_context": ("订单",),
+    },
+    "cs_query_logistics": {
+        "family": "order_logistics",
+        "actions": ("查", "查询", "看看"),
+        "objects": ("物流", "快递"),
+        "shared_context": ("物流", "快递", "订单"),
+    },
+    "cs_cancel_order": {
+        "family": "order_cancel",
+        "actions": ("取消", "撤销", "撤单", "不要了"),
+        "objects": ("订单",),
+        "shared_context": ("订单",),
+    },
+}
diff --git a/intelligent_cabin/app/services/response_policy.py b/intelligent_cabin/app/services/response_policy.py
new file mode 100644
index 0000000..0312596
--- /dev/null
+++ b/intelligent_cabin/app/services/response_policy.py
@@ -0,0 +1,299 @@
+from __future__ import annotations
+
+import re
+from typing import Any
+
+from app.schemas.intent import IntentDefinition
+
+
+class ResponsePolicy:
+    _DEFAULT_INTENT_HINTS = {
+        "cabin_nav_to": "导航",
+        "cabin_nav_cancel": "结束导航",
+        "cabin_set_ac": "调空调",
+        "cabin_ac_on": "打开空调",
+        "cabin_ac_off": "关闭空调",
+        "cabin_fan_up": "调大风量",
+        "cabin_fan_down": "调小风量",
+        "cabin_defog_front_on": "打开前挡除雾",
+        "cabin_defog_rear_on": "打开后挡除雾",
+        "cabin_window_open": "打开车窗",
+        "cabin_window_close": "关闭车窗",
+        "cabin_sunroof_open": "打开天窗",
+        "cabin_sunroof_close": "关闭天窗",
+        "cabin_trunk_open": "打开后备箱",
+        "cabin_trunk_close": "关闭后备箱",
+        "cabin_lock_doors": "锁车门",
+        "cabin_unlock_doors": "解锁车门",
+        "cabin_play_music": "播放音乐",
+        "cabin_pause_music": "暂停音乐",
+        "cabin_next_track": "下一首",
+        "cabin_previous_track": "上一首",
+        "cabin_volume_up": "调大音量",
+        "cabin_volume_down": "调小音量",
+        "cabin_volume_mute": "静音",
+        "cabin_lights_on": "打开车灯",
+        "cabin_lights_off": "关闭车灯",
+        "cabin_reading_light_on": "打开阅读灯",
+        "cabin_reading_light_off": "关闭阅读灯",
+        "cabin_seat_heat_on": "打开座椅加热",
+        "cabin_seat_heat_off": "关闭座椅加热",
+        "cabin_seat_vent_on": "打开座椅通风",
+        "cabin_seat_vent_off": "关闭座椅通风",
+        "cabin_mirror_fold": "折叠后视镜",
+        "cabin_mirror_unfold": "展开后视镜",
+        "cabin_wiper_on": "打开雨刷",
+        "cabin_wiper_off": "关闭雨刷",
+        "cabin_screen_brightness_up": "调亮屏幕",
+        "cabin_screen_brightness_down": "调暗屏幕",
+        "cabin_answer_call": "接听电话",
+        "cabin_hang_up_call": "挂断电话",
+        "cs_query_order": "查订单",
+        "cs_query_logistics": "查物流",
+        "cs_cancel_order": "取消订单",
+        "cs_transfer_human": "转人工",
+    }
+
+    def __init__(
+        self,
+        templates: dict[str, str] | None = None,
+        intent_hints: dict[str, str] | None = None,
+    ) -> None:
+        self._templates = templates or {}
+        self._intent_hints = {**self._DEFAULT_INTENT_HINTS, **(intent_hints or {})}
+
+    def ask_for_slot(self, intent: IntentDefinition, slot_name: str, default_template: str) -> str:
+        if slot_name == "order_id":
+            if intent.intent_id == "cs_cancel_order":
+                return self._template("ask_cancel_order_id", "请告诉我订单号。")
+            return self._template("ask_order_id", "请提供订单号。")
+        if slot_name == "destination":
+            return self._template("ask_destination", "请告诉我要去哪里。")
+        if slot_name == "temperature":
+            return self._template("ask_temperature", "请告诉我要设置多少度。")
+        if slot_name == "media_query":
+            return self._template("ask_media_query", "想听什么风格或者具体的歌名？")
+        return default_template.strip() or "请补充一个关键信息。"
+
+    def workflow_result(self, intent: IntentDefinition, plugin_result: dict[str, Any]) -> str:
+        if not plugin_result.get("success", True):
+            return self._template("workflow_failed", "这次没处理成功，请稍后再试。")
+        message = str(plugin_result.get("message") or "").strip()
+        if not message:
+            return self.ack(intent)
+        if len(message) > 42:
+            return message[:39].rstrip("，。；; ") + "..."
+        return message
+
+    def workflow_summary(self, messages: list[str]) -> str:
+        cleaned = [item.strip() for item in messages if item and item.strip()]
+        if not cleaned:
+            return self._template("workflow_summary_empty", "好的，已经处理完成。")
+        if len(cleaned) == 1:
+            return cleaned[0]
+        natural_clauses: list[str] = []
+        previous_subject: str | None = None
+        for index, item in enumerate(cleaned[:3]):
+            clause, subject = self._vehicle_style_clause(item, index=index, previous_subject=previous_subject)
+            natural_clauses.append(clause)
+            previous_subject = subject or previous_subject
+        summary = f"好，{'，'.join(natural_clauses)}。"
+        if len(cleaned) > 3:
+            summary = summary.rstrip("。") + "，其余步骤也已完成。"
+        if len(summary) > 70:
+            return summary[:67].rstrip("，。；; ") + "..."
+        return summary
+
+    def ask_for_confirmation(self, intent: IntentDefinition, detail: str | None = None) -> str:
+        if intent.intent_id == "cs_cancel_order":
+            if detail:
+                return f"即将取消订单，{detail}。请回复“确认”或“取消”。"
+            return "即将取消订单。请回复“确认”或“取消”。"
+        if detail:
+            return f"{detail}。请回复“确认”或“取消”。"
+        return "请确认是否继续执行。回复“确认”或“取消”。"
+
+    def confirm_retry(self) -> str:
+        return self._template("confirm_retry", "我需要一个明确确认。请回复“确认”继续，或回复“取消”终止。")
+
+    def confirm_cancelled(self) -> str:
+        return self._template("confirm_cancelled", "好的，已取消这一步。")
+
+    def step_skipped(self, intent: IntentDefinition, reason: str | None = None) -> str:
+        if intent.intent_id == "cs_cancel_order":
+            base = "订单取消步骤未执行。"
+        else:
+            base = "这一步已跳过。"
+        if reason:
+            return f"{base}{reason}"
+        return base
+
+    def ack(self, intent: IntentDefinition | None = None) -> str:
+        if intent is None:
+            return self._template("ack_default", "收到，马上处理。")
+        if intent.domain == "cabin":
+            return self._template("ack_cabin", "好的，马上处理。")
+        return self._template("ack_service", "收到，我来处理。")
+
+    def reject(self) -> str:
+        return self._template("reject", "这个我暂时做不了，但我可以帮你查询、控制或转人工。")
+
+    def short_social(self, social_kind: str) -> str:
+        if social_kind == "greeting":
+            return self._template("short_social_greeting", "你好，我在。")
+        if social_kind == "thanks":
+            return self._template("short_social_thanks", "不客气。")
+        if social_kind == "goodbye":
+            return self._template("short_social_goodbye", "好的，有需要再叫我。")
+        if social_kind == "capability":
+            return self._template(
+                "short_social_capability",
+                "我可以帮你查订单、查物流、取消订单、导航、调空调、播放音乐或转人工。",
+            )
+        return self._template("short_social_default", "我在。")
+
+    def open_social_fallback(self) -> str:
+        return self._template("open_social_fallback", "可以和你聊两句，你也可以继续告诉我想处理什么。")
+
+    def with_pending_hint(self, text: str, pending_hint: str | None = None) -> str:
+        base = text.strip() or self.open_social_fallback()
+        hint = (pending_hint or "").strip()
+        if not hint:
+            return base
+        return f"{base} {hint}"
+
+    def pending_task_hint(self, status: str, pending_slots: list[str], current_intent: str | None = None) -> str | None:
+        if status == "waiting_confirmation":
+            return self._template("pending_confirmation_hint", "当前这一步还在等你确认，回复“确认”或“取消”即可。")
+        if status == "waiting_slot" and pending_slots:
+            if pending_slots[0] == "order_id":
+                return self._template("pending_slot_order_id", "当前还缺订单号，你继续告诉我订单号就行。")
+            if pending_slots[0] == "temperature":
+                return self._template("pending_slot_temperature", "当前还缺温度，你继续告诉我要设置多少度就行。")
+            if pending_slots[0] == "destination":
+                return self._template("pending_slot_destination", "当前还缺目的地，你继续告诉我要去哪里就行。")
+            if pending_slots[0] == "media_query":
+                return self._template("pending_slot_media_query", "当前还缺歌名或风格，你直接说歌名、歌手或风格就行。")
+            return self._template("pending_slot_default", "当前还缺一个关键信息，你继续补充就行。")
+        if status == "running" and current_intent:
+            return self._template("pending_running", "当前任务还在继续，你也可以直接继续下一个指令。")
+        return None
+
+    def task_stopped(self) -> str:
+        return self._template("task_stopped", "好的，已停止当前任务。")
+
+    def clarify(self, candidate_intents: list[str]) -> str:
+        options = [
+            self._intent_hints.get(intent_id, intent_id)
+            for intent_id in candidate_intents
+            if intent_id
+        ]
+        deduped: list[str] = []
+        for item in options:
+            if item not in deduped:
+                deduped.append(item)
+        if not deduped:
+            return "我理解得还不够确定，你是想查询、控制，还是转人工？"
+        if len(deduped) == 1:
+            return f"请确认一下，你是想{deduped[0]}吗？"
+        if len(deduped) == 2:
+            return f"请确认一下，你是想{deduped[0]}还是{deduped[1]}？"
+        return f"请确认一下，你是想{deduped[0]}、{deduped[1]}，还是{deduped[2]}？"
+
+    def fallback(self) -> str:
+        return self._template("fallback", "我还没完全听懂，你可以换个简短说法，或告诉我是查询、控制还是转人工。")
+
+    def _template(self, key: str, default: str) -> str:
+        value = str(self._templates.get(key, default)).strip()
+        return value or default
+
+    def _naturalize_workflow_message(self, text: str) -> str:
+        normalized = text.strip().rstrip("。；; ")
+        normalized = re.sub(r"^好的[，,\s]*", "", normalized)
+        normalized = re.sub(r"^收到[，,\s]*", "", normalized)
+        if normalized.startswith("已将"):
+            normalized = normalized[2:]
+        elif normalized.startswith("已经将"):
+            normalized = normalized[3:]
+        elif normalized.startswith("已经"):
+            normalized = normalized[2:]
+        elif normalized.startswith("已"):
+            normalized = normalized[1:]
+        normalized = normalized.strip("，, ")
+        if not normalized:
+            return "已经处理好了"
+        if normalized.endswith("了"):
+            return normalized
+        return f"{normalized}了"
+
+    def _vehicle_style_clause(
+        self,
+        text: str,
+        *,
+        index: int,
+        previous_subject: str | None = None,
+    ) -> tuple[str, str | None]:
+        normalized = self._naturalize_workflow_message(text)
+
+        match = re.match(r"^(打开|关闭)(.+)了$", normalized)
+        if match:
+            action, subject = match.groups()
+            subject = subject.strip()
+            if action == "打开":
+                if previous_subject and previous_subject == subject:
+                    return "也打开了", subject
+                if index > 0:
+                    return f"{subject}也打开了", subject
+                return f"{subject}已经打开了", subject
+            if previous_subject and previous_subject == subject:
+                return "也帮你关上了", subject
+            if index > 0:
+                return f"{subject}也帮你关上了", subject
+            return f"{subject}已经关上了", subject
+
+        match = re.match(r"^(锁定|解锁)(.+)了$", normalized)
+        if match:
+            action, subject = match.groups()
+            subject = subject.strip()
+            action_text = "锁好了" if action == "锁定" else "解锁了"
+            if previous_subject and previous_subject == subject:
+                return f"也{action_text}", subject
+            if index > 0:
+                return f"{subject}也{action_text}", subject
+            return f"{subject}已经{action_text}", subject
+
+        match = re.match(r"^(.+)调到\s*(.+)度了$", normalized)
+        if match:
+            subject, value = match.groups()
+            subject = subject.strip()
+            value = value.strip()
+            if previous_subject and previous_subject == subject:
+                return f"也调到 {value} 度了", subject
+            if index > 0:
+                return f"{subject}也调到 {value} 度了", subject
+            return f"{subject}调到 {value} 度了", subject
+
+        match = re.match(r"^(调大|调小)(.+)了$", normalized)
+        if match:
+            action, subject = match.groups()
+            subject = subject.strip()
+            if previous_subject and previous_subject == subject:
+                return f"也{action}了", subject
+            if index > 0:
+                return f"{subject}也{action}了", subject
+            return f"{subject}已经{action}了", subject
+
+        if normalized.startswith("正在播放 "):
+            target = normalized[len("正在播放 ") :].strip()
+            if index > 0:
+                return f"也开始播放 {target} 了", "播放"
+            return f"开始播放 {target} 了", "播放"
+
+        if normalized.startswith("订单 ") and normalized.endswith(" 已取消"):
+            order_text = normalized[:-4].strip()
+            return f"{order_text}已经取消了", "订单"
+
+        if normalized.startswith("订单 ") and "当前" in normalized:
+            return normalized, "订单"
+
+        return normalized, None
diff --git a/intelligent_cabin/app/services/rewrite_engine.py b/intelligent_cabin/app/services/rewrite_engine.py
new file mode 100644
index 0000000..2b2b569
--- /dev/null
+++ b/intelligent_cabin/app/services/rewrite_engine.py
@@ -0,0 +1,108 @@
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass, field
+from typing import Any
+
+from app.schemas.configuration import ContextRewriteConfig, ParamContextDefinition
+from app.services.session_store import SessionState
+
+
+@dataclass
+class RewriteResult:
+    original_text: str
+    rewritten_text: str
+    applied: bool = False
+    reason: str | None = None
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+
+class ContextRewriteEngine:
+    """
+    将短句 follow-up（如"再快一点"、"电压高一点"）改写为完整命令（如"速度设为 85 mm/min"），
+    使其能复用本地快路径而不必每轮重做完整规划。
+
+    改写规则完全由外部配置文件（context_rewrite.yml）驱动，不硬编码业务参数，
+    适用于不同设备（线切割 / 激光切割 / 注塑机等）的部署切换。
+    """
+
+    def __init__(self, config: ContextRewriteConfig | None = None) -> None:
+        self._config = config or ContextRewriteConfig()
+        # 构建反向索引：intent_id → ParamContextDefinition
+        self._intent_index: dict[str, ParamContextDefinition] = {}
+        for ctx in self._config.param_contexts:
+            for intent_id in ctx.intent_ids:
+                self._intent_index[intent_id] = ctx
+
+    # ------------------------------------------------------------------ public
+
+    def rewrite(self, text: str, session: SessionState) -> RewriteResult:
+        normalized = text.strip()
+        if not normalized:
+            return RewriteResult(original_text=text, rewritten_text=text)
+
+        current_intent = session.current_intent
+        if current_intent and current_intent in self._intent_index:
+            ctx = self._intent_index[current_intent]
+            result = self._rewrite_param_adjustment(normalized, session, ctx)
+            if result.applied:
+                return result
+
+        return RewriteResult(original_text=text, rewritten_text=text)
+
+    # ----------------------------------------------------------------- private
+
+    def _rewrite_param_adjustment(
+        self,
+        text: str,
+        session: SessionState,
+        ctx: ParamContextDefinition,
+    ) -> RewriteResult:
+        direction: str | None = None
+        if any(phrase and phrase in text for phrase in ctx.up_phrases):
+            direction = "up"
+        elif any(phrase and phrase in text for phrase in ctx.down_phrases):
+            direction = "down"
+
+        if direction is None:
+            return RewriteResult(original_text=text, rewritten_text=text)
+
+        previous_value = self._last_slot_value(session, ctx.slot_name)
+        base_value = previous_value if previous_value is not None else ctx.default_value
+        delta = ctx.step if direction == "up" else -ctx.step
+
+        if isinstance(ctx.min_value, float) or isinstance(ctx.max_value, float) or isinstance(ctx.step, float):
+            next_value: int | float = max(float(ctx.min_value), min(float(ctx.max_value), float(base_value) + float(delta)))
+        else:
+            next_value = max(int(ctx.min_value), min(int(ctx.max_value), int(base_value) + int(delta)))
+
+        rewritten = ctx.rewrite_template.format(value=next_value)
+        return RewriteResult(
+            original_text=text,
+            rewritten_text=rewritten,
+            applied=True,
+            reason=f"normalize relative {ctx.slot_name} adjustment into an explicit target",
+            metadata={
+                "cache_hit": True,
+                "rewrite_type": "param_adjustment",
+                "slot_name": ctx.slot_name,
+                "direction": direction,
+                "previous_value": previous_value,
+                "base_value": base_value,
+                "next_value": next_value,
+            },
+        )
+
+    @staticmethod
+    def _last_slot_value(session: SessionState, slot_name: str) -> int | float | None:
+        raw = session.context_memory.get(f"last_{slot_name}", session.slots.get(slot_name))
+        if raw is None:
+            return None
+        if isinstance(raw, (int, float)):
+            return raw
+        if isinstance(raw, str):
+            try:
+                return int(raw) if raw.isdigit() else float(raw)
+            except ValueError:
+                pass
+        return None
diff --git a/intelligent_cabin/app/services/router.py b/intelligent_cabin/app/services/router.py
new file mode 100644
index 0000000..32eab01
--- /dev/null
+++ b/intelligent_cabin/app/services/router.py
@@ -0,0 +1,407 @@
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass
+from time import perf_counter
+from typing import Any, Protocol
+
+from app.schemas.debug import IntentCandidate, MatcherStageDebug, RoutingDebug
+from app.schemas.intent import IntentDefinition
+from app.services.classifier import IntentClassifier
+from app.services.intent_registry import IntentRegistry
+from app.services.joint_nlu import JointBertNLU
+
+
+@dataclass
+class IntentMatchResult:
+    intent: IntentDefinition | None
+    stage_debug: MatcherStageDebug
+
+
+@dataclass
+class RouteMatchResult:
+    intent: IntentDefinition | None
+    debug: RoutingDebug
+
+
+class IntentMatcher(Protocol):
+    def match(self, text: str) -> IntentMatchResult:
+        ...
+
+
+class SlotExtractor(Protocol):
+    def extract(self, text: str, intent: IntentDefinition) -> dict[str, Any]:
+        ...
+
+
+class ClassifierIntentMatcher:
+    def __init__(self, registry: IntentRegistry, classifier: IntentClassifier) -> None:
+        self._registry = registry
+        self._classifier = classifier
+
+    def match(self, text: str) -> IntentMatchResult:
+        result = self._classifier.predict(text, self._registry.list())
+        ranked_classifier_scores = sorted(
+            [float(score) for _, score in (result.candidates or [])],
+            reverse=True,
+        )
+        classifier_top_margin = (
+            ranked_classifier_scores[0] - ranked_classifier_scores[1]
+            if len(ranked_classifier_scores) >= 2
+            else ranked_classifier_scores[0] if ranked_classifier_scores else 0.0
+        )
+        candidates = [
+            IntentCandidate(
+                intent_id=intent.intent_id,
+                score=score,
+                reason="classifier candidate",
+                model_name=result.backend_name or result.model_name,
+                raw_label=next(
+                    (
+                        item.get("label")
+                        for item in (result.raw_candidates or [])
+                        if item.get("intent_id") == intent.intent_id and float(item.get("score", 0.0)) == score
+                    ),
+                    intent.intent_id,
+                ),
+            )
+            for intent, score in (result.candidates or [])
+        ]
+        metadata: dict[str, Any] = {
+            "decision_model": result.model_name,
+            "threshold": getattr(self._classifier, "_threshold", None),
+            "raw_candidates": result.raw_candidates or [],
+            "top_margin": round(classifier_top_margin, 4),
+        }
+        if result.fallback_reason:
+            metadata["fallback_reason"] = result.fallback_reason
+        if result.intent is None:
+            return IntentMatchResult(
+                intent=None,
+                stage_debug=MatcherStageDebug(
+                    stage="classifier",
+                    accepted=False,
+                    score=result.score,
+                    reason=result.fallback_reason or "classifier below threshold or no intent selected",
+                    model_name=result.model_name,
+                    backend=result.backend_name or result.model_name,
+                    fallback_used=result.used_fallback,
+                    raw_label=result.raw_label,
+                    error_message=result.error_message,
+                    metadata=metadata,
+                    candidates=candidates,
+                ),
+            )
+        return IntentMatchResult(
+            intent=result.intent,
+            stage_debug=MatcherStageDebug(
+                stage="classifier",
+                accepted=True,
+                selected_intent=result.intent.intent_id,
+                score=result.score,
+                reason=(
+                    "bert classifier selected best candidate"
+                    if not result.used_fallback
+                    else f"fallback selected best candidate: {result.fallback_reason}"
+                ),
+                model_name=result.model_name,
+                backend=result.backend_name or result.model_name,
+                fallback_used=result.used_fallback,
+                raw_label=result.raw_label,
+                error_message=result.error_message,
+                metadata=metadata,
+                candidates=candidates,
+            ),
+        )
+
+
+class MultiStageIntentMatcher:
+    def __init__(
+        self,
+        registry: IntentRegistry,
+        matchers: list[IntentMatcher],
+        route_to_cloud_threshold: float = 0.75,
+        clarify_margin_threshold: float = 0.12,
+        classifier_execute_score_threshold: float = 0.55,
+        classifier_execute_margin_threshold: float = 0.18,
+    ) -> None:
+        self._registry = registry
+        self._matchers = matchers
+        self._route_to_cloud_threshold = route_to_cloud_threshold
+        self._clarify_margin_threshold = clarify_margin_threshold
+        self._classifier_execute_score_threshold = classifier_execute_score_threshold
+        self._classifier_execute_margin_threshold = classifier_execute_margin_threshold
+
+    def match(self, text: str) -> RouteMatchResult:
+        stage_traces: list[MatcherStageDebug] = []
+        match_started_at = perf_counter()
+        for matcher in self._matchers:
+            stage_started_at = perf_counter()
+            result = matcher.match(text)
+            result.stage_debug.elapsed_ms = round((perf_counter() - stage_started_at) * 1000, 3)
+            stage_traces.append(result.stage_debug)
+        fusion_started_at = perf_counter()
+        fusion_stage = self._build_fusion_stage(stage_traces)
+        fusion_stage.elapsed_ms = round((perf_counter() - fusion_started_at) * 1000, 3)
+        stage_traces.append(fusion_stage)
+        total_match_latency_ms = round((perf_counter() - match_started_at) * 1000, 3)
+        decision = str(fusion_stage.metadata.get("decision", "reject"))
+        confidence_grade = str(fusion_stage.metadata.get("grade", "low"))
+        unknown_detected = bool(fusion_stage.metadata.get("unknown_detected", False))
+        decision_reason = fusion_stage.reason
+        if decision == "execute" and fusion_stage.selected_intent is not None:
+            intent = self._registry.get(fusion_stage.selected_intent)
+            return RouteMatchResult(
+                intent=intent,
+                debug=RoutingDebug(
+                    selected_intent=fusion_stage.selected_intent,
+                    matched_stage=fusion_stage.stage,
+                    decision=decision,
+                    decision_reason=decision_reason,
+                    confidence_grade=confidence_grade,
+                    total_match_latency_ms=total_match_latency_ms,
+                    unknown_detected=unknown_detected,
+                    stages=stage_traces,
+                ),
+            )
+        return RouteMatchResult(
+            intent=None,
+            debug=RoutingDebug(
+                selected_intent=None,
+                matched_stage=fusion_stage.stage,
+                decision=decision,
+                decision_reason=decision_reason,
+                confidence_grade=confidence_grade,
+                total_match_latency_ms=total_match_latency_ms,
+                unknown_detected=unknown_detected,
+                stages=stage_traces,
+            ),
+        )
+
+    def _build_fusion_stage(self, stage_traces: list[MatcherStageDebug]) -> MatcherStageDebug:
+        classifier_stage = next((stage for stage in stage_traces if stage.stage == "classifier"), None)
+        if classifier_stage is None:
+            return MatcherStageDebug(
+                stage="fusion",
+                accepted=False,
+                reason="classifier stage is missing",
+                model_name="fusion-router",
+                backend="bert-first-fusion",
+                metadata={
+                    "grade": "low",
+                    "decision": "reject",
+                    "unknown_detected": True,
+                    "ranked_intents": [],
+                },
+                candidates=[],
+            )
+        ranked_candidates = list(classifier_stage.candidates or [])
+        if not ranked_candidates and classifier_stage.selected_intent is not None:
+            ranked_candidates = [
+                IntentCandidate(
+                    intent_id=classifier_stage.selected_intent,
+                    score=classifier_stage.score,
+                    reason="classifier selected intent",
+                    model_name=classifier_stage.model_name,
+                )
+            ]
+        if not ranked_candidates:
+            return MatcherStageDebug(
+                stage="fusion",
+                accepted=False,
+                reason="classifier did not produce a usable candidate",
+                model_name="fusion-router",
+                backend="bert-first-fusion",
+                metadata={
+                    "grade": "low",
+                    "decision": "reject",
+                    "unknown_detected": True,
+                    "ranked_intents": [],
+                },
+                candidates=[],
+            )
+        selected_candidate = ranked_candidates[0]
+        selected_intent = selected_candidate.intent_id
+        top_score = float(selected_candidate.score)
+        second_score = float(ranked_candidates[1].score) if len(ranked_candidates) > 1 else 0.0
+        top_margin = top_score - second_score
+        grade = self._fusion_grade(top_score)
+        classifier_backend = str(classifier_stage.backend or classifier_stage.model_name or "")
+        classifier_signal = top_score
+        classifier_margin = top_margin
+        bert_classifier_confident = (
+            "bert" in classifier_backend
+            and classifier_signal >= self._classifier_execute_score_threshold
+            and classifier_margin >= self._classifier_execute_margin_threshold
+        )
+        ambiguous = (
+            classifier_signal >= self._route_to_cloud_threshold
+            and classifier_signal < self._classifier_execute_score_threshold
+            and top_margin < self._clarify_margin_threshold
+            and len(ranked_candidates) > 1
+        )
+        accepted = bert_classifier_confident and not ambiguous
+        possible_known_intent = classifier_signal >= self._route_to_cloud_threshold or classifier_signal >= 0.24
+        unknown_detected = not accepted and not possible_known_intent
+        if accepted:
+            decision = "execute"
+            reason = f"bert classifier is confident enough to execute (grade={grade})"
+        elif ambiguous:
+            decision = "clarify"
+            reason = "bert top candidates are too close and require a short clarification"
+        elif possible_known_intent:
+            decision = "route_to_cloud"
+            reason = "bert signal is not stable enough, routing to cloud planner"
+        else:
+            decision = "reject"
+            reason = "bert signal is below local capability threshold"
+        metadata = {
+            "grade": grade,
+            "classifier_signal": round(classifier_signal, 4),
+            "classifier_margin": round(classifier_margin, 4),
+            "classifier_backend": classifier_backend or None,
+            "bert_classifier_confident": bert_classifier_confident,
+            "top_margin": round(top_margin, 4),
+            "route_to_cloud_threshold": self._route_to_cloud_threshold,
+            "clarify_margin_threshold": self._clarify_margin_threshold,
+            "classifier_execute_score_threshold": self._classifier_execute_score_threshold,
+            "classifier_execute_margin_threshold": self._classifier_execute_margin_threshold,
+            "decision": decision,
+            "unknown_detected": unknown_detected,
+            "ranked_intents": [
+                {"intent_id": item.intent_id, "score": round(float(item.score), 4)}
+                for item in ranked_candidates[:5]
+            ],
+        }
+        return MatcherStageDebug(
+            stage="fusion",
+            accepted=accepted or ambiguous,
+            selected_intent=selected_intent if decision in {"execute", "clarify", "route_to_cloud"} else None,
+            score=top_score,
+            reason=reason,
+            model_name="fusion-router",
+            backend="bert-first-fusion",
+            metadata=metadata,
+            candidates=ranked_candidates[:5],
+        )
+
+    def _fusion_grade(self, score: float) -> str:
+        if score >= self._classifier_execute_score_threshold:
+            return "high"
+        if score >= self._route_to_cloud_threshold:
+            return "medium"
+        return "low"
+
+
+class HeuristicSlotExtractor:
+    def extract(self, text: str, intent: IntentDefinition) -> dict[str, Any]:
+        slots: dict[str, Any] = {}
+        order_id_match = re.search(r"\b[A-Za-z]\d{5,}\b", text)
+        if order_id_match:
+            slots["order_id"] = order_id_match.group(0)
+
+        temperature_match = re.search(r"(\d{2})\s*度", text)
+        if temperature_match:
+            slots["temperature"] = int(temperature_match.group(1))
+
+        if intent.intent_id == "cabin_nav_to":
+            destination = self._extract_destination(text)
+            if destination:
+                slots["destination"] = destination
+
+        if intent.intent_id == "cabin_play_music":
+            if "播放" in text:
+                music_target = text.split("播放", maxsplit=1)[-1].strip(" ，。")
+                if music_target:
+                    slots["song"] = music_target
+            elif "音乐" in text:
+                slots["genre"] = "轻音乐"
+
+        return slots
+
+    def _extract_destination(self, text: str) -> str | None:
+        patterns = [
+            r"导航去(?P<destination>.+)",
+            r"导航到(?P<destination>.+)",
+            r"去(?P<destination>.+)",
+        ]
+        for pattern in patterns:
+            match = re.search(pattern, text)
+            if match:
+                destination = match.group("destination").strip(" ，。")
+                if destination:
+                    return destination
+        return None
+
+
+class JointBertSlotExtractor:
+    def __init__(self, nlu: JointBertNLU) -> None:
+        self._nlu = nlu
+
+    def extract(self, text: str, intent: IntentDefinition) -> dict[str, Any]:
+        return self._nlu.extract_slots(text, intent)
+
+
+class Router(Protocol):
+    def route(self, text: str) -> RouteMatchResult:
+        ...
+
+    def match_intent(self, text: str) -> IntentDefinition | None:
+        ...
+
+    def extract_slots(self, text: str, intent: IntentDefinition) -> dict[str, Any]:
+        ...
+
+
+class IntentRouter:
+    def __init__(self, matcher: IntentMatcher, slot_extractor: SlotExtractor) -> None:
+        self._matcher = matcher
+        self._slot_extractor = slot_extractor
+
+    def route(self, text: str) -> RouteMatchResult:
+        return self._matcher.match(text)
+
+    def match_intent(self, text: str) -> IntentDefinition | None:
+        return self.route(text).intent
+
+    def extract_slots(self, text: str, intent: IntentDefinition) -> dict[str, Any]:
+        return self._slot_extractor.extract(text, intent)
+
+
+def build_matcher_pipeline(
+    registry: IntentRegistry,
+    stages: list[str],
+    classifier: IntentClassifier | None = None,
+    route_to_cloud_threshold: float = 0.75,
+    clarify_margin_threshold: float = 0.12,
+    classifier_execute_score_threshold: float = 0.55,
+    classifier_execute_margin_threshold: float = 0.18,
+) -> MultiStageIntentMatcher:
+    normalized_stages = [stage.strip() for stage in stages if stage.strip()]
+    if not normalized_stages:
+        normalized_stages = ["classifier"]
+    if normalized_stages != ["classifier"]:
+        raise ValueError("Only classifier matcher pipeline is supported in bert-first mode")
+    matcher = ClassifierIntentMatcher(registry, classifier) if classifier is not None else NullIntentMatcher()
+    return MultiStageIntentMatcher(
+        registry,
+        [matcher],
+        route_to_cloud_threshold=route_to_cloud_threshold,
+        clarify_margin_threshold=clarify_margin_threshold,
+        classifier_execute_score_threshold=classifier_execute_score_threshold,
+        classifier_execute_margin_threshold=classifier_execute_margin_threshold,
+    )
+
+
+class NullIntentMatcher:
+    def match(self, text: str) -> IntentMatchResult:
+        _ = text
+        return IntentMatchResult(
+            intent=None,
+            stage_debug=MatcherStageDebug(
+                stage="null",
+                accepted=False,
+                reason="matcher unavailable",
+                candidates=[],
+            ),
+        )
diff --git a/intelligent_cabin/app/services/session_store.py b/intelligent_cabin/app/services/session_store.py
new file mode 100644
index 0000000..368152e
--- /dev/null
+++ b/intelligent_cabin/app/services/session_store.py
@@ -0,0 +1,125 @@
+from __future__ import annotations
+
+import importlib
+import json
+from dataclasses import dataclass, field
+from typing import Any
+from typing import Protocol
+
+
+@dataclass
+class SessionState:
+    session_id: str
+    user_id: str
+    channel: str
+    status: str = "idle"
+    current_intent: str | None = None
+    pending_slots: list[str] = field(default_factory=list)
+    slots: dict[str, Any] = field(default_factory=dict)
+    workflow: dict[str, Any] | None = None
+    routing_debug: dict[str, Any] | None = None
+    last_user_text: str | None = None
+    last_agent_text: str | None = None
+    context_memory: dict[str, Any] = field(default_factory=dict)
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "session_id": self.session_id,
+            "user_id": self.user_id,
+            "channel": self.channel,
+            "status": self.status,
+            "current_intent": self.current_intent,
+            "pending_slots": self.pending_slots,
+            "slots": self.slots,
+            "workflow": self.workflow,
+            "routing_debug": self.routing_debug,
+            "last_user_text": self.last_user_text,
+            "last_agent_text": self.last_agent_text,
+            "context_memory": self.context_memory,
+        }
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "SessionState":
+        return cls(
+            session_id=data["session_id"],
+            user_id=data["user_id"],
+            channel=data.get("channel", "app"),
+            status=data.get("status", "idle"),
+            current_intent=data.get("current_intent"),
+            pending_slots=list(data.get("pending_slots", [])),
+            slots=dict(data.get("slots", {})),
+            workflow=data.get("workflow"),
+            routing_debug=data.get("routing_debug"),
+            last_user_text=data.get("last_user_text"),
+            last_agent_text=data.get("last_agent_text"),
+            context_memory=dict(data.get("context_memory", {})),
+        )
+
+
+class SessionStore(Protocol):
+    def get_or_create(self, session_id: str, user_id: str, channel: str = "app") -> SessionState:
+        ...
+
+    def get(self, session_id: str) -> SessionState | None:
+        ...
+
+    def save(self, session: SessionState) -> SessionState:
+        ...
+
+
+class InMemorySessionStore:
+    def __init__(self) -> None:
+        self._sessions: dict[str, SessionState] = {}
+
+    def get_or_create(self, session_id: str, user_id: str, channel: str = "app") -> SessionState:
+        session = self._sessions.get(session_id)
+        if session is None:
+            session = SessionState(session_id=session_id, user_id=user_id, channel=channel)
+            self._sessions[session_id] = session
+        return session
+
+    def get(self, session_id: str) -> SessionState | None:
+        return self._sessions.get(session_id)
+
+    def save(self, session: SessionState) -> SessionState:
+        self._sessions[session.session_id] = session
+        return session
+
+
+class RedisSessionStore:
+    def __init__(
+        self,
+        redis_url: str,
+        key_prefix: str = "agent:session",
+        ttl_seconds: int = 86400,
+    ) -> None:
+        redis_module = importlib.import_module("redis")
+        self._client = redis_module.Redis.from_url(redis_url, decode_responses=True)
+        self._key_prefix = key_prefix
+        self._ttl_seconds = ttl_seconds
+
+    def get_or_create(self, session_id: str, user_id: str, channel: str = "app") -> SessionState:
+        session = self.get(session_id)
+        if session is not None:
+            return session
+
+        session = SessionState(session_id=session_id, user_id=user_id, channel=channel)
+        self.save(session)
+        return session
+
+    def get(self, session_id: str) -> SessionState | None:
+        payload = self._client.get(self._build_key(session_id))
+        if payload is None:
+            return None
+        return SessionState.from_dict(json.loads(payload))
+
+    def save(self, session: SessionState) -> SessionState:
+        self._client.set(
+            self._build_key(session.session_id),
+            json.dumps(session.to_dict(), ensure_ascii=False),
+            ex=self._ttl_seconds,
+        )
+        return session
+
+    def _build_key(self, session_id: str) -> str:
+        return f"{self._key_prefix}:{session_id}"
diff --git a/intelligent_cabin/app/services/social.py b/intelligent_cabin/app/services/social.py
new file mode 100644
index 0000000..ec978c5
--- /dev/null
+++ b/intelligent_cabin/app/services/social.py
@@ -0,0 +1,250 @@
+from __future__ import annotations
+
+import json
+import re
+from dataclasses import dataclass, field
+from typing import Any, Literal, Protocol
+from urllib import error, request
+
+from app.services.session_store import SessionState
+
+
+SocialCategory = Literal["none", "open_social"]
+ShortSocialKind = Literal["greeting", "thanks", "goodbye", "capability"]
+
+
+@dataclass
+class SocialRouteResult:
+    category: SocialCategory
+    reason: str
+    short_kind: ShortSocialKind | None = None
+
+
+@dataclass
+class SocialReplyResult:
+    text: str
+    backend: str
+    model_name: str
+    error_message: str | None = None
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+
+class SocialResponder(Protocol):
+    def reply(self, text: str, session: SessionState) -> SocialReplyResult:
+        ...
+
+
+class SocialRouter:
+    _SHORT_SOCIAL_PATTERNS: dict[ShortSocialKind, tuple[str, ...]] = {
+        "greeting": ("你好", "您好", "嗨", "哈喽", "hi", "hello", "在吗", "在不在"),
+        "thanks": ("谢谢", "谢啦", "多谢", "thanks", "thank you", "辛苦了"),
+        "goodbye": ("再见", "拜拜", "回头见", "bye", "goodbye"),
+        "capability": (
+            "你是谁",
+            "你叫什么",
+            "你叫什么名字",
+            "你叫啥",
+            "怎么称呼你",
+            "介绍一下你自己",
+            "你能做什么",
+            "你会什么",
+            "你可以做什么",
+        ),
+    }
+    _OPEN_SOCIAL_PATTERNS: tuple[str, ...] = (
+        "天气",
+        "讲个笑话",
+        "笑话",
+        "无聊",
+        "有点累",
+        "有点困",
+        "有点烦",
+        "开心",
+        "不开心",
+        "真不错",
+        "真好",
+        "聊聊天",
+        "你觉得",
+        "你怎么看",
+        "你说呢",
+    )
+    _OPEN_SOCIAL_REGEXES: tuple[re.Pattern[str], ...] = (
+        re.compile(r"今天.*(不错|真好|挺好|真舒服)"),
+        re.compile(r"(好|真)热啊"),
+        re.compile(r"(好|真)冷啊"),
+        re.compile(r"我今天.*(累|困|烦|开心|难过)"),
+        re.compile(r".*(怎么样|如何|咋样)[？?]?$"),
+    )
+    _CAPABILITY_REGEXES: tuple[re.Pattern[str], ...] = (
+        re.compile(r"你.*(叫.*名字|叫什么|叫啥)[？?]?$"),
+        re.compile(r"(怎么称呼你|介绍一下你自己)[？?]?$"),
+        re.compile(r"你.*(能做什么|会什么|可以做什么)[？?]?$"),
+    )
+    _TASK_KEYWORDS: tuple[str, ...] = (
+        "订单",
+        "物流",
+        "取消",
+        "转人工",
+        "导航",
+        "去",
+        "到",
+        "空调",
+        "温度",
+        "调到",
+        "播放",
+        "音乐",
+        "歌曲",
+        "车窗",
+        "座椅",
+        "后视镜",
+        "灯光",
+        "除雾",
+        "确认",
+        "不用",
+    )
+
+    def route(self, text: str, session: SessionState) -> SocialRouteResult:
+        normalized = self._normalize(text)
+        if not normalized:
+            return SocialRouteResult(category="none", reason="empty text")
+        if self._looks_like_task(normalized):
+            return SocialRouteResult(category="none", reason="contains task keywords")
+        for short_kind, patterns in self._SHORT_SOCIAL_PATTERNS.items():
+            if any(pattern in normalized for pattern in patterns):
+                return SocialRouteResult(
+                    category="open_social",
+                    short_kind=short_kind,
+                    reason=f"matched social pattern routed to llm: {short_kind}",
+                )
+        if any(regex.search(normalized) for regex in self._CAPABILITY_REGEXES):
+            return SocialRouteResult(
+                category="open_social",
+                short_kind="capability",
+                reason="matched capability social regex routed to llm",
+            )
+        if any(pattern in normalized for pattern in self._OPEN_SOCIAL_PATTERNS):
+            return SocialRouteResult(category="open_social", reason="matched open social phrase")
+        if any(regex.search(normalized) for regex in self._OPEN_SOCIAL_REGEXES):
+            return SocialRouteResult(category="open_social", reason="matched open social regex")
+        if session.context_memory.get("last_dialog_mode") == "open_social" and len(normalized) <= 14:
+            return SocialRouteResult(category="open_social", reason="follow-up to previous open social turn")
+        return SocialRouteResult(category="none", reason="no social pattern matched")
+
+    def _normalize(self, text: str) -> str:
+        return re.sub(r"\s+", "", text.strip().lower())
+
+    def _looks_like_task(self, normalized: str) -> bool:
+        if any(keyword in normalized for keyword in self._TASK_KEYWORDS):
+            return True
+        return bool(re.match(r"^(查|帮我查|打开|关闭|设置|调|导航|播放|取消|转)(.+)", normalized))
+
+
+class DashScopeSocialResponder:
+    def __init__(
+        self,
+        base_url: str,
+        api_key: str,
+        model_name: str,
+        timeout_seconds: float = 6.0,
+    ) -> None:
+        self._base_url = base_url.rstrip("/")
+        self._api_key = api_key
+        self._model_name = model_name
+        self._timeout_seconds = timeout_seconds
+
+    def reply(self, text: str, session: SessionState) -> SocialReplyResult:
+        if not self._base_url or not self._api_key or not self._model_name:
+            return SocialReplyResult(
+                text="可以和你聊两句，你也可以继续让我处理查询或控制。",
+                backend="local-fallback",
+                model_name="social-fallback",
+                error_message="social responder is not configured",
+            )
+
+        payload = {
+            "model": self._model_name,
+            "temperature": 0.6,
+            "enable_thinking": False,
+            "max_tokens": 120,
+            "messages": [
+                {
+                    "role": "system",
+                    "content": (
+                        "你是智能座舱助手，负责处理所有闲聊、问候、身份问答、能力介绍和开放聊天。"
+                        "请用自然、口语化、简短的中文回答，优先 1-3 句，总长度尽量不超过 50 个字。"
+                        "如果用户在打招呼、问你是谁、问你叫什么名字、问你会什么，请直接自然回答，不要像固定菜单。"
+                        "可以结合用户上下文自然接话，但不要过度展开。"
+                        "不要编造已经执行了任何车辆或客服动作。"
+                        "不要输出 JSON，不要长篇解释。"
+                    ),
+                },
+                {
+                    "role": "user",
+                    "content": json.dumps(
+                        {
+                            "text": text,
+                            "context": {
+                                "last_user_text": session.last_user_text,
+                                "last_agent_text": session.last_agent_text,
+                                "current_intent": session.current_intent,
+                                "status": session.status,
+                            },
+                        },
+                        ensure_ascii=False,
+                    ),
+                },
+            ],
+        }
+        req = request.Request(
+            self._endpoint(),
+            data=json.dumps(payload).encode("utf-8"),
+            headers={
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {self._api_key}",
+            },
+            method="POST",
+        )
+        try:
+            with request.urlopen(req, timeout=self._timeout_seconds) as response:
+                data = json.loads(response.read().decode("utf-8"))
+        except (error.URLError, TimeoutError, ValueError) as exc:
+            return SocialReplyResult(
+                text="是啊，听起来今天状态不错。",
+                backend="local-fallback",
+                model_name="social-fallback",
+                error_message=str(exc),
+            )
+
+        content = self._extract_content(data)
+        if not content:
+            return SocialReplyResult(
+                text="可以和你聊两句，你也可以继续说说看。",
+                backend="local-fallback",
+                model_name="social-fallback",
+                error_message="empty social response",
+            )
+        return SocialReplyResult(
+            text=content,
+            backend="dashscope",
+            model_name=self._model_name,
+        )
+
+    def _endpoint(self) -> str:
+        if self._base_url.endswith("/chat/completions"):
+            return self._base_url
+        return f"{self._base_url}/chat/completions"
+
+    def _extract_content(self, payload: dict[str, Any]) -> str:
+        choices = payload.get("choices")
+        if not isinstance(choices, list) or not choices:
+            return ""
+        message = choices[0].get("message", {})
+        content = message.get("content", "")
+        if isinstance(content, list):
+            parts = [
+                str(item.get("text", "")).strip()
+                for item in content
+                if isinstance(item, dict) and item.get("type") == "text"
+            ]
+            return "".join(parts).strip()
+        return str(content).strip()
diff --git a/intelligent_cabin/archive/demo/demo.html b/intelligent_cabin/archive/demo/demo.html
new file mode 100644
index 0000000..f206d7f
--- /dev/null
+++ b/intelligent_cabin/archive/demo/demo.html
@@ -0,0 +1,2254 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+  <meta charset="UTF-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intelligent Cabin Agent Demo</title>
+  <style>
+    :root {
+      color-scheme: light;
+      --bg: #f5f7fb;
+      --panel: #ffffff;
+      --line: #dde3ee;
+      --text: #1f2937;
+      --sub: #6b7280;
+      --brand: #2563eb;
+      --user: #eff6ff;
+      --agent: #f3f4f6;
+      --accent: #eef2ff;
+      --success: #047857;
+      --warn: #b45309;
+      --danger: #b91c1c;
+      --dark: #0f172a;
+    }
+
+    * { box-sizing: border-box; }
+
+    html, body {
+      height: 100%;
+    }
+
+    body {
+      margin: 0;
+      font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
+      background: var(--bg);
+      color: var(--text);
+      overflow: hidden;
+    }
+
+    .page {
+      display: grid;
+      grid-template-columns: minmax(0, 1.5fr) minmax(320px, 420px);
+      gap: 16px;
+      height: 100vh;
+      padding: 16px;
+      overflow: hidden;
+    }
+
+    .page.debug-collapsed {
+      grid-template-columns: minmax(0, 1fr);
+    }
+
+    .panel {
+      background: var(--panel);
+      border: 1px solid var(--line);
+      border-radius: 16px;
+      box-shadow: 0 6px 24px rgba(15, 23, 42, 0.06);
+      overflow: hidden;
+      min-height: 0;
+    }
+
+    .header {
+      padding: 18px 20px;
+      border-bottom: 1px solid var(--line);
+      display: flex;
+      align-items: center;
+      justify-content: space-between;
+      gap: 12px;
+    }
+
+    .title {
+      font-size: 18px;
+      font-weight: 700;
+    }
+
+    .sub, .hint {
+      color: var(--sub);
+      font-size: 13px;
+    }
+
+    .header-actions {
+      display: flex;
+      align-items: center;
+      gap: 10px;
+      flex-wrap: wrap;
+      justify-content: flex-end;
+    }
+
+    .chat-panel {
+      display: grid;
+      grid-template-rows: auto 1fr auto;
+      height: calc(100vh - 32px);
+      min-height: 0;
+    }
+
+    .controls {
+      display: grid;
+      grid-template-columns: repeat(2, minmax(0, 1fr));
+      gap: 12px;
+      padding: 16px 20px 8px;
+    }
+
+    .control {
+      display: flex;
+      flex-direction: column;
+      gap: 6px;
+    }
+
+    label {
+      font-size: 12px;
+      font-weight: 600;
+      color: var(--sub);
+    }
+
+    input, textarea, select, button {
+      font: inherit;
+    }
+
+    input, textarea, select {
+      width: 100%;
+      border: 1px solid var(--line);
+      border-radius: 10px;
+      padding: 10px 12px;
+      background: #fff;
+      color: var(--text);
+    }
+
+    textarea {
+      min-height: 82px;
+      resize: vertical;
+    }
+
+    .messages {
+      padding: 12px 20px 16px;
+      overflow: auto;
+      display: flex;
+      flex-direction: column;
+      gap: 14px;
+      min-height: 0;
+      background: linear-gradient(180deg, #f8fbff 0%, #ffffff 100%);
+      scroll-behavior: smooth;
+    }
+
+    .message-row {
+      display: flex;
+      flex-direction: column;
+      gap: 4px;
+    }
+
+    .message-row.user {
+      align-items: flex-end;
+    }
+
+    .message-row.agent {
+      align-items: flex-start;
+    }
+
+    .message-row.system {
+      align-items: center;
+    }
+
+    .message-head {
+      padding: 0 2px;
+      font-size: 12px;
+      color: var(--sub);
+    }
+
+    .message-name {
+      font-weight: 600;
+    }
+
+    .message {
+      max-width: min(82%, 720px);
+      padding: 12px 14px;
+      border-radius: 14px;
+      line-height: 1.5;
+      white-space: pre-wrap;
+      word-break: break-word;
+      border: 1px solid transparent;
+    }
+
+    .message.user {
+      background: var(--user);
+      border-color: #bfdbfe;
+    }
+
+    .message.agent {
+      background: var(--agent);
+      border-color: #e5e7eb;
+    }
+
+    .message.system {
+      max-width: min(92%, 760px);
+      background: #fff;
+      border-color: var(--line);
+      color: var(--sub);
+      font-size: 13px;
+    }
+
+    .message-note {
+      margin-top: 8px;
+      padding: 8px 10px;
+      border-radius: 10px;
+      background: rgba(255, 255, 255, 0.65);
+      color: #334155;
+      font-size: 12px;
+      border: 1px dashed rgba(148, 163, 184, 0.7);
+    }
+
+    .meta {
+      margin-top: 6px;
+      font-size: 12px;
+      color: var(--sub);
+    }
+
+    .composer {
+      border-top: 1px solid var(--line);
+      padding: 16px 20px 20px;
+      display: grid;
+      gap: 10px;
+    }
+
+    .composer-actions, .toolbar {
+      display: flex;
+      gap: 10px;
+      justify-content: space-between;
+      align-items: center;
+      flex-wrap: wrap;
+    }
+
+    .btn-row {
+      display: flex;
+      gap: 10px;
+      flex-wrap: wrap;
+    }
+
+    button {
+      border: none;
+      border-radius: 10px;
+      padding: 10px 14px;
+      cursor: pointer;
+      transition: opacity 0.2s ease;
+    }
+
+    button:hover { opacity: 0.92; }
+    button:disabled { opacity: 0.6; cursor: not-allowed; }
+
+    .primary { background: var(--brand); color: #fff; }
+    .secondary { background: var(--accent); color: #3730a3; }
+    .ghost { background: #f8fafc; color: var(--text); border: 1px solid var(--line); }
+
+    .status {
+      font-size: 13px;
+      color: var(--sub);
+    }
+
+    .side {
+      display: flex;
+      flex-direction: column;
+      height: calc(100vh - 32px);
+      min-height: 0;
+      overflow: auto;
+    }
+
+    .page.debug-collapsed .side {
+      display: none;
+    }
+
+    .section {
+      padding: 16px 20px;
+      border-bottom: 1px solid var(--line);
+    }
+
+    .section h3 {
+      margin: 0 0 10px;
+      font-size: 15px;
+    }
+
+    .grid-2 {
+      display: grid;
+      grid-template-columns: repeat(2, minmax(0, 1fr));
+      gap: 10px;
+    }
+
+    .chips {
+      display: flex;
+      flex-wrap: wrap;
+      gap: 8px;
+    }
+
+    .chip {
+      border: 1px solid var(--line);
+      background: #fff;
+      border-radius: 999px;
+      padding: 6px 10px;
+      font-size: 12px;
+      cursor: pointer;
+    }
+
+    .chip.active {
+      background: #dbeafe;
+      border-color: #93c5fd;
+      color: #1d4ed8;
+    }
+
+    .kv {
+      display: grid;
+      grid-template-columns: 88px 1fr;
+      gap: 8px 10px;
+      font-size: 13px;
+    }
+
+    .kv .k { color: var(--sub); }
+
+    .cabin-demo {
+      display: grid;
+      gap: 12px;
+    }
+
+    .demo-scene {
+      display: grid;
+      grid-template-columns: 1.2fr 0.8fr;
+      gap: 12px;
+      align-items: stretch;
+    }
+
+    .demo-card {
+      border: 1px solid var(--line);
+      border-radius: 14px;
+      background: linear-gradient(180deg, #f8fbff 0%, #ffffff 100%);
+      padding: 12px;
+      position: relative;
+      overflow: hidden;
+    }
+
+    .demo-card.pulse {
+      animation: demoPulse 0.85s ease;
+    }
+
+    @keyframes demoPulse {
+      0% { box-shadow: 0 0 0 rgba(37, 99, 235, 0.0); }
+      30% { box-shadow: 0 0 0 6px rgba(37, 99, 235, 0.12); }
+      100% { box-shadow: 0 0 0 rgba(37, 99, 235, 0.0); }
+    }
+
+    .demo-card h4 {
+      margin: 0 0 8px;
+      font-size: 13px;
+    }
+
+    .demo-card .hint {
+      font-size: 12px;
+    }
+
+    .car-canvas {
+      position: relative;
+      height: 168px;
+      border-radius: 12px;
+      background: linear-gradient(180deg, #dbeafe 0%, #eff6ff 38%, #f8fafc 38%, #f8fafc 100%);
+      overflow: hidden;
+      margin-top: 10px;
+    }
+
+    .car-body-shape {
+      position: absolute;
+      left: 20px;
+      right: 20px;
+      bottom: 22px;
+      height: 64px;
+      background: linear-gradient(180deg, #1d4ed8 0%, #1e40af 100%);
+      border-radius: 18px 18px 14px 14px;
+    }
+
+    .car-roof {
+      position: absolute;
+      left: 58px;
+      right: 58px;
+      bottom: 76px;
+      height: 44px;
+      background: linear-gradient(180deg, #2563eb 0%, #1d4ed8 100%);
+      border-radius: 18px 18px 8px 8px;
+    }
+
+    .window-frame {
+      position: absolute;
+      left: 74px;
+      right: 74px;
+      bottom: 84px;
+      height: 30px;
+      border-radius: 10px;
+      background: rgba(15, 23, 42, 0.15);
+      overflow: hidden;
+      border: 2px solid rgba(255, 255, 255, 0.5);
+    }
+
+    .window-glass {
+      position: absolute;
+      inset: 0;
+      background: linear-gradient(180deg, rgba(191, 219, 254, 0.95) 0%, rgba(125, 211, 252, 0.88) 100%);
+      transform: translateY(0%);
+      transition: transform 0.75s ease;
+    }
+
+    .window-glass.open {
+      transform: translateY(72%);
+    }
+
+    .wheel {
+      position: absolute;
+      bottom: 10px;
+      width: 26px;
+      height: 26px;
+      border-radius: 50%;
+      background: #0f172a;
+      border: 4px solid #94a3b8;
+    }
+
+    .wheel.left { left: 42px; }
+    .wheel.right { right: 42px; }
+
+    .window-status {
+      margin-top: 10px;
+      font-size: 12px;
+      color: var(--sub);
+    }
+
+    .ac-panel {
+      display: grid;
+      gap: 10px;
+      height: 100%;
+    }
+
+    .ac-unit {
+      margin-top: 6px;
+      border-radius: 14px;
+      background: #0f172a;
+      color: #e2e8f0;
+      min-height: 132px;
+      padding: 14px 12px;
+      display: grid;
+      grid-template-rows: auto auto 1fr;
+      align-items: start;
+    }
+
+    .ac-display {
+      font-size: 30px;
+      font-weight: 700;
+      letter-spacing: 0.02em;
+    }
+
+    .ac-display small {
+      font-size: 13px;
+      color: #93c5fd;
+      margin-left: 4px;
+    }
+
+    .ac-mode {
+      margin-top: 4px;
+      font-size: 12px;
+      color: #94a3b8;
+    }
+
+    .ac-waves {
+      position: relative;
+      display: flex;
+      align-items: center;
+      justify-content: center;
+      gap: 8px;
+      margin-top: 10px;
+    }
+
+    .ac-wave {
+      width: 10px;
+      height: 52px;
+      border-radius: 999px;
+      background: linear-gradient(180deg, rgba(96, 165, 250, 0.18) 0%, rgba(56, 189, 248, 0.2) 100%);
+      opacity: 0.28;
+      transform: scaleY(0.35);
+      transform-origin: center bottom;
+      transition: opacity 0.35s ease, transform 0.35s ease;
+    }
+
+    .ac-waves.active .ac-wave {
+      opacity: 1;
+      animation: acBreeze 1.2s ease-in-out infinite;
+    }
+
+    .ac-waves.active .ac-wave:nth-child(2) { animation-delay: 0.12s; }
+    .ac-waves.active .ac-wave:nth-child(3) { animation-delay: 0.24s; }
+
+    @keyframes acBreeze {
+      0%, 100% { transform: scaleY(0.45); }
+      50% { transform: scaleY(1); }
+    }
+
+    .demo-summary {
+      border-radius: 12px;
+      background: #f8fafc;
+      border: 1px dashed var(--line);
+      padding: 10px 12px;
+      font-size: 12px;
+      line-height: 1.6;
+      color: var(--sub);
+    }
+
+    .code {
+      margin: 0;
+      padding: 12px;
+      border-radius: 12px;
+      background: var(--dark);
+      color: #e2e8f0;
+      overflow: auto;
+      font-size: 12px;
+      line-height: 1.5;
+      min-height: 220px;
+    }
+
+    .sessions {
+      display: flex;
+      flex-direction: column;
+      gap: 8px;
+      max-height: 220px;
+      overflow: auto;
+    }
+
+    .session-item {
+      padding: 10px 12px;
+      border: 1px solid var(--line);
+      border-radius: 12px;
+      cursor: pointer;
+      background: #fff;
+    }
+
+    .session-item.active {
+      border-color: #93c5fd;
+      background: #eff6ff;
+    }
+
+    .session-item .s-top {
+      font-size: 13px;
+      font-weight: 600;
+    }
+
+    .session-item .s-sub {
+      margin-top: 4px;
+      font-size: 12px;
+      color: var(--sub);
+      overflow: hidden;
+      text-overflow: ellipsis;
+      white-space: nowrap;
+    }
+
+    .stage-list {
+      display: flex;
+      flex-direction: column;
+      gap: 10px;
+      max-height: 240px;
+      overflow: auto;
+    }
+
+    .stage-card {
+      border: 1px solid var(--line);
+      border-radius: 12px;
+      padding: 10px 12px;
+      background: #fff;
+    }
+
+    .stage-card.accepted {
+      background: #eff6ff;
+      border-color: #93c5fd;
+    }
+
+    .stage-header {
+      display: flex;
+      justify-content: space-between;
+      gap: 8px;
+      font-size: 13px;
+      font-weight: 600;
+    }
+
+    .stage-meta {
+      margin-top: 6px;
+      font-size: 12px;
+      color: var(--sub);
+      line-height: 1.5;
+    }
+
+    .candidate-list {
+      margin-top: 8px;
+      display: flex;
+      flex-wrap: wrap;
+      gap: 6px;
+    }
+
+    .candidate {
+      padding: 4px 8px;
+      border-radius: 999px;
+      border: 1px solid var(--line);
+      background: #f8fafc;
+      font-size: 12px;
+    }
+
+    .raw-json {
+      margin-top: 8px;
+      padding: 10px 12px;
+      border-radius: 10px;
+      background: #0f172a;
+      color: #e2e8f0;
+      font-size: 12px;
+      line-height: 1.5;
+      overflow: auto;
+      white-space: pre-wrap;
+      word-break: break-word;
+    }
+
+    .planner-summary {
+      font-size: 12px;
+      color: var(--sub);
+      line-height: 1.6;
+    }
+
+    .planner-steps {
+      margin-top: 10px;
+      display: flex;
+      flex-direction: column;
+      gap: 10px;
+      max-height: 280px;
+      overflow: auto;
+    }
+
+    .planner-step {
+      border: 1px solid var(--line);
+      border-radius: 12px;
+      padding: 10px 12px;
+      background: #f8fafc;
+    }
+
+    .planner-step-title {
+      font-size: 13px;
+      font-weight: 600;
+    }
+
+    .planner-step-meta {
+      margin-top: 6px;
+      font-size: 12px;
+      color: var(--sub);
+      line-height: 1.5;
+    }
+
+    .runtime-badges {
+      display: flex;
+      flex-wrap: wrap;
+      gap: 8px;
+      margin-top: 8px;
+    }
+
+    .badge {
+      padding: 5px 10px;
+      border-radius: 999px;
+      font-size: 12px;
+      background: #f8fafc;
+      border: 1px solid var(--line);
+    }
+
+    .ok { color: var(--success); }
+    .warn { color: var(--warn); }
+    .error { color: var(--danger); }
+
+    .process-summary {
+      padding: 10px 12px;
+      border: 1px solid var(--line);
+      border-radius: 12px;
+      background: linear-gradient(180deg, #eff6ff 0%, #ffffff 100%);
+      font-size: 13px;
+      line-height: 1.6;
+      color: #334155;
+      white-space: pre-wrap;
+    }
+
+    .process-timeline {
+      margin-top: 10px;
+      display: flex;
+      flex-direction: column;
+      gap: 10px;
+      max-height: 360px;
+      overflow: auto;
+    }
+
+    .process-step {
+      border: 1px solid var(--line);
+      border-radius: 12px;
+      padding: 12px;
+      background: #fff;
+    }
+
+    .process-step.ok {
+      border-color: #93c5fd;
+      background: #eff6ff;
+    }
+
+    .process-step.warn {
+      border-color: #fcd34d;
+      background: #fffbeb;
+    }
+
+    .process-step.error {
+      border-color: #fca5a5;
+      background: #fef2f2;
+    }
+
+    .process-step-head {
+      display: flex;
+      justify-content: space-between;
+      gap: 10px;
+      align-items: center;
+      font-size: 13px;
+      font-weight: 700;
+    }
+
+    .process-step-status {
+      font-size: 12px;
+      font-weight: 600;
+      color: var(--sub);
+      white-space: nowrap;
+    }
+
+    .process-step-summary {
+      margin-top: 6px;
+      font-size: 13px;
+      line-height: 1.6;
+      color: #111827;
+      white-space: pre-wrap;
+      word-break: break-word;
+    }
+
+    .process-step-detail {
+      margin-top: 8px;
+      display: flex;
+      flex-wrap: wrap;
+      gap: 6px;
+    }
+
+    .process-pill {
+      padding: 4px 8px;
+      border-radius: 999px;
+      font-size: 12px;
+      background: #f8fafc;
+      border: 1px solid var(--line);
+      color: #475569;
+    }
+
+    @media (max-width: 1100px) {
+      body {
+        overflow: auto;
+      }
+
+      .page {
+        grid-template-columns: 1fr;
+        height: auto;
+        overflow: visible;
+        padding: 12px;
+      }
+
+      .chat-panel, .side {
+        height: auto;
+      }
+
+      .controls {
+        grid-template-columns: 1fr;
+      }
+
+      .message {
+        max-width: 92%;
+      }
+    }
+  </style>
+</head>
+<body>
+  <div class="page" id="page">
+    <section class="panel chat-panel">
+      <div>
+        <div class="header">
+          <div>
+            <div class="title">Agent 演示台</div>
+            <div class="sub">Bert-first 意图识别，多轮补槽续跑，右侧展示每轮请求的完整处理链路</div>
+          </div>
+          <div class="header-actions">
+            <div class="hint" id="serverStatus">服务状态：检测中</div>
+            <button class="ghost" id="toggleDebugBtn" type="button">收起调试</button>
+          </div>
+        </div>
+        <div class="controls">
+          <div class="control">
+            <label for="sessionId">Session ID</label>
+            <input id="sessionId" />
+          </div>
+          <div class="control">
+            <label for="userId">User ID</label>
+            <input id="userId" />
+          </div>
+        </div>
+      </div>
+
+      <div class="messages" id="messages"></div>
+
+      <div class="composer">
+        <textarea id="messageInput" placeholder="请输入用户指令，例如：帮我取消订单"></textarea>
+        <div class="composer-actions">
+          <div class="status" id="composerStatus">当前会自动判断是首次请求还是补槽续跑。</div>
+          <div class="btn-row">
+            <button class="ghost" id="copySummaryBtn" type="button">复制摘要</button>
+            <button class="ghost" id="copyJsonBtn" type="button">复制 JSON</button>
+            <button class="ghost" id="newSessionBtn">新建会话</button>
+            <button class="secondary" id="clearBtn">清空聊天</button>
+            <button class="primary" id="sendBtn">发送</button>
+          </div>
+        </div>
+      </div>
+    </section>
+
+    <aside class="panel side">
+      <div class="section">
+        <h3>运行配置</h3>
+        <div class="grid-2">
+          <div class="control">
+            <label for="matcherPipeline">Intent Route</label>
+            <select id="matcherPipeline">
+              <option value="classifier">classifier</option>
+            </select>
+          </div>
+          <div class="control">
+            <label for="classifierBackend">Classifier Backend</label>
+            <select id="classifierBackend">
+              <option value="mock">mock</option>
+              <option value="bert">bert</option>
+              <option value="joint_bert">joint_bert</option>
+              <option value="remote">remote</option>
+            </select>
+          </div>
+          <div class="control">
+            <label for="sessionBackend">Session Backend</label>
+            <select id="sessionBackend">
+              <option value="memory">memory</option>
+              <option value="redis">redis</option>
+            </select>
+          </div>
+          <div class="control">
+            <label>&nbsp;</label>
+            <button class="primary" id="applyRuntimeBtn">应用配置</button>
+          </div>
+        </div>
+        <div class="runtime-badges" id="runtimeBadges"></div>
+        <div class="hint" id="runtimeStatus">配置加载中</div>
+      </div>
+
+      <div class="section">
+        <h3>演示示例</h3>
+        <div class="chips" id="exampleChips"></div>
+      </div>
+
+      <div class="section">
+        <h3>本地会话历史</h3>
+        <div class="toolbar">
+          <div class="hint">浏览器会保存每个 session 的聊天记录和最后一次 workflow。</div>
+          <button class="ghost" id="refreshSessionsBtn">刷新列表</button>
+        </div>
+        <div class="sessions" id="sessionList"></div>
+      </div>
+
+      <div class="section">
+        <h3>当前状态</h3>
+        <div class="kv">
+          <div class="k">Intent</div><div id="currentIntent">-</div>
+          <div class="k">Matcher</div><div id="currentMatchedStage">-</div>
+          <div class="k">Status</div><div id="currentStatus">-</div>
+          <div class="k">Pending</div><div id="pendingSlots">-</div>
+          <div class="k">首响</div><div id="firstLatency">-</div>
+          <div class="k">总耗时</div><div id="totalLatency">-</div>
+          <div class="k">分解</div><div id="timingBreakdown">-</div>
+          <div class="k">Trace ID</div><div id="traceId">-</div>
+        </div>
+      </div>
+
+      <div class="section">
+        <h3>座舱演示</h3>
+        <div class="hint">根据当前执行结果，在右侧模拟车窗开合和空调状态变化，方便演示座舱控制。</div>
+        <div class="cabin-demo">
+          <div class="demo-scene">
+            <div class="demo-card" id="windowCard">
+              <h4>车窗</h4>
+              <div class="car-canvas">
+                <div class="car-roof"></div>
+                <div class="window-frame">
+                  <div class="window-glass" id="windowGlass"></div>
+                </div>
+                <div class="car-body-shape"></div>
+                <div class="wheel left"></div>
+                <div class="wheel right"></div>
+              </div>
+              <div class="window-status" id="windowStatus">当前状态：已关闭</div>
+            </div>
+            <div class="demo-card" id="acCard">
+              <h4>空调</h4>
+              <div class="ac-panel">
+                <div class="ac-unit">
+                  <div class="ac-display"><span id="acTempText">--</span><small id="acTempUnit">°C</small></div>
+                  <div class="ac-mode" id="acModeText">当前状态：关闭</div>
+                  <div class="ac-waves" id="acWaves">
+                    <div class="ac-wave"></div>
+                    <div class="ac-wave"></div>
+                    <div class="ac-wave"></div>
+                  </div>
+                </div>
+              </div>
+            </div>
+          </div>
+          <div class="demo-summary" id="demoStatusText">等待控制指令，右侧将展示座舱演示动画。</div>
+        </div>
+      </div>
+
+      <div class="section">
+        <h3>本轮流程</h3>
+        <div class="hint">按输入、改写、Bert、决策、规划、执行和回复的顺序展示，便于直观看清楚每一步做了什么。</div>
+        <div class="process-summary" id="processSummary">等待一次新的请求。</div>
+        <div class="process-timeline" id="processTimeline"></div>
+        <pre class="raw-json" id="executionProcess">暂无执行过程。</pre>
+      </div>
+
+      <div class="section">
+        <h3>路由调试</h3>
+        <div class="hint">展示每一层 matcher 的候选意图、分数和最终命中原因。</div>
+        <div class="stage-list" id="routingDebugList"></div>
+      </div>
+
+      <div class="section" style="border-bottom: none;">
+        <h3>Planner 调试</h3>
+        <div class="hint">展示云端 planner 的拆分步骤、归一化槽位和原始 JSON。</div>
+        <div class="planner-summary" id="plannerSummary">当前还没有 planner 结果。</div>
+        <div class="planner-summary" id="clauseSummary">当前还没有子句拆解结果。</div>
+        <div class="planner-steps" id="clauseAnalysisList"></div>
+        <div class="planner-steps" id="plannerSteps"></div>
+        <pre class="raw-json" id="plannerRawJson">{}</pre>
+      </div>
+
+      <div class="section" style="border-bottom: none;">
+        <h3>Workflow JSON</h3>
+        <pre class="code" id="workflowJson">{}</pre>
+      </div>
+    </aside>
+  </div>
+
+  <script>
+    const STORAGE_INDEX_KEY = "agent-demo:sessions";
+    const DEBUG_VISIBILITY_KEY = "agent-demo:debug-visible";
+
+    const state = {
+      pendingSlots: [],
+      lastWorkflow: null,
+      lastResponse: null,
+      messages: [],
+      runtime: null,
+      debugVisible: true,
+      cabinDemo: {
+        windowOpen: false,
+        acPower: false,
+        temperature: 24,
+        lastAction: "等待控制指令，右侧将展示座舱演示动画。",
+      },
+    };
+
+    const els = {
+      page: document.getElementById("page"),
+      sessionId: document.getElementById("sessionId"),
+      userId: document.getElementById("userId"),
+      messageInput: document.getElementById("messageInput"),
+      messages: document.getElementById("messages"),
+      sendBtn: document.getElementById("sendBtn"),
+      clearBtn: document.getElementById("clearBtn"),
+      copySummaryBtn: document.getElementById("copySummaryBtn"),
+      copyJsonBtn: document.getElementById("copyJsonBtn"),
+      newSessionBtn: document.getElementById("newSessionBtn"),
+      refreshSessionsBtn: document.getElementById("refreshSessionsBtn"),
+      currentIntent: document.getElementById("currentIntent"),
+      currentMatchedStage: document.getElementById("currentMatchedStage"),
+      currentStatus: document.getElementById("currentStatus"),
+      pendingSlots: document.getElementById("pendingSlots"),
+      firstLatency: document.getElementById("firstLatency"),
+      totalLatency: document.getElementById("totalLatency"),
+      timingBreakdown: document.getElementById("timingBreakdown"),
+      traceId: document.getElementById("traceId"),
+      executionProcess: document.getElementById("executionProcess"),
+      processSummary: document.getElementById("processSummary"),
+      processTimeline: document.getElementById("processTimeline"),
+      windowCard: document.getElementById("windowCard"),
+      acCard: document.getElementById("acCard"),
+      windowGlass: document.getElementById("windowGlass"),
+      windowStatus: document.getElementById("windowStatus"),
+      acWaves: document.getElementById("acWaves"),
+      acTempText: document.getElementById("acTempText"),
+      acTempUnit: document.getElementById("acTempUnit"),
+      acModeText: document.getElementById("acModeText"),
+      demoStatusText: document.getElementById("demoStatusText"),
+      workflowJson: document.getElementById("workflowJson"),
+      routingDebugList: document.getElementById("routingDebugList"),
+      exampleChips: document.getElementById("exampleChips"),
+      serverStatus: document.getElementById("serverStatus"),
+      composerStatus: document.getElementById("composerStatus"),
+      matcherPipeline: document.getElementById("matcherPipeline"),
+      classifierBackend: document.getElementById("classifierBackend"),
+      sessionBackend: document.getElementById("sessionBackend"),
+      applyRuntimeBtn: document.getElementById("applyRuntimeBtn"),
+      runtimeStatus: document.getElementById("runtimeStatus"),
+      runtimeBadges: document.getElementById("runtimeBadges"),
+      sessionList: document.getElementById("sessionList"),
+      plannerSummary: document.getElementById("plannerSummary"),
+      clauseSummary: document.getElementById("clauseSummary"),
+      clauseAnalysisList: document.getElementById("clauseAnalysisList"),
+      plannerSteps: document.getElementById("plannerSteps"),
+      plannerRawJson: document.getElementById("plannerRawJson"),
+      toggleDebugBtn: document.getElementById("toggleDebugBtn"),
+    };
+
+    const demoExamples = [
+      "帮我查询订单 A123456",
+      "帮我取消订单",
+      "订单号是 A654321",
+      "我的订单现在什么情况 A701111",
+      "来点轻音乐",
+      "导航去公司",
+      "把空调调到 22 度",
+      "帮我转人工客服"
+    ];
+
+    function randomId(prefix) {
+      return `${prefix}_${Math.random().toString(36).slice(2, 10)}`;
+    }
+
+    function sessionStorageKey(sessionId) {
+      return `agent-demo:session:${sessionId}`;
+    }
+
+    function getSessionIndex() {
+      try {
+        return JSON.parse(localStorage.getItem(STORAGE_INDEX_KEY) || "[]");
+      } catch (_) {
+        return [];
+      }
+    }
+
+    function saveSessionIndex(index) {
+      localStorage.setItem(STORAGE_INDEX_KEY, JSON.stringify(index.slice(0, 20)));
+    }
+
+    function touchSessionIndex(sessionId, userId) {
+      const index = getSessionIndex().filter((item) => item.session_id !== sessionId);
+      const preview = [...state.messages].reverse().find((item) => item.role === "user")?.text || "暂无消息";
+      index.unshift({
+        session_id: sessionId,
+        user_id: userId,
+        preview,
+        updated_at: new Date().toISOString(),
+      });
+      saveSessionIndex(index);
+    }
+
+    function persistSession() {
+      const sessionId = els.sessionId.value.trim();
+      const userId = els.userId.value.trim();
+      if (!sessionId || !userId) return;
+
+      const payload = {
+        session_id: sessionId,
+        user_id: userId,
+        messages: state.messages.filter((item) => !item.transient),
+        pending_slots: state.pendingSlots,
+        workflow: state.lastWorkflow,
+        response: state.lastResponse,
+      };
+      localStorage.setItem(sessionStorageKey(sessionId), JSON.stringify(payload));
+      touchSessionIndex(sessionId, userId);
+      renderSessionList();
+    }
+
+    function loadSession(sessionId) {
+      const raw = localStorage.getItem(sessionStorageKey(sessionId));
+      if (!raw) {
+        state.messages = [];
+        state.pendingSlots = [];
+        state.lastWorkflow = null;
+        state.lastResponse = null;
+        renderMessages();
+        updateResponsePanel({ pending_slots: [], workflow: {}, intent: "-", status: "-", trace_id: "-" });
+        renderCabinDemo(null, { reset: true });
+        return false;
+      }
+
+      try {
+        const data = JSON.parse(raw);
+        els.sessionId.value = data.session_id || sessionId;
+        els.userId.value = data.user_id || randomId("user");
+        state.messages = Array.isArray(data.messages) ? data.messages : [];
+        state.pendingSlots = Array.isArray(data.pending_slots) ? data.pending_slots : [];
+        state.lastWorkflow = data.workflow || null;
+        state.lastResponse = data.response || null;
+        renderMessages();
+        updateResponsePanel(data.response || { pending_slots: state.pendingSlots, workflow: state.lastWorkflow || {}, intent: "-", status: "-", trace_id: "-" });
+        return true;
+      } catch (_) {
+        return false;
+      }
+    }
+
+    function initIds() {
+      els.sessionId.value = randomId("sess");
+      els.userId.value = randomId("user");
+    }
+
+    function setDebugVisible(visible) {
+      state.debugVisible = visible;
+      els.page.classList.toggle("debug-collapsed", !visible);
+      els.toggleDebugBtn.textContent = visible ? "收起调试" : "显示调试";
+      localStorage.setItem(DEBUG_VISIBILITY_KEY, visible ? "1" : "0");
+    }
+
+    function messageRoleName(role) {
+      if (role === "user") return "你";
+      if (role === "agent") return "助手";
+      return "系统";
+    }
+
+    function addMessage(role, text, metaText = "", detailText = "", options = {}) {
+      state.messages.push({
+        role,
+        text,
+        metaText,
+        detailText,
+        timestamp: new Date().toISOString(),
+        transient: Boolean(options.transient),
+      });
+      renderMessages();
+      persistSession();
+    }
+
+    function clearTransientMessages() {
+      const before = state.messages.length;
+      state.messages = state.messages.filter((item) => !item.transient);
+      if (state.messages.length !== before) {
+        renderMessages();
+        persistSession();
+      }
+    }
+
+    function renderMessages() {
+      els.messages.innerHTML = "";
+      for (const item of state.messages) {
+        const row = document.createElement("div");
+        row.className = `message-row ${item.role}`;
+        if (item.role !== "system") {
+          const head = document.createElement("div");
+          head.className = "message-head";
+          head.innerHTML = `<span class="message-name">${messageRoleName(item.role)}</span>`;
+          row.appendChild(head);
+        }
+        const div = document.createElement("div");
+        div.className = `message ${item.role}`;
+        div.textContent = item.text;
+        if (item.detailText) {
+          const note = document.createElement("div");
+          note.className = "message-note";
+          note.textContent = item.detailText;
+          div.appendChild(note);
+        }
+        const metaParts = [];
+        if (item.timestamp) {
+          metaParts.push(new Date(item.timestamp).toLocaleTimeString());
+        }
+        if (item.metaText) {
+          metaParts.push(item.metaText);
+        }
+        if (metaParts.length) {
+          const meta = document.createElement("div");
+          meta.className = "meta";
+          meta.textContent = metaParts.join(" | ");
+          div.appendChild(meta);
+        }
+        row.appendChild(div);
+        els.messages.appendChild(row);
+      }
+      requestAnimationFrame(() => {
+        els.messages.scrollTop = els.messages.scrollHeight;
+      });
+    }
+
+    function getRewriteStage(routingDebug) {
+      if (!routingDebug || !Array.isArray(routingDebug.stages)) {
+        return null;
+      }
+      return routingDebug.stages.find((stage) => stage.stage === "rewrite") || null;
+    }
+
+    function buildRewriteNote(routingDebug) {
+      const rewriteStage = getRewriteStage(routingDebug);
+      const meta = rewriteStage?.metadata || {};
+      const original = String(meta.original_text || "").trim();
+      const rewritten = String(meta.rewritten_text || "").trim();
+      if (!rewriteStage?.accepted || !original || !rewritten || original === rewritten) {
+        return "";
+      }
+      return `按上下文理解为：${rewritten}`;
+    }
+
+    function formatLatency(value) {
+      return typeof value === "number" && Number.isFinite(value) ? `${(value / 1000).toFixed(3)} s` : "-";
+    }
+
+    function formatBreakdown(breakdown) {
+      if (!breakdown || typeof breakdown !== "object") {
+        return "-";
+      }
+      const parts = Object.entries(breakdown)
+        .filter(([, value]) => typeof value === "number" && Number.isFinite(value))
+        .sort((a, b) => b[1] - a[1])
+        .map(([key, value]) => `${key}=${(value / 1000).toFixed(3)}s`);
+      return parts.length ? parts.join(" | ") : "-";
+    }
+
+    function defaultCabinDemoState() {
+      return {
+        windowOpen: false,
+        acPower: false,
+        temperature: 24,
+        lastAction: "等待控制指令，右侧将展示座舱演示动画。",
+      };
+    }
+
+    function animateDemoCard(el) {
+      if (!el) return;
+      el.classList.remove("pulse");
+      void el.offsetWidth;
+      el.classList.add("pulse");
+    }
+
+    function extractTemperature(value) {
+      if (typeof value === "number" && Number.isFinite(value)) {
+        return Math.round(value);
+      }
+      if (typeof value === "string") {
+        const match = value.match(/(\d{2})/);
+        if (match) return Number(match[1]);
+      }
+      return null;
+    }
+
+    function applyCabinIntent(next, intentId, slots = {}) {
+      let changed = false;
+      if (intentId === "cabin_window_open") {
+        changed = changed || !next.windowOpen;
+        next.windowOpen = true;
+        next.lastAction = "已演示车窗打开动画。";
+      } else if (intentId === "cabin_window_close") {
+        changed = changed || next.windowOpen;
+        next.windowOpen = false;
+        next.lastAction = "已演示车窗关闭动画。";
+      } else if (intentId === "cabin_ac_on") {
+        changed = changed || !next.acPower;
+        next.acPower = true;
+        next.lastAction = "已演示空调启动送风。";
+      } else if (intentId === "cabin_ac_off") {
+        changed = changed || next.acPower;
+        next.acPower = false;
+        next.lastAction = "已演示空调关闭。";
+      } else if (intentId === "cabin_set_ac") {
+        const nextTemp = extractTemperature(slots.temperature);
+        changed = changed || !next.acPower;
+        next.acPower = true;
+        if (nextTemp !== null && next.temperature !== nextTemp) {
+          changed = true;
+          next.temperature = nextTemp;
+        }
+        next.lastAction = `已演示空调调到 ${next.temperature} 度。`;
+      }
+      return changed;
+    }
+
+    function deriveCabinDemoState(data) {
+      const next = { ...state.cabinDemo };
+      let windowChanged = false;
+      let acChanged = false;
+      const workflowSteps = Array.isArray(data.workflow?.steps) ? data.workflow.steps : [];
+      const actionableSteps = workflowSteps.filter((step) => step && step.status === "completed");
+
+      if (actionableSteps.length) {
+        for (const step of actionableSteps) {
+          const changed = applyCabinIntent(next, step.intent_id, step.slots || {});
+          if (step.intent_id?.startsWith("cabin_window_")) windowChanged = windowChanged || changed;
+          if (step.intent_id?.startsWith("cabin_ac") || step.intent_id === "cabin_set_ac") acChanged = acChanged || changed;
+        }
+      } else if ((data.reply_type === "workflow_result" || data.status === "completed") && data.intent) {
+        const changed = applyCabinIntent(next, data.intent, data.filled_slots || {});
+        if (data.intent.startsWith("cabin_window_")) windowChanged = changed;
+        if (data.intent.startsWith("cabin_ac") || data.intent === "cabin_set_ac") acChanged = changed;
+      }
+
+      if (windowChanged || acChanged) {
+        return { next, windowChanged, acChanged };
+      }
+      return { next: state.cabinDemo, windowChanged: false, acChanged: false };
+    }
+
+    function renderCabinDemo(data, options = {}) {
+      const { reset = false } = options;
+      if (reset) {
+        state.cabinDemo = defaultCabinDemoState();
+      } else if (data) {
+        const derived = deriveCabinDemoState(data);
+        state.cabinDemo = derived.next;
+        if (derived.windowChanged) animateDemoCard(els.windowCard);
+        if (derived.acChanged) animateDemoCard(els.acCard);
+      }
+
+      const demo = state.cabinDemo;
+      els.windowGlass.classList.toggle("open", demo.windowOpen);
+      els.windowStatus.textContent = `当前状态：${demo.windowOpen ? "已打开" : "已关闭"}`;
+      els.acWaves.classList.toggle("active", demo.acPower);
+      els.acTempText.textContent = demo.acPower ? String(demo.temperature) : "--";
+      els.acTempUnit.textContent = demo.acPower ? "°C" : "";
+      els.acModeText.textContent = demo.acPower ? `当前状态：送风中，目标 ${demo.temperature} 度` : "当前状态：关闭";
+      els.demoStatusText.textContent = demo.lastAction;
+    }
+
+    function latestUserMessage() {
+      return [...state.messages].reverse().find((item) => item.role === "user")?.text || "-";
+    }
+
+    function buildExecutionProcessText(data) {
+      const routingDebug = data.routing_debug || {};
+      const stages = Array.isArray(routingDebug.stages) ? routingDebug.stages : [];
+      const plannerStage = getPlannerStage(routingDebug);
+      const plannerMeta = plannerStage?.metadata || {};
+      const clauseAnalysis = Array.isArray(plannerMeta.clause_analysis) ? plannerMeta.clause_analysis : [];
+      const lines = [];
+      lines.push(`输入: ${latestUserMessage()}`);
+      lines.push(`回复: ${data.reply_text || "-"}`);
+      lines.push(`意图: ${data.intent || "-"} | 回复类型: ${data.reply_type || "-"} | 状态: ${data.status || "-"}`);
+      lines.push(`首响: ${formatLatency(data.first_response_latency_ms)} | 总耗时: ${formatLatency(data.total_latency_ms)}`);
+      if (data.processing_breakdown && Object.keys(data.processing_breakdown).length) {
+        lines.push(`处理分解: ${formatBreakdown(data.processing_breakdown)}`);
+      }
+      lines.push("");
+      lines.push("阶段过程:");
+      if (!stages.length) {
+        lines.push("- 暂无阶段数据");
+        return lines.join("\n");
+      }
+      stages.forEach((stage, index) => {
+        const bits = [];
+        bits.push(`${index + 1}. ${stage.stage}`);
+        bits.push(stage.accepted ? "accepted" : "pass");
+        if (typeof stage.elapsed_ms === "number") {
+          bits.push(formatLatency(stage.elapsed_ms));
+        }
+        if (stage.selected_intent) {
+          bits.push(`intent=${stage.selected_intent}`);
+        }
+        if (stage.backend) {
+          bits.push(`backend=${stage.backend}`);
+        }
+        if (stage.model_name) {
+          bits.push(`model=${stage.model_name}`);
+        }
+        if (stage.reason) {
+          bits.push(stage.reason);
+        }
+        lines.push(`- ${bits.join(" | ")}`);
+      });
+      if (clauseAnalysis.length) {
+        lines.push("");
+        lines.push("子句拆解:");
+        clauseAnalysis.forEach((item, index) => {
+          const candidates = Array.isArray(item?.candidates) ? item.candidates : [];
+          const candidateText = candidates.length
+            ? candidates.map((candidate) => {
+                const candidateScore = typeof candidate.score === "number" ? candidate.score.toFixed(3) : "-";
+                return `${candidate.intent_id}(${candidateScore})`;
+              }).join(", ")
+            : "-";
+          lines.push(
+            `- ${index + 1}. text=${item?.clause_text || "-"} | intent=${item?.selected_intent_id || "-"} | score=${
+              typeof item?.score === "number" ? item.score.toFixed(3) : "-"
+            } | slots=${JSON.stringify(item?.slots || {})} | candidates=${candidateText}`
+          );
+        });
+      }
+      return lines.join("\n");
+    }
+
+    function buildExportSummary() {
+      const data = state.lastResponse || {};
+      const runtime = state.runtime || {};
+      const plannerStage = getPlannerStage(data.routing_debug || {});
+      const plannerMeta = plannerStage?.metadata || {};
+      const clauseAnalysis = Array.isArray(plannerMeta.clause_analysis) ? plannerMeta.clause_analysis : [];
+      const normalizedSteps = Array.isArray(plannerMeta.normalized_steps) ? plannerMeta.normalized_steps : [];
+      return [
+        "## 测试结果",
+        `- session_id: ${els.sessionId.value.trim() || "-"}`,
+        `- user_id: ${els.userId.value.trim() || "-"}`,
+        `- pipeline: ${runtime.matcher_pipeline || "-"}`,
+        `- classifier: ${runtime.classifier_backend || "-"}`,
+        `- planner: ${runtime.planner_backend || "-"}`,
+        `- 最新输入: ${latestUserMessage()}`,
+        `- 最新回复: ${data.reply_text || "-"}`,
+        `- intent: ${data.intent || "-"}`,
+        `- status: ${data.status || "-"}`,
+        `- 首响: ${formatLatency(data.first_response_latency_ms)}`,
+        `- 总耗时: ${formatLatency(data.total_latency_ms)}`,
+        `- trace_id: ${data.trace_id || "-"}`,
+        `- multi_intent_detected: ${plannerMeta.multi_intent_detected ? "yes" : "no"}`,
+        `- clause_count: ${clauseAnalysis.length}`,
+        `- planner_steps: ${normalizedSteps.length}`,
+        "",
+        "```text",
+        buildExecutionProcessText(data),
+        "```",
+      ].join("\n");
+    }
+
+    function buildExportJson() {
+      return JSON.stringify({
+        session_id: els.sessionId.value.trim(),
+        user_id: els.userId.value.trim(),
+        runtime: state.runtime,
+        last_response: state.lastResponse,
+        messages_tail: state.messages.slice(-8),
+      }, null, 2);
+    }
+
+    async function copyTextToClipboard(text, successText) {
+      try {
+        await navigator.clipboard.writeText(text);
+        addMessage("system", successText);
+      } catch (err) {
+        addMessage("system", `复制失败：${err.message}`);
+      }
+    }
+
+    async function readNdjsonStream(response, onEvent) {
+      if (!response.body) {
+        throw new Error("流式响应不可用");
+      }
+      const reader = response.body.getReader();
+      const decoder = new TextDecoder("utf-8");
+      let buffer = "";
+      while (true) {
+        const { value, done } = await reader.read();
+        if (done) break;
+        buffer += decoder.decode(value, { stream: true });
+        const lines = buffer.split("\n");
+        buffer = lines.pop() || "";
+        for (const line of lines) {
+          const trimmed = line.trim();
+          if (!trimmed) continue;
+          onEvent(JSON.parse(trimmed));
+        }
+      }
+      const tail = buffer.trim();
+      if (tail) {
+        onEvent(JSON.parse(tail));
+      }
+    }
+
+    function buildAgentMeta(data) {
+      const statusMap = {
+        completed: "已完成",
+        waiting_slot: "待补槽",
+        waiting_confirmation: "待确认",
+        clarify: "待澄清",
+        rejected: "已拒识",
+        fallback: "回退",
+      };
+      const metaParts = [];
+      if (statusMap[data.status]) {
+        metaParts.push(statusMap[data.status]);
+      }
+      if (data.reply_type === "reject") {
+        metaParts.push("超出当前能力范围");
+      } else if (data.reply_type === "clarify") {
+        metaParts.push("需要你再确认一下");
+      }
+      if (typeof data.first_response_latency_ms === "number") {
+        metaParts.push(`首响 ${formatLatency(data.first_response_latency_ms)}`);
+      }
+      if (typeof data.total_latency_ms === "number") {
+        metaParts.push(`总耗时 ${formatLatency(data.total_latency_ms)}`);
+      }
+      return metaParts.join(" | ");
+    }
+
+    function formatObjectSummary(value) {
+      if (!value || typeof value !== "object" || Array.isArray(value)) {
+        return "-";
+      }
+      const entries = Object.entries(value);
+      if (!entries.length) {
+        return "{}";
+      }
+      return entries.map(([key, item]) => `${key}=${typeof item === "object" ? JSON.stringify(item) : item}`).join(" | ");
+    }
+
+    function getLatestUserMessage() {
+      for (let index = state.messages.length - 1; index >= 0; index -= 1) {
+        const item = state.messages[index];
+        if (item.role === "user") {
+          return item.text || "";
+        }
+      }
+      return "";
+    }
+
+    function createProcessStep(step) {
+      const block = document.createElement("div");
+      block.className = `process-step ${step.tone || ""}`.trim();
+
+      const head = document.createElement("div");
+      head.className = "process-step-head";
+      head.innerHTML = `<span>${step.title}</span><span class="process-step-status">${step.status || ""}</span>`;
+      block.appendChild(head);
+
+      const summary = document.createElement("div");
+      summary.className = "process-step-summary";
+      summary.textContent = step.summary || "-";
+      block.appendChild(summary);
+
+      const details = Array.isArray(step.details) ? step.details.filter(Boolean) : [];
+      if (details.length) {
+        const detailWrap = document.createElement("div");
+        detailWrap.className = "process-step-detail";
+        for (const item of details) {
+          const pill = document.createElement("div");
+          pill.className = "process-pill";
+          pill.textContent = item;
+          detailWrap.appendChild(pill);
+        }
+        block.appendChild(detailWrap);
+      }
+      return block;
+    }
+
+    function formatCandidateList(candidates) {
+      if (!Array.isArray(candidates) || !candidates.length) {
+        return "无候选意图";
+      }
+      return candidates
+        .slice(0, 3)
+        .map((candidate) => {
+          const score = typeof candidate.score === "number" ? candidate.score.toFixed(3) : "-";
+          return `${candidate.intent_id}(${score})`;
+        })
+        .join(" | ");
+    }
+
+    function formatWorkflowExecutionSummary(workflowSteps, replyType) {
+      if (!Array.isArray(workflowSteps) || !workflowSteps.length) {
+        if (replyType === "ask_slot") {
+          return "当前进入待补槽阶段，执行尚未开始。";
+        }
+        if (replyType === "ask_confirmation") {
+          return "当前进入待确认阶段，等待用户确认后执行。";
+        }
+        return "当前没有可展示的 workflow 执行步骤。";
+      }
+      return workflowSteps
+        .map((step, index) => `${index + 1}. ${step.intent_id || step.action || "unknown"} -> ${step.status || "pending"}`)
+        .join("\n");
+    }
+
+    function renderProcessFlow(data) {
+      const routingDebug = data.routing_debug || {};
+      const stages = Array.isArray(routingDebug.stages) ? routingDebug.stages : [];
+      const rewriteStage = getRewriteStage(routingDebug);
+      const classifierStage = stages.find((stage) => stage.stage === "classifier") || null;
+      const fusionStage = stages.find((stage) => stage.stage === "fusion") || null;
+      const plannerStage = getPlannerStage(routingDebug);
+      const latestUserText = getLatestUserMessage();
+      const workflow = data.workflow || {};
+      const workflowSteps = Array.isArray(workflow.steps) ? workflow.steps : [];
+      const extractedSlots = routingDebug.extracted_slots || {};
+      const filledSlots = data.filled_slots || {};
+      const responseDecision = data.decision || routingDebug.decision || "-";
+      const steps = [];
+
+      steps.push({
+        title: "1. 用户输入",
+        status: "received",
+        tone: "",
+        summary: latestUserText || "当前没有可展示的输入文本",
+        details: [
+          data.reply_type ? `reply_type=${data.reply_type}` : "",
+          data.status ? `status=${data.status}` : "",
+        ],
+      });
+
+      const rewriteMeta = rewriteStage?.metadata || {};
+      const original = String(rewriteMeta.original_text || "").trim();
+      const rewritten = String(rewriteMeta.rewritten_text || "").trim();
+      const changed = Boolean(rewriteStage?.accepted && original && rewritten && original !== rewritten);
+      steps.push({
+        title: "2. 上下文改写",
+        status: changed ? "rewritten" : "no rewrite",
+        tone: changed ? "ok" : "",
+        summary: changed ? `${original}\n-> ${rewritten}` : "当前没有发生上下文改写，直接使用原始输入进入 Bert。",
+        details: [
+          typeof rewriteStage?.elapsed_ms === "number" ? `latency=${formatLatency(rewriteStage.elapsed_ms)}` : "",
+          rewriteStage?.reason || "",
+        ],
+      });
+
+      steps.push({
+        title: "3. Bert 意图识别",
+        status: classifierStage ? (classifierStage.accepted ? "accepted" : "below threshold") : "missing",
+        tone: classifierStage ? (classifierStage.accepted ? "ok" : "warn") : "error",
+        summary: classifierStage
+          ? (classifierStage.selected_intent
+              ? `${classifierStage.selected_intent} (${(classifierStage.score || 0).toFixed(3)})\n候选: ${formatCandidateList(classifierStage.candidates)}`
+              : `当前没有足够高置信的本地意图\n候选: ${formatCandidateList(classifierStage.candidates)}`)
+          : "当前响应里没有 classifier 阶段数据。",
+        details: [
+          classifierStage?.backend ? `backend=${classifierStage.backend}` : "",
+          classifierStage?.raw_label ? `raw_label=${classifierStage.raw_label}` : "",
+          classifierStage?.metadata?.top_margin !== undefined ? `top_margin=${classifierStage.metadata.top_margin}` : "",
+          typeof classifierStage?.elapsed_ms === "number" ? `latency=${formatLatency(classifierStage.elapsed_ms)}` : "",
+        ],
+      });
+
+      const fusionDecision = fusionStage?.metadata?.decision || responseDecision;
+      steps.push({
+        title: "4. 本地决策",
+        status: fusionDecision,
+        tone: fusionDecision === "execute" ? "ok" : (fusionDecision === "reject" ? "error" : "warn"),
+        summary: fusionStage
+          ? `${fusionStage.reason || data.decision_reason || "无决策说明"}\n候选: ${formatCandidateList(fusionStage.candidates)}`
+          : (data.decision_reason || "当前响应里没有 fusion 决策数据。"),
+        details: [
+          fusionStage?.selected_intent ? `selected=${fusionStage.selected_intent}` : "",
+          fusionStage?.metadata?.classifier_signal !== undefined ? `signal=${fusionStage.metadata.classifier_signal}` : "",
+          fusionStage?.metadata?.classifier_margin !== undefined ? `margin=${fusionStage.metadata.classifier_margin}` : "",
+          typeof fusionStage?.elapsed_ms === "number" ? `latency=${formatLatency(fusionStage.elapsed_ms)}` : "",
+        ],
+      });
+
+      const normalizedSteps = Array.isArray(plannerStage?.metadata?.normalized_steps) ? plannerStage.metadata.normalized_steps : workflowSteps;
+      const clauseAnalysis = Array.isArray(plannerStage?.metadata?.clause_analysis) ? plannerStage.metadata.clause_analysis : [];
+      steps.push({
+        title: "5. Planner / Workflow",
+        status: plannerStage ? (plannerStage.accepted ? "planner used" : "planner reviewed") : "not used",
+        tone: plannerStage ? "ok" : "",
+        summary: normalizedSteps.length
+          ? normalizedSteps.map((item, index) => `${index + 1}. ${item.intent_id || item.action || "unknown"}`).join("\n")
+          : "当前没有进入 planner，直接按单意图链路继续处理。",
+        details: [
+          plannerStage?.backend ? `backend=${plannerStage.backend}` : "",
+          workflow.workflow_type ? `workflow=${workflow.workflow_type}` : "",
+          workflowSteps.length ? `steps=${workflowSteps.length}` : "",
+          clauseAnalysis.length ? `clauses=${clauseAnalysis.length}` : "",
+        ],
+      });
+
+      const mergedSlots = { ...extractedSlots, ...filledSlots };
+      steps.push({
+        title: "6. 槽位与执行",
+        status: workflowSteps.length ? "workflow executed" : (Object.keys(mergedSlots).length ? "slots ready" : "no slots"),
+        tone: workflowSteps.length || Object.keys(mergedSlots).length ? "ok" : "",
+        summary: [
+          Object.keys(mergedSlots).length
+            ? `槽位: ${formatObjectSummary(mergedSlots)}`
+            : "当前没有提取到可展示的槽位，或该意图无需槽位。",
+          formatWorkflowExecutionSummary(workflowSteps, data.reply_type),
+        ].join("\n"),
+        details: [
+          Object.keys(extractedSlots).length ? `router_extract=${formatObjectSummary(extractedSlots)}` : "",
+          Object.keys(filledSlots).length ? `filled=${formatObjectSummary(filledSlots)}` : "",
+          data.pending_slots?.length ? `pending=${data.pending_slots.join(", ")}` : "",
+        ],
+      });
+
+      steps.push({
+        title: "7. 最终回复",
+        status: data.reply_type || "-",
+        tone: data.reply_type === "reject" ? "error" : (data.reply_type === "clarify" ? "warn" : "ok"),
+        summary: data.reply_text || "无回复文本",
+        details: [
+          data.intent ? `intent=${data.intent}` : "",
+          typeof data.first_response_latency_ms === "number" ? `first=${formatLatency(data.first_response_latency_ms)}` : "",
+          typeof data.total_latency_ms === "number" ? `total=${formatLatency(data.total_latency_ms)}` : "",
+        ],
+      });
+
+      els.processTimeline.innerHTML = "";
+      steps.forEach((step) => els.processTimeline.appendChild(createProcessStep(step)));
+      els.processSummary.textContent = [
+        `本轮输入: ${latestUserText || "-"}`,
+        `最终决策: ${responseDecision}`,
+        `最终意图: ${data.intent || routingDebug.selected_intent || "-"}`,
+        `响应类型: ${data.reply_type || "-"}`,
+        `首响 / 总耗时: ${formatLatency(data.first_response_latency_ms)} / ${formatLatency(data.total_latency_ms)}`,
+      ].join("\n");
+    }
+
+    function renderRoutingDebug(routingDebug) {
+      els.routingDebugList.innerHTML = "";
+      if (!routingDebug || !Array.isArray(routingDebug.stages) || !routingDebug.stages.length) {
+        const empty = document.createElement("div");
+        empty.className = "hint";
+        empty.textContent = "当前还没有 matcher 调试信息，发起一次聊天请求后会显示。";
+        els.routingDebugList.appendChild(empty);
+        return;
+      }
+
+      for (const stage of routingDebug.stages) {
+        const block = document.createElement("div");
+        block.className = `stage-card ${stage.accepted ? "accepted" : ""}`;
+
+        const header = document.createElement("div");
+        header.className = "stage-header";
+        header.innerHTML = `
+          <span>${stage.stage}</span>
+          <span>${stage.accepted ? "accepted" : "pass"}</span>
+        `;
+        block.appendChild(header);
+
+        const meta = document.createElement("div");
+        const parts = [];
+        if (stage.selected_intent) {
+          parts.push(`intent=${stage.selected_intent}`);
+        }
+        if (typeof stage.score === "number" && stage.score > 0) {
+          parts.push(`score=${stage.score.toFixed(3)}`);
+        }
+        if (typeof stage.elapsed_ms === "number") {
+          parts.push(`latency=${formatLatency(stage.elapsed_ms)}`);
+        }
+        if (stage.model_name) {
+          parts.push(`decision=${stage.model_name}`);
+        }
+        if (stage.backend) {
+          parts.push(`backend=${stage.backend}`);
+        }
+        if (stage.fallback_used) {
+          parts.push("fallback=yes");
+        }
+        if (stage.raw_label) {
+          parts.push(`raw_label=${stage.raw_label}`);
+        }
+        if (stage.reason) {
+          parts.push(stage.reason);
+        }
+        if (stage.metadata && stage.metadata.decision) {
+          parts.push(`decision=${stage.metadata.decision}`);
+        }
+        if (stage.metadata && stage.metadata.grade) {
+          parts.push(`grade=${stage.metadata.grade}`);
+        }
+        if (stage.metadata && stage.metadata.unknown_detected) {
+          parts.push("unknown=yes");
+        }
+        if (stage.error_message) {
+          parts.push(`error=${stage.error_message}`);
+        }
+        meta.className = "stage-meta";
+        meta.textContent = parts.join(" | ") || "无调试说明";
+        block.appendChild(meta);
+
+        if (Array.isArray(stage.candidates) && stage.candidates.length) {
+          const candidateList = document.createElement("div");
+          candidateList.className = "candidate-list";
+          for (const candidate of stage.candidates) {
+            const item = document.createElement("div");
+            item.className = "candidate";
+            const score = typeof candidate.score === "number" ? candidate.score.toFixed(3) : "0.000";
+            const rawLabel = candidate.raw_label && candidate.raw_label !== candidate.intent_id
+              ? ` / ${candidate.raw_label}`
+              : "";
+            item.textContent = `${candidate.intent_id}${rawLabel} (${score})`;
+            candidateList.appendChild(item);
+          }
+          block.appendChild(candidateList);
+        }
+
+        if (stage.metadata && Object.keys(stage.metadata).length) {
+          const raw = document.createElement("pre");
+          raw.className = "raw-json";
+          raw.textContent = JSON.stringify(stage.metadata, null, 2);
+          block.appendChild(raw);
+        }
+
+        els.routingDebugList.appendChild(block);
+      }
+    }
+
+    function getPlannerStage(routingDebug) {
+      if (!routingDebug || !Array.isArray(routingDebug.stages)) {
+        return null;
+      }
+      return routingDebug.stages.find((stage) => stage.stage === "planner") || null;
+    }
+
+    function renderClauseAnalysis(clauseAnalysis, multiIntentDetected) {
+      els.clauseAnalysisList.innerHTML = "";
+      if (!Array.isArray(clauseAnalysis) || !clauseAnalysis.length) {
+        els.clauseSummary.textContent = "当前还没有子句拆解结果。";
+        return;
+      }
+
+      const matchedCount = clauseAnalysis.filter((item) => item && item.selected_intent_id).length;
+      els.clauseSummary.textContent = [
+        `clauses=${clauseAnalysis.length}`,
+        `matched=${matchedCount}`,
+        `multi_intent=${multiIntentDetected ? "yes" : "no"}`,
+      ].join(" | ");
+
+      clauseAnalysis.forEach((item, index) => {
+        const block = document.createElement("div");
+        block.className = "planner-step";
+
+        const title = document.createElement("div");
+        title.className = "planner-step-title";
+        const selected = item?.selected_intent_id || "未命中";
+        const score = typeof item?.score === "number" ? item.score.toFixed(3) : "-";
+        title.textContent = `子句 #${index + 1} | ${selected} | score=${score}`;
+        block.appendChild(title);
+
+        const stepMeta = document.createElement("div");
+        stepMeta.className = "planner-step-meta";
+        const parts = [];
+        if (item?.clause_text) {
+          parts.push(`text=${item.clause_text}`);
+        }
+        if (item?.reason) {
+          parts.push(`reason=${item.reason}`);
+        }
+        if (item?.slots && Object.keys(item.slots).length) {
+          parts.push(`slots=${JSON.stringify(item.slots)}`);
+        }
+        const candidates = Array.isArray(item?.candidates) ? item.candidates : [];
+        if (candidates.length) {
+          parts.push(`candidates=${candidates.map((candidate) => {
+            const candidateScore = typeof candidate.score === "number" ? candidate.score.toFixed(3) : "-";
+            return `${candidate.intent_id}(${candidateScore})`;
+          }).join(", ")}`);
+        }
+        stepMeta.textContent = parts.join(" | ") || "无子句调试信息";
+        block.appendChild(stepMeta);
+        els.clauseAnalysisList.appendChild(block);
+      });
+    }
+
+    function renderPlannerDebug(routingDebug, workflow) {
+      const plannerStage = getPlannerStage(routingDebug);
+      els.clauseAnalysisList.innerHTML = "";
+      els.clauseSummary.textContent = "当前还没有子句拆解结果。";
+      els.plannerSteps.innerHTML = "";
+      els.plannerRawJson.textContent = "{}";
+
+      if (!plannerStage) {
+        els.plannerSummary.textContent = "当前请求没有触发 planner，多命令或复杂请求时这里会展示云端拆分结果。";
+        return;
+      }
+
+      const meta = plannerStage.metadata || {};
+      renderClauseAnalysis(meta.clause_analysis, Boolean(meta.multi_intent_detected));
+      const workflowType = meta.workflow_type || workflow?.workflow_type || "-";
+      const reason = plannerStage.reason || "无说明";
+      els.plannerSummary.textContent = [
+        `accepted=${plannerStage.accepted ? "yes" : "no"}`,
+        `backend=${plannerStage.backend || "-"}`,
+        `model=${plannerStage.model_name || "-"}`,
+        `workflow=${workflowType}`,
+        `latency=${formatLatency(plannerStage.elapsed_ms)}`,
+        reason,
+      ].join(" | ");
+
+      const normalizedSteps = Array.isArray(meta.normalized_steps) ? meta.normalized_steps : [];
+      const stepItems = normalizedSteps.length
+        ? normalizedSteps
+        : (Array.isArray(plannerStage.candidates) ? plannerStage.candidates.map((item) => ({
+            intent_id: item.intent_id,
+            normalized_slots: item.metadata?.slots || {},
+            reason: item.reason || "",
+          })) : []);
+
+      if (!stepItems.length) {
+        const empty = document.createElement("div");
+        empty.className = "hint";
+        empty.textContent = "planner 已触发，但当前没有可展示的 step。";
+        els.plannerSteps.appendChild(empty);
+      }
+
+      stepItems.forEach((item, index) => {
+        const block = document.createElement("div");
+        block.className = "planner-step";
+        const title = document.createElement("div");
+        title.className = "planner-step-title";
+        title.textContent = `#${index + 1} ${item.intent_id || "unknown"}`;
+        block.appendChild(title);
+
+        const stepMeta = document.createElement("div");
+        stepMeta.className = "planner-step-meta";
+        const parts = [];
+        if (item.clause_text) {
+          parts.push(`clause=${item.clause_text}`);
+        }
+        if (item.reason) {
+          parts.push(`reason=${item.reason}`);
+        }
+        if (Array.isArray(item.depends_on) && item.depends_on.length) {
+          parts.push(`depends_on=${JSON.stringify(item.depends_on)}`);
+        }
+        if (item.condition && Object.keys(item.condition).length) {
+          parts.push(`condition=${JSON.stringify(item.condition)}`);
+        }
+        if (item.requires_confirmation) {
+          parts.push("confirm=yes");
+        }
+        const slots = item.normalized_slots || item.slots || {};
+        if (Object.keys(slots).length) {
+          parts.push(`slots=${JSON.stringify(slots, null, 0)}`);
+        } else {
+          parts.push("slots={}");
+        }
+        if (item.cloud_slots && Object.keys(item.cloud_slots).length) {
+          parts.push(`cloud=${JSON.stringify(item.cloud_slots, null, 0)}`);
+        }
+        if (item.clause_slots && Object.keys(item.clause_slots).length) {
+          parts.push(`clause_extract=${JSON.stringify(item.clause_slots, null, 0)}`);
+        }
+        stepMeta.textContent = parts.join(" | ");
+        block.appendChild(stepMeta);
+        els.plannerSteps.appendChild(block);
+      });
+
+      const rawJson = meta.parsed_plan || meta.raw_response || workflow?.meta?.planner_debug?.parsed_plan || {};
+      if (typeof rawJson === "string") {
+        try {
+          els.plannerRawJson.textContent = JSON.stringify(JSON.parse(rawJson), null, 2);
+        } catch (_) {
+          els.plannerRawJson.textContent = rawJson;
+        }
+      } else {
+        els.plannerRawJson.textContent = JSON.stringify(rawJson, null, 2);
+      }
+    }
+
+    function updateResponsePanel(data) {
+      state.pendingSlots = Array.isArray(data.pending_slots) ? data.pending_slots : [];
+      state.lastWorkflow = data.workflow || null;
+      state.lastResponse = data;
+      const routingDebug = data.routing_debug || {};
+
+      els.currentIntent.textContent = data.intent || "-";
+      const currentStage = routingDebug.matched_stage || "-";
+      const currentDecision = routingDebug.decision ? ` / ${routingDebug.decision}` : "";
+      els.currentMatchedStage.textContent = `${currentStage}${currentDecision}`;
+      els.currentStatus.textContent = data.status || "-";
+      els.pendingSlots.textContent = state.pendingSlots.length ? state.pendingSlots.join(", ") : "-";
+      els.firstLatency.textContent = formatLatency(data.first_response_latency_ms);
+      els.totalLatency.textContent = formatLatency(data.total_latency_ms);
+      els.timingBreakdown.textContent = formatBreakdown(data.processing_breakdown);
+      els.traceId.textContent = data.trace_id || "-";
+      els.executionProcess.textContent = buildExecutionProcessText(data);
+      renderProcessFlow(data);
+      els.workflowJson.textContent = JSON.stringify(data.workflow || {}, null, 2);
+      renderCabinDemo(data);
+      renderRoutingDebug(routingDebug);
+      renderPlannerDebug(routingDebug, data.workflow || {});
+
+      if (state.pendingSlots.length) {
+        els.composerStatus.textContent = `等待补槽: ${state.pendingSlots.join(", ")}，下一条消息会走 fill-slots。`;
+      } else {
+        els.composerStatus.textContent = "当前会自动判断是首次请求还是补槽续跑。";
+      }
+      persistSession();
+    }
+
+    function renderRuntimeBadges(runtime) {
+      els.runtimeBadges.innerHTML = "";
+      const badges = [
+        `intent_route=bert-first`,
+        `pipeline=${runtime.matcher_pipeline}`,
+        `classifier=${runtime.classifier_backend}`,
+        `session=${runtime.session_backend}`,
+        `slot=${runtime.slot_extractor_backend}`,
+        `planner=${runtime.planner_backend}`,
+        `planner_model=${runtime.planner_model_name || "-"}`,
+      ];
+      for (const item of badges) {
+        const badge = document.createElement("div");
+        badge.className = "badge";
+        badge.textContent = item;
+        els.runtimeBadges.appendChild(badge);
+      }
+    }
+
+    async function fetchRuntimeConfig() {
+      const res = await fetch("/api/v1/demo/runtime");
+      const runtime = await res.json();
+      state.runtime = runtime;
+      els.matcherPipeline.value = "classifier";
+      els.classifierBackend.value = runtime.classifier_backend;
+      els.sessionBackend.value = runtime.session_backend;
+      renderRuntimeBadges(runtime);
+      els.runtimeStatus.textContent = "当前页面和后端运行配置已同步。";
+    }
+
+    async function applyRuntimeConfig() {
+      const payload = {
+        matcher_pipeline: "classifier",
+        classifier_backend: els.classifierBackend.value,
+        session_backend: els.sessionBackend.value,
+      };
+      els.applyRuntimeBtn.disabled = true;
+      els.runtimeStatus.textContent = "正在应用配置...";
+      try {
+        const res = await fetch("/api/v1/demo/runtime", {
+          method: "POST",
+          headers: { "Content-Type": "application/json" },
+          body: JSON.stringify(payload),
+        });
+        const data = await res.json();
+        if (!res.ok) {
+          throw new Error(data.detail || "配置切换失败");
+        }
+        state.runtime = data;
+        renderRuntimeBadges(data);
+        els.runtimeStatus.textContent = "配置已应用，后续请求将使用新的运行时设置。";
+        addMessage("system", `演示配置已切换：${data.matcher_pipeline} / ${data.classifier_backend} / ${data.session_backend}`);
+      } catch (err) {
+        els.runtimeStatus.textContent = `配置切换失败：${err.message}`;
+      } finally {
+        els.applyRuntimeBtn.disabled = false;
+      }
+    }
+
+    async function sendMessage() {
+      const text = els.messageInput.value.trim();
+      if (!text) return;
+
+      const isFillSlots = state.pendingSlots.length > 0;
+      addMessage("user", text, isFillSlots ? "继续当前任务" : "");
+      els.messageInput.value = "";
+
+      const endpoint = isFillSlots ? "/api/v1/agent/fill-slots" : "/api/v1/agent/chat";
+      const streamEndpoint = "/api/v1/agent/chat-stream";
+      const payload = isFillSlots
+        ? {
+            session_id: els.sessionId.value.trim(),
+            user_id: els.userId.value.trim(),
+            input_text: text,
+          }
+        : {
+            session_id: els.sessionId.value.trim(),
+            user_id: els.userId.value.trim(),
+            channel: "demo-web",
+            input_text: text,
+            input_type: "text",
+          };
+
+      els.sendBtn.disabled = true;
+      try {
+        if (!isFillSlots) {
+          const streamRes = await fetch(streamEndpoint, {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify(payload),
+          });
+          if (!streamRes.ok) {
+            throw new Error(`流式请求失败: ${streamRes.status}`);
+          }
+          let finalData = null;
+          await readNdjsonStream(streamRes, (event) => {
+            if (!event || typeof event !== "object") return;
+            if (event.type === "ack") {
+              clearTransientMessages();
+              addMessage("agent", event.reply_text || "好的，正在处理中，请稍等一下。", "处理中", "", { transient: true });
+              return;
+            }
+            if (event.type === "final") {
+              finalData = event.data || null;
+              return;
+            }
+            if (event.type === "error") {
+              throw new Error(event.message || "流式处理失败");
+            }
+          });
+          if (!finalData) {
+            throw new Error("流式响应未返回最终结果");
+          }
+          clearTransientMessages();
+          addMessage(
+            "agent",
+            finalData.reply_text || "无返回文本",
+            buildAgentMeta(finalData),
+            buildRewriteNote(finalData.routing_debug),
+          );
+          updateResponsePanel(finalData);
+          return;
+        }
+
+        const res = await fetch(endpoint, {
+          method: "POST",
+          headers: { "Content-Type": "application/json" },
+          body: JSON.stringify(payload),
+        });
+        const data = await res.json();
+        if (!res.ok) {
+          throw new Error(data.detail || "请求失败");
+        }
+        addMessage(
+          "agent",
+          data.reply_text || "无返回文本",
+          buildAgentMeta(data),
+          buildRewriteNote(data.routing_debug),
+        );
+        updateResponsePanel(data);
+      } catch (err) {
+        addMessage("system", `请求失败：${err.message}`);
+      } finally {
+        els.sendBtn.disabled = false;
+        els.messageInput.focus();
+      }
+    }
+
+    function clearChat() {
+      state.messages = [];
+      state.pendingSlots = [];
+      state.lastWorkflow = null;
+      state.lastResponse = null;
+      persistSession();
+      renderMessages();
+      updateResponsePanel({ pending_slots: [], workflow: {}, intent: "-", status: "-", trace_id: "-" });
+      renderCabinDemo(null, { reset: true });
+      addMessage("system", "聊天记录已清空，当前 session 保持不变。");
+    }
+
+    function newSession() {
+      initIds();
+      state.messages = [];
+      state.pendingSlots = [];
+      state.lastWorkflow = null;
+      state.lastResponse = null;
+      renderMessages();
+      updateResponsePanel({ pending_slots: [], workflow: {}, intent: "-", status: "-", trace_id: "-" });
+      renderCabinDemo(null, { reset: true });
+      addMessage("system", "已创建新的演示会话。");
+      persistSession();
+    }
+
+    function renderExamples() {
+      els.exampleChips.innerHTML = "";
+      for (const text of demoExamples) {
+        const chip = document.createElement("button");
+        chip.className = "chip";
+        chip.textContent = text;
+        chip.addEventListener("click", () => {
+          els.messageInput.value = text;
+          els.messageInput.focus();
+        });
+        els.exampleChips.appendChild(chip);
+      }
+    }
+
+    function renderSessionList() {
+      const index = getSessionIndex();
+      const currentSessionId = els.sessionId.value.trim();
+      els.sessionList.innerHTML = "";
+      if (!index.length) {
+        const empty = document.createElement("div");
+        empty.className = "hint";
+        empty.textContent = "还没有本地历史 session，发几条消息后会出现在这里。";
+        els.sessionList.appendChild(empty);
+        return;
+      }
+
+      for (const item of index) {
+        const block = document.createElement("div");
+        block.className = `session-item ${item.session_id === currentSessionId ? "active" : ""}`;
+        block.innerHTML = `
+          <div class="s-top">${item.session_id}</div>
+          <div class="s-sub">${item.preview || "暂无消息"}</div>
+          <div class="s-sub">${item.user_id} · ${new Date(item.updated_at).toLocaleString()}</div>
+        `;
+        block.addEventListener("click", () => {
+          if (loadSession(item.session_id)) {
+            renderSessionList();
+            addMessage("system", `已恢复本地会话 ${item.session_id}`);
+          }
+        });
+        els.sessionList.appendChild(block);
+      }
+    }
+
+    async function checkHealth() {
+      try {
+        const res = await fetch("/health");
+        const data = await res.json();
+        els.serverStatus.textContent = `服务状态：${data.status} / ${data.env}`;
+      } catch (_) {
+        els.serverStatus.textContent = "服务状态：连接失败";
+      }
+    }
+
+    function bindEvents() {
+      els.sendBtn.addEventListener("click", sendMessage);
+      els.clearBtn.addEventListener("click", clearChat);
+      els.copySummaryBtn.addEventListener("click", () => copyTextToClipboard(buildExportSummary(), "已复制测试摘要，可直接粘贴给我。"));
+      els.copyJsonBtn.addEventListener("click", () => copyTextToClipboard(buildExportJson(), "已复制测试 JSON，可直接粘贴给我。"));
+      els.newSessionBtn.addEventListener("click", newSession);
+      els.refreshSessionsBtn.addEventListener("click", renderSessionList);
+      els.applyRuntimeBtn.addEventListener("click", applyRuntimeConfig);
+      els.toggleDebugBtn.addEventListener("click", () => setDebugVisible(!state.debugVisible));
+      els.sessionId.addEventListener("change", () => {
+        if (!loadSession(els.sessionId.value.trim())) {
+          state.messages = [];
+          renderMessages();
+          updateResponsePanel({ pending_slots: [], workflow: {}, intent: "-", status: "-", trace_id: "-" });
+          renderCabinDemo(null, { reset: true });
+          persistSession();
+        }
+        renderSessionList();
+      });
+      els.userId.addEventListener("change", persistSession);
+      els.messageInput.addEventListener("keydown", (event) => {
+        if ((event.metaKey || event.ctrlKey) && event.key === "Enter") {
+          sendMessage();
+        }
+      });
+    }
+
+    async function init() {
+      const storedDebugVisible = localStorage.getItem(DEBUG_VISIBILITY_KEY);
+      setDebugVisible(storedDebugVisible === null ? window.innerWidth > 1280 : storedDebugVisible === "1");
+      initIds();
+      renderExamples();
+      bindEvents();
+      await checkHealth();
+      await fetchRuntimeConfig();
+      addMessage("system", "演示页面已就绪。输入一条用户指令开始体验。");
+      updateResponsePanel({ pending_slots: [], workflow: {}, intent: "-", status: "-", trace_id: "-" });
+      renderCabinDemo(null, { reset: true });
+      renderSessionList();
+    }
+
+    init();
+  </script>
+</body>
+</html>
diff --git a/intelligent_cabin/archive/docs/current_system_flow.md b/intelligent_cabin/archive/docs/current_system_flow.md
new file mode 100644
index 0000000..73a655b
--- /dev/null
+++ b/intelligent_cabin/archive/docs/current_system_flow.md
@@ -0,0 +1,1722 @@
+# 当前项目完整流程说明
+
+## 1. 文档目标
+
+本文档描述当前项目在代码层面的真实运行流程，不是目标态蓝图，而是“现在这个仓库实际如何工作”。
+
+覆盖范围包括：
+
+- 服务启动与运行时装配
+- API 请求入口与返回结构
+- 会话状态管理
+- 输入改写
+- 本地路由与多阶段融合
+- 社交闲聊分流
+- planner 触发与多步骤 workflow 生成
+- 单步执行、多步执行、条件执行、确认执行
+- 配置驱动加载方式
+- 当前使用的技术、模型、阈值、分支条件
+- 主要风险点与当前边界
+
+---
+
+## 2. 系统定位
+
+当前项目是一个面向“智能座舱 + 客服”的执行型 Agent 服务，后端基于 FastAPI，核心特点如下：
+
+- 配置驱动：意图、动作、响应模板、表单、规则、workflow 模板都来自 `config/`
+- 本地优先：优先走 `rewrite -> keyword/classifier/retrieval -> fusion`
+- planner 不是默认入口：只有复杂句、多意图、条件句、低置信句才触发 planner
+- 会话显式维护：多轮状态不依赖 LLM 记忆，而依赖 `SessionState`
+- 社交闲聊单独分流：问候、感谢、能力问答、开放闲聊走 `SocialRouter`
+- 插件执行统一抽象：所有意图最终映射到 `plugin_id`
+
+---
+
+## 3. 总体架构图
+
+```mermaid
+flowchart TD
+    A[HTTP 请求 /api/v1/agent/chat] --> B[AgentService.handle_chat]
+    B --> C[SessionStore get_or_create]
+    C --> D[Dialog Act 更新]
+    D --> E{是否停止当前任务}
+    E -->|是| F[停止任务并返回 stopped]
+    E -->|否| G{是否社交闲聊}
+    G -->|是| H[SocialRouter + SocialResponder]
+    G -->|否| I[ContextRewriteEngine 改写]
+    I --> J[Router.route]
+
+    J --> J1[keyword matcher]
+    J --> J2[classifier matcher]
+    J --> J3[retrieval matcher]
+    J1 --> J4[fusion 决策]
+    J2 --> J4
+    J3 --> J4
+
+    J4 --> K{是否需要 planner}
+    K -->|否| L{fusion 决策结果}
+    K -->|是| M[WorkflowPlanner.plan]
+
+    M --> M1[TemplateWorkflowPlanner]
+    M --> M2[HeuristicWorkflowPlanner]
+    M --> M3[DashScopeWorkflowPlanner 可选]
+    M1 --> N{planner accepted?}
+    M2 --> N
+    M3 --> N
+
+    N -->|是| O[构建 Workflow 并执行]
+    N -->|否| L
+
+    L -->|execute| P[槽位提取]
+    P --> Q{缺槽位?}
+    Q -->|是| R[ask_slot]
+    Q -->|否| S[执行单步插件]
+    L -->|clarify| T[clarify]
+    L -->|reject| U[reject]
+    L -->|route_to_cloud| V[clarify / fallback / reject]
+
+    O --> W{多步流程继续执行}
+    W -->|缺槽位| X[ask_slot]
+    W -->|需确认| Y[ask_confirmation]
+    W -->|条件不满足| Z[skip step]
+    W -->|可执行| AA[调用插件]
+    AA --> AB[workflow_summary]
+```
+
+---
+
+## 4. 启动与装配流程
+
+### 4.1 入口
+
+应用入口是 `app/main.py`，FastAPI 在 import 阶段完成以下事情：
+
+1. 初始化 `FastAPI(title=settings.app_name)`
+2. 初始化 demo runtime 配置
+3. 调用 `build_agent_service_with_runtime(...)`
+4. 调用 `build_intent_registry()`
+5. 暴露 `/health`、`/demo`、`/api/v1/agent/chat`、`/api/v1/agent/chat-stream`、`/api/v1/agent/fill-slots`
+
+### 4.2 运行时装配
+
+`build_agent_service_with_runtime()` 会组装完整执行链：
+
+1. `ConfigLoader.load()` 读取配置文件
+2. 构建 `IntentRegistry`
+3. 构建 classifier
+4. 构建 multi-intent detector
+5. 构建 matcher pipeline 和 router
+6. 构建 session store
+7. 构建 `ResponsePolicy`
+8. 构建 `DialogRuleEngine`
+9. 构建 `DialogActEngine`
+10. 构建 planner
+11. 注册 mock plugin
+12. 构建 `SocialRouter` 和 `DashScopeSocialResponder`
+13. 最终实例化 `AgentService`
+
+### 4.3 启动装配图
+
+```mermaid
+flowchart TD
+    A[build_agent_service_with_runtime] --> B[ConfigLoader.load]
+    B --> C[IntentRegistry]
+    B --> D[Response templates]
+    B --> E[Dialog rules]
+    B --> F[Dialog acts]
+    B --> G[Workflow templates]
+
+    A --> H[build_classifier]
+    H --> H1[MockIntentClassifier]
+    H --> H2[BertIntentClassifier]
+    H --> H3[RemoteIntentClassifier]
+
+    A --> I[build_multi_intent_detector]
+    I --> I1[BertMultiIntentDetector]
+
+    A --> J[build_router]
+    J --> J1[build_matcher_pipeline]
+    J --> J2[HeuristicSlotExtractor]
+
+    A --> K[build_session_store]
+    K --> K1[InMemorySessionStore]
+    K --> K2[RedisSessionStore]
+
+    A --> L[build_planner]
+    L --> L1[TemplateWorkflowPlanner]
+    L --> L2[HeuristicWorkflowPlanner]
+    L --> L3[DashScopeWorkflowPlanner]
+
+    A --> M[MockPluginExecutor.register]
+    A --> N[SocialRouter]
+    A --> O[DashScopeSocialResponder]
+    A --> P[AgentService]
+```
+
+---
+
+## 5. 配置驱动体系
+
+当前运行时主要依赖这些配置文件：
+
+- `config/domain.yml`
+- `config/actions.yml`
+- `config/forms.yml`
+- `config/responses.yml`
+- `config/rules.yml`
+- `config/dialog_acts.yml`
+- `config/workflows.yml`
+
+### 5.1 各配置文件职责
+
+`domain.yml`
+
+- 定义意图目录
+- 定义 `intent_id`
+- 定义领域 `domain`
+- 定义 label、keywords、examples
+- 指向 `action_id`
+
+`actions.yml`
+
+- 将 `action_id` 绑定到 `plugin_id`
+- 指定风险等级、描述等动作元信息
+
+`forms.yml`
+
+- 定义每个意图需要的 `required_slots`
+- 定义槽位缺失时的 `ask_templates`
+
+`responses.yml`
+
+- 定义系统通用话术模板
+- 包含 ask / ack / reject / fallback / confirm 等模板
+
+`rules.yml`
+
+- 定义停止词
+- 定义确认正负词
+- 定义哪些 intent 或哪些 risk level 必须确认
+
+`dialog_acts.yml`
+
+- 定义 `affirm / deny / cancel / modify / chitchat / request / inform`
+
+`workflows.yml`
+
+- 定义局部固定 workflow 模板
+- 当前已有顺序模板和条件模板
+
+### 5.2 加载流程
+
+```mermaid
+flowchart LR
+    A[ConfigLoader] --> B[domain.yml]
+    A --> C[actions.yml]
+    A --> D[forms.yml]
+    A --> E[responses.yml]
+    A --> F[rules.yml]
+    A --> G[dialog_acts.yml]
+    A --> H[workflows.yml]
+
+    B --> I[DomainConfig]
+    C --> J[ActionsConfig]
+    D --> K[FormsConfig]
+    E --> L[ResponsesConfig]
+    F --> M[DialogRulesConfig]
+    G --> N[DialogActsConfig]
+    H --> O[WorkflowTemplatesConfig]
+
+    I --> P[IntentRegistry]
+    J --> P
+    K --> P
+    L --> Q[ResponsePolicy]
+    M --> R[DialogRuleEngine]
+    N --> S[DialogActEngine]
+    O --> T[WorkflowPlanner]
+```
+
+---
+
+## 6. 请求入口层
+
+### 6.1 `/api/v1/agent/chat`
+
+这是主同步接口：
+
+1. 接收 `ChatRequest`
+2. 直接调用 `agent_service.handle_chat(request)`
+3. 返回 `ChatResponse`
+
+### 6.2 `/api/v1/agent/chat-stream`
+
+这是流式接口：
+
+1. 用线程池异步执行 `agent_service.handle_chat`
+2. 如果 1 秒内拿到结果，直接返回 `final`
+3. 如果 1 秒内没拿到结果：
+   - 先检查 `_should_emit_processing_hint(text)`
+   - 如果命中工具型关键词，就先发一个 `ack`
+   - 再等待最终结果
+4. 最终以 NDJSON 流输出
+
+注意：
+
+- 这里的流式 `ack` 仍然是“基于输入文本 token 的启发式判断”
+- 它并不等于“真实插件已开始执行”
+- 这是 HTTP streaming 层的反馈策略，不是 AgentService 内部 workflow 执行层
+
+### 6.3 `/api/v1/agent/fill-slots`
+
+这是补槽位和确认续跑接口：
+
+1. 根据 `session_id` 取已有 session
+2. 若没有有效 session 或没有 `current_intent`，返回 fallback
+3. 如果当前在 `waiting_confirmation`，优先处理确认
+4. 如果当前是多步 workflow，继续推进 workflow
+5. 否则按当前 intent 继续抽槽并补全
+
+---
+
+## 7. Session 设计
+
+### 7.1 SessionState 字段
+
+`SessionState` 当前包含：
+
+- `session_id`
+- `user_id`
+- `channel`
+- `status`
+- `current_intent`
+- `pending_slots`
+- `slots`
+- `workflow`
+- `routing_debug`
+- `last_user_text`
+- `last_agent_text`
+- `context_memory`
+
+### 7.2 状态含义
+
+当前项目中常见状态：
+
+- `idle`
+- `understanding`
+- `waiting_slot`
+- `waiting_confirmation`
+- `running`
+- `completed`
+- `rejected`
+- `fallback`
+- `stopped`
+- `social`
+
+### 7.3 Session Backend
+
+支持两种 session backend：
+
+- `memory`
+- `redis`
+
+`memory`
+
+- 进程内字典保存
+- 重启丢失
+- 适合本地开发
+
+`redis`
+
+- JSON 序列化保存
+- key 带前缀和 TTL
+- 适合跨进程和长会话
+
+### 7.4 Session 状态图
+
+```mermaid
+stateDiagram-v2
+    [*] --> idle
+    idle --> understanding: 收到普通任务
+    idle --> social: 收到社交闲聊
+    understanding --> waiting_slot: 缺槽位
+    understanding --> ready_to_execute: 槽位齐全
+    ready_to_execute --> completed: 单步插件执行完成
+    ready_to_execute --> running: 多步 workflow 开始
+    running --> waiting_slot: 某一步缺槽位
+    running --> waiting_confirmation: 某一步需确认
+    waiting_slot --> running: 用户补槽后继续
+    waiting_confirmation --> running: 用户确认继续
+    waiting_confirmation --> completed: 用户取消该步后流程继续或结束
+    understanding --> rejected: reject
+    understanding --> fallback: fallback
+    running --> stopped: 用户停止
+    completed --> idle: 下轮新任务
+```
+
+---
+
+## 8. AgentService 主流程
+
+`AgentService.handle_chat()` 是整个服务的核心编排器。
+
+### 8.1 主链路顺序
+
+1. 读取或创建 session
+2. 更新 dialog act
+3. 检查是否停止任务
+4. 检查是否社交闲聊
+5. 运行 rewrite
+6. 路由匹配
+7. 判断是否要启 planner
+8. 如果 planner 接受，直接走多步 workflow
+9. 如果 planner 不接受，则按 fusion 决策处理
+10. 如果决策是 execute，则抽槽并执行
+11. 记录 turn
+12. 保存 session
+13. 填充 latency breakdown
+
+### 8.2 主流程图
+
+```mermaid
+flowchart TD
+    A[handle_chat] --> B[get_or_create session]
+    B --> C[update dialog act]
+    C --> D{stop request?}
+    D -->|yes| E[reset active task + return stopped]
+    D -->|no| F{social turn?}
+    F -->|yes| G[build social response]
+    F -->|no| H[rewrite]
+    H --> I[router.route]
+    I --> J{should use planner?}
+    J -->|yes| K[planner.plan]
+    K --> L{accepted and has steps?}
+    L -->|yes| M[start planned workflow]
+    L -->|no| N[handle route decision]
+    J -->|no| N
+    N -->|has response| O[clarify / reject / route_to_cloud]
+    N -->|none| P{intent found?}
+    P -->|no| Q[fallback]
+    P -->|yes| R[extract slots]
+    R --> S[update session + context memory]
+    S --> T[build response from session]
+    T --> U[record turn + save session + finalize]
+```
+
+### 8.3 Timing 统计
+
+当前 `ChatResponse` 会附带处理耗时细分，典型字段包括：
+
+- `session_get_or_create_ms`
+- `dialog_act_ms`
+- `stop_check_ms`
+- `social_route_ms`
+- `rewrite_ms`
+- `route_ms`
+- `planner_ms`
+- `decision_response_ms`
+- `slot_extract_ms`
+- `response_build_ms`
+- `record_turn_ms`
+- `session_save_ms`
+- `match_pipeline_ms`
+- `first_response_latency_ms`
+- `total_latency_ms`
+
+---
+
+## 9. Dialog Act 与停止分支
+
+### 9.1 Dialog Act
+
+系统会在请求一开始调用 `DialogActEngine.detect(text)`，当前 act 包括：
+
+- `affirm`
+- `deny`
+- `cancel`
+- `modify`
+- `chitchat`
+- `request`
+- `inform`
+- `unknown`
+
+当前 dialog act 的用途主要是：
+
+- 保存会话理解上下文
+- 给后续确认/修改/闲聊处理提供辅助信号
+
+### 9.2 停止分支
+
+停止请求由 `DialogRuleEngine.is_stop_request()` 判断，典型词包括：
+
+- 不用了
+- 算了
+- 先这样吧
+- 停一下
+- 停止
+- 结束这次操作
+
+只有满足以下两个条件才会真正停止：
+
+1. 输入命中 stop phrase
+2. session 当前有 active task
+
+命中后动作：
+
+- reset active task
+- 清空 pending_slots
+- workflow 置空
+- 返回 `status=stopped`
+
+---
+
+## 10. 社交闲聊分支
+
+### 10.1 为什么先做社交分流
+
+社交闲聊与任务型请求的目标不同：
+
+- 任务型请求要落到 intent / slot / workflow / plugin
+- 社交闲聊只需要自然回复，不应误触发任务执行
+
+### 10.2 SocialRouter 路由逻辑
+
+`SocialRouter.route(text, session)` 流程如下：
+
+1. 归一化文本
+2. 如果看起来像任务型请求，直接返回 `category=none`
+3. 如果命中短社交模式，返回 `open_social`
+4. 如果命中 capability regex，返回 `open_social`
+5. 如果命中开放闲聊短语或 regex，返回 `open_social`
+6. 如果上一轮是 `open_social` 且当前文本很短，作为闲聊续接
+7. 否则不是社交
+
+### 10.3 社交分支的两个层次
+
+第一层：`SocialRouter`
+
+- 只判断要不要走社交链路
+
+第二层：`DashScopeSocialResponder`
+
+- 真正生成自然语言回复
+- 使用 DashScope 兼容 OpenAI 的 `/chat/completions`
+- `temperature=0.6`
+- `max_tokens=120`
+- 系统提示词要求：
+  - 简短
+  - 口语化
+  - 不编造已执行动作
+  - 不输出 JSON
+
+### 10.4 社交回复的补充逻辑
+
+如果当前 session 还有未完成任务：
+
+- `ResponsePolicy.pending_task_hint()` 会追加提示
+- 例如还在等确认、还在等槽位、还在运行中
+
+因此社交回复不一定纯闲聊，可能附带任务续接提示
+
+### 10.5 社交流程图
+
+```mermaid
+flowchart TD
+    A[用户输入] --> B[SocialRouter.route]
+    B --> C{looks_like_task?}
+    C -->|yes| D[回主任务链路]
+    C -->|no| E{命中社交模式?}
+    E -->|no| D
+    E -->|yes| F{是否配置 social responder?}
+    F -->|no| G[open_social_fallback]
+    F -->|yes| H[DashScopeSocialResponder.reply]
+    H --> I{返回为空或报错?}
+    I -->|yes| G
+    I -->|no| J[自然回复]
+    G --> K[附加 pending task hint]
+    J --> K
+    K --> L[返回 social/text response]
+```
+
+---
+
+## 11. Rewrite 流程
+
+### 11.1 Rewrite 的定位
+
+`ContextRewriteEngine` 不是通用 LLM 改写器，而是轻量、本地、规则型短句恢复器。
+
+目的：
+
+- 把“再低一点”“下一首”“不要高速”这种短句补成完整命令
+- 让下一轮仍然能命中本地快链路
+- 减少每轮都走 planner 或云端
+
+### 11.2 当前支持的 rewrite 场景
+
+空调 follow-up
+
+- 当前 intent 属于空调相关：
+  - `cabin_set_ac`
+  - `cabin_ac_on`
+  - `cabin_ac_off`
+  - `cabin_fan_up`
+  - `cabin_fan_down`
+- 输入匹配“高一点 / 低一点 / 调高一点 / 调低一点”
+- 根据 `context_memory.last_temperature` 或 `session.slots.temperature`
+- 生成显式句式：
+  - `把空调调到 21 度`
+
+音乐 follow-up
+
+- 当前 intent 为 `cabin_play_music`
+- 输入匹配：
+  - 再来一首
+  - 换一首
+  - 下一首
+- 改写为：
+  - `播放下一首歌`
+
+导航 follow-up
+
+- 当前 intent 为 `cabin_nav_to`
+- 如果输入含“不要高速”
+- 且 session 中能取到上次目的地
+- 改写为：
+  - `导航去 xxx，不要高速`
+
+### 11.3 Rewrite 分支图
+
+```mermaid
+flowchart TD
+    A[原始输入] --> B{current_intent}
+    B -->|空调相关| C[_rewrite_ac_adjustment]
+    B -->|播放音乐| D[_rewrite_music_followup]
+    B -->|导航| E[_rewrite_navigation_followup]
+    B -->|其他| F[不改写]
+    C --> G{命中规则?}
+    D --> G
+    E --> G
+    G -->|yes| H[输出 rewritten_text + metadata]
+    G -->|no| F
+```
+
+---
+
+## 12. Router 与本地多阶段匹配
+
+### 12.1 Router 组成
+
+当前 Router 是 `RuleBasedRouter`，内部包含：
+
+- `matcher`: `MultiStageIntentMatcher`
+- `slot_extractor`: `HeuristicSlotExtractor`
+
+### 12.2 Matcher Pipeline
+
+可配置的匹配阶段：
+
+- `keyword`
+- `classifier`
+- `retrieval`
+- `llm` 占位，当前未实现
+
+默认项目原则上使用：
+
+- `keyword,classifier,retrieval`
+
+### 12.3 各阶段职责
+
+`KeywordIntentMatcher`
+
+- 遍历每个 intent 的 keywords
+- 命中即给 1.0 分
+- 候选 reason 是 `keyword:xxx`
+
+`ClassifierIntentMatcher`
+
+- 调用 `IntentClassifier.predict`
+- 可能接后端：
+  - `mock`
+  - `bert`
+  - `remote`
+- 输出 top-k candidates
+- 附带 raw_label、raw_candidates、top_margin、fallback_reason
+
+`RetrievalIntentMatcher`
+
+- 对输入做 token / n-gram 切分
+- 与 intent 的 keywords + examples 做重叠评分
+- 分数来源：
+  - overlap
+  - coverage
+  - keyword bonus
+
+`LlmIntentMatcher`
+
+- 当前是占位实现
+- 永远返回 not implemented
+
+### 12.4 Fusion 评分机制
+
+fusion 会聚合所有 stage 的候选：
+
+- keyword 权重：`1.15`
+- classifier 权重：`1.0`
+- retrieval 权重：`0.75`
+- llm 权重：`1.1`
+
+额外规则：
+
+- 某 stage accepted 且 selected_intent 命中，会加 accepted bonus
+- classifier 若走 fallback，会减分
+- BERT classifier 会用不同归一化规则
+- fusion 最终得到 ranked intents
+
+### 12.5 Fusion 决策阈值
+
+核心阈值来自环境变量：
+
+- `local_execute_threshold = 1.65`
+- `local_route_to_cloud_threshold = 0.75`
+- `local_clarify_margin_threshold = 0.12`
+- `local_classifier_execute_score_threshold = 0.55`
+- `local_classifier_execute_margin_threshold = 0.18`
+
+### 12.6 Fusion 决策逻辑
+
+`execute`
+
+满足任一：
+
+- `top_score >= execute_threshold`
+- 至少两路 accepted 支持，或有强 symbolic support
+- 纯 BERT 路径但分数和 margin 足够高
+
+`clarify`
+
+满足：
+
+- top_score 介于 `route_to_cloud_threshold` 和 `execute_threshold`
+- top_margin 小于 clarify margin
+- 存在多候选竞争
+
+`route_to_cloud`
+
+满足：
+
+- 本地信号说明“像已知意图”
+- 但不够稳定执行
+
+`reject`
+
+满足：
+
+- 本地没有足够信号
+- 且不认为是已知能力内请求
+
+### 12.7 Router 总流程图
+
+```mermaid
+flowchart TD
+    A[rewrite 后文本] --> B[keyword]
+    A --> C[classifier]
+    A --> D[retrieval]
+    B --> E[fusion]
+    C --> E
+    D --> E
+
+    E --> F{top_score / margin / support}
+    F -->|高置信| G[decision=execute]
+    F -->|接近但歧义| H[decision=clarify]
+    F -->|像已知意图但不稳| I[decision=route_to_cloud]
+    F -->|未知或过低| J[decision=reject]
+```
+
+### 12.8 Routing Debug
+
+每次 route 都会构造 `RoutingDebug`，包括：
+
+- `selected_intent`
+- `matched_stage`
+- `decision`
+- `decision_reason`
+- `confidence_grade`
+- `unknown_detected`
+- `stages`
+- `total_match_latency_ms`
+
+每个 stage 又包含：
+
+- accepted
+- selected_intent
+- score
+- reason
+- model_name
+- backend
+- fallback_used
+- raw_label
+- error_message
+- metadata
+- candidates
+
+这也是 demo 面板能展示详细匹配过程的原因。
+
+---
+
+## 13. Classifier 技术细节
+
+### 13.1 支持的 classifier backend
+
+`mock`
+
+- 本地规则化 token overlap stub
+- 主要用于开发或 fallback
+
+`bert`
+
+- 使用 `transformers.pipeline("text-classification")`
+- 模型目录来自 `AGENT_CLASSIFIER_MODEL_PATH`
+- 支持 label map
+- 支持 warmup
+
+`remote`
+
+- 请求远端分类服务
+- 传入 text、top_k、labels
+- 返回 intent_id / score / raw candidates
+
+### 13.2 BERT classifier 行为
+
+本地 BERT 分类器：
+
+- 初始化时不一定立刻加载模型，但 `build_classifier()` 会按配置触发 warmup
+- warmup 默认文本：`打开车窗`
+- 输出 top-k candidates
+- 分数低于阈值时可 fallback 到 mock
+
+### 13.3 当前模型相关配置
+
+- `classifier_backend`
+- `classifier_model_path`
+- `classifier_label_map_path`
+- `classifier_top_k`
+- `classifier_bert_threshold`
+- `classifier_warmup_enabled`
+- `classifier_warmup_text`
+
+---
+
+## 14. Multi-Intent Detector 技术细节
+
+### 14.1 当前 detector 的角色
+
+当前多标签 detector 不是 planner 主入口，而是 planner 二阶段中的先验信号。
+
+它的职责是：
+
+- 从整句维度给出可能共现的多个 intent
+- 生成 `detector_prior`
+- 在 clause classifier 融合时增强多意图解析
+
+### 14.2 当前实现方式
+
+`BertMultiIntentDetector`
+
+- 加载独立多标签模型目录
+- 运行 `AutoTokenizer` + `AutoModelForSequenceClassification`
+- 对 logits 做 sigmoid
+- 用 threshold 过滤
+- 屏蔽：
+  - `__social__`
+  - `__out_of_scope__`
+- 最多输出 `max_labels`
+
+### 14.3 detector 配置
+
+- `planner_multi_intent_detector_enabled`
+- `planner_multi_intent_detector_model_path`
+- `planner_multi_intent_detector_threshold`
+- `planner_multi_intent_detector_top_k`
+- `planner_multi_intent_detector_max_labels`
+
+---
+
+## 15. Planner 触发机制
+
+### 15.1 何时调用 planner
+
+`AgentService._should_use_planner()` 当前规则：
+
+满足任一就会进入 planner：
+
+1. 文本包含复杂连接词：
+   - 然后
+   - 并且
+   - 同时
+   - 如果
+   - 若
+   - 先
+   - 后
+2. 文本包含明显分隔符：
+   - `，`
+   - `,`
+   - `；`
+   - `;`
+3. fusion stage 最终未 accepted
+
+这意味着：
+
+- planner 不是全量调用
+- 单条明确命令通常不会调 planner
+- 复杂句、歧义句、低置信句会调 planner
+
+### 15.2 planner 组合结构
+
+当前 `build_planner()` 构成如下：
+
+本地层：
+
+- `TemplateWorkflowPlanner`
+- `HeuristicWorkflowPlanner`
+
+云端层：
+
+- `DashScopeWorkflowPlanner`
+
+最终组合方式：
+
+- 如果 `planner_backend=heuristic`
+  - 返回 `Composite(local_template, local_heuristic)`
+- 如果 `planner_backend=dashscope`
+  - 返回 `Composite(local_first, dashscope)`
+
+重点：
+
+- 始终是 local-first
+- 云端 planner 只是后面的补充层
+- 云端失败时会 fallback 到本地 heuristic planner
+
+### 15.3 planner 链路图
+
+```mermaid
+flowchart TD
+    A[planner.plan] --> B[TemplateWorkflowPlanner]
+    B --> C{accepted?}
+    C -->|yes| D[返回模板 workflow]
+    C -->|no| E[HeuristicWorkflowPlanner]
+    E --> F{accepted?}
+    F -->|yes| G[返回本地 heuristic workflow]
+    F -->|no| H{planner_backend=dashscope?}
+    H -->|no| I[返回未接受]
+    H -->|yes| J[DashScopeWorkflowPlanner]
+    J --> K{云端成功?}
+    K -->|yes| L[返回 cloud workflow]
+    K -->|no| M[fallback 到本地 heuristic 结果]
+```
+
+---
+
+## 16. TemplateWorkflowPlanner 细节
+
+### 16.1 作用
+
+对已经定义好的典型固定组合优先做模板命中。
+
+当前 `workflows.yml` 里已有：
+
+- `window_then_ac_sequence`
+- `query_then_cancel_if_pending`
+
+### 16.2 工作方式
+
+1. 调 `_analyze_multi_intent()`
+2. 拿到 clause 分析结果
+3. 如果匹配的 intent 少于 2 个，不接受
+4. 逐个比对 workflow 模板
+5. 如果模板命中，则生成 `PlannedStep`
+
+### 16.3 模板命中条件
+
+需要同时满足：
+
+- `matched_ids[:len(intent_sequence)] == template.intent_sequence`
+- 若模板定义了 `trigger_keywords`，文本中必须都出现
+
+这是一种严格模板匹配，不是模糊相似模板匹配。
+
+---
+
+## 17. HeuristicWorkflowPlanner 细节
+
+### 17.1 输入分析
+
+`_analyze_multi_intent()` 做这些事：
+
+1. 如果配置了多标签 detector，先对整句做 multi-intent detect
+2. 生成 `detector_prior`
+3. 按连接词和标点做 clause split
+4. 对每个 clause 做 heuristic + classifier + detector 融合
+5. 汇总成 `MultiIntentParseResult`
+
+### 17.2 Clause Split 规则
+
+当前切分词包括：
+
+- 然后
+- 并且
+- 同时
+- 再
+- 顺便
+- 接着
+- 并
+- `,`
+- `，`
+- `；`
+- `;`
+
+### 17.3 Clause 评分信号
+
+每个 clause 对每个 intent 的启发式评分来源：
+
+- keyword 命中
+- example 命中
+- action 命中
+- object 命中
+- qualifier 命中
+- shared context 命中
+- 显式 temperature
+- 显式 order_id
+
+### 17.4 Clause 融合
+
+最终 clause 分数 = 启发式分数 + `model_score * classifier_weight` + detector bonus + 一致性 bonus
+
+补充规则：
+
+- 如果启发式没有命中，但 model 分数足够高，允许 `bert_bootstrap`
+- 如果 clause 内看起来像并列复合子句，还会尝试抽出多个 parallel candidates
+
+### 17.5 Workflow 类型推断
+
+- 如果文本有条件模式：
+  - `如果`
+  - `若`
+  - `还没`
+  - `未发货`
+  - `没发货`
+  - 则推断 `conditional`
+- 否则若存在多 clause，通常推断 `sequence`
+- 匹配数不足 2 个时，仍回落为 `single`
+
+### 17.6 条件流程修正
+
+`_apply_conditional_hints()` 当前专门处理：
+
+- `cs_query_order -> cs_cancel_order`
+- 且文本包含“还没发货 / 未发货 / 没发货”
+
+则自动补：
+
+- `depends_on=[query_step]`
+- `condition.field=order_status`
+- `condition.operator=equals`
+- `condition.value=pending_shipment`
+- `requires_confirmation=true`
+
+---
+
+## 18. DashScopeWorkflowPlanner 细节
+
+### 18.1 调用方式
+
+云端 planner 使用 DashScope 兼容 OpenAI 的 `POST /chat/completions`
+
+配置项：
+
+- `planner_base_url`
+- `planner_api_key`
+- `planner_model_name`
+- `planner_timeout_seconds`
+
+### 18.2 Prompt 设计目标
+
+云端 planner 的 system prompt 要求：
+
+- 只返回严格 JSON
+- 只能使用 catalog 中已有的 `intent_id`
+- 单命令返回一个 step
+- 多命令返回 sequence steps
+- 条件命令返回 conditional steps
+- 显式抽取 slots
+- 高风险动作标记 `requires_confirmation=true`
+
+### 18.3 结果归一化
+
+云端返回后会做：
+
+1. 提取 content
+2. 去掉 markdown code fence
+3. 解析 JSON
+4. 校验 intent 是否在 catalog 中
+5. 合并 cloud slots + clause slots + full_text slots
+6. 归一化 depends_on 和 condition
+7. 如果文本本身是条件句，再补条件 hints
+
+### 18.4 云端 planner 失败分支
+
+若出现以下问题：
+
+- 未配置
+- HTTP 请求失败
+- 超时
+- 返回空内容
+- JSON 非法
+
+则返回 fallback 结果：
+
+- backend 记为 `dashscope-fallback`
+- 实际 steps 来自本地 heuristic planner
+
+---
+
+## 19. 单步执行流程
+
+当 planner 没接管，且 fusion 决策为 `execute` 时，系统进入单步执行。
+
+### 19.1 单步执行顺序
+
+1. 如果当前识别的 intent 与 session 上一个 intent 不同：
+   - 清空 `pending_slots`
+   - 清空 `slots`
+   - 清空 `workflow`
+2. 更新 `session.current_intent`
+3. `status=understanding`
+4. 抽取槽位
+5. 更新 `session.slots`
+6. 更新 `context_memory`
+7. 调 `_build_response_from_session()`
+
+### 19.2 `_build_response_from_session()` 分支
+
+如果缺槽位：
+
+- `status=waiting_slot`
+- 返回 `ask_slot`
+- 生成单步 workflow 展示缺失字段
+
+如果槽位齐全：
+
+- 直接调用 plugin
+- `status=completed`
+- 返回 `workflow_result`
+
+### 19.3 单步执行图
+
+```mermaid
+flowchart TD
+    A[decision=execute] --> B[extract slots]
+    B --> C[update session.slots]
+    C --> D{pending slots?}
+    D -->|yes| E[ask_slot + waiting_slot]
+    D -->|no| F[plugins.execute]
+    F --> G[workflow_result]
+    G --> H[record turn + save session]
+```
+
+---
+
+## 20. 多步 Workflow 执行流程
+
+### 20.1 启动
+
+如果 planner 返回：
+
+- `accepted=True`
+- 且 `steps` 非空
+
+则 `AgentService._start_planned_workflow()` 会：
+
+1. 调 `_build_planned_workflow()`
+2. 将 workflow 写入 session
+3. 将 routing_debug 写入 session
+4. 立即调用 `_continue_planned_workflow()`
+
+### 20.2 WorkflowStep 结构
+
+每一步包含：
+
+- `step`
+- `step_id`
+- `intent_id`
+- `plugin_id`
+- `action`
+- `status`
+- `depends_on`
+- `slots`
+- `condition`
+- `requires_confirmation`
+
+### 20.3 `_continue_planned_workflow()` 的核心逻辑
+
+系统按 step 顺序遍历：
+
+1. 如果 step 已完成或已跳过，跳过
+2. 如果依赖步骤未完成，跳过
+3. 合并 session slots 与 step slots
+4. 做 intent 级 slot normalize
+5. 检查当前 step 是否缺必填槽位
+6. 如果缺槽位，暂停整个 workflow，返回 `ask_slot`
+7. 若有 condition，先评估条件
+8. 条件不满足则 `skipped`
+9. 如果此步需要确认且还未确认，返回 `ask_confirmation`
+10. 否则执行 plugin
+11. 记录 step result
+12. 更新 session slots 与 context memory
+13. 收集每步 message
+14. 全部结束后生成自然语言 workflow summary
+
+### 20.4 多步执行图
+
+```mermaid
+flowchart TD
+    A[workflow ready] --> B[for step in steps]
+    B --> C{已完成或已跳过?}
+    C -->|yes| B
+    C -->|no| D{depends_on satisfied?}
+    D -->|no| B
+    D -->|yes| E[merge slots]
+    E --> F{缺槽位?}
+    F -->|yes| G[ask_slot + pause workflow]
+    F -->|no| H{condition exists?}
+    H -->|yes| I[评估条件]
+    I -->|false| J[skip step]
+    I -->|true/none| K{requires confirmation?}
+    H -->|no| K
+    K -->|yes| L[ask_confirmation + pause workflow]
+    K -->|no| M[plugins.execute]
+    M --> N[记录 step_results]
+    N --> B
+    B --> O[全部结束]
+    O --> P[workflow_summary]
+```
+
+---
+
+## 21. 条件流程与确认流程
+
+### 21.1 条件流程
+
+目前条件执行能力是 workflow 层完成的，不是 plugin 层完成的。
+
+机制如下：
+
+1. 某 step 带 `condition`
+2. 条件里描述：
+   - `source_step`
+   - `field`
+   - `operator`
+   - `value`
+3. 执行依赖步骤后，从 `workflow.meta.step_results[source_step]` 里取 `data`
+4. 读出对应 field
+5. 做 equals / not_equals / in 判断
+6. 如果不满足：
+   - step.status = skipped
+   - 拼接 skip message
+
+### 21.2 当前确认机制
+
+确认触发来源有两种：
+
+1. workflow step 显式写了 `requires_confirmation`
+2. `DialogRuleEngine.requires_confirmation(intent_id, risk_level)` 返回 true
+
+当前典型需要确认的动作：
+
+- `cs_cancel_order`
+- 风险等级为 `high` 的动作
+
+### 21.3 确认处理流程
+
+当 workflow 卡在确认步骤时：
+
+1. `status=waiting_confirmation`
+2. `pending_slots=["confirmation"]`
+3. session.workflow.meta 里写入 `pending_confirmation`
+
+之后用户通过 `fill-slots` 或后续续聊输入：
+
+- 如果明确为正确认：
+  - 将 step_id 加入 `confirmed_steps`
+  - 继续跑 workflow
+- 如果明确为负确认：
+  - 将该 step 标记 `skipped`
+  - 继续后面的 workflow
+- 如果无法判断：
+  - 返回 `confirm_retry`
+
+### 21.4 确认时序图
+
+```mermaid
+sequenceDiagram
+    participant U as 用户
+    participant A as AgentService
+    participant W as Workflow
+    participant P as Plugin
+
+    A->>W: 发现当前 step requires_confirmation
+    W-->>U: ask_confirmation
+    U->>A: 确认 / 取消 / 模糊回复
+    A->>A: parse_confirmation_decision
+    alt 明确确认
+        A->>W: 标记 confirmed_steps
+        A->>P: 执行该 step
+        P-->>A: success
+        A-->>U: workflow_result
+    else 明确取消
+        A->>W: 标记 step skipped
+        A-->>U: 后续 summary
+    else 无法判断
+        A-->>U: confirm_retry
+    end
+```
+
+---
+
+## 22. Fill-Slots 续跑流程
+
+`handle_fill_slots()` 是状态恢复和续跑的重要入口。
+
+### 22.1 主要分支
+
+1. 读取 session
+2. 如果无 session 或无 current_intent，fallback
+3. 更新 dialog act
+4. 检查 stop request
+5. 如果在 `waiting_confirmation`，优先尝试确认处理
+6. 如果是 social turn，也可先走社交
+7. 如果还在 `waiting_confirmation`，继续确认流程
+8. 如果 session 内已有非 single workflow：
+   - 只抽本轮补充槽位
+   - 继续 `_continue_planned_workflow()`
+9. 否则：
+   - 按当前单 intent 抽槽并继续 `_build_response_from_session()`
+
+### 22.2 为什么 fill-slots 很关键
+
+它让系统具备这些能力：
+
+- 缺槽位后可续跑
+- 条件确认后可续跑
+- 多步 workflow 在中断后可继续
+- 不需要整轮重新识别 planner
+
+---
+
+## 23. Plugin 执行层
+
+### 23.1 抽象方式
+
+当前插件层由 `PluginRegistry` 统一管理：
+
+- `register(plugin_id, handler)`
+- `execute(plugin_id, slots)`
+
+### 23.2 当前插件实现
+
+现在实际接的是 `MockPluginExecutor`，它注册了一批 mock handler，包括：
+
+客服类：
+
+- `plugin.order.query`
+- `plugin.logistics.query`
+- `plugin.order.cancel`
+- `plugin.service.transfer_human`
+
+座舱类：
+
+- `plugin.cabin.navigation`
+- `plugin.cabin.navigation.cancel`
+- `plugin.cabin.ac.on`
+- `plugin.cabin.ac.off`
+- `plugin.cabin.ac_control`
+- `plugin.cabin.fan.up`
+- `plugin.cabin.fan.down`
+- `plugin.cabin.defog.front_on`
+- `plugin.cabin.defog.rear_on`
+- `plugin.cabin.window.open`
+- `plugin.cabin.window.close`
+- `plugin.cabin.sunroof.open`
+- `plugin.cabin.sunroof.close`
+- `plugin.cabin.doors.lock`
+- `plugin.cabin.doors.unlock`
+- `plugin.cabin.music_play`
+- `plugin.cabin.music.pause`
+- `plugin.cabin.music.next`
+- `plugin.cabin.music.previous`
+- `plugin.cabin.volume.up`
+- `plugin.cabin.volume.down`
+- `plugin.cabin.volume.mute`
+- `plugin.cabin.lights.on`
+- `plugin.cabin.lights.off`
+- `plugin.cabin.seat_heat.on`
+- `plugin.cabin.seat_heat.off`
+- `plugin.cabin.mirror.fold`
+- `plugin.cabin.mirror.unfold`
+- `plugin.cabin.wiper.on`
+- `plugin.cabin.wiper.off`
+
+### 23.3 plugin 返回结构
+
+每个 plugin 返回统一 dict：
+
+- `success`
+- `message`
+- `data`
+
+这使得：
+
+- ResponsePolicy 能统一生成自然回复
+- workflow condition 能统一读取 `data.field`
+
+---
+
+## 24. ResponsePolicy 与最终话术
+
+### 24.1 ResponsePolicy 职责
+
+`ResponsePolicy` 是统一话术策略层，负责：
+
+- `ask_for_slot`
+- `workflow_result`
+- `workflow_summary`
+- `ask_for_confirmation`
+- `confirm_retry`
+- `confirm_cancelled`
+- `step_skipped`
+- `ack`
+- `reject`
+- `short_social`
+- `open_social_fallback`
+- `with_pending_hint`
+- `pending_task_hint`
+- `task_stopped`
+- `clarify`
+- `fallback`
+
+### 24.2 workflow_summary 的特殊点
+
+多步结果不是简单拼接，而会做一定“车机式口语化聚合”，例如：
+
+- `车窗已经打开了`
+- `空调也调到 20 度了`
+- `也开始播放 民谣 了`
+
+最终形态类似：
+
+- `好，车窗已经打开了，空调也调到 20 度了。`
+
+### 24.3 clarify / reject / fallback 区别
+
+`clarify`
+
+- 已知能力内
+- 但候选有歧义
+
+`reject`
+
+- 当前判断超能力边界
+- 或 planner 明确指出 out of scope
+
+`fallback`
+
+- 没有稳定理解
+- 也没有足够候选可澄清
+
+---
+
+## 25. Debug 与可观测性
+
+当前项目对调试非常友好，主要体现在：
+
+- route 每个 stage 都保留详细 debug
+- rewrite 会带 applied / reason / metadata
+- planner 会追加 planner stage 到 routing_debug
+- clause_analysis 会放入 planner metadata
+- multi_intent_detector 原始 top scores 会保留
+- processing breakdown 会附带每阶段耗时
+
+因此一次响应通常能回放出：
+
+1. 原始输入
+2. 是否改写
+3. route 经过哪些 stage
+4. 每个 stage 给了什么 candidates
+5. fusion 为什么 execute / clarify / reject / route_to_cloud
+6. planner 是否触发
+7. planner 为什么 accepted / rejected
+8. workflow 长什么样
+9. 每步执行结果如何
+
+---
+
+## 26. 当前支持的核心能力
+
+### 26.1 客服域
+
+- 查订单
+- 查物流
+- 取消订单
+- 转人工
+
+### 26.2 座舱域
+
+- 导航去某地
+- 结束导航
+- 打开/关闭空调
+- 调空调温度
+- 调大/调小风量
+- 前挡除雾
+- 后挡除雾
+- 打开/关闭车窗
+- 打开/关闭天窗
+- 锁/解锁车门
+- 播放/暂停音乐
+- 上一首/下一首
+- 调大/调小音量
+- 静音
+- 打开/关闭车灯
+- 打开/关闭座椅加热
+- 折叠/展开后视镜
+- 打开/关闭雨刷
+
+### 26.3 对话辅助能力
+
+- 问候
+- 感谢
+- 再见
+- 能力问答
+- 开放闲聊
+- 多轮短句改写
+- 条件执行
+- 高风险确认
+- 停止当前任务
+
+---
+
+## 27. 分支矩阵
+
+### 27.1 输入到输出的主要分支
+
+| 输入类型 | 前置判断 | 走向 | 最终输出类型 |
+| --- | --- | --- | --- |
+| 停止词 + 有活动任务 | stop rule | reset task | `text/stopped` |
+| 问候/感谢/闲聊 | social route | social responder | `text/social` |
+| 单命令高置信 | fusion execute | 单步执行 | `workflow_result` |
+| 单命令缺槽位 | execute + missing slots | ask slot | `ask_slot` |
+| 多命令/条件句 | planner accepted | workflow | `workflow_result / ask_slot / ask_confirmation` |
+| 本地歧义 | fusion clarify | clarify | `clarify` |
+| 本地像已知意图但不稳 | route_to_cloud | clarify / fallback / reject | `clarify/fallback/reject` |
+| 完全未知 | reject | reject | `reject` |
+| session 丢失或无 current intent 的 fill-slots | invalid continuation | fallback | `fallback` |
+
+### 27.2 route_to_cloud 的三种最终表现
+
+当 fusion 决策为 `route_to_cloud` 时，不一定真的“去云端执行完就返回结果”，当前表现取决于 planner stage 和候选情况：
+
+1. planner 明确提示 out-of-scope
+   - 返回 `reject`
+2. 还有可用候选 intent
+   - 返回 `clarify`
+3. 没候选也没明确拒绝
+   - 返回 `fallback`
+
+---
+
+## 28. 当前技术栈与模型清单
+
+### 28.1 基础框架
+
+- Python
+- FastAPI
+- Pydantic Settings
+- YAML / JSON 配置文件
+
+### 28.2 本地理解层
+
+- keyword matcher
+- heuristic retrieval matcher
+- mock classifier
+- local BERT classifier
+- local multi-label BERT detector
+
+### 28.3 云端能力层
+
+- DashScope OpenAI-compatible chat completion
+- 云端 workflow planner
+- 云端 social responder
+
+### 28.4 状态与数据层
+
+- In-memory session
+- Redis session
+
+### 28.5 执行层
+
+- PluginRegistry
+- MockPluginExecutor
+
+### 28.6 前端与调试
+
+- demo.html
+- runtime backend 切换
+- routing debug panel
+- workflow JSON 展示
+
+---
+
+## 29. 真实请求样例时序
+
+### 29.1 单命令：打开车窗
+
+```mermaid
+sequenceDiagram
+    participant U as 用户
+    participant API as FastAPI
+    participant S as AgentService
+    participant R as Router
+    participant P as Plugin
+
+    U->>API: /chat 打开车窗
+    API->>S: handle_chat
+    S->>S: get/create session
+    S->>S: rewrite(无改写)
+    S->>R: route
+    R-->>S: decision=execute,intent=cabin_window_open
+    S->>S: extract slots(空)
+    S->>P: plugin.cabin.window.open
+    P-->>S: success + message
+    S-->>API: workflow_result
+    API-->>U: 好的，已打开车窗
+```
+
+### 29.2 条件句：查订单，如果没发货就取消
+
+```mermaid
+sequenceDiagram
+    participant U as 用户
+    participant S as AgentService
+    participant PL as Planner
+    participant W as Workflow
+    participant P as Plugin
+
+    U->>S: 查订单A123，如果没发货就取消
+    S->>PL: planner.plan
+    PL-->>S: conditional workflow
+    S->>W: build workflow
+    W->>P: query_order
+    P-->>W: data.order_status=pending_shipment
+    W-->>U: ask_confirmation
+    U->>S: 确认
+    S->>W: continue workflow
+    W->>P: cancel_order
+    P-->>W: success
+    W-->>U: workflow_summary
+```
+
+### 29.3 多轮补槽：导航去哪里
+
+```mermaid
+sequenceDiagram
+    participant U as 用户
+    participant S as AgentService
+    participant P as Plugin
+
+    U->>S: 导航
+    S-->>U: ask_slot(请告诉我要去哪里)
+    U->>S: 去公司
+    S->>S: fill-slots
+    S->>P: plugin.cabin.navigation(destination=公司)
+    P-->>S: success
+    S-->>U: workflow_result
+```
+
+---
+
+## 30. 当前边界与限制
+
+### 30.1 还不是完整语音链路
+
+当前项目只有文本 Agent 服务：
+
+- 没有内建 ASR
+- 没有内建 TTS
+- 没有真正的车机设备控制链路
+
+### 30.2 plugin 仍然是 mock
+
+虽然 plugin 结构已经有了，但当前执行结果仍然是 mock handler 返回，不是真实业务系统结果。
+
+### 30.3 LLM matcher 尚未实现
+
+`matcher_pipeline` 虽然支持 `llm` 这个 stage 名字，但实现仍是 placeholder。
+
+### 30.4 流式 ack 与真实执行未完全打通
+
+`chat-stream` 中 1 秒后的 `ack` 仍然依赖输入文本 token，不是插件真实启动事件。
+
+### 30.5 多意图 detector 已独立训练，但泛化仍需继续补数据
+
+目前多标签 detector 已接入为真正训练过的多标签模型，但对独立口语化长尾场景仍需要继续补强训练数据。
+
+### 30.6 NER / Token Classification 尚未接入
+
+当前动作-对象-槽位边界主要依赖：
+
+- heuristic slot extraction
+- clause heuristic
+- classifier 与 detector 融合
+
+还没有真正的 token classification 层。
+
+---
+
+## 31. 一句话总结当前项目真实流程
+
+当前项目的真实运行方式可以概括为：
+
+```text
+FastAPI 接收请求
+-> AgentService 读取 session
+-> 先处理 stop / social
+-> rewrite 做短句补全
+-> keyword/classifier/retrieval 多阶段并行打分
+-> fusion 决定 execute / clarify / reject / route_to_cloud
+-> 复杂句触发 local-first planner
+-> 生成 single/sequence/conditional workflow
+-> 缺槽位就 ask_slot
+-> 高风险就 ask_confirmation
+-> PluginRegistry 执行
+-> ResponsePolicy 生成最终自然回复
+-> Session 持久化并返回完整 debug 与时延指标
+```
+
+---
+
+## 32. 后续阅读建议
+
+如果要继续往下深挖，建议按这个顺序看代码：
+
+1. `app/main.py`
+2. `app/core/bootstrap.py`
+3. `app/services/agent_service.py`
+4. `app/services/router.py`
+5. `app/services/planner.py`
+6. `app/services/classifier.py`
+7. `app/services/multi_intent_detector.py`
+8. `app/services/rewrite_engine.py`
+9. `app/services/social.py`
+10. `app/services/session_store.py`
+11. `config/domain.yml`
+12. `config/workflows.yml`
+
diff --git a/intelligent_cabin/archive/docs/design.md b/intelligent_cabin/archive/docs/design.md
new file mode 100644
index 0000000..938d01b
--- /dev/null
+++ b/intelligent_cabin/archive/docs/design.md
@@ -0,0 +1,2104 @@
+# 面向客服/售后/前台/智能座舱的高响应 Agent 技术方案
+
+## 1. 文档目标
+
+本文档用于设计一套可落地的 Agent 系统，满足以下核心诉求：
+
+- 响应快：用户发起操作后，系统能够在极短时间内给出首响应或执行反馈。
+- 执行准：对高频业务请求识别准确，对复杂请求可拆解、可校验、可执行。
+- 可多轮：支持上下文记忆、槽位补全、断点续跑和任务恢复。
+- 可扩展：适配客服、售后、前台接待、智能座舱等不同业务场景。
+- 可运营：意图、槽位、规则、话术、插件均支持配置化管理。
+
+本方案不是单纯的问答机器人，而是一个“可理解用户意图、可生成执行计划、可调用业务能力完成任务”的 Agent 平台。
+
+## 2. 目标场景
+
+### 2.1 客服场景
+
+- 查询订单、物流、退款进度
+- 识别催单、投诉、转人工、取消订单等诉求
+- 多轮追问补全订单号、手机号、收货人等信息
+
+### 2.2 售后场景
+
+- 退货、换货、维修、预约上门
+- 收集故障描述、图片、时间、地址等槽位
+- 根据政策规则决定是否可直接处理、升级或转人工
+
+### 2.3 前台场景
+
+- 来访登记、会议室预约、路线引导、通知被访人
+- 识别身份、预约信息、时间、地点等关键字段
+- 支持多步骤任务执行和结果确认
+
+### 2.4 智能座舱场景
+
+- 导航、空调、车窗、音乐、电话、座椅等车控指令
+- 识别复合指令，如“打开空调并导航去公司”
+- 在驾驶场景下保证低延迟、强确定性和安全边界
+
+## 3. 总体设计原则
+
+### 3.1 快慢分层
+
+高频、标准化、可确定执行的请求走“快路径”；复杂、低频、跨任务、带条件逻辑的请求走“慢路径”。
+
+- 快路径：小模型分类 + 规则校验 + 直接执行
+- 慢路径：RAG 召回 + LLM 精判 + 工作流生成 + 插件执行
+
+这样可以同时满足“响应速度”和“复杂理解能力”。
+
+### 3.2 LLM 不直接控制业务
+
+LLM 只负责：
+
+- 意图识别与精判
+- 多意图拆分
+- 槽位提取
+- 执行计划生成
+- 回复文案生成
+
+真正的业务执行由规则引擎、状态管理器和插件执行器完成，避免 LLM 幻觉带来的误操作。
+
+### 3.3 会话状态中心是核心
+
+多轮对话不能依赖模型“记忆”，必须建设独立的 Session State。
+
+状态中心负责：
+
+- 保存当前会话上下文
+- 保存已识别意图和已填充槽位
+- 保存当前任务状态和执行进度
+- 记录待补充信息和断点恢复位置
+
+### 3.4 配置优先、插件扩展
+
+以下能力尽量配置化，而不是写死在代码中：
+
+- 意图定义
+- 槽位定义
+- 规则表达式
+- 反问话术
+- 权限与风控限制
+- 插件路由关系
+
+## 4. 建设目标
+
+### 4.1 业务目标
+
+- 支持不少于 100 个可配置意图
+- 支持单意图、多意图、条件意图三类输入
+- 支持跨轮补充槽位并继续执行
+- 支持知识问答、事务办理、设备控制三类能力
+
+### 4.2 体验目标
+
+- 首字响应时间小于 800ms
+- 高频标准请求平均响应时间 500ms 到 1500ms
+- 复杂规划请求平均响应时间 1500ms 到 3500ms
+- 多轮槽位补全过程中保持上下文一致
+
+### 4.3 质量目标
+
+- 高频意图识别准确率大于 95%
+- 槽位抽取 F1 大于 90%
+- 高风险动作误执行率接近 0
+- 转人工触发符合业务策略并可追踪
+
+## 5. 能力边界与功能点
+
+### 5.1 核心能力
+
+- 意图识别：识别用户到底要做什么
+- 槽位提取：提取时间、地点、金额、对象、设备等参数
+- 多意图拆分：识别一句话中多个任务
+- 逻辑解析：识别顺序、条件、重试、并行关系
+- 会话管理：跟踪当前任务、历史轮次和上下文变量
+- 任务执行：调用业务接口、设备控制接口或人工服务接口
+- 结果回复：结构化返回执行结果，必要时生成自然语言
+
+### 5.2 平台能力
+
+- 意图知识库管理
+- 规则与话术配置平台
+- 插件注册中心
+- 会话状态存储
+- 指标监控与日志追踪
+- 安全审计与权限控制
+
+### 5.3 场景化能力
+
+- 客服：FAQ、工单、订单操作、转人工
+- 售后：退换修、预约、故障诊断、流程推进
+- 前台：访客接待、通知、预约、引导
+- 智能座舱：设备控制、导航、多设备联动、安全确认
+
+## 6. 系统总体架构
+
+```text
+用户输入
+  -> 接入层（Web/App/语音/车机）
+  -> 预处理层（ASR/文本归一化/敏感词/语言检测）
+  -> 路由层
+      -> 快路径：意图分类模型 + 规则匹配 + 插件执行
+      -> 慢路径：向量召回 + LLM 精判 + Workflow 生成
+  -> 状态中心（Session/Context/Slots/Current Step）
+  -> 执行引擎（规则引擎 + 插件调度 + 断点恢复）
+  -> 回复生成层（模板/LLM）
+  -> 用户
+```
+
+## 7. 核心处理链路
+
+### 7.1 快路径
+
+适用于客服、前台、座舱中的高频标准请求，例如：
+
+- “查一下我的订单”
+- “打开空调”
+- “帮我通知张三来前台”
+
+执行流程：
+
+1. 对输入做标准化与轻量实体提取
+2. 进入轻量意图分类模型
+3. 命中高置信度意图后，进行槽位校验
+4. 槽位齐全则直接调用插件执行
+5. 槽位缺失则触发反问并记录状态
+
+优势：
+
+- 延迟低
+- 成本低
+- 可控性强
+
+### 7.2 慢路径
+
+适用于复杂请求，例如：
+
+- “帮我查一下订单，如果还没发货就取消”
+- “导航去公司，然后把空调调到 22 度，再给我播放轻音乐”
+- “我要退货，若超过 7 天就帮我转人工”
+
+执行流程：
+
+1. 向量召回 Top-K 候选意图
+2. LLM 对候选意图做精判
+3. 对输入做多意图拆分
+4. 生成结构化 Workflow JSON
+5. 执行引擎按步骤调度插件
+6. 缺槽位时暂停并反问
+7. 用户补充后从断点恢复执行
+
+## 8. 推荐的多层 Agent 架构
+
+### 8.1 第 1 层：输入理解层
+
+职责：
+
+- 文本清洗
+- 口语归一化
+- 同义词映射
+- 设备指令别名统一
+- 敏感/危险指令预过滤
+
+### 8.2 第 2 层：意图路由层
+
+职责：
+
+- 判断是否命中高频快路径
+- 判断是否需要进入复杂规划链路
+- 判断是问答类、事务类还是控制类任务
+
+推荐策略：
+
+- 高频意图使用专门分类器
+- 低频和新增意图使用向量召回
+- 复杂指令交给 LLM 做精判和拆解
+
+### 8.3 第 3 层：会话状态层
+
+核心数据：
+
+- session_id
+- user_id / device_id
+- current_intent
+- current_workflow
+- completed_steps
+- pending_slots
+- business_context
+- risk_level
+
+### 8.4 第 4 层：执行编排层
+
+职责：
+
+- 执行工作流
+- 管理步骤状态
+- 根据规则判断分支
+- 调用插件
+- 处理重试、超时和回滚
+
+### 8.5 第 5 层：能力插件层
+
+插件示例：
+
+- 查询订单插件
+- 取消订单插件
+- 创建售后工单插件
+- 访客登记插件
+- 通知员工插件
+- 导航插件
+- 空调控制插件
+- 音乐控制插件
+
+### 8.6 第 6 层：回复生成层
+
+优先级建议：
+
+- 优先模板化返回
+- 复杂解释场景再用 LLM 生成自然语言
+- 高风险动作必须做明确确认和结果回执
+
+## 9. 意图、槽位、工作流设计
+
+### 9.1 意图设计
+
+建议每个意图至少包含：
+
+- intent_id
+- intent_name
+- domain
+- description
+- examples
+- boundary
+- required_slots
+- optional_slots
+- execution_plugin
+- risk_level
+
+示例：
+
+```json
+{
+  "intent_id": "cabin_set_ac_temperature",
+  "intent_name": "设置空调温度",
+  "domain": "cabin_control",
+  "required_slots": ["temperature"],
+  "optional_slots": ["zone"],
+  "execution_plugin": "ac_control_plugin",
+  "risk_level": "low"
+}
+```
+
+### 9.2 槽位设计
+
+每个槽位建议配置：
+
+- 名称
+- 类型
+- 是否必填
+- 默认值
+- 校验规则
+- 提问模板
+- 错误提示
+- 来源优先级
+
+### 9.3 Workflow 设计
+
+复杂请求最终统一转成结构化执行计划。
+
+示例：
+
+```json
+{
+  "workflow_id": "wf_1001",
+  "logic_type": "sequence",
+  "steps": [
+    {
+      "step": 1,
+      "intent": "query_order_status",
+      "slots": {
+        "order_id": "A123"
+      }
+    },
+    {
+      "step": 2,
+      "condition": "order_status == 'pending_shipment'",
+      "intent": "cancel_order"
+    }
+  ]
+}
+```
+
+## 10. 多轮对话设计
+
+### 10.1 多轮的本质
+
+多轮不是每一轮都重新做完整识别，而是：
+
+- 首轮生成任务或命中任务
+- 后续轮次只做补槽位、确认、修正、继续执行
+
+### 10.2 状态机设计
+
+建议会话状态包括：
+
+- `idle`：空闲
+- `understanding`：理解中
+- `waiting_slot`：等待用户补充信息
+- `ready_to_execute`：准备执行
+- `executing`：执行中
+- `paused`：中断等待
+- `completed`：完成
+- `fallback`：兜底或转人工
+
+### 10.3 断点续跑
+
+示例：
+
+1. 用户说“我要退货”
+2. 系统识别退货意图，但缺少订单号
+3. 系统反问“请提供订单号”
+4. 用户回复“A123”
+5. 系统只做槽位提取，不重新做全量规划
+6. 系统恢复原任务继续执行
+
+### 10.4 多轮省略恢复与上下文改写
+
+参考车机场景的公开方案，多轮对话不能只靠“读取上一轮状态”，还应增加“相邻轮改写”和“高频缓存命中”能力。
+
+适用场景：
+
+- “音量调大” -> “再大一点”
+- “导航去公司” -> “不要高速”
+- “查一下北京天气” -> “上海呢”
+
+建议新增一层 `context rewrite engine`，位于 `Session State` 和 `Router` 之间：
+
+1. 读取上一轮已确认的主任务、领域和关键槽位
+2. 将“当前轮短句 + 上一轮已确认语义”送入高频缓存引擎查询
+3. 若命中缓存模板，直接改写为完整指令
+4. 若缓存未命中，再使用轻量改写模型生成补全后的标准句
+5. 改写后再进入意图识别与槽位提取
+
+示例：
+
+- 上一轮：`空调调高`
+- 当前轮：`再高一点`
+- 改写后：`把空调温度再调高一点`
+
+缓存建议：
+
+- key：`previous_intent + current_utterance_pattern`
+- value：`rewritten_text / target_intent / slot_delta`
+- 数据来源：高频真实语料、线上点击日志、人工标注样本
+
+收益：
+
+- 显著提升省略表达、连续调节、改口表达的识别率
+- 避免每一轮都走完整大模型规划
+- 将多轮短句场景时延压缩到接近单轮快路径
+
+## 11. 模型方案
+
+### 11.1 选型原则
+
+- 高频场景优先低延迟
+- 复杂场景优先理解能力
+- 高风险场景优先可控和可审计
+- 模型职责必须拆开，不要一个模型包打天下
+
+### 11.2 推荐模型组合
+
+#### A. 轻量意图识别模型
+
+用途：
+
+- 高频标准请求分类
+- 第一时间做快路径路由
+
+推荐方向：
+
+- BERT / RoBERTa 中文分类模型
+- 小型指令模型蒸馏版
+- ONNX / TensorRT 部署后的轻量分类模型
+
+适合场景：
+
+- 客服高频查询
+- 前台固定流程
+- 座舱高频控制
+
+#### B. 向量召回模型
+
+用途：
+
+- 动态意图库检索
+- 召回候选意图与候选技能
+
+推荐方向：
+
+- BGE-M3
+- BCE Embedding
+- text-embedding 系列中文向量模型
+
+#### C. 主 LLM
+
+用途：
+
+- 候选意图精判
+- 多意图拆分
+- 槽位抽取
+- Workflow 生成
+- 自然语言回复
+
+推荐方向：
+
+- 通义千问系列
+- DeepSeek 系列
+- GLM 系列
+- GPT 类模型
+
+建议：
+
+- 线上使用支持函数调用、结构化输出、低温度推理的模型
+- 对复杂规划与多轮状态更新使用 JSON schema 约束输出
+
+#### D. ASR / TTS 模型
+
+用于前台语音交互和智能座舱：
+
+- ASR：语音转文本
+- TTS：语音播报反馈
+- VAD：语音活动检测，提升交互流畅度
+
+### 11.3 推荐模型职责拆分
+
+- 小模型：意图分类、风险识别、简单槽位抽取
+- 向量模型：候选意图召回
+- 大模型：复杂理解、规划和自然语言生成
+- 规则引擎：确定性判断与业务约束
+
+这是兼顾速度、成本和准确性的最佳组合。
+
+### 11.4 端云协同模型分工
+
+要达到接近车机助手的体验，必须采用“本地优先、云端增强、结果融合”的模型架构，而不是所有请求都走一个远端大模型。
+
+建议分工如下：
+
+- 本地 ASR / 文本归一化：负责第一时间得到稳定文本或拼音特征
+- 本地快分支 1：关键词/规则/Trie 高频模式匹配
+- 本地快分支 2：轻量 BERT 意图分类模型
+- 本地快分支 3：多轮缓存改写 + 连续调节识别
+- 本地快分支 4：轻量检索或 FAQ 命中
+- 云端慢分支：复杂语义理解、多意图拆分、条件规划、知识问答、RAG、API 预测
+
+融合原则：
+
+- 本地结果若达到最高置信等级，则直接触发首响应与本地执行
+- 云端结果若在可接受时间窗口内返回，则可覆盖或补充本地结果
+- 对高风险动作，云端只能提供建议，最终执行仍由规则与插件层裁决
+
+### 11.5 声学语义大模型的落地方式
+
+参考公开专利中的“声学编码 + 字符转写 + 检索增强 + 大模型”思路，后续如果要做语音版车机，不建议继续把 ASR、NLU、知识检索和 API 预测完全割裂成独立长链路。
+
+推荐两阶段落地：
+
+- 第一期：保留模块化工程架构，但缩短串行链路，做到 `ASR -> local router -> plugin/cloud planner`
+- 第二期：引入端到端语音语义模型，使其直接输出：
+  - intent
+  - slots
+  - candidate_api
+  - reject / clarify / execute decision
+
+这样可以同时保留工程可控性和后续车机语音体验的升级空间。
+
+## 12. 技术栈建议
+
+### 12.1 服务端
+
+- Python：适合快速构建 AI 编排、模型接入、规则处理
+- FastAPI：提供高性能 API 服务
+- Celery / 队列系统：处理异步任务和外部接口调用
+
+如果团队更偏 Java，也可使用：
+
+- Java + Spring Boot
+- LangChain4j 或自研 Agent 编排层
+
+### 12.2 检索与存储
+
+- Redis：会话状态、热点缓存、限流计数
+- PostgreSQL：业务数据、配置中心、日志审计
+- Elasticsearch：检索与日志分析
+- Qdrant / Milvus：向量检索与意图知识库召回
+
+### 12.3 Agent 编排与规则
+
+- 自研 Workflow Engine 或基于状态机的编排引擎
+- JSON Schema：约束结构化输出
+- 规则引擎：表达式解析、条件判断、路由控制
+
+### 12.4 前端与接入
+
+- Web 前端 / App SDK
+- 呼叫中心接入
+- 企业 IM 接入
+- 车机系统接入
+- 语音网关接入
+
+### 12.5 可观测与运维
+
+- Prometheus + Grafana：监控
+- OpenTelemetry：链路追踪
+- Loki / ELK：日志采集与分析
+
+## 13. 响应速度优化方案
+
+这是本系统成败的关键。
+
+### 13.1 低延迟设计
+
+- 高频意图前置分类，不直接把所有请求都交给 LLM
+- 热门意图和常用知识走缓存
+- 首响应和最终响应分离，先反馈“正在处理”
+- LLM 使用流式输出
+- 复杂任务异步执行，前台先返回受理状态
+
+### 13.2 结构化提速
+
+- 只给 LLM 候选意图，不让模型自由发散
+- 后续轮次只做槽位提取，不重复做全流程规划
+- 采用模板化回复代替不必要的长文本生成
+
+### 13.3 工程提速
+
+- 模型服务常驻内存
+- Embedding 批量化和缓存
+- 插件接口超时控制与降级
+- 预加载高频配置与词典
+
+### 13.4 参考车机方案的极速响应架构
+
+为了逼近“小鹏 P7 类”体验，建议在当前快慢路径基础上，升级为“本地多支路并发 + 结果分级融合 + 云端延迟覆盖”的架构。
+
+```text
+用户输入
+  -> 并发启动本地多支路
+      -> Branch A: keyword / rule / trie
+      -> Branch B: local bert classifier
+      -> Branch C: context rewrite + cache engine
+      -> Branch D: retrieval matcher
+  -> 结果分级器
+      -> high grade: 直接首响应 / 直接执行
+      -> medium grade: 等待 100~300ms 观察其他分支
+      -> low grade: 进入云端理解
+  -> 云端 planner / rag / llm
+  -> 融合器输出目标结果
+```
+
+分级融合建议使用以下特征：
+
+- ASR 置信度 / 清晰度
+- 本地意图分类置信度
+- 领域置信度
+- 槽位完备度
+- 是否命中高频缓存
+- 是否命中历史会话上下文
+- 是否属于高风险动作
+
+推荐时延预算：
+
+- 0~150ms：文本标准化、关键词、Trie、缓存改写
+- 150~350ms：本地 BERT / 本地检索 / 本地槽位提取
+- 350~600ms：返回首响应或执行简单任务
+- 600~1500ms：云端补充复杂理解或条件规划结果
+
+首响应策略：
+
+- 能本地直接执行的，优先回短反馈，如“好的，正在导航”
+- 需要云端复杂规划的，先回受理反馈，如“收到，我先帮你确认一下”
+- 云端超时不阻塞首响应，必要时走兜底或稍后补充结果
+
+该策略是实现 `<1s` 体验的关键，不要求所有任务都在 1 秒内完成，但要求绝大多数任务在 1 秒内给出可感知反馈。
+
+## 14. 准确率与执行可靠性设计
+
+### 14.1 准确率保障
+
+- 高频意图单独建模
+- 意图边界描述清晰化
+- 引入负样本和相似意图对比训练
+- 使用 RAG 缩小候选范围后再让 LLM 精判
+
+### 14.2 执行可靠性保障
+
+- 高风险动作必须二次确认
+- 插件执行前后都记录审计日志
+- 核心步骤支持幂等、重试和回滚
+- 输出必须是结构化结果，禁止自由文本直接驱动操作
+
+### 14.3 风控策略
+
+- 敏感词和危险动作拦截
+- 用户身份和权限校验
+- 场景安全等级区分
+- 智能座舱中行车状态下限制高风险控制动作
+
+### 14.4 多命令拆分与任务栈
+
+为了支持“打开空调并导航去公司，再播放轻音乐”这一类车机复合指令，建议在路由层之后增加 `command splitter` 和 `task stack manager`。
+
+输出结构建议：
+
+```json
+{
+  "task_id": "task_001",
+  "logic_type": "sequence",
+  "commands": [
+    {"intent": "cabin_set_ac", "slots": {"temperature": 22}},
+    {"intent": "cabin_nav_to", "slots": {"destination": "公司"}},
+    {"intent": "cabin_play_music", "slots": {"genre": "轻音乐"}}
+  ]
+}
+```
+
+执行要求：
+
+- 简单并列命令支持顺序执行
+- 需要条件判断的命令转为 workflow condition
+- 允许用户在执行中打断、改口和取消后续任务
+- 为每个任务保留任务栈、当前步骤和已完成步骤
+
+打断策略：
+
+- 新请求与当前任务冲突时，允许暂停旧任务并保存上下文
+- 明确打断词出现时，清空当前任务栈或清理对应子任务上下文
+- 对高风险动作，打断后不得默认自动恢复
+
+### 14.5 拒答、澄清与简短反馈策略
+
+要实现“反馈迅速而简短，不会的内容有合适拒绝话术”，不能只靠模型生成，应建设统一的 `response policy layer`。
+
+回复规则：
+
+- 简单执行成功：一句话短反馈，长度优先控制在 8~20 个字
+- 缺槽位：只追问一个最关键槽位，不一次性问太多
+- 不支持的请求：明确说明边界并给出可做事项
+- 风险动作：先确认再执行
+- 云端失败：给出降级反馈，不暴露内部错误
+
+建议内置 5 类模板：
+
+- `ack`：收到，马上处理
+- `clarify`：信息不够，请补一个关键字段
+- `confirm`：高风险动作确认
+- `reject`：能力边界拒答
+- `fallback`：系统忙或结果不稳定时的兜底
+
+示例：
+
+- `ack`：`好的，正在为你导航`
+- `clarify`：`请告诉我订单号`
+- `confirm`：`确认要取消这个订单吗`
+- `reject`：`这个我暂时做不了，但我可以帮你导航、查订单或调空调`
+- `fallback`：`我先记下你的需求，稍后再试一次`
+
+核心原则：
+
+- 首反馈短
+- 首反馈稳
+- 不会就明确拒答
+- 不确定就先澄清
+- 不让模型自由发挥高风险话术
+
+## 15. 配置化设计
+
+建议把以下内容全部放入配置中心：
+
+- 意图定义
+- 槽位定义
+- 规则表达式
+- 反问模板
+- 插件映射
+- 风险等级
+- 转人工策略
+- 场景开关
+
+这样新增业务时，尽量做到“不改核心代码，只加配置和插件”。
+
+## 16. 效果预期
+
+### 16.1 对业务的价值
+
+- 降低人工客服和前台重复劳动
+- 提升售后处理自动化比例
+- 提高智能座舱交互的自然度和完成率
+- 缩短用户从提问到执行完成的路径
+
+### 16.2 对用户的体验
+
+- 回答更快
+- 多轮更顺
+- 执行更稳
+- 出错可解释
+- 失败可转人工
+
+### 16.3 可量化指标
+
+- 首响应耗时
+- 请求完成率
+- 单轮解决率
+- 多轮补槽成功率
+- 插件执行成功率
+- 转人工率
+- 用户满意度
+
+## 17. 分阶段落地建议
+
+### 17.1 第一阶段：POC
+
+目标：
+
+- 先验证快路径和多轮补槽是否可用
+
+范围：
+
+- 10 到 20 个高频意图
+- 单领域试点，如客服或智能座舱
+
+方案：
+
+- 轻量分类模型 + Redis 状态中心 + 少量插件 + 模板回复
+
+### 17.2 第二阶段：商用 MVP
+
+目标：
+
+- 覆盖主要业务链路，支持转人工和监控闭环
+
+范围：
+
+- 50 到 100 个意图
+- 问答 + 事务 + 执行三类能力
+
+方案：
+
+- 分类模型 + 向量召回 + LLM 精判 + 执行引擎 + 配置中心
+
+### 17.3 第三阶段：平台化
+
+目标：
+
+- 多场景复用，支持客服、售后、前台、座舱统一底座
+
+范围：
+
+- 多租户、多场景、多插件
+
+方案：
+
+- 插件平台、规则平台、意图库平台、评测平台、可观测平台全面建设
+
+## 18. 最终建议
+
+对于“回答迅速且执行准确”的 Agent，不建议直接用单一大模型硬做，而应采用以下混合架构：
+
+- 高频请求：小模型分类直达执行
+- 动态意图：向量召回缩小范围
+- 复杂请求：LLM 做拆分、精判和工作流生成
+- 多轮会话：状态中心负责记忆与恢复
+- 最终执行：插件和规则引擎负责落地
+
+一句话总结：
+
+这是一个“快路径保证速度、慢路径保证理解、状态中心保证多轮、插件执行保证结果”的 Agent 架构。
+
+如果后续继续推进，下一步建议补两份文档：
+
+- 一份《系统架构图 + 时序图》
+- 一份《意图表、槽位表、插件表、接口协议表》
+
+## 19. 系统架构图
+
+### 19.1 总体架构图
+
+```mermaid
+flowchart LR
+    A[用户<br/>App / Web / 呼叫中心 / 车机] --> B[接入层<br/>API Gateway / WS / 语音网关]
+    B --> C[预处理层<br/>ASR / 文本归一化 / 安全过滤 / 语言识别]
+    C --> D[意图路由层]
+
+    D --> E[快路径<br/>轻量分类模型 + 规则匹配]
+    D --> F[慢路径<br/>向量召回 + LLM 精判 + Workflow 生成]
+
+    E --> G[槽位校验]
+    F --> H[复杂任务解析]
+
+    G --> I[状态中心<br/>Session / Slots / Context / Current Step]
+    H --> I
+
+    I --> J[执行引擎<br/>状态机 / 规则引擎 / 插件调度]
+    J --> K[插件层<br/>订单 / 售后 / 前台 / 座舱 / 转人工]
+
+    K --> L[业务系统<br/>CRM / ERP / 工单 / 地图 / IoT / 车控]
+    J --> M[回复生成层<br/>模板回复 / LLM 润色 / TTS]
+    M --> N[用户反馈]
+
+    I --> O[Redis]
+    J --> P[PostgreSQL]
+    F --> Q[向量库 Qdrant / Milvus]
+    J --> R[监控审计<br/>日志 / Trace / Metrics]
+```
+
+### 19.2 模块职责说明
+
+- 接入层：统一承接文本、语音、车机和 IM 渠道请求。
+- 预处理层：负责 ASR、文本标准化、敏感词过滤、别名归一。
+- 意图路由层：判断进入快路径还是慢路径。
+- 状态中心：保存会话、槽位、上下文和当前执行进度。
+- 执行引擎：按规则与工作流调度插件，控制暂停、恢复、重试和回滚。
+- 插件层：承接具体业务动作，保证执行可控和可审计。
+
+## 20. 核心时序图
+
+### 20.1 高频请求快路径时序图
+
+适用场景：
+
+- “打开空调”
+- “查询订单”
+- “通知张三到前台”
+
+```mermaid
+sequenceDiagram
+    participant U as 用户
+    participant G as 接入层
+    participant R as 路由层
+    participant C as 轻量分类模型
+    participant S as 状态中心
+    participant E as 执行引擎
+    participant P as 业务插件
+    participant B as 业务系统
+
+    U->>G: 发起请求
+    G->>R: 预处理后的文本
+    R->>C: 意图分类
+    C-->>R: 高频意图 + 置信度
+    R->>S: 读取/更新会话状态
+    R->>E: 发起执行请求
+    E->>P: 调用目标插件
+    P->>B: 调用业务接口
+    B-->>P: 返回结果
+    P-->>E: 执行结果
+    E->>S: 更新步骤状态
+    E-->>G: 结构化结果
+    G-->>U: 返回执行结果
+```
+
+### 20.2 复杂请求慢路径时序图
+
+适用场景：
+
+- “查订单，如果没发货就取消”
+- “导航去公司，再把空调调到 22 度”
+
+```mermaid
+sequenceDiagram
+    participant U as 用户
+    participant G as 接入层
+    participant R as 路由层
+    participant V as 向量召回
+    participant L as LLM
+    participant S as 状态中心
+    participant E as 执行引擎
+    participant P as 插件层
+
+    U->>G: 发起复杂请求
+    G->>R: 预处理后的输入
+    R->>V: 召回候选意图 Top-K
+    V-->>R: 候选意图列表
+    R->>L: 候选意图 + 用户输入
+    L-->>R: 精判结果 + Workflow JSON
+    R->>S: 保存 workflow 和 slots
+    R->>E: 启动执行
+    E->>P: 按步骤调度插件
+    P-->>E: 每步执行结果
+    E->>S: 更新 current_step / context
+    E-->>G: 汇总结果
+    G-->>U: 返回执行反馈
+```
+
+### 20.3 多轮补槽与断点续跑时序图
+
+适用场景：
+
+- “我要退货”
+- “帮我预约维修”
+- “导航去机场，走高速”
+
+```mermaid
+sequenceDiagram
+    participant U as 用户
+    participant G as 接入层
+    participant R as 路由层
+    participant L as LLM/抽取模型
+    participant S as 状态中心
+    participant E as 执行引擎
+    participant P as 插件层
+
+    U->>G: 我要退货
+    G->>R: 首轮请求
+    R->>L: 识别意图并抽取槽位
+    L-->>R: 退货意图，缺少 order_id
+    R->>S: 保存 pending_slots=order_id
+    R-->>G: 生成追问
+    G-->>U: 请提供订单号
+
+    U->>G: A123456
+    G->>S: 带 session_id 读取上下文
+    S-->>G: 返回待补槽位信息
+    G->>L: 仅做槽位提取
+    L-->>G: order_id=A123456
+    G->>S: 更新槽位并置为 ready_to_execute
+    G->>E: 从断点恢复执行
+    E->>P: 调用退货插件
+    P-->>E: 返回结果
+    E->>S: 更新为 completed
+    E-->>G: 执行成功
+    G-->>U: 已为你提交退货申请
+```
+
+## 21. 场景执行图
+
+### 21.1 智能座舱复合指令执行图
+
+示例指令：
+
+- “打开空调到 22 度，导航去公司，再播放轻音乐”
+
+```mermaid
+flowchart TD
+    A[用户输入复合指令] --> B[意图拆分]
+    B --> C1[设置空调]
+    B --> C2[导航到公司]
+    B --> C3[播放轻音乐]
+
+    C1 --> D[槽位校验 temperature=22]
+    C2 --> E[槽位校验 destination=公司]
+    C3 --> F[槽位校验 genre=轻音乐]
+
+    D --> G[空调插件]
+    E --> H[导航插件]
+    F --> I[音乐插件]
+
+    G --> J[更新座舱上下文]
+    H --> J
+    I --> J
+    J --> K[统一反馈执行结果]
+```
+
+### 21.2 客服售后条件流程图
+
+示例指令：
+
+- “帮我查订单，如果还没发货就取消”
+
+```mermaid
+flowchart TD
+    A[用户输入] --> B[识别查询订单 + 取消订单]
+    B --> C[生成条件工作流]
+    C --> D[执行查询订单插件]
+    D --> E{是否已发货}
+    E -- 否 --> F[执行取消订单插件]
+    E -- 是 --> G[返回不可取消说明]
+    F --> H[返回取消成功]
+    G --> I[结束]
+    H --> I
+```
+
+## 22. 时序图对应的工程说明
+
+### 22.1 为什么要先路由再规划
+
+- 不是所有请求都需要 LLM。
+- 高频请求先经过快路径，能够显著降低响应延迟和调用成本。
+- 复杂请求才进入召回与规划链路，保证资源集中用在真正困难的问题上。
+
+### 22.2 为什么多轮只做补槽不重规划
+
+- 这样可以减少模型重复推理，显著缩短第二轮及后续轮次响应时间。
+- 可以避免上下文漂移，保证任务持续围绕原始目标推进。
+- 更适合客服、售后、前台、座舱这类明确任务型交互。
+
+### 22.3 为什么执行一定要经过状态中心
+
+- 执行过程需要可追踪、可暂停、可恢复。
+- 插件失败、超时、用户打断等情况都需要状态中心统一管理。
+- 后续做审计、风控、会话分析时，也必须依赖完整状态记录。
+
+## 23. 下一步详细设计建议
+
+在当前架构图和时序图基础上，建议下一步输出以下内容：
+
+1. 意图清单：按客服、售后、前台、座舱四大域拆分。
+2. 槽位字典：定义每个意图所需字段、校验规则和追问模板。
+3. 插件接口表：定义入参、出参、幂等键、超时与错误码。
+4. MVP 范围：先选 10 到 20 个高频意图，构建第一版闭环。
+
+## 24. 意图清单初稿
+
+### 24.1 字段说明
+
+建议每个意图至少包含以下字段：
+
+- `intent_id`：意图唯一标识
+- `intent_name`：意图名称
+- `domain`：所属业务域
+- `type`：问答 / 事务 / 控制
+- `priority`：优先级
+- `risk_level`：风险等级
+- `entry_path`：快路径 / 慢路径
+- `required_slots`：必填槽位
+- `plugin_id`：执行插件标识
+
+### 24.2 客服域意图表
+
+| intent_id | intent_name | type | risk_level | entry_path | required_slots | plugin_id |
+| --- | --- | --- | --- | --- | --- | --- |
+| cs_query_order | 查询订单状态 | 事务 | low | 快路径 | order_id | plugin.order.query |
+| cs_query_logistics | 查询物流 | 事务 | low | 快路径 | order_id | plugin.logistics.query |
+| cs_cancel_order | 取消订单 | 事务 | medium | 快路径 | order_id | plugin.order.cancel |
+| cs_refund_status | 查询退款进度 | 事务 | low | 快路径 | refund_id/order_id | plugin.refund.query |
+| cs_transfer_human | 转人工 | 事务 | low | 快路径 | reason | plugin.service.transfer_human |
+| cs_complaint_submit | 提交投诉 | 事务 | medium | 慢路径 | complaint_type, content | plugin.ticket.complaint |
+
+### 24.3 售后域意图表
+
+| intent_id | intent_name | type | risk_level | entry_path | required_slots | plugin_id |
+| --- | --- | --- | --- | --- | --- | --- |
+| af_apply_return | 申请退货 | 事务 | medium | 快路径 | order_id, reason | plugin.aftersale.return |
+| af_apply_exchange | 申请换货 | 事务 | medium | 慢路径 | order_id, reason | plugin.aftersale.exchange |
+| af_apply_repair | 申请维修 | 事务 | medium | 慢路径 | product_id, fault_desc | plugin.aftersale.repair |
+| af_book_service | 预约上门服务 | 事务 | medium | 慢路径 | address, time_range, phone | plugin.aftersale.booking |
+| af_query_ticket | 查询工单进度 | 事务 | low | 快路径 | ticket_id | plugin.ticket.query |
+| af_upload_material | 补充售后材料 | 事务 | low | 快路径 | ticket_id, material_type | plugin.ticket.material |
+
+### 24.4 前台域意图表
+
+| intent_id | intent_name | type | risk_level | entry_path | required_slots | plugin_id |
+| --- | --- | --- | --- | --- | --- | --- |
+| front_visitor_register | 来访登记 | 事务 | medium | 慢路径 | visitor_name, phone, host_name | plugin.frontdesk.visitor_register |
+| front_notify_host | 通知被访人 | 事务 | low | 快路径 | host_name | plugin.frontdesk.notify_host |
+| front_book_meeting_room | 预约会议室 | 事务 | medium | 慢路径 | room_name, start_time, end_time | plugin.frontdesk.book_room |
+| front_route_guide | 路线引导 | 问答 | low | 快路径 | destination | plugin.frontdesk.route_guide |
+| front_check_appointment | 查询预约信息 | 事务 | low | 快路径 | phone/appointment_id | plugin.frontdesk.query_appointment |
+| front_print_badge | 打印访客证 | 控制 | medium | 快路径 | visitor_name, company | plugin.frontdesk.print_badge |
+
+### 24.5 智能座舱域意图表
+
+| intent_id | intent_name | type | risk_level | entry_path | required_slots | plugin_id |
+| --- | --- | --- | --- | --- | --- | --- |
+| cabin_nav_to | 导航到目的地 | 控制 | medium | 快路径 | destination | plugin.cabin.navigation |
+| cabin_set_ac | 设置空调 | 控制 | low | 快路径 | temperature | plugin.cabin.ac_control |
+| cabin_window_control | 控制车窗 | 控制 | medium | 快路径 | position, action | plugin.cabin.window_control |
+| cabin_play_music | 播放音乐 | 控制 | low | 快路径 | song/genre | plugin.cabin.music_play |
+| cabin_call_contact | 拨打电话 | 控制 | high | 慢路径 | contact_name | plugin.cabin.call_contact |
+| cabin_multi_command | 复合指令执行 | 控制 | medium | 慢路径 | workflow | plugin.cabin.workflow_executor |
+
+### 24.6 通用兜底意图
+
+| intent_id | intent_name | type | risk_level | entry_path | required_slots | plugin_id |
+| --- | --- | --- | --- | --- | --- | --- |
+| common_faq | 知识问答 | 问答 | low | 快路径 | 无 | plugin.knowledge.faq |
+| common_clarify | 澄清意图 | 问答 | low | 慢路径 | 无 | plugin.dialog.clarify |
+| common_reject | 拒绝执行 | 问答 | high | 快路径 | 无 | plugin.guard.reject |
+| common_fallback | 兜底处理 | 问答 | low | 慢路径 | 无 | plugin.dialog.fallback |
+
+## 25. 槽位字典初稿
+
+### 25.1 通用槽位表
+
+| slot_name | type | required | example | validate_rule | ask_template | used_by |
+| --- | --- | --- | --- | --- | --- | --- |
+| order_id | string | 是 | A123456 | 长度 6-32，字母数字 | 请提供订单号 | 客服、售后 |
+| refund_id | string | 否 | R2025001 | 长度 6-32 | 请提供退款单号 | 客服 |
+| ticket_id | string | 是 | T90001 | 长度 4-32 | 请提供工单号 | 售后 |
+| appointment_id | string | 否 | AP2026001 | 长度 4-32 | 请提供预约编号 | 前台 |
+| product_id | string | 是 | SKU12345 | 长度 4-64 | 请提供商品编号或设备编号 | 售后 |
+| reason | string | 是 | 不想要了 | 长度 2-200 | 请说明原因 | 客服、售后 |
+| fault_desc | string | 是 | 无法开机 | 长度 5-300 | 请描述故障现象 | 售后 |
+| complaint_type | enum | 是 | 服务问题 | 枚举校验 | 请问是商品问题、物流问题还是服务问题 | 客服 |
+| content | string | 是 | 客服态度差 | 长度 5-500 | 请描述具体情况 | 客服 |
+| phone | string | 是 | 13800138000 | 手机号格式 | 请提供手机号 | 售后、前台 |
+| time_range | string | 是 | 明天下午 2 点到 4 点 | 可解析时间范围 | 请提供预约时间 | 售后、前台 |
+| address | string | 是 | 上海浦东新区 XX 路 | 长度 5-200 | 请提供服务地址 | 售后 |
+| material_type | enum | 是 | 故障图片 | 枚举校验 | 请说明要补充的材料类型 | 售后 |
+| visitor_name | string | 是 | 张三 | 长度 2-50 | 请提供来访人姓名 | 前台 |
+| host_name | string | 是 | 李经理 | 长度 2-50 | 请问要拜访谁 | 前台 |
+| company | string | 否 | 某某科技 | 长度 2-100 | 请提供访客单位名称 | 前台 |
+| room_name | string | 是 | 3F-A01 | 会议室编码格式 | 请提供会议室名称或编号 | 前台 |
+| start_time | datetime | 是 | 2026-05-10 14:00 | 标准时间格式 | 请提供开始时间 | 前台 |
+| end_time | datetime | 是 | 2026-05-10 15:00 | 晚于开始时间 | 请提供结束时间 | 前台 |
+| destination | string | 是 | 公司/虹桥机场 | POI 或地址可解析 | 请告诉我要去哪里 | 前台、座舱 |
+| temperature | number | 是 | 22 | 16-30 | 请问要设置多少度 | 座舱 |
+| position | enum | 是 | 主驾窗 | 枚举校验 | 请问要控制哪个车窗 | 座舱 |
+| action | enum | 是 | 打开/关闭 | 枚举校验 | 请问要打开还是关闭 | 座舱 |
+| song | string | 否 | 夜曲 | 长度 1-100 | 请问要播放哪首歌 | 座舱 |
+| genre | string | 否 | 轻音乐 | 长度 1-50 | 请问要听什么类型的音乐 | 座舱 |
+| contact_name | string | 是 | 王总 | 长度 2-50 | 请问要拨打给谁 | 座舱 |
+| workflow | json | 是 | 复合动作 JSON | JSON Schema 校验 | 正在解析复合指令 | 座舱 |
+
+### 25.2 槽位配置建议
+
+每个槽位建议增加如下元数据：
+
+- `normalize_fn`：归一化方法，例如金额、时间、地址标准化
+- `source_priority`：优先从用户当前输入、历史会话还是业务上下文中取值
+- `confirm_required`：是否需要用户确认
+- `risk_binding`：是否与风险动作绑定
+- `rewrite_prompt`：槽位识别失败时的重试提示
+
+### 25.3 槽位提取策略
+
+- 首轮请求：意图识别与槽位提取同时进行
+- 补槽轮次：只提取 `pending_slots`
+- 高风险槽位：提取后必须二次确认
+- 可继承槽位：如 `phone`、`host_name`，可从历史会话或用户档案中补全
+
+## 26. 插件接口规范初稿
+
+### 26.1 插件统一接口
+
+所有业务插件建议遵循统一接口，便于执行引擎调度：
+
+```json
+{
+  "plugin_id": "plugin.order.cancel",
+  "request_id": "req_001",
+  "session_id": "sess_001",
+  "user_id": "user_001",
+  "intent_id": "cs_cancel_order",
+  "slots": {
+    "order_id": "A123456",
+    "reason": "不想要了"
+  },
+  "context": {
+    "channel": "app",
+    "trace_id": "trace_001"
+  }
+}
+```
+
+统一响应格式建议如下：
+
+```json
+{
+  "success": true,
+  "code": "OK",
+  "message": "订单取消成功",
+  "data": {
+    "order_id": "A123456",
+    "status": "cancelled"
+  },
+  "need_confirm": false,
+  "need_more_slots": [],
+  "retryable": false
+}
+```
+
+### 26.2 插件表初稿
+
+| plugin_id | plugin_name | domain | input_slots | output_fields | timeout_ms | retry_policy | idempotent_key |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| plugin.order.query | 查询订单 | 客服 | order_id | order_status, pay_status | 1500 | 失败重试 1 次 | session_id + order_id |
+| plugin.logistics.query | 查询物流 | 客服 | order_id | logistics_status, trace_list | 1500 | 失败重试 1 次 | session_id + order_id |
+| plugin.order.cancel | 取消订单 | 客服 | order_id, reason | cancel_status | 2000 | 不自动重试 | session_id + order_id |
+| plugin.service.transfer_human | 转人工 | 客服 | reason | queue_no, wait_time | 1000 | 不自动重试 | session_id + reason |
+| plugin.aftersale.return | 申请退货 | 售后 | order_id, reason | return_ticket_id | 2000 | 不自动重试 | session_id + order_id |
+| plugin.aftersale.repair | 申请维修 | 售后 | product_id, fault_desc | repair_ticket_id | 2500 | 不自动重试 | session_id + product_id |
+| plugin.aftersale.booking | 上门预约 | 售后 | address, time_range, phone | booking_id | 2500 | 失败重试 1 次 | session_id + phone + time_range |
+| plugin.frontdesk.visitor_register | 来访登记 | 前台 | visitor_name, phone, host_name | visit_id, qr_code | 2000 | 不自动重试 | session_id + phone |
+| plugin.frontdesk.notify_host | 通知被访人 | 前台 | host_name | notify_status | 1000 | 失败重试 1 次 | session_id + host_name |
+| plugin.frontdesk.book_room | 预约会议室 | 前台 | room_name, start_time, end_time | booking_status, booking_id | 2000 | 不自动重试 | session_id + room_name + start_time |
+| plugin.cabin.navigation | 导航 | 座舱 | destination | route_id, eta | 1200 | 失败重试 1 次 | session_id + destination |
+| plugin.cabin.ac_control | 空调控制 | 座舱 | temperature | ac_status | 800 | 不自动重试 | session_id + temperature |
+| plugin.cabin.window_control | 车窗控制 | 座舱 | position, action | window_status | 800 | 不自动重试 | session_id + position + action |
+| plugin.cabin.music_play | 音乐播放 | 座舱 | song/genre | play_status, media_id | 1000 | 失败重试 1 次 | session_id + song + genre |
+
+### 26.3 插件错误码建议
+
+| code | meaning | action |
+| --- | --- | --- |
+| OK | 执行成功 | 正常返回 |
+| MISSING_SLOT | 缺少必要槽位 | 转为追问流程 |
+| INVALID_SLOT | 槽位不合法 | 触发重试提示 |
+| NEED_CONFIRM | 需要用户确认 | 进入确认节点 |
+| NO_PERMISSION | 权限不足 | 拒绝执行并解释原因 |
+| RISK_BLOCKED | 风险拦截 | 终止执行并记录审计 |
+| BIZ_TIMEOUT | 下游超时 | 可重试或降级 |
+| BIZ_FAILED | 业务失败 | 给出失败原因 |
+| SYSTEM_ERROR | 系统异常 | 兜底或转人工 |
+
+### 26.4 插件开发约束
+
+- 插件必须无状态，状态统一交给状态中心管理。
+- 插件必须返回结构化结果，不能只返回自然语言。
+- 高风险插件必须支持 `need_confirm` 控制。
+- 写操作类插件必须支持幂等键。
+- 插件必须记录 trace_id，支持全链路审计。
+
+## 27. 接口协议建议
+
+### 27.1 对话入口接口
+
+`POST /api/v1/agent/chat`
+
+请求示例：
+
+```json
+{
+  "session_id": "sess_001",
+  "user_id": "user_001",
+  "channel": "app",
+  "input_text": "帮我查一下订单，如果没发货就取消",
+  "input_type": "text",
+  "metadata": {
+    "device_id": "dev_001",
+    "tenant_id": "tenant_a"
+  }
+}
+```
+
+响应示例：
+
+```json
+{
+  "session_id": "sess_001",
+  "reply_type": "workflow_result",
+  "reply_text": "已为你查询订单，当前未发货，正在为你取消。",
+  "intent": "cs_cancel_order",
+  "status": "executing",
+  "pending_slots": [],
+  "workflow_id": "wf_001",
+  "trace_id": "trace_001"
+}
+```
+
+### 27.2 补槽接口
+
+`POST /api/v1/agent/fill-slots`
+
+请求示例：
+
+```json
+{
+  "session_id": "sess_001",
+  "user_id": "user_001",
+  "input_text": "订单号是 A123456"
+}
+```
+
+响应示例：
+
+```json
+{
+  "session_id": "sess_001",
+  "status": "ready_to_execute",
+  "filled_slots": {
+    "order_id": "A123456"
+  },
+  "pending_slots": [],
+  "reply_text": "收到，正在继续为你处理。"
+}
+```
+
+### 27.3 管理后台接口建议
+
+- `GET /api/v1/intents`
+- `POST /api/v1/intents`
+- `GET /api/v1/slots`
+- `POST /api/v1/plugins/register`
+- `GET /api/v1/sessions/{session_id}`
+- `GET /api/v1/traces/{trace_id}`
+
+## 28. MVP 一期范围建议
+
+### 28.1 一期目标
+
+目标不是一开始覆盖所有场景，而是优先打通一条“能快速响应、能多轮补槽、能真正执行”的闭环。
+
+### 28.2 推荐一期场景
+
+建议先选两个最有代表性的场景：
+
+- 客服：查询订单、查物流、取消订单、转人工
+- 智能座舱：导航、空调控制、音乐播放、复合指令
+
+这样可以同时覆盖：
+
+- 标准事务型任务
+- 高实时控制型任务
+- 多轮补槽能力
+- 复杂复合指令能力
+
+### 28.3 一期推荐意图
+
+建议第一期先落 12 个意图：
+
+| domain | intent_id | intent_name | priority |
+| --- | --- | --- | --- |
+| 客服 | cs_query_order | 查询订单状态 | P0 |
+| 客服 | cs_query_logistics | 查询物流 | P0 |
+| 客服 | cs_cancel_order | 取消订单 | P0 |
+| 客服 | cs_transfer_human | 转人工 | P0 |
+| 售后 | af_apply_return | 申请退货 | P1 |
+| 售后 | af_query_ticket | 查询工单进度 | P1 |
+| 前台 | front_notify_host | 通知被访人 | P1 |
+| 前台 | front_route_guide | 路线引导 | P1 |
+| 座舱 | cabin_nav_to | 导航到目的地 | P0 |
+| 座舱 | cabin_set_ac | 设置空调 | P0 |
+| 座舱 | cabin_play_music | 播放音乐 | P0 |
+| 座舱 | cabin_multi_command | 复合指令执行 | P0 |
+
+### 28.4 一期必须具备的能力
+
+- 会话状态中心
+- 快路径意图分类
+- 基础槽位提取
+- 补槽追问与断点恢复
+- 插件统一协议
+- 至少 8 个可执行插件
+- 基础监控和审计日志
+
+### 28.5 一期不建议过早投入的能力
+
+- 全量复杂规则平台
+- 多租户运营后台
+- 自动训练平台
+- 端到端自主规划型 Agent
+
+### 28.6 一期验收标准
+
+| metric | target |
+| --- | --- |
+| 高频快路径平均响应时间 | <= 1.5s |
+| 复杂慢路径平均响应时间 | <= 3.5s |
+| 多轮补槽成功率 | >= 85% |
+| 插件执行成功率 | >= 95% |
+| 高频意图识别准确率 | >= 95% |
+| 高风险误执行率 | 0 |
+
+## 29. 开发拆分建议
+
+### 29.1 服务拆分
+
+- `gateway-service`：统一接入与鉴权
+- `nlu-service`：分类、抽取、召回、精判
+- `session-service`：会话状态管理
+- `orchestrator-service`：工作流执行与插件调度
+- `plugin-service`：业务插件实现
+- `ops-console`：配置与观测后台
+
+### 29.2 迭代顺序
+
+1. 先做会话状态中心和统一协议。
+2. 再做快路径分类与基础插件闭环。
+3. 然后接入向量召回和 LLM 精判。
+4. 最后补复合指令、多轮恢复、监控审计。
+
+### 29.3 人员建议
+
+- 后端工程师：2 人
+- AI/NLP 工程师：1 到 2 人
+- 前端或客户端工程师：1 人
+- 测试工程师：1 人
+- 产品经理：1 人
+
+## 30. 下一步建议
+
+基于当前文档，下一步最适合继续补的是：
+
+1. 数据库表结构设计
+2. Redis Session 结构设计
+3. Workflow JSON Schema
+4. 插件 SDK 规范
+5. 评测集与测试用例设计
+
+## 31. 数据库表结构设计
+
+### 31.1 设计原则
+
+- 业务配置与运行态数据分离。
+- 高并发状态放 Redis，持久化和审计放 PostgreSQL。
+- 配置表优先满足可运营、可灰度、可版本化。
+- 运行表优先满足可追踪、可排障、可回放。
+
+### 31.2 核心表清单
+
+| table_name | purpose |
+| --- | --- |
+| intents | 意图定义表 |
+| intent_examples | 意图示例语料表 |
+| slots | 槽位定义表 |
+| intent_slot_bindings | 意图和槽位绑定表 |
+| plugins | 插件注册表 |
+| plugin_routes | 意图到插件路由表 |
+| workflow_templates | 工作流模板表 |
+| sessions | 会话主表 |
+| session_messages | 会话消息表 |
+| task_executions | 任务执行表 |
+| task_steps | 任务步骤表 |
+| plugin_calls | 插件调用日志表 |
+| audit_logs | 审计日志表 |
+| evaluation_cases | 评测样本表 |
+
+### 31.3 关键表字段示例
+
+#### intents
+
+| field | type | desc |
+| --- | --- | --- |
+| id | bigint pk | 主键 |
+| intent_id | varchar(64) unique | 意图唯一标识 |
+| intent_name | varchar(128) | 意图名称 |
+| domain | varchar(64) | 业务域 |
+| type | varchar(32) | 问答/事务/控制 |
+| risk_level | varchar(16) | 风险等级 |
+| entry_path | varchar(16) | 快路径/慢路径 |
+| enabled | boolean | 是否启用 |
+| version | int | 配置版本 |
+| description | text | 说明 |
+| created_at | timestamp | 创建时间 |
+| updated_at | timestamp | 更新时间 |
+
+#### slots
+
+| field | type | desc |
+| --- | --- | --- |
+| id | bigint pk | 主键 |
+| slot_name | varchar(64) unique | 槽位名称 |
+| slot_type | varchar(32) | string/number/enum/json |
+| required | boolean | 是否必填 |
+| validate_rule | varchar(256) | 校验规则 |
+| ask_template | varchar(256) | 追问模板 |
+| normalize_fn | varchar(64) | 归一化方法 |
+| confirm_required | boolean | 是否需要确认 |
+| enabled | boolean | 是否启用 |
+| created_at | timestamp | 创建时间 |
+| updated_at | timestamp | 更新时间 |
+
+#### intent_slot_bindings
+
+| field | type | desc |
+| --- | --- | --- |
+| id | bigint pk | 主键 |
+| intent_id | varchar(64) | 意图标识 |
+| slot_name | varchar(64) | 槽位名称 |
+| required | boolean | 对该意图是否必填 |
+| priority | int | 提问优先级 |
+| source_priority | varchar(128) | 来源优先级 |
+| created_at | timestamp | 创建时间 |
+
+#### plugins
+
+| field | type | desc |
+| --- | --- | --- |
+| id | bigint pk | 主键 |
+| plugin_id | varchar(128) unique | 插件标识 |
+| plugin_name | varchar(128) | 插件名称 |
+| domain | varchar(64) | 所属域 |
+| endpoint | varchar(256) | 插件地址或服务名 |
+| timeout_ms | int | 超时时间 |
+| retry_policy | varchar(64) | 重试策略 |
+| idempotent_rule | varchar(128) | 幂等规则 |
+| enabled | boolean | 是否启用 |
+| created_at | timestamp | 创建时间 |
+| updated_at | timestamp | 更新时间 |
+
+#### sessions
+
+| field | type | desc |
+| --- | --- | --- |
+| id | bigint pk | 主键 |
+| session_id | varchar(64) unique | 会话标识 |
+| user_id | varchar(64) | 用户标识 |
+| channel | varchar(32) | 渠道 |
+| current_intent | varchar(64) | 当前意图 |
+| current_status | varchar(32) | 当前状态 |
+| current_workflow_id | varchar(64) | 当前工作流 |
+| risk_level | varchar(16) | 当前风险等级 |
+| last_message_at | timestamp | 最后活跃时间 |
+| created_at | timestamp | 创建时间 |
+| updated_at | timestamp | 更新时间 |
+
+#### session_messages
+
+| field | type | desc |
+| --- | --- | --- |
+| id | bigint pk | 主键 |
+| session_id | varchar(64) | 会话标识 |
+| role | varchar(16) | user/assistant/system |
+| message_text | text | 文本内容 |
+| structured_payload | jsonb | 结构化载荷 |
+| trace_id | varchar(64) | 链路标识 |
+| created_at | timestamp | 创建时间 |
+
+#### task_executions
+
+| field | type | desc |
+| --- | --- | --- |
+| id | bigint pk | 主键 |
+| task_id | varchar(64) unique | 任务标识 |
+| session_id | varchar(64) | 会话标识 |
+| workflow_id | varchar(64) | 工作流标识 |
+| status | varchar(32) | pending/running/paused/completed/failed |
+| current_step | int | 当前步骤 |
+| context_snapshot | jsonb | 上下文快照 |
+| error_code | varchar(64) | 错误码 |
+| error_message | varchar(256) | 错误信息 |
+| created_at | timestamp | 创建时间 |
+| updated_at | timestamp | 更新时间 |
+
+#### task_steps
+
+| field | type | desc |
+| --- | --- | --- |
+| id | bigint pk | 主键 |
+| task_id | varchar(64) | 任务标识 |
+| step_no | int | 步骤号 |
+| intent_id | varchar(64) | 步骤意图 |
+| plugin_id | varchar(128) | 插件标识 |
+| status | varchar(32) | 步骤状态 |
+| input_payload | jsonb | 入参 |
+| output_payload | jsonb | 出参 |
+| started_at | timestamp | 开始时间 |
+| ended_at | timestamp | 结束时间 |
+
+#### plugin_calls
+
+| field | type | desc |
+| --- | --- | --- |
+| id | bigint pk | 主键 |
+| trace_id | varchar(64) | 链路标识 |
+| task_id | varchar(64) | 任务标识 |
+| plugin_id | varchar(128) | 插件标识 |
+| request_payload | jsonb | 请求内容 |
+| response_payload | jsonb | 响应内容 |
+| success | boolean | 是否成功 |
+| latency_ms | int | 调用耗时 |
+| created_at | timestamp | 创建时间 |
+
+### 31.4 建表索引建议
+
+- `intents(intent_id)` 唯一索引
+- `slots(slot_name)` 唯一索引
+- `plugins(plugin_id)` 唯一索引
+- `sessions(session_id)` 唯一索引
+- `sessions(user_id, updated_at)` 组合索引
+- `session_messages(session_id, created_at)` 组合索引
+- `task_executions(session_id, status)` 组合索引
+- `plugin_calls(trace_id)` 普通索引
+
+## 32. Redis Session 结构设计
+
+### 32.1 设计目标
+
+- 支持毫秒级读取会话状态
+- 支持多轮补槽和断点恢复
+- 支持任务并发控制和幂等去重
+- 支持热点上下文缓存
+
+### 32.2 Key 设计
+
+| key_pattern | type | purpose | ttl |
+| --- | --- | --- | --- |
+| `agent:session:{session_id}` | Hash | 会话主状态 | 24h |
+| `agent:slots:{session_id}` | Hash | 当前已填槽位 | 24h |
+| `agent:pending:{session_id}` | List | 待补槽位队列 | 24h |
+| `agent:workflow:{session_id}` | String | 当前 Workflow JSON | 24h |
+| `agent:context:{session_id}` | String | 上下文快照 JSON | 24h |
+| `agent:lock:{session_id}` | String | 会话执行锁 | 30s |
+| `agent:idempotent:{key}` | String | 幂等去重键 | 10m |
+| `agent:cache:intent:{text_hash}` | String | 高频意图缓存 | 30m |
+
+### 32.3 Session Hash 示例
+
+Key: `agent:session:sess_001`
+
+```json
+{
+  "session_id": "sess_001",
+  "user_id": "user_001",
+  "channel": "app",
+  "status": "waiting_slot",
+  "current_intent": "af_apply_return",
+  "workflow_id": "wf_001",
+  "current_step": "1",
+  "risk_level": "medium",
+  "last_active_at": "2026-05-09T10:00:00Z"
+}
+```
+
+### 32.4 Slots Hash 示例
+
+Key: `agent:slots:sess_001`
+
+```json
+{
+  "order_id": "A123456",
+  "reason": "不想要了"
+}
+```
+
+### 32.5 Pending Slots List 示例
+
+Key: `agent:pending:sess_001`
+
+```json
+[
+  "order_id",
+  "reason"
+]
+```
+
+### 32.6 Context JSON 示例
+
+Key: `agent:context:sess_001`
+
+```json
+{
+  "biz_context": {
+    "order_status": "pending_shipment"
+  },
+  "history_summary": "用户发起退货申请",
+  "plugin_results": {
+    "plugin.order.query": {
+      "success": true,
+      "status": "pending_shipment"
+    }
+  }
+}
+```
+
+### 32.7 Redis 操作建议
+
+- 进入执行前对 `agent:lock:{session_id}` 加分布式锁。
+- 每次补槽后同时更新 `session`、`slots`、`pending`。
+- 工作流执行完成后，可保留短时缓存用于追问和结果追溯。
+- 长会话可异步落盘 PostgreSQL 后缩短 Redis TTL。
+
+## 33. Workflow JSON Schema 设计
+
+### 33.1 设计目标
+
+- 让 LLM 输出严格受控
+- 让执行引擎易于解析
+- 支持单步、顺序、条件、并行和补槽暂停
+
+### 33.2 Workflow 顶层结构
+
+```json
+{
+  "workflow_id": "wf_001",
+  "workflow_type": "sequence",
+  "domain": "customer_service",
+  "intent_id": "cs_cancel_order",
+  "status": "ready",
+  "risk_level": "medium",
+  "slots": {
+    "order_id": "A123456"
+  },
+  "missing_slots": [],
+  "steps": [],
+  "meta": {
+    "source": "llm_planner",
+    "version": "1.0"
+  }
+}
+```
+
+### 33.3 Step 结构
+
+```json
+{
+  "step": 1,
+  "step_id": "step_001",
+  "intent_id": "cs_query_order",
+  "plugin_id": "plugin.order.query",
+  "action": "query_order",
+  "status": "pending",
+  "depends_on": [],
+  "slots": {
+    "order_id": "A123456"
+  },
+  "on_success": "next",
+  "on_failure": "fallback",
+  "timeout_ms": 1500
+}
+```
+
+### 33.4 条件步骤结构
+
+```json
+{
+  "step": 2,
+  "step_id": "step_002",
+  "type": "condition",
+  "condition": {
+    "expr": "context.order_status == 'pending_shipment'"
+  },
+  "if_true": {
+    "intent_id": "cs_cancel_order",
+    "plugin_id": "plugin.order.cancel"
+  },
+  "if_false": {
+    "action": "reply",
+    "message": "订单已发货，暂时无法取消。"
+  }
+}
+```
+
+### 33.5 补槽暂停结构
+
+```json
+{
+  "status": "waiting_slot",
+  "missing_slots": [
+    {
+      "slot_name": "order_id",
+      "ask_template": "请提供订单号",
+      "priority": 1
+    }
+  ]
+}
+```
+
+### 33.6 JSON Schema 示例
+
+```json
+{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "title": "AgentWorkflow",
+  "type": "object",
+  "required": ["workflow_id", "workflow_type", "intent_id", "status", "steps"],
+  "properties": {
+    "workflow_id": {
+      "type": "string"
+    },
+    "workflow_type": {
+      "type": "string",
+      "enum": ["single", "sequence", "conditional", "parallel"]
+    },
+    "domain": {
+      "type": "string"
+    },
+    "intent_id": {
+      "type": "string"
+    },
+    "status": {
+      "type": "string",
+      "enum": ["ready", "waiting_slot", "running", "completed", "failed"]
+    },
+    "risk_level": {
+      "type": "string",
+      "enum": ["low", "medium", "high"]
+    },
+    "slots": {
+      "type": "object"
+    },
+    "missing_slots": {
+      "type": "array"
+    },
+    "steps": {
+      "type": "array",
+      "items": {
+        "type": "object",
+        "required": ["step", "step_id"],
+        "properties": {
+          "step": {
+            "type": "integer"
+          },
+          "step_id": {
+            "type": "string"
+          },
+          "intent_id": {
+            "type": "string"
+          },
+          "plugin_id": {
+            "type": "string"
+          },
+          "action": {
+            "type": "string"
+          },
+          "status": {
+            "type": "string"
+          },
+          "depends_on": {
+            "type": "array"
+          },
+          "slots": {
+            "type": "object"
+          }
+        }
+      }
+    },
+    "meta": {
+      "type": "object"
+    }
+  }
+}
+```
+
+### 33.7 约束建议
+
+- LLM 只能从候选意图和候选插件中选择，不能自由生成未知标识。
+- 所有工作流必须通过 JSON Schema 校验后才能进入执行引擎。
+- 高风险动作必须显式输出 `risk_level` 和 `need_confirm`。
+- 补槽状态和执行状态必须严格区分，避免重复执行。
+
+## 34. 开工建议
+
+在完成以上设计后，建议按以下顺序正式开工：
+
+1. 初始化后端项目骨架。
+2. 实现配置模型、会话模型和 Workflow Schema。
+3. 打通 `/chat` 和 `/fill-slots` 两个核心接口。
+4. 先接入 mock 插件，完成完整闭环。
+5. 再逐步接入真实业务系统和模型服务。
+
+## 35. 多标签 Detector 过渡方案
+
+### 35.1 背景
+
+当前多意图链路已经具备以下结构：
+
+- `clause split` 负责切分明显并列或条件语句
+- `clause classifier` 负责对单个子句提供语义补偿
+- `multi_intent_detector` 负责在整句粒度提供多标签先验
+- `planner fusion` 负责合并 heuristic、classifier 和 detector 的信号
+
+现阶段的问题不在 `planner` 主骨架，而在 detector 的训练方式。当前 detector 基于单标签分类头的 logits 做 sigmoid 解释，只能作为过渡信号，不能代表真正学到的“多标签共现关系”。
+
+### 35.2 迁移目标
+
+本阶段的目标不是重写规划器，而是把 detector 从“推理时解释型多标签”升级成“训练时显式监督的多标签模型”。
+
+迁移后应满足：
+
+- `planner` 继续消费 `detector_prior`，主骨架保持稳定
+- detector 使用独立模型目录，不与单标签 classifier 共享分类头
+- 训练样本显式表达一个句子对应多个 intent 的监督关系
+- 评测指标从单一准确率升级为多标签指标
+- 为下一阶段 `NER / token classification` 提供更稳定的候选意图集合
+
+### 35.3 数据格式
+
+保留现有单标签训练数据：
+
+```json
+{"text": "打开车窗", "intent_id": "cabin_window_open"}
+```
+
+新增多标签训练数据：
+
+```json
+{"text": "打开车窗并播放音乐", "intent_ids": ["cabin_window_open", "cabin_play_music"]}
+```
+
+约束建议：
+
+- 单标签样本可以直接提升为 `intent_ids` 长度为 1 的多标签样本
+- 多标签样本优先覆盖座舱高频并列控制场景
+- `__social__` 与 `__out_of_scope__` 不作为多标签共现目标，避免污染业务动作组合学习
+- 多标签语料要覆盖口语连接词、顺序词、弱连接和省略表达
+
+### 35.4 模型与训练
+
+建议为 detector 使用独立训练脚本和独立输出目录：
+
+- 训练脚本：`scripts/train_local_bert_multi_intent.py`
+- 评测脚本：`scripts/eval_local_bert_multi_intent.py`
+- 模型目录：`models/local_bert_multi_intent/`
+
+训练方式：
+
+- 底座继续使用本地 MacBERT
+- 任务类型设置为 `multi_label_classification`
+- 标签为多热向量，损失函数为 `BCEWithLogitsLoss`
+- 输出为每个意图的独立概率，不再依赖单标签 softmax 头的副产物
+
+### 35.5 运行时接入
+
+运行时只替换 detector 的模型来源和解释方式，不改 `planner` 主逻辑。
+
+接入原则：
+
+- `classifier` 继续服务单子句语义分类
+- `multi_intent_detector` 独立加载多标签模型
+- detector 输出仍然是 `candidates -> detector_prior`
+- detector 默认屏蔽 `__social__` 和 `__out_of_scope__`
+- 当 detector 不可用时，planner 仍可退化到 `clause split + clause classifier + heuristic`
+
+### 35.6 与 NER 的衔接
+
+在真正多标签 detector 稳定前，不建议直接推进 `NER / token classification` 作为主增量。
+
+原因：
+
+- NER 擅长抽取边界，不擅长先决定整句有几个意图
+- 如果上游 detector 仍是假多标签，NER 很容易围绕错误意图做精细抽取
+- 先把多标签 detector 训练扎实，可以让 NER 在“候选意图集合已收敛”的前提下工作
+
+因此推荐顺序固定为：
+
+1. 真正多标签 detector
+2. detector 线上评测和阈值稳定
+3. NER / token classification
+
+### 35.7 本阶段验收标准
+
+本阶段完成后至少应满足：
+
+- detector 支持独立训练、独立加载、独立评测
+- 对典型并列座舱指令，`recall@k` 和 `micro_f1` 明显优于现有 sigmoid 解释方案
+- `planner` 主骨架无需改写，现有 fusion 逻辑保持兼容
+- 后续引入 NER 时，只需增加边界抽取模块，不必再次调整 detector 接口
+
+## 36. Joint NLU 升级方案
+
+### 36.1 目标
+
+本阶段不再继续扩旧的 heuristic 槽位提取逻辑，而是把本地 NLU 快链路升级成真正的 `Joint Intent + Slot` 联合识别。
+
+升级目标：
+
+- 用一个共享编码器同时完成意图识别和槽位抽取
+- 替换当前 `HeuristicSlotExtractor` 的主路径职责
+- 保留 `planner / workflow / session / response policy` 主骨架
+- 多意图整句仍走 `clause split + multi-intent detector + planner`
+- 单句或单 clause 的语义理解改为 `JointBERT`
+
+### 36.2 为什么不直接推倒整条链
+
+当前系统已经有稳定的上层编排能力：
+
+- `fusion` 负责 `execute / clarify / reject / route_to_cloud`
+- `planner` 负责 `sequence / conditional workflow`
+- `workflow executor` 负责多步执行、条件判断、确认和补槽
+
+这些能力并不是 `JointBERT` 能替代的。
+
+因此本次升级只替换本地理解层，不重写上层编排层。
+
+### 36.3 新架构边界
+
+升级后的本地链路定义为：
+
+```text
+text
+-> rewrite
+-> Joint NLU
+   - intent head
+   - slot tagging head
+-> fusion / planner
+-> workflow executor
+```
+
+新的职责划分：
+
+- `JointBERT`：单句或单 clause 的 intent + slot
+- `multi-intent detector`：整句多标签先验
+- `planner`：多意图、条件句、多步骤 workflow
+- `workflow executor`：补槽、确认、执行、汇总
+
+### 36.4 Joint NLU 数据格式
+
+新增联合训练数据，采用 span 标注格式：
+
+```json
+{
+  "text": "把空调调到22度",
+  "intent_id": "cabin_set_ac",
+  "slots": [
+    {"slot_name": "temperature", "value": "22度", "start": 6, "end": 9}
+  ]
+}
+```
+
+说明：
+
+- 单条样本只标注一个主意图
+- 多意图句在训练阶段优先拆成 clause 级样本
+- 无槽位意图的 `slots` 为空数组
+- 运行时将 span 转换为 token-level BIO 标签
+
+### 36.5 模型结构
+
+采用共享编码器的双头结构：
+
+- 编码器：`MacBERT`
+- 意图头：句级单标签分类
+- 槽位头：token classification，输出 BIO 标签
+
+第一版先不引入复杂的 slot-gate，先保证：
+
+- 共享编码器
+- 联合训练
+- 意图与槽位共享语义表征
+
+在当前项目里，这已经比“句分类 + 规则抽槽”前进一步。
+
+### 36.6 接入方式
+
+运行时新增 `JointNLU` 服务，供两处复用：
+
+1. `classifier backend = joint_bert`
+   - 用联合模型的 intent head 输出意图分数
+2. `slot_extractor backend = joint_bert`
+   - 用联合模型的 slot head 输出槽位
+
+要求：
+
+- 两个 backend 共享同一个模型实例，避免重复冷启动
+- 对同一文本允许做短时预测缓存，避免一次请求重复前向
+- 若联合模型不可用，直接报错或显式 fallback，不继续隐式走旧 heuristic 逻辑
+
+### 36.7 与多意图链路的关系
+
+`JointBERT` 不直接负责整句多意图 workflow 规划。
+
+多意图流程保持：
+
+1. `multi-intent detector` 对整句给出多标签先验
+2. `planner` 进行 clause split
+3. 每个 clause 用 `Joint NLU` 做 intent + slot
+4. `planner` 汇总 clause 结果为 workflow
+
+这样做的原因：
+
+- 经典 JointBERT 更适合单句单意图
+- 当前系统已经有成熟的 clause-level planner 骨架
+- 直接让一个单模型接管整句多步骤规划，风险更高
+
+### 36.8 替换原则
+
+本次升级的明确原则：
+
+- 默认主链路不再依赖 `HeuristicSlotExtractor`
+- planner 内部的 clause slots 也优先改为 `Joint NLU`
+- 原 heuristic 抽槽只保留为测试对照或应急兜底，不再作为主路径
+
+### 36.9 验收标准
+
+升级完成后至少满足：
+
+- 单步任务的槽位抽取来自 `Joint NLU`
+- 缺槽位逻辑、确认逻辑、多步 workflow 逻辑保持兼容
+- `router` 和 `planner` 都能读取联合模型输出
+- 本地常见控制句的槽位抽取精度高于旧 heuristic
+- 冷启动通过 warmup 控制，首轮推理不发生首次懒加载抖动
diff --git a/intelligent_cabin/archive/docs/solution_review.md b/intelligent_cabin/archive/docs/solution_review.md
new file mode 100644
index 0000000..e6f64c8
--- /dev/null
+++ b/intelligent_cabin/archive/docs/solution_review.md
@@ -0,0 +1,467 @@
+# 对齐车机方案的实施流程复审
+
+## 1. 目标
+
+本文档用于重新审视当前项目的实现方向，并将“对齐小鹏车机类方案”的核心流程、分支决策和典型场景演示明确下来。
+
+目标不是做一个普通聊天机器人，而是做一个面向车机/客服/前台场景的低延迟执行型 Agent：
+
+- 简单请求本地快速闭环
+- 复杂请求云端增强处理
+- 多轮短句能够恢复上下文
+- 多命令能够拆分为 workflow 执行
+- 超出能力边界时明确拒绝或澄清
+
+---
+
+## 2. 对齐车机方案的核心原则
+
+结合前面参考的小鹏公开专利，可以抽象出下面几个工程原则：
+
+- 本地优先，云端增强，不是所有请求都直接走远端大模型
+- 本地不是单模型，而是多支路并发
+- 快反馈和最终反馈分离，首反馈必须短而稳
+- 多轮短句优先靠上下文改写和缓存恢复，不是每轮重做完整规划
+- 高风险动作必须确认
+- 超能力边界时必须拒答，不能强行分类执行
+
+---
+
+## 3. 总体流程图
+
+```mermaid
+flowchart TD
+    A[用户语音输入] --> B[ASR 转文本]
+    B --> C[文本归一化]
+    C --> D[本地多支路并发]
+
+    D --> D1[keyword / rule / trie]
+    D --> D2[local bert classifier]
+    D --> D3[context rewrite / cache]
+    D --> D4[retrieval matcher]
+
+    D1 --> E[本地融合分级器]
+    D2 --> E
+    D3 --> E
+    D4 --> E
+
+    E -->|高置信| F[直接执行或直接生成 workflow]
+    E -->|中置信| G[等待 100~300ms 观察补充分支/云端结果]
+    E -->|低置信| H[云端 planner / LLM / RAG]
+
+    G --> H
+    H --> I[生成 intent / workflow / clarify / reject]
+
+    F --> J[插件执行]
+    I --> J
+
+    J --> K[首反馈 ack / progress]
+    J --> L[最终反馈 result / clarify / reject]
+```
+
+---
+
+## 4. 本地与云端分工
+
+### 4.1 本地快链路
+
+本地负责：
+
+- 高频固定控制类命令
+- 已知业务集合内的快速意图识别
+- 短句、省略句、连续调节的上下文恢复
+- 简单任务的直接执行
+- `<1s` 以内的首响应
+
+本地组件：
+
+- `keyword / rule / trie`
+- `local bert classifier`
+- `context rewrite / cache`
+- `retrieval matcher`
+- `fusion grader`
+
+### 4.2 云端慢链路
+
+云端负责：
+
+- 多命令拆分
+- 条件型请求理解
+- 歧义消解
+- 复杂问答
+- planner 级 workflow 生成
+
+---
+
+## 5. 本地分级决策图
+
+```mermaid
+flowchart TD
+    A[本地多分支结果] --> B{融合分级}
+    B -->|high| C[直接执行]
+    B -->|medium| D[等待 100~300ms]
+    B -->|low| E[不直接执行]
+
+    D --> F{云端是否及时返回}
+    F -->|是| G[采用云端结果或覆盖本地]
+    F -->|否| H{本地是否达到最低执行阈值}
+
+    H -->|是| I[执行本地结果]
+    H -->|否| J[澄清或拒答]
+
+    E --> K[云端理解 / clarify / reject]
+```
+
+关键修正点：
+
+- 不是“本地 BERT 有结果就执行”
+- 不是“云端没返回就一定执行本地”
+- 必须先判断本地结果是否达到最低可执行阈值
+- 高风险动作即使高置信，也不能直接执行
+
+---
+
+## 6. 用户反馈状态图
+
+```mermaid
+stateDiagram-v2
+    [*] --> Received
+    Received --> Ack: 已接收请求
+    Ack --> ExecutingFast: 本地快执行
+    Ack --> WaitingCloud: 等待云端/复杂规划
+    Ack --> Clarify: 缺关键槽位
+    Ack --> Confirm: 高风险动作
+    Ack --> Reject: 超能力边界
+
+    ExecutingFast --> Result
+    WaitingCloud --> Result
+    Clarify --> Result
+    Confirm --> Result
+    Reject --> [*]
+    Result --> [*]
+```
+
+反馈规则：
+
+- `ack`：收到，马上处理
+- `progress`：正在为你处理
+- `result`：执行完成或查询完成
+- `clarify`：信息不足，补一个关键字段
+- `confirm`：高风险动作确认
+- `reject`：能力边界拒答
+
+---
+
+## 7. 反馈模板策略
+
+### 7.1 快执行
+
+适用：
+
+- 打开车窗
+- 调低空调
+- 播放音乐
+- 导航去公司
+
+推荐反馈：
+
+- 首反馈：`好的，正在打开车窗`
+- 最终反馈：`车窗已打开`
+
+如果设备动作极快，也可以直接播报最终反馈：
+
+- `车窗已打开`
+
+### 7.2 慢执行
+
+适用：
+
+- 查订单
+- 查物流
+- 多命令复杂规划
+- 条件型任务
+
+推荐反馈：
+
+- 首反馈：`收到，我先帮你查一下`
+- 最终反馈：`订单还没发货`
+
+### 7.3 复合命令
+
+例如：
+
+- `打开车窗，空调调低至20度`
+
+推荐反馈：
+
+- 首反馈：`好的，正在为你打开车窗并调低空调`
+- 最终反馈：`车窗已打开，空调已调到20度`
+
+### 7.4 边界外请求
+
+例如：
+
+- `打开飞机门`
+
+推荐反馈：
+
+- `这个我暂时做不了，但我可以帮你导航、查订单、调空调或播放音乐`
+
+---
+
+## 8. 多轮上下文恢复流程
+
+```mermaid
+flowchart TD
+    A[当前轮输入: 再低一点] --> B[读取 session context]
+    B --> C{是否命中高频改写缓存}
+    C -->|是| D[改写为完整句]
+    C -->|否| E[轻量改写模型/规则补全]
+    D --> F[进入 router]
+    E --> F
+    F --> G[意图识别 + 槽位提取 + 执行]
+```
+
+说明：
+
+- 这里的“上下文能力”不是 BERT 自己缓存的
+- 而是 Agent 在 `session state` 中保存上轮任务和关键槽位
+- 再由 `rewrite engine` 完成短句恢复
+
+---
+
+## 9. 典型场景流程演示
+
+### 9.1 场景一：快执行单命令
+
+用户输入：
+
+- `打开车窗`
+
+处理流程：
+
+1. ASR 转文本：`打开车窗`
+2. 文本归一化
+3. 本地多支路并发
+4. 若本地高置信命中 `cabin_open_window`
+5. 直接执行车窗插件
+6. 播报：`车窗已打开`
+
+时序演示：
+
+```mermaid
+sequenceDiagram
+    participant U as 用户
+    participant A as ASR
+    participant R as 本地路由
+    participant P as 插件执行
+    participant T as TTS
+
+    U->>A: 打开车窗
+    A->>R: 打开车窗
+    R->>P: 执行 open_window
+    P-->>R: success
+    R->>T: 车窗已打开
+    T-->>U: 车窗已打开
+```
+
+### 9.2 场景二：复合命令快执行
+
+用户输入：
+
+- `打开车窗，空调调低至20度`
+
+处理流程：
+
+1. 文本进入本地路由
+2. 判断为多命令
+3. planner 或本地 splitter 输出两个 step
+4. 生成 sequence workflow
+5. 顺序执行：
+   - 打开车窗
+   - 空调调到 20 度
+6. 汇总反馈：`车窗已打开，空调已调到20度`
+
+时序演示：
+
+```mermaid
+sequenceDiagram
+    participant U as 用户
+    participant A as ASR
+    participant F as 融合分级器
+    participant W as Workflow
+    participant P as 插件层
+    participant T as TTS
+
+    U->>A: 打开车窗，空调调低至20度
+    A->>F: 规范化文本
+    F->>W: 输出 sequence workflow
+    W->>P: step1 open_window
+    P-->>W: success
+    W->>P: step2 set_ac(20)
+    P-->>W: success
+    W->>T: 车窗已打开，空调已调到20度
+    T-->>U: 车窗已打开，空调已调到20度
+```
+
+### 9.3 场景三：慢执行查询
+
+用户输入：
+
+- `帮我查一下订单A123456`
+
+处理流程：
+
+1. 本地高频分支命中订单查询
+2. 首反馈先给：
+   - `收到，我帮你查一下`
+3. 调用订单查询插件
+4. 最终反馈：
+   - `订单A123456当前待发货`
+
+### 9.4 场景四：条件型请求
+
+用户输入：
+
+- `查一下订单A123456，如果还没发货就取消`
+
+处理流程：
+
+1. 本地识别该请求复杂，进入云端 planner
+2. planner 输出 conditional workflow：
+   - step1: query_order
+   - step2: cancel_order
+   - condition: order_status == pending_shipment
+3. 先执行 step1
+4. 若满足条件，则进入确认
+5. 用户回复确认后，再执行取消
+6. 最终反馈：
+   - `订单A123456已取消`
+
+时序演示：
+
+```mermaid
+sequenceDiagram
+    participant U as 用户
+    participant R as 本地融合器
+    participant C as 云端 Planner
+    participant W as Workflow
+    participant P as 插件层
+    participant T as TTS
+
+    U->>R: 查一下订单A123456，如果还没发货就取消
+    R->>C: 复杂条件请求
+    C-->>W: conditional workflow
+    W->>P: step1 query_order
+    P-->>W: order_status=pending_shipment
+    W->>T: 即将取消订单，仅在订单未发货时取消。请回复确认或取消
+    T-->>U: 确认提示
+    U->>W: 确认
+    W->>P: step2 cancel_order
+    P-->>W: success
+    W->>T: 订单A123456已取消
+    T-->>U: 订单A123456已取消
+```
+
+### 9.5 场景五：多轮短句恢复
+
+对话过程：
+
+1. 用户：`把空调调到22度`
+2. 系统：`空调已调到22度`
+3. 用户：`再低一点`
+4. 系统读取 `last_intent=cabin_set_ac` 和 `last_temperature=22`
+5. rewrite engine 改写为：`把空调调到21度`
+6. 再进入意图识别和执行
+7. 系统反馈：`空调已调到21度`
+
+### 9.6 场景六：边界外请求
+
+用户输入：
+
+- `打开飞机门`
+
+正确处理：
+
+1. 本地分支都无法稳定支持
+2. 若低于执行阈值，不得直接执行已有意图
+3. 进入：
+   - reject
+   - 或云端澄清
+4. 反馈：
+   - `这个我暂时做不了，但我可以帮你导航、查订单、调空调或播放音乐`
+
+这类场景必须通过 `unknown / out_of_scope` 机制处理，不能靠封闭集分类硬选。
+
+---
+
+## 10. 当前项目与目标方案的对应关系
+
+### 10.1 已经具备的能力
+
+- 本地 `keyword / classifier / retrieval / fusion`
+- 本地 BERT 分类器
+- `session state`
+- `context rewrite`
+- `planner`
+- `sequence / conditional workflow`
+- 高风险确认
+- demo 调试面板
+
+### 10.2 还需要补齐的关键能力
+
+- `unknown / out_of_scope`
+- 低分拒识策略
+- 明确的 `execute / reject / route_to_cloud` 决策建议
+- 更多真实车机意图
+- 真实插件接入
+- 语音前端与 ASR/TTS 完整接入
+
+---
+
+## 11. 当前阶段的正式实施结论
+
+当前方向可以继续，但必须明确：
+
+- 本地 BERT 是本地快分支之一，不是整个系统的唯一裁决者
+- 最终执行依据应来自“本地融合分级器 + planner + 风险规则”
+- 用户体验的关键不只是识别正确，还包括：
+  - 首反馈是否快
+  - 多轮是否顺
+  - 边界是否清楚
+  - 风险是否可控
+
+因此，当前正式方案应定义为：
+
+```text
+车机型 Agent = 本地并发快链路
+              + 上下文改写缓存
+              + 分级融合决策
+              + 云端 planner
+              + workflow 执行
+              + 风险确认
+              + reject / clarify / fallback 策略
+```
+
+---
+
+## 12. 下一步执行优先级
+
+建议按以下顺序继续实现：
+
+1. 补 `unknown / out_of_scope` 和拒识阈值
+2. 输出统一执行建议：
+   - `execute`
+   - `clarify`
+   - `reject`
+   - `route_to_cloud`
+3. 扩真实车机场景意图：
+   - 车窗
+   - 车门
+   - 座椅
+   - 灯光
+   - 后视镜
+   - 除雾
+4. 强化 rewrite/cache 高频模式
+5. 接真实插件与真实语音链路
+
diff --git a/intelligent_cabin/archive/patents/202510261979_CN120089140A.pdf b/intelligent_cabin/archive/patents/202510261979_CN120089140A.pdf
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diff --git a/intelligent_cabin/archive/patents/texts/202510261979_CN120089140A.txt b/intelligent_cabin/archive/patents/texts/202510261979_CN120089140A.txt
new file mode 100644
index 0000000..d943007
--- /dev/null
+++ b/intelligent_cabin/archive/patents/texts/202510261979_CN120089140A.txt
@@ -0,0 +1,1195 @@
+(19)国家知识产权局
+
+(12)发明专利申请
+(10)申请公布号 CN 120089140 A
+(43)申请公布日 2025.06.03
+(21)申请号 202510261979 .6
+(22)申请日 2025 .03 .05
+
+G06F 40/253 (2020 .01)
+G06F 40/30 (2020 .01)
+
+(71)申请人 广州小鹏汽车科技有限公司
+地址 510000 广东省广州市天河区岑村松
+岗大街8号
+(72)发明人 王小平 赵群 李晓辰 支淑婷
+孟菲 王文杰
+(74)专利代理机构 北京清亦华知识产权代理事
+务所(普通合伙) 11201
+专利代理师 魏宇晴
+(51)Int .Cl .
+
+G10L 15/26 (2006 .01)
+G10L 15/02 (2006 .01)
+G10L 15/18 (2013 .01)
+G06F 40/211 (2020 .01)
+
+CN 120089140 A
+
+权利要求书2页
+
+(54)发明名称
+声学语义大模型、服务器、语音交互方法和
+存储介质
+(57)摘要
+本申请公开了一种声学语义大模型、服务
+器、语音交互方法和计算机可读存储介质。声学
+语义大模型包括声学编码模块、字符转写模块、
+知识检索模块和大语言模型模块。声学编码模块
+被配置为根据输入的语音请求，
+生成语音请求的
+声学特征向量。字符转写模块被配置为将语音请
+求转写为相对应的字符序列，字符序列包括语音
+请求中各文字相对应的字符。知识检索模块被配
+置为根据字符序列，自外部知识库中，获取补充
+信息。大语言模型模块被配置为根据声学特征向
+量和补充信息，确定自然语言处理结果。如此，通
+过端到端的声学语义大模型，减少了多个模块的
+串行处理，降低了处理语音请求的时延，提升了
+模型响应速度，从而增强用户体验。
+
+说明书13页
+
+附图4页
+
+CN 120089140 A
+
+权
+
+利
+
+要
+
+求
+
+书
+
+1/2 页
+
+1 .一种声学语义大模型，其特征在于，所述声学语义大模型包括声学编码模块、字符转
+写模块、
+知识检索模块和大语言模型模块；
+所述声学编码模块被配置为根据输入的语音请求，生成所述语音请求的声学特征向
+量；
+所述字符转写模块被配置为将所述语音请求转写为相对应的字符序列，所述字符序列
+包括所述语音请求中各文字相对应的字符；
+所述知识检索模块被配置为根据所述字符序列，
+自外部知识库中，获取补充信息；
+所述大语言模型模块被配置为根据所述声学特征向量和所述补充信息，确定自然语言
+处理结果。
+2 .根据权利要求1所述的声学语义大模型，其特征在于，所述声学编码模块被配置为：
+对所述语音请求进行特征提取处理，
+生成语义声学特征；
+对所述语义声学特征进行编码处理，
+生成所述声学特征向量。
+3 .根据权利要求1所述的声学语义大模型，其特征在于，所述字符转写模块被配置为：
+基于预设算法，对所述声学特征向量进行映射处理，以将所述语音请求转写为所述字
+符序列。
+4 .根据权利要求1所述的声学语义大模型，其特征在于，所述外部知识库基于键信息和
+值信息构建，
+其中，所述键信息基于汉语拼音和英文字母构建，所述值信息基于汉字构建；
+所述外部知识库包括基础知识库，并以预定周期对所述基础知识库进行迭代更新。
+5 .根据权利要求4所述的声学语义大模型，其特征在于，所述字符包括汉语拼音和/或
+英文字母，所述知识检索模块被配置为：
+根据所述字符序列，
+自所述外部知识库中，
+确定与所述字符序列相对应的候选键信息；
+根据所述候选键信息，将与所述候选键信息相对应的候选值信息，确定为所述候选信
+息；
+根据所述候选信息的引用次数排序结果，
+自所述候选信息中，
+确定所述补充信息。
+6 .根据权利要求1所述的声学语义大模型，其特征在于，所述大语言模型模块被配置
+为：
+对所述声学特征向量和所述补充信息进行融合处理，
+生成融合信息；
+根据所述融合信息，
+确定所述自然语言处理结果。
+7 .根据权利要求6所述的声学语义大模型，其特征在于，所述大语言模型模块被配置
+为：
+对所述融合信息进行槽位识别，
+得到槽位识别结果；
+对所述融合信息进行应用程序接口预测，
+得到预测应用接口；
+根据所述槽位识别结果和所述预测应用接口、
+选择所述预测应用接口执行应用程序接
+口参数填充，
+得到所述自然语言处理结果。
+8 .一种服务器，其特征在于，所述服务器包括处理器和存储器，所述存储器上存储有计
+算机程序，当所述计算机程序被所述处理器执行时，实现权利要求1‑7任一项所述的声学语
+义大模型。
+9 .一种语音交互方法，其特征在于，所述语音交互方法基于如权利要求1‑7任意一项所
+述的声学语义大模型，所述方法包括：
+2
+
+CN 120089140 A
+
+权
+
+利
+
+要
+
+求
+
+书
+
+2/2 页
+
+获取当前语音请求；
+基于所述声学语义大模型，根据所述当前语音请求，确定与所述当前语音请求相对应
+的车辆控制指令，进行所述语音交互。
+10 .一种计算机可读存储介质，其上存储有计算机程序，其特征在于，所述程序被处理
+器执行的情况下，
+实现如权利要求9所述的方法的步骤。
+
+3
+
+CN 120089140 A
+
+说
+
+明
+
+书
+
+1/13 页
+
+声学语义大模型、服务器、语音交互方法和存储介质
+技术领域
+[0001] 本申请涉及语音交互领域，
+更具体而言，涉及一种声学语义大模型、服务器、语音
+交互方法和计算机可读存储介质。
+背景技术
+[0002] 相关技术中，
+为方便用户驾驶车辆，车载智能助手通常通过多个模块对语音请求
+的串行处理，包括通过声学拒识模块对语音请求进行声学拒识处理、通过语音识别模块对
+语音请求进行语音识别处理、
+基于知识检索模块对语音请求进行检索处理和利用自然语言
+处理模块对语音请求进行自然语言处理。然而如此，需要等待每个串行模块对语音请求的
+处理完成后，才能生成能够满足用户需求的自然语言理解结果，整体耗时较长，用户体验较
+差。
+发明内容
+[0003] 本申请提供了一种声学语义大模型、
+服务器、语音交互方法和计算机可读存储介
+质。
+[0004] 本申请实施方式提供一种声学语义大模型，
+所述声学语义大模型包括声学编码模
+块、
+字符转写模块、
+知识检索模块和大语言模型模块；
+[0005] 所述声学编码模块被配置为根据输入的语音请求，
+生成所述语音请求的声学特征
+向量；
+[0006] 所述字符转写模块被配置为将所述语音请求转写为相对应的字符序列，
+所述字符
+序列包括所述语音请求中各文字相对应的字符；
+[0007] 所述知识检索模块被配置为根据所述字符序列，
+自外部知识库中，获取补充信息；
+[0008] 所述大语言模型模块被配置为根据所述声学特征向量和所述补充信息，
+确定自然
+语言处理结果。
+[0009] 如此，
+声学语义大模型包括声学编码模块、
+字符转写模块、知识检索模块和大语言
+模型模块。其中，声学编码模块能够根据输入的语音请求，生成语音请求的声学特征向量。
+字符转写模块能够将语音请求转写为相对应的字符序列，字符序列包括语音请求中各文字
+相对应的字符。知识检索模块能够根据字符序列，
+自外部知识库中，获取补充信息。大语言
+模型模块能够根据声学特征向量和补充信息，确定自然语言处理结果。
+这样，通过端到端的
+声学语义大模型，减少了多个模块的串行处理，
+降低了处理语音请求的时延，提升了模型响
+应速度，从而增强用户体验。并且，通过汉语拼音或英文字母的匹配进行知识检索，避免了
+汉字解码的困难，提升了语音请求的识别准确性，从而增强声学语义大模型的语义理解能
+力和鲁棒性。
+[0010] 某些实施方式中，
+所述声学编码模块被配置为：
+[0011] 对所述语音请求进行特征提取处理，
+生成语义声学特征；
+[0012] 对所述语义声学特征进行编码处理，
+生成所述声学特征向量。
+4
+
+CN 120089140 A
+
+说
+
+明
+
+[0013]
+
+书
+
+2/13 页
+
+如此，声学编码模块能够对语音请求进行特征提取处理，
+生成语义声学特征。并能
+够对语义声学特征进行编码处理，生成声学特征向量。这样，通过提取语义声学特征，能够
+准确地捕捉语音信号的语义信息，并能够降低噪声等因素对语音识别的影响，提高声学语
+义大模型的语义理解能力和鲁棒性。
+[0014] 在某些实施方式中，
+所述字符转写模块被配置为：
+[0015] 基于预设算法，
+对所述声学特征向量进行映射处理，以将所述语音请求转写为所
+述字符序列。
+[0016] 如此，
+字符转写模块能够基于预设算法，对声学特征向量进行映射处理，
+以将语音
+请求转写为字符序列。
+这样，通过预设算法对声学特征向量进行映射处理，能够准确地将语
+音信号转换为字符序列，为后续的知识检索提供基础。
+[0017] 在某些实施方式中，
+所述外部知识库基于键信息和值信息构建，其中，所述键信息
+基于汉语拼音和英文字母构建，所述值信息基于汉字构建；
+[0018] 所述外部知识库包括基础知识库，
+并以预定周期对所述基础知识库进行迭代更
+新。
+[0019] 如此，
+外部知识库基于键信息和值信息构建，其中，键信息基于汉语拼音和英文字
+母构建，值信息基于汉字构建。此外，外部知识库包括基础知识库，并以预定周期对基础知
+识库进行迭代更新。
+这样，
+由于拼音和字母的索引方式比直接使用汉字更为简单和快速，通
+过使用汉语拼音和英文字母作为键信息，能够快速进行索引和检索。并且，通过预定周期对
+基础知识库进行迭代更新，
+可以确保外部知识库中内容的实时性。
+[0020] 在某些实施方式中，
+所述字符包括汉语拼音和/或英文字母，所述知识检索模块被
+配置为：
+[0021] 根据所述字符序列，
+自所述外部知识库中，确定与所述字符序列相对应的候选键
+信息；
+[0022] 根据所述候选键信息，
+将与所述候选键信息相对应的候选值信息，确定为所述候
+选信息；
+[0023] 根据所述候选信息的引用次数排序结果，
+自所述候选信息中，
+确定所述补充信息。
+[0024] 如此，
+知识检索模块能够根据字符序列，
+自外部知识库中，确定与字符序列相对应
+的候选键信息。接着，知识检索模块能够根据候选键信息，将与候选键信息相对应的候选值
+信息，确定为候选信息。最后，知识检索模块能够根据候选信息的引用次数排序结果，
+自候
+选信息中，确定补充信息。这样，通过候选键信息和字符序列的匹配，可以确保检索到的补
+充信息与用户输入的字符序列相匹配，从而提高知识检索的准确性。并且，通过候选信息引
+用次数的排序，
+可以优先选择引用次数最高的信息，从而合理地处理多义字的情况。
+[0025] 在某些实施方式中，
+所述大语言模型模块被配置为：
+[0026] 对所述声学特征向量和所述补充信息进行融合处理，
+生成融合信息；
+[0027] 根据所述融合信息，
+确定所述自然语言处理结果。
+[0028] 如此，
+大语言模型模块能够对声学特征向量和补充信息进行融合处理，生成融合
+信息。接着，大语言模型模块能够根据融合信息，确定自然语言处理结果。
+这样，通过融合声
+学特征向量和补充信息，能够帮助大语言模型准确地理解用户意图，从而提高识别准确率，
+增强用户体验。
+5
+
+CN 120089140 A
+
+说
+
+明
+
+书
+
+3/13 页
+
+[0029]
+
+在某些实施方式中，所述大语言模型模块被配置为：
+[0030] 对所述融合信息进行槽位识别，
+得到槽位识别结果；
+[0031] 对所述融合信息进行应用程序接口预测，
+得到预测应用接口；
+[0032] 根据所述槽位识别结果和所述预测应用接口、
+选择所述预测应用接口执行应用程
+序接口参数填充，
+得到所述自然语言处理结果。
+[0033] 如此，
+大语言模型模块能够对融合信息进行槽位识别，得到槽位识别结果。接着，
+大语言模型模块能够对融合信息进行应用程序接口预测，得到预测应用接口。最后，大语言
+模型模块能够根据槽位识别结果和预测应用接口、
+选择预测应用接口执行应用程序接口参
+数填充，得到自然语言处理结果。这样，通过槽位识别，大语言模型能够准确地理解用户指
+令中各个实体的含义，从而提升用户体验。通过应用接口预测和参数填充，大语言模型能够
+根据用户指令选择合适的接口并填充相应的参数，实现灵活的指令处理，从而提升用户体
+验。
+[0034] 本申请实施方式提供了一种服务器，
+所述服务器部署有上述的声学语义大模型。
+[0035] 本申请实施方式提供一种语音交互方法，
+所述方法包括：
+[0036] 获取当前语音请求；
+[0037] 基于所述声学语义大模型，
+根据所述当前语音请求，确定与所述当前语音请求相
+对应的车辆控制指令，进行所述语音交互。
+[0038] 如此，
+获取当前语音请求。接着，基于声学语义大模型，根据当前语音请求，确定与
+当前语音请求相对应的车辆控制指令，进行语音交互。这样，基于上述的声学语义大模型，
+能够迅速且准确地对用户语音请求进行处理，增强用户体验。
+[0039] 本申请实施方式提供了一种计算机可读存储介质，
+其上存储有计算机程序，所述
+计算机程序被处理器执行的情况下，
+实现如上述的语音交互方法的步骤。
+[0040] 本申请的实施方式的附加方面和优点将在下面的描述中部分给出，
+部分将从下面
+的描述中变得明显，或通过本申请的实施方式的实践了解到。
+附图说明
+[0041] 本申请的上述和/或附加的方面和优点从结合下面附图对实施方式的描述中将变
+得明显和容易理解，其中：
+[0042] 图1是本申请实施方式的声学语义大模型的结构示意图；
+[0043] 图2是本申请实施方式的语音交互方法的流程示意图之一；
+[0044] 图3是本申请实施方式的语音交互方法的流程示意图之二；
+[0045] 图4是本申请实施方式的语音交互方法的流程示意图之三；
+[0046] 图5是本申请实施方式的语音交互方法的流程示意图之四；
+[0047] 图6是本申请实施方式的语音交互方法的流程示意图之五；
+[0048] 图7是本申请实施方式的语音交互方法的流程示意图之六；
+[0049] 图8是本申请实施方式的语音交互方法的流程示意图之七。
+具体实施方式
+[0050] 下面详细描述本申请的实施方式，
+实施方式的示例在附图中示出，其中，相同或类
+6
+
+CN 120089140 A
+
+说
+
+明
+
+书
+
+4/13 页
+
+似的标号自始至终表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附
+图描述的实施方式是示例性的，仅用于解释本申请的实施方式，而不能理解为对本申请的
+实施方式的限制。
+[0051] 相关技术中，
+为了方便用户在驾驶车辆时能够安全、
+有效地进行交互，往往使用车
+载智能助手辅助用户及时车辆。对于用户发出的语音请求，车载智能助手普遍采用多个模
+块对语音请求进行串行处理：
+[0052] 首先，
+通过声学拒识模块对用户的语音请求进行声学拒识处理，声学拒识模型分
+析语音请求的音频质量，判断是否为噪声或受损音频。若语音请求的音频质量不良 (如背景
+嘈杂或断断续续)，则判定为无效语音请求，直接拒识，
+结束流程。若语音请求的音频质量良
+好，则判定为有效语音请求，放行语音请求至下一模块。
+[0053] 接着，
+有效语音请求会被送入语音识别模块，语音识别模块能够将用户的语音请
+求转换为文字，即对语音信号进行解码，将其转换为可理解的文本信息。然而，若是用户语
+音请求表述不标准，语音识别模块可能无法将用户语音请求转换为正确的文本信息。例如，
+用户语音请求为“我想我妈了，帮我打电话给她”，语音识别模块有可能因为口音问题，将语
+音请求转换为“我想我马了，帮我打电话给她”。这样，后续步骤将基于错误文本“我想我马
+了，帮我打电话给她”进行，
+无法满足用户需求。
+[0054] 然后，
+转换后的文本信息会进入知识检索模块，知识检索模块能够根据用户的请
+求，在车载智能助手的知识库中检索相关的信息。若检索到相关条目，将其作为补充知识。
+若未检索到(如无匹配内容或相似度低于阈值)，则不添加额外知识。例如，如果用户询问附
+近的餐馆，知识检索模块会搜索用户当前的位置信息，并检索附近的餐馆信息。
+[0055] 最后，
+检索到的信息会进入自然语言处理模块，自然语言处理模块能够理解文本
+信息和补充信息的含义，包括但不限于词法分析、句法分析、
+语义理解等，最终生成满足用
+户需求的自然语言理解结果。
+[0056] 然而，
+上述串行处理流程需要依次等待每个模块完成处理，导致整体响应时间较
+长，用户体验较差，
+尤其是在需要快速响应的场景下，例如导航或紧急呼叫等。
+[0057] 基于上述的问题，
+请参阅图1，本申请实施方式提供一种声学语义大模型1000，声
+学语义大模型1000包括声学编码模块100、
+字符转写模块200、
+知识检索模块300和大语言模
+型模块400。
+[0058] 其中，
+声学编码模块100能够根据输入的语音请求，生成语音请求的声学特征向
+量；
+[0059] 字符转写模块200能够将语音请求转写为相对应的字符序列，
+字符序列包括语音
+请求中各文字相对应的字符；
+[0060] 知识检索模块300能够根据字符序列，
+自外部知识库中，获取补充信息；
+[0061] 大语言模型模块400能够根据声学特征向量和补充信息，
+确定自然语言处理结果。
+[0062] 具体地，
+声学语义大模型1000是一个端到端系统，通过将声学编码处理、拼音转写
+处理、
+知识检索处理和自然语言处理融合在一起，实现了端到端的处理，避免了传统方案中
+各个模块之间需要单独训练和调优的复杂性，从而提高了系统的响应速度。在某些实施方
+式中，声学语义大模型1000通常采用深度学习技术构建，例如卷积神经网络(CNN)、循环神
+经网络(RNN)或Transformer模型等，在此不做限定。声学语义大模型1000能够有效地捕捉
+7
+
+CN 120089140 A
+
+说
+
+明
+
+书
+
+5/13 页
+
+语音信号的复杂特征和语义信息，从而提高语音识别和语义理解的准确率。并且，声学语义
+大模型1000通过转写为字符序列，能够适应不同的说话人、
+口音和噪声环境，具有很强的鲁
+棒性。需要说明地，该声学语义大模型1000不仅能够部署在车载智能助中，在智能家居、智
+能客服等领域也有着广泛的应用前景。
+[0063] 声学编码模块100是声学语义大模型1000中负责将语音信号转换为声学特征向量
+的模块，通过信号处理技术，例如傅里叶变换、滤波器组等，将语音信号转换成声学特征向
+量，为后续的任务提供数据信息支持。声学编码模块100相当于传统方案中声学拒识模块和
+语音识别模块的结合，能够实现声学拒识模块和语音识别模块的部分功能。
+这样，声学编码
+模块100将声学特征提取和语音识别融合在一起，实现了端到端的处理，从而提高了系统的
+响应速度。
+[0064] 语音请求指的是用户通过语音方式向智能座舱系统发出的指令或提问，
+包括多种
+类型的信息。例如，例如，
+“播放歌手A的歌曲B”、
+“导航去北京”、
+“今天的天气怎么样”、
+“附近
+有什么餐厅”、
+“打开空调”和“讲个笑话”等。
+[0065] 声学特征向量指的是从语音请求中提取的能够表征语音请求声学特性的多维向
+量，包括语音请求的重要信息，例如音高、
+音强和音色等，是语音识别、
+语音合成和说话人识
+别等语音处理任务的基础。
+[0066] 字符转写模块200是声学语义大模型1000中将语音识别模型输出的声学特征向量
+转换为拼音序列的模块，为知识检索模块300提供输入，并提高大语言模型模块400的准确
+率。
+[0067] 字符序列指的是由汉语拼音和/或英文字母等字符组成的序列，
+用于表示语音请
+求中的文字信息，
+可以用于检索与语音请求相关的知识。
+[0068] 知识检索模块300是声学语义大模型1000中根据字符序列从外部知识库中检索相
+关知识的模块，能够从外部知识库中获取补充信息，并应用于后续模块。
+[0069] 外部知识库存储有各种知识信息，
+能够为知识检索模块300提供知识来源。
+[0070] 补充信息指的是从外部知识库中检索到的与拼音序列相关的知识，
+为大语言模型
+模块400提供额外的知识，帮助其更好地理解输入文本，并做出更准确的响应。
+[0071] 大语言模型模块400指的是声学语义大模型1000中根据声学特征向量和补充信
+息，确定自然语言处理结果的模块，能够理解和生成自然语言，并进行语义理解和意图识
+别，输出自然语言处理结果，如应用程序接口API和对应的填充参数。在某些实施方式中，大
+语言模型模块400中的大语言模型可以是如BERT等基于Transformer的模型，在此不做限
+定。
+[0072] 首先，
+声学编码模块100接收输入的语音请求，并将语音请求转换为声学特征向
+量。接着，字符转写模块200将语音请求转写为相对应的字符序列，字符序列包括语音请求
+中各文字相对应的字符，即汉语拼音和/或英文字母。由于汉语拼音和英文字母的粒度较
+粗，解码准确性更高。然后，知识检索模块300根据拼音序列，从外部知识库中检索与用户输
+入相关的额外知识，例如歌曲信息、
+地名信息等。最后，大语言模型模块400结合声学特征向
+量和检索到的额外知识，理解用户意图并生成自然语言处理结果，例如API和Arguments。
+[0073] 综上，
+本申请提供的声学语义大模型1000包括声学编码模块100、字符转写模块
+200、
+知识检索模块300和大语言模型模块400。其中，声学编码模块100能够根据输入的语音
+8
+
+CN 120089140 A
+
+说
+
+明
+
+书
+
+6/13 页
+
+请求，
+生成语音请求的声学特征向量。字符转写模块200能够将语音请求转写为相对应的字
+符序列，字符序列包括语音请求中各文字相对应的字符。知识检索模块300能够根据字符序
+列，
+自外部知识库中，获取补充信息。大语言模型模块400能够根据声学特征向量和补充信
+息，确定自然语言处理结果。这样，通过端到端的声学语义大模型1000，减少了多个模块的
+串行处理，
+降低了处理语音请求的时延，提升了模型响应速度，从而增强用户体验。并且，通
+过汉语拼音或英文字母的匹配进行知识检索，避免了汉字解码的困难，提升了语音请求的
+识别准确性，从而增强声学语义大模型1000的语义理解能力和鲁棒性。
+[0074] 在某些实施方式中，
+声学编码模块100能够对语音请求进行特征提取处理，
+生成语
+义声学特征。并能够对语义声学特征进行编码处理，
+生成声学特征向量。
+[0075] 具体地，
+特征提取处理指的是从原始语音请求中提取出与语音识别、语义理解等
+任务相关的有用信息的过程，包括噪声处理、归一化处理、帧处理，特征计算处理和特征归
+一化处理等。
+特征提取处理类似于人类听觉系统对声音的感知过程，即，从声音中提取出有
+用的信息，例如声音的音高、
+音量、
+音色等，并根据这些信息理解声音的含义。
+特征提取处理
+通过模拟人类听觉系统对声音的感知过程，将语音信号转换成计算机可以理解的特征，以
+便进行后续的语音识别、
+语义理解等任务。
+[0076] 语义声学特征指的是从语音请求信号中提取出的与语义信息相关的声学特征，
+是
+连接语音信号和语义信息之间的桥梁。语义声学特征包括音素特征、音节特征和韵律特征
+等语音请求信号中与语义相关的信息。
+[0077] 其中，
+音素特征指的是指与单个音素相关的声学特征，包括音素时长、
+音素能量和
+音素频率等，音素是语音学中用来描述语音的最小单位，它具有区别意义的作用。例如，汉
+语中的“妈”和“马”的发音区别在于“a”和“a”的音素不同，前者是开口呼，后者是合口呼。音
+素时长指的是音素在语音中的持续时间。音素能量指的是音素的能量强度。音素频率指的
+是音素的频率成分。
+[0078] 音节特征指的是指与单个音节相关的声学特征，
+包括音节时长、
+音节能量、
+音节时
+长分布和音节能量分布等，音节是语音结构的基本单位，
+由一个或多个音素组成，并包括一
+个核心音素。音节是语言中可以独立发音的最小单位，例如汉语中的“啊”和“衣”等都是一
+个音节。音节时长指的是音节在语音中的持续时间。音节能量指的是音节的能量强度。音节
+时长分布指的是音节内部各音素时长的分布情况。音节能量分布指的是音节内部各音素能
+量的分布情况。
+[0079] 韵律特征指的是与语音韵律相关的声学特征，
+包括语调、
+语速和停顿等。语调指的
+是语音的升降变化，例如升调、
+降调、
+平调等。
+语速指的是语音的快慢程度。停顿指的是语音
+的停顿位置和停顿时间。
+[0080] 以“播放歌手A的歌曲B”
+这个语音请求为例，
+语义声学特征包括以下内容：
+[0081] 音素特征：
+例如“b”、
+“o”、
+“f”、
+“a”、
+“ng”等音素的时长、
+能量等。
+[0082] 音节特征：
+例如“bo”、
+“fang”、
+“ge”、
+“shou”、
+“A”、
+“ge”、
+“qu”、
+“B”等音节的时长、
+能
+量和时长分布等。
+[0083] 韵律特征：
+例如语调的升降、
+语速的快慢、
+停顿的位置等。
+[0084] 声学特征向量指的是将语音请求信号转换成计算机可以理解的数学形式，
+包括语
+音信号中与语义相关的信息，可以用于后续的自然语言处理和知识检索等。声学特征向量
+9
+
+CN 120089140 A
+
+说
+
+明
+
+书
+
+7/13 页
+
+通常包括如梅尔频率倒谱系数特征值、音素特征值、音节特征值和韵律特征值等多个特征
+值。梅尔频率倒谱系数特征值(Mel‑frequency Cepstral Coefficients，MFCC)是一种常用
+的语音特征参数，它通过将语音信号转换到梅尔频率域，并对对数谱进行离散余弦变换来
+提取特征。
+[0085] 声学编码模块100首先对输入的语音请求进行特征提取，
+生成语义声学特征。接
+着，声学编码模块100将语义声学特征编码成声学特征向量。
+这些声学特征向量是高维空间
+中的点，能够代表语音信号的复杂特性。
+[0086] 如此，
+声学编码模块100能够对语音请求进行特征提取处理，生成语义声学特征。
+并能够对语义声学特征进行编码处理，生成声学特征向量。这样，通过提取语义声学特征，
+能够准确地捕捉语音信号的语义信息，并能够降低噪声等因素对语音识别的影响，提高声
+学语义大模型1000的语义理解能力和鲁棒性。
+[0087] 在某些实施方式中，
+字符转写模块200能够基于预设算法，对声学特征向量进行映
+射处理，
+以将语音请求转写为字符序列。
+[0088] 具体地，
+字符转写模块200是一个预训练完成的模型，能够将声学特征向量映射为
+字符序列。在某些实施方式中，字符转写模块200能够将Recurrent Neural Network、
+Convolutional Neural Network和ransformer深度神经网络模型其中之一作为基座模型。
+随后，根据预设训练数据对基座模型进行训练，并选择相应预设算法，构建得到字符转写模
+块200。需要说明地，根据预设训练数据对基座模型进行训练时，主要学习将声学特征向量
+映射为汉语拼音和如何将声学特征向量映射为26个英文字母。相比于使用汉字进行训练，
+通过使用拼音和英文字母进行训练，字符转写模块200的建模的粒度更粗、建模难度更低，
+并且训练完成的字符转写模块200解码准确性更高。例如，用户语音请求“我想我妈(由于地
+域性的口音，说成了第三声)了，帮我打电话给她”，若是直接转为汉字可能就是“我想我马
+了，帮我打电话给她”，影响后续处理。而转为汉语拼音“wo xiang wo ma le ,bang wo da
+dian hua gei ta”，在后续大语言模型模块400进行处理时，能够发现并纠正。
+[0089] 此外，
+使用拼音和英文字母进行建模，可以在外部知识库中根据拼音和英文字母
+进行知识匹配，例如可以匹配“ge shou A”和“ge qu B”等拼音序列，从而提高知识检索的
+准确性。并且，
+使用拼音和英文字母进行建模，可以更好地处理新词、
+热词和地名，即使模型
+训练时没有见过这些词，也能够通过拼音进行匹配，例如可以解码出“ge qu B”等新词，从
+而提高字符转写模块200的鲁棒性。
+[0090] 预设算法指的是字符转写模块200中的解码算法，
+用于指导字符序列转写的过程。
+在某些实施方式中，预设算法能够是贪心算法、Beam Search算法、Prefix Beam Search算
+法和Modified Beam Search算法中的任意一种，在此不做限定。在本申请实施方式所举示
+例中，考虑到声学语义大模型1000的时延要求，使用贪心算法作为预设算法。
+贪心算法一种
+启发式搜索算法，
+通过在每一步选择当前看起来最优的选项，
+来尝试找到问题的最优解。
+[0091] 拼音转写模块接收声学编码模块100输出的声学特征向量。
+接着，拼音转写模块使
+用预设算法对声学特征向量进行映射处理，将声学特征向量转换为字符序列，这些字符序
+列对应于输入声学语义大模型1000的语音请求。
+[0092] 如此，
+字符转写模块200能够基于预设算法，对声学特征向量进行映射处理，以将
+语音请求转写为字符序列。
+这样，通过预设算法对声学特征向量进行映射处理，能够准确地
+10
+
+CN 120089140 A
+
+说
+
+明
+
+书
+
+8/13 页
+
+将语音信号转换为字符序列，为后续的知识检索提供基础。
+[0093] 在某些实施方式中，
+外部知识库基于键信息和值信息构建，其中，键信息基于汉语
+拼音和英文字母构建，值信息基于汉字构建。并且。外部知识库包括基础知识库，并以预定
+周期对基础知识库进行迭代更新。
+[0094] 具体地，
+外部知识库基于键信息和值信息构建。其中，键信息(Key)由汉语拼音和
+英文字母构建，这意味着外部知识库中的每个条目都通过拼音和字母进行索引，而不是直
+接使用汉字，相比于直接使用汉字进行匹配，拼音匹配可以更好地应对新词、
+热词和地名等
+变化，例如用户可以说“qi che shou ce”，外部知识库能够找到“汽车使用手册”的相关信
+息。值信息(Value)由汉字构建，即具体的知识内容，
+使用汉字进行描述，每个键信息都对应
+一个或多个值信息。在某些实施方式中，外部知识库的构建流程如下：
+第一，进行数据收集，
+从互联网、
+书籍、
+百科全书等来源收集知识数据，例如歌词、
+电影台词、
+新闻等。
+第二，进行数
+据清洗，对收集到的数据进行清洗，去除噪声、重复信息等，并进行分词和词性标注。第三，
+将清洗后的数据构建成键值对形式，其中键信息基于汉语拼音和英文字母构建，值信息基
+于汉字构建。例如，将“汽车”的键信息设置为“qi che”，值信息设置为“汽车是一种代步工
+具”。
+第四，知识库构建，将构建好的键值对存储在外部知识库中，并按照拼音字母顺序进行
+组织。
+[0095] 外部知识库以基础知识库作为外部知识库的主体部分，
+基础知识库中包括大量的
+基础知识和信息。并且，基础知识库会定期进行更新，以保证知识的时效性和准确性，更好
+地满足用户的需求。
+更新周期是预定的，
+可以是每天、
+每周、
+每月等。
+[0096] 如此，
+外部知识库基于键信息和值信息构建，其中，键信息基于汉语拼音和英文字
+母构建，值信息基于汉字构建。此外，外部知识库包括基础知识库，并以预定周期对基础知
+识库进行迭代更新。
+这样，
+由于拼音和字母的索引方式比直接使用汉字更为简单和快速，通
+过使用汉语拼音和英文字母作为键信息，能够快速进行索引和检索。并且，通过预定周期对
+基础知识库进行迭代更新，
+可以确保外部知识库中内容的实时性。
+[0097] 在某些实施方式中，
+知识检索模块300能够根据字符序列，自外部知识库中，确定
+与字符序列相对应的候选键信息。
+[0098] 接着，
+知识检索模块300能够根据候选键信息，将与候选键信息相对应的候选值信
+息，
+确定为候选信息。
+[0099] 最后，
+知识检索模块300能够根据候选信息的引用次数排序结果，
+自候选信息中，
+确定补充信息。
+[0100] 具体地，
+候选键信息是与用户输入的字符序列相匹配的信息，可以作为检索知识
+库的线索，即“索引”或“关键词”，能够帮助我们缩小检索范围，快速找到与用户输入相关的
+信息。例如，输入字符序列为“qi che”，候选键信息可能为“qi che an quan”、
+“qi che jia
+shi qing kuang”和“qi che bao yang qing kuang”等。
+[0101] 候选值信息是指与候选键信息相对应的，
+从外部知识库中提取出来的信息。候选
+值信息是与输入的字符序列相关的，可以帮助用户获取更全面的信息，即“详细信息”，提供
+了与用户输入相关的更具体的信息。
+[0102] 候选信息指的知识检索过程中，
+从外部知识库中检索到的与输入的字符序列相关
+的所有知识片段。
+11
+
+CN 120089140 A
+
+说
+
+明
+
+[0103]
+
+书
+
+9/13 页
+
+候选信息的引用次数指的是每个候选信息在知识库中被引用的次数，可以理解为
+该候选信息的重要程度或热度。引用次数排序结果指的是根据候选信息的引用次数，将这
+些候选信息按照重要程度或热度进行排序。例如，输入的字符序列为“ge shou A”进行知识
+检索，知识库中检索到了以下三个与“ge shou A”相关的候选信息：候选信息A:ge shou A，
+著名歌手，代表作有《ge qu B》、
+《ge qu C》等。候选信息B:ge shou A，知名演员，代表作有
+《ying shi D》、
+《ying shi F》等。候选信息C:ge shou A，著名篮球运动员，效力于知名球
+队。假设候选信息A的引用次数最高，候选信息B的引用次数次之，候选信息C的引用次数最
+低。那么，根据候选信息的引用次数排序结果，排名由高到低，这三个候选信息将被排序为：
+候选信息A、
+候选信息B和候选信息C。
+这样，通过候选信息的排序，可以优先选择引用次数最
+高的信息，从而更好地处理多义字的情况。
+[0104] 需要说明地，
+知识检索模块300采用检索增强生成技术(Retrieval Augmented
+Generation，RAG)，RAG知识检索技术是一种结合信息检索和自然语言生成的技术，旨在利
+用外部知识库来增强模型的性能。
+[0105] 如此，
+知识检索模块300能够根据字符序列，自外部知识库中，确定与字符序列相
+对应的候选键信息。接着，知识检索模块300能够根据候选键信息，将与候选键信息相对应
+的候选值信息，确定为候选信息。最后，知识检索模块300能够根据候选信息的引用次数排
+序结果，
+自候选信息中，确定补充信息。
+这样，通过候选键信息和字符序列的匹配，可以确保
+检索到的补充信息与用户输入的字符序列相匹配，从而提高知识检索的准确性。并且，通过
+候选信息引用次数的排序，可以优先选择引用次数最高的信息，从而合理地处理多义字的
+情况。
+[0106] 在某些实施方式中，
+大语言模型模块400能够对声学特征向量和补充信息进行融
+合处理，
+生成融合信息。
+[0107] 接着，
+大语言模型模块400能够根据融合信息，
+确定自然语言处理结果。
+[0108] 具体地，
+融合处理指的是将声学特征向量和补充信息结合起来，以便大语言模型
+模块400更好地理解和处理用户语音请求。在某些实施方式中，融合处理的方式包括特征拼
+接和特征加权拼接等，
+特征拼接指的是将声学特征向量和补充信息直接拼接成一个更长的
+特征向量。
+特征加权拼接指的是对声学特征向量和补充信息进行加权，
+然后进行拼接。
+[0109] 将声学特征向量和补充信息输入到大语言模型模块400，
+该模块会对这些信息进
+行融合处理，
+结合声学特征向量和补充信息，
+生成语义理解更加全面的融合信息。声学特征
+向量包含了丰富的声学信息，补充信息则提供了额外的语义信息，两者的融合能够帮助大
+语言模型更准确地理解用户意图，从而提高识别准确率。
+[0110] 接着，
+大语言模型模块400根据融合信息，确定自然语言处理结果，例如识别用户
+指令、
+回答用户问题等。
+[0111] 如此，
+大语言模型模块400能够对声学特征向量和补充信息进行融合处理，
+生成融
+合信息。接着，大语言模型模块400能够根据融合信息，确定自然语言处理结果。这样，通过
+融合声学特征向量和补充信息，能够帮助大语言模型准确地理解用户意图，从而提高识别
+准确率，增强用户体验。
+[0112] 在某些实施方式中，
+大语言模型模块400能够对融合信息进行槽位识别，得到槽位
+识别结果。
+12
+
+CN 120089140 A
+[0113]
+
+说
+
+明
+
+书
+
+10/13 页
+
+接着，大语言模型模块400能够对融合信息进行应用程序接口预测，得到预测应用
+
+接口。
+[0114]
+
+最后，大语言模型模块400能够根据槽位识别结果和预测应用接口、
+选择预测应用
+接口执行应用程序接口参数填充，
+得到自然语言处理结果。
+[0115] 具体地，
+槽位识别指的是从用户的输入中提取特定的信息片段，这些信息片段通
+常被称为"槽位"(slots)。槽位通常是完成某个任务或请求所必需的关键信息，如时间、
+地
+点、
+对象等。
+以用户语音请求为“明天温度多少”为例，进行槽位识别可以得到的槽位信息包
+括[“明天”——日期(Date)]，即槽位信息包括槽位取值和槽位类型，其中“明天”为槽位取
+值，日期(Date)为槽位类型。以用户语音请求“导航到地址Q”为例，进行槽位识别可以得到
+的槽位信息为[“地址Q”——地名(Place)]，其中“中关村”为槽位取值，
+地名(Place)为槽位
+类型。槽位识别是自然语言处理中的一个重要任务，其目标是识别文本中的特定实体和属
+性，并将其与预定义的槽位对应起来。槽位识别可以帮助大语言模型模块400理解用户的意
+图，并生成更准确、
+更自然的回复。
+[0116] 槽位识别结果指的是对语音请求进行槽位识别，
+得到的命名实体。如上述的槽位
+信息[“明天”——日期(Date)]和槽位信息[“地址Q”——地名(Place)]等。
+[0117] 应用程序接口预测指的是根据输入文本的语义，
+预测出与输入文本对应的操作类
+型，并生成相应的应用程序接口调用指令。应用程序接口预测可以帮助大语言模型模块400
+理解用户的意图，并执行相应的操作。
+[0118] 应用程序接口参数填充是自然语言处理中的一个任务，
+其目标是根据输入文本的
+语义和应用程序接口调用指令，为应用程序接口调用指令中的参数指定具体的值。
+[0119] 大语言模型模块400首先对融合信息进行槽位识别，
+将用户的指令分解为不同的
+槽位，例如歌曲名、
+歌手名和播放模式等。接着，大语言模型模块400根据融合信息进行应用
+程序接口预测，预测用户需要执行的应用程序接口，例如“播放歌曲”和“查询天气”等。最
+后，大语言模型模块400根据槽位识别结果和预测应用接口，选择相应的预测应用接口执行
+应用程序接口参数填充，例如将歌曲名和歌手名作为参数填充到“播放歌曲”接口中。
+[0120] 最终，
+大语言模型模块400输出自然语言处理结果，例如识别用户指令为“播放歌
+曲”，并调用音乐播放器播放用户指定的歌曲。
+[0121] 如此，
+大语言模型模块400能够对融合信息进行槽位识别，得到槽位识别结果。接
+着，大语言模型模块400能够对融合信息进行应用程序接口预测，得到预测应用接口。最后，
+大语言模型模块400能够根据槽位识别结果和预测应用接口、选择预测应用接口执行应用
+程序接口参数填充，得到自然语言处理结果。这样，通过槽位识别，大语言模型能够准确地
+理解用户指令中各个实体的含义，从而提升用户体验。通过应用接口预测和参数填充，大语
+言模型能够根据用户指令选择合适的接口并填充相应的参数，实现灵活的指令处理，从而
+提升用户体验。
+[0122] 本申请实施方式提供了一种服务器，
+服务器部署有上述的声学语义大模型1000。
+[0123] 具体地，
+服务器是运行应用程序并提供服务的硬件设备，能够为声学语义大模型
+1000提供运行环境，包括计算资源、
+存储空间和网络连接等。并能够将训练好的声学语义大
+模型1000部署到线上环境，
+使其能够对外提供服务。需要说明地，车辆的算力足够声学语义
+大模型1000使用时，服务器能够部署在车辆本地。
+反之，则部署在云端。
+13
+
+CN 120089140 A
+
+说
+
+明
+
+[0124]
+
+书
+
+11/13 页
+
+服务器接收来自用户的语音输入，并将处理结果返回给用户。
+[0125] 如此，
+服务器将声学语义大模型1000集成到一个完整的系统中，实现从语音输入
+到自然语言处理结果输出的端到端流程，快速响应用户的语音指令，并提供准确的结果，从
+而提升用户体验。
+[0126] 请参阅图2，
+本申请实施方式提供一种语音交互方法，方法包括：
+[0127] 01：
+获取当前语音请求；
+[0128] 02：
+基于声学语义大模型，根据当前语音请求，确定与当前语音请求相对应的车辆
+控制指令，进行语音交互。
+[0129] 具体地，
+系统通过麦克风等硬件设备接收用户发出的当前语音请求。接着，声学语
+义大模型1000对当前语音请求进行理解和分析，识别出用户的意图和目标，并将其转换为
+车辆可以理解的车辆控制指令。
+[0130] 请参阅图3，
+在某些实施方式中，步骤02(基于声学语义大模型，根据当前语音请
+求，
+确定与当前语音请求相对应的车辆控制指令)，
+包括：
+[0131] 021：
+根据输入的语音请求，
+生成语音请求的声学特征向量；
+[0132] 022：
+将语音请求转写为相对应的字符序列；
+[0133] 023：
+根据字符序列，
+自外部知识库中，获取补充信息；
+[0134] 024：
+根据声学特征向量和补充信息，
+确定自然语言处理结果。
+[0135] 如此，
+通过端到端的声学语义大模型，减少了多个模块的串行处理，
+降低了处理语
+音请求的时延，提升了模型响应速度，从而增强用户体验。并且，通过汉语拼音或英文字母
+的匹配进行知识检索，避免了汉字解码的困难，提升了语音请求的识别准确性，从而增强声
+学语义大模型的语义理解能力和鲁棒性。
+[0136] 请参阅图4，
+在某些实施方式中，步骤021(根据输入的语音请求，生成语音请求的
+声学特征向量)，
+包括：
+[0137] 0211：
+对语音请求进行特征提取处理，
+生成语义声学特征；
+[0138] 0212：
+对语义声学特征进行编码处理，
+生成声学特征向量。
+[0139] 如此，
+通过提取语义声学特征，能够准确地捕捉语音信号的语义信息，并能够降低
+噪声等因素对语音识别的影响，
+提高声学语义大模型的语义理解能力和鲁棒性。
+[0140] 请参阅图5，
+在某些实施方式中，步骤022(将语音请求转写为相对应的字符序列)，
+包括：
+[0141] 0221：
+基于预设算法，对声学特征向量进行映射处理，
+以将语音请求转写为字符序
+列。
+[0142] 如此，
+通过预设算法对声学特征向量进行映射处理，能够准确地将语音信号转换
+为字符序列，为后续的知识检索提供基础。
+[0143] 在某些实施方式中，
+外部知识库基于键信息和值信息构建，其中，键信息基于汉语
+拼音和英文字母构建，值信息基于汉字构建。并且，外部知识库包括基础知识库，并以预定
+周期对基础知识库进行迭代更新。
+[0144] 如此，
+由于拼音和字母的索引方式比直接使用汉字更为简单和快速，通过使用汉
+语拼音和英文字母作为键信息，能够快速进行索引和检索。并且，通过预定周期对基础知识
+库进行迭代更新，
+可以确保外部知识库中内容的实时性。
+14
+
+CN 120089140 A
+
+说
+
+明
+
+[0145]
+
+书
+
+12/13 页
+
+请参阅图6，在某些实施方式中，字符包括汉语拼音和/或英文字母，步骤023(根据
+字符序列，
+自外部知识库中，获取补充信息)，
+包括：
+[0146] 0231：
+根据字符序列，
+自外部知识库中，
+确定与字符序列相对应的候选键信息；
+[0147] 0232：
+根据候选键信息，将与候选键信息相对应的候选值信息，
+确定为候选信息；
+[0148] 0233：
+根据候选信息的引用次数排序结果，
+自候选信息中，
+确定补充信息。
+[0149] 如此，
+通过候选键信息和字符序列的匹配，可以确保检索到的补充信息与用户输
+入的字符序列相匹配，从而提高知识检索的准确性。并且，通过候选信息引用次数的排序，
+可以优先选择引用次数最高的信息，从而合理地处理多义字的情况。
+[0150] 请参阅图7，
+在某些实施方式中，步骤024(根据声学特征向量和补充信息，确定自
+然语言处理结果)，
+包括：
+[0151] 0241：
+对声学特征向量和补充信息进行融合处理，
+生成融合信息；
+[0152] 0242：
+根据融合信息，
+确定自然语言处理结果。
+[0153] 如此，
+通过融合声学特征向量和补充信息，能够帮助大语言模型准确地理解用户
+意图，从而提高识别准确率，增强用户体验。
+[0154] 请参阅图8，
+在某些实施方式中，步骤0242(根据融合信息，确定自然语言处理结
+果)，
+包括：
+[0155] 02421：
+对融合信息进行槽位识别，
+得到槽位识别结果；
+[0156] 02422：
+对融合信息进行应用程序接口预测，
+得到预测应用接口；
+[0157] 02423：
+根据槽位识别结果和预测应用接口、
+选择预测应用接口执行应用程序接口
+参数填充，
+得到自然语言处理结果。
+[0158] 如此，
+通过槽位识别，大语言模型能够准确地理解用户指令中各个实体的含义，从
+而提升用户体验。通过应用接口预测和参数填充，大语言模型能够根据用户指令选择合适
+的接口并填充相应的参数，
+实现灵活的指令处理，从而提升用户体验。
+[0159] 需要说明地，
+本申请实施提供的语音交互方法基于前述声学语义大模型1000实
+现。具体而言，语音交互方法的各个步骤均依赖于声学语义大模型1000的各个模块。即，步
+骤021及其子步骤0211和子步骤0212基于声学编码模块100实现，步骤022及其子步骤0221
+基于字符转写模块200实现，步骤023及其子步骤0231、子步骤0232和子步骤0233基于知识
+检索模块300实现，步骤024、步骤024的子步骤0241和子步骤0242、
+及子步骤0242的次步骤
+02421、
+次步骤02422和次步骤02423基于大语言模型模块400实现。其中，关于语音交互方法
+的方法步骤中涉及的技术术语和解释说明，请参考对声学语义大模型各个模块的详细描
+述，
+此处不再赘述。
+[0160] 综上，
+本申请实施方式提供的语音交互方法，基于上述的声学语义大模型1000实
+现，能够迅速且准确地对用户语音请求进行处理，增强用户体验。
+[0161] 本申请还提供了一种计算机可读存储介质，
+其上存储有计算机程序。当计算机程
+序处理器执行的情况下实现如上述的车辆控制方法的步骤。
+[0162] 可以理解，
+计算机程序包括计算机程序代码。
+计算机程序代码可以为源代码形式、
+对象代码形式、
+可执行文件或某些中间形式等。
+计算机可读存储介质可以包括：能够携带计
+算机程序代码的任何实体或装置、
+记录介质、U盘、
+移动硬盘、磁碟、光盘、
+计算机存储器、只
+读存储器(ROM，Read‑Only Memory)、
+随机存取存储器(RAM，Random Access Memory)、
+以及
+15
+
+CN 120089140 A
+
+说
+
+明
+
+书
+
+13/13 页
+
+软件分发介质等。
+[0163] 在本说明书的描述中，
+参考术语“具体地”、
+“进一步地”、
+“特别地”、
+“可以理解地”
+等的描述意指结合实施方式或示例描述的具体特征、
+结构、材料或者特点包含于本申请的
+至少一个实施方式或示例中。在本说明书中，对上述术语的示意性表述不预定指的是相同
+的实施方式或示例。而且，描述的具体特征、
+结构、材料或者特点可以在任何的一个或多个
+实施方式或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可
+以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。
+[0164] 流程图中或在此以其他方式描述的任何过程或方法描述可以被理解为，
+表示包括
+一个或更多个用于实现特定逻辑功能或过程的步骤的可执行指令的代码的模块、
+片段或部
+分，并且本申请的优选实施方式的范围包括另外的实现，其中可以不按所示出或讨论的顺
+序，包括根据所涉及的功能按基本同时的方式或按相反的顺序，来执行功能，
+这应被本申请
+的实施例所属技术领域的技术人员所理解。
+[0165] 尽管上面已经示出和描述了本申请的实施方式，
+可以理解的是，上述实施方式是
+示例性的，不能理解为对本申请的限制，本领域的普通技术人员在本申请的范围内可以对
+上述实施方式进行变化、
+修改、
+替换和变型。
+
+16
+
+CN 120089140 A
+
+说
+
+明
+
+书
+
+图1
+
+图2
+
+17
+
+附
+
+图
+
+1/4 页
+
+CN 120089140 A
+
+说
+
+明
+
+书
+
+图3
+
+图4
+
+18
+
+附
+
+图
+
+2/4 页
+
+CN 120089140 A
+
+说
+
+明
+
+书
+
+图5
+
+图6
+
+19
+
+附
+
+图
+
+3/4 页
+
+CN 120089140 A
+
+说
+
+明
+
+书
+
+图7
+
+图8
+
+20
+
+附
+
+图
+
+4/4 页
+
+
\ No newline at end of file
diff --git a/intelligent_cabin/archive/patents/texts/CN114299931B.txt b/intelligent_cabin/archive/patents/texts/CN114299931B.txt
new file mode 100644
index 0000000..9f9ab02
--- /dev/null
+++ b/intelligent_cabin/archive/patents/texts/CN114299931B.txt
@@ -0,0 +1,1095 @@
+(19)国家知识产权局
+
+(12)发明专利
+(10)授权公告号 CN 114299931 B
+(45)授权公告日 2025.05.30
+(21)申请号 202111574477 .7
+(22)申请日 2021 .12 .21
+(65)同一申请的已公布的文献号
+申请公布号 CN 114299931 A
+(43)申请公布日 2022 .04 .08
+(73)专利权人 广州小鹏汽车科技有限公司
+地址 510000 广东省广州市天河区岑村松
+岗大街8号
+
+G10L 15/22 (2006 .01)
+G10L 15/26 (2006 .01)
+G10L 25/51 (2013 .01)
+G06F 40/30 (2020 .01)
+B60R 16/023 (2006 .01)
+(56)对比文件
+CN 112581955 A ,2021 .03 .30
+CN 113806470 A ,2021 .12 .17
+审查员 白雪旻
+
+(72)发明人 赵群 张天宇 王亭玉 孙文平
+潘晓彤 赵恒艺
+(74)专利代理机构 北京清亦华知识产权代理事
+务所(普通合伙) 11201
+专利代理师 赵静
+
+CN 114299931 B
+
+(51)Int .Cl .
+G10L 15/06 (2013 .01)
+(54)发明名称
+语音交互方法及其装置、
+服务器和可读存储
+介质
+(57)摘要
+本发明公开了一种语音交互方法及其装置、
+服务器和可读存储介质。该语音交互方法包括：
+接收车辆转发的对车辆预设功能调节的当前轮
+的语音请求，
+预设功能指模拟对车辆零部件的操
+作进行刻度调节的功能；读取对车辆预设功能调
+节的上一轮的语音请求；根据当前轮的语音请求
+和上一轮的语音请求在缓存引擎中进行缓存查
+询；在缓存查询的结果为未能查询到对应缓存的
+情况下，利用上一轮的语音请求对当前轮的语音
+请求进行改写；对改写后的当前轮的语音请求进
+行意图识别；根据意图识别的结果完成语音交
+互。本发明结合两轮语音请求，使用高频缓存引
+擎与意图识别相结合的方式识别语音请求的意
+图，
+实现多轮语音请求下，用户意图的准确识别。
+
+权利要求书2页
+
+说明书11页
+
+附图7页
+
+CN 114299931 B
+
+权
+
+利
+
+要
+
+求
+
+书
+
+1/2 页
+
+1 .一种语音交互方法，其特征在于，
+包括：
+接收车辆转发的对车辆预设功能调节的当前轮的语音请求，所述预设功能指模拟对车
+辆零部件的操作进行刻度调节的功能；
+读取对车辆预设功能调节的上一轮的语音请求；
+根据所述当前轮的语音请求和所述上一轮的语音请求在缓存引擎中进行缓存查询；
+在所述缓存查询的结果为未能查询到对应缓存的情况下，利用所述上一轮的语音请求
+对所述当前轮的语音请求进行改写；
+对改写后的所述当前轮的语音请求进行意图识别；
+根据所述意图识别的结果完成语音交互；
+所述语音交互方法包括：
+将出现频率大于预设频率的相邻两轮语音请求添加到所述缓存引擎。
+2 .根据权利要求1所述的语音交互方法，其特征在于，所述语音交互方法包括：
+建立当前轮的语音请求与预设意图之间的映射关系。
+3 .根据权利要求2所述的语音交互方法，其特征在于，所述语音交互方法包括：
+在所述缓存查询的结果为查询到对应缓存的情况下，根据所述映射关系确定所述当前
+轮的语音请求对应的预设意图为目标意图以完成语音交互。
+4 .根据权利要求1所述的语音交互方法，其特征在于，所述利用所述上一轮的语音请求
+对所述当前轮的语音请求进行改写，
+包括：
+通过改写训练数据训练得到改写模型，所述改写训练数据包括相邻两轮语音请求；
+利用所述上一轮的语音请求和所述改写模型对所述当前轮的语音请求进行改写。
+5 .根据权利要求1所述的语音交互方法，其特征在于，所述对改写后的所述当前轮的语
+音请求进行意图识别，
+包括：
+通过意图训练数据训练得到意图识别模型，所述意图训练数据与可进行刻度调节的车
+辆零部件和所述车辆零部件的刻度调节范围相关；
+利用所述意图识别模型对改写后的所述当前轮的语音请求进行意图识别。
+6 .根据权利要求5所述的语音交互方法，其特征在于，所述根据所述意图识别的结果完
+成语音交互，
+包括：
+获取所述意图识别的结果对应各个预设意图的意图判别概率；
+将所述意图判别概率大于概率阈值的一个所述预设意图确定为所述当前轮的语音请
+求对应的目标意图以完成语音交互。
+7 .根据权利要求6所述的语音交互方法，其特征在于，所述预设意图包括：音量调大、
+音
+量调小、
+风量调大、
+风量调小、温度调高、温度调低、
+地图放大、
+地图缩小、屏幕调亮、屏幕调
+暗、屏幕上滑、屏幕下滑、
+仪表调亮、
+仪表调暗、氛围灯调亮、氛围灯调暗、座椅向前、座椅向
+后、
+座椅升高、
+座椅降低、
+椅背向前、
+椅背向后、
+车窗上升和车窗下降中的至少一种。
+8 .根据权利要求6所述的语音交互方法，其特征在于，所述语音交互方法包括：
+在各个所述预设意图的所述意图判别概率均不大于概率阈值的情况下，确定所述当前
+轮的语音请求的意图为非刻度调节意图。
+9 .一种语音交互装置，其特征在于，所述语音交互装置包括：
+接收指令模块，所述接收指令模块用于接收车辆转发的对车辆预设功能调节的当前轮
+2
+
+CN 114299931 B
+
+权
+
+利
+
+要
+
+求
+
+书
+
+2/2 页
+
+的语音请求，所述预设功能指模拟对车辆零部件的操作进行刻度调节的功能；
+读取指令模块，所述读取指令模块用于读取对车辆预设功能调节的上一轮的语音请
+求；
+查询模块，所述查询模块用于根据所述当前轮的语音请求和所述上一轮的语音请求在
+缓存引擎中进行缓存查询；
+改写模块，所述改写模块用于在所述缓存查询的结果为未能查询到对应缓存的情况
+下，利用所述上一轮的语音请求对所述当前轮的语音请求进行改写；
+意图识别模块，所述意图识别模块用于对改写后的所述当前轮的语音请求进行意图识
+别；
+交互模块，所述交互模块用于根据所述意图识别的结果完成语音交互；
+所述查询模块还用于将出现频率大于预设频率的相邻两轮语音请求添加到所述缓存
+引擎。
+10 .一种服务器，其特征在于，所述服务器包括处理器和存储器，所述存储器上存储有
+计算机程序，当所述计算机程序被所述处理器执行时，实现权利要求1‑8任一项所述的语音
+交互方法。
+11 .一种包含有计算机程序的非易失性计算机可读存储介质，其特征在于，当所述计算
+机程序被一个或多个处理器执行时，
+实现权利要求1‑8任一项所述的语音交互方法。
+
+3
+
+CN 114299931 B
+
+说
+
+明
+
+书
+
+1/11 页
+
+语音交互方法及其装置、服务器和可读存储介质
+技术领域
+[0001] 本发明涉及语音技术领域，
+特别涉及一种语音交互方法及其装置、服务器和可读
+存储介质。
+背景技术
+[0002] 目前在智能汽车场景中，
+可以应用语音交互实现用户对车辆零部件的控制，比如
+“打开车窗”，
+“音量调高”等，但是，对于用户希望进行连续调节的场景，在语音场景下体现
+为多轮交互，用户在上一轮语音交互后很自然地省略后续每轮对话的部分内容，例如以下
+用户跟语音助手小P的对话：
+[0003]
+用户：
+今天天气怎么样？
+[0004] 小P：
+广州今天晴天26‑30°
+。
+[0005]
+用户：上海(的天气)呢？
+[0006]
+在多轮对话中，像上述第一个示例中用户字面含义是问上海，但其实是想问上海
+的天气，将部分内容省略符合人对话的习惯，但是这样可能会导致车辆的车载系统对某些
+轮次的语音请求不能准确识别，或者提示听不懂。
+[0007] 进一步地，
+如果用户需要调整音量，可以通过对车上调整汽车音量的机械旋钮进
+行操作，将机械旋钮旋转到想要的音量，但是如果使用语音调整音量，只能够调大或者调
+小。在如下第二个示例中：
+[0008]
+用户：音量调大
+[0009] 小P：
+音量已调大
+[0010]
+用户：
+大大大
+[0011] 从第二个示例可以看出，
+当前车辆的车载系统对当前轮的大大大不能准确识别，
+或者提示听不懂，这样情况不能满足用户对类似机械旋钮一样精准的刻度连续调节的需
+求。
+发明内容
+[0012] 本发明实施方式提供一种语音交互方法及其装置、
+服务器和可读存储介质。
+[0013] 本发明实施方式提供一种语音交互方法。
+该语音交互方法包括：接收车辆转发的
+对车辆预设功能调节的当前轮的语音请求，所述预设功能指模拟对车辆零部件的操作进行
+刻度调节的功能；读取对车辆预设功能调节的上一轮的语音请求；根据所述当前轮的语音
+请求和所述上一轮的语音请求在缓存引擎中进行缓存查询；在所述缓存查询的结果为未能
+查询到对应缓存的情况下，利用所述上一轮的语音请求对所述当前轮的语音请求进行改
+写；对改写后的所述当前轮的语音请求进行意图识别；根据所述意图识别的结果完成语音
+交互。
+[0014] 如此，
+本发明的语音交互方法可以在接收到用户对于车辆零部件进行刻度调节的
+语音请求后，通过读取上一轮的语音请求，
+结合两轮语音请求查询是否命中缓存，在查询不
+4
+
+CN 114299931 B
+
+说
+
+明
+
+书
+
+2/11 页
+
+到对应缓存的情况下，利用上一轮的语音请求改写当前轮的语音请求，使得改写后的语音
+请求可以被车辆的车载系统识别出相应的意图，进而根据意图识别的结果以语音交互方式
+实现对车辆零部件进行刻度调节。
+使用高频缓存引擎与意图识别相结合的方式识别语音请
+求的意图，
+实现多轮语音请求下，用户意图的准确识别。
+[0015] 所述语音交互方法包括:将出现频率大于预设频率的相邻两轮语音请求添加到所
+述缓存引擎。
+[0016] 如此，
+本发明缓存引擎的缓存由出现频率大于预设频率相邻两轮语音请求构成，
+实现高频集语音请求的统计。
+[0017] 所述语音交互方法包括：
+建立当前轮的语音请求与预设意图之间的映射关系。
+[0018] 如此，
+本发明建立当前轮的语音请求与预设意图之间的映射关系后，将每个预设
+意图与对应的相邻两轮语音请求相关联，从而在缓存引擎中查询可以实现语音请求对应意
+图的确定。
+[0019] 所述语音交互方法包括：
+在所述缓存查询的结果为查询到对应缓存的情况下，根
+据所述映射关系确定所述当前轮的语音请求对应的预设意图为目标意图以完成语音交互。
+[0020] 如此，
+本发明在缓存引擎中查询到当前轮的语音请求和相邻的上一轮的语音请
+求，根据建立好的映射关系，可以直接确定当前轮的语音请求的目标意图，从而可以根据确
+定好的当前轮的语音请求对应的目标意图完成语音交互。
+[0021] 所述利用所述上一轮的语音请求对所述当前轮的语音请求进行改写，
+包括：通过
+改写训练数据训练得到改写模型，所述改写训练数据包括相邻两轮语音请求；利用所述上
+一轮的语音请求和所述改写模型对所述当前轮的语音请求进行改写。
+[0022] 如此，
+本发明通过机器学习的方式，
+由相邻两轮语音请求训练得到改写模型，从而
+根据上一轮的语音请求和改写模型可以实现对当前轮的语音请求的改写，使得改写后的语
+音请求可以被车辆的车载系统识别出相应的意图。
+[0023] 所述对改写后的所述当前轮的语音请求进行意图识别，
+包括：通过意图训练数据
+训练得到意图识别模型，所述意图训练数据与可进行刻度调节的车辆零部件和所述车辆零
+部件的刻度调节范围相关；利用所述意图识别模型对改写后的所述当前轮的语音请求进行
+意图识别。
+[0024] 如此，
+本发明通过机器学习的方式，由可进行刻度调节的车辆零部件和所述车辆
+零部件的刻度调节范围对应的训练数据训练得到意图识别模型，进而对改写后的语音请求
+进行意图识别，
+实现用户意图的准确识别。
+[0025] 所述根据所述意图识别的结果完成语音交互，
+包括：获取所述意图识别的结果对
+应各个预设意图的意图判别概率；将所述意图判别概率大于概率阈值的一个所述预设意图
+确定为所述当前轮的语音请求对应的目标意图以完成语音交互。
+[0026] 如此，
+本发明的语音交互方法可以获取意图识别的结果对应各个预设意图的意图
+判别概率，将意图判别概率大于概率阈值的一个预设意图确定为语音请求对应的目标意
+图，从而识别用户精准调节车辆零部件的意图。
+[0027] 所述预设意图包括：
+音量调大、音量调小、风量调大、风量调小、温度调高、温度调
+低、
+地图放大、
+地图缩小、
+屏幕调亮、
+屏幕调暗、
+屏幕上滑、
+屏幕下滑、
+仪表调亮、
+仪表调暗、
+氛
+围灯调亮、氛围灯调暗、座椅向前、座椅向后、座椅升高、座椅降低、椅背向前、椅背向后、
+车
+5
+
+CN 114299931 B
+
+说
+
+明
+
+书
+
+3/11 页
+
+窗上升和车窗下降中的至少一种。
+[0028] 如此，
+设置了多种预设意图可以进一步为识别用户的语音交互意图奠定基础。
+[0029] 所述语音交互方法包括：
+在各个所述预设意图的所述意图判别概率均不大于概率
+阈值的情况下，
+确定所述当前轮的语音请求的意图为非刻度调节意图。
+[0030] 如此，
+在各个预设意图的意图判别概率均不大于概率阈值的情况下，确定该语音
+请求为非刻度调节意图，
+可以排除非刻度调节意图的语音请求。
+[0031] 本发明还提供一种语音交互装置。
+所述语音交互装置包括：接收指令模块、
+读取指
+令模块、查询模块、改写模块、意图识别模块和交互模块。所述接收指令模块用于接收车辆
+转发的对车辆预设功能调节的当前轮的语音请求，所述预设功能指模拟对车辆零部件的操
+作进行刻度调节的功能；所述读取指令模块用于读取对车辆预设功能调节的上一轮的语音
+请求；所述查询模块用于根据所述当前轮的语音请求和所述上一轮的语音请求在缓存引擎
+中进行缓存查询；所述改写模块用于在所述缓存查询的结果为未能查询到对应缓存的情况
+下，利用所述上一轮的语音请求对所述当前轮的语音请求进行改写；所述意图识别模块用
+于对改写后的所述当前轮的语音请求进行意图识别；所述交互模块用于根据所述意图识别
+的结果完成语音交互。
+[0032] 如此，
+本发明的语音交互装置可以在接收到用户对于车辆零部件进行刻度调节的
+语音请求后，通过读取上一轮的语音请求，
+结合两轮语音请求查询是否命中缓存，在查询不
+到对应缓存的情况下，利用上一轮的语音请求改写当前轮的语音请求以使得改写后的语音
+请求可以被车辆的车载系统识别出相应的意图，进而根据意图识别结果以语音交互方式实
+现对车辆零部件进行刻度调节。
+使用高频缓存引擎与意图识别相结合的方式识别语音请求
+的意图，
+实现多轮语音请求下，用户意图的准确识别。
+[0033] 本发明提供一种服务器。
+所述服务器包括处理器和存储器，所述存储器上存储有
+计算机程序，当所述计算机程序被所述处理器执行时，实现上述任意一项实施方式所述的
+语音交互方法。
+[0034] 如此，
+本发明的服务器通过处理器执行计算机程序，可以在接收到用户对于车辆
+零部件进行刻度调节的语音请求后，通过读取上一轮的语音请求，结合两轮语音请求查询
+是否命中缓存，在查询不到对应缓存的情况下，利用上一轮的语音请求改写当前轮的语音
+请求以使得改写后的语音请求可以被车辆的车载系统识别出相应的意图，进而根据意图识
+别结果以语音交互方式实现对车辆零部件进行刻度调节。
+使用高频缓存引擎与意图识别相
+结合的方式识别语音请求的意图，
+实现多轮语音请求下，用户意图的准确识别。
+[0035] 本发明实施方式还提供一种包含有计算机程序的非易失性计算机可读存储介质。
+当所述计算机程序被一个或多个处理器执行时，实现上述任意一项实施方式所述的语音交
+互方法。
+[0036] 如此，
+本发明的可读存储介质存储的计算机程序被处理器执行时，可以在接收到
+用户对于车辆零部件进行刻度调节的语音请求后，通过读取上一轮的语音请求，结合两轮
+语音请求查询是否命中缓存，在查询不到对应缓存的情况下，利用上一轮的语音请求改写
+当前轮的语音请求以使得改写后的语音请求可以被车辆的车载系统识别出相应的意图，进
+而根据意图识别结果以语音交互方式实现对车辆零部件进行刻度调节。
+使用高频缓存引擎
+与意图识别相结合的方式识别语音请求的意图，实现多轮语音请求下，用户意图的准确识
+6
+
+CN 114299931 B
+
+说
+
+明
+
+书
+
+4/11 页
+
+别。
+[0037]
+
+本发明实施方式的附加方面和优点将在下面的描述中部分给出，部分将从下面的
+描述中变得明显，或通过本发明的实践了解到。
+
+附图说明
+[0038] 本发明的上述和/或附加的方面和优点可以从结合下面附图对实施方式的描述中
+将变得明显和容易理解，其中：
+[0039] 图1是本发明的语音交互方法的流程示意图；
+[0040] 图2是本发明的语音交互装置的结构示意图；
+[0041] 图3是本发明的语音交互方法的流程示意图；
+[0042] 图4是本发明的语音交互装置的结构示意图；
+[0043] 图5是本发明的语音交互方法的流程示意图；
+[0044] 图6是本发明的语音交互方法的流程示意图；
+[0045] 图7是本发明的语音交互方法的流程示意图；
+[0046] 图8是本发明的语音交互装置的结构示意图；
+[0047] 图9是本发明的语音交互方法的流程示意图；
+[0048] 图10是本发明的语音交互装置的结构示意图；
+[0049] 图11是本发明的语音交互方法的流程示意图；
+[0050] 图12是本发明的语音交互装置中的交互模块的结构示意图；
+[0051] 图13是本发明的服务器的结构示意图；
+[0052] 图14是本发明的计算机可读存储介质的结构示意图。
+具体实施方式
+[0053] 下面详细描述本发明的实施方式，
+所述实施方式的示例在附图中示出，其中，相同
+或类似的标号自始至终表示相同或类似的元件或具有相同或类似功能的元件。下面通过参
+考附图描述的实施方式是示例性的，仅用于解释本发明的实施方式，而不能理解为对本发
+明的实施方式的限制。
+[0054] 目前，
+车辆的语音交互系统在用户进行多轮语音请求的情况下，例如，用户第一轮
+语音请求为“屏幕调亮”，而第二轮语音请求采用精简语音请求“亮亮亮”的情况下，语音交
+互系统不能根据用户的语音请求精准识别出用户第二轮的需求为屏幕亮度提高3个刻度，
+不能正确的下发车辆控制指令，使得屏幕亮度准确提高用户需要的三个亮度，用户体验不
+佳。
+[0055] 为了解决上述问题，
+请参阅图1，本发明提供了一种语音交互方法。该语音交互方
+法包括：
+[0056] 01，
+接收车辆转发的对车辆预设功能调节的当前轮的语音请求，预设功能指模拟
+对车辆零部件的操作进行刻度调节的功能；
+[0057] 02，
+读取对车辆预设功能调节的上一轮的语音请求；
+[0058] 03，
+根据当前轮的语音请求和上一轮的语音请求在缓存引擎中进行缓存查询；
+[0059] 04，
+在缓存查询的结果为未能查询到对应缓存的情况下，利用上一轮的语音请求
+7
+
+CN 114299931 B
+
+说
+
+明
+
+书
+
+5/11 页
+
+对当前轮的语音请求进行改写；
+[0060] 05，
+对改写后的当前轮的语音请求进行意图识别；
+[0061] 06，
+根据意图识别的结果完成语音交互。
+[0062] 请参阅图2，
+本发明还提供一种语音交互装置10。语音交互装置10包括：接收指令
+模块11、
+读取指令模块12、
+查询模块13、
+改写模块14、
+意图识别模块15和交互模块16。
+[0063] 步骤01可以由接收指令模块11实现，
+步骤02可以由读取指令模块12实现，步骤03
+可以由查询模块13实现，步骤04可以由改写模块14实现，步骤05可以由意图识别模块15实
+现和步骤06可以由交互模块16实现。也即是说，接收指令模块11可以用于接收车辆转发的
+对车辆预设功能调节的当前轮的语音请求，
+预设功能指模拟对车辆零部件的操作进行刻度
+调节的功能；读取指令模块12可以用于读取对车辆预设功能调节的上一轮的语音请求；查
+询模块13可以用于根据当前轮的语音请求和上一轮的语音请求在缓存引擎中进行缓存查
+询；改写模块14可以用于在缓存查询的结果为未能查询到对应缓存的情况下，利用上一轮
+的语音请求对当前轮的语音请求进行改写；意图识别模块15可以用于对改写后的当前轮的
+语音请求进行意图识别；交互模块16可以用于根据意图识别的结果完成语音交互。
+[0064]
+用户使用语音交互模拟对车辆零部件进行刻度调节的过程，对应的语音请求可以
+包括但不限于“屏幕亮亮亮”、
+“音量大大大”、
+“座椅后后后”。其中，预设功能是指通过车辆
+零部件完成刻度调节的功能，其中的车辆零部件可以指机械旋钮或按钮等实体部件，这些
+是可以进行调节刻度的部件。
+目前在智能汽车中，对于用户希望进行连续调节的场景，在语
+音场景下体现为多轮交互，比如用户上一轮的语音请求为“音量大大大”，系统音量调大后，
+用户又发出当前轮的语音请求“小一点小一点”，这时候系统针对当前轮的语音请求会不识
+别，或者提示听不懂，不能满足用户对类似机械旋钮一样精准的刻度连续调节的需求。
+[0065] 本发明可以在接收到用户对于车辆预设功能的语音请求后，
+通过读取上一轮的语
+音请求，
+结合两轮语音请求查询是否命中缓存，在查询不到对应缓存的情况下，利用上一轮
+的语音请求改写当前轮的语音请求以使得改写后的语音请求可以被系统识别出相应的意
+图，从而对改写后的当前轮的语音请求进行意图识别后，可以准确识别出用户的意图，进而
+可以根据意图识别结果下发控制指令，
+以控制对应的车辆零部件，完成语音交互。
+使用高频
+缓存引擎与意图识别相结合的方式识别语音请求的意图，实现多轮语音请求下，准确识别
+出通过语音交互来模拟对车辆零部件的操作实现刻度调节的用户意图。
+[0066] 需要说明的是，
+在接收到用户对于车辆预设功能的当前轮的语音请求后，对接收
+到的当前轮的语音请求进行语音识别得到当前轮语音识别文本以便后续处理，例如，对用
+户输入的当前轮的语音请求“屏幕亮亮亮”进行语音识别，得到的当前轮识别文本即为“屏
+幕亮亮亮”。
+[0067]
+在实际情况下可能受到车辆硬件限制，或者因为网络不稳定性，用户表述口语化
+或者方言化等原因导致ASR识别后的文本指令不够清晰准确，可以对接收到的当前轮的语
+音请求进行预处理，
+预处理包括对一些常规文本纠错，比如“音量深深深深深”纠正为“音量
+增增增增增”，
+以及一些无意义词语的去除等，
+比如“啊”，
+“请”等。
+[0068] 请结合图3，
+在步骤01之前，
+语音交互方法可以包括：
+[0069] 011，
+确定车辆零部件的控制范围及非控制范围。
+[0070] 请结合图4，
+语音交互装置10还包括第一确定模块111。
+8
+
+CN 114299931 B
+
+说
+
+明
+
+[0071]
+
+书
+
+6/11 页
+
+步骤011可以由第一确定模块111实现。也即是说，
+第一确定模块111可以用于确定
+车辆零部件的控制范围及非控制范围。
+[0072] 可以理解地，
+车辆并非所有功能的调节都可以、能够或有需要进行精准的刻度调
+节。例如，座椅在各个方向上的移动可以通过车辆零部件进行调节。而车门则没有类似旋
+钮、按键等车辆零部件来实现刻度调节，而通常仅通过车门把手进行开关。因此，座椅调节
+是属于车辆零部件的控制范围、
+而车门调节则属于车辆零部件的非控制范围。
+[0073] 获取车辆零部件的信息，
+根据车辆零部件的信息，确定可通过零部件进行刻度调
+节的硬件，确定为车辆零部件的控制范围，将不可通过车辆零部件进行调节的硬件确定为
+非控制范围。
+[0074] 首先，
+确定在车辆上可以进行刻度调节的零部件，例如：
+“音量旋钮”，
+“屏幕亮度按
+钮”，
+“空调风量旋钮/按钮”，
+“座椅调节旋钮/按钮”等。进一步，确定车辆零部件的控制范围
+可包括：车载音响、
+车辆内的屏幕、
+车辆空调、
+车辆座椅、
+车内的氛围灯、
+车辆外部的车灯、
+或
+车窗等。
+车辆零部件的非控制范围可包括：
+车门、
+后视镜、
+后备箱等。
+[0075]
+在后续语音交互的过程中，可在语音请求针对车辆零部件的非控制范围的情况下
+进行语音提示。
+[0076] 如此，
+通过收集车辆零部件信息，确认可通过零部件进行刻度调节的功能，从而确
+定车辆零部件的控制范围，
+也即是可通过语音交互进行刻度调节的控制范围。
+[0077] 语音交互方法还包括：
+[0078] 012，
+确定车辆零部件的可调节范围。
+[0079] 语音交互装置10还包括第二确定模块112。
+[0080] 步骤012可以由第二确定模块112实现。
+也即是说，
+第二确定模块112用于确定车辆
+零部件的可调节范围。
+[0081] 可以理解，
+在确定车辆零部件的控制范围和非控制范围后，需要针对控制范围中
+的每一个车辆零部件确定的可调节范围。车辆零部件的可调节范围与通过操作该车辆零部
+件进行调节的刻度范围相对应。对应不同车辆零部件，可调节范围可以是档位或量程。例
+如，屏幕亮度按钮累计连续按压5次，屏幕亮度依次调整1至5个档位的亮度至最大亮度，则
+该屏幕亮度按钮的可调节范围为1至5个档位。又如，对座椅进行前后调节的旋钮的总刻度
+值为90，则该座椅调节旋钮的可调节范围为刻度值1～90。
+[0082] 语音交互方法还包括：
+[0083] 013，
+将控制范围和可调节范围映射到预设意图。
+[0084] 语音交互装置100还包括映射模块113。
+[0085] 步骤013可以由映射模块113实现。
+也即是说，映射模块113可以用于将控制范围和
+可调节范围映射到预设意图。
+[0086] 将车辆零部件的控制范围和每个车辆零部件的可调节范围，
+映射到意图识别模型
+所能够理解的意图体系。针对车辆零部件的控制范围中的对象和对应的车辆零部件的可调
+节范围均制定一个相应的预设意图。例如：system_volume_up代表着预设意图“音量调大”
+和system_volume_down代表着预设意图“音量调小”，并且包含了所有可调节的范围说法，
+例如“音量大大大”为预设意图对应的system_volume_up，
+“音量大大大大”也会对此意图。
+如此，
+针对零部件控制范围和车辆零部件的可调节范围制定了一套具体的意图映射体系。
+9
+
+CN 114299931 B
+
+说
+
+明
+
+[0087]
+
+书
+
+7/11 页
+
+预设意图可以包括：音量调大、音量调小、风量调大、风量调小、温度调高、温度调
+低、
+地图放大、
+地图缩小、
+屏幕调亮、
+屏幕调暗、
+屏幕上滑、
+屏幕下滑、
+仪表调亮、
+仪表调暗、
+氛
+围灯调亮、氛围灯调暗、座椅向前、座椅向后、座椅升高、座椅降低、椅背向前、椅背向后、
+车
+窗上升和车窗下降中的至少一种。
+[0088] 如此，
+设置了多种预设意图可以进一步为识别用户的语音交互意图奠定基础，根
+据用户提供的带有精简词的语音请求进行不同意图的识别，从而实现相应的目标意图。
+[0089] 请结合图5，
+语音交互方法包括:
+[0090] 014，
+将出现频率大于预设频率的相邻两轮语音请求添加到缓存引擎。
+[0091] 步骤014可以由查询模块13实现。
+也即是说，查询模块13可以用于将出现频率大于
+预设频率的相邻两轮语音请求添加到缓存引擎。
+[0092] 如此，
+本发明缓存引擎的缓存由出现频率大于预设频率相邻两轮语音请求构成，
+实现高频集语音请求的统计。
+[0093] 首先服务器可以在用户允许的情况下收集一段时间内用户的历史语音请求，
+此处
+收集到的语音请求需要至少包含两轮语音请求。其中，
+预计需要收集超过1万条历史语音请
+求。
+[0094] 其次，
+服务器可以对收集到的历史语音请求进行简单的筛选，以筛选掉明显语义
+不明确的语音请求，以及一些只包含语气词，比如“啊”，
+“哦”等较短的语音请求，留下语义
+明确同时包含具体目的的语音请求，比如“导航到公司”，
+“帮我打开空调”，
+“搜一下附近的
+医院”，
+“播放歌手A的歌曲”，
+“今天天气怎么样”等；并且在筛选中去掉只有一轮的语音请
+求。
+[0095] 服务器可以对筛选后的语音请求进行高频统计，
+统计相邻两轮语音请求的出现频
+率。其中，统计相邻两轮语音请求作为唯一值出现的次数，当出现次数大于一定次数时，可
+以认为对应的相邻两轮语音请求的出现频率大于预设频率。
+[0096] 比如，
+上一轮的语音请求为“音量大一点大一点”，当前轮的语音请求为“小一点”
+的情况，在筛选出来的语音请求中出现的次数超过预定次数，则可以将“音量大一点大一
+点”和“小一点”的相邻两轮语音请求加入到缓存引擎中。
+[0097] 语音交互方法还包括：
+[0098] 015，
+建立当前轮的语音请求与预设意图之间的映射关系。
+[0099] 步骤015可以由查询模块13实现。
+也即是说，查询模块13可以用于建立当前轮的语
+音请求与预设意图之间的映射关系。
+[0100] 如此，
+本发明建立当前轮的语音请求与预设意图之间的映射关系后，将每个预设
+意图与对应的相邻两轮语音请求相关联，从而在缓存引擎中查询可以实现语音请求对应意
+图的确定。
+[0101] 应当理解，
+上一轮的语音请求和当前轮的语音请求为相邻的两轮，其中确定缓存
+引擎的相邻两轮语音请求中，当前轮的语音请求与预设意图之间的映射关系后，即可确定
+上一轮的语音请求和当前轮的语音请求结合是否属于高频集指令，
+以及是否可以根据高频
+集指令对应的预设意图确定当前轮的语音请求的目标意图。
+[0102] 比如，
+上一轮的语音请求为“音量大一点大一点”，当前轮的语音请求为“小一点”，
+那么可以关联“音量大一点大一点”和“小一点”这相邻两轮语音请求中，当前轮的语音请求
+10
+
+CN 114299931 B
+
+说
+
+明
+
+书
+
+8/11 页
+
+“小一点”对应的预设意图为“音量调小”。
+[0103] 请结合图6，
+语音交互方法包括：
+[0104] 07，
+在缓存查询的结果为查询到对应缓存的情况下，根据映射关系确定当前轮的
+语音请求对应的预设意图为目标意图以完成语音交互。
+[0105] 步骤07可以由交互模块16实现。
+也即是说，交互模块16可以用于在缓存查询的结
+果为查询到对应缓存的情况下，根据映射关系确定当前轮的语音请求对应的预设意图为目
+标意图以完成语音交互。
+[0106] 本发明在缓存引擎中查询到与当前轮的语音请求和上一轮的语音请求相应的相
+邻两轮语音请求的情况下，根据建立好的当前轮的语音请求与预设意图之间的映射关系，
+可以直接确定当前轮的语音请求对应的目标意图，从而可以根据确定好的当前轮的语音请
+求对应的目标意图完成语音交互。
+[0107] 例如，
+上一轮的语音请求为“音量大一点大一点”，当前轮的语音请求为“小一点”，
+若在缓存引擎中查询到缓存的相邻两轮语音请求为“音量大一点大一点”和“小一点”，且
+“小一点”对应的预设意图为“音量调小”，则可以直接确定当前轮的语音请求的目标意图为
+查询到的预设意图“音量调小”，从而可以根据“音量调小”的意图通过语音交互模拟对车辆
+零部件的操作，
+实现多轮语音请求下，用户意图的准确识别。
+[0108] 请结合图7，
+步骤04包括：
+[0109] 041，
+通过改写训练数据训练得到改写模型，改写训练数据包括相邻两轮语音请
+求；
+[0110] 042，
+利用上一轮的语音请求和改写模型对当前轮的语音请求进行改写。
+[0111] 请结合图8，
+语音交互装置10包括改写训练模块114。
+[0112] 步骤041可以用于改写训练模块114实现，
+步骤042可以由改写模块14实现。也即是
+说，改写训练模块114可以用于通过改写训练数据训练得到改写模型。改写模块14可以用于
+利用上一轮的语音请求和改写模型对当前轮的语音请求进行改写。
+[0113] 本发明通过机器学习的方式，
+由相邻两轮语音请求训练得到改写模型，从而根据
+上一轮的语音请求和改写模型可以实现对当前轮的语音请求的改写，使得改写后的语音请
+求 可 以 被 车 辆 的 车 载 系 统 识 别 出 相 应 的 意 图 。其 中 ，对 于 改 写 模 型 ，可 使 用 B E R T
+(Bidirectional Encoder Representation from Transformers)和序列标注的方式进行
+模型训练，从而获取训练好的改写模型。
+[0114] 其中，
+改写数据可以是上述筛选后的语音请求中的相邻两轮语音请求经过标注后
+得到，可以由人工对相邻两轮语音请求中的当前轮的语音请求进行改写标注，例如，上一轮
+的语音请求为“音量大一点大一点”，当前轮的语音请求为“小一点”，那么可以将当前轮的
+语音请求改写标注为“音量小一点”。如此，将标注好的相邻两轮语音请求输入到建立好的
+改写模型，在训练过程中，改写模型可以通过特征提取的方式学习如何通过相邻两轮语音
+请求将标注前的当前轮的语音请求改写成标注后的当前轮的语音请求。
+[0115]
+在训练过程中，标注后的语音请求中的相邻两轮语音请求划分为改写训练集和改
+写验证集，划分比例可根据需求设定，在此不作限定。例如改写训练集80％，改写验证集为
+20％。对于建立好的改写模型，先通改写训练集中的至少部分数据用于训练改写模型，然后
+利用改写验证集的至少部分数据对训练后的改写模型的准确率进行改写验证。在改写验证
+11
+
+CN 114299931 B
+
+说
+
+明
+
+书
+
+9/11 页
+
+的准确率没有达到改写准确率阈值的情况下，再次通过改写训练集的至少另一部分数据对
+改写模型进行训练，
+以及再次利用改写验证集的至少另一部分数据对再次训练后的改写模
+型的准确率进行改写验证，如此重复训练和改写验证的过程，直到改写验证的准确率达到
+改写准确率阈值时，
+可以认为改写模型已经达标，
+完成改写模型的训练。
+[0116] 需要说明的是，
+改写训练集和改写验证集中的每个数据均只使用一次，在改写模
+型遍历改写训练集和改写验证集的所有数据均未能训练达标的情况下，可以再次在用户允
+许的情况下收集更多的语音请求，从而筛选并标注得到更多的改写训练数据对改写模型进
+行训练，从而保证改写模型能够准确对语音请求进行改写。
+[0117] 请结合图9，
+步骤05包括：
+[0118] 051，
+通过意图训练数据训练得到意图识别模型，意图训练数据与可进行刻度调节
+的车辆零部件和车辆零部件的刻度调节范围相关；
+[0119] 052，
+利用意图识别模型对改写后的当前轮的语音请求进行意图识别。
+[0120] 请结合图10，
+语音交互装置10包括意图训练模块115。
+[0121] 步骤051可以由意图训练模块115实现，
+步骤052可以由意图识别模块15实现。也即
+是说，意图训练模块115可以用于通过意图训练数据训练得到意图识别模型。意图识别模块
+15可以用于利用意图识别模型对改写后的当前轮的语音请求进行意图识别。
+[0122] 本发明通过机器学习的方式，
+由可进行刻度调节的车辆零部件和车辆零部件的刻
+度调节范围对应的训练数据训练得到意图识别模型，进而对改写后的当前轮的语音请求进
+行意图识别，实现用户意图的准确识别。其中，模型训练可以利用BERT、ALBERT、XLNet、
+RoBERTa等模型。
+[0123] 其中，
+意图训练数据与可进行刻度调节的车辆零部件和零部件的刻度调节范围相
+关。车辆零部件指的是在智能汽车上可以进行刻度调节的零部件，例如：
+“音量旋钮”，
+“屏幕
+亮度按钮”，
+“空调风量旋钮/按钮”，
+“座椅调节旋钮/按钮”等。车辆零部件的可调节范围与
+与通过操作该车辆零部件进行调节的刻度范围相对应。对应不同车辆零部件，可调节范围
+可以是档位或量程。
+[0124] 其中，
+意图训练数据可以是上述筛选后的语音请求中的相邻两轮语音请求中上一
+轮的语音请求经过标注后得到，可以由人工对相邻两轮语音请求中的上一轮的语音请求进
+行意图标注，可以理解，上一轮的语音请求中应当包括用户需要调节的意图相关的内容，例
+如，上一轮的语音请求为“音量大一点大一点”，用户需要调节的是音量调大2次，此时，可以
+通过人工将上一轮的语音请求对应的意图标注为“音量调大”。如此，将标注好的上一轮的
+语音请求给到建立好的意图识别模型，在训练过程中，意图识别模型可以通过特征提取学
+习如何通过输入的语音请求识别出用户想要实现的目标意图。
+[0125] 在训练过程中，
+可以将标注后的上一轮的语音请求划分为意图训练集和意图验证
+集，划分比例可根据需求设定，在此不作限定。例如意图训练集80％，意图验证集为20％。对
+于建立好的意图识别模型，先利用意图训练集中的至少部分数据用于训练意图识别模型，
+然后利用意图验证集的至少部分数据对训练后的意图识别模型的准确率进行意图验证。在
+意图验证的准确率没有达到意图准确率阈值的情况下，再次通过意图训练集的至少另一部
+分数据对意图识别模型进行训练，
+以及再次利用意图验证集的另一部分数据对再次训练后
+的意图识别模型的准确率进行意图验证，如此重复训练和意图验证的过程，直到意图验证
+12
+
+CN 114299931 B
+
+说
+
+明
+
+书
+
+10/11 页
+
+的准确率达到意图准确率阈值时，可以认为意图识别模型已经达标，完成意图识别模型的
+训练。
+[0126] 需要说明的是，
+意图训练集和意图验证集中的每个数据均只使用一次，在意图识
+别模型遍历意图训练集和意图验证集的所有数据均未能训练达标的情况下，可以再次在用
+户允许的情况下收集更多的语音请求，从而筛选并标注得到更多的意图训练数据对意图识
+别模型进行训练，从而保证意图识别模型能够准确识别输入的语音请求对应的意图。
+[0127] 可以理解，
+上述改写模型和意图识别模型的训练可以离线进行训练，将离线训练
+好的改写模型和意图识别模型部署到服务器后，服务器可以在接收到当前轮的语音请求后
+利用上一轮的语音请求改写模型对当前轮的语音请求进行改写，
+以及利用意图识别模型对
+改写后的当前轮的语音请求进行意图识别。
+[0128] 请结合图11，
+步骤06包括：
+[0129] 061，
+获取意图识别的结果对应各个预设意图的意图判别概率；
+[0130] 062，
+将意图判别概率大于概率阈值的一个预设意图确定为当前轮的语音请求对
+应的目标意图以完成语音交互。
+[0131] 请结合图12，
+交互模块16包括获取单元161和意图确定单元162。
+[0132] 步骤061可以由获取单元161实现，
+步骤062可以由意图确定单元162实现。也即是
+说，获取单元161可以用于获取意图识别的结果对应各个预设意图的意图判别概率。意图确
+定单元162可以用于将意图判别概率大于概率阈值的一个预设意图确定为语音请求对应的
+目标意图以完成语音交互。
+[0133] 根据多个类别的预设意图对应的每个预设意图类别的识别结果，
+意图识别模块15
+可以给出各个预设意图相匹配的意图判别概率，则可以得到多个意图判别概率。若概率阈
+值为0 .9，则意图识别的结果为某个类别的预设意图的意图判别概率超过0 .9，那么服务端
+认为该类别的预设意图就是当前用户的语音请求的目标意图。概率阈值也可以为其他数
+值，概率阈值可以为默认设置的数值，
+也可以根据用户需要自行设定，在此不作限制。
+[0134] 如此，
+本发明的语音交互方法可以获取意图识别的结果对应各个预设意图的意图
+判别概率，将意图判别概率大于概率阈值的一个预设意图确定为语音请求对应的目标意
+图，从而实现识别用户精准调节车辆零部件的意图的需求。
+[0135] 语音交互方法包括：
+[0136] 063，
+在各个预设意图的意图判别概率均不大于概率阈值的情况下，确定当前轮的
+语音请求的意图为非刻度调节意图。
+[0137] 步骤063可以由意图确定单元162实现。
+也即是说，意图确定单元162可以用于在各
+个预设意图的意图判别概率均不大于概率阈值的情况下，确定语音请求为非刻度调节意
+图。
+[0138] 例如，
+当根据各个类别的预设意图对应得到的意图判别概率均不大于概率阈值的
+情况，即根据语音请求得到用户的意图识别结果与各个类别预设意图像匹配的概率比较
+低，均低于概率阈值，例如概率阈值可以为0 .9，则确定该语音请求为非刻度调节意图，非刻
+度调节意图指的是对不能用带有刻度的旋钮或按钮调节的零部件的用户意图，例如，用户
+输入的语音请求为“车门开开开”，因为车门不是用带有刻度的旋钮或按钮进行调节的零部
+件，因此，
+该语音请求“车门开开开”是非刻度调节意图。
+13
+
+CN 114299931 B
+
+说
+
+明
+
+[0139]
+
+书
+
+11/11 页
+
+如此，在各个预设意图的意图判别概率均不大于概率阈值的情况下，确定该语音
+请求为非刻度调节意图，
+可以排除非刻度调节意图的语音请求。
+[0140] 请参阅图13，
+本发明还提供一种服务器20。该服务器20包括处理器21和存储器22，
+存储器22上存储有计算机程序221，当计算机程序221被处理器21执行时，实现上述任意一
+个实施例中所述的语音交互方法。
+[0141] 本发明的服务器可以通过处理器21执行计算机程序221，
+可以在接收到用户对于
+车辆预设功能的语音请求后，通过读取上一轮的语音请求，结合两轮语音请求查询是否命
+中缓存，在查询不到对应缓存的情况下，利用上一轮的语音请求改写当前轮的语音请求以
+使得改写后的语音请求可以被系统识别出相应的意图，从而对改写后的当前轮的语音请求
+进行意图识别后，可以准确识别出用户的意图，进而可以根据意图识别结果下发控制指令，
+以控制对应的车辆零部件，完成语音交互。使用高频缓存引擎与意图识别相结合的方式识
+别语音请求的意图，实现多轮语音请求下，准确识别出通过语音交互来模拟对车辆零部件
+的操作实现刻度调节的用户意图。
+[0142] 请参阅图14，
+本发明还提供一种包含有计算机程序31的非易失性计算机可读存储
+介质30。当计算机程序31被一个或多个处理器40执行时，实现上述任意实施条例的语音交
+互方法。
+[0143] 例如，
+计算机程序31被处理器40执行时实现以下数据处理方法的步骤：
+[0144] 01，
+接收车辆转发的对车辆预设功能调节的当前轮的语音请求，预设功能指模拟
+对车辆零部件的操作进行刻度调节的功能；
+[0145] 02，
+读取对车辆预设功能调节的上一轮的语音请求；
+[0146] 03，
+根据当前轮的语音请求和上一轮的语音请求在缓存引擎中进行缓存查询；
+[0147] 04，
+在缓存查询的结果为未能查询到对应缓存的情况下，利用上一轮的语音请求
+对当前轮的语音请求进行改写；
+[0148] 05，
+对改写后的当前轮的语音请求进行意图识别；
+[0149] 06，
+根据意图识别的结果完成语音交互。
+[0150] 可以理解地，
+计算机程序包括计算机程序代码。计算机程序代码可以为源代码形
+式、对象代码形式、可执行文件或某些中间形式等。计算机可读存储介质可以包括：能够携
+带计算机程序代码的任何实体或装置、记录介质、U盘、
+移动硬盘、磁碟、光盘、计算机存储
+器、只读存储器(ROM，
+Read‑Only Memory)、
+随机存取存储器(RAM，
+Random Access Memory)、
+以及软件分发介质等。
+[0151] 本发明的计算机可读存储介质30存储的计算机程序31被处理器40执行时，
+可以在
+接收到用户对于车辆预设功能的语音请求后，通过读取上一轮的语音请求，结合两轮语音
+请求查询是否命中缓存，在查询不到对应缓存的情况下，利用上一轮的语音请求改写当前
+轮的语音请求以使得改写后的语音请求可以被系统识别出相应的意图，从而对改写后的当
+前轮的语音请求进行意图识别后，可以准确识别出用户的意图，进而可以根据意图识别结
+果下发控制指令，
+以控制对应的车辆零部件，完成语音交互。
+使用高频缓存引擎与意图识别
+相结合的方式识别语音请求的意图，实现多轮语音请求下，准确识别出通过语音交互来模
+拟对车辆零部件的操作实现刻度调节的用户意图。
+
+14
+
+CN 114299931 B
+
+说
+
+明
+
+书
+
+图1
+
+15
+
+附
+
+图
+
+1/7 页
+
+CN 114299931 B
+
+说
+
+明
+
+书
+
+图2
+
+图3
+
+16
+
+附
+
+图
+
+2/7 页
+
+CN 114299931 B
+
+说
+
+明
+
+书
+
+图4
+
+图5
+
+17
+
+附
+
+图
+
+3/7 页
+
+CN 114299931 B
+
+说
+
+明
+
+书
+
+图6
+
+图7
+18
+
+附
+
+图
+
+4/7 页
+
+CN 114299931 B
+
+说
+
+明
+
+书
+
+图8
+
+图9
+
+19
+
+附
+
+图
+
+5/7 页
+
+CN 114299931 B
+
+说
+
+明
+
+书
+
+图10
+
+图11
+
+20
+
+附
+
+图
+
+6/7 页
+
+CN 114299931 B
+
+说
+
+明
+
+书
+
+图12
+
+图13
+
+图14
+
+21
+
+附
+
+图
+
+7/7 页
+
+
\ No newline at end of file
diff --git a/intelligent_cabin/archive/patents/texts/CN115394300A.txt b/intelligent_cabin/archive/patents/texts/CN115394300A.txt
new file mode 100644
index 0000000..274928f
--- /dev/null
+++ b/intelligent_cabin/archive/patents/texts/CN115394300A.txt
@@ -0,0 +1,1814 @@
+(19)国家知识产权局
+
+(12)发明专利申请
+(10)申请公布号 CN 115394300 A
+(43)申请公布日 2022.11.25
+(21)申请号 202211332377 .8
+(22)申请日 2022 .10 .28
+(71)申请人 广州小鹏汽车科技有限公司
+地址 510000 广东省广州市天河区岑村松
+岗大街8号
+(72)发明人 唐祥光
+鲍鹏丽
+
+胡梓垣 孙仿逊
+王合心
+
+左佑
+
+(74)专利代理机构 北京清亦华知识产权代理事
+务所(普通合伙) 11201
+专利代理师 雷玉龙
+(51)Int .Cl .
+G10L 15/26 (2006 .01)
+G06F 40/30 (2020 .01)
+
+G10L 15/34 (2013 .01)
+B60R 16/037 (2006 .01)
+权利要求书2页
+
+CN 115394300 A
+
+(54)发明名称
+语音交互方法、语音交互装置、车辆和可读
+存储介质
+(57)摘要
+本发明公开了一种语音交互方法、
+语音交互
+装置、车辆和可读存储介质，
+属于车载语音交互
+技术领域。所述语音交互方法，
+包括：
+获取本地端
+确定的至少一路第一对话结果；
+对第一对话结果
+进行分级处理，
+确定各第一对话结果对应的融合
+等级；
+在未接收到云端发送的第二对话结果，
+且
+将第一对话结果对应的融合等级确定为最高级
+的情况下，
+将最高级对应的第一对话结果确定为
+目标对话结果；
+在接收到云端发送的第二对话结
+果的情况下，
+将第二对话结果确定为目标对话结
+果；
+按目标对话结果执行语音交互。本发明的语
+音交互方法，
+能在保证识别准确性的同时，
+提高
+语音交互系统的响应速度及灵敏性，
+在保证准确
+性的同时体验更快，
+实现极速对话。
+
+说明书16页
+
+附图4页
+
+CN 115394300 A
+
+权
+
+利
+
+要
+
+求
+
+书
+
+1/2 页
+
+1 .一种语音交互方法，
+其特征在于，
+包括：
+获取本地端确定的至少一路第一对话结果；
+所述本地端包括至少一个支路，
+各所述支
+路基于车辆座舱的用户语音请求确定与所述支路对应的第一对话结果，
+各所述支路的计算
+时延不同；
+对所述第一对话结果进行分级处理，
+确定各所述第一对话结果对应的融合等级；
+在未接收到云端发送的第二对话结果，
+且将所述第一对话结果对应的融合等级确定为
+最高级的情况下，
+将所述最高级对应的第一对话结果确定为目标对话结果；
+在接收到云端发送的第二对话结果的情况下，
+将所述第二对话结果确定为目标对话结
+果；
+按所述目标对话结果执行语音交互；
+其中，
+所述第二对话结果为所述云端基于所述用户语音请求确定的。
+2 .根据权利要求1所述的语音交互方法，
+其特征在于，
+在所述用户语音请求包括多个连
+续的子语音请求的情况下，
+所述对所述第一对话结果进行分级处理，
+确定各所述第一对话
+结果对应的融合等级，
+包括：
+在第一目标子对话结果为所述云端识别的结果的情况下，
+确定第二目标子对话结果对
+应的融合等级为第二级；
+在第一目标子对话结果为所述本地端识别的结果的情况下，
+确定第二目标子对话结果
+对应的融合等级为所述最高级；
+其中，
+所述第一目标子对话结果为第一目标子语音请求对应的目标对话结果，
+所述第
+二目标子对话结果为第二目标子语音请求对应的第一对话结果，
+所述第一目标子语音请求
+为所述多个连续的子语音请求中，
+位于所述第二目标子语音请求之前，
+且与所述第二目标
+子语音请求相邻的子语音请求。
+3 .根据权利要求1所述的语音交互方法，
+其特征在于，
+所述对所述第一对话结果进行分
+级处理，
+确定各所述第一对话结果对应的融合等级，
+包括：
+对所述第一对话结果进行基于文本识别结果和所述第一对话结果中的至少一种结果
+的分级处理，
+确定所述第一对话结果对应的融合等级；
+其中，
+所述文本识别结果为对所述用户语音请求进行文本识别确定的 ，
+所述第一对话
+结果为对所述文本识别结果进行语义理解确定的。
+4 .根据权利要求3所述的语音交互方法，
+其特征在于，
+所述对所述第一对话结果进行基
+于文本识别结果和所述第一对话结果中的至少一种结果的分级处理，
+确定所述第一对话结
+果对应的融合等级，
+包括：
+获取所述文本识别结果中的文本识别置信度和文本识别清晰度，
+获取所述第一对话结
+果中的领域、
+所述领域对应的第一置信度、
+意图和所述意图对应的第二置信度；
+基于所述文本识别置信度、文本识别清晰度、所述领域、所述第一置信度、所述意图和
+所述第二置信度中的至少两种，
+确定所述第一对话结果对应的融合等级。
+5 .根据权利要求1‑4任一项所述的语音交互方法，
+其特征在于，
+在所述获取本地端确定
+的至少一路第一对话结果之后，
+且在所述对所述第一对话结果进行分级处理，
+确定各所述
+第一对话结果对应的融合等级之前，
+所述方法还包括：
+基于所述第一对话结果，
+确定所述第一对话结果对应的频率等级和所述第一对话结果
+2
+
+CN 115394300 A
+
+权
+
+利
+
+要
+
+求
+
+书
+
+2/2 页
+
+对应的可信度；
+在所述可信度大于目标阈值，
+且所述频率等级为最高频的情况下，
+确定所述第一对话
+结果对应的融合等级为所述最高级。
+6 .根据权利要求5所述的语音交互方法，
+其特征在于，
+所述基于所述第一对话结果，
+确
+定所述第一对话结果对应的频率等级和所述第一对话结果对应的可信度，
+包括：
+获取文本识别结果中的文本识别清晰度，
+获取所述第一对话结果中的领域对应的第一
+置信度、
+意图对应的第二置信度和所述用户语音请求；
+其中，
+所述文本识别结果为对所述用
+户语音请求进行文本识别确定的；
+采用前缀树匹配所述用户语音请求，
+确定所述频率等级；
+基于所述文本识别清晰度、
+所述第一置信度和所述第二置信度，
+确定所述可信度。
+7 .根据权利要求1‑4任一项所述的语音交互方法，
+其特征在于，
+在所述按所述目标对话
+结果执行语音交互之后，
+所述方法还包括：
+基于所述目标对话结果，
+更新所述用户语音请求对应的初始上下文信息，
+所述初始上
+下文信息为所述本地端或所述云端基于所述用户语音请求确定。
+8 .根据权利要求1‑4任一项所述的语音交互方法，
+其特征在于，
+在所述用户语音请求包
+括多个连续的子语音请求的情况下，
+所述方法还包括：
+接收目标设备发送的打断信号，
+所述打断信号包括被打断的任务ID；
+响应于所述打断信号，
+清空所述任务ID对应的任务栈及所述任务ID对应的上下文信
+息。
+9 .一种语音交互装置，
+其特征在于，
+包括：
+第一获取模块，用于获取本地端确定的至少一路第一对话结果；
+所述本地端包括至少
+一个支路，
+各所述支路基于车辆座舱的用户语音请求确定与所述支路对应的第一对话结
+果，
+各所述支路的计算时延不同；
+第一处理模块，用于对所述第一对话结果进行分级处理，
+确定各所述第一对话结果对
+应的融合等级；
+第二处理模块，用于在未接收到云端发送的第二对话结果，
+且将所述第一对话结果对
+应的融合等级确定为最高级的情况下，
+将所述最高级对应的第一对话结果确定为目标对话
+结果；
+第三处理模块，用于在接收到云端发送的第二对话结果的情况下，
+将所述第二对话结
+果确定为目标对话结果；
+第四处理模块，
+用于按所述目标对话结果执行语音交互；
+其中，
+所述第二对话结果为所述云端基于所述用户语音请求确定的。
+10 .一种车辆，
+包括存储器、
+处理器及存储在存储器上并可在处理器上运行的计算机程
+序，
+其特征在于，
+所述处理器执行所述程序时实现如权利要求1‑8任一项所述的语音交互方
+法。
+11 .一种非暂态计算机可读存储介质，
+其上存储有计算机程序，
+其特征在于，
+该计算机
+程序被处理器执行时实现如权利要求1‑8任一项所述的语音交互方法。
+
+3
+
+CN 115394300 A
+
+说
+
+明
+
+书
+
+1/16 页
+
+语音交互方法、语音交互装置、车辆和可读存储介质
+技术领域
+[0001] 本发明属于车载语音交互技术领域，
+尤其涉及一种语音交互方法、
+语音交互装置、
+车辆和可读存储介质。
+背景技术
+[0002] 随着车载语音系统的广泛应用，
+用户越来越注重车载语音系统的识别精确度以及
+响应速度。相关技术中，
+往往通过将自然语言解析服务放置于云端以提高语音识别的精确
+度，
+但耗时较高，
+导致响应速度较慢，
+从而影响用户的使用体验。
+发明内容
+[0003] 本发明旨在至少解决现有技术中存在的技术问题之一。
+为此，
+本发明提出一种语
+音交互方法、语音交互装置、
+车辆和可读存储介质，
+在保证识别准确性的同时，
+提高语音交
+互系统的响应速度以及灵敏性。
+[0004] 第一方面，
+本发明提供了一种语音交互方法，
+该方法包括：
+所述本地端包括至少一个支路，
+各所
+获取本地端确定的至少一路第一对话结果；
+述支路基于车辆座舱的用户语音请求确定与所述支路对应的第一对话结果，
+各所述支路的
+计算时延不同；
+对所述第一对话结果进行分级处理，
+确定各所述第一对话结果对应的融合等级；
+在未接收到云端发送的第二对话结果，
+且将所述第一对话结果对应的融合等级确
+定为最高级的情况下，
+将所述最高级对应的第一对话结果确定为目标对话结果；
+在接收到云端发送的第二对话结果的情况下，
+将所述第二对话结果确定为目标对
+话结果；
+按所述目标对话结果执行语音交互；
+其中，
+所述第二对话结果为所述云端基于所述用户语音请求确定的。
+[0005] 根据本发明的语音交互方法，
+通过对本地端多支路发送的第一对话结果进行分级
+处理以得到对应的融合等级，
+并基于融合等级以及是否接收到云端发送的第二对话结果来
+确定最终用于执行语音交互的目标对话结果，
+有助于在保证识别准确性的同时，
+提高语音
+交互系统的响应速度以及灵敏性，
+在保证准确性的同时体验更快，
+实现极速对话，
+从而提高
+用户的使用体验。
+[0006] 根据本发明的语音交互方法，
+在所述用户语音请求包括多个连续的子语音请求的
+情况下，
+所述对所述第一对话结果进行分级处理，
+确定各所述第一对话结果对应的融合等
+级，
+包括：
+在第一目标子对话结果为所述云端识别的结果的情况下，
+确定第二目标子对话结
+果对应的融合等级为第二级；
+确定第二目标子对话
+在第一目标子对话结果为所述本地端识别的结果的情况下，
+结果对应的融合等级为所述最高级；
+4
+
+CN 115394300 A
+
+说
+
+明
+
+书
+
+2/16 页
+
+其中，
+所述第一目标子对话结果为第一目标子语音请求对应的目标对话结果，
+所
+述第二目标子对话结果为第二目标子语音请求对应的第一对话结果，
+所述第一目标子语音
+且与所述第二
+请求为所述多个连续的子语音请求中，
+位于所述第二目标子语音请求之前，
+目标子语音请求相邻的子语音请求。
+[0007] 根据本发明的语音交互方法，
+所述对所述第一对话结果进行分级处理，
+确定各所
+述第一对话结果对应的融合等级，
+包括：
+对所述第一对话结果进行基于文本识别结果和所述第一对话结果中的至少一种
+结果的分级处理，
+确定所述第一对话结果对应的融合等级；
+其中，
+所述文本识别结果为对所述用户语音请求进行文本识别确定的，
+所述第一
+对话结果为对所述文本识别结果进行语义理解确定的。
+[0008] 根据本发明的语音交互方法，
+所述对所述第一对话结果进行基于文本识别结果和
+所述第一对话结果中的至少一种结果的分级处理，确定所述第一对话结果对应的融合等
+级，
+包括：
+获取所述文本识别结果中的文本识别置信度和文本识别清晰度，
+获取所述第一对
+话结果中的领域、
+所述领域对应的第一置信度、
+意图和所述意图对应的第二置信度；
+基于所述文本识别置信度、文本识别清晰度、所述领域、所述第一置信度、所述意
+图和所述第二置信度中的至少两种，
+确定所述第一对话结果对应的融合等级。
+[0009] 根据本发明的语音交互方法，
+在所述获取本地端确定的至少一路第一对话结果之
+后，
+且在所述对所述第一对话结果进行分级处理，
+确定各所述第一对话结果对应的融合等
+级之前，
+所述方法还包括：
+基于所述第一对话结果，
+确定所述第一对话结果对应的频率等级和所述第一对话
+结果对应的可信度；
+在所述可信度大于目标阈值，
+且所述频率等级为最高频的情况下，
+确定所述第一
+对话结果对应的融合等级为所述最高级。
+[0010] 根据本发明的语音交互方法，
+所述基于所述第一对话结果，
+确定所述第一对话结
+果对应的频率等级和所述第一对话结果对应的可信度，
+包括：
+获取文本识别结果中的文本识别清晰度，
+获取所述第一对话结果中的领域对应的
+第一置信度、
+意图对应的第二置信度和所述用户语音请求；
+其中，
+所述文本识别结果为对所
+述用户语音请求进行文本识别确定的；
+采用前缀树匹配所述用户语音请求，
+确定所述频率等级；
+基于所述文本识别清晰度、
+所述第一置信度和所述第二置信度，
+确定所述可信度。
+[0011] 根据本发明的语音交互方法，
+在所述按所述目标对话结果执行语音交互之后，
+所
+述方法还包括：
+基于所述目标对话结果，
+更新所述用户语音请求对应的初始上下文信息，
+所述初
+始上下文信息为所述本地端或所述云端基于所述用户语音请求确定。
+[0012] 根据本发明的语音交互方法，
+在所述用户语音请求包括多个连续的子语音请求的
+情况下，
+所述方法还包括：
+接收目标设备发送的打断信号，
+所述打断信号包括被打断的任务ID；
+响应于所述打断信号，
+清空所述任务ID对应的任务栈及所述任务ID对应的上下文
+5
+
+CN 115394300 A
+
+说
+
+明
+
+书
+
+3/16 页
+
+信息。
+[0013]
+
+第二方面，
+本发明提供了一种语音交互装置，
+该装置包括：
+所述本地端包括
+第一获取模块，用于获取本地端确定的至少一路第一对话结果；
+至少一个支路，
+各所述支路基于车辆座舱的用户语音请求确定与所述支路对应的第一对话
+结果，
+各所述支路的计算时延不同；
+第一处理模块，用于对所述第一对话结果进行分级处理，
+确定各所述第一对话结
+果对应的融合等级；
+第二处理模块，用于在未接收到云端发送的第二对话结果，
+且将所述第一对话结
+果对应的融合等级确定为最高级的情况下，
+将所述最高级对应的第一对话结果确定为目标
+对话结果；
+第三处理模块，用于在接收到云端发送的第二对话结果的情况下，
+将所述第二对
+话结果确定为目标对话结果；
+第四处理模块，
+用于按所述目标对话结果执行语音交互；
+其中，
+所述第二对话结果为所述云端基于所述用户语音请求确定的。
+[0014] 根据本发明的语音交互装置，
+通过对本地端多支路发送的第一对话结果进行分级
+处理以得到对应的融合等级，
+并基于融合等级以及是否接收到云端发送的第二对话结果来
+提高语音
+确定最终用于执行语音交互的目标对话结果，
+有助于在保证识别准确性的同时，
+交互系统的响应速度以及灵敏性，
+在保证准确性的同时体验更快，
+实现极速对话，
+从而提高
+用户的使用体验。
+[0015] 第三方面，
+本发明提供了一种车辆，
+包括存储器、
+处理器及存储在所述存储器上并
+可在所述处理器上运行的计算机程序，
+所述处理器执行所述计算机程序时实现如上述第一
+方面所述的语音交互方法。
+[0016] 第四方面，
+本发明提供了一种非暂态计算机可读存储介质，
+其上存储有计算机程
+序，
+所述计算机程序被处理器执行时实现如上述第一方面所述的语音交互方法。
+[0017] 第五方面，
+本发明提供了一种芯片，
+所述芯片包括处理器和通信接口，
+所述通信接
+口和所述处理器耦合，
+所述处理器用于运行程序或指令，
+实现如第一方面所述的语音交互
+方法。
+[0018] 第六方面，
+本发明提供了一种计算机程序产品，
+包括计算机程序，
+所述计算机程序
+被处理器执行时实现如上述第一方面所述的语音交互方法。
+[0019] 本发明的上述一个或多个技术方案，
+至少具有如下技术效果之一：
+通过对本地端多支路发送的第一对话结果进行分级处理以得到对应的融合等级，
+并基于融合等级以及是否接收到云端发送的第二对话结果来确定最终用于执行语音交互
+的目标对话结果，
+有助于在保证识别准确性的同时，
+提高语音交互系统的响应速度以及灵
+敏性，
+在保证准确性的同时体验更快，
+实现极速对话，
+从而提高用户的使用体验。
+[0020] 进一步的，
+通过在多轮对话场景下，
+基于上一轮的结果对下一轮的融合等级进行
+调整，
+实现融合等级的动态分级，
+从而能够保障不同架构不同体系的端云服务能较好地兼
+容，
+降低实施难度。
+[0021] 更进一步的，
+通过在进行分级处理之前，
+对第一对话结果进行高频分级处理，
+以判
+断第一对话结果是否为用户的高频语音指令，
+当确定第一对话结果为用户在车载环境下的
+6
+
+CN 115394300 A
+
+说
+
+明
+
+书
+
+4/16 页
+
+高频语音指令的情况下，
+则将该第一对话结果确定为目标对话结果，
+在保证精确性的基础
+上，
+还能进一步提高响应速率。
+[0022] 再进一步的，
+通过对第一对话结果进行基于文本识别结果和第一对话结果中的至
+少一种结果的分级处理，
+确定第一对话结果对应的融合等级，
+能够提高最终所确定的融合
+等级的精确性以及准确性，
+从而有助于提高语音交互系统的准确性。
+[0023] 本发明的附加方面和优点将在下面的描述中部分给出，
+部分将从下面的描述中变
+得明显，
+或通过本发明的实践了解到。
+附图说明
+[0024] 本发明的上述和/或附加的方面和优点从结合下面附图对实施例的描述中将变得
+明显和容易理解，
+其中：
+图1是本发明提供的语音交互方法的流程示意图之一；
+图2是本发明提供的语音交互方法的流程示意图之二；
+图3是本发明提供的语音交互方法的流程示意图之三；
+图4是本发明提供的语音交互方法的流程示意图之四；
+图5是本发明提供的语音交互方法的流程示意图之五；
+图6是本发明提供的语音交互装置的结构示意图；
+图7是本发明提供的车辆的结构示意图。
+具体实施方式
+[0025] 下面将结合本发明实施例中的附图，
+对本发明实施例中的技术方案进行清楚地描
+述，
+显然，
+所描述的实施例是本发明一部分实施例，
+而不是全部的实施例。基于本发明中的
+实施例，
+本领域普通技术人员获得的所有其他实施例，
+都属于本发明保护的范围。
+[0026] 本发明的说明书和权利要求书中的术语“第一”、
+“第二”等是用于区别类似的对
+象，
+而不用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互
+换，
+以便本发明的实施例能够以除了在这里图示或描述的那些以外的顺序实施，
+且“第一”、
+“第二”等所区分的对象通常为一类，
+并不限定对象的个数，
+例如第一对象可以是一个，
+也可
+以是多个。此外，
+说明书以及权利要求中“和/或”表示所连接对象的至少其中之一，
+字符
+“/”，
+一般表示前后关联对象是一种“或”的关系。
+[0027]
+相关技术中，
+语音交互有三种方式：
+其一，
+将车载语音系统中的大量神经网络系统部署于云端以进行语音识别，
+然后
+通过本地端来执行语音识别结果，
+通过网络进行云端与本地端的通信；
+然而该方法耗时较
+长，
+且响应速度较慢，
+从而导致用户体验差，
+极大降低了用户的语音交互欲望。
+[0028] 其二，
+同时部署云服务和端服务，
+在本地端无法支持语音识别时再采用云服务进
+行语音识别；
+然而该方法极大地影响了在本地端无法支持语音识别的情况下的响应速度，
+无法满足较为复杂的语音识别情境下的快速响应，
+从而导致用户体验差。
+[0029] 其三，
+采用边缘计算进行语音识别，
+然而该方法需要外接较多的设备，
+导致设计成
+本较高，
+且边缘计算不稳定且算力具有局限性，
+导致语音识别系统的大多功能无法实现，
+影
+响用户的使用体验。
+7
+
+CN 115394300 A
+
+说
+
+明
+
+[0030]
+
+书
+
+5/16 页
+
+下面结合附图 ，
+通过具体的实现方式及其应用场景对本发明提供的语音交互方
+法、
+语音交互装置、
+车辆和可读存储介质进行详细地说明。
+[0031] 其中，
+语音交互方法可应用于终端，
+具体可由，
+终端中的硬件或软件执行。
+[0032] 该终端可以为车机，
+终端可以为包括诸如拾音器或触控面板或其它物理用户接口
+的设备。
+[0033] 以下各个实现方式中，
+描述了包括显示器和触摸敏感表面的终端。然而，
+应当理解
+的是，
+终端可以包括诸如物理键盘、
+鼠标和控制杆的一个或多个其它物理用户接口设备。
+[0034] 本发明提供的语音交互方法，
+该语音交互方法的执行主体可以为车机或者车机中
+能够实现该语音交互方法的功能模块或功能实体（如端云融合管理系统）。在车载环境下，
+由于网络情况复杂，
+比如车辆行驶时，
+由于地点切换导致网络状态动态变化，
+语音交互的复
+杂性以及计算时延也远高于居家环境的语音交互。
+[0035] 如图1所示，
+该语音交互方法包括：
+步骤110、步骤120、步骤130、步骤140和步骤
+150。
+[0036] 步骤110、
+获取本地端确定的至少一路第一对话结果；
+本地端包括至少一个支路，
+各支路基于车辆座舱的用户语音请求确定与支路对应的第一对话结果，
+各支路的计算时延
+不同；
+在该步骤中，用户语音请求为车辆座舱的用户在进行语音交互情境下，
+所发出的
+语音信息。
+[0037] 可以理解的是，
+语音交互场景包括单轮对话场景和多轮对话场景。
+[0038] 其中，
+在单轮对话场景下，用户语音请求为一条请求；
+在多轮对话场景下，用户语
+音请求可以包括多条连续的子语音请求，
+后一条子语音请求为基于上一条子语音请求对应
+的执行结果所发出的后续请求指令。
+[0039] 本地端包括至少一路支路，
+各支路独立对用户语音请求进行识别。
+[0040] 不同的支路设置有不同的算力，
+对应有不同的计算时延，
+例如，
+可阶梯式逐级提升
+各支路的算力。
+[0041] 第一对话结果为由本地端的支路对用户语音请求进行语音识别所得到的结果。
+[0042] 在实际执行过程中，
+各支路应对同一用户语音请求分别进行独立识别，
+以生成与
+各支路对应的第一对话结果。
+[0043] 第一对话结果的确定方式将在下文中进行说明，
+在此暂不作赘述。
+[0044] 步骤120、
+对第一对话结果进行分级处理，
+确定各第一对话结果对应的融合等级；
+在该步骤中，
+融合等级用于表征第一对话结果的优先级。
+[0045]
+例如，
+融合等级可以包括A/B/C/D/E等多个等级，
+各等级对应的优先级依次降低。
+[0046] 可以理解的是，
+不同支路处理得到的第一对话结果的精确度可能各不相同，
+不同
+的精确度对应有不同的优先级。
+[0047] 在后续执行过程中，
+可基于融合等级从得到的多个第一对话结果中确定一个进行
+执行。
+[0048] 在实际执行过程中，
+可以采用预训练的神经网络模型对第一对话结果进行分级处
+理，
+以得到各第一对话结果对应的融合等级，
+其中，
+神经网络模型可以为任意可实现的模
+型。
+8
+
+CN 115394300 A
+
+说
+
+明
+
+[0049]
+
+书
+
+6/16 页
+
+在一些实施方式中，
+步骤120，
+可以包括：
+对第一对话结果进行基于文本识别结果和第一对话结果中的至少一种结果的分
+确定第一对话结果对应的融合等级；
+级处理，
+其中，
+文本识别结果为对用户语音请求进行文本识别确定的，
+第一对话结果为对
+文本识别结果进行语义理解确定的。
+[0050] 在该实施方式中，
+文本识别结果为对用户语音请求进行文本识别（ASR）所得到的。
+[0051] 在得到文本识别结果后，
+对所得到的文本识别结果进行语义理解（NLU）即可获取
+第一对话结果。
+[0052] 在实际执行过程中，
+可对进行ASR处理后得到的文本识别结果进行保存，
+在需要时
+进行调取即可。
+[0053] 在得到第一对话结果后，
+对第一对话结果对应的文本识别结果进行分级处理；
+或
+者对第一对话结果进行分级处理；
+或者对第一对话结果以及第一对话结果对应的文本识别
+即可确定第一对话结果对应的融合等级。
+结果进行分级处理，
+[0054] 根据本发明提供的语音交互方法，
+通过对第一对话结果进行基于文本识别结果和
+第一对话结果中的至少一种结果的分级处理，
+确定第一对话结果对应的融合等级，
+能够提
+高最终所确定的融合等级的精确性以及准确性，
+从而有助于提高语音交互系统的准确性。
+[0055] 在一些实施方式中，
+对第一对话结果进行基于文本识别结果和第一对话结果中的
+确定第一对话结果对应的融合等级，
+可以包括：
+至少一种结果的分级处理，
+获取文本识别结果中的文本识别置信度和文本识别清晰度，
+获取第一对话结果中
+的领域、
+领域对应的第一置信度、
+意图和意图对应的第二置信度；
+基于文本识别置信度、文本识别清晰度、领域、第一置信度、意图和第二置信度中
+的至少两种，
+确定第一对话结果对应的融合等级。
+[0056] 在该实施方式中，
+文本识别结果可以包括文本识别置信度（ASR nbest置信度）和
+文本识别清晰度（ASR清晰度）。
+[0057] 第一对话结果可以包括：
+领域、
+领域对应的第一置信度（即领域置信度）、
+意图以及
+意图对应的第二置信度（即意图置信度）。
+[0058] 对ASR nbest置信度、
+ASR清晰度、领域、第一置信度、意图和第二置信度中的至少
+两种进行处理，
+确定第一对话结果对应的融合等级。
+[0059] 在实际执行过程中，
+可以采用预先训练的分级模型来执行该步骤。
+[0060] 如图2所示，
+在得到第一对话结果和文本识别结果后，
+将ASR nbest置信度、ASR清
+晰度、
+领域、第一置信度、
+意图和第二置信度输入至分级模型，
+由分级模型对ASR nbest置信
+度进行计算方差以及计算均值等处理，
+对ASR清晰度、
+领域、第一置信度、
+意图和第二置信度
+等进行其他处理，
+然后对得到的结果进行cat以及linear等处理，
+以输出第一对话结果对应
+的融合等级。
+[0061] 表1示例了分别对各支路输出的第一对话结果进行分级处理后所得到的各第一对
+话结果对应的融合等级，
+其中，
+第一对话结果为各支路对用户语音请求“打开车窗”进行识
+别后得到的。
+[0062] 表1
+
+9
+
+CN 115394300 A
+
+说
+
+明
+
+书
+
+7/16 页
+
+其中，
+支路2所输出的第一对话结果对应的融合等级最高，
+为A级。
+[0063] 需要说明的是，
+分级模型可以基于样本第一对话结果以及样本第一对话结果对应
+的样本融合等级训练得到；
+或者分级模型可以基于样本文本识别结果以及样本文本识别结
+果对应的样本融合等级训练得到；
+或者分级模型还可以基于样本文本识别结果、样本文本
+识别结果以及与样本文本识别结果对应的样本融合等级训练得到。
+[0064] 下面对分级模型的训练过程进行说明。
+[0065] 1、
+数据采集与标注
+先对样本用户语音请求进行听音，
+并标注其对应的领域以及意图等，
+获取样本领
+域和样本意图；
+将样本领域以及样本意图标注是否在可抢跑清单里面；
+根据样本用户语音的清晰度和完整度判定融合等级（如A/B/C/D/E等），
+并对等级
+进行标注以获取样本融合等级；
+基于上述所获取的数据即可获取分级模型的训练样本。
+[0066] 2、
+训练
+样本用户语音请求过一遍ASR以获取ASR nbest置信度，
+过一遍AJ系统（其他语音
+模型）以获取ASR清晰度；
+将ASR nbest置信度、
+ASR清晰度，
+样本领域及其置信度、
+样本意图及其置信度分别
+编码后即可训练多类模型。
+[0067] 3、
+训练语料示例。
+[0068] 4、
+标注标准可以基于清晰度（可以包括0、
+0 .3、
+0 .6以及1等级别）、
+样本领域置信度
+（可以包括0、
+0 .5以及1等级别），
+样本意图置信度（可以包括0、
+0 .5以及1等级别）和样本用户
+语音请求是否为背景音以及是否属于随意聊天内容等进行降级处理来确定。
+[0069] 在本发明中，
+通过采用预训练的分级模型确定第一对话结果的融合等级，
+可以提
+10
+
+CN 115394300 A
+
+说
+
+明
+
+书
+
+8/16 页
+
+高计算结果的准确性以及计算速率，
+且具有较高的学习能力。
+[0070] 根据本发明提供的语音交互方法，
+基于文本识别置信度、文本识别清晰度、领域、
+确定第一对话结果对应的融合等级，
+能够提
+第一置信度、
+意图和第二置信度中的至少两种，
+高最终所确定的融合等级的精确性以及准确性，
+从而有助于提高语音交互系统的准确性。
+[0071] 步骤130、
+在未接收到云端发送的第二对话结果，
+且将第一对话结果对应的融合等
+级确定为最高级的情况下，
+将最高级对应的第一对话结果确定为目标对话结果；
+在该步骤中，
+目标对话结果为最终用于执行的对话结果。
+[0072] 其中，
+第二对话结果为云端基于用户语音请求确定的，
+该用户语音请求与本地端
+对应的用户语音请求相同。
+[0073] 需要说明的是，
+在本发明中，
+默认将云端发送的第二对话结果对应的融合等级确
+定为最高级，
+即确定为A级。
+[0074] 可以理解的是，
+在车机正常工作时，
+用户唤醒语音交互系统后，
+用户语音请求可以
+通过本地端的至少一条支路以及云端的处理路线进行处理，
+云端的计算时延可能会高于本
+地端部分支路的计算时延。在接收云端发送的第二对话结果之前，
+可能会接收到一路或多
+路本地端的支路所发送的第一对话结果。
+[0075] 在未接收到第二对话结果之前，
+则比较已接收的各支路发送的第一对话结果对应
+的融合等级，
+若确定存在最高级对应的第一对话结果（即A级对应的第一对话结果），
+则将A
+级对应的第一对话结果确定为目标对话结果，
+即由本地端识别得到的该A级对应的第一对
+话结果为本轮用于执行的对话结果。
+[0076] 在未接收到第二对话结果且未得到最高级对应的第一对话结果的情况下，
+则持续
+接收各支路发送的第一对话结果并对第一对话结果进行分级处理，
+基于融合等级进行如下
+处理。
+[0077] 在一些示例中，
+在目标时段内，
+在未接收到第二对话结果且未得到最高级对应的
+第一对话结果的情况下，
+将确定的融合等级为第二级（即B级）的第一对话结果确定为目标
+对话结果。
+[0078] 其中，
+目标时段可以基于用户自定义，
+如设置为自本轮语音交互开始起第3s至第
+5s之间的时段，
+当然，
+也可以基于实际需求设置为其他时间段，
+如第2s至第4秒等，
+本发明不
+作限定。
+[0079] 在另一些示例中，
+在目标时段内，
+在未接收到第二对话结果且未得到最高级对应
+的第一对话结果以及第二级对应的第一对话结果的情况下，
+则结束本轮语音交互。
+[0080]
+例如，
+在第3s‑5s期间，
+在未接收到第二对话结果的情况下，
+若得到融合等级为A级
+的第一对话结果，
+则将融合等级为A级的第一对话结果确定为目标对话结果；
+在第3s‑5s期间，
+在未接收到第二对话结果且未得到A级的第一对话结果的情况
+下，
+则还可以将在第3s‑5s期间得到的融合等级为B级的第一对话结果确定为目标对话结
+果。
+[0081] 在第3s‑5s期间，
+在未接收到第二对话结果，
+未得到A级的第一对话结果，
+且未得到
+融合等级为B级的第一对话结果的情况下，
+则结束本轮语音交互。
+[0082] 步骤140、
+在接收到云端发送的第二对话结果的情况下，
+将第二对话结果确定为目
+标对话结果；
+11
+
+CN 115394300 A
+
+说
+
+明
+
+书
+
+9/16 页
+
+在该步骤中，
+在未得到最高级对应的第一对话结果且接收到云端发送的第二对话
+结果的情况下，
+则将第二对话结果的等级确定为最高级，
+并将第二对话结果确定为目标对
+话结果，
+即由云端识别得到的第二对话结果为本轮用于执行的对话结果。
+[0083] 步骤150、
+按目标对话结果执行语音交互。
+[0084] 在该步骤中，
+目标对话结果为第一对话结果和第二对话结果中的一种。
+[0085] 目标对话结果对应的融合等级为最高级。
+[0086] 执行语音交互可以有多种形式：
+其一，
+执行语音交互对应的控制指令。
+[0087] 比如，
+用户语音请求为“打开天窗”，
+执行语音交互可以包括开启天窗。
+[0088] 其二，
+播报语音回复。
+[0089] 比如，
+用户语音请求为“还有多久到目的地”，
+执行语音交互可以包括播报“离目的
+地还有30分钟路程”。
+[0090] 其三，
+执行语音交互对应的控制指令且播报语音回复。
+[0091] 比如，
+用户语音请求为“打开天窗”，
+执行语音交互可以包括开启天窗且播报“天窗
+已开启”。
+[0092] 在本发明中，
+通过阶梯式配置多通道+端云融合的半无状态特性，
+可以解决云服务
+和端服务协作时无法权衡响应速度和准确性的问题；
+通过端云融合管理技术可以解决端服
+务和云服务完全割裂的问题。
+[0093] 根据本发明提供的语音交互方法，
+通过对本地端多支路发送的第一对话结果进行
+分级处理以得到对应的融合等级，
+并基于融合等级以及是否接收到云端发送的第二对话结
+果来确定最终用于执行语音交互的目标对话结果，
+有助于在保证识别准确性的同时，
+提高
+语音交互系统的响应速度以及灵敏性，
+在保证准确性的同时体验更快，
+实现极速对话，
+从而
+提高用户的使用体验。
+[0094] 在一些实施方式中，
+在步骤110之后且在步骤120之前，
+该方法还可以包括：
+基于第一对话结果，
+确定第一对话结果对应的频率等级和第一对话结果对应的可
+信度；
+在可信度大于目标阈值，
+且频率等级为最高频的情况下，
+确定第一对话结果对应
+的融合等级为最高级。
+[0095] 在该实施方式中，
+频率等级用于表征第一对话结果是否为车辆座舱的用户日常使
+用的高频语音指令。
+[0096] 可以理解的是，
+在车载环境下，
+存在有该环境下特有的高频语音指令，
+如“打开车
+窗”以及“导航至XX地点”等。
+[0097] 目标阈值可以基于用户自定义，
+如设置为0 .99、
+0 .98或0 .8等，
+本发明不作限定，
+在
+实际应用过程中，
+可基于实际需求确定最佳的数值。
+[0098] 在进行分级处理之前，
+可以先进行高频分级处理。通过对第一对话结果进行处理
+以确定第一对话结果对应的频率等级和第一对话结果对应的可信度，
+并将可信度与目标阈
+值进行比较。
+[0099] 需要说明的是，
+在本发明中，
+当基于频率等级和第一对话结果对应的可信度确定
+将第一对话结果的融合等级确定为最高级后，
+则可不进行步骤120，
+跳转至步骤130。
+12
+
+说
+
+CN 115394300 A
+[0100]
+
+例如，
+在可信度
+
+明
+
+书
+
+10/16 页
+
+＞0 .99且第一对话结果对应的频率等级为高频的情况下，
+
+则将该第一对话结果对应的融合等级确定为A级；
+在将该第一对话结果对应的融合等级确
+定为A级后，
+在未接收到云端发送的第二对话结果的情况下，
+则将该A级对应的第一对话结
+果确定为目标对话结果。
+[0101] 在实际执行过程中，
+可以采用训练好的高频分级模型对第一对话结果进行高频分
+级处理，
+以确定第一对话结果的融合等级。
+[0102] 可以理解的是，
+高频分级处理对应的算法的时延应低于上文通用的分级处理的算
+法的时延。
+[0103] 如图3所示，
+将第一对话结果输入至高频分级模型，
+获取高频分级模型输出的融合
+等级，
+并判断融合等级。
+[0104] 在高频分级模型输出的融合等级为最高级的情况下，
+则直接将该最高级对应的第
+一对话结果确定为目标对话结果；
+在高频分级模型输出的融合等级非最高级的情况下，
+则将第一对话结果输入至分
+级模型进行进一步地分级处理，
+具体分级处理过程已在上文实施方式中进行了说明，
+在此
+不作赘述。
+[0105] 根据本发明提供的语音交互方法，
+通过在进行分级处理之前，
+对第一对话结果进
+行高频分级处理，
+以判断第一对话结果是否为用户的高频语音指令，
+当确定第一对话结果
+为用户在车载环境下的高频语音指令的情况下，
+则将该第一对话结果确定为目标对话结
+果，
+在保证精确性的基础上，
+还能进一步提高响应速率。
+[0106] 在一些实施方式中，
+基于第一对话结果，
+确定第一对话结果对应的频率等级和第
+一对话结果对应的可信度，
+可以包括：
+获取文本识别结果中的文本识别清晰度，
+获取第一对话结果中的领域对应的第一
+置信度、
+意图对应的第二置信度和用户语音请求；
+其中，
+文本识别结果为对用户语音请求进
+行文本识别确定的；
+采用前缀树匹配用户语音请求，
+确定频率等级；
+基于文本识别清晰度、
+第一置信度和第二置信度，
+确定可信度。
+[0107] 在该实施方式中，
+文本识别清晰度即ASR清晰度。
+[0108] 领域对应的第一置信度以及意图对应的第二置信度即入NLU的领域意图的置信
+度。
+[0109]
+用户语音请求即Query。
+[0110]
+例如，
+在实际执行过程中，
+可以输入NLU的领域意图的置信度、ASR清晰度和Query
+至高频分级模型；
+采用前缀树匹配Query是否属于高频，
+以确定频率等级；
+并通过如下公式
+计算可信度。
+
+[0111]
+
+其中，
+
+为可信度；
+
+为第一置信度；
+
+置信度；
+a、
+b和c分别为调节因子，
+浮点数标量。
+
+13
+
+为第二
+
+CN 115394300 A
+[0112]
+
+在一些示例中，
+当
+
+说
+
+明
+
+书
+
+11/16 页
+
+>0 .99且频率等级属于高频时，
+则将融合等级确定为A，
+
+其他情况分配默认等级D。
+[0113] 根据本发明提供的语音交互方法，
+采用前缀树匹配用户语音请求，
+确定频率等级，
+确定可信度，
+具有较高的准确度和精确
+并基于文本识别清晰度、第一置信度和第二置信度，
+度。
+[0114] 在一些实施方式中，
+在步骤150之后，
+该方法还可以包括：
+基于目标对话结果，
+更新
+用户语音请求对应的初始上下文信息，
+初始上下文信息为本地端或云端基于用户语音请求
+确定。
+[0115] 在该实施方式中，
+初始上下文信息包括初始识别的领域、
+意图和状态等信息。
+[0116] 如图4所示，
+对经NLU处理后得到的第一对话结果进行分级处理，
+并由车载大屏执
+行器采纳某一路结果后，
+将采纳信号（即目标对话结果）和采纳详情通知部署在车载大屏的
+端云融合管理系统，
+由其进行状态和信息的融合。
+[0117] 大屏执行器采用后回传的结果包括领域、
+意图、状态（continue/end）、采纳来源
+（端/云）以及通道ID等信息。
+[0118] 然后更新上下文，
+读取暂存区中与采纳结果最相近的上下文，
+其中，
+若是采用本地
+端的识别结果则根据通道ID读取，
+若是采用云端的识别结果则读取任一个端结果；用回传
+的信息来校验初始上下文信息。
+[0119] 最后基于采纳状态更新任务栈。
+其中，
+如果采纳状态=end，
+则退出语音助手，
+清空
+整个对话管理系统；
+在确定为新任
+如果采纳状态=continue，
+则判定当前用户语音请求是否为新任务，
+务的情况下则退出旧任务，
+进入新任务；
+在确定为旧任务的情况下则更新领域以及意图等
+信息。
+[0120] 根据本发明提供的语音交互方法，
+基于目标对话结果，
+更新用户语音请求对应的
+初始上下文信息，
+在实现快速响应的基础上，
+还能进一步实现信息的及时更新，
+确保端云信
+息保持一致。
+[0121] 下面对多轮对话场景下本发明的实现方式进行说明。
+[0122] 在一些实施方式中，
+在用户语音请求包括多个连续的子语音请求的情况下，
+步骤
+120，
+可以包括：
+在第一目标子对话结果为云端识别的结果的情况下，
+确定第二目标子对话结果对
+应的融合等级为第二级；
+在第一目标子对话结果为本地端识别的结果的情况下，
+确定第二目标子对话结果
+对应的融合等级为最高级；
+其中，
+第一目标子对话结果为第一目标子语音请求对应的目标对话结果，
+第二目
+标子对话结果为第二目标子语音请求对应的第一对话结果，
+第一目标子语音请求为多个连
+续的子语音请求中，
+位于第二目标子语音请求之前，
+且与第二目标子语音请求相邻的子语
+音请求。
+[0123] 在该实施方式中，
+第一目标子语音请求为多轮对话场景下，
+上一轮已执行的子语
+音请求。可以理解的是，
+第一目标子对话结果可能为云端输出的第二对话结果，
+也可能为本
+14
+
+CN 115394300 A
+
+说
+
+明
+
+书
+
+12/16 页
+
+地端某支路输出的第一对话结果。
+[0124]
+第二目标子语音请求为多轮对话场景下，
+本轮将要执行的子语音请求。第二目标
+子对话结果基于上一轮所执行的端的类型来确定。
+[0125] 需要说明的是，
+在多轮对话场景下，
+得出初步的分级结果后，
+在发给车载大屏执行
+器之前，
+需要针对多轮任务的情况做分级调整，
+以保障不同架构不同体系的端云服务能较
+好地兼容，
+降低实施难度。
+[0126]
+例如，
+在多轮对话场景下，
+若上一轮采用是本地端输出的第一对话结果，
+则本轮融
+合等级分级强制确定为A级；
+若上一轮采用的是云端输出的第二对话结果，
+则本轮融合等级分级强制确定为B
+级。
+[0127] 将最终确定的分级结果发送至车载大屏执行器进行执行。
+[0128] 在本发明中，
+通过动态分级技术，
+能够解决以往单纯靠一级服务完全无法承接才
+退化到二级服务的简单协作方式的问题。
+[0129] 根据本发明提供的语音交互方法，
+通过在多轮对话场景下，
+基于上一轮的结果对
+下一轮的融合等级进行调整，
+实现融合等级的动态分级，
+从而能够保障不同架构不同体系
+的端云服务能较好地兼容，
+降低实施难度。
+[0130] 在一些实施方式中，
+在将分级结果发给车载大屏执行器之后，
+且在端云融合前，
+还
+可以将结果暂存至暂存队列。
+[0131] 暂存期间不更新上下文，
+并且可以接受同一个msgId的不同通道多次请求，
+如图5
+所示。
+[0132] 若msgId1在处理后采纳前 ，
+msgId2开始处理并采纳msgId2，则抛弃msgId1 ，且
+msgId1和msgId2的上下文是同一份，
+msgId1并未被采纳来更新上下文（即上下文更新的条
+件是采纳信号）。
+[0133] 在一些实施方式中，
+在用户语音请求包括多个连续的子语音请求的情况下，
+该方
+法还可以包括：
+接收目标设备发送的打断信号，
+打断信号包括被打断的任务ID；
+响应于打断信号，
+清空任务ID对应的任务栈及任务ID对应的上下文信息。
+[0134] 在该实施方式中，
+表2示例了一种分级处理结果以及打断情况下的处理结果。
+[0135] 表2
+
+15
+
+CN 115394300 A
+
+说
+
+明
+
+书
+
+13/16 页
+
+可以理解的是，
+在多轮对话情境下，
+可能会出现异常打断，
+即某一些操作导致当前
+任务无法继续，
+需通过打断源广播出打断信号和打断详情，
+以维护了多轮状态的各个系统
+或者模块进行状态更新。
+[0136] 目标设备可以为任意车载设备，
+如车载大屏或车载大屏的UI管理器等。
+[0137]
+例如，
+在UI变动引起的任务打断的情况下，
+车载大屏的UI管理器给端云融合管理
+系统发送打断信号，
+告知被打断的任务ID；
+端云融合管理系统接收到任务ID判断是否需要
+销毁任务栈里面的对应任务，
+在确定需要清空的情况下则清空任务ID对应的任务栈及任务
+ID对应的上下文信息。
+[0138] 在语音切换意图导致的任务打断的情况下，
+端云融合管理系统给车载大屏UI管理
+器发送打断信号，
+告知被打断的任务ID；
+车载大屏UI管理器判断是否需要销毁UI显示的卡
+片和其维护的其他资源。
+[0139] 根据本发明提供的语音交互方法，
+通过在多轮对话场景下，
+当出现异常打断时，
+发
+送包括被打断的任务ID的打断信号以清空任务ID对应的任务栈及任务ID对应的上下文信
+息，
+能够有效维护更系统的工作状态，
+保证其处于正常工作状态。
+[0140] 本发明提供的语音交互方法，
+执行主体可以为语音交互装置。本发明中以语音交
+互装置执行语音交互方法为例，
+说明本发明提供的语音交互装置。
+
+16
+
+CN 115394300 A
+
+说
+
+明
+
+[0141]
+
+书
+
+14/16 页
+
+本发明还提供一种语音交互装置。
+[0142] 如图6所示，
+该语音交互装置包括：
+第一获取模块610、第一处理模块620、第二处理
+第三处理模块640和第四处理模块650。
+模块630、
+[0143] 第一获取模块610，
+用于获取本地端确定的至少一路第一对话结果；
+本地端包括至
+少一个支路，
+各支路基于车辆座舱的用户语音请求确定与支路对应的第一对话结果，
+各支
+路的计算时延不同；
+第一处理模块620，
+用于对第一对话结果进行分级处理，
+确定各第一对话结果对应
+的融合等级；
+第二处理模块630，
+用于在未接收到云端发送的第二对话结果，
+且将第一对话结果
+对应的融合等级确定为最高级的情况下，
+将最高级对应的第一对话结果确定为目标对话结
+果；
+第三处理模块640，
+用于在接收到云端发送的第二对话结果的情况下，
+将第二对话
+结果确定为目标对话结果；
+第四处理模块650，
+用于按目标对话结果执行语音交互；
+其中，
+第二对话结果为云端基于用户语音请求确定的。
+[0144] 根据本发明提供的语音交互装置，
+通过对本地端多支路发送的第一对话结果进行
+分级处理以得到对应的融合等级，
+并基于融合等级以及是否接收到云端发送的第二对话结
+果来确定最终用于执行语音交互的目标对话结果，
+有助于在保证识别准确性的同时，
+提高
+语音交互系统的响应速度以及灵敏性，
+在保证准确性的同时体验更快，
+实现极速对话，
+从而
+提高用户的使用体验。
+[0145] 在一些示例中，
+在用户语音请求包括多个连续的子语音请求的情况下，
+第一处理
+模块620，
+还可以用于：
+在第一目标子对话结果为云端识别的结果的情况下，
+确定第二目标子对话结果对
+应的融合等级为第二级；
+在第一目标子对话结果为本地端识别的结果的情况下，
+确定第二目标子对话结果
+对应的融合等级为最高级；
+其中，
+第一目标子对话结果为第一目标子语音请求对应的目标对话结果，
+第二目
+标子对话结果为第二目标子语音请求对应的第一对话结果，
+第一目标子语音请求为多个连
+续的子语音请求中，
+位于第二目标子语音请求之前，
+且与第二目标子语音请求相邻的子语
+音请求。
+[0146] 在一些示例中，
+第一处理模块620，
+还可以用于：
+对第一对话结果进行基于文本识别结果和第一对话结果中的至少一种结果的分
+级处理，
+确定第一对话结果对应的融合等级；
+其中，
+文本识别结果为对用户语音请求进行文本识别确定的，
+第一对话结果为对
+文本识别结果进行语义理解确定的。
+[0147] 在一些示例中，
+第一处理模块620，
+还可以用于：
+获取文本识别结果中的文本识别置信度和文本识别清晰度，
+获取第一对话结果中
+的领域、
+领域对应的第一置信度、
+意图和意图对应的第二置信度；
+基于文本识别置信度、文本识别清晰度、领域、第一置信度、意图和第二置信度中
+17
+
+CN 115394300 A
+
+说
+
+明
+
+书
+
+15/16 页
+
+的至少两种，
+确定第一对话结果对应的融合等级。
+[0148] 在一些示例中，
+该装置还可以包括：
+且在对第
+第五处理模块，用于在获取本地端确定的至少一路第一对话结果之后，
+一对话结果进行分级处理，
+确定各第一对话结果对应的融合等级之前，
+基于第一对话结果，
+确定第一对话结果对应的频率等级和第一对话结果对应的可信度；
+第六处理模块，
+用于在可信度大于目标阈值，
+且频率等级为最高频的情况下，
+确定
+第一对话结果对应的融合等级为最高级。
+[0149] 在一些示例中，
+第五处理模块，
+还可以用于：
+获取文本识别结果中的文本识别清晰度，
+获取第一对话结果中的领域对应的第一
+置信度、
+意图对应的第二置信度和用户语音请求；
+其中，
+文本识别结果为对用户语音请求进
+行文本识别确定的；
+采用前缀树匹配用户语音请求，
+确定频率等级；
+基于文本识别清晰度、
+第一置信度和第二置信度，
+确定可信度。
+[0150] 在一些示例中，
+该装置还可以包括：
+第七处理模块，
+用于在按目标对话结果执行语
+音交互之后，
+基于目标对话结果，
+更新用户语音请求对应的初始上下文信息，
+初始上下文信
+息为本地端或云端基于用户语音请求确定。
+[0151] 在一些示例中，
+该装置还可以包括：
+第七处理模块，用于在用户语音请求包括多个连续的子语音请求的情况下，
+接收
+目标设备发送的打断信号，
+打断信号包括被打断的任务ID；
+第八处理模块，用于响应于打断信号，
+清空任务ID对应的任务栈及任务ID对应的
+上下文信息。
+[0152] 本发明中的语音交互装置可以是电子设备，
+也可以是电子设备中的部件，
+例如集
+成电路或芯片。该电子设备可以是终端，
+也可以为除终端之外的其他设备。示例性的，
+电子
+设备可以为车辆或车辆上的车机等，
+本发明不作具体限定。
+[0153] 本发明中的语音交互装置可以为具有操作系统的装置。
+该操作系统可以为安卓
+（Android）操作系统，
+可以为IOS操作系统，
+还可以为其他可能的操作系统，
+本发明不作具体
+限定。
+[0154] 本发明提供的语音交互装置能够实现图1至图5的方法示例实现的各个过程，
+为避
+免重复，
+这里不再赘述。
+[0155] 在一些示例中，
+如图7所示，
+本发明还提供一种车辆700，
+包括处理器701、存储器
+702及存储在存储器702上并可在处理器701上运行的计算机程序，
+该程序被处理器701执行
+时实现上述语音交互方法示例的各个过程，
+且能达到相同的技术效果，
+为避免重复，
+这里不
+再赘述。
+[0156] 本发明还提供一种非暂态计算机可读存储介质，
+该非暂态计算机可读存储介质上
+存储有计算机程序，
+该计算机程序被处理器执行时实现上述语音交互方法示例的各个过
+程，
+且能达到相同的技术效果，
+为避免重复，
+这里不再赘述。
+[0157] 其中，
+所述处理器为上述示例中所述的电子设备中的处理器。所述可读存储介质，
+包括计算机可读存储介质，
+如计算机只读存储器ROM、
+随机存取存储器RAM、磁碟或者光盘
+等。
+18
+
+CN 115394300 A
+
+说
+
+明
+
+[0158]
+
+书
+
+16/16 页
+
+本发明还提供一种计算机程序产品，
+包括计算机程序，
+该计算机程序被处理器执
+行时实现上述语音交互方法。
+[0159] 其中，
+所述处理器为上述示例中所述的电子设备中的处理器。所述可读存储介质，
+包括计算机可读存储介质，
+如计算机只读存储器ROM、
+随机存取存储器RAM、磁碟或者光盘
+等。
+[0160] 本发明另提供了一种芯片，
+所述芯片包括处理器和通信接口，
+所述通信接口和所
+述处理器耦合，
+所述处理器用于运行程序或指令，
+实现上述语音交互方法示例的各个过程，
+且能达到相同的技术效果，
+为避免重复，
+这里不再赘述。
+[0161] 应理解，
+本发明提到的芯片还可以称为系统级芯片、系统芯片、
+芯片系统或片上系
+统芯片等。
+[0162] 需要说明的是，
+在本文中，
+术语“包括”、
+“包含”或者其任何其他变体意在涵盖非排
+他性的包含，
+从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，
+而
+或者是还包括为这种过程、
+方法、
+物品或者装置所固有
+且还包括没有明确列出的其他要素，
+的要素。在没有更多限制的情况下，
+由语句“包括一个……”限定的要素，
+并不排除在包括该
+要素的过程、
+方法、
+物品或者装置中还存在另外的相同要素。此外，
+需要指出的是，
+本发明实
+施方式中的方法和装置的范围不限按示出或讨论的顺序来执行功能，
+还可包括根据所涉及
+例如，
+可以按不同于所描述的次序
+的功能按基本同时的方式或按相反的顺序来执行功能，
+来执行所描述的方法，
+并且还可以添加、
+省去、
+或组合各种步骤。另外，
+参照某些示例所描述
+的特征可在其他示例中被组合。
+[0163] 通过以上的实施方式的描述，
+本领域的技术人员可以清楚地了解到上述示例方法
+可借助软件加必需的通用硬件平台的方式来实现，
+当然也可以通过硬件，
+但很多情况下前
+者是更佳的实施方式。基于这样的理解，
+本发明的技术方案本质上或者说对现有技术做出
+贡献的部分可以以计算机软件产品的形式体现出来，
+该计算机软件产品存储在一个存储介
+质（如ROM/RAM、
+磁碟、
+光盘）中，
+包括若干指令用以使得一台终端（可以是手机，
+计算机，
+服务
+器，
+或者网络设备等）执行本发明各个示例所述的方法。
+[0164] 上面结合附图对本发明的示例进行了描述，
+但是本发明并不局限于上述的具体实
+施方式，
+上述的具体实施方式仅仅是示意性的，
+而不是限制性的，
+本领域的普通技术人员在
+本发明的启示下，
+在不脱离本发明宗旨和权利要求所保护的范围情况下，
+还可做出很多形
+式，
+均属于本发明的保护之内。
+[0165] 在本说明书的描述中，
+参考术语“一个示例”、
+“一些示例”、
+“示意性示例”、
+“示例”、
+“具体示例”、
+或“一些示例”等的描述意指结合该示例或示例描述的具体特征、
+结构、
+材料或
+者特点包含于本发明的至少一个示例或示例中。在本说明书中，
+对上述术语的示意性表述
+不一定指的是相同的示例或示例。而且，
+描述的具体特征、
+结构、
+材料或者特点可以在任何
+的一个或多个示例或示例中以合适的方式结合。
+[0166] 尽管已经示出和描述了本发明的示例，
+本领域的普通技术人员可以理解：
+在不脱
+离本发明的原理和宗旨的情况下可以对这些示例进行多种变化、
+修改、
+替换和变型，
+本发明
+的范围由权利要求及其等同物限定。
+
+19
+
+CN 115394300 A
+
+说
+
+明
+
+书
+
+图1
+
+图2
+
+20
+
+附
+
+图
+
+1/4 页
+
+CN 115394300 A
+
+说
+
+明
+
+书
+
+图3
+
+21
+
+附
+
+图
+
+2/4 页
+
+CN 115394300 A
+
+说
+
+明
+
+书
+
+图4
+
+图5
+22
+
+附
+
+图
+
+3/4 页
+
+CN 115394300 A
+
+说
+
+明
+
+书
+
+图6
+
+图7
+
+23
+
+附
+
+图
+
+4/4 页
+
+
\ No newline at end of file
diff --git a/intelligent_cabin/archive/reports/bert_local_test_report.md b/intelligent_cabin/archive/reports/bert_local_test_report.md
new file mode 100644
index 0000000..5e2ba3f
--- /dev/null
+++ b/intelligent_cabin/archive/reports/bert_local_test_report.md
@@ -0,0 +1,65 @@
+# 本地 BERT 意图识别测试报告
+
+## 概览
+- 模型目录：`/Users/hwp/Documents/trae_projects/intelligent_cabin/models/local_bert_intent`
+- 评测集：`/Users/hwp/Documents/trae_projects/intelligent_cabin/app/data/bert_intent_eval_independent.jsonl`
+- 评测阈值：`0.0`
+- 测试样本数：`42`
+- 总体准确率：`0.9762`
+
+## 训练摘要
+- 基座模型：`hfl/chinese-macbert-base`
+- 训练集 / 验证集：`1557 / 401`
+- 最佳验证准确率：`0.9875`
+- 训练设备：`mps`
+
+## 分类别结果
+- `business`: 33/34 = 0.9706
+- `out_of_scope`: 4/4 = 1.0
+- `social`: 4/4 = 1.0
+
+## 分标签结果
+- `__out_of_scope__` (out_of_scope): 4/4 = 1.0
+- `__social__` (social): 4/4 = 1.0
+- `cabin_ac_off` (business): 1/1 = 1.0
+- `cabin_ac_on` (business): 1/1 = 1.0
+- `cabin_defog_front_on` (business): 1/1 = 1.0
+- `cabin_defog_rear_on` (business): 1/1 = 1.0
+- `cabin_fan_down` (business): 1/1 = 1.0
+- `cabin_fan_up` (business): 1/1 = 1.0
+- `cabin_lights_off` (business): 1/1 = 1.0
+- `cabin_lights_on` (business): 1/1 = 1.0
+- `cabin_lock_doors` (business): 1/1 = 1.0
+- `cabin_mirror_fold` (business): 1/1 = 1.0
+- `cabin_mirror_unfold` (business): 1/1 = 1.0
+- `cabin_nav_cancel` (business): 1/1 = 1.0
+- `cabin_nav_to` (business): 1/1 = 1.0
+- `cabin_next_track` (business): 1/1 = 1.0
+- `cabin_pause_music` (business): 1/1 = 1.0
+- `cabin_play_music` (business): 1/1 = 1.0
+- `cabin_previous_track` (business): 1/1 = 1.0
+- `cabin_seat_heat_off` (business): 1/1 = 1.0
+- `cabin_seat_heat_on` (business): 1/1 = 1.0
+- `cabin_set_ac` (business): 1/1 = 1.0
+- `cabin_sunroof_close` (business): 1/1 = 1.0
+- `cabin_sunroof_open` (business): 1/1 = 1.0
+- `cabin_unlock_doors` (business): 1/1 = 1.0
+- `cabin_volume_down` (business): 1/1 = 1.0
+- `cabin_volume_mute` (business): 1/1 = 1.0
+- `cabin_volume_up` (business): 1/1 = 1.0
+- `cabin_window_close` (business): 1/1 = 1.0
+- `cabin_window_open` (business): 0/1 = 0.0
+- `cabin_wiper_off` (business): 1/1 = 1.0
+- `cabin_wiper_on` (business): 1/1 = 1.0
+- `cs_cancel_order` (business): 1/1 = 1.0
+- `cs_query_logistics` (business): 1/1 = 1.0
+- `cs_query_order` (business): 1/1 = 1.0
+- `cs_transfer_human` (business): 1/1 = 1.0
+
+## 错误样例
+- 文本：`左前窗打开一点` | 类别：`business` | 期望：`cabin_window_open` | 预测：`cabin_defog_front_on` | 分数：`0.9951`
+
+## 结论
+- 当前本地 MacBERT 已具备较强的业务意图识别能力，可作为本地快链路分类器。
+- 误判主要集中在方向相反或语义接近的控制指令，下一步应补充对抗样本和真实口语表达。
+- 上线前建议继续补充 ASR 错字、多轮短句和多意图子句级样本。
diff --git a/intelligent_cabin/archive/reports/bert_local_test_result.json b/intelligent_cabin/archive/reports/bert_local_test_result.json
new file mode 100644
index 0000000..ae459d5
--- /dev/null
+++ b/intelligent_cabin/archive/reports/bert_local_test_result.json
@@ -0,0 +1,426 @@
+{
+  "model_dir": "/Users/hwp/Documents/trae_projects/intelligent_cabin/models/local_bert_intent",
+  "threshold": 0.0,
+  "test_path": "/Users/hwp/Documents/trae_projects/intelligent_cabin/app/data/bert_intent_eval_independent.jsonl",
+  "test_case_count": 42,
+  "accuracy": 0.9762,
+  "train_summary": {
+    "base_model": "hfl/chinese-macbert-base",
+    "epochs": 16,
+    "batch_size": 8,
+    "learning_rate": 2e-05,
+    "train_size": 1557,
+    "dev_size": 401,
+    "best_dev_accuracy": 0.9875,
+    "device": "mps"
+  },
+  "per_category": [
+    {
+      "category": "business",
+      "total": 34,
+      "correct": 33,
+      "accuracy": 0.9706
+    },
+    {
+      "category": "out_of_scope",
+      "total": 4,
+      "correct": 4,
+      "accuracy": 1.0
+    },
+    {
+      "category": "social",
+      "total": 4,
+      "correct": 4,
+      "accuracy": 1.0
+    }
+  ],
+  "per_label": [
+    {
+      "label": "__out_of_scope__",
+      "category": "out_of_scope",
+      "total": 4,
+      "correct": 4,
+      "accuracy": 1.0
+    },
+    {
+      "label": "__social__",
+      "category": "social",
+      "total": 4,
+      "correct": 4,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_ac_off",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_ac_on",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_defog_front_on",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_defog_rear_on",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_fan_down",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_fan_up",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_lights_off",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_lights_on",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_lock_doors",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_mirror_fold",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_mirror_unfold",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_nav_cancel",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_nav_to",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_next_track",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_pause_music",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_play_music",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_previous_track",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_seat_heat_off",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_seat_heat_on",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_set_ac",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_sunroof_close",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_sunroof_open",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_unlock_doors",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_volume_down",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_volume_mute",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_volume_up",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_window_close",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_window_open",
+      "category": "business",
+      "total": 1,
+      "correct": 0,
+      "accuracy": 0.0
+    },
+    {
+      "label": "cabin_wiper_off",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cabin_wiper_on",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cs_cancel_order",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cs_query_logistics",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cs_query_order",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    },
+    {
+      "label": "cs_transfer_human",
+      "category": "business",
+      "total": 1,
+      "correct": 1,
+      "accuracy": 1.0
+    }
+  ],
+  "errors": [
+    {
+      "text": "左前窗打开一点",
+      "category": "business",
+      "expected_label": "cabin_window_open",
+      "predicted_label": "cabin_defog_front_on",
+      "score": 0.9951,
+      "raw_label": "cabin_defog_front_on",
+      "ok": false,
+      "top_candidates": [
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.9951
+        },
+        {
+          "intent_id": "cabin_sunroof_open",
+          "score": 0.0005
+        },
+        {
+          "intent_id": "cabin_lights_on",
+          "score": 0.0004
+        }
+      ]
+    }
+  ],
+  "confusion": {
+    "cabin_ac_off": {
+      "cabin_ac_off": 1
+    },
+    "cabin_ac_on": {
+      "cabin_ac_on": 1
+    },
+    "cabin_defog_front_on": {
+      "cabin_defog_front_on": 1
+    },
+    "cabin_defog_rear_on": {
+      "cabin_defog_rear_on": 1
+    },
+    "cabin_fan_down": {
+      "cabin_fan_down": 1
+    },
+    "cabin_fan_up": {
+      "cabin_fan_up": 1
+    },
+    "cabin_lights_off": {
+      "cabin_lights_off": 1
+    },
+    "cabin_lights_on": {
+      "cabin_lights_on": 1
+    },
+    "cabin_lock_doors": {
+      "cabin_lock_doors": 1
+    },
+    "cabin_mirror_fold": {
+      "cabin_mirror_fold": 1
+    },
+    "cabin_mirror_unfold": {
+      "cabin_mirror_unfold": 1
+    },
+    "cabin_nav_cancel": {
+      "cabin_nav_cancel": 1
+    },
+    "cabin_nav_to": {
+      "cabin_nav_to": 1
+    },
+    "cabin_next_track": {
+      "cabin_next_track": 1
+    },
+    "cabin_pause_music": {
+      "cabin_pause_music": 1
+    },
+    "cabin_play_music": {
+      "cabin_play_music": 1
+    },
+    "cabin_previous_track": {
+      "cabin_previous_track": 1
+    },
+    "cabin_seat_heat_off": {
+      "cabin_seat_heat_off": 1
+    },
+    "cabin_seat_heat_on": {
+      "cabin_seat_heat_on": 1
+    },
+    "cabin_set_ac": {
+      "cabin_set_ac": 1
+    },
+    "cabin_sunroof_close": {
+      "cabin_sunroof_close": 1
+    },
+    "cabin_sunroof_open": {
+      "cabin_sunroof_open": 1
+    },
+    "cabin_unlock_doors": {
+      "cabin_unlock_doors": 1
+    },
+    "cabin_volume_down": {
+      "cabin_volume_down": 1
+    },
+    "cabin_volume_mute": {
+      "cabin_volume_mute": 1
+    },
+    "cabin_volume_up": {
+      "cabin_volume_up": 1
+    },
+    "cabin_window_close": {
+      "cabin_window_close": 1
+    },
+    "cabin_window_open": {
+      "cabin_defog_front_on": 1
+    },
+    "cabin_wiper_off": {
+      "cabin_wiper_off": 1
+    },
+    "cabin_wiper_on": {
+      "cabin_wiper_on": 1
+    },
+    "cs_cancel_order": {
+      "cs_cancel_order": 1
+    },
+    "cs_query_logistics": {
+      "cs_query_logistics": 1
+    },
+    "cs_query_order": {
+      "cs_query_order": 1
+    },
+    "cs_transfer_human": {
+      "cs_transfer_human": 1
+    },
+    "__social__": {
+      "__social__": 4
+    },
+    "__out_of_scope__": {
+      "__out_of_scope__": 4
+    }
+  }
+}
\ No newline at end of file
diff --git a/intelligent_cabin/archive/reports/bert_multi_intent_independent_report.md b/intelligent_cabin/archive/reports/bert_multi_intent_independent_report.md
new file mode 100644
index 0000000..8c9a5ad
--- /dev/null
+++ b/intelligent_cabin/archive/reports/bert_multi_intent_independent_report.md
@@ -0,0 +1,47 @@
+# 本地多标签 Detector 独立评测报告
+
+## 概览
+- 模型目录：`/Users/hwp/Documents/trae_projects/intelligent_cabin/models/local_bert_multi_intent`
+- 评测集：`/Users/hwp/Documents/trae_projects/intelligent_cabin/app/data/bert_intent_multilabel_eval_independent.jsonl`
+- 样本数：`37`
+- 阈值 / top_k / max_labels：`0.45 / 8 / 4`
+- `micro_precision`：`0.9362`
+- `micro_recall`：`0.6377`
+- `micro_f1`：`0.7586`
+- `exact_match`：`0.5135`
+- `multi_sentence_recall`：`0.4138`
+- `single_guard_false_alarm_rate`：`0.0`
+
+## 分类别结果
+- `cabin_parallel`: count=15 micro_f1=0.807 exact_match=0.4667
+- `cabin_sequence`: count=9 micro_f1=0.5385 exact_match=0.3333
+- `cs_conditional`: count=3 micro_f1=0.9091 exact_match=0.6667
+- `cs_sequence`: count=2 micro_f1=0.6667 exact_match=0.0
+- `single_guard`: count=8 micro_f1=0.875 exact_match=0.875
+
+## 主要混淆
+- 漏掉 `cabin_sunroof_open`，同时误报 `cabin_window_open`：`1` 次
+- 漏掉 `cabin_pause_music`，同时误报 `cabin_play_music`：`1` 次
+- 漏掉 `cabin_window_open`，同时误报 `cabin_defog_front_on`：`1` 次
+
+## 错误样例
+- 文本：`锁车门，再把后视镜收起来` | 类别：`cabin_sequence` | 期望：`['cabin_lock_doors', 'cabin_mirror_fold']` | 预测：`[]`
+- 文本：`把车门解锁，再把镜子展开` | 类别：`cabin_sequence` | 期望：`['cabin_mirror_unfold', 'cabin_unlock_doors']` | 预测：`[]`
+- 文本：`路线别导了，音乐也停一下` | 类别：`cabin_parallel` | 期望：`['cabin_nav_cancel', 'cabin_pause_music']` | 预测：`[]`
+- 文本：`雨停了，雨刮关掉，再把窗开一点` | 类别：`cabin_sequence` | 期望：`['cabin_window_open', 'cabin_wiper_off']` | 预测：`[]`
+- 文本：`把天窗合上，然后把音乐暂停` | 类别：`cabin_sequence` | 期望：`['cabin_pause_music', 'cabin_sunroof_close']` | 预测：`[]`
+- 文本：`先把音量调大，再切下一首` | 类别：`cabin_parallel` | 期望：`['cabin_next_track', 'cabin_volume_up']` | 预测：`[]`
+- 文本：`静音之后切回上一首` | 类别：`cabin_sequence` | 期望：`['cabin_previous_track', 'cabin_volume_mute']` | 预测：`[]`
+- 文本：`把天窗打开透口气，再开空调` | 类别：`cabin_parallel` | 期望：`['cabin_ac_on', 'cabin_sunroof_open']` | 预测：`['cabin_ac_on', 'cabin_window_open']`
+- 文本：`音乐停一下，然后导航到公司` | 类别：`cabin_sequence` | 期望：`['cabin_nav_to', 'cabin_pause_music']` | 预测：`['cabin_nav_to', 'cabin_play_music']`
+- 文本：`把左前窗降一点` | 类别：`single_guard` | 期望：`['cabin_window_open']` | 预测：`['cabin_defog_front_on']`
+- 文本：`车里闷，给我透个气，再放点轻松的歌` | 类别：`cabin_parallel` | 期望：`['cabin_play_music', 'cabin_window_open']` | 预测：`['cabin_play_music']`
+- 文本：`把空调开了，风别太小，再来首歌` | 类别：`cabin_parallel` | 期望：`['cabin_ac_on', 'cabin_fan_up', 'cabin_play_music']` | 预测：`['cabin_ac_on', 'cabin_play_music']`
+- 文本：`开导航去徐家汇，顺便把风量调大` | 类别：`cabin_parallel` | 期望：`['cabin_fan_up', 'cabin_nav_to']` | 预测：`['cabin_nav_to']`
+- 文本：`温度调到二十三度，风稍微小一点` | 类别：`cabin_parallel` | 期望：`['cabin_fan_down', 'cabin_set_ac']` | 预测：`['cabin_set_ac']`
+- 文本：`帮我看A812302物流，要是太慢就转人工` | 类别：`cs_conditional` | 期望：`['cs_query_logistics', 'cs_transfer_human']` | 预测：`['cs_query_logistics']`
+
+## 结论建议
+- 先看多意图句是否存在系统性漏召回，再看单意图是否被误报成多意图。
+- 若 `single_guard_false_alarm_rate` 偏高，需要先收紧 detector 阈值或补单意图负样本，再考虑进入 NER。
+- 若 `multi_sentence_recall` 不稳定，应继续补条件句、弱连接句和口语化多动作语料。
diff --git a/intelligent_cabin/archive/reports/bert_multi_intent_independent_result.json b/intelligent_cabin/archive/reports/bert_multi_intent_independent_result.json
new file mode 100644
index 0000000..91c1e83
--- /dev/null
+++ b/intelligent_cabin/archive/reports/bert_multi_intent_independent_result.json
@@ -0,0 +1,2582 @@
+{
+  "model_path": "/Users/hwp/Documents/trae_projects/intelligent_cabin/models/local_bert_multi_intent",
+  "test_path": "/Users/hwp/Documents/trae_projects/intelligent_cabin/app/data/bert_intent_multilabel_eval_independent.jsonl",
+  "threshold": 0.45,
+  "top_k": 8,
+  "max_labels": 4,
+  "sample_count": 37,
+  "metrics": {
+    "micro_precision": 0.9362,
+    "micro_recall": 0.6377,
+    "micro_f1": 0.7586,
+    "exact_match": 0.5135,
+    "multi_sentence_recall": 0.4138,
+    "single_guard_false_alarm_rate": 0.0
+  },
+  "per_category": [
+    {
+      "category": "cabin_parallel",
+      "sample_count": 15,
+      "metrics": {
+        "micro_precision": 0.9583,
+        "micro_recall": 0.697,
+        "micro_f1": 0.807,
+        "exact_match": 0.4667,
+        "multi_sentence_recall": 0.4667,
+        "single_guard_false_alarm_rate": 0.0
+      }
+    },
+    {
+      "category": "cabin_sequence",
+      "sample_count": 9,
+      "metrics": {
+        "micro_precision": 0.875,
+        "micro_recall": 0.3889,
+        "micro_f1": 0.5385,
+        "exact_match": 0.3333,
+        "multi_sentence_recall": 0.3333,
+        "single_guard_false_alarm_rate": 0.0
+      }
+    },
+    {
+      "category": "cs_conditional",
+      "sample_count": 3,
+      "metrics": {
+        "micro_precision": 1.0,
+        "micro_recall": 0.8333,
+        "micro_f1": 0.9091,
+        "exact_match": 0.6667,
+        "multi_sentence_recall": 0.6667,
+        "single_guard_false_alarm_rate": 0.0
+      }
+    },
+    {
+      "category": "cs_sequence",
+      "sample_count": 2,
+      "metrics": {
+        "micro_precision": 1.0,
+        "micro_recall": 0.5,
+        "micro_f1": 0.6667,
+        "exact_match": 0.0,
+        "multi_sentence_recall": 0.0,
+        "single_guard_false_alarm_rate": 0.0
+      }
+    },
+    {
+      "category": "single_guard",
+      "sample_count": 8,
+      "metrics": {
+        "micro_precision": 0.875,
+        "micro_recall": 0.875,
+        "micro_f1": 0.875,
+        "exact_match": 0.875,
+        "multi_sentence_recall": 0.0,
+        "single_guard_false_alarm_rate": 0.0
+      }
+    }
+  ],
+  "top_confusions": [
+    {
+      "expected_missing": "cabin_sunroof_open",
+      "wrong_extra": "cabin_window_open",
+      "count": 1
+    },
+    {
+      "expected_missing": "cabin_pause_music",
+      "wrong_extra": "cabin_play_music",
+      "count": 1
+    },
+    {
+      "expected_missing": "cabin_window_open",
+      "wrong_extra": "cabin_defog_front_on",
+      "count": 1
+    }
+  ],
+  "error_examples": [
+    {
+      "text": "锁车门，再把后视镜收起来",
+      "category": "cabin_sequence",
+      "expected_intent_ids": [
+        "cabin_lock_doors",
+        "cabin_mirror_fold"
+      ],
+      "predicted_intent_ids": [],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=0 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_mirror_fold",
+          "score": 0.3966
+        },
+        {
+          "intent_id": "cabin_unlock_doors",
+          "score": 0.0406
+        },
+        {
+          "intent_id": "cabin_defog_rear_on",
+          "score": 0.0313
+        },
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.0293
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.0276
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.0271
+        },
+        {
+          "intent_id": "cabin_lock_doors",
+          "score": 0.0267
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0257
+        }
+      ]
+    },
+    {
+      "text": "把车门解锁，再把镜子展开",
+      "category": "cabin_sequence",
+      "expected_intent_ids": [
+        "cabin_mirror_unfold",
+        "cabin_unlock_doors"
+      ],
+      "predicted_intent_ids": [],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=0 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_unlock_doors",
+          "score": 0.2038
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.1351
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.0637
+        },
+        {
+          "intent_id": "cabin_defog_rear_on",
+          "score": 0.0486
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.045
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.0298
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.0261
+        },
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.0202
+        }
+      ]
+    },
+    {
+      "text": "路线别导了，音乐也停一下",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_nav_cancel",
+        "cabin_pause_music"
+      ],
+      "predicted_intent_ids": [],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=0 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_pause_music",
+          "score": 0.2378
+        },
+        {
+          "intent_id": "cabin_nav_cancel",
+          "score": 0.1729
+        },
+        {
+          "intent_id": "cs_transfer_human",
+          "score": 0.0294
+        },
+        {
+          "intent_id": "cabin_mirror_fold",
+          "score": 0.0288
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0272
+        },
+        {
+          "intent_id": "cabin_volume_down",
+          "score": 0.0238
+        },
+        {
+          "intent_id": "cabin_next_track",
+          "score": 0.0207
+        },
+        {
+          "intent_id": "cs_query_logistics",
+          "score": 0.0201
+        }
+      ]
+    },
+    {
+      "text": "雨停了，雨刮关掉，再把窗开一点",
+      "category": "cabin_sequence",
+      "expected_intent_ids": [
+        "cabin_window_open",
+        "cabin_wiper_off"
+      ],
+      "predicted_intent_ids": [],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=0 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_wiper_off",
+          "score": 0.2446
+        },
+        {
+          "intent_id": "cabin_wiper_on",
+          "score": 0.176
+        },
+        {
+          "intent_id": "cabin_ac_off",
+          "score": 0.0339
+        },
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.0315
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.0291
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.0278
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0248
+        },
+        {
+          "intent_id": "cabin_defog_rear_on",
+          "score": 0.0223
+        }
+      ]
+    },
+    {
+      "text": "把天窗合上，然后把音乐暂停",
+      "category": "cabin_sequence",
+      "expected_intent_ids": [
+        "cabin_pause_music",
+        "cabin_sunroof_close"
+      ],
+      "predicted_intent_ids": [],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=0 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_pause_music",
+          "score": 0.2731
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0735
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.0337
+        },
+        {
+          "intent_id": "cabin_nav_cancel",
+          "score": 0.027
+        },
+        {
+          "intent_id": "cabin_volume_down",
+          "score": 0.0262
+        },
+        {
+          "intent_id": "cabin_mirror_fold",
+          "score": 0.0259
+        },
+        {
+          "intent_id": "cabin_sunroof_close",
+          "score": 0.0216
+        },
+        {
+          "intent_id": "cabin_previous_track",
+          "score": 0.0208
+        }
+      ]
+    },
+    {
+      "text": "先把音量调大，再切下一首",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_next_track",
+        "cabin_volume_up"
+      ],
+      "predicted_intent_ids": [],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=0 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_next_track",
+          "score": 0.1388
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0573
+        },
+        {
+          "intent_id": "cabin_volume_up",
+          "score": 0.0556
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.0266
+        },
+        {
+          "intent_id": "cabin_previous_track",
+          "score": 0.0256
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.0227
+        },
+        {
+          "intent_id": "cabin_lock_doors",
+          "score": 0.021
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.019
+        }
+      ]
+    },
+    {
+      "text": "静音之后切回上一首",
+      "category": "cabin_sequence",
+      "expected_intent_ids": [
+        "cabin_previous_track",
+        "cabin_volume_mute"
+      ],
+      "predicted_intent_ids": [],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=0 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_volume_mute",
+          "score": 0.3221
+        },
+        {
+          "intent_id": "cabin_volume_down",
+          "score": 0.0552
+        },
+        {
+          "intent_id": "cs_transfer_human",
+          "score": 0.031
+        },
+        {
+          "intent_id": "cabin_previous_track",
+          "score": 0.0289
+        },
+        {
+          "intent_id": "cabin_lock_doors",
+          "score": 0.0251
+        },
+        {
+          "intent_id": "cabin_volume_up",
+          "score": 0.0247
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0239
+        },
+        {
+          "intent_id": "cabin_sunroof_close",
+          "score": 0.0213
+        }
+      ]
+    },
+    {
+      "text": "把天窗打开透口气，再开空调",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_ac_on",
+        "cabin_sunroof_open"
+      ],
+      "predicted_intent_ids": [
+        "cabin_ac_on",
+        "cabin_window_open"
+      ],
+      "detected": true,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=2 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.7857
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.5555
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.0827
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0748
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.0513
+        },
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.0492
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.0451
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0421
+        }
+      ]
+    },
+    {
+      "text": "音乐停一下，然后导航到公司",
+      "category": "cabin_sequence",
+      "expected_intent_ids": [
+        "cabin_nav_to",
+        "cabin_pause_music"
+      ],
+      "predicted_intent_ids": [
+        "cabin_nav_to",
+        "cabin_play_music"
+      ],
+      "detected": true,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=2 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.7234
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.5356
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.1071
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.0318
+        },
+        {
+          "intent_id": "cabin_lights_on",
+          "score": 0.0284
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.027
+        },
+        {
+          "intent_id": "cabin_pause_music",
+          "score": 0.027
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.0187
+        }
+      ]
+    },
+    {
+      "text": "把左前窗降一点",
+      "category": "single_guard",
+      "expected_intent_ids": [
+        "cabin_window_open"
+      ],
+      "predicted_intent_ids": [
+        "cabin_defog_front_on"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.5524
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0541
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.0379
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.0376
+        },
+        {
+          "intent_id": "cabin_defog_rear_on",
+          "score": 0.0363
+        },
+        {
+          "intent_id": "cabin_mirror_fold",
+          "score": 0.0264
+        },
+        {
+          "intent_id": "cabin_sunroof_open",
+          "score": 0.0237
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.0236
+        }
+      ]
+    },
+    {
+      "text": "车里闷，给我透个气，再放点轻松的歌",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_play_music",
+        "cabin_window_open"
+      ],
+      "predicted_intent_ids": [
+        "cabin_play_music"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.9513
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.2538
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.2177
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.1694
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.0903
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.0392
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0387
+        },
+        {
+          "intent_id": "cabin_sunroof_close",
+          "score": 0.0313
+        }
+      ]
+    },
+    {
+      "text": "把空调开了，风别太小，再来首歌",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_ac_on",
+        "cabin_fan_up",
+        "cabin_play_music"
+      ],
+      "predicted_intent_ids": [
+        "cabin_ac_on",
+        "cabin_play_music"
+      ],
+      "detected": true,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=2 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.7546
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.6847
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.3138
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.2437
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.1289
+        },
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.0679
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.0662
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.0591
+        }
+      ]
+    },
+    {
+      "text": "开导航去徐家汇，顺便把风量调大",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_fan_up",
+        "cabin_nav_to"
+      ],
+      "predicted_intent_ids": [
+        "cabin_nav_to"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.6332
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.1716
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.1066
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.0919
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0398
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.031
+        },
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.0303
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.0223
+        }
+      ]
+    },
+    {
+      "text": "温度调到二十三度，风稍微小一点",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_fan_down",
+        "cabin_set_ac"
+      ],
+      "predicted_intent_ids": [
+        "cabin_set_ac"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.4948
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.339
+        },
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.1272
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0348
+        },
+        {
+          "intent_id": "cabin_ac_off",
+          "score": 0.0342
+        },
+        {
+          "intent_id": "cs_query_logistics",
+          "score": 0.0261
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.0256
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.0237
+        }
+      ]
+    },
+    {
+      "text": "帮我看A812302物流，要是太慢就转人工",
+      "category": "cs_conditional",
+      "expected_intent_ids": [
+        "cs_query_logistics",
+        "cs_transfer_human"
+      ],
+      "predicted_intent_ids": [
+        "cs_query_logistics"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cs_query_logistics",
+          "score": 0.7622
+        },
+        {
+          "intent_id": "cs_transfer_human",
+          "score": 0.1558
+        },
+        {
+          "intent_id": "cs_query_order",
+          "score": 0.0761
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.03
+        },
+        {
+          "intent_id": "cs_cancel_order",
+          "score": 0.0223
+        },
+        {
+          "intent_id": "cabin_pause_music",
+          "score": 0.0217
+        },
+        {
+          "intent_id": "cabin_defog_rear_on",
+          "score": 0.0207
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.0197
+        }
+      ]
+    }
+  ],
+  "results": [
+    {
+      "text": "车里闷，给我透个气，再放点轻松的歌",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_play_music",
+        "cabin_window_open"
+      ],
+      "predicted_intent_ids": [
+        "cabin_play_music"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.9513
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.2538
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.2177
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.1694
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.0903
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.0392
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0387
+        },
+        {
+          "intent_id": "cabin_sunroof_close",
+          "score": 0.0313
+        }
+      ]
+    },
+    {
+      "text": "先把空调开起来，顺手把窗户关好",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_ac_on",
+        "cabin_window_close"
+      ],
+      "predicted_intent_ids": [
+        "cabin_ac_on",
+        "cabin_window_close"
+      ],
+      "detected": true,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=2 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.8218
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.8106
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.1734
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.0839
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0676
+        },
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.0584
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.0544
+        },
+        {
+          "intent_id": "cabin_defog_rear_on",
+          "score": 0.0507
+        }
+      ]
+    },
+    {
+      "text": "带我去公司，路上播点民谣",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_nav_to",
+        "cabin_play_music"
+      ],
+      "predicted_intent_ids": [
+        "cabin_nav_to",
+        "cabin_play_music"
+      ],
+      "detected": true,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=2 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.8145
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.8009
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.2145
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.0544
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.0349
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.0335
+        },
+        {
+          "intent_id": "cabin_lights_on",
+          "score": 0.0314
+        },
+        {
+          "intent_id": "cabin_next_track",
+          "score": 0.0266
+        }
+      ]
+    },
+    {
+      "text": "有点热，把温度打到二十一度，再来点音乐",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_play_music",
+        "cabin_set_ac"
+      ],
+      "predicted_intent_ids": [
+        "cabin_set_ac",
+        "cabin_play_music"
+      ],
+      "detected": true,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=2 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.9261
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.7769
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.0962
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.0945
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0821
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.0756
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.0574
+        },
+        {
+          "intent_id": "cs_query_order",
+          "score": 0.0416
+        }
+      ]
+    },
+    {
+      "text": "导航去虹桥站，然后把空调打开",
+      "category": "cabin_sequence",
+      "expected_intent_ids": [
+        "cabin_ac_on",
+        "cabin_nav_to"
+      ],
+      "predicted_intent_ids": [
+        "cabin_ac_on",
+        "cabin_nav_to"
+      ],
+      "detected": true,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=2 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.8224
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.6724
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.1293
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.065
+        },
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.0446
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.04
+        },
+        {
+          "intent_id": "cabin_lights_on",
+          "score": 0.0375
+        },
+        {
+          "intent_id": "cabin_volume_up",
+          "score": 0.0371
+        }
+      ]
+    },
+    {
+      "text": "前挡看不清了，开除雾，风也加大一点",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_defog_front_on",
+        "cabin_fan_up"
+      ],
+      "predicted_intent_ids": [
+        "cabin_defog_front_on",
+        "cabin_fan_up"
+      ],
+      "detected": true,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=2 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.7406
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.7131
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.1019
+        },
+        {
+          "intent_id": "cabin_next_track",
+          "score": 0.0539
+        },
+        {
+          "intent_id": "cabin_mirror_fold",
+          "score": 0.0496
+        },
+        {
+          "intent_id": "cabin_defog_rear_on",
+          "score": 0.0479
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.0475
+        },
+        {
+          "intent_id": "cs_query_order",
+          "score": 0.0405
+        }
+      ]
+    },
+    {
+      "text": "后面玻璃有雾，先除雾，再把窗关上",
+      "category": "cabin_sequence",
+      "expected_intent_ids": [
+        "cabin_defog_rear_on",
+        "cabin_window_close"
+      ],
+      "predicted_intent_ids": [
+        "cabin_window_close",
+        "cabin_defog_rear_on"
+      ],
+      "detected": true,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=2 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.8096
+        },
+        {
+          "intent_id": "cabin_defog_rear_on",
+          "score": 0.7042
+        },
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.0704
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.0592
+        },
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.0537
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.0485
+        },
+        {
+          "intent_id": "cabin_sunroof_open",
+          "score": 0.0445
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0442
+        }
+      ]
+    },
+    {
+      "text": "把空调开了，风别太小，再来首歌",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_ac_on",
+        "cabin_fan_up",
+        "cabin_play_music"
+      ],
+      "predicted_intent_ids": [
+        "cabin_ac_on",
+        "cabin_play_music"
+      ],
+      "detected": true,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=2 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.7546
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.6847
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.3138
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.2437
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.1289
+        },
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.0679
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.0662
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.0591
+        }
+      ]
+    },
+    {
+      "text": "去浦东机场，车里凉一点，顺便放点歌",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_nav_to",
+        "cabin_play_music",
+        "cabin_set_ac"
+      ],
+      "predicted_intent_ids": [
+        "cabin_play_music",
+        "cabin_nav_to",
+        "cabin_set_ac"
+      ],
+      "detected": true,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=3 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.8592
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.6897
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.4792
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.3405
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.0717
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.0628
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0531
+        },
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.0361
+        }
+      ]
+    },
+    {
+      "text": "先开一点窗，别那么闷，再把温度调低",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_set_ac",
+        "cabin_window_open"
+      ],
+      "predicted_intent_ids": [
+        "cabin_set_ac",
+        "cabin_window_open"
+      ],
+      "detected": true,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=2 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.8216
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.6359
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.2656
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.218
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.1838
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0775
+        },
+        {
+          "intent_id": "cabin_wiper_off",
+          "score": 0.0396
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.0372
+        }
+      ]
+    },
+    {
+      "text": "把四个窗都关了，然后播点轻音乐",
+      "category": "cabin_sequence",
+      "expected_intent_ids": [
+        "cabin_play_music",
+        "cabin_window_close"
+      ],
+      "predicted_intent_ids": [
+        "cabin_play_music",
+        "cabin_window_close"
+      ],
+      "detected": true,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=2 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.8155
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.7628
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.0844
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.0655
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.0584
+        },
+        {
+          "intent_id": "cabin_sunroof_close",
+          "score": 0.0397
+        },
+        {
+          "intent_id": "cabin_lights_off",
+          "score": 0.0338
+        },
+        {
+          "intent_id": "cabin_sunroof_open",
+          "score": 0.029
+        }
+      ]
+    },
+    {
+      "text": "把天窗打开透口气，再开空调",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_ac_on",
+        "cabin_sunroof_open"
+      ],
+      "predicted_intent_ids": [
+        "cabin_ac_on",
+        "cabin_window_open"
+      ],
+      "detected": true,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=2 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.7857
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.5555
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.0827
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0748
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.0513
+        },
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.0492
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.0451
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0421
+        }
+      ]
+    },
+    {
+      "text": "开导航去徐家汇，顺便把风量调大",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_fan_up",
+        "cabin_nav_to"
+      ],
+      "predicted_intent_ids": [
+        "cabin_nav_to"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.6332
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.1716
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.1066
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.0919
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0398
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.031
+        },
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.0303
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.0223
+        }
+      ]
+    },
+    {
+      "text": "音乐停一下，然后导航到公司",
+      "category": "cabin_sequence",
+      "expected_intent_ids": [
+        "cabin_nav_to",
+        "cabin_pause_music"
+      ],
+      "predicted_intent_ids": [
+        "cabin_nav_to",
+        "cabin_play_music"
+      ],
+      "detected": true,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=2 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.7234
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.5356
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.1071
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.0318
+        },
+        {
+          "intent_id": "cabin_lights_on",
+          "score": 0.0284
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.027
+        },
+        {
+          "intent_id": "cabin_pause_music",
+          "score": 0.027
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.0187
+        }
+      ]
+    },
+    {
+      "text": "锁车门，再把后视镜收起来",
+      "category": "cabin_sequence",
+      "expected_intent_ids": [
+        "cabin_lock_doors",
+        "cabin_mirror_fold"
+      ],
+      "predicted_intent_ids": [],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=0 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_mirror_fold",
+          "score": 0.3966
+        },
+        {
+          "intent_id": "cabin_unlock_doors",
+          "score": 0.0406
+        },
+        {
+          "intent_id": "cabin_defog_rear_on",
+          "score": 0.0313
+        },
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.0293
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.0276
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.0271
+        },
+        {
+          "intent_id": "cabin_lock_doors",
+          "score": 0.0267
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0257
+        }
+      ]
+    },
+    {
+      "text": "把车门解锁，再把镜子展开",
+      "category": "cabin_sequence",
+      "expected_intent_ids": [
+        "cabin_mirror_unfold",
+        "cabin_unlock_doors"
+      ],
+      "predicted_intent_ids": [],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=0 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_unlock_doors",
+          "score": 0.2038
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.1351
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.0637
+        },
+        {
+          "intent_id": "cabin_defog_rear_on",
+          "score": 0.0486
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.045
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.0298
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.0261
+        },
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.0202
+        }
+      ]
+    },
+    {
+      "text": "路线别导了，音乐也停一下",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_nav_cancel",
+        "cabin_pause_music"
+      ],
+      "predicted_intent_ids": [],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=0 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_pause_music",
+          "score": 0.2378
+        },
+        {
+          "intent_id": "cabin_nav_cancel",
+          "score": 0.1729
+        },
+        {
+          "intent_id": "cs_transfer_human",
+          "score": 0.0294
+        },
+        {
+          "intent_id": "cabin_mirror_fold",
+          "score": 0.0288
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0272
+        },
+        {
+          "intent_id": "cabin_volume_down",
+          "score": 0.0238
+        },
+        {
+          "intent_id": "cabin_next_track",
+          "score": 0.0207
+        },
+        {
+          "intent_id": "cs_query_logistics",
+          "score": 0.0201
+        }
+      ]
+    },
+    {
+      "text": "温度调到二十三度，风稍微小一点",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_fan_down",
+        "cabin_set_ac"
+      ],
+      "predicted_intent_ids": [
+        "cabin_set_ac"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.4948
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.339
+        },
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.1272
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0348
+        },
+        {
+          "intent_id": "cabin_ac_off",
+          "score": 0.0342
+        },
+        {
+          "intent_id": "cs_query_logistics",
+          "score": 0.0261
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.0256
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.0237
+        }
+      ]
+    },
+    {
+      "text": "查下订单A812301，如果还没发货就取消掉",
+      "category": "cs_conditional",
+      "expected_intent_ids": [
+        "cs_cancel_order",
+        "cs_query_order"
+      ],
+      "predicted_intent_ids": [
+        "cs_cancel_order",
+        "cs_query_order"
+      ],
+      "detected": true,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=2 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cs_cancel_order",
+          "score": 0.7303
+        },
+        {
+          "intent_id": "cs_query_order",
+          "score": 0.6821
+        },
+        {
+          "intent_id": "cs_query_logistics",
+          "score": 0.0692
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.0578
+        },
+        {
+          "intent_id": "cs_transfer_human",
+          "score": 0.0532
+        },
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.0392
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0357
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0328
+        }
+      ]
+    },
+    {
+      "text": "帮我看A812302物流，要是太慢就转人工",
+      "category": "cs_conditional",
+      "expected_intent_ids": [
+        "cs_query_logistics",
+        "cs_transfer_human"
+      ],
+      "predicted_intent_ids": [
+        "cs_query_logistics"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cs_query_logistics",
+          "score": 0.7622
+        },
+        {
+          "intent_id": "cs_transfer_human",
+          "score": 0.1558
+        },
+        {
+          "intent_id": "cs_query_order",
+          "score": 0.0761
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.03
+        },
+        {
+          "intent_id": "cs_cancel_order",
+          "score": 0.0223
+        },
+        {
+          "intent_id": "cabin_pause_music",
+          "score": 0.0217
+        },
+        {
+          "intent_id": "cabin_defog_rear_on",
+          "score": 0.0207
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.0197
+        }
+      ]
+    },
+    {
+      "text": "先查一下A812303这单进度，再帮我转人工客服",
+      "category": "cs_sequence",
+      "expected_intent_ids": [
+        "cs_query_order",
+        "cs_transfer_human"
+      ],
+      "predicted_intent_ids": [
+        "cs_transfer_human"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cs_transfer_human",
+          "score": 0.7768
+        },
+        {
+          "intent_id": "cs_query_order",
+          "score": 0.3466
+        },
+        {
+          "intent_id": "cs_query_logistics",
+          "score": 0.3133
+        },
+        {
+          "intent_id": "cabin_previous_track",
+          "score": 0.0374
+        },
+        {
+          "intent_id": "cabin_sunroof_open",
+          "score": 0.0347
+        },
+        {
+          "intent_id": "cabin_volume_mute",
+          "score": 0.0322
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.0314
+        },
+        {
+          "intent_id": "cabin_nav_cancel",
+          "score": 0.0307
+        }
+      ]
+    },
+    {
+      "text": "订单A812304先查下状态，再看看物流到了哪",
+      "category": "cs_sequence",
+      "expected_intent_ids": [
+        "cs_query_logistics",
+        "cs_query_order"
+      ],
+      "predicted_intent_ids": [
+        "cs_query_logistics"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cs_query_logistics",
+          "score": 0.7938
+        },
+        {
+          "intent_id": "cs_query_order",
+          "score": 0.1886
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.0281
+        },
+        {
+          "intent_id": "cs_cancel_order",
+          "score": 0.0246
+        },
+        {
+          "intent_id": "cs_transfer_human",
+          "score": 0.0244
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.023
+        },
+        {
+          "intent_id": "cabin_unlock_doors",
+          "score": 0.0206
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0195
+        }
+      ]
+    },
+    {
+      "text": "我想先看看A812305有没有发货，没发的话直接撤单",
+      "category": "cs_conditional",
+      "expected_intent_ids": [
+        "cs_cancel_order",
+        "cs_query_order"
+      ],
+      "predicted_intent_ids": [
+        "cs_cancel_order",
+        "cs_query_order"
+      ],
+      "detected": true,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=2 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cs_cancel_order",
+          "score": 0.624
+        },
+        {
+          "intent_id": "cs_query_order",
+          "score": 0.5238
+        },
+        {
+          "intent_id": "cs_query_logistics",
+          "score": 0.1303
+        },
+        {
+          "intent_id": "cs_transfer_human",
+          "score": 0.0431
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.0387
+        },
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.0286
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0285
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0231
+        }
+      ]
+    },
+    {
+      "text": "把空调关掉",
+      "category": "single_guard",
+      "expected_intent_ids": [
+        "cabin_ac_off"
+      ],
+      "predicted_intent_ids": [
+        "cabin_ac_off"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_ac_off",
+          "score": 0.6784
+        },
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.0443
+        },
+        {
+          "intent_id": "cabin_seat_heat_off",
+          "score": 0.0441
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.0431
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.041
+        },
+        {
+          "intent_id": "cabin_wiper_off",
+          "score": 0.0406
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.0329
+        },
+        {
+          "intent_id": "cabin_lights_off",
+          "score": 0.0289
+        }
+      ]
+    },
+    {
+      "text": "帮我开一下前挡除雾",
+      "category": "single_guard",
+      "expected_intent_ids": [
+        "cabin_defog_front_on"
+      ],
+      "predicted_intent_ids": [
+        "cabin_defog_front_on"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.6609
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0538
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.0446
+        },
+        {
+          "intent_id": "cabin_defog_rear_on",
+          "score": 0.0396
+        },
+        {
+          "intent_id": "cs_query_order",
+          "score": 0.0311
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.0266
+        },
+        {
+          "intent_id": "cabin_sunroof_open",
+          "score": 0.0252
+        },
+        {
+          "intent_id": "cabin_mirror_fold",
+          "score": 0.0247
+        }
+      ]
+    },
+    {
+      "text": "风太大了，往小调一点",
+      "category": "single_guard",
+      "expected_intent_ids": [
+        "cabin_fan_down"
+      ],
+      "predicted_intent_ids": [
+        "cabin_fan_down"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.783
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0674
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0432
+        },
+        {
+          "intent_id": "cabin_defog_rear_on",
+          "score": 0.0417
+        },
+        {
+          "intent_id": "cabin_volume_down",
+          "score": 0.0393
+        },
+        {
+          "intent_id": "cabin_previous_track",
+          "score": 0.0377
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.0327
+        },
+        {
+          "intent_id": "cabin_ac_off",
+          "score": 0.0324
+        }
+      ]
+    },
+    {
+      "text": "给我导航到龙阳路",
+      "category": "single_guard",
+      "expected_intent_ids": [
+        "cabin_nav_to"
+      ],
+      "predicted_intent_ids": [
+        "cabin_nav_to"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.8217
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0264
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.024
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.0214
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.0212
+        },
+        {
+          "intent_id": "cabin_lights_on",
+          "score": 0.0194
+        },
+        {
+          "intent_id": "cabin_volume_up",
+          "score": 0.0173
+        },
+        {
+          "intent_id": "cabin_wiper_on",
+          "score": 0.0141
+        }
+      ]
+    },
+    {
+      "text": "来点轻音乐",
+      "category": "single_guard",
+      "expected_intent_ids": [
+        "cabin_play_music"
+      ],
+      "predicted_intent_ids": [
+        "cabin_play_music"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.8947
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.0407
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.0396
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.032
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.0258
+        },
+        {
+          "intent_id": "cabin_next_track",
+          "score": 0.0215
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.0206
+        },
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.0167
+        }
+      ]
+    },
+    {
+      "text": "把左前窗降一点",
+      "category": "single_guard",
+      "expected_intent_ids": [
+        "cabin_window_open"
+      ],
+      "predicted_intent_ids": [
+        "cabin_defog_front_on"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.5524
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0541
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.0379
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.0376
+        },
+        {
+          "intent_id": "cabin_defog_rear_on",
+          "score": 0.0363
+        },
+        {
+          "intent_id": "cabin_mirror_fold",
+          "score": 0.0264
+        },
+        {
+          "intent_id": "cabin_sunroof_open",
+          "score": 0.0237
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.0236
+        }
+      ]
+    },
+    {
+      "text": "订单A812306不要了，直接取消",
+      "category": "single_guard",
+      "expected_intent_ids": [
+        "cs_cancel_order"
+      ],
+      "predicted_intent_ids": [
+        "cs_cancel_order"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cs_cancel_order",
+          "score": 0.8392
+        },
+        {
+          "intent_id": "cs_query_order",
+          "score": 0.0384
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0307
+        },
+        {
+          "intent_id": "cs_query_logistics",
+          "score": 0.0273
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.0266
+        },
+        {
+          "intent_id": "cabin_seat_heat_off",
+          "score": 0.0249
+        },
+        {
+          "intent_id": "cabin_wiper_off",
+          "score": 0.0207
+        },
+        {
+          "intent_id": "cabin_sunroof_close",
+          "score": 0.0204
+        }
+      ]
+    },
+    {
+      "text": "A812307这个快递到哪了",
+      "category": "single_guard",
+      "expected_intent_ids": [
+        "cs_query_logistics"
+      ],
+      "predicted_intent_ids": [
+        "cs_query_logistics"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cs_query_logistics",
+          "score": 0.8186
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.0285
+        },
+        {
+          "intent_id": "cs_query_order",
+          "score": 0.0284
+        },
+        {
+          "intent_id": "cabin_unlock_doors",
+          "score": 0.0246
+        },
+        {
+          "intent_id": "cs_cancel_order",
+          "score": 0.0223
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.0204
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0199
+        },
+        {
+          "intent_id": "cabin_lights_on",
+          "score": 0.018
+        }
+      ]
+    },
+    {
+      "text": "导航去公司，再把空调开开，歌也放起来",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_ac_on",
+        "cabin_nav_to",
+        "cabin_play_music"
+      ],
+      "predicted_intent_ids": [
+        "cabin_ac_on",
+        "cabin_play_music",
+        "cabin_nav_to"
+      ],
+      "detected": true,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=3 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.8751
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.8661
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.6692
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.161
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.1466
+        },
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.0777
+        },
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.0622
+        },
+        {
+          "intent_id": "cabin_lights_on",
+          "score": 0.0571
+        }
+      ]
+    },
+    {
+      "text": "把雨刮打开，顺便关下车窗",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_window_close",
+        "cabin_wiper_on"
+      ],
+      "predicted_intent_ids": [
+        "cabin_window_close"
+      ],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=1 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.4675
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.1444
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0462
+        },
+        {
+          "intent_id": "cabin_wiper_on",
+          "score": 0.035
+        },
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.0292
+        },
+        {
+          "intent_id": "cabin_lights_on",
+          "score": 0.0283
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.023
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0226
+        }
+      ]
+    },
+    {
+      "text": "雨停了，雨刮关掉，再把窗开一点",
+      "category": "cabin_sequence",
+      "expected_intent_ids": [
+        "cabin_window_open",
+        "cabin_wiper_off"
+      ],
+      "predicted_intent_ids": [],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=0 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_wiper_off",
+          "score": 0.2446
+        },
+        {
+          "intent_id": "cabin_wiper_on",
+          "score": 0.176
+        },
+        {
+          "intent_id": "cabin_ac_off",
+          "score": 0.0339
+        },
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.0315
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.0291
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.0278
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0248
+        },
+        {
+          "intent_id": "cabin_defog_rear_on",
+          "score": 0.0223
+        }
+      ]
+    },
+    {
+      "text": "把天窗合上，然后把音乐暂停",
+      "category": "cabin_sequence",
+      "expected_intent_ids": [
+        "cabin_pause_music",
+        "cabin_sunroof_close"
+      ],
+      "predicted_intent_ids": [],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=0 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_pause_music",
+          "score": 0.2731
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0735
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.0337
+        },
+        {
+          "intent_id": "cabin_nav_cancel",
+          "score": 0.027
+        },
+        {
+          "intent_id": "cabin_volume_down",
+          "score": 0.0262
+        },
+        {
+          "intent_id": "cabin_mirror_fold",
+          "score": 0.0259
+        },
+        {
+          "intent_id": "cabin_sunroof_close",
+          "score": 0.0216
+        },
+        {
+          "intent_id": "cabin_previous_track",
+          "score": 0.0208
+        }
+      ]
+    },
+    {
+      "text": "先把音量调大，再切下一首",
+      "category": "cabin_parallel",
+      "expected_intent_ids": [
+        "cabin_next_track",
+        "cabin_volume_up"
+      ],
+      "predicted_intent_ids": [],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=0 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_next_track",
+          "score": 0.1388
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0573
+        },
+        {
+          "intent_id": "cabin_volume_up",
+          "score": 0.0556
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.0266
+        },
+        {
+          "intent_id": "cabin_previous_track",
+          "score": 0.0256
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.0227
+        },
+        {
+          "intent_id": "cabin_lock_doors",
+          "score": 0.021
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.019
+        }
+      ]
+    },
+    {
+      "text": "静音之后切回上一首",
+      "category": "cabin_sequence",
+      "expected_intent_ids": [
+        "cabin_previous_track",
+        "cabin_volume_mute"
+      ],
+      "predicted_intent_ids": [],
+      "detected": false,
+      "backend_name": "bert-multi-label",
+      "reason": "bert multi-label candidates=0 threshold=0.45",
+      "raw_top_scores": [
+        {
+          "intent_id": "cabin_volume_mute",
+          "score": 0.3221
+        },
+        {
+          "intent_id": "cabin_volume_down",
+          "score": 0.0552
+        },
+        {
+          "intent_id": "cs_transfer_human",
+          "score": 0.031
+        },
+        {
+          "intent_id": "cabin_previous_track",
+          "score": 0.0289
+        },
+        {
+          "intent_id": "cabin_lock_doors",
+          "score": 0.0251
+        },
+        {
+          "intent_id": "cabin_volume_up",
+          "score": 0.0247
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0239
+        },
+        {
+          "intent_id": "cabin_sunroof_close",
+          "score": 0.0213
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/intelligent_cabin/archive/reports/joint_nlu_independent_report.md b/intelligent_cabin/archive/reports/joint_nlu_independent_report.md
new file mode 100644
index 0000000..e5e656c
--- /dev/null
+++ b/intelligent_cabin/archive/reports/joint_nlu_independent_report.md
@@ -0,0 +1,40 @@
+# Joint NLU 独立评测报告
+
+## 概览
+- 模型目录：`/Users/hwp/Documents/trae_projects/intelligent_cabin/models/local_joint_bert_nlu`
+- 评测集：`/Users/hwp/Documents/trae_projects/intelligent_cabin/app/data/joint_nlu_eval_independent.jsonl`
+- 样本数：`43`
+- `intent_accuracy`：`0.9302`
+- `slot_exact_match`：`1.0`
+- `joint_exact_match`：`0.9302`
+- `slot_micro_precision`：`1.0`
+- `slot_micro_recall`：`1.0`
+- `slot_micro_f1`：`1.0`
+
+## 训练摘要
+- 训练集 / 评测集：`337 / 10`
+- 训练阶段 `intent_accuracy`：`1.0`
+- 训练阶段 `slot_exact_match`：`0.8`
+
+## 分类别结果
+- `failure_replay`: count=12 intent_acc=0.75 slot_exact=1.0 joint_exact=0.75
+- `no_slot_control`: count=14 intent_acc=1.0 slot_exact=1.0 joint_exact=1.0
+- `slot_destination`: count=4 intent_acc=1.0 slot_exact=1.0 joint_exact=1.0
+- `slot_music`: count=5 intent_acc=1.0 slot_exact=1.0 joint_exact=1.0
+- `slot_order`: count=4 intent_acc=1.0 slot_exact=1.0 joint_exact=1.0
+- `slot_temperature`: count=4 intent_acc=1.0 slot_exact=1.0 joint_exact=1.0
+
+## 主要意图混淆
+- 期望 `cabin_window_open`，预测成 `None`：`1` 次
+- 期望 `cabin_window_open`，预测成 `cabin_play_music`：`1` 次
+- 期望 `cabin_fan_up`，预测成 `cabin_fan_down`：`1` 次
+
+## 失败样例回放
+- 文本：`把左前窗降一点` | 类别：`failure_replay` | 期望意图：`cabin_window_open` | 预测意图：`None` | 期望槽位：`{}` | 预测槽位：`{}` | 缺失槽位：`[]` | 多出槽位：`[]`
+- 文本：`给我透个气` | 类别：`failure_replay` | 期望意图：`cabin_window_open` | 预测意图：`cabin_play_music` | 期望槽位：`{}` | 预测槽位：`{}` | 缺失槽位：`[]` | 多出槽位：`[]`
+- 文本：`风别太小` | 类别：`failure_replay` | 期望意图：`cabin_fan_up` | 预测意图：`cabin_fan_down` | 期望槽位：`{}` | 预测槽位：`{}` | 缺失槽位：`[]` | 多出槽位：`[]`
+
+## 结论
+- 先看 `failure_replay` 是否仍然错，能直接判断先前多意图失败到底是联合模型本体问题还是上层组合问题。
+- 若 `slot_music` 或 `slot_destination` 仍不稳，优先补 span 标注，不要回退到规则抽槽。
+- 若 `no_slot_control` 很稳但 `failure_replay` 中仍有大量错误，下一步应补长尾控制语义数据，而不是急着上更复杂结构。
diff --git a/intelligent_cabin/archive/reports/joint_nlu_independent_result.json b/intelligent_cabin/archive/reports/joint_nlu_independent_result.json
new file mode 100644
index 0000000..4d9122b
--- /dev/null
+++ b/intelligent_cabin/archive/reports/joint_nlu_independent_result.json
@@ -0,0 +1,1706 @@
+{
+  "model_path": "/Users/hwp/Documents/trae_projects/intelligent_cabin/models/local_joint_bert_nlu",
+  "test_path": "/Users/hwp/Documents/trae_projects/intelligent_cabin/app/data/joint_nlu_eval_independent.jsonl",
+  "sample_count": 43,
+  "metrics": {
+    "intent_accuracy": 0.9302,
+    "slot_exact_match": 1.0,
+    "joint_exact_match": 0.9302,
+    "slot_micro_precision": 1.0,
+    "slot_micro_recall": 1.0,
+    "slot_micro_f1": 1.0
+  },
+  "per_category": [
+    {
+      "category": "failure_replay",
+      "sample_count": 12,
+      "metrics": {
+        "intent_accuracy": 0.75,
+        "slot_exact_match": 1.0,
+        "joint_exact_match": 0.75,
+        "slot_micro_precision": 0.0,
+        "slot_micro_recall": 0.0,
+        "slot_micro_f1": 0.0
+      }
+    },
+    {
+      "category": "no_slot_control",
+      "sample_count": 14,
+      "metrics": {
+        "intent_accuracy": 1.0,
+        "slot_exact_match": 1.0,
+        "joint_exact_match": 1.0,
+        "slot_micro_precision": 0.0,
+        "slot_micro_recall": 0.0,
+        "slot_micro_f1": 0.0
+      }
+    },
+    {
+      "category": "slot_destination",
+      "sample_count": 4,
+      "metrics": {
+        "intent_accuracy": 1.0,
+        "slot_exact_match": 1.0,
+        "joint_exact_match": 1.0,
+        "slot_micro_precision": 1.0,
+        "slot_micro_recall": 1.0,
+        "slot_micro_f1": 1.0
+      }
+    },
+    {
+      "category": "slot_music",
+      "sample_count": 5,
+      "metrics": {
+        "intent_accuracy": 1.0,
+        "slot_exact_match": 1.0,
+        "joint_exact_match": 1.0,
+        "slot_micro_precision": 1.0,
+        "slot_micro_recall": 1.0,
+        "slot_micro_f1": 1.0
+      }
+    },
+    {
+      "category": "slot_order",
+      "sample_count": 4,
+      "metrics": {
+        "intent_accuracy": 1.0,
+        "slot_exact_match": 1.0,
+        "joint_exact_match": 1.0,
+        "slot_micro_precision": 1.0,
+        "slot_micro_recall": 1.0,
+        "slot_micro_f1": 1.0
+      }
+    },
+    {
+      "category": "slot_temperature",
+      "sample_count": 4,
+      "metrics": {
+        "intent_accuracy": 1.0,
+        "slot_exact_match": 1.0,
+        "joint_exact_match": 1.0,
+        "slot_micro_precision": 1.0,
+        "slot_micro_recall": 1.0,
+        "slot_micro_f1": 1.0
+      }
+    }
+  ],
+  "top_confusions": [
+    {
+      "expected": "cabin_window_open",
+      "predicted": "None",
+      "count": 1
+    },
+    {
+      "expected": "cabin_window_open",
+      "predicted": "cabin_play_music",
+      "count": 1
+    },
+    {
+      "expected": "cabin_fan_up",
+      "predicted": "cabin_fan_down",
+      "count": 1
+    }
+  ],
+  "failure_examples": [
+    {
+      "text": "把左前窗降一点",
+      "category": "failure_replay",
+      "expected_intent_id": "cabin_window_open",
+      "predicted_intent_id": "None",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.2812,
+      "intent_ok": false,
+      "slot_exact": true,
+      "joint_ok": false,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.2812
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.1715
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.0986
+        }
+      ]
+    },
+    {
+      "text": "给我透个气",
+      "category": "failure_replay",
+      "expected_intent_id": "cabin_window_open",
+      "predicted_intent_id": "cabin_play_music",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.9331,
+      "intent_ok": false,
+      "slot_exact": true,
+      "joint_ok": false,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.9331
+        },
+        {
+          "intent_id": "cabin_next_track",
+          "score": 0.0138
+        },
+        {
+          "intent_id": "cabin_pause_music",
+          "score": 0.0031
+        }
+      ]
+    },
+    {
+      "text": "风别太小",
+      "category": "failure_replay",
+      "expected_intent_id": "cabin_fan_up",
+      "predicted_intent_id": "cabin_fan_down",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.6053,
+      "intent_ok": false,
+      "slot_exact": true,
+      "joint_ok": false,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.6053
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.1549
+        },
+        {
+          "intent_id": "cabin_volume_down",
+          "score": 0.0286
+        }
+      ]
+    }
+  ],
+  "train_summary": {
+    "train_size": 337,
+    "eval_size": 10,
+    "metrics": {
+      "intent_accuracy": 1.0,
+      "slot_token_accuracy": 0.9412,
+      "slot_exact_match": 0.8,
+      "intent_label_count": 34.0,
+      "slot_label_count": 11.0,
+      "train_loss": 0.2596
+    }
+  },
+  "results": [
+    {
+      "text": "把空调调到22度",
+      "category": "slot_temperature",
+      "expected_intent_id": "cabin_set_ac",
+      "predicted_intent_id": "cabin_set_ac",
+      "expected_slots": {
+        "temperature": 22
+      },
+      "predicted_slots": {
+        "temperature": 22
+      },
+      "intent_score": 0.9879,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 1,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.9879
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0017
+        },
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.0011
+        }
+      ]
+    },
+    {
+      "text": "空调给我调到20度",
+      "category": "slot_temperature",
+      "expected_intent_id": "cabin_set_ac",
+      "predicted_intent_id": "cabin_set_ac",
+      "expected_slots": {
+        "temperature": 20
+      },
+      "predicted_slots": {
+        "temperature": 20
+      },
+      "intent_score": 0.9897,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 1,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.9897
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0014
+        },
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.0011
+        }
+      ]
+    },
+    {
+      "text": "车里温度设成24度",
+      "category": "slot_temperature",
+      "expected_intent_id": "cabin_set_ac",
+      "predicted_intent_id": "cabin_set_ac",
+      "expected_slots": {
+        "temperature": 24
+      },
+      "predicted_slots": {
+        "temperature": 24
+      },
+      "intent_score": 0.9908,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 1,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.9908
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0009
+        },
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.0006
+        }
+      ]
+    },
+    {
+      "text": "把温度打到21度",
+      "category": "slot_temperature",
+      "expected_intent_id": "cabin_set_ac",
+      "predicted_intent_id": "cabin_set_ac",
+      "expected_slots": {
+        "temperature": 21
+      },
+      "predicted_slots": {
+        "temperature": 21
+      },
+      "intent_score": 0.9886,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 1,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_set_ac",
+          "score": 0.9886
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0011
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.0008
+        }
+      ]
+    },
+    {
+      "text": "导航去公司停车场",
+      "category": "slot_destination",
+      "expected_intent_id": "cabin_nav_to",
+      "predicted_intent_id": "cabin_nav_to",
+      "expected_slots": {
+        "destination": "公司停车场"
+      },
+      "predicted_slots": {
+        "destination": "公司停车场"
+      },
+      "intent_score": 0.9858,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 1,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.9858
+        },
+        {
+          "intent_id": "cabin_nav_cancel",
+          "score": 0.0018
+        },
+        {
+          "intent_id": "cabin_lights_on",
+          "score": 0.001
+        }
+      ]
+    },
+    {
+      "text": "带我去浦东机场",
+      "category": "slot_destination",
+      "expected_intent_id": "cabin_nav_to",
+      "predicted_intent_id": "cabin_nav_to",
+      "expected_slots": {
+        "destination": "浦东机场"
+      },
+      "predicted_slots": {
+        "destination": "浦东机场"
+      },
+      "intent_score": 0.9893,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 1,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.9893
+        },
+        {
+          "intent_id": "cs_query_logistics",
+          "score": 0.0008
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0007
+        }
+      ]
+    },
+    {
+      "text": "导航到南京东路",
+      "category": "slot_destination",
+      "expected_intent_id": "cabin_nav_to",
+      "predicted_intent_id": "cabin_nav_to",
+      "expected_slots": {
+        "destination": "南京东路"
+      },
+      "predicted_slots": {
+        "destination": "南京东路"
+      },
+      "intent_score": 0.9893,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 1,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.9893
+        },
+        {
+          "intent_id": "cabin_nav_cancel",
+          "score": 0.0012
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0007
+        }
+      ]
+    },
+    {
+      "text": "去虹桥机场",
+      "category": "slot_destination",
+      "expected_intent_id": "cabin_nav_to",
+      "predicted_intent_id": "cabin_nav_to",
+      "expected_slots": {
+        "destination": "虹桥机场"
+      },
+      "predicted_slots": {
+        "destination": "虹桥机场"
+      },
+      "intent_score": 0.9862,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 1,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.9862
+        },
+        {
+          "intent_id": "cabin_nav_cancel",
+          "score": 0.0012
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0011
+        }
+      ]
+    },
+    {
+      "text": "查一下订单A123456",
+      "category": "slot_order",
+      "expected_intent_id": "cs_query_order",
+      "predicted_intent_id": "cs_query_order",
+      "expected_slots": {
+        "order_id": "A123456"
+      },
+      "predicted_slots": {
+        "order_id": "A123456"
+      },
+      "intent_score": 0.9921,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 1,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cs_query_order",
+          "score": 0.9921
+        },
+        {
+          "intent_id": "cs_cancel_order",
+          "score": 0.001
+        },
+        {
+          "intent_id": "cs_query_logistics",
+          "score": 0.0007
+        }
+      ]
+    },
+    {
+      "text": "帮我看看订单A808001",
+      "category": "slot_order",
+      "expected_intent_id": "cs_query_order",
+      "predicted_intent_id": "cs_query_order",
+      "expected_slots": {
+        "order_id": "A808001"
+      },
+      "predicted_slots": {
+        "order_id": "A808001"
+      },
+      "intent_score": 0.9922,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 1,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cs_query_order",
+          "score": 0.9922
+        },
+        {
+          "intent_id": "cs_cancel_order",
+          "score": 0.0013
+        },
+        {
+          "intent_id": "cs_query_logistics",
+          "score": 0.0007
+        }
+      ]
+    },
+    {
+      "text": "快递A998877到哪了",
+      "category": "slot_order",
+      "expected_intent_id": "cs_query_logistics",
+      "predicted_intent_id": "cs_query_logistics",
+      "expected_slots": {
+        "order_id": "A998877"
+      },
+      "predicted_slots": {
+        "order_id": "A998877"
+      },
+      "intent_score": 0.9931,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 1,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cs_query_logistics",
+          "score": 0.9931
+        },
+        {
+          "intent_id": "cabin_volume_mute",
+          "score": 0.0005
+        },
+        {
+          "intent_id": "cabin_defog_rear_on",
+          "score": 0.0005
+        }
+      ]
+    },
+    {
+      "text": "取消订单A556677",
+      "category": "slot_order",
+      "expected_intent_id": "cs_cancel_order",
+      "predicted_intent_id": "cs_cancel_order",
+      "expected_slots": {
+        "order_id": "A556677"
+      },
+      "predicted_slots": {
+        "order_id": "A556677"
+      },
+      "intent_score": 0.9911,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 1,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cs_cancel_order",
+          "score": 0.9911
+        },
+        {
+          "intent_id": "cabin_pause_music",
+          "score": 0.001
+        },
+        {
+          "intent_id": "cs_query_order",
+          "score": 0.0007
+        }
+      ]
+    },
+    {
+      "text": "来一首青花瓷",
+      "category": "slot_music",
+      "expected_intent_id": "cabin_play_music",
+      "predicted_intent_id": "cabin_play_music",
+      "expected_slots": {
+        "song": "青花瓷"
+      },
+      "predicted_slots": {
+        "song": "青花瓷"
+      },
+      "intent_score": 0.9881,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 1,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.9881
+        },
+        {
+          "intent_id": "cabin_next_track",
+          "score": 0.002
+        },
+        {
+          "intent_id": "cabin_pause_music",
+          "score": 0.0005
+        }
+      ]
+    },
+    {
+      "text": "播放夜的第七章",
+      "category": "slot_music",
+      "expected_intent_id": "cabin_play_music",
+      "predicted_intent_id": "cabin_play_music",
+      "expected_slots": {
+        "song": "夜的第七章"
+      },
+      "predicted_slots": {
+        "song": "夜的第七章"
+      },
+      "intent_score": 0.9828,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 1,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.9828
+        },
+        {
+          "intent_id": "cabin_nav_to",
+          "score": 0.0015
+        },
+        {
+          "intent_id": "cabin_pause_music",
+          "score": 0.0012
+        }
+      ]
+    },
+    {
+      "text": "来点爵士",
+      "category": "slot_music",
+      "expected_intent_id": "cabin_play_music",
+      "predicted_intent_id": "cabin_play_music",
+      "expected_slots": {
+        "genre": "爵士"
+      },
+      "predicted_slots": {
+        "genre": "爵士"
+      },
+      "intent_score": 0.9908,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 1,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.9908
+        },
+        {
+          "intent_id": "cabin_next_track",
+          "score": 0.0007
+        },
+        {
+          "intent_id": "cabin_pause_music",
+          "score": 0.0006
+        }
+      ]
+    },
+    {
+      "text": "放点摇滚",
+      "category": "slot_music",
+      "expected_intent_id": "cabin_play_music",
+      "predicted_intent_id": "cabin_play_music",
+      "expected_slots": {
+        "genre": "摇滚"
+      },
+      "predicted_slots": {
+        "genre": "摇滚"
+      },
+      "intent_score": 0.9915,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 1,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.9915
+        },
+        {
+          "intent_id": "cabin_pause_music",
+          "score": 0.0006
+        },
+        {
+          "intent_id": "cabin_next_track",
+          "score": 0.0006
+        }
+      ]
+    },
+    {
+      "text": "给我播点民谣",
+      "category": "slot_music",
+      "expected_intent_id": "cabin_play_music",
+      "predicted_intent_id": "cabin_play_music",
+      "expected_slots": {
+        "genre": "民谣"
+      },
+      "predicted_slots": {
+        "genre": "民谣"
+      },
+      "intent_score": 0.9917,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 1,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.9917
+        },
+        {
+          "intent_id": "cabin_next_track",
+          "score": 0.0007
+        },
+        {
+          "intent_id": "cabin_sunroof_open",
+          "score": 0.0005
+        }
+      ]
+    },
+    {
+      "text": "把车窗打开",
+      "category": "no_slot_control",
+      "expected_intent_id": "cabin_window_open",
+      "predicted_intent_id": "cabin_window_open",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.9474,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.9474
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.0074
+        },
+        {
+          "intent_id": "cabin_sunroof_open",
+          "score": 0.0053
+        }
+      ]
+    },
+    {
+      "text": "把车窗关上",
+      "category": "no_slot_control",
+      "expected_intent_id": "cabin_window_close",
+      "predicted_intent_id": "cabin_window_close",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.9544,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.9544
+        },
+        {
+          "intent_id": "cabin_lock_doors",
+          "score": 0.0088
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.0065
+        }
+      ]
+    },
+    {
+      "text": "把天窗打开",
+      "category": "no_slot_control",
+      "expected_intent_id": "cabin_sunroof_open",
+      "predicted_intent_id": "cabin_sunroof_open",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.9715,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_sunroof_open",
+          "score": 0.9715
+        },
+        {
+          "intent_id": "cabin_sunroof_close",
+          "score": 0.0032
+        },
+        {
+          "intent_id": "cabin_wiper_on",
+          "score": 0.0018
+        }
+      ]
+    },
+    {
+      "text": "把天窗合上",
+      "category": "no_slot_control",
+      "expected_intent_id": "cabin_sunroof_close",
+      "predicted_intent_id": "cabin_sunroof_close",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.7244,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_sunroof_close",
+          "score": 0.7244
+        },
+        {
+          "intent_id": "cabin_sunroof_open",
+          "score": 0.204
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.008
+        }
+      ]
+    },
+    {
+      "text": "把空调打开",
+      "category": "no_slot_control",
+      "expected_intent_id": "cabin_ac_on",
+      "predicted_intent_id": "cabin_ac_on",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.9545,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.9545
+        },
+        {
+          "intent_id": "cabin_ac_off",
+          "score": 0.0079
+        },
+        {
+          "intent_id": "cabin_sunroof_open",
+          "score": 0.0029
+        }
+      ]
+    },
+    {
+      "text": "把空调关掉",
+      "category": "no_slot_control",
+      "expected_intent_id": "cabin_ac_off",
+      "predicted_intent_id": "cabin_ac_off",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.9673,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_ac_off",
+          "score": 0.9673
+        },
+        {
+          "intent_id": "cabin_ac_on",
+          "score": 0.0035
+        },
+        {
+          "intent_id": "cabin_lights_off",
+          "score": 0.0032
+        }
+      ]
+    },
+    {
+      "text": "把风量调大一点",
+      "category": "no_slot_control",
+      "expected_intent_id": "cabin_fan_up",
+      "predicted_intent_id": "cabin_fan_up",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.5975,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.5975
+        },
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.3425
+        },
+        {
+          "intent_id": "cabin_volume_up",
+          "score": 0.0044
+        }
+      ]
+    },
+    {
+      "text": "风太大了，往小调一点",
+      "category": "no_slot_control",
+      "expected_intent_id": "cabin_fan_down",
+      "predicted_intent_id": "cabin_fan_down",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.6892,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.6892
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.2052
+        },
+        {
+          "intent_id": "cabin_volume_down",
+          "score": 0.014
+        }
+      ]
+    },
+    {
+      "text": "把音乐暂停",
+      "category": "no_slot_control",
+      "expected_intent_id": "cabin_pause_music",
+      "predicted_intent_id": "cabin_pause_music",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.9672,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_pause_music",
+          "score": 0.9672
+        },
+        {
+          "intent_id": "cabin_volume_mute",
+          "score": 0.0039
+        },
+        {
+          "intent_id": "cabin_next_track",
+          "score": 0.0025
+        }
+      ]
+    },
+    {
+      "text": "帮我切到下一首",
+      "category": "no_slot_control",
+      "expected_intent_id": "cabin_next_track",
+      "predicted_intent_id": "cabin_next_track",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.9667,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_next_track",
+          "score": 0.9667
+        },
+        {
+          "intent_id": "cabin_previous_track",
+          "score": 0.0104
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.0022
+        }
+      ]
+    },
+    {
+      "text": "切回上一首",
+      "category": "no_slot_control",
+      "expected_intent_id": "cabin_previous_track",
+      "predicted_intent_id": "cabin_previous_track",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.9694,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_previous_track",
+          "score": 0.9694
+        },
+        {
+          "intent_id": "cabin_next_track",
+          "score": 0.0096
+        },
+        {
+          "intent_id": "cabin_nav_cancel",
+          "score": 0.0018
+        }
+      ]
+    },
+    {
+      "text": "先静音",
+      "category": "no_slot_control",
+      "expected_intent_id": "cabin_volume_mute",
+      "predicted_intent_id": "cabin_volume_mute",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.9763,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_volume_mute",
+          "score": 0.9763
+        },
+        {
+          "intent_id": "cabin_pause_music",
+          "score": 0.0022
+        },
+        {
+          "intent_id": "cs_query_logistics",
+          "score": 0.0016
+        }
+      ]
+    },
+    {
+      "text": "把音量调大一点",
+      "category": "no_slot_control",
+      "expected_intent_id": "cabin_volume_up",
+      "predicted_intent_id": "cabin_volume_up",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.6867,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_volume_up",
+          "score": 0.6867
+        },
+        {
+          "intent_id": "cabin_volume_down",
+          "score": 0.2533
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.0067
+        }
+      ]
+    },
+    {
+      "text": "把音量调小一点",
+      "category": "no_slot_control",
+      "expected_intent_id": "cabin_volume_down",
+      "predicted_intent_id": "cabin_volume_down",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.6048,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_volume_down",
+          "score": 0.6048
+        },
+        {
+          "intent_id": "cabin_volume_up",
+          "score": 0.327
+        },
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.009
+        }
+      ]
+    },
+    {
+      "text": "把后视镜收起来",
+      "category": "failure_replay",
+      "expected_intent_id": "cabin_mirror_fold",
+      "predicted_intent_id": "cabin_mirror_fold",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.9329,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_mirror_fold",
+          "score": 0.9329
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.0315
+        },
+        {
+          "intent_id": "cabin_wiper_off",
+          "score": 0.0046
+        }
+      ]
+    },
+    {
+      "text": "把镜子展开",
+      "category": "failure_replay",
+      "expected_intent_id": "cabin_mirror_unfold",
+      "predicted_intent_id": "cabin_mirror_unfold",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.7236,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.7236
+        },
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.025
+        },
+        {
+          "intent_id": "cabin_mirror_fold",
+          "score": 0.0239
+        }
+      ]
+    },
+    {
+      "text": "锁车门",
+      "category": "failure_replay",
+      "expected_intent_id": "cabin_lock_doors",
+      "predicted_intent_id": "cabin_lock_doors",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.9345,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_lock_doors",
+          "score": 0.9345
+        },
+        {
+          "intent_id": "cabin_unlock_doors",
+          "score": 0.0328
+        },
+        {
+          "intent_id": "cabin_window_close",
+          "score": 0.0061
+        }
+      ]
+    },
+    {
+      "text": "把车门解锁",
+      "category": "failure_replay",
+      "expected_intent_id": "cabin_unlock_doors",
+      "predicted_intent_id": "cabin_unlock_doors",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.9194,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_unlock_doors",
+          "score": 0.9194
+        },
+        {
+          "intent_id": "cabin_lock_doors",
+          "score": 0.0488
+        },
+        {
+          "intent_id": "cabin_seat_heat_off",
+          "score": 0.0032
+        }
+      ]
+    },
+    {
+      "text": "路线别导了",
+      "category": "failure_replay",
+      "expected_intent_id": "cabin_nav_cancel",
+      "predicted_intent_id": "cabin_nav_cancel",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.6978,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_nav_cancel",
+          "score": 0.6978
+        },
+        {
+          "intent_id": "cabin_pause_music",
+          "score": 0.069
+        },
+        {
+          "intent_id": "cs_cancel_order",
+          "score": 0.0353
+        }
+      ]
+    },
+    {
+      "text": "音乐停一下",
+      "category": "failure_replay",
+      "expected_intent_id": "cabin_pause_music",
+      "predicted_intent_id": "cabin_pause_music",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.9077,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_pause_music",
+          "score": 0.9077
+        },
+        {
+          "intent_id": "cabin_volume_mute",
+          "score": 0.012
+        },
+        {
+          "intent_id": "cabin_lights_off",
+          "score": 0.009
+        }
+      ]
+    },
+    {
+      "text": "雨刮关掉",
+      "category": "failure_replay",
+      "expected_intent_id": "cabin_wiper_off",
+      "predicted_intent_id": "cabin_wiper_off",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.9638,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_wiper_off",
+          "score": 0.9638
+        },
+        {
+          "intent_id": "cabin_wiper_on",
+          "score": 0.0044
+        },
+        {
+          "intent_id": "cabin_lights_off",
+          "score": 0.0033
+        }
+      ]
+    },
+    {
+      "text": "把雨刮打开",
+      "category": "failure_replay",
+      "expected_intent_id": "cabin_wiper_on",
+      "predicted_intent_id": "cabin_wiper_on",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.9066,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_wiper_on",
+          "score": 0.9066
+        },
+        {
+          "intent_id": "cabin_wiper_off",
+          "score": 0.0363
+        },
+        {
+          "intent_id": "cabin_sunroof_open",
+          "score": 0.0069
+        }
+      ]
+    },
+    {
+      "text": "把左前窗降一点",
+      "category": "failure_replay",
+      "expected_intent_id": "cabin_window_open",
+      "predicted_intent_id": "None",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.2812,
+      "intent_ok": false,
+      "slot_exact": true,
+      "joint_ok": false,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_defog_front_on",
+          "score": 0.2812
+        },
+        {
+          "intent_id": "cabin_window_open",
+          "score": 0.1715
+        },
+        {
+          "intent_id": "cabin_mirror_unfold",
+          "score": 0.0986
+        }
+      ]
+    },
+    {
+      "text": "给我透个气",
+      "category": "failure_replay",
+      "expected_intent_id": "cabin_window_open",
+      "predicted_intent_id": "cabin_play_music",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.9331,
+      "intent_ok": false,
+      "slot_exact": true,
+      "joint_ok": false,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_play_music",
+          "score": 0.9331
+        },
+        {
+          "intent_id": "cabin_next_track",
+          "score": 0.0138
+        },
+        {
+          "intent_id": "cabin_pause_music",
+          "score": 0.0031
+        }
+      ]
+    },
+    {
+      "text": "风别太小",
+      "category": "failure_replay",
+      "expected_intent_id": "cabin_fan_up",
+      "predicted_intent_id": "cabin_fan_down",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.6053,
+      "intent_ok": false,
+      "slot_exact": true,
+      "joint_ok": false,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cabin_fan_down",
+          "score": 0.6053
+        },
+        {
+          "intent_id": "cabin_fan_up",
+          "score": 0.1549
+        },
+        {
+          "intent_id": "cabin_volume_down",
+          "score": 0.0286
+        }
+      ]
+    },
+    {
+      "text": "要是太慢就转人工",
+      "category": "failure_replay",
+      "expected_intent_id": "cs_transfer_human",
+      "predicted_intent_id": "cs_transfer_human",
+      "expected_slots": {},
+      "predicted_slots": {},
+      "intent_score": 0.9849,
+      "intent_ok": true,
+      "slot_exact": true,
+      "joint_ok": true,
+      "slot_diff": {
+        "missing_keys": [],
+        "extra_keys": [],
+        "wrong_values": [],
+        "matched_keys": 0,
+        "exact": true
+      },
+      "top_candidates": [
+        {
+          "intent_id": "cs_transfer_human",
+          "score": 0.9849
+        },
+        {
+          "intent_id": "cs_query_logistics",
+          "score": 0.0018
+        },
+        {
+          "intent_id": "cs_cancel_order",
+          "score": 0.0016
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/intelligent_cabin/archive/scripts/eval_joint_bert_multi_intent_independent.py b/intelligent_cabin/archive/scripts/eval_joint_bert_multi_intent_independent.py
new file mode 100644
index 0000000..cac4865
--- /dev/null
+++ b/intelligent_cabin/archive/scripts/eval_joint_bert_multi_intent_independent.py
@@ -0,0 +1,92 @@
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from collections import Counter
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from app.core.bootstrap import build_intent_registry
+from app.services.joint_nlu import JointBertNLU
+
+
+DEFAULT_TEST_PATH = PROJECT_ROOT / "app/data/bert_intent_multilabel_eval_independent.jsonl"
+
+
+def load_cases(path: Path) -> list[dict[str, object]]:
+    rows: list[dict[str, object]] = []
+    for line in path.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line:
+            continue
+        payload = json.loads(line)
+        rows.append(payload)
+    return rows
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Joint BERT 多意图独立评测")
+    parser.add_argument("--model-path", type=str, default="models/local_joint_bert_nlu")
+    parser.add_argument("--test-path", type=str, default=str(DEFAULT_TEST_PATH))
+    args = parser.parse_args()
+
+    registry = build_intent_registry()
+    nlu = JointBertNLU(model_path=args.model_path)
+    cases = load_cases(Path(args.test_path))
+
+    tp = 0
+    fp = 0
+    fn = 0
+    exact = 0
+    failures: list[dict[str, object]] = []
+    category_correct: Counter[str] = Counter()
+    category_total: Counter[str] = Counter()
+
+    for case in cases:
+        text = str(case["text"])
+        expected = sorted({str(item) for item in case.get("expected_intent_ids", [])})
+        predicted = sorted(item.intent_id for item in nlu.predict_multi_intents(text, registry.list(), top_k=8, max_labels=4))
+        expected_set = set(expected)
+        predicted_set = set(predicted)
+        tp += len(expected_set & predicted_set)
+        fp += len(predicted_set - expected_set)
+        fn += len(expected_set - predicted_set)
+        category = str(case.get("category") or "unknown")
+        category_total[category] += 1
+        if expected_set == predicted_set:
+            exact += 1
+            category_correct[category] += 1
+        else:
+            failures.append(
+                {
+                    "text": text,
+                    "expected_intent_ids": expected,
+                    "predicted_intent_ids": predicted,
+                    "category": category,
+                }
+            )
+
+    precision = tp / max(tp + fp, 1)
+    recall = tp / max(tp + fn, 1)
+    f1 = (2 * precision * recall / (precision + recall)) if (precision + recall) else 0.0
+    result = {
+        "sample_count": len(cases),
+        "micro_precision": round(precision, 4),
+        "micro_recall": round(recall, 4),
+        "micro_f1": round(f1, 4),
+        "exact_match": round(exact / max(len(cases), 1), 4),
+        "per_category_exact_match": {
+            category: round(category_correct[category] / max(total, 1), 4)
+            for category, total in sorted(category_total.items())
+        },
+        "failures": failures[:20],
+    }
+    print(json.dumps(result, ensure_ascii=False, indent=2))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/intelligent_cabin/archive/scripts/eval_joint_bert_nlu.py b/intelligent_cabin/archive/scripts/eval_joint_bert_nlu.py
new file mode 100644
index 0000000..83878f9
--- /dev/null
+++ b/intelligent_cabin/archive/scripts/eval_joint_bert_nlu.py
@@ -0,0 +1,59 @@
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from app.core.bootstrap import build_intent_registry
+from app.services.joint_nlu import JointBertNLU
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="评测 Joint BERT NLU 单句意图与槽位输出")
+    parser.add_argument("--text", type=str, required=True, help="待评测文本")
+    parser.add_argument("--model-path", type=str, default="models/local_joint_bert_nlu", help="模型目录")
+    args = parser.parse_args()
+
+    registry = build_intent_registry()
+    nlu = JointBertNLU(model_path=args.model_path)
+    result = nlu.predict(args.text, registry.list())
+    print(
+        json.dumps(
+            {
+                "text": args.text,
+                "intent_id": result.intent_id,
+                "intent_score": round(result.intent_score, 4),
+                "candidates": [
+                    {"intent_id": item.intent_id, "score": round(item.score, 4)}
+                    for item in result.candidates
+                ],
+                "multi_intent_candidates": [
+                    {"intent_id": item.intent_id, "score": round(item.score, 4)}
+                    for item in result.multi_intent_candidates
+                ],
+                "slots": result.slots,
+                "slot_items": [
+                    {
+                        "slot_name": item.slot_name,
+                        "value": item.value,
+                        "start": item.start,
+                        "end": item.end,
+                        "score": item.score,
+                    }
+                    for item in result.slot_items
+                ],
+                "error_message": result.error_message,
+            },
+            ensure_ascii=False,
+            indent=2,
+        )
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/intelligent_cabin/archive/scripts/eval_joint_nlu_independent.py b/intelligent_cabin/archive/scripts/eval_joint_nlu_independent.py
new file mode 100644
index 0000000..c17dc12
--- /dev/null
+++ b/intelligent_cabin/archive/scripts/eval_joint_nlu_independent.py
@@ -0,0 +1,275 @@
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from collections import Counter, defaultdict
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from app.core.bootstrap import build_intent_registry
+from app.services.joint_nlu import JointBertNLU
+
+
+TEST_PATH = PROJECT_ROOT / "app/data/joint_nlu_eval_independent.jsonl"
+MODEL_DIR = PROJECT_ROOT / "models/local_joint_bert_nlu"
+REPORT_DIR = PROJECT_ROOT / "reports"
+RESULT_PATH = REPORT_DIR / "joint_nlu_independent_result.json"
+REPORT_PATH = REPORT_DIR / "joint_nlu_independent_report.md"
+TRAIN_SUMMARY_PATH = MODEL_DIR / "train_summary.json"
+
+
+def load_cases(file_path: Path) -> list[dict[str, object]]:
+    cases: list[dict[str, object]] = []
+    for line in file_path.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line:
+            continue
+        payload = json.loads(line)
+        expected_intent_id = str(payload.get("expected_intent_id") or payload.get("intent_id") or "").strip()
+        if not expected_intent_id:
+            continue
+        cases.append(
+            {
+                "text": str(payload["text"]),
+                "expected_intent_id": expected_intent_id,
+                "expected_slots": dict(payload.get("expected_slots") or {}),
+                "category": str(payload.get("category") or "unknown"),
+            }
+        )
+    return cases
+
+
+def load_train_summary(file_path: Path) -> dict[str, object]:
+    if not file_path.exists():
+        return {}
+    return json.loads(file_path.read_text(encoding="utf-8"))
+
+
+def compare_slots(expected: dict[str, object], predicted: dict[str, object]) -> dict[str, object]:
+    expected_keys = set(expected)
+    predicted_keys = set(predicted)
+    missing_keys = sorted(expected_keys - predicted_keys)
+    extra_keys = sorted(predicted_keys - expected_keys)
+    wrong_values: list[dict[str, object]] = []
+    matched_keys = 0
+    for key in sorted(expected_keys & predicted_keys):
+        if expected[key] == predicted[key]:
+            matched_keys += 1
+        else:
+            wrong_values.append(
+                {
+                    "slot_name": key,
+                    "expected": expected[key],
+                    "predicted": predicted[key],
+                }
+            )
+    exact = not missing_keys and not extra_keys and not wrong_values
+    return {
+        "missing_keys": missing_keys,
+        "extra_keys": extra_keys,
+        "wrong_values": wrong_values,
+        "matched_keys": matched_keys,
+        "exact": exact,
+    }
+
+
+def compute_metrics(results: list[dict[str, object]]) -> dict[str, float]:
+    total = len(results)
+    intent_correct = sum(1 for item in results if item["intent_ok"])
+    slot_exact = sum(1 for item in results if item["slot_exact"])
+    joint_exact = sum(1 for item in results if item["joint_ok"])
+
+    slot_tp = 0
+    slot_fp = 0
+    slot_fn = 0
+    for item in results:
+        expected = dict(item["expected_slots"])
+        predicted = dict(item["predicted_slots"])
+        expected_keys = set(expected)
+        predicted_keys = set(predicted)
+        slot_tp += sum(1 for key in expected_keys & predicted_keys if expected[key] == predicted[key])
+        slot_fp += len(predicted_keys - expected_keys)
+        slot_fn += len(expected_keys - predicted_keys)
+        slot_fp += sum(1 for key in expected_keys & predicted_keys if expected[key] != predicted[key])
+        slot_fn += sum(1 for key in expected_keys & predicted_keys if expected[key] != predicted[key])
+
+    precision = slot_tp / (slot_tp + slot_fp) if (slot_tp + slot_fp) else 0.0
+    recall = slot_tp / (slot_tp + slot_fn) if (slot_tp + slot_fn) else 0.0
+    slot_f1 = (2 * precision * recall / (precision + recall)) if (precision + recall) else 0.0
+    return {
+        "intent_accuracy": round(intent_correct / total, 4) if total else 0.0,
+        "slot_exact_match": round(slot_exact / total, 4) if total else 0.0,
+        "joint_exact_match": round(joint_exact / total, 4) if total else 0.0,
+        "slot_micro_precision": round(precision, 4),
+        "slot_micro_recall": round(recall, 4),
+        "slot_micro_f1": round(slot_f1, 4),
+    }
+
+
+def summarize_by_category(results: list[dict[str, object]]) -> list[dict[str, object]]:
+    grouped: dict[str, list[dict[str, object]]] = defaultdict(list)
+    for item in results:
+        grouped[str(item["category"])].append(item)
+    summary: list[dict[str, object]] = []
+    for category, items in sorted(grouped.items()):
+        summary.append(
+            {
+                "category": category,
+                "sample_count": len(items),
+                "metrics": compute_metrics(items),
+            }
+        )
+    return summary
+
+
+def collect_top_confusions(results: list[dict[str, object]], limit: int = 12) -> list[dict[str, object]]:
+    counter: Counter[tuple[str, str]] = Counter()
+    for item in results:
+        if item["intent_ok"]:
+            continue
+        counter[(str(item["expected_intent_id"]), str(item["predicted_intent_id"]))] += 1
+    return [
+        {"expected": expected, "predicted": predicted, "count": count}
+        for (expected, predicted), count in counter.most_common(limit)
+    ]
+
+
+def collect_failures(results: list[dict[str, object]], limit: int = 20) -> list[dict[str, object]]:
+    failures = [item for item in results if not item["joint_ok"]]
+
+    def sort_key(item: dict[str, object]) -> tuple[int, int, int]:
+        slot_errors = len(item["slot_diff"]["missing_keys"]) + len(item["slot_diff"]["extra_keys"]) + len(item["slot_diff"]["wrong_values"])
+        return (0 if item["intent_ok"] else 1, slot_errors, len(str(item["text"])))
+
+    return sorted(failures, key=sort_key, reverse=True)[:limit]
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Joint NLU 独立评测与失败样例回放")
+    parser.add_argument("--test-path", type=str, default=str(TEST_PATH), help="评测集路径")
+    parser.add_argument("--model-path", type=str, default=str(MODEL_DIR), help="Joint NLU 模型路径")
+    parser.add_argument("--result-path", type=str, default=str(RESULT_PATH), help="结构化结果输出路径")
+    parser.add_argument("--report-path", type=str, default=str(REPORT_PATH), help="Markdown 报告输出路径")
+    args = parser.parse_args()
+
+    cases = load_cases(Path(args.test_path))
+    registry = build_intent_registry()
+    nlu = JointBertNLU(model_path=args.model_path)
+    results: list[dict[str, object]] = []
+    for case in cases:
+        prediction = nlu.predict(str(case["text"]), registry.list())
+        predicted_slots = dict(prediction.slots)
+        slot_diff = compare_slots(dict(case["expected_slots"]), predicted_slots)
+        predicted_intent_id = prediction.intent_id or "None"
+        intent_ok = predicted_intent_id == case["expected_intent_id"]
+        joint_ok = intent_ok and bool(slot_diff["exact"])
+        results.append(
+            {
+                "text": case["text"],
+                "category": case["category"],
+                "expected_intent_id": case["expected_intent_id"],
+                "predicted_intent_id": predicted_intent_id,
+                "expected_slots": case["expected_slots"],
+                "predicted_slots": predicted_slots,
+                "intent_score": round(prediction.intent_score, 4),
+                "intent_ok": intent_ok,
+                "slot_exact": bool(slot_diff["exact"]),
+                "joint_ok": joint_ok,
+                "slot_diff": slot_diff,
+                "top_candidates": [
+                    {"intent_id": item.intent_id, "score": round(item.score, 4)}
+                    for item in prediction.candidates
+                ],
+            }
+        )
+
+    summary = {
+        "model_path": args.model_path,
+        "test_path": args.test_path,
+        "sample_count": len(results),
+        "metrics": compute_metrics(results),
+        "per_category": summarize_by_category(results),
+        "top_confusions": collect_top_confusions(results),
+        "failure_examples": collect_failures(results),
+        "train_summary": load_train_summary(TRAIN_SUMMARY_PATH),
+        "results": results,
+    }
+    REPORT_DIR.mkdir(parents=True, exist_ok=True)
+    Path(args.result_path).write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8")
+    Path(args.report_path).write_text(render_report(summary), encoding="utf-8")
+    print(json.dumps({"sample_count": summary["sample_count"], "metrics": summary["metrics"]}, ensure_ascii=False))
+
+
+def render_report(summary: dict[str, object]) -> str:
+    metrics = summary["metrics"]
+    per_category = summary["per_category"]
+    confusions = summary["top_confusions"]
+    failures = summary["failure_examples"]
+    train_summary = summary.get("train_summary") or {}
+    lines = [
+        "# Joint NLU 独立评测报告",
+        "",
+        "## 概览",
+        f"- 模型目录：`{summary['model_path']}`",
+        f"- 评测集：`{summary['test_path']}`",
+        f"- 样本数：`{summary['sample_count']}`",
+        f"- `intent_accuracy`：`{metrics['intent_accuracy']}`",
+        f"- `slot_exact_match`：`{metrics['slot_exact_match']}`",
+        f"- `joint_exact_match`：`{metrics['joint_exact_match']}`",
+        f"- `slot_micro_precision`：`{metrics['slot_micro_precision']}`",
+        f"- `slot_micro_recall`：`{metrics['slot_micro_recall']}`",
+        f"- `slot_micro_f1`：`{metrics['slot_micro_f1']}`",
+        "",
+        "## 训练摘要",
+    ]
+    if train_summary:
+        lines.extend(
+            [
+                f"- 训练集 / 评测集：`{train_summary.get('train_size', 'unknown')} / {train_summary.get('eval_size', 'unknown')}`",
+                f"- 训练阶段 `intent_accuracy`：`{train_summary.get('metrics', {}).get('intent_accuracy', 'unknown')}`",
+                f"- 训练阶段 `slot_exact_match`：`{train_summary.get('metrics', {}).get('slot_exact_match', 'unknown')}`",
+                "",
+            ]
+        )
+    else:
+        lines.extend(["- 未找到训练摘要。", ""])
+    lines.extend(["## 分类别结果"])
+    for item in per_category:
+        category_metrics = item["metrics"]
+        lines.append(
+            f"- `{item['category']}`: count={item['sample_count']} intent_acc={category_metrics['intent_accuracy']} slot_exact={category_metrics['slot_exact_match']} joint_exact={category_metrics['joint_exact_match']}"
+        )
+    lines.extend(["", "## 主要意图混淆"])
+    if not confusions:
+        lines.append("- 未发现意图混淆。")
+    else:
+        for item in confusions:
+            lines.append(f"- 期望 `{item['expected']}`，预测成 `{item['predicted']}`：`{item['count']}` 次")
+    lines.extend(["", "## 失败样例回放"])
+    if not failures:
+        lines.append("- 无失败样例。")
+    else:
+        for item in failures:
+            slot_diff = item["slot_diff"]
+            lines.append(
+                f"- 文本：`{item['text']}` | 类别：`{item['category']}` | 期望意图：`{item['expected_intent_id']}` | 预测意图：`{item['predicted_intent_id']}` | 期望槽位：`{item['expected_slots']}` | 预测槽位：`{item['predicted_slots']}` | 缺失槽位：`{slot_diff['missing_keys']}` | 多出槽位：`{slot_diff['extra_keys']}`"
+            )
+    lines.extend(
+        [
+            "",
+            "## 结论",
+            "- 先看 `failure_replay` 是否仍然错，能直接判断先前多意图失败到底是联合模型本体问题还是上层组合问题。",
+            "- 若 `slot_music` 或 `slot_destination` 仍不稳，优先补 span 标注，不要回退到规则抽槽。",
+            "- 若 `no_slot_control` 很稳但 `failure_replay` 中仍有大量错误，下一步应补长尾控制语义数据，而不是急着上更复杂结构。",
+            "",
+        ]
+    )
+    return "\n".join(lines)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/intelligent_cabin/archive/scripts/eval_local_bert_intent.py b/intelligent_cabin/archive/scripts/eval_local_bert_intent.py
new file mode 100644
index 0000000..939ad42
--- /dev/null
+++ b/intelligent_cabin/archive/scripts/eval_local_bert_intent.py
@@ -0,0 +1,231 @@
+from __future__ import annotations
+
+import argparse
+import json
+from collections import Counter, defaultdict
+from pathlib import Path
+import sys
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from app.core.bootstrap import build_intent_registry
+from app.services.classifier import BertIntentClassifier
+
+
+TEST_PATH = PROJECT_ROOT / "app/data/bert_intent_eval_independent.jsonl"
+MODEL_DIR = PROJECT_ROOT / "models/local_bert_intent"
+REPORT_DIR = PROJECT_ROOT / "reports"
+REPORT_PATH = REPORT_DIR / "bert_local_test_report.md"
+RESULT_PATH = REPORT_DIR / "bert_local_test_result.json"
+BERT_THRESHOLD = 0.0
+TRAIN_SUMMARY_PATH = MODEL_DIR / "train_summary.json"
+
+
+def load_cases(file_path: Path) -> list[dict[str, str]]:
+    cases: list[dict[str, str]] = []
+    for line in file_path.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line:
+            continue
+        payload = json.loads(line)
+        expected_label = str(payload.get("expected_label") or payload.get("intent_id") or "").strip()
+        if not expected_label:
+            continue
+        category = str(payload.get("category") or infer_category(expected_label)).strip()
+        cases.append(
+            {
+                "text": str(payload["text"]),
+                "expected_label": expected_label,
+                "category": category,
+            }
+        )
+    return cases
+
+
+def load_train_summary(file_path: Path) -> dict[str, object]:
+    if not file_path.exists():
+        return {}
+    return json.loads(file_path.read_text(encoding="utf-8"))
+
+
+def infer_category(label: str) -> str:
+    if label == "__social__":
+        return "social"
+    if label == "__out_of_scope__":
+        return "out_of_scope"
+    return "business"
+
+
+def resolve_predicted_label(result) -> str:
+    if result.intent is not None:
+        return result.intent.intent_id
+    if result.raw_label:
+        return str(result.raw_label)
+    return "None"
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="本地 BERT 独立评测脚本")
+    parser.add_argument("--test-path", type=str, default=str(TEST_PATH), help="评测集路径")
+    parser.add_argument("--result-path", type=str, default=str(RESULT_PATH), help="结构化评测结果输出路径")
+    parser.add_argument("--report-path", type=str, default=str(REPORT_PATH), help="Markdown 评测报告输出路径")
+    args = parser.parse_args()
+
+    intent_registry = build_intent_registry()
+    intents = intent_registry.list()
+    classifier = BertIntentClassifier(
+        model_path=str(MODEL_DIR),
+        threshold=BERT_THRESHOLD,
+        label_map_path=str(MODEL_DIR / "label_map.json"),
+        fallback=None,
+        top_k=3,
+    )
+    cases = load_cases(Path(args.test_path))
+
+    results: list[dict[str, object]] = []
+    confusion: dict[str, Counter[str]] = defaultdict(Counter)
+    category_confusion: dict[str, Counter[str]] = defaultdict(Counter)
+    correct = 0
+
+    for case in cases:
+        result = classifier.predict(case["text"], intents)
+        predicted = resolve_predicted_label(result)
+        expected = case["expected_label"]
+        ok = predicted == expected
+        if ok:
+            correct += 1
+        confusion[expected][predicted] += 1
+        category_confusion[case["category"]]["correct" if ok else "wrong"] += 1
+        results.append(
+            {
+                "text": case["text"],
+                "category": case["category"],
+                "expected_label": expected,
+                "predicted_label": predicted,
+                "score": round(result.score, 4),
+                "raw_label": result.raw_label,
+                "ok": ok,
+                "top_candidates": [
+                    {"intent_id": intent.intent_id, "score": round(score, 4)}
+                    for intent, score in (result.candidates or [])
+                ],
+            }
+        )
+
+    accuracy = correct / len(cases) if cases else 0.0
+    train_summary = load_train_summary(TRAIN_SUMMARY_PATH)
+    per_label_stats: list[dict[str, object]] = []
+    for label in sorted({case["expected_label"] for case in cases}):
+        label_cases = [item for item in results if item["expected_label"] == label]
+        label_correct = sum(1 for item in label_cases if item["ok"])
+        per_label_stats.append(
+            {
+                "label": label,
+                "category": infer_category(label),
+                "total": len(label_cases),
+                "correct": label_correct,
+                "accuracy": round(label_correct / len(label_cases), 4) if label_cases else 0.0,
+            }
+        )
+    per_category_stats: list[dict[str, object]] = []
+    for category in sorted({case["category"] for case in cases}):
+        category_cases = [item for item in results if item["category"] == category]
+        category_correct = sum(1 for item in category_cases if item["ok"])
+        per_category_stats.append(
+            {
+                "category": category,
+                "total": len(category_cases),
+                "correct": category_correct,
+                "accuracy": round(category_correct / len(category_cases), 4) if category_cases else 0.0,
+            }
+        )
+
+    errors = [item for item in results if not item["ok"]]
+    summary = {
+        "model_dir": str(MODEL_DIR),
+        "threshold": BERT_THRESHOLD,
+        "test_path": str(args.test_path),
+        "test_case_count": len(cases),
+        "accuracy": round(accuracy, 4),
+        "train_summary": train_summary,
+        "per_category": per_category_stats,
+        "per_label": per_label_stats,
+        "errors": errors,
+        "confusion": {key: dict(value) for key, value in confusion.items()},
+    }
+
+    REPORT_DIR.mkdir(parents=True, exist_ok=True)
+    Path(args.result_path).write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8")
+    Path(args.report_path).write_text(render_report(summary), encoding="utf-8")
+    print(json.dumps({"accuracy": summary["accuracy"], "test_case_count": len(cases), "error_count": len(errors)}, ensure_ascii=False))
+
+
+def render_report(summary: dict[str, object]) -> str:
+    per_category = summary["per_category"]
+    per_label = summary["per_label"]
+    errors = summary["errors"]
+    train_summary = summary.get("train_summary") or {}
+    lines = [
+        "# 本地 BERT 意图识别测试报告",
+        "",
+        "## 概览",
+        f"- 模型目录：`{summary['model_dir']}`",
+        f"- 评测集：`{summary['test_path']}`",
+        f"- 评测阈值：`{summary['threshold']}`",
+        f"- 测试样本数：`{summary['test_case_count']}`",
+        f"- 总体准确率：`{summary['accuracy']}`",
+        "",
+        "## 训练摘要",
+    ]
+    if train_summary:
+        lines.extend(
+            [
+                f"- 基座模型：`{train_summary.get('base_model', 'unknown')}`",
+                f"- 训练集 / 验证集：`{train_summary.get('train_size', 'unknown')} / {train_summary.get('dev_size', 'unknown')}`",
+                f"- 最佳验证准确率：`{train_summary.get('best_dev_accuracy', 'unknown')}`",
+                f"- 训练设备：`{train_summary.get('device', 'unknown')}`",
+                "",
+            ]
+        )
+    else:
+        lines.extend(["- 未找到训练摘要。", ""])
+
+    lines.extend(
+        [
+        "## 分类别结果",
+        ]
+    )
+    for item in per_category:
+        lines.append(
+            f"- `{item['category']}`: {item['correct']}/{item['total']} = {item['accuracy']}"
+        )
+    lines.extend(["", "## 分标签结果"])
+    for item in per_label:
+        lines.append(
+            f"- `{item['label']}` ({item['category']}): {item['correct']}/{item['total']} = {item['accuracy']}"
+        )
+    lines.extend(["", "## 错误样例"])
+    if not errors:
+        lines.append("- 无错误样例。")
+    else:
+        for item in errors[:10]:
+            lines.append(
+                f"- 文本：`{item['text']}` | 类别：`{item['category']}` | 期望：`{item['expected_label']}` | 预测：`{item['predicted_label']}` | 分数：`{item['score']}`"
+            )
+    lines.extend(
+        [
+            "",
+            "## 结论",
+            "- 当前本地 MacBERT 已具备较强的业务意图识别能力，可作为本地快链路分类器。",
+            "- 误判主要集中在方向相反或语义接近的控制指令，下一步应补充对抗样本和真实口语表达。",
+            "- 上线前建议继续补充 ASR 错字、多轮短句和多意图子句级样本。",
+            "",
+        ]
+    )
+    return "\n".join(lines)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/intelligent_cabin/archive/scripts/eval_local_bert_multi_intent.py b/intelligent_cabin/archive/scripts/eval_local_bert_multi_intent.py
new file mode 100644
index 0000000..0598986
--- /dev/null
+++ b/intelligent_cabin/archive/scripts/eval_local_bert_multi_intent.py
@@ -0,0 +1,123 @@
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+import sys
+
+import torch
+from torch.utils.data import DataLoader
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from scripts.train_local_bert_multi_intent import (
+    BATCH_SIZE,
+    OUTPUT_DIR,
+    TOP_K,
+    THRESHOLD,
+    MultiLabelIntentDataset,
+    load_all_samples,
+    split_samples,
+    set_seed,
+)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Evaluate local BERT multi-intent detector.")
+    parser.add_argument("--model-path", default=str(OUTPUT_DIR), help="Path to trained multi-intent model.")
+    parser.add_argument("--threshold", type=float, default=THRESHOLD, help="Probability threshold.")
+    parser.add_argument("--top-k", type=int, default=TOP_K, help="Top-k for recall@k.")
+    parser.add_argument(
+        "--dataset",
+        choices=("dev", "all"),
+        default="dev",
+        help="Evaluate on the held-out dev split or all combined samples.",
+    )
+    return parser.parse_args()
+
+
+def compute_metrics(
+    probabilities: list[list[float]],
+    targets: list[list[float]],
+    threshold: float,
+    top_k: int,
+) -> dict[str, float]:
+    true_positive = 0
+    false_positive = 0
+    false_negative = 0
+    exact_match = 0
+    recall_at_k_total = 0.0
+    total = len(probabilities)
+    for scores, target in zip(probabilities, targets):
+        predicted = {index for index, score in enumerate(scores) if score >= threshold}
+        expected = {index for index, value in enumerate(target) if value >= 0.5}
+        if predicted == expected:
+            exact_match += 1
+        true_positive += len(predicted & expected)
+        false_positive += len(predicted - expected)
+        false_negative += len(expected - predicted)
+        top_indices = sorted(range(len(scores)), key=lambda index: scores[index], reverse=True)[:top_k]
+        if expected:
+            recall_at_k_total += len(set(top_indices) & expected) / len(expected)
+    precision = true_positive / (true_positive + false_positive) if (true_positive + false_positive) else 0.0
+    recall = true_positive / (true_positive + false_negative) if (true_positive + false_negative) else 0.0
+    micro_f1 = (2 * precision * recall / (precision + recall)) if (precision + recall) else 0.0
+    return {
+        "micro_precision": round(precision, 4),
+        "micro_recall": round(recall, 4),
+        "micro_f1": round(micro_f1, 4),
+        "exact_match": round(exact_match / total, 4) if total else 0.0,
+        "recall_at_k": round(recall_at_k_total / total, 4) if total else 0.0,
+    }
+
+
+def main() -> None:
+    args = parse_args()
+    set_seed(42)
+    samples = load_all_samples()
+    _, dev_samples = split_samples(samples)
+    eval_samples = samples if args.dataset == "all" else dev_samples
+    model_path = Path(args.model_path)
+    if not model_path.exists():
+        raise FileNotFoundError(f"model path not found: {model_path}")
+
+    model = AutoModelForSequenceClassification.from_pretrained(model_path)
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    label_to_id = {str(label): int(index) for label, index in (model.config.label2id or {}).items()}
+    if not label_to_id:
+        raise RuntimeError("label2id is missing from model config")
+
+    dataset = MultiLabelIntentDataset(eval_samples, tokenizer, label_to_id)
+    loader = DataLoader(dataset, batch_size=BATCH_SIZE)
+    device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
+    model.to(device)
+    model.eval()
+
+    probabilities: list[list[float]] = []
+    targets: list[list[float]] = []
+    with torch.no_grad():
+        for batch in loader:
+            input_ids = batch["input_ids"].to(device)
+            attention_mask = batch["attention_mask"].to(device)
+            labels = batch["labels"].to(device)
+            outputs = model(input_ids=input_ids, attention_mask=attention_mask)
+            probabilities.extend(torch.sigmoid(outputs.logits).detach().cpu().tolist())
+            targets.extend(labels.detach().cpu().tolist())
+
+    metrics = compute_metrics(probabilities, targets, threshold=args.threshold, top_k=args.top_k)
+    result = {
+        "model_path": str(model_path),
+        "dataset": args.dataset,
+        "sample_size": len(eval_samples),
+        "threshold": args.threshold,
+        "top_k": args.top_k,
+        "metrics": metrics,
+    }
+    print(json.dumps(result, ensure_ascii=False, indent=2))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/intelligent_cabin/archive/scripts/eval_local_bert_multi_intent_independent.py b/intelligent_cabin/archive/scripts/eval_local_bert_multi_intent_independent.py
new file mode 100644
index 0000000..604f1b3
--- /dev/null
+++ b/intelligent_cabin/archive/scripts/eval_local_bert_multi_intent_independent.py
@@ -0,0 +1,247 @@
+from __future__ import annotations
+
+import argparse
+import json
+from collections import Counter, defaultdict
+from pathlib import Path
+import sys
+
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from app.core.bootstrap import build_intent_registry
+from app.services.multi_intent_detector import BertMultiIntentDetector
+
+
+TEST_PATH = PROJECT_ROOT / "app/data/bert_intent_multilabel_eval_independent.jsonl"
+MODEL_DIR = PROJECT_ROOT / "models/local_bert_multi_intent"
+REPORT_DIR = PROJECT_ROOT / "reports"
+RESULT_PATH = REPORT_DIR / "bert_multi_intent_independent_result.json"
+REPORT_PATH = REPORT_DIR / "bert_multi_intent_independent_report.md"
+THRESHOLD = 0.45
+TOP_K = 8
+MAX_LABELS = 4
+
+
+def load_cases(file_path: Path) -> list[dict[str, object]]:
+    cases: list[dict[str, object]] = []
+    for line in file_path.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line:
+            continue
+        payload = json.loads(line)
+        expected = sorted({str(item).strip() for item in payload.get("expected_intent_ids") or [] if str(item).strip()})
+        if not expected:
+            continue
+        cases.append(
+            {
+                "text": str(payload["text"]),
+                "expected_intent_ids": expected,
+                "category": str(payload.get("category") or "unknown"),
+            }
+        )
+    return cases
+
+
+def compute_set_metrics(results: list[dict[str, object]]) -> dict[str, float]:
+    true_positive = 0
+    false_positive = 0
+    false_negative = 0
+    exact_match = 0
+    multi_recall_hit = 0
+    single_false_alarm = 0
+    total = len(results)
+    single_guard_total = 0
+    for item in results:
+        expected = set(item["expected_intent_ids"])
+        predicted = set(item["predicted_intent_ids"])
+        if expected == predicted:
+            exact_match += 1
+        true_positive += len(expected & predicted)
+        false_positive += len(predicted - expected)
+        false_negative += len(expected - predicted)
+        if len(expected) >= 2 and expected.issubset(predicted):
+            multi_recall_hit += 1
+        if len(expected) == 1:
+            single_guard_total += 1
+            if len(predicted) > 1:
+                single_false_alarm += 1
+    precision = true_positive / (true_positive + false_positive) if (true_positive + false_positive) else 0.0
+    recall = true_positive / (true_positive + false_negative) if (true_positive + false_negative) else 0.0
+    micro_f1 = (2 * precision * recall / (precision + recall)) if (precision + recall) else 0.0
+    multi_total = sum(1 for item in results if len(item["expected_intent_ids"]) >= 2)
+    return {
+        "micro_precision": round(precision, 4),
+        "micro_recall": round(recall, 4),
+        "micro_f1": round(micro_f1, 4),
+        "exact_match": round(exact_match / total, 4) if total else 0.0,
+        "multi_sentence_recall": round(multi_recall_hit / multi_total, 4) if multi_total else 0.0,
+        "single_guard_false_alarm_rate": round(single_false_alarm / single_guard_total, 4) if single_guard_total else 0.0,
+    }
+
+
+def summarize_by_category(results: list[dict[str, object]]) -> list[dict[str, object]]:
+    grouped: dict[str, list[dict[str, object]]] = defaultdict(list)
+    for item in results:
+        grouped[str(item["category"])].append(item)
+    summary: list[dict[str, object]] = []
+    for category, items in sorted(grouped.items()):
+        summary.append(
+            {
+                "category": category,
+                "sample_count": len(items),
+                "metrics": compute_set_metrics(items),
+            }
+        )
+    return summary
+
+
+def collect_error_examples(results: list[dict[str, object]], limit: int = 15) -> list[dict[str, object]]:
+    errors = [item for item in results if set(item["expected_intent_ids"]) != set(item["predicted_intent_ids"])]
+    def sort_key(item: dict[str, object]) -> tuple[int, int]:
+        expected = set(item["expected_intent_ids"])
+        predicted = set(item["predicted_intent_ids"])
+        miss = len(expected - predicted)
+        extra = len(predicted - expected)
+        return (miss + extra, miss)
+    return sorted(errors, key=sort_key, reverse=True)[:limit]
+
+
+def top_confusions(results: list[dict[str, object]], limit: int = 12) -> list[dict[str, object]]:
+    counter: Counter[tuple[str, str]] = Counter()
+    for item in results:
+        expected = set(item["expected_intent_ids"])
+        predicted = set(item["predicted_intent_ids"])
+        for miss in sorted(expected - predicted):
+            for extra in sorted(predicted - expected):
+                counter[(miss, extra)] += 1
+    return [
+        {"expected_missing": pair[0], "wrong_extra": pair[1], "count": count}
+        for pair, count in counter.most_common(limit)
+    ]
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="本地多标签 detector 独立评测脚本")
+    parser.add_argument("--test-path", type=str, default=str(TEST_PATH), help="独立评测集路径")
+    parser.add_argument("--model-path", type=str, default=str(MODEL_DIR), help="多标签模型路径")
+    parser.add_argument("--threshold", type=float, default=THRESHOLD, help="检测阈值")
+    parser.add_argument("--top-k", type=int, default=TOP_K, help="输出 top-k 原始分数")
+    parser.add_argument("--max-labels", type=int, default=MAX_LABELS, help="最多返回标签数")
+    parser.add_argument("--result-path", type=str, default=str(RESULT_PATH), help="结构化结果输出路径")
+    parser.add_argument("--report-path", type=str, default=str(REPORT_PATH), help="Markdown 报告输出路径")
+    args = parser.parse_args()
+
+    cases = load_cases(Path(args.test_path))
+    intents = build_intent_registry().list()
+    detector = BertMultiIntentDetector(
+        model_path=args.model_path,
+        threshold=args.threshold,
+        top_k=args.top_k,
+        max_labels=args.max_labels,
+    )
+
+    results: list[dict[str, object]] = []
+    for case in cases:
+        detection = detector.detect(str(case["text"]), intents)
+        predicted = [candidate.intent_id for candidate in detection.candidates]
+        raw_top = [
+            {
+                "intent_id": str(item.get("intent_id") or item.get("label") or ""),
+                "score": round(float(item.get("score", 0.0)), 4),
+            }
+            for item in detection.raw_scores
+        ]
+        results.append(
+            {
+                "text": case["text"],
+                "category": case["category"],
+                "expected_intent_ids": case["expected_intent_ids"],
+                "predicted_intent_ids": predicted,
+                "detected": detection.detected,
+                "backend_name": detection.backend_name,
+                "reason": detection.reason,
+                "raw_top_scores": raw_top,
+            }
+        )
+
+    summary = {
+        "model_path": args.model_path,
+        "test_path": args.test_path,
+        "threshold": args.threshold,
+        "top_k": args.top_k,
+        "max_labels": args.max_labels,
+        "sample_count": len(results),
+        "metrics": compute_set_metrics(results),
+        "per_category": summarize_by_category(results),
+        "top_confusions": top_confusions(results),
+        "error_examples": collect_error_examples(results),
+        "results": results,
+    }
+
+    REPORT_DIR.mkdir(parents=True, exist_ok=True)
+    Path(args.result_path).write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8")
+    Path(args.report_path).write_text(render_report(summary), encoding="utf-8")
+    print(json.dumps({"sample_count": len(results), "metrics": summary["metrics"]}, ensure_ascii=False))
+
+
+def render_report(summary: dict[str, object]) -> str:
+    metrics = summary["metrics"]
+    per_category = summary["per_category"]
+    confusions = summary["top_confusions"]
+    errors = summary["error_examples"]
+    lines = [
+        "# 本地多标签 Detector 独立评测报告",
+        "",
+        "## 概览",
+        f"- 模型目录：`{summary['model_path']}`",
+        f"- 评测集：`{summary['test_path']}`",
+        f"- 样本数：`{summary['sample_count']}`",
+        f"- 阈值 / top_k / max_labels：`{summary['threshold']} / {summary['top_k']} / {summary['max_labels']}`",
+        f"- `micro_precision`：`{metrics['micro_precision']}`",
+        f"- `micro_recall`：`{metrics['micro_recall']}`",
+        f"- `micro_f1`：`{metrics['micro_f1']}`",
+        f"- `exact_match`：`{metrics['exact_match']}`",
+        f"- `multi_sentence_recall`：`{metrics['multi_sentence_recall']}`",
+        f"- `single_guard_false_alarm_rate`：`{metrics['single_guard_false_alarm_rate']}`",
+        "",
+        "## 分类别结果",
+    ]
+    for item in per_category:
+        category_metrics = item["metrics"]
+        lines.append(
+            f"- `{item['category']}`: count={item['sample_count']} micro_f1={category_metrics['micro_f1']} exact_match={category_metrics['exact_match']}"
+        )
+    lines.extend(["", "## 主要混淆"])
+    if not confusions:
+        lines.append("- 未发现明显混淆对。")
+    else:
+        for item in confusions:
+            lines.append(
+                f"- 漏掉 `{item['expected_missing']}`，同时误报 `{item['wrong_extra']}`：`{item['count']}` 次"
+            )
+    lines.extend(["", "## 错误样例"])
+    if not errors:
+        lines.append("- 无错误样例。")
+    else:
+        for item in errors:
+            lines.append(
+                f"- 文本：`{item['text']}` | 类别：`{item['category']}` | 期望：`{item['expected_intent_ids']}` | 预测：`{item['predicted_intent_ids']}`"
+            )
+    lines.extend(
+        [
+            "",
+            "## 结论建议",
+            "- 先看多意图句是否存在系统性漏召回，再看单意图是否被误报成多意图。",
+            "- 若 `single_guard_false_alarm_rate` 偏高，需要先收紧 detector 阈值或补单意图负样本，再考虑进入 NER。",
+            "- 若 `multi_sentence_recall` 不稳定，应继续补条件句、弱连接句和口语化多动作语料。",
+            "",
+        ]
+    )
+    return "\n".join(lines)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/intelligent_cabin/archive/scripts/test_local_bert_intent.py b/intelligent_cabin/archive/scripts/test_local_bert_intent.py
new file mode 100644
index 0000000..46adcbd
--- /dev/null
+++ b/intelligent_cabin/archive/scripts/test_local_bert_intent.py
@@ -0,0 +1,97 @@
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from app.core.config import settings
+from app.core.bootstrap import build_intent_registry
+from app.services.classifier import BertIntentClassifier
+from app.services.router import build_matcher_pipeline
+
+
+DEFAULT_MODEL_DIR = PROJECT_ROOT / "models/local_bert_intent"
+DEFAULT_LABEL_MAP = DEFAULT_MODEL_DIR / "label_map.json"
+
+
+def build_classifier(threshold: float, top_k: int) -> BertIntentClassifier:
+    return BertIntentClassifier(
+        model_path=str(DEFAULT_MODEL_DIR),
+        threshold=threshold,
+        label_map_path=str(DEFAULT_LABEL_MAP),
+        fallback=None,
+        top_k=top_k,
+    )
+
+
+def predict_once(text: str, threshold: float, top_k: int) -> dict[str, object]:
+    classifier = build_classifier(threshold=threshold, top_k=top_k)
+    registry = build_intent_registry()
+    intents = registry.list()
+    result = classifier.predict(text, intents)
+    matcher = build_matcher_pipeline(
+        registry,
+        ["classifier"],
+        classifier=classifier,
+        route_to_cloud_threshold=settings.local_route_to_cloud_threshold,
+        clarify_margin_threshold=settings.local_clarify_margin_threshold,
+    )
+    route_result = matcher.match(text)
+    fusion_stage = next((stage for stage in reversed(route_result.debug.stages) if stage.stage == "fusion"), None)
+    return {
+        "text": text,
+        "predicted_intent": result.intent.intent_id if result.intent is not None else None,
+        "score": round(result.score, 4),
+        "model_name": result.model_name,
+        "backend": result.backend_name,
+        "raw_label": result.raw_label,
+        "fallback_reason": result.fallback_reason,
+        "error_message": result.error_message,
+        "decision": route_result.debug.decision,
+        "decision_reason": route_result.debug.decision_reason,
+        "confidence_grade": route_result.debug.confidence_grade,
+        "unknown_detected": route_result.debug.unknown_detected,
+        "fusion_top_score": round(fusion_stage.score, 4) if fusion_stage is not None else None,
+        "top_candidates": [
+            {"intent_id": intent.intent_id, "score": round(score, 4)}
+            for intent, score in (result.candidates or [])
+        ],
+    }
+
+
+def interactive_loop(threshold: float, top_k: int) -> None:
+    print("本地 BERT 意图测试已启动，输入一句话直接查看预测结果，输入 exit 退出。")
+    while True:
+        try:
+            text = input("\n请输入问题> ").strip()
+        except EOFError:
+            print()
+            break
+        if not text:
+            continue
+        if text.lower() in {"exit", "quit", "q"}:
+            break
+        result = predict_once(text, threshold=threshold, top_k=top_k)
+        print(json.dumps(result, ensure_ascii=False, indent=2))
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="本地 BERT 意图识别测试脚本")
+    parser.add_argument("--text", type=str, default="", help="单次测试文本")
+    parser.add_argument("--threshold", type=float, default=0.0, help="BERT 置信度阈值")
+    parser.add_argument("--top-k", type=int, default=3, help="返回候选数量")
+    args = parser.parse_args()
+
+    if args.text.strip():
+        print(json.dumps(predict_once(args.text.strip(), threshold=args.threshold, top_k=args.top_k), ensure_ascii=False, indent=2))
+        return
+    interactive_loop(threshold=args.threshold, top_k=args.top_k)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/intelligent_cabin/archive/scripts/train_joint_bert_nlu.py b/intelligent_cabin/archive/scripts/train_joint_bert_nlu.py
new file mode 100644
index 0000000..d85c558
--- /dev/null
+++ b/intelligent_cabin/archive/scripts/train_joint_bert_nlu.py
@@ -0,0 +1,500 @@
+from __future__ import annotations
+
+import json
+import random
+import re
+import sys
+from dataclasses import dataclass
+from pathlib import Path
+
+import torch
+from torch.utils.data import DataLoader, Dataset
+from transformers import AutoTokenizer
+import yaml
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from app.services.joint_nlu import JointBertForNLU
+
+
+TRAIN_PATH = PROJECT_ROOT / "app/data/bert_intent_train.jsonl"
+MULTI_TRAIN_PATH = PROJECT_ROOT / "app/data/bert_intent_multilabel_train.jsonl"
+SEED_PATH = PROJECT_ROOT / "app/data/joint_nlu_seed.jsonl"
+EVAL_PATH = PROJECT_ROOT / "app/data/joint_nlu_eval.jsonl"
+MULTI_EVAL_PATH = PROJECT_ROOT / "app/data/joint_nlu_multilabel_eval.jsonl"
+DOMAIN_PATH = PROJECT_ROOT / "config/domain.yml"
+OUTPUT_DIR = PROJECT_ROOT / "models/local_joint_bert_nlu"
+DEFAULT_BASE_MODEL = "hfl/chinese-macbert-base"
+MAX_LENGTH = 64
+BATCH_SIZE = 8
+EPOCHS = 8
+LEARNING_RATE = 2e-5
+SEED = 42
+IGNORE_INDEX = -100
+GENRE_KEYWORDS = ("轻音乐", "摇滚", "古典", "民谣", "爵士", "流行", "儿歌")
+DEFAULT_INTENT_THRESHOLD = 0.3
+MULTI_INTENT_REPEAT = 6
+THRESHOLD_CANDIDATES = [0.1, 0.12, 0.15, 0.18, 0.2, 0.22, 0.25, 0.28, 0.3, 0.33, 0.35, 0.38, 0.4, 0.45]
+
+
+@dataclass
+class JointSample:
+    text: str
+    intent_ids: list[str]
+    slots: list[dict[str, object]]
+
+
+class JointDataset(Dataset):
+    def __init__(
+        self,
+        samples: list[JointSample],
+        tokenizer,
+        intent_to_index: dict[str, int],
+        slot_to_index: dict[str, int],
+    ) -> None:
+        self._samples = samples
+        self._tokenizer = tokenizer
+        self._intent_to_index = intent_to_index
+        self._slot_to_index = slot_to_index
+
+    def __len__(self) -> int:
+        return len(self._samples)
+
+    def __getitem__(self, index: int) -> dict[str, torch.Tensor]:
+        sample = self._samples[index]
+        encoded = self._tokenizer(
+            sample.text,
+            truncation=True,
+            max_length=MAX_LENGTH,
+            padding="max_length",
+            return_offsets_mapping=True,
+            return_tensors="pt",
+        )
+        offset_mapping = encoded.pop("offset_mapping")[0].tolist()
+        slot_labels = [IGNORE_INDEX] * len(offset_mapping)
+        char_labels = ["O"] * len(sample.text)
+        for slot in sample.slots:
+            start = int(slot["start"])
+            end = int(slot["end"])
+            slot_name = str(slot["slot_name"])
+            if start < 0 or end > len(sample.text) or start >= end:
+                continue
+            char_labels[start] = f"B-{slot_name}"
+            for pos in range(start + 1, end):
+                char_labels[pos] = f"I-{slot_name}"
+
+        for token_index, (start, end) in enumerate(offset_mapping):
+            if end <= start:
+                continue
+            label = char_labels[start]
+            slot_labels[token_index] = self._slot_to_index.get(label, self._slot_to_index["O"])
+        intent_vector = torch.zeros(len(self._intent_to_index), dtype=torch.float32)
+        for intent_id in sample.intent_ids:
+            if intent_id in self._intent_to_index:
+                intent_vector[self._intent_to_index[intent_id]] = 1.0
+
+        return {
+            "input_ids": encoded["input_ids"][0],
+            "attention_mask": encoded["attention_mask"][0],
+            "intent_labels": intent_vector,
+            "slot_labels": torch.tensor(slot_labels, dtype=torch.long),
+        }
+
+
+def set_seed() -> None:
+    random.seed(SEED)
+    torch.manual_seed(SEED)
+
+
+def load_jsonl(path: Path) -> list[dict[str, object]]:
+    rows: list[dict[str, object]] = []
+    with path.open("r", encoding="utf-8") as handle:
+        for line in handle:
+            line = line.strip()
+            if not line:
+                continue
+            rows.append(json.loads(line))
+    return rows
+
+
+def find_order_id_span(text: str) -> tuple[str, int, int] | None:
+    match = re.search(r"[A-Za-z]\d{5,}", text)
+    if not match:
+        return None
+    return match.group(0), match.start(), match.end()
+
+
+def find_temperature_span(text: str) -> tuple[str, int, int] | None:
+    match = re.search(r"(\d{2}\s*度)", text)
+    if not match:
+        return None
+    return match.group(1), match.start(), match.end()
+
+
+def find_destination_span(text: str) -> tuple[str, int, int] | None:
+    for pattern in (
+        r"导航去(?P<destination>.+)",
+        r"导航到(?P<destination>.+)",
+        r"带我去(?P<destination>.+)",
+        r"送我去(?P<destination>.+)",
+        r"去(?P<destination>.+)",
+    ):
+        match = re.search(pattern, text)
+        if not match:
+            continue
+        destination = re.split(r"(?:然后|并且|同时|再|,|，|；|;)", match.group("destination"), maxsplit=1)[0].strip(" ，。")
+        if not destination:
+            continue
+        start = text.find(destination)
+        if start >= 0:
+            return destination, start, start + len(destination)
+    return None
+
+
+def find_music_span(text: str) -> tuple[str, str, int, int] | None:
+    for genre in GENRE_KEYWORDS:
+        start = text.find(genre)
+        if start >= 0:
+            return "genre", genre, start, start + len(genre)
+    for trigger in ("播放", "来点", "放点", "听", "来首", "来一首", "放一首"):
+        if trigger not in text:
+            continue
+        target = text.split(trigger, maxsplit=1)[-1]
+        target = re.split(r"(?:然后|并且|同时|再|,|，|；|;)", target, maxsplit=1)[0].strip(" 的一首首个歌曲音乐吧呀啊，。")
+        if not target or target in {"歌", "音乐"}:
+            continue
+        for genre in GENRE_KEYWORDS:
+            if genre in target:
+                start = text.find(genre)
+                return "genre", genre, start, start + len(genre)
+        start = text.find(target)
+        if start >= 0:
+            return "song", target, start, start + len(target)
+    return None
+
+
+def annotate_slots(text: str, intent_id: str) -> list[dict[str, object]]:
+    slots: list[dict[str, object]] = []
+    if intent_id in {"cs_query_order", "cs_query_logistics", "cs_cancel_order"}:
+        matched = find_order_id_span(text)
+        if matched is not None:
+            value, start, end = matched
+            slots.append({"slot_name": "order_id", "value": value, "start": start, "end": end})
+    elif intent_id == "cabin_set_ac":
+        matched = find_temperature_span(text)
+        if matched is not None:
+            value, start, end = matched
+            slots.append({"slot_name": "temperature", "value": value, "start": start, "end": end})
+    elif intent_id == "cabin_nav_to":
+        matched = find_destination_span(text)
+        if matched is not None:
+            value, start, end = matched
+            slots.append({"slot_name": "destination", "value": value, "start": start, "end": end})
+    elif intent_id == "cabin_play_music":
+        matched = find_music_span(text)
+        if matched is not None:
+            slot_name, value, start, end = matched
+            slots.append({"slot_name": slot_name, "value": value, "start": start, "end": end})
+    return slots
+
+
+def annotate_slots_for_intents(text: str, intent_ids: list[str]) -> list[dict[str, object]]:
+    merged: list[dict[str, object]] = []
+    seen: set[tuple[str, int, int]] = set()
+    for intent_id in intent_ids:
+        for slot in annotate_slots(text, intent_id):
+            key = (str(slot["slot_name"]), int(slot["start"]), int(slot["end"]))
+            if key in seen:
+                continue
+            seen.add(key)
+            merged.append(slot)
+    merged.sort(key=lambda item: (int(item["start"]), int(item["end"])))
+    return merged
+
+
+def build_train_samples() -> list[JointSample]:
+    samples: list[JointSample] = []
+    seen: set[tuple[str, tuple[str, ...]]] = set()
+    domain_data = yaml.safe_load(DOMAIN_PATH.read_text(encoding="utf-8")) or {}
+    for intent in domain_data.get("intents", []):
+        intent_id = str(intent.get("intent_id", "")).strip()
+        if not intent_id:
+            continue
+        for text in list(intent.get("examples", [])) + list(intent.get("keywords", [])):
+            text = str(text).strip()
+            if not text:
+                continue
+            key = (text, (intent_id,))
+            if key in seen:
+                continue
+            seen.add(key)
+            samples.append(JointSample(text=text, intent_ids=[intent_id], slots=annotate_slots(text, intent_id)))
+    for row in load_jsonl(TRAIN_PATH):
+        text = str(row["text"])
+        intent_id = str(row["intent_id"])
+        key = (text, (intent_id,))
+        if key in seen:
+            continue
+        seen.add(key)
+        samples.append(JointSample(text=text, intent_ids=[intent_id], slots=annotate_slots(text, intent_id)))
+    for row in load_jsonl(SEED_PATH):
+        text = str(row["text"])
+        intent_id = str(row["intent_id"])
+        key = (text, (intent_id,))
+        if key in seen:
+            continue
+        seen.add(key)
+        samples.append(JointSample(text=text, intent_ids=[intent_id], slots=list(row.get("slots", []))))
+    for row in load_jsonl(MULTI_TRAIN_PATH):
+        text = str(row["text"]).strip()
+        intent_ids = sorted({str(item).strip() for item in row.get("intent_ids", []) if str(item).strip()})
+        if not text or not intent_ids:
+            continue
+        key = (text, tuple(intent_ids))
+        if key in seen:
+            continue
+        seen.add(key)
+        slots = list(row.get("slots") or annotate_slots_for_intents(text, intent_ids))
+        samples.append(JointSample(text=text, intent_ids=intent_ids, slots=slots))
+        if len(intent_ids) >= 2:
+            for _ in range(MULTI_INTENT_REPEAT - 1):
+                samples.append(JointSample(text=text, intent_ids=intent_ids, slots=list(slots)))
+    random.shuffle(samples)
+    return samples
+
+
+def build_eval_samples() -> list[JointSample]:
+    rows = load_jsonl(EVAL_PATH)
+    samples = [
+        JointSample(
+            text=str(row["text"]),
+            intent_ids=[str(row["intent_id"])],
+            slots=list(row.get("slots", [])),
+        )
+        for row in rows
+    ]
+    if MULTI_EVAL_PATH.exists():
+        for row in load_jsonl(MULTI_EVAL_PATH):
+            samples.append(
+                JointSample(
+                    text=str(row["text"]),
+                    intent_ids=sorted({str(item).strip() for item in row.get("intent_ids", []) if str(item).strip()}),
+                    slots=list(row.get("slots") or annotate_slots_for_intents(str(row["text"]), list(row.get("intent_ids", [])))),
+                )
+            )
+    return samples
+
+
+def build_slot_labels(samples: list[JointSample]) -> list[str]:
+    slot_names = sorted({str(slot["slot_name"]) for sample in samples for slot in sample.slots})
+    labels = ["O"]
+    for name in slot_names:
+        labels.append(f"B-{name}")
+        labels.append(f"I-{name}")
+    return labels
+
+
+def compute_metrics(
+    model: JointBertForNLU,
+    dataloader: DataLoader,
+    device: torch.device,
+    intent_labels: list[str],
+    slot_labels: list[str],
+    threshold: float,
+) -> dict[str, float]:
+    model.eval()
+    intent_tp = 0
+    intent_fp = 0
+    intent_fn = 0
+    single_intent_correct = 0
+    single_intent_total = 0
+    intent_exact_match = 0
+    correct_slot_tokens = 0
+    total_slot_tokens = 0
+    exact_slot_samples = 0
+    total_samples = 0
+    with torch.no_grad():
+        for batch in dataloader:
+            batch = {key: value.to(device) for key, value in batch.items()}
+            intent_logits, slot_logits = model(batch["input_ids"], batch["attention_mask"])
+            predicted_probs = torch.sigmoid(intent_logits)
+            predicted_multi = predicted_probs >= threshold
+            gold_multi = batch["intent_labels"] > 0.5
+            intent_tp += int((predicted_multi & gold_multi).sum().item())
+            intent_fp += int((predicted_multi & ~gold_multi).sum().item())
+            intent_fn += int((~predicted_multi & gold_multi).sum().item())
+            intent_exact_match += int((predicted_multi == gold_multi).all(dim=1).sum().item())
+            top_predicted = torch.argmax(predicted_probs, dim=-1)
+            gold_counts = gold_multi.sum(dim=-1)
+            single_mask = gold_counts == 1
+            if int(single_mask.sum().item()) > 0:
+                gold_top = torch.argmax(gold_multi.float(), dim=-1)
+                single_intent_correct += int((top_predicted[single_mask] == gold_top[single_mask]).sum().item())
+                single_intent_total += int(single_mask.sum().item())
+
+            predicted_slots = torch.argmax(slot_logits, dim=-1)
+            mask = batch["slot_labels"] != IGNORE_INDEX
+            correct_slot_tokens += int(((predicted_slots == batch["slot_labels"]) & mask).sum().item())
+            total_slot_tokens += int(mask.sum().item())
+
+            for index in range(batch["slot_labels"].size(0)):
+                gold = batch["slot_labels"][index][mask[index]]
+                pred = predicted_slots[index][mask[index]]
+                exact_slot_samples += int(torch.equal(gold, pred))
+                total_samples += 1
+    precision = intent_tp / max(intent_tp + intent_fp, 1)
+    recall = intent_tp / max(intent_tp + intent_fn, 1)
+    f1 = (2 * precision * recall / (precision + recall)) if (precision + recall) else 0.0
+    return {
+        "intent_threshold": round(threshold, 4),
+        "intent_micro_precision": round(precision, 4),
+        "intent_micro_recall": round(recall, 4),
+        "intent_micro_f1": round(f1, 4),
+        "intent_exact_match": round(intent_exact_match / max(total_samples, 1), 4),
+        "single_intent_top1_accuracy": round(single_intent_correct / max(single_intent_total, 1), 4),
+        "slot_token_accuracy": round(correct_slot_tokens / max(total_slot_tokens, 1), 4),
+        "slot_exact_match": round(exact_slot_samples / max(total_samples, 1), 4),
+        "intent_label_count": float(len(intent_labels)),
+        "slot_label_count": float(len(slot_labels)),
+    }
+
+
+def search_best_threshold(
+    model: JointBertForNLU,
+    dataloader: DataLoader,
+    device: torch.device,
+    intent_labels: list[str],
+    slot_labels: list[str],
+) -> dict[str, float]:
+    best_metrics: dict[str, float] | None = None
+    for threshold in THRESHOLD_CANDIDATES:
+        metrics = compute_metrics(
+            model,
+            dataloader,
+            device,
+            intent_labels,
+            slot_labels,
+            threshold=threshold,
+        )
+        if best_metrics is None:
+            best_metrics = metrics
+            continue
+        current_score = (metrics["intent_micro_f1"], metrics["intent_exact_match"], metrics["slot_exact_match"])
+        best_score = (
+            best_metrics["intent_micro_f1"],
+            best_metrics["intent_exact_match"],
+            best_metrics["slot_exact_match"],
+        )
+        if current_score > best_score:
+            best_metrics = metrics
+    assert best_metrics is not None
+    return best_metrics
+
+
+def build_pos_weight(samples: list[JointSample], intent_labels: list[str]) -> torch.Tensor:
+    positive_counts = {label: 0 for label in intent_labels}
+    for sample in samples:
+        sample_intents = set(sample.intent_ids)
+        for label in intent_labels:
+            if label in sample_intents:
+                positive_counts[label] += 1
+    total = max(len(samples), 1)
+    weights: list[float] = []
+    for label in intent_labels:
+        positives = max(positive_counts[label], 1)
+        negatives = max(total - positives, 1)
+        weight = negatives / positives
+        weights.append(min(max(weight, 1.0), 12.0))
+    return torch.tensor(weights, dtype=torch.float32)
+
+
+def main() -> None:
+    set_seed()
+    train_samples = build_train_samples()
+    eval_samples = build_eval_samples()
+    intent_labels = sorted({intent_id for sample in train_samples + eval_samples for intent_id in sample.intent_ids})
+    slot_labels = build_slot_labels(train_samples + eval_samples)
+    intent_to_index = {label: index for index, label in enumerate(intent_labels)}
+    slot_to_index = {label: index for index, label in enumerate(slot_labels)}
+    tokenizer = AutoTokenizer.from_pretrained(DEFAULT_BASE_MODEL)
+    train_dataset = JointDataset(train_samples, tokenizer, intent_to_index, slot_to_index)
+    eval_dataset = JointDataset(eval_samples, tokenizer, intent_to_index, slot_to_index)
+    train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
+    eval_loader = DataLoader(eval_dataset, batch_size=BATCH_SIZE, shuffle=False)
+
+    device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
+    model = JointBertForNLU(
+        base_model_name=DEFAULT_BASE_MODEL,
+        num_intents=len(intent_labels),
+        num_slot_labels=len(slot_labels),
+    )
+    model.to(device)
+    optimizer = torch.optim.AdamW(model.parameters(), lr=LEARNING_RATE)
+    pos_weight = build_pos_weight(train_samples, intent_labels).to(device)
+    best_metrics: dict[str, float] | None = None
+    best_state: dict[str, torch.Tensor] | None = None
+
+    for epoch in range(EPOCHS):
+        model.train()
+        epoch_loss = 0.0
+        for batch in train_loader:
+            batch = {key: value.to(device) for key, value in batch.items()}
+            optimizer.zero_grad()
+            intent_logits, slot_logits = model(batch["input_ids"], batch["attention_mask"])
+            intent_loss = torch.nn.functional.binary_cross_entropy_with_logits(
+                intent_logits,
+                batch["intent_labels"],
+                pos_weight=pos_weight,
+            )
+            slot_loss = torch.nn.functional.cross_entropy(
+                slot_logits.view(-1, slot_logits.size(-1)),
+                batch["slot_labels"].view(-1),
+                ignore_index=IGNORE_INDEX,
+            )
+            loss = intent_loss + slot_loss
+            loss.backward()
+            optimizer.step()
+            epoch_loss += float(loss.item())
+
+        metrics = search_best_threshold(model, eval_loader, device, intent_labels, slot_labels)
+        metrics["train_loss"] = round(epoch_loss / max(len(train_loader), 1), 4)
+        print(json.dumps({"epoch": epoch + 1, **metrics}, ensure_ascii=False))
+        if best_metrics is None or metrics["intent_micro_f1"] > best_metrics["intent_micro_f1"]:
+            best_metrics = metrics
+            best_state = {key: value.detach().cpu() for key, value in model.state_dict().items()}
+
+    if best_state is None or best_metrics is None:
+        raise RuntimeError("joint nlu training did not produce a best checkpoint")
+
+    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+    tokenizer.save_pretrained(OUTPUT_DIR)
+    torch.save(best_state, OUTPUT_DIR / "model_state.pt")
+    config = {
+        "base_model_name": DEFAULT_BASE_MODEL,
+        "intent_task": "multi_label",
+        "intent_labels": intent_labels,
+        "slot_labels": slot_labels,
+        "max_length": MAX_LENGTH,
+        "intent_threshold": float(best_metrics["intent_threshold"]),
+        "multi_intent_threshold": float(best_metrics["intent_threshold"]),
+        "max_multi_intents": 4,
+    }
+    (OUTPUT_DIR / "joint_nlu_config.json").write_text(json.dumps(config, ensure_ascii=False, indent=2), encoding="utf-8")
+    (OUTPUT_DIR / "train_summary.json").write_text(
+        json.dumps(
+            {
+                "train_size": len(train_samples),
+                "eval_size": len(eval_samples),
+                "metrics": best_metrics,
+            },
+            ensure_ascii=False,
+            indent=2,
+        ),
+        encoding="utf-8",
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/intelligent_cabin/archive/scripts/train_local_bert_intent.py b/intelligent_cabin/archive/scripts/train_local_bert_intent.py
new file mode 100644
index 0000000..d0398e1
--- /dev/null
+++ b/intelligent_cabin/archive/scripts/train_local_bert_intent.py
@@ -0,0 +1,684 @@
+from __future__ import annotations
+
+import json
+import os
+import random
+from dataclasses import dataclass
+from pathlib import Path
+
+import torch
+from torch.utils.data import DataLoader, Dataset
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import yaml
+
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+TRAIN_PATH = PROJECT_ROOT / "app/data/bert_intent_train.jsonl"
+DOMAIN_PATH = PROJECT_ROOT / "config/domain.yml"
+OUTPUT_DIR = PROJECT_ROOT / "models/local_bert_intent"
+DEFAULT_BASE_MODEL = "hfl/chinese-macbert-base"
+MAX_LENGTH = 48
+BATCH_SIZE = 8
+EPOCHS = 16
+LEARNING_RATE = 2e-5
+SEED = 42
+
+ORDER_IDS = ["A123456", "A700001", "A800002", "A900005", "A202501", "A808001"]
+DESTINATIONS = ["公司停车场", "浦东机场", "徐家汇", "虹桥机场", "最近的充电站", "南京东路"]
+TEMPERATURES = [18, 20, 21, 22, 23, 24, 26]
+SONGS = ["夜曲", "稻香", "青花瓷", "晴天", "告白气球"]
+GENRES = ["轻音乐", "摇滚", "古典音乐", "民谣", "爵士"]
+SOCIAL_LABEL = "__social__"
+OUT_OF_SCOPE_LABEL = "__out_of_scope__"
+
+TEMPLATES: dict[str, list[str]] = {
+    "cs_query_order": [
+        "查一下订单{order_id}现在什么状态",
+        "我的订单{order_id}到哪一步了",
+        "帮我看看{order_id}这个订单",
+        "确认下{order_id}订单状态",
+        "订单{order_id}现在处理到哪里",
+        "看下{order_id}这单进度",
+        "订单号{order_id}目前怎么样",
+        "帮忙确认订单{order_id}",
+        "订单{order_id}有结果了吗",
+        "帮我追一下订单{order_id}",
+        "订单{order_id}现在受理了吗",
+        "看看{order_id}这单现在啥情况",
+        "帮我查查{order_id}订单进展",
+        "{order_id}这个订单处理好了没",
+        "{order_id}这笔订单现在进展到哪了",
+    ],
+    "cs_query_logistics": [
+        "快递{order_id}到哪儿了",
+        "帮我查{order_id}物流进度",
+        "看看{order_id}配送状态",
+        "订单{order_id}物流更新了吗",
+        "查询{order_id}的快递信息",
+        "我的{order_id}现在派送到哪了",
+        "查一下{order_id}这单物流",
+        "配送单{order_id}走到哪里了",
+        "帮我看下{order_id}快递到没到",
+        "物流单号{order_id}现在在哪",
+        "订单{order_id}物流到哪一步了",
+        "{order_id}这单现在派件了吗",
+        "帮我追踪{order_id}运输轨迹",
+        "{order_id}快件现在运到哪里了",
+        "我想看{order_id}的配送更新",
+    ],
+    "cs_cancel_order": [
+        "帮我取消{order_id}这个订单",
+        "{order_id}别要了给我撤销",
+        "把订单{order_id}取消掉",
+        "我不要{order_id}了",
+        "撤销一下{order_id}订单",
+        "订单{order_id}不要发了",
+        "帮我把{order_id}退掉并取消",
+        "把{order_id}这一单停掉",
+        "{order_id}这单直接取消",
+        "订单号{order_id}撤回一下",
+        "订单{order_id}我不想要了",
+        "{order_id}这笔订单先别发了",
+        "把{order_id}这单给我撤单",
+        "订单{order_id}停掉吧",
+        "{order_id}这个快给我取消了",
+    ],
+    "cs_transfer_human": [
+        "我要找人工客服处理",
+        "现在转人工",
+        "麻烦给我接人工服务",
+        "帮我呼叫真人客服",
+        "别机器人了我要人工",
+        "转真人客服",
+        "我要人工坐席",
+        "帮我接人工处理",
+        "叫人工客服来",
+        "直接给我转人工",
+        "这个问题给我人工跟进",
+        "安排真人客服接手",
+        "机器人处理不了，转人工",
+        "帮我叫个客服专员",
+        "我要人工来处理这事",
+    ],
+    "cabin_nav_to": [
+        "导航到{destination}",
+        "带我去{destination}",
+        "我要去{destination}",
+        "去{destination}",
+        "开导航去{destination}",
+        "帮我导航到{destination}",
+        "送我去{destination}",
+        "现在去{destination}",
+        "带路到{destination}",
+        "去一下{destination}",
+        "规划路线去{destination}",
+        "直接开去{destination}",
+        "给我导到{destination}",
+        "{destination}怎么走，导航一下",
+        "出发去{destination}",
+    ],
+    "cabin_set_ac": [
+        "把空调设到{temperature}度",
+        "车里温度调成{temperature}度",
+        "冷气开到{temperature}度",
+        "空调给我调到{temperature}度",
+        "温度改成{temperature}度",
+        "车内设成{temperature}度",
+        "把温度打到{temperature}度",
+        "空调调为{temperature}度",
+        "帮我把车里调成{temperature}度",
+        "冷风调到{temperature}度",
+        "把车内温度设为{temperature}度",
+        "空调温度改到{temperature}度",
+        "冷气帮我调到{temperature}度",
+        "舱内调成{temperature}度",
+        "给我把温度定在{temperature}度",
+        "把车里弄凉快点",
+        "车里太热了，降一点",
+        "把里面调凉快一点",
+        "有点热，降温",
+        "空调再冷一点",
+        "车内温度低一点",
+        "把里面弄暖和点",
+        "车里太冷了，升一点温度",
+    ],
+    "cabin_ac_on": [
+        "把空调打开",
+        "开一下冷气",
+        "把冷风开起来",
+        "车里热，空调开开",
+        "打开制冷",
+        "空调启动一下",
+    ],
+    "cabin_window_open": [
+        "把车窗打开",
+        "开下窗",
+        "窗户开一点",
+        "帮我透透气",
+        "车里太闷了，开下窗",
+        "顺便开下车窗",
+        "把窗户降一点",
+        "把玻璃打开一点",
+    ],
+    "cabin_window_close": [
+        "把车窗关上",
+        "窗户关一下",
+        "把窗升起来",
+        "外面太吵了，把窗关了",
+        "把窗户关严",
+    ],
+    "cabin_fan_down": [
+        "风别这么大",
+        "风小一点",
+        "别吹这么猛",
+        "把风量调小一点",
+        "出风弱一点",
+    ],
+    "cabin_fan_up": [
+        "风再大一点",
+        "把风量开大点",
+        "出风强一点",
+        "风不够，调大些",
+    ],
+    "cabin_defog_front_on": [
+        "前挡起雾了，除一下",
+        "把前挡风玻璃雾气清掉",
+        "前窗看不清了，开除雾",
+    ],
+    "cabin_defog_rear_on": [
+        "后挡有雾，开下除雾",
+        "后玻璃起雾了，清一下",
+        "后窗看不清了，除雾",
+    ],
+    "cabin_play_music": [
+        "播放一首{genre}",
+        "来点{genre}",
+        "我想听{genre}",
+        "给我播点{genre}",
+        "放一首{song}",
+        "来一首{song}",
+        "播放{song}",
+        "放点音乐，来个{genre}",
+        "我想听首{song}",
+        "给我来点歌，放{song}",
+        "随机放点{genre}",
+        "帮我播首{song}",
+        "来点适合开车听的{genre}",
+        "打开音乐，放{song}",
+        "给我放一些{genre}",
+        "放点歌",
+        "来首歌",
+        "整点音乐",
+        "车里放点歌",
+        "来点能听的",
+    ],
+    SOCIAL_LABEL: [
+        "你好",
+        "嗨",
+        "哈喽",
+        "早上好",
+        "晚上好",
+        "你叫什么名字",
+        "你是谁",
+        "你能做什么",
+        "今天天气不错",
+        "陪我聊聊天",
+    ],
+    OUT_OF_SCOPE_LABEL: [
+        "帮我点个外卖",
+        "订一张去北京的机票",
+        "帮我买杯咖啡",
+        "给我订一家酒店",
+        "人类诞生的意义是什么",
+        "帮我写一份年终总结",
+        "推荐一部电影",
+        "讲个笑话",
+        "帮我做一道数学题",
+        "去美团叫个外卖",
+    ],
+}
+
+INTENT_REPLACEMENTS: dict[str, list[tuple[str, str]]] = {
+    "cs_query_order": [
+        ("订单", "这单"),
+        ("查一下", "看一下"),
+        ("帮我", "麻烦帮我"),
+        ("现在什么状态", "现在啥状态"),
+        ("处理到哪里", "进展到哪里"),
+    ],
+    "cs_query_logistics": [
+        ("物流", "快递"),
+        ("快递", "配送"),
+        ("配送", "派送"),
+        ("帮我", "麻烦帮我"),
+        ("现在在哪", "现在到哪了"),
+    ],
+    "cs_cancel_order": [
+        ("取消", "撤销"),
+        ("撤销", "撤单"),
+        ("订单", "这单"),
+        ("帮我", "麻烦帮我"),
+        ("不要发了", "别发了"),
+    ],
+    "cs_transfer_human": [
+        ("人工客服", "真人客服"),
+        ("人工", "人工坐席"),
+        ("帮我", "麻烦帮我"),
+    ],
+    "cabin_nav_to": [
+        ("导航", "带路"),
+        ("带我", "送我"),
+        ("去", "前往"),
+    ],
+    "cabin_set_ac": [
+        ("空调", "车里温度"),
+        ("调到", "设到"),
+        ("温度", "车内温度"),
+        ("凉快点", "冷一点"),
+        ("暖和点", "热一点"),
+    ],
+    "cabin_ac_on": [
+        ("空调", "冷气"),
+        ("打开", "开"),
+        ("冷风", "制冷"),
+    ],
+    "cabin_window_open": [
+        ("车窗", "窗户"),
+        ("打开", "开"),
+        ("透透气", "通通风"),
+    ],
+    "cabin_window_close": [
+        ("关上", "关掉"),
+        ("车窗", "窗户"),
+        ("关严", "关好"),
+    ],
+    "cabin_fan_down": [
+        ("风量", "风"),
+        ("调小", "调低"),
+        ("别吹这么猛", "风小一点"),
+    ],
+    "cabin_fan_up": [
+        ("风量", "风"),
+        ("调大", "调高"),
+    ],
+    "cabin_defog_front_on": [
+        ("前挡", "前窗"),
+        ("除雾", "除一下"),
+    ],
+    "cabin_defog_rear_on": [
+        ("后挡", "后窗"),
+        ("除雾", "清一下雾"),
+    ],
+    "cabin_play_music": [
+        ("播放", "放"),
+        ("来点", "播点"),
+        ("我想听", "给我来点"),
+        ("放点歌", "来首歌"),
+    ],
+    SOCIAL_LABEL: [
+        ("你好", "您好"),
+        ("哈喽", "hello"),
+        ("你叫什么名字", "怎么称呼你"),
+    ],
+    OUT_OF_SCOPE_LABEL: [
+        ("点个外卖", "叫个外卖"),
+        ("订一家酒店", "订个酒店"),
+        ("讲个笑话", "说个笑话"),
+    ],
+}
+
+
+@dataclass
+class Sample:
+    text: str
+    intent_id: str
+
+
+HARD_NEGATIVE_RAW_SAMPLES: list[tuple[str, str]] = [
+    ("订单A700101物流到哪了", "cs_query_logistics"),
+    ("帮我看下订单A700102配送到哪里了", "cs_query_logistics"),
+    ("订单A700103现在派件了吗", "cs_query_logistics"),
+    ("A700104这单物流有没有更新", "cs_query_logistics"),
+    ("查一下订单A700105运输轨迹", "cs_query_logistics"),
+    ("订单A700106不要了，帮我撤单", "cs_cancel_order"),
+    ("A700107这单别发了，直接取消", "cs_cancel_order"),
+    ("把订单A700108停掉吧", "cs_cancel_order"),
+    ("A700109这个订单我不想要了", "cs_cancel_order"),
+    ("订单A700110给我撤回", "cs_cancel_order"),
+    ("订单A700111现在受理了吗", "cs_query_order"),
+    ("帮我看看A700112这单处理得怎么样了", "cs_query_order"),
+    ("A700113订单目前进展如何", "cs_query_order"),
+    ("查下订单A700114现在什么情况", "cs_query_order"),
+    ("帮我确认订单A700115是否已经处理", "cs_query_order"),
+    ("你好呀", SOCIAL_LABEL),
+    ("嗨，在吗", SOCIAL_LABEL),
+    ("今天天气真不错", SOCIAL_LABEL),
+    ("你叫什么名字呀", SOCIAL_LABEL),
+    ("你是做什么的", SOCIAL_LABEL),
+    ("陪我随便聊聊", SOCIAL_LABEL),
+    ("帮我透透气", "cabin_window_open"),
+    ("车里太闷了，开下窗", "cabin_window_open"),
+    ("把窗户降一点", "cabin_window_open"),
+    ("前挡起雾了，除一下", "cabin_defog_front_on"),
+    ("后挡有雾，开下除雾", "cabin_defog_rear_on"),
+    ("把车里弄凉快点", "cabin_set_ac"),
+    ("车里太热了，降一点", "cabin_set_ac"),
+    ("空调再冷一点", "cabin_set_ac"),
+    ("把里面弄暖和点", "cabin_set_ac"),
+    ("风别这么大", "cabin_fan_down"),
+    ("别吹这么猛", "cabin_fan_down"),
+    ("风再大一点", "cabin_fan_up"),
+    ("放点歌", "cabin_play_music"),
+    ("来首歌", "cabin_play_music"),
+    ("整点音乐", "cabin_play_music"),
+    ("透透气", "cabin_window_open"),
+    ("通通风", "cabin_window_open"),
+    ("车里太闷了", "cabin_window_open"),
+    ("凉快点", "cabin_set_ac"),
+    ("暖和点", "cabin_set_ac"),
+    ("帮我点一份麻辣烫", OUT_OF_SCOPE_LABEL),
+    ("给我订今晚的酒店", OUT_OF_SCOPE_LABEL),
+    ("帮我买张电影票", OUT_OF_SCOPE_LABEL),
+    ("人为什么会做梦", OUT_OF_SCOPE_LABEL),
+    ("帮我做个旅游攻略", OUT_OF_SCOPE_LABEL),
+    ("帮我点肯德基外卖", OUT_OF_SCOPE_LABEL),
+    ("透透气，别给我除雾", "cabin_window_open"),
+    ("后挡有雾，不是开窗，是除雾", "cabin_defog_rear_on"),
+    ("前挡看不清了，除雾不要开窗", "cabin_defog_front_on"),
+    ("凉快点，不是把风量调小", "cabin_set_ac"),
+    ("别吹这么猛，不是降温", "cabin_fan_down"),
+    ("来首歌，不是切下一首", "cabin_play_music"),
+    ("放点歌，不是暂停音乐", "cabin_play_music"),
+]
+
+HARD_NEGATIVE_SAMPLES: list[Sample] = [
+    Sample(text=text, intent_id=intent_id) for text, intent_id in HARD_NEGATIVE_RAW_SAMPLES
+]
+
+
+class IntentDataset(Dataset):
+    def __init__(self, samples: list[Sample], tokenizer, label_to_id: dict[str, int]) -> None:
+        self._samples = samples
+        self._tokenizer = tokenizer
+        self._label_to_id = label_to_id
+
+    def __len__(self) -> int:
+        return len(self._samples)
+
+    def __getitem__(self, index: int) -> dict[str, torch.Tensor]:
+        sample = self._samples[index]
+        encoded = self._tokenizer(
+            sample.text,
+            truncation=True,
+            padding="max_length",
+            max_length=MAX_LENGTH,
+            return_tensors="pt",
+        )
+        return {
+            "input_ids": encoded["input_ids"].squeeze(0),
+            "attention_mask": encoded["attention_mask"].squeeze(0),
+            "labels": torch.tensor(self._label_to_id[sample.intent_id], dtype=torch.long),
+        }
+
+
+def set_seed(seed: int) -> None:
+    random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.backends.mps.is_available():
+        torch.mps.manual_seed(seed)
+
+
+def load_samples(file_path: Path) -> list[Sample]:
+    samples: list[Sample] = []
+    if not file_path.exists():
+        return samples
+    for line in file_path.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line:
+            continue
+        payload = json.loads(line)
+        samples.append(Sample(text=str(payload["text"]), intent_id=str(payload["intent_id"])))
+    return samples
+
+
+def load_domain_samples(file_path: Path) -> list[Sample]:
+    if not file_path.exists():
+        return []
+    payload = yaml.safe_load(file_path.read_text(encoding="utf-8")) or {}
+    intents = payload.get("intents", [])
+    samples: list[Sample] = []
+    seen: set[tuple[str, str]] = set()
+    for item in intents:
+        intent_id = str(item.get("intent_id") or "").strip()
+        if not intent_id:
+            continue
+        seed_texts = list(item.get("examples") or [])
+        seed_texts.extend(item.get("keywords") or [])
+        label = str(item.get("label") or "").strip()
+        if label:
+            seed_texts.append(label)
+        for text in seed_texts:
+            normalized = str(text).strip()
+            if not normalized:
+                continue
+            for variant in expand_seed_variants(normalized):
+                key = (variant, intent_id)
+                if key in seen:
+                    continue
+                seen.add(key)
+                samples.append(Sample(text=variant, intent_id=intent_id))
+    return samples
+
+
+def expand_seed_variants(text: str) -> list[str]:
+    normalized = text.strip().strip("，。！？；; ")
+    if not normalized:
+        return []
+    variants = {
+        normalized,
+        normalized.replace("一下", "").strip(),
+        normalized.replace("帮我", "").strip(),
+        normalized.replace("请", "").strip(),
+        f"帮我{normalized}",
+        f"请{normalized}",
+        f"{normalized}一下",
+    }
+    cleaned: list[str] = []
+    for item in variants:
+        compact = " ".join(item.split()).strip("，。！？；; ")
+        if compact:
+            cleaned.append(compact)
+    return cleaned
+
+
+def load_training_samples() -> list[Sample]:
+    samples = load_samples(TRAIN_PATH)
+    samples.extend(load_domain_samples(DOMAIN_PATH))
+    deduped: list[Sample] = []
+    seen: set[tuple[str, str]] = set()
+    for sample in samples:
+        key = (sample.text, sample.intent_id)
+        if key in seen:
+            continue
+        seen.add(key)
+        deduped.append(sample)
+    return deduped
+
+
+def augment_samples(samples: list[Sample]) -> list[Sample]:
+    augmented = list(samples)
+    seen = {(sample.text, sample.intent_id) for sample in augmented}
+    for intent_id, templates in TEMPLATES.items():
+        for index, template in enumerate(templates):
+            sample = render_template(intent_id, template, index)
+            key = (sample.text, sample.intent_id)
+            if key not in seen:
+                augmented.append(sample)
+                seen.add(key)
+
+    for sample in HARD_NEGATIVE_SAMPLES:
+        key = (sample.text, sample.intent_id)
+        if key not in seen:
+            augmented.append(sample)
+            seen.add(key)
+
+    for sample in list(augmented):
+        text = sample.text
+        for source, target in INTENT_REPLACEMENTS.get(sample.intent_id, []):
+            if source in text:
+                variant = text.replace(source, target, 1)
+                key = (variant, sample.intent_id)
+                if key not in seen:
+                    augmented.append(Sample(text=variant, intent_id=sample.intent_id))
+                    seen.add(key)
+
+        compact = text
+        for token in ("帮我", "麻烦", "请", "一下"):
+            if token in compact:
+                compact = compact.replace(token, "", 1)
+        compact = compact.strip(" ，。！？")
+        if compact and compact != text:
+            key = (compact, sample.intent_id)
+            if key not in seen:
+                augmented.append(Sample(text=compact, intent_id=sample.intent_id))
+                seen.add(key)
+    random.shuffle(augmented)
+    return augmented
+
+
+def render_template(intent_id: str, template: str, index: int) -> Sample:
+    order_id = ORDER_IDS[index % len(ORDER_IDS)]
+    destination = DESTINATIONS[index % len(DESTINATIONS)]
+    temperature = TEMPERATURES[index % len(TEMPERATURES)]
+    song = SONGS[index % len(SONGS)]
+    genre = GENRES[index % len(GENRES)]
+    text = template.format(
+        order_id=order_id,
+        destination=destination,
+        temperature=temperature,
+        song=song,
+        genre=genre,
+    )
+    return Sample(text=text, intent_id=intent_id)
+
+
+def split_samples(samples: list[Sample]) -> tuple[list[Sample], list[Sample]]:
+    grouped: dict[str, list[Sample]] = {}
+    for sample in samples:
+        grouped.setdefault(sample.intent_id, []).append(sample)
+    train_samples: list[Sample] = []
+    dev_samples: list[Sample] = []
+    for items in grouped.values():
+        random.shuffle(items)
+        cut = max(1, int(len(items) * 0.8))
+        train_samples.extend(items[:cut])
+        dev_samples.extend(items[cut:])
+    random.shuffle(train_samples)
+    random.shuffle(dev_samples)
+    return train_samples, dev_samples
+
+
+def accuracy(model, loader: DataLoader, device: torch.device) -> float:
+    model.eval()
+    correct = 0
+    total = 0
+    with torch.no_grad():
+        for batch in loader:
+            input_ids = batch["input_ids"].to(device)
+            attention_mask = batch["attention_mask"].to(device)
+            labels = batch["labels"].to(device)
+            outputs = model(input_ids=input_ids, attention_mask=attention_mask)
+            preds = outputs.logits.argmax(dim=-1)
+            correct += int((preds == labels).sum().item())
+            total += int(labels.numel())
+    return correct / total if total else 0.0
+
+
+def resolve_base_model() -> str:
+    configured = os.getenv("AGENT_BERT_BASE_MODEL", "").strip()
+    if configured:
+        return configured
+    return DEFAULT_BASE_MODEL
+
+
+def main() -> None:
+    set_seed(SEED)
+    samples = augment_samples(load_training_samples())
+    intents = sorted({sample.intent_id for sample in samples})
+    label_to_id = {intent_id: index for index, intent_id in enumerate(intents)}
+    id_to_label = {index: intent_id for intent_id, index in label_to_id.items()}
+
+    train_samples, dev_samples = split_samples(samples)
+    base_model = resolve_base_model()
+    tokenizer = AutoTokenizer.from_pretrained(base_model)
+    train_dataset = IntentDataset(train_samples, tokenizer, label_to_id)
+    dev_dataset = IntentDataset(dev_samples, tokenizer, label_to_id)
+
+    train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
+    dev_loader = DataLoader(dev_dataset, batch_size=BATCH_SIZE)
+
+    model = AutoModelForSequenceClassification.from_pretrained(
+        base_model,
+        num_labels=len(intents),
+        id2label=id_to_label,
+        label2id=label_to_id,
+    )
+
+    device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
+    model.to(device)
+    optimizer = torch.optim.AdamW(model.parameters(), lr=LEARNING_RATE)
+
+    best_dev_acc = 0.0
+    best_state = None
+
+    for epoch in range(1, EPOCHS + 1):
+        model.train()
+        total_loss = 0.0
+        for batch in train_loader:
+            optimizer.zero_grad()
+            input_ids = batch["input_ids"].to(device)
+            attention_mask = batch["attention_mask"].to(device)
+            labels = batch["labels"].to(device)
+            outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
+            loss = outputs.loss
+            loss.backward()
+            optimizer.step()
+            total_loss += float(loss.item())
+
+        dev_acc = accuracy(model, dev_loader, device)
+        avg_loss = total_loss / max(len(train_loader), 1)
+        print(f"epoch={epoch} loss={avg_loss:.4f} dev_acc={dev_acc:.4f}")
+        if dev_acc >= best_dev_acc:
+            best_dev_acc = dev_acc
+            best_state = {key: value.detach().cpu().clone() for key, value in model.state_dict().items()}
+
+    if best_state is not None:
+        model.load_state_dict(best_state)
+
+    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+    model.save_pretrained(OUTPUT_DIR)
+    tokenizer.save_pretrained(OUTPUT_DIR)
+    label_map = {f"LABEL_{index}": intent_id for index, intent_id in id_to_label.items()}
+    (OUTPUT_DIR / "label_map.json").write_text(
+        json.dumps(label_map, ensure_ascii=False, indent=2),
+        encoding="utf-8",
+    )
+    train_summary = {
+        "base_model": base_model,
+        "epochs": EPOCHS,
+        "batch_size": BATCH_SIZE,
+        "learning_rate": LEARNING_RATE,
+        "train_size": len(train_samples),
+        "dev_size": len(dev_samples),
+        "best_dev_accuracy": round(best_dev_acc, 4),
+        "device": str(device),
+    }
+    (OUTPUT_DIR / "train_summary.json").write_text(
+        json.dumps(train_summary, ensure_ascii=False, indent=2),
+        encoding="utf-8",
+    )
+    print(json.dumps(train_summary, ensure_ascii=False, indent=2))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/intelligent_cabin/archive/scripts/train_local_bert_multi_intent.py b/intelligent_cabin/archive/scripts/train_local_bert_multi_intent.py
new file mode 100644
index 0000000..4fb1d1f
--- /dev/null
+++ b/intelligent_cabin/archive/scripts/train_local_bert_multi_intent.py
@@ -0,0 +1,415 @@
+from __future__ import annotations
+
+import json
+import os
+import random
+from dataclasses import dataclass
+from pathlib import Path
+
+import torch
+from torch.utils.data import DataLoader, Dataset
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import yaml
+
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+SINGLE_LABEL_PATH = PROJECT_ROOT / "app/data/bert_intent_train.jsonl"
+MULTI_LABEL_PATH = PROJECT_ROOT / "app/data/bert_intent_multilabel_train.jsonl"
+DOMAIN_PATH = PROJECT_ROOT / "config/domain.yml"
+OUTPUT_DIR = PROJECT_ROOT / "models/local_bert_multi_intent"
+DEFAULT_BASE_MODEL = "hfl/chinese-macbert-base"
+SOCIAL_LABEL = "__social__"
+OUT_OF_SCOPE_LABEL = "__out_of_scope__"
+BLOCKED_LABELS = {SOCIAL_LABEL, OUT_OF_SCOPE_LABEL}
+MAX_LENGTH = 48
+BATCH_SIZE = 8
+EPOCHS = 12
+LEARNING_RATE = 2e-5
+THRESHOLD = 0.5
+TOP_K = 4
+SEED = 42
+
+CONNECTOR_VARIANTS: tuple[tuple[str, str], ...] = (
+    ("并", "然后"),
+    ("然后", "并"),
+    ("顺便", "再"),
+    ("再", "顺便"),
+)
+
+
+@dataclass(frozen=True)
+class MultiLabelSample:
+    text: str
+    intent_ids: tuple[str, ...]
+
+
+class MultiLabelIntentDataset(Dataset):
+    def __init__(
+        self,
+        samples: list[MultiLabelSample],
+        tokenizer,
+        label_to_id: dict[str, int],
+    ) -> None:
+        self._samples = samples
+        self._tokenizer = tokenizer
+        self._label_to_id = label_to_id
+        self._label_size = len(label_to_id)
+
+    def __len__(self) -> int:
+        return len(self._samples)
+
+    def __getitem__(self, index: int) -> dict[str, torch.Tensor]:
+        sample = self._samples[index]
+        encoded = self._tokenizer(
+            sample.text,
+            truncation=True,
+            padding="max_length",
+            max_length=MAX_LENGTH,
+            return_tensors="pt",
+        )
+        labels = torch.zeros(self._label_size, dtype=torch.float32)
+        for intent_id in sample.intent_ids:
+            labels[self._label_to_id[intent_id]] = 1.0
+        return {
+            "input_ids": encoded["input_ids"].squeeze(0),
+            "attention_mask": encoded["attention_mask"].squeeze(0),
+            "labels": labels,
+        }
+
+
+def set_seed(seed: int) -> None:
+    random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.backends.mps.is_available():
+        torch.mps.manual_seed(seed)
+
+
+def resolve_base_model() -> str:
+    configured = os.getenv("AGENT_BERT_BASE_MODEL", "").strip()
+    if configured:
+        return configured
+    return DEFAULT_BASE_MODEL
+
+
+def normalize_text(text: str) -> str:
+    return " ".join(str(text).strip().split())
+
+
+def normalize_intent_ids(intent_ids: list[str] | tuple[str, ...]) -> tuple[str, ...]:
+    cleaned = sorted(
+        {
+            str(intent_id).strip()
+            for intent_id in intent_ids
+            if str(intent_id).strip() and str(intent_id).strip() not in BLOCKED_LABELS
+        }
+    )
+    return tuple(cleaned)
+
+
+def expand_single_label_variants(text: str) -> list[str]:
+    normalized = text.strip().strip("，。！？；; ")
+    if not normalized:
+        return []
+    variants = {
+        normalized,
+        normalized.replace("一下", "").strip(),
+        normalized.replace("帮我", "").strip(),
+        normalized.replace("请", "").strip(),
+        f"帮我{normalized}",
+        f"请{normalized}",
+        f"{normalized}一下",
+    }
+    cleaned: list[str] = []
+    for item in variants:
+        compact = " ".join(item.split()).strip("，。！？；; ")
+        if compact:
+            cleaned.append(compact)
+    return cleaned
+
+
+def load_single_label_samples(file_path: Path) -> list[MultiLabelSample]:
+    samples: list[MultiLabelSample] = []
+    if not file_path.exists():
+        return samples
+    for line in file_path.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line:
+            continue
+        payload = json.loads(line)
+        intent_ids = normalize_intent_ids([str(payload.get("intent_id") or "")])
+        if not intent_ids:
+            continue
+        text = normalize_text(str(payload.get("text") or ""))
+        if not text:
+            continue
+        samples.append(MultiLabelSample(text=text, intent_ids=intent_ids))
+    return samples
+
+
+def load_domain_samples(file_path: Path) -> list[MultiLabelSample]:
+    if not file_path.exists():
+        return []
+    payload = yaml.safe_load(file_path.read_text(encoding="utf-8")) or {}
+    intents = payload.get("intents", [])
+    samples: list[MultiLabelSample] = []
+    seen: set[tuple[str, tuple[str, ...]]] = set()
+    for item in intents:
+        intent_ids = normalize_intent_ids([str(item.get("intent_id") or "")])
+        if not intent_ids:
+            continue
+        seed_texts = list(item.get("examples") or [])
+        seed_texts.extend(item.get("keywords") or [])
+        label = str(item.get("label") or "").strip()
+        if label:
+            seed_texts.append(label)
+        for text in seed_texts:
+            normalized = normalize_text(text)
+            if not normalized:
+                continue
+            for variant in expand_single_label_variants(normalized):
+                key = (variant, intent_ids)
+                if key in seen:
+                    continue
+                seen.add(key)
+                samples.append(MultiLabelSample(text=variant, intent_ids=intent_ids))
+    return samples
+
+
+def load_multilabel_samples(file_path: Path) -> list[MultiLabelSample]:
+    samples: list[MultiLabelSample] = []
+    if not file_path.exists():
+        return samples
+    for line in file_path.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line:
+            continue
+        payload = json.loads(line)
+        intent_ids = normalize_intent_ids(list(payload.get("intent_ids") or []))
+        if len(intent_ids) < 2:
+            continue
+        text = normalize_text(str(payload.get("text") or ""))
+        if not text:
+            continue
+        samples.append(MultiLabelSample(text=text, intent_ids=intent_ids))
+    return samples
+
+
+def augment_multilabel_samples(samples: list[MultiLabelSample]) -> list[MultiLabelSample]:
+    augmented = list(samples)
+    seen = {(sample.text, sample.intent_ids) for sample in augmented}
+    for sample in list(samples):
+        variants = {
+            sample.text,
+            f"帮我{sample.text}",
+            f"请{sample.text}",
+            sample.text.replace("，", ", "),
+            sample.text.replace("，", ""),
+        }
+        for source, target in CONNECTOR_VARIANTS:
+            if source in sample.text:
+                variants.add(sample.text.replace(source, target, 1))
+        for variant in variants:
+            normalized = normalize_text(variant).strip("，。！？；; ")
+            key = (normalized, sample.intent_ids)
+            if normalized and key not in seen:
+                augmented.append(MultiLabelSample(text=normalized, intent_ids=sample.intent_ids))
+                seen.add(key)
+    return augmented
+
+
+def load_all_samples() -> list[MultiLabelSample]:
+    samples = load_single_label_samples(SINGLE_LABEL_PATH)
+    samples.extend(load_domain_samples(DOMAIN_PATH))
+    samples.extend(augment_multilabel_samples(load_multilabel_samples(MULTI_LABEL_PATH)))
+    deduped: list[MultiLabelSample] = []
+    seen: set[tuple[str, tuple[str, ...]]] = set()
+    for sample in samples:
+        key = (sample.text, sample.intent_ids)
+        if key in seen:
+            continue
+        seen.add(key)
+        deduped.append(sample)
+    random.shuffle(deduped)
+    return deduped
+
+
+def split_samples(samples: list[MultiLabelSample]) -> tuple[list[MultiLabelSample], list[MultiLabelSample]]:
+    grouped: dict[tuple[str, ...], list[MultiLabelSample]] = {}
+    for sample in samples:
+        grouped.setdefault(sample.intent_ids, []).append(sample)
+    train_samples: list[MultiLabelSample] = []
+    dev_samples: list[MultiLabelSample] = []
+    for items in grouped.values():
+        random.shuffle(items)
+        if len(items) == 1:
+            train_samples.extend(items)
+            continue
+        cut = max(1, int(len(items) * 0.8))
+        if cut >= len(items):
+            cut = len(items) - 1
+        train_samples.extend(items[:cut])
+        dev_samples.extend(items[cut:])
+    if not dev_samples:
+        dev_samples = train_samples[-max(1, min(32, len(train_samples) // 5 or 1)) :]
+        train_samples = train_samples[: len(train_samples) - len(dev_samples)]
+    random.shuffle(train_samples)
+    random.shuffle(dev_samples)
+    return train_samples, dev_samples
+
+
+def logits_to_probabilities(logits: torch.Tensor) -> list[list[float]]:
+    return torch.sigmoid(logits).detach().cpu().tolist()
+
+
+def compute_metrics(
+    probabilities: list[list[float]],
+    targets: list[list[float]],
+    threshold: float,
+    top_k: int,
+) -> dict[str, float]:
+    true_positive = 0
+    false_positive = 0
+    false_negative = 0
+    exact_match = 0
+    recall_at_k_total = 0.0
+    total = len(probabilities)
+    for scores, target in zip(probabilities, targets):
+        predicted = {index for index, score in enumerate(scores) if score >= threshold}
+        expected = {index for index, value in enumerate(target) if value >= 0.5}
+        if predicted == expected:
+            exact_match += 1
+        true_positive += len(predicted & expected)
+        false_positive += len(predicted - expected)
+        false_negative += len(expected - predicted)
+        top_indices = sorted(range(len(scores)), key=lambda index: scores[index], reverse=True)[:top_k]
+        if expected:
+            recall_at_k_total += len(set(top_indices) & expected) / len(expected)
+    precision = true_positive / (true_positive + false_positive) if (true_positive + false_positive) else 0.0
+    recall = true_positive / (true_positive + false_negative) if (true_positive + false_negative) else 0.0
+    micro_f1 = (2 * precision * recall / (precision + recall)) if (precision + recall) else 0.0
+    return {
+        "micro_precision": round(precision, 4),
+        "micro_recall": round(recall, 4),
+        "micro_f1": round(micro_f1, 4),
+        "exact_match": round(exact_match / total, 4) if total else 0.0,
+        "recall_at_k": round(recall_at_k_total / total, 4) if total else 0.0,
+    }
+
+
+def evaluate(model, loader: DataLoader, device: torch.device, threshold: float, top_k: int) -> tuple[float, dict[str, float]]:
+    model.eval()
+    total_loss = 0.0
+    probabilities: list[list[float]] = []
+    targets: list[list[float]] = []
+    with torch.no_grad():
+        for batch in loader:
+            input_ids = batch["input_ids"].to(device)
+            attention_mask = batch["attention_mask"].to(device)
+            labels = batch["labels"].to(device)
+            outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
+            total_loss += float(outputs.loss.item())
+            probabilities.extend(logits_to_probabilities(outputs.logits))
+            targets.extend(labels.detach().cpu().tolist())
+    avg_loss = total_loss / max(len(loader), 1)
+    return avg_loss, compute_metrics(probabilities, targets, threshold=threshold, top_k=top_k)
+
+
+def main() -> None:
+    set_seed(SEED)
+    samples = load_all_samples()
+    intents = sorted({intent_id for sample in samples for intent_id in sample.intent_ids})
+    label_to_id = {intent_id: index for index, intent_id in enumerate(intents)}
+    id_to_label = {index: intent_id for intent_id, index in label_to_id.items()}
+
+    train_samples, dev_samples = split_samples(samples)
+    base_model = resolve_base_model()
+    tokenizer = AutoTokenizer.from_pretrained(base_model)
+    train_dataset = MultiLabelIntentDataset(train_samples, tokenizer, label_to_id)
+    dev_dataset = MultiLabelIntentDataset(dev_samples, tokenizer, label_to_id)
+
+    train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
+    dev_loader = DataLoader(dev_dataset, batch_size=BATCH_SIZE)
+
+    model = AutoModelForSequenceClassification.from_pretrained(
+        base_model,
+        num_labels=len(intents),
+        id2label=id_to_label,
+        label2id=label_to_id,
+        problem_type="multi_label_classification",
+    )
+
+    device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
+    model.to(device)
+    optimizer = torch.optim.AdamW(model.parameters(), lr=LEARNING_RATE)
+
+    best_dev_f1 = 0.0
+    best_state = None
+    best_metrics: dict[str, float] = {}
+
+    for epoch in range(1, EPOCHS + 1):
+        model.train()
+        total_loss = 0.0
+        for batch in train_loader:
+            optimizer.zero_grad()
+            input_ids = batch["input_ids"].to(device)
+            attention_mask = batch["attention_mask"].to(device)
+            labels = batch["labels"].to(device)
+            outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
+            loss = outputs.loss
+            loss.backward()
+            optimizer.step()
+            total_loss += float(loss.item())
+
+        dev_loss, dev_metrics = evaluate(model, dev_loader, device, threshold=THRESHOLD, top_k=TOP_K)
+        avg_loss = total_loss / max(len(train_loader), 1)
+        print(
+            " ".join(
+                [
+                    f"epoch={epoch}",
+                    f"train_loss={avg_loss:.4f}",
+                    f"dev_loss={dev_loss:.4f}",
+                    f"dev_micro_f1={dev_metrics['micro_f1']:.4f}",
+                    f"dev_exact_match={dev_metrics['exact_match']:.4f}",
+                ]
+            )
+        )
+        if dev_metrics["micro_f1"] >= best_dev_f1:
+            best_dev_f1 = dev_metrics["micro_f1"]
+            best_metrics = dict(dev_metrics)
+            best_state = {key: value.detach().cpu().clone() for key, value in model.state_dict().items()}
+
+    if best_state is not None:
+        model.load_state_dict(best_state)
+
+    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+    model.save_pretrained(OUTPUT_DIR)
+    tokenizer.save_pretrained(OUTPUT_DIR)
+    label_map = {f"LABEL_{index}": intent_id for index, intent_id in id_to_label.items()}
+    (OUTPUT_DIR / "label_map.json").write_text(
+        json.dumps(label_map, ensure_ascii=False, indent=2),
+        encoding="utf-8",
+    )
+    train_summary = {
+        "task_type": "multi_label_intent_detection",
+        "base_model": base_model,
+        "epochs": EPOCHS,
+        "batch_size": BATCH_SIZE,
+        "learning_rate": LEARNING_RATE,
+        "threshold": THRESHOLD,
+        "top_k": TOP_K,
+        "train_size": len(train_samples),
+        "dev_size": len(dev_samples),
+        "label_count": len(intents),
+        "labels": intents,
+        "best_dev_metrics": best_metrics,
+        "device": str(device),
+    }
+    (OUTPUT_DIR / "train_summary.json").write_text(
+        json.dumps(train_summary, ensure_ascii=False, indent=2),
+        encoding="utf-8",
+    )
+    print(json.dumps(train_summary, ensure_ascii=False, indent=2))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/intelligent_cabin/archive/tests/test_agent_cloud_route.py b/intelligent_cabin/archive/tests/test_agent_cloud_route.py
new file mode 100644
index 0000000..7e44554
--- /dev/null
+++ b/intelligent_cabin/archive/tests/test_agent_cloud_route.py
@@ -0,0 +1,132 @@
+from __future__ import annotations
+
+import unittest
+
+from app.plugins.base import PluginRegistry
+from app.schemas.chat import ChatRequest
+from app.schemas.debug import IntentCandidate, MatcherStageDebug, RoutingDebug
+from app.schemas.intent import IntentDefinition
+from app.services.agent_service import AgentService
+from app.services.intent_registry import IntentRegistry
+from app.services.planner import PlanningResult
+from app.services.session_store import InMemorySessionStore
+
+
+class _RouteToCloudRouter:
+    def route(self, text: str):
+        _ = text
+        return type(
+            "RouteResult",
+            (),
+            {
+                "intent": None,
+                "debug": RoutingDebug(
+                    selected_intent="cabin_nav_to",
+                    matched_stage="fusion",
+                    decision="route_to_cloud",
+                    decision_reason="local signal is not stable enough, routing to cloud planner",
+                    confidence_grade="low",
+                    stages=[
+                        MatcherStageDebug(
+                            stage="fusion",
+                            accepted=False,
+                            selected_intent="cabin_nav_to",
+                            score=0.88,
+                            reason="route to cloud",
+                            candidates=[
+                                IntentCandidate(intent_id="cabin_nav_to", score=0.88, reason="fusion", model_name="fusion"),
+                                IntentCandidate(intent_id="cabin_play_music", score=0.75, reason="fusion", model_name="fusion"),
+                            ],
+                        )
+                    ],
+                ),
+            },
+        )()
+
+    def extract_slots(self, text: str, intent: IntentDefinition) -> dict[str, object]:
+        _ = (text, intent)
+        return {}
+
+
+class _PlannerRejects:
+    def plan(self, text: str, intents: list[IntentDefinition], context: dict[str, object] | None = None) -> PlanningResult:
+        _ = (text, intents, context)
+        return PlanningResult(
+            accepted=False,
+            workflow_type="single",
+            model_name="qwen3.5-plus",
+            backend="dashscope",
+            reason="cloud planner could not produce a stable executable step",
+        )
+
+
+class _PlannerOutOfScope:
+    def plan(self, text: str, intents: list[IntentDefinition], context: dict[str, object] | None = None) -> PlanningResult:
+        _ = (text, intents, context)
+        return PlanningResult(
+            accepted=False,
+            workflow_type="single",
+            model_name="qwen3.5-plus",
+            backend="dashscope",
+            reason="The provided intent catalog only contains cabin and service actions. There is no matching intent for ordering food via a third-party app action.",
+        )
+
+
+def _intent(intent_id: str) -> IntentDefinition:
+    return IntentDefinition(
+        intent_id=intent_id,
+        plugin_id=f"mock.{intent_id}",
+        domain="cabin",
+        keywords=[],
+        examples=[],
+    )
+
+
+class AgentCloudRouteTests(unittest.TestCase):
+    def test_route_to_cloud_returns_explicit_clarify_feedback_when_planner_does_not_accept(self) -> None:
+        service = AgentService(
+            intent_registry=IntentRegistry([_intent("cabin_nav_to"), _intent("cabin_play_music")]),
+            router=_RouteToCloudRouter(),
+            plugins=PluginRegistry(),
+            session_store=InMemorySessionStore(),
+            planner=_PlannerRejects(),
+        )
+
+        response = service.handle_chat(
+            ChatRequest(
+                session_id="sess_cloud_route",
+                user_id="user_1",
+                input_text="带我过去",
+            )
+        )
+
+        self.assertEqual(response.decision, "route_to_cloud")
+        self.assertEqual(response.reply_type, "clarify")
+        self.assertEqual(response.status, "route_to_cloud")
+        self.assertIn("请确认一下", response.reply_text)
+
+    def test_route_to_cloud_rejects_when_planner_marks_request_out_of_scope(self) -> None:
+        service = AgentService(
+            intent_registry=IntentRegistry([_intent("cabin_nav_to"), _intent("cabin_play_music")]),
+            router=_RouteToCloudRouter(),
+            plugins=PluginRegistry(),
+            session_store=InMemorySessionStore(),
+            planner=_PlannerOutOfScope(),
+        )
+
+        response = service.handle_chat(
+            ChatRequest(
+                session_id="sess_cloud_route_reject",
+                user_id="user_1",
+                input_text="去美团叫个外卖",
+            )
+        )
+
+        self.assertEqual(response.reply_type, "reject")
+        self.assertEqual(response.decision, "reject")
+        self.assertEqual(response.status, "rejected")
+        self.assertIn("做不了", response.reply_text)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/intelligent_cabin/archive/tests/test_bert.py b/intelligent_cabin/archive/tests/test_bert.py
new file mode 100644
index 0000000..fb70dba
--- /dev/null
+++ b/intelligent_cabin/archive/tests/test_bert.py
@@ -0,0 +1,235 @@
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from app.core.config import settings
+from app.core.bootstrap import build_intent_registry
+from app.services.classifier import BertIntentClassifier
+from app.services.router import build_matcher_pipeline
+
+
+DEFAULT_MODEL_DIR = PROJECT_ROOT / "models" / "local_bert_intent"
+NON_BUSINESS_LABELS = {"__social__", "__out_of_scope__"}
+
+
+def resolve_model_path(model_path: str) -> Path:
+    configured = (model_path or settings.classifier_model_path).strip()
+    if configured:
+        return Path(configured)
+    return DEFAULT_MODEL_DIR
+
+
+def resolve_label_map_path(label_map_path: str, model_path: Path) -> Path:
+    configured = (label_map_path or settings.classifier_label_map_path).strip()
+    if configured:
+        return Path(configured)
+    return model_path / "label_map.json"
+
+
+def build_classifier(
+    *,
+    model_path: Path,
+    label_map_path: Path,
+    threshold: float,
+    top_k: int,
+) -> BertIntentClassifier:
+    return BertIntentClassifier(
+        model_path=str(model_path),
+        threshold=threshold,
+        label_map_path=str(label_map_path),
+        fallback=None,
+        top_k=top_k,
+    )
+
+
+def predict_once(
+    text: str,
+    *,
+    model_path: Path,
+    label_map_path: Path,
+    threshold: float,
+    top_k: int,
+    warmup: bool,
+) -> dict[str, object]:
+    registry = build_intent_registry()
+    classifier = build_classifier(
+        model_path=model_path,
+        label_map_path=label_map_path,
+        threshold=threshold,
+        top_k=top_k,
+    )
+    warmup_ok = None
+    if warmup:
+        warmup_ok = classifier.warmup(settings.classifier_warmup_text)
+        if not warmup_ok:
+            error_message = getattr(classifier, "_warmup_error_message", None) or "BERT warmup failed"
+            raise RuntimeError(error_message)
+
+    intents = registry.list()
+    result = classifier.predict(text, intents)
+    matcher = build_matcher_pipeline(
+        registry,
+        ["classifier"],
+        classifier=classifier,
+        route_to_cloud_threshold=settings.local_route_to_cloud_threshold,
+        clarify_margin_threshold=settings.local_clarify_margin_threshold,
+        classifier_execute_score_threshold=settings.local_classifier_execute_score_threshold,
+        classifier_execute_margin_threshold=settings.local_classifier_execute_margin_threshold,
+    )
+    route_result = matcher.match(text)
+    fusion_stage = next((stage for stage in reversed(route_result.debug.stages) if stage.stage == "fusion"), None)
+    classifier_stage = next((stage for stage in reversed(route_result.debug.stages) if stage.stage == "classifier"), None)
+
+    return {
+        "text": text,
+        "config": {
+            "model_path": str(model_path),
+            "label_map_path": str(label_map_path),
+            "threshold": threshold,
+            "top_k": top_k,
+            "warmup_requested": warmup,
+            "warmup_ok": warmup_ok,
+            "warmup_elapsed_ms": getattr(classifier, "_warmup_elapsed_ms", None),
+            "warmup_error_message": getattr(classifier, "_warmup_error_message", None),
+        },
+        "classifier_result": {
+            "predicted_intent": result.intent.intent_id if result.intent is not None else None,
+            "score": round(result.score, 4),
+            "model_name": result.model_name,
+            "backend_name": result.backend_name,
+            "used_fallback": result.used_fallback,
+            "fallback_reason": result.fallback_reason,
+            "error_message": result.error_message,
+            "raw_label": result.raw_label,
+            "raw_candidates": result.raw_candidates or [],
+            "known_candidates": [
+                {"intent_id": intent.intent_id, "score": round(score, 4)}
+                for intent, score in (result.candidates or [])
+            ],
+        },
+        "route_result": {
+            "decision": route_result.debug.decision,
+            "decision_reason": route_result.debug.decision_reason,
+            "matched_stage": route_result.debug.matched_stage,
+            "selected_intent": route_result.debug.selected_intent,
+            "confidence_grade": route_result.debug.confidence_grade,
+            "unknown_detected": route_result.debug.unknown_detected,
+            "classifier_score": round(classifier_stage.score, 4) if classifier_stage is not None else None,
+            "fusion_score": round(fusion_stage.score, 4) if fusion_stage is not None else None,
+        },
+    }
+
+
+def summarize_business_view(result: dict[str, object]) -> dict[str, object]:
+    classifier_result = dict(result.get("classifier_result") or {})
+    route_result = dict(result.get("route_result") or {})
+    predicted_intent = classifier_result.get("predicted_intent")
+    raw_label = classifier_result.get("raw_label")
+    effective_label = raw_label if raw_label in NON_BUSINESS_LABELS else predicted_intent
+    if effective_label in NON_BUSINESS_LABELS:
+        classifier_result["predicted_intent"] = None
+        classifier_result["non_business_label"] = effective_label
+        classifier_result["business_interpretation"] = "non_business_label_detected"
+        route_result["selected_intent"] = None
+        route_result["decision"] = "reject"
+        route_result["decision_reason"] = "classifier detected a non-business label"
+        route_result["unknown_detected"] = True
+    else:
+        classifier_result["non_business_label"] = None
+        classifier_result["business_interpretation"] = "known_business_intent_or_uncertain"
+    return {
+        "text": result.get("text"),
+        "config": result.get("config"),
+        "classifier_result": classifier_result,
+        "route_result": route_result,
+    }
+
+
+def interactive_loop(
+    *,
+    model_path: Path,
+    label_map_path: Path,
+    threshold: float,
+    top_k: int,
+    warmup: bool,
+    mode: str,
+) -> None:
+    print("当前 BERT 测试已启动，输入一句话直接查看预测结果，输入 exit 退出。")
+    print(f"model_path={model_path}")
+    print(f"label_map_path={label_map_path}")
+    print(f"threshold={threshold} top_k={top_k} warmup={warmup} mode={mode}")
+    while True:
+        try:
+            text = input("\n请输入问题> ").strip()
+        except EOFError:
+            print()
+            break
+        if not text:
+            continue
+        if text.lower() in {"exit", "quit", "q"}:
+            break
+        result = predict_once(
+            text,
+            model_path=model_path,
+            label_map_path=label_map_path,
+            threshold=threshold,
+            top_k=top_k,
+            warmup=warmup,
+        )
+        if mode == "business":
+            result = summarize_business_view(result)
+        print(json.dumps(result, ensure_ascii=False, indent=2))
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="当前项目 BERT 意图识别测试脚本")
+    parser.add_argument("--text", type=str, default="", help="单次测试文本")
+    parser.add_argument("--threshold", type=float, default=settings.classifier_bert_threshold, help="BERT 置信度阈值")
+    parser.add_argument("--top-k", type=int, default=settings.classifier_top_k, help="返回候选数量")
+    parser.add_argument("--model-path", type=str, default="", help="模型目录，默认取 .env 或 models/local_bert_intent")
+    parser.add_argument("--label-map-path", type=str, default="", help="标签映射文件，默认取 .env 或 model_path/label_map.json")
+    parser.add_argument("--warmup", action="store_true", help="先执行一次 warmup 再预测")
+    parser.add_argument(
+        "--mode",
+        choices=("classifier", "business"),
+        default="classifier",
+        help="classifier 显示原始分类结果；business 会把非业务标签折叠成未命中业务意图",
+    )
+    args = parser.parse_args()
+
+    model_path = resolve_model_path(args.model_path)
+    label_map_path = resolve_label_map_path(args.label_map_path, model_path)
+
+    if args.text.strip():
+        result = predict_once(
+            args.text.strip(),
+            model_path=model_path,
+            label_map_path=label_map_path,
+            threshold=args.threshold,
+            top_k=args.top_k,
+            warmup=args.warmup,
+        )
+        if args.mode == "business":
+            result = summarize_business_view(result)
+        print(json.dumps(result, ensure_ascii=False, indent=2))
+        return
+
+    interactive_loop(
+        model_path=model_path,
+        label_map_path=label_map_path,
+        threshold=args.threshold,
+        top_k=args.top_k,
+        warmup=args.warmup,
+        mode=args.mode,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/intelligent_cabin/archive/tests/test_chat_stream.py b/intelligent_cabin/archive/tests/test_chat_stream.py
new file mode 100644
index 0000000..6a8bf6c
--- /dev/null
+++ b/intelligent_cabin/archive/tests/test_chat_stream.py
@@ -0,0 +1,109 @@
+from __future__ import annotations
+
+import json
+import os
+import time
+import unittest
+from unittest.mock import patch
+
+from fastapi.testclient import TestClient
+
+os.environ["AGENT_CLASSIFIER_BACKEND"] = "mock"
+os.environ["AGENT_CLASSIFIER_WARMUP_ENABLED"] = "false"
+
+from app.main import app
+from app.schemas.chat import ChatResponse
+
+
+def _fake_response() -> ChatResponse:
+    return ChatResponse(
+        session_id="sess_stream_1",
+        reply_type="workflow_result",
+        reply_text="好，空调已经打开了。",
+        intent="cabin_ac_on",
+        status="completed",
+        trace_id="trace_stream_1",
+    )
+
+
+class ChatStreamTests(unittest.TestCase):
+    def test_chat_stream_returns_final_only_when_fast(self) -> None:
+        client = TestClient(app)
+        with patch("app.main.agent_service.handle_chat", return_value=_fake_response()):
+            response = client.post(
+                "/api/v1/agent/chat-stream",
+                json={
+                    "session_id": "sess_stream_1",
+                    "user_id": "user_stream_1",
+                    "channel": "test",
+                    "input_text": "打开车窗",
+                    "input_type": "text",
+                },
+            )
+
+        self.assertEqual(response.status_code, 200)
+        lines = [line.strip() for line in response.text.splitlines() if line.strip()]
+        self.assertEqual(len(lines), 1)
+        final_event = json.loads(lines[0])
+        self.assertEqual(final_event.get("type"), "final")
+
+    def test_chat_stream_returns_ack_then_final_when_slow_request(self) -> None:
+        client = TestClient(app)
+
+        def _slow_handle_chat(_request):
+            time.sleep(1.2)
+            return _fake_response()
+
+        with patch("app.main.agent_service.handle_chat", side_effect=_slow_handle_chat):
+            response = client.post(
+                "/api/v1/agent/chat-stream",
+                json={
+                    "session_id": "sess_stream_1",
+                    "user_id": "user_stream_1",
+                    "channel": "test",
+                    "input_text": "打开车窗",
+                    "input_type": "text",
+                },
+            )
+
+        self.assertEqual(response.status_code, 200)
+        lines = [line.strip() for line in response.text.splitlines() if line.strip()]
+        self.assertGreaterEqual(len(lines), 2)
+
+        ack_event = json.loads(lines[0])
+        final_event = json.loads(lines[-1])
+        self.assertEqual(ack_event.get("type"), "ack")
+        self.assertEqual(final_event.get("type"), "final")
+        self.assertIn("data", final_event)
+        self.assertIn("reply_text", final_event["data"])
+
+    def test_chat_stream_returns_ack_then_final_when_slow_social_request(self) -> None:
+        client = TestClient(app)
+
+        def _slow_handle_chat(_request):
+            time.sleep(1.2)
+            return _fake_response()
+
+        with patch("app.main.agent_service.handle_chat", side_effect=_slow_handle_chat):
+            response = client.post(
+                "/api/v1/agent/chat-stream",
+                json={
+                    "session_id": "sess_stream_1",
+                    "user_id": "user_stream_1",
+                    "channel": "test",
+                    "input_text": "今天天气如何",
+                    "input_type": "text",
+                },
+            )
+
+        self.assertEqual(response.status_code, 200)
+        lines = [line.strip() for line in response.text.splitlines() if line.strip()]
+        self.assertGreaterEqual(len(lines), 2)
+        ack_event = json.loads(lines[0])
+        final_event = json.loads(lines[-1])
+        self.assertEqual(ack_event.get("type"), "ack")
+        self.assertEqual(final_event.get("type"), "final")
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/intelligent_cabin/archive/tests/test_config_loader.py b/intelligent_cabin/archive/tests/test_config_loader.py
new file mode 100644
index 0000000..a318153
--- /dev/null
+++ b/intelligent_cabin/archive/tests/test_config_loader.py
@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+import unittest
+from unittest.mock import patch
+
+from app.core.bootstrap import build_planner, load_runtime_bundle
+from app.core.config import settings
+from app.services.planner import CompositeWorkflowPlanner
+from app.services.config_loader import ConfigLoader
+from app.services.dialog_rules import DialogRuleEngine
+from app.services.response_policy import ResponsePolicy
+
+
+class ConfigLoaderTests(unittest.TestCase):
+    def test_loader_reads_domain_actions_and_responses(self) -> None:
+        bundle = ConfigLoader(
+            domain_path="config/domain.yml",
+            action_path="config/actions.yml",
+            response_path="config/responses.yml",
+            form_path="config/forms.yml",
+            rule_path="config/rules.yml",
+            dialog_act_path="config/dialog_acts.yml",
+            workflow_path="config/workflows.yml",
+            legacy_intent_path="app/data/intents.json",
+        ).load()
+
+        self.assertGreaterEqual(len(bundle.intent_registry.list()), 30)
+        self.assertEqual(bundle.intent_registry.get("cabin_window_open").plugin_id, "plugin.cabin.window.open")
+        self.assertEqual(bundle.intent_hints.get("cabin_window_open"), "打开车窗")
+        self.assertEqual(bundle.response_templates.get("task_stopped"), "好的，已停止当前任务。")
+        self.assertEqual(bundle.intent_registry.get("cabin_set_ac").required_slots, ["temperature"])
+        self.assertTrue(bundle.dialog_rules.is_stop_request("先不要了"))
+        self.assertEqual(bundle.dialog_rules.parse_confirmation_decision("确认"), True)
+        self.assertEqual(bundle.dialog_act_engine.detect("你好"), "chitchat")
+        self.assertGreaterEqual(len(bundle.workflow_templates.templates), 2)
+
+    def test_bootstrap_runtime_bundle_is_available(self) -> None:
+        bundle = load_runtime_bundle()
+
+        self.assertGreaterEqual(len(bundle.intent_registry.list()), 30)
+        self.assertIn("fallback", bundle.response_templates)
+        self.assertEqual(bundle.dialog_act_engine.detect("确认"), "affirm")
+
+    def test_response_policy_can_be_driven_by_config_templates(self) -> None:
+        policy = ResponsePolicy(
+            templates={"reject": "这个能力暂未开通。"},
+            intent_hints={"cabin_window_open": "开车窗"},
+        )
+
+        self.assertEqual(policy.reject(), "这个能力暂未开通。")
+        self.assertEqual(policy.clarify(["cabin_window_open"]), "请确认一下，你是想开车窗吗？")
+
+    def test_response_policy_formats_multi_step_summary_naturally(self) -> None:
+        policy = ResponsePolicy()
+
+        summary = policy.workflow_summary(["好的，已打开空调。", "已将空调调到 20 度。"])
+
+        self.assertEqual(summary, "好，空调已经打开了，也调到 20 度了。")
+
+    def test_response_policy_formats_multi_step_summary_in_vehicle_style(self) -> None:
+        policy = ResponsePolicy()
+
+        summary = policy.workflow_summary(["好的，已打开空调。", "好的，已关闭车窗。"])
+
+        self.assertEqual(summary, "好，空调已经打开了，车窗也帮你关上了。")
+
+    def test_build_planner_prefers_local_planners_before_cloud(self) -> None:
+        with patch.object(settings, "planner_backend", "dashscope"):
+            planner = build_planner()
+
+        self.assertIsInstance(planner, CompositeWorkflowPlanner)
+        self.assertIsInstance(planner._planners[0], CompositeWorkflowPlanner)
+
+    def test_dialog_rule_engine_supports_configured_confirmation_and_stop(self) -> None:
+        rules = DialogRuleEngine(
+            stop_phrases=("先不用了",),
+            positive_confirmation_tokens=("好，继续",),
+            negative_confirmation_tokens=("取消吧",),
+            confirmation_required_intents=("foo",),
+            confirmation_required_risk_levels=("high",),
+        )
+
+        self.assertTrue(rules.is_stop_request("先不用了"))
+        self.assertTrue(rules.parse_confirmation_decision("好，继续"))
+        self.assertFalse(rules.parse_confirmation_decision("取消吧"))
+        self.assertTrue(rules.requires_confirmation("foo", "low"))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/intelligent_cabin/archive/tests/test_dialog_continuation_and_multi_intent.py b/intelligent_cabin/archive/tests/test_dialog_continuation_and_multi_intent.py
new file mode 100644
index 0000000..50a951c
--- /dev/null
+++ b/intelligent_cabin/archive/tests/test_dialog_continuation_and_multi_intent.py
@@ -0,0 +1,202 @@
+from __future__ import annotations
+
+import unittest
+
+from app.plugins.base import PluginRegistry
+from app.plugins.mock import MockPluginExecutor
+from app.schemas.chat import ChatRequest
+from app.schemas.debug import IntentCandidate, MatcherStageDebug, RoutingDebug
+from app.schemas.workflow import Workflow, WorkflowStep
+from app.services.agent_service import AgentService
+from app.services.intent_registry import IntentRegistry
+from app.services.planner import HeuristicWorkflowPlanner
+from app.services.response_policy import ResponsePolicy
+from app.services.rewrite_engine import ContextRewriteEngine
+from app.services.router import RouteMatchResult
+from app.services.session_store import InMemorySessionStore
+
+
+class _FailIfCalledPlanner:
+    def plan(self, text, intents, context=None):
+        _ = (intents, context)
+        raise AssertionError(f"planner should not be called for single intent request: {text}")
+
+
+class _ScriptedRouter:
+    def __init__(self, registry: IntentRegistry) -> None:
+        self._registry = registry
+        self._route_map = {
+            "来点music": self._route_result("cabin_play_music", ["cabin_play_music"]),
+            "打开车窗和空调": self._route_result("cabin_window_open", ["cabin_window_open", "cabin_ac_on"]),
+            "关闭车窗": self._route_result("cabin_window_close", ["cabin_window_close", "cabin_window_open"]),
+        }
+        self._slot_map = {
+            ("播放黄昏", "cabin_play_music"): {"song": "黄昏"},
+            ("来一首黄昏", "cabin_play_music"): {"song": "黄昏"},
+            ("来点黄昏", "cabin_play_music"): {"song": "黄昏"},
+        }
+
+    def route(self, text: str) -> RouteMatchResult:
+        if text not in self._route_map:
+            raise AssertionError(f"unexpected route request: {text}")
+        return self._route_map[text]
+
+    def extract_slots(self, text: str, intent) -> dict[str, object]:
+        return dict(self._slot_map.get((text, intent.intent_id), {}))
+
+    def _route_result(self, selected_intent: str, candidates: list[str]) -> RouteMatchResult:
+        intent = self._registry.get(selected_intent)
+        stage = MatcherStageDebug(
+            stage="fusion",
+            accepted=True,
+            selected_intent=selected_intent,
+            score=1.0,
+            candidates=[
+                IntentCandidate(intent_id=intent_id, score=max(0.5, 1.0 - index * 0.1))
+                for index, intent_id in enumerate(candidates)
+            ],
+        )
+        return RouteMatchResult(
+            intent=intent,
+            debug=RoutingDebug(
+                selected_intent=selected_intent,
+                matched_stage="fusion",
+                decision="execute",
+                stages=[stage],
+            ),
+        )
+
+
+class DialogContinuationAndMultiIntentTests(unittest.TestCase):
+    def setUp(self) -> None:
+        self.registry = IntentRegistry.from_json("app/data/intents.json")
+        self.plugins = PluginRegistry()
+        MockPluginExecutor().register(self.plugins)
+        self.service = AgentService(
+            intent_registry=self.registry,
+            router=_ScriptedRouter(self.registry),
+            plugins=self.plugins,
+            session_store=InMemorySessionStore(),
+            rewrite_engine=ContextRewriteEngine(),
+            response_policy=ResponsePolicy(),
+            planner=HeuristicWorkflowPlanner(),
+        )
+
+    def test_music_followup_in_chat_continues_waiting_slot(self) -> None:
+        first = self.service.handle_chat(
+            ChatRequest(
+                session_id="sess_music_followup",
+                user_id="user_1",
+                input_text="来点music",
+            )
+        )
+
+        self.assertEqual(first.reply_type, "ask_slot")
+        self.assertEqual(first.pending_slots, ["media_query"])
+
+        second = self.service.handle_chat(
+            ChatRequest(
+                session_id="sess_music_followup",
+                user_id="user_1",
+                input_text="黄昏",
+            )
+        )
+
+        self.assertEqual(second.reply_type, "workflow_result")
+        self.assertEqual(second.intent, "cabin_play_music")
+        self.assertEqual(second.filled_slots.get("song"), "黄昏")
+        self.assertIn("黄昏", second.reply_text)
+
+    def test_parallel_compound_request_enters_planner(self) -> None:
+        response = self.service.handle_chat(
+            ChatRequest(
+                session_id="sess_parallel_compound",
+                user_id="user_1",
+                input_text="打开车窗和空调",
+            )
+        )
+
+        self.assertEqual(response.reply_type, "workflow_result")
+        self.assertEqual(response.workflow.workflow_type, "sequence")
+        step_intents = [step.intent_id for step in response.workflow.steps]
+        self.assertEqual(step_intents, ["cabin_window_open", "cabin_ac_on"])
+        self.assertIn("车窗", response.reply_text)
+        self.assertIn("空调", response.reply_text)
+
+    def test_single_cabin_intent_does_not_enter_planner_from_top2_domain_candidates(self) -> None:
+        service = AgentService(
+            intent_registry=self.registry,
+            router=_ScriptedRouter(self.registry),
+            plugins=self.plugins,
+            session_store=InMemorySessionStore(),
+            rewrite_engine=ContextRewriteEngine(),
+            response_policy=ResponsePolicy(),
+            planner=_FailIfCalledPlanner(),
+        )
+
+        response = service.handle_chat(
+            ChatRequest(
+                session_id="sess_single_cabin_no_planner",
+                user_id="user_1",
+                input_text="关闭车窗",
+            )
+        )
+
+        self.assertEqual(response.reply_type, "workflow_result")
+        self.assertEqual(response.intent, "cabin_window_close")
+        self.assertEqual(response.routing_debug.decision, "execute")
+        self.assertFalse(any(stage.stage == "planner" for stage in response.routing_debug.stages))
+
+    def test_waiting_confirmation_can_continue_via_chat(self) -> None:
+        session = self.service.session_store.get_or_create("sess_confirm_chat", "user_1")
+        session.current_intent = "cs_cancel_order"
+        session.status = "waiting_confirmation"
+        session.pending_slots = ["confirmation"]
+        session.slots = {"order_id": "A123456"}
+        session.workflow = Workflow(
+            workflow_id="wf_confirm_chat",
+            workflow_type="conditional",
+            domain="customer_service",
+            intent_id="cs_cancel_order",
+            status="waiting_confirmation",
+            risk_level="high",
+            slots={"order_id": "A123456"},
+            steps=[
+                WorkflowStep(
+                    step=1,
+                    step_id="step_cancel",
+                    intent_id="cs_cancel_order",
+                    plugin_id="plugin.order.cancel",
+                    action="cancel_order",
+                    status="waiting_confirmation",
+                    slots={"order_id": "A123456"},
+                    requires_confirmation=True,
+                )
+            ],
+            meta={
+                "pending_confirmation": {
+                    "step_id": "step_cancel",
+                    "intent_id": "cs_cancel_order",
+                    "detail": "确认取消订单 A123456",
+                },
+                "step_results": {},
+                "confirmed_steps": [],
+            },
+        ).model_dump()
+        self.service.session_store.save(session)
+
+        response = self.service.handle_chat(
+            ChatRequest(
+                session_id="sess_confirm_chat",
+                user_id="user_1",
+                input_text="确认",
+            )
+        )
+
+        self.assertEqual(response.reply_type, "workflow_result")
+        self.assertEqual(response.status, "completed")
+        self.assertIn("A123456", response.reply_text)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/intelligent_cabin/archive/tests/test_intent_coverage_and_stop.py b/intelligent_cabin/archive/tests/test_intent_coverage_and_stop.py
new file mode 100644
index 0000000..ff0020b
--- /dev/null
+++ b/intelligent_cabin/archive/tests/test_intent_coverage_and_stop.py
@@ -0,0 +1,149 @@
+from __future__ import annotations
+
+import unittest
+
+from app.plugins.base import PluginRegistry
+from app.plugins.mock import MockPluginExecutor
+from app.services.classifier import MockIntentClassifier
+from app.services.agent_service import AgentService
+from app.services.intent_registry import IntentRegistry
+from app.services.response_policy import ResponsePolicy
+from app.services.rewrite_engine import ContextRewriteEngine
+from app.services.router import HeuristicSlotExtractor, IntentRouter, build_matcher_pipeline
+from app.services.session_store import InMemorySessionStore
+from app.schemas.chat import ChatRequest, FillSlotsRequest
+
+
+class _BertLikeMockClassifier(MockIntentClassifier):
+    def predict(self, text, intents):
+        result = super().predict(text, intents)
+        result.model_name = "bert-local"
+        result.backend_name = "bert-local"
+        return result
+
+
+def _build_service() -> AgentService:
+    registry = IntentRegistry.from_json("app/data/intents.json")
+    plugins = PluginRegistry()
+    MockPluginExecutor().register(plugins)
+    router = IntentRouter(
+        matcher=build_matcher_pipeline(
+            registry,
+            ["classifier"],
+            classifier=_BertLikeMockClassifier(threshold=0.0, top_k=3),
+        ),
+        slot_extractor=HeuristicSlotExtractor(),
+    )
+    return AgentService(
+        intent_registry=registry,
+        router=router,
+        plugins=plugins,
+        session_store=InMemorySessionStore(),
+        rewrite_engine=ContextRewriteEngine(),
+        response_policy=ResponsePolicy(),
+        planner=None,
+    )
+
+
+class IntentCoverageAndStopTests(unittest.TestCase):
+    def setUp(self) -> None:
+        self.service = _build_service()
+        self.registry = IntentRegistry.from_json("app/data/intents.json")
+
+    def test_intent_catalog_has_at_least_30_items(self) -> None:
+        self.assertGreaterEqual(len(self.registry.list()), 30)
+
+    def test_close_ac_routes_to_power_off(self) -> None:
+        response = self.service.handle_chat(
+            ChatRequest(
+                session_id="sess_close_ac",
+                user_id="user_1",
+                input_text="关闭空调",
+            )
+        )
+
+        self.assertEqual(response.reply_type, "workflow_result")
+        self.assertEqual(response.intent, "cabin_ac_off")
+        self.assertIn("已关闭空调", response.reply_text)
+
+    def test_open_window_is_covered(self) -> None:
+        response = self.service.handle_chat(
+            ChatRequest(
+                session_id="sess_window_open",
+                user_id="user_1",
+                input_text="打开车窗",
+            )
+        )
+
+        self.assertEqual(response.reply_type, "workflow_result")
+        self.assertEqual(response.intent, "cabin_window_open")
+        self.assertIn("已打开车窗", response.reply_text)
+
+    def test_stop_current_task_while_waiting_for_slot(self) -> None:
+        first = self.service.handle_chat(
+            ChatRequest(
+                session_id="sess_stop_task",
+                user_id="user_1",
+                input_text="空调调到",
+            )
+        )
+        self.assertEqual(first.reply_type, "ask_slot")
+        self.assertEqual(first.pending_slots, ["temperature"])
+
+        stopped = self.service.handle_fill_slots(
+            FillSlotsRequest(
+                session_id="sess_stop_task",
+                user_id="user_1",
+                input_text="不用了",
+            )
+        )
+
+        self.assertEqual(stopped.reply_type, "text")
+        self.assertEqual(stopped.status, "stopped")
+        self.assertEqual(stopped.pending_slots, [])
+        self.assertIn("已停止当前任务", stopped.reply_text)
+
+    def test_relative_ac_adjustment_uses_two_degree_step(self) -> None:
+        session = self.service.session_store.get_or_create("sess_ac_lower", "user_1")
+        session.current_intent = "cabin_set_ac"
+        session.context_memory["last_temperature"] = 24
+        self.service.session_store.save(session)
+
+        response = self.service.handle_chat(
+            ChatRequest(
+                session_id="sess_ac_lower",
+                user_id="user_1",
+                input_text="调低一点",
+            )
+        )
+
+        self.assertEqual(response.reply_type, "workflow_result")
+        self.assertEqual(response.intent, "cabin_set_ac")
+        self.assertEqual(response.filled_slots.get("temperature"), 22)
+        self.assertIn("22", response.reply_text)
+
+    def test_relative_ac_adjustment_without_history_uses_default_baseline(self) -> None:
+        session = self.service.session_store.get_or_create("sess_ac_lower_default", "user_1")
+        session.current_intent = "cabin_ac_on"
+        self.service.session_store.save(session)
+
+        response = self.service.handle_chat(
+            ChatRequest(
+                session_id="sess_ac_lower_default",
+                user_id="user_1",
+                input_text="调低一点",
+            )
+        )
+
+        self.assertEqual(response.reply_type, "workflow_result")
+        self.assertEqual(response.intent, "cabin_set_ac")
+        self.assertEqual(response.filled_slots.get("temperature"), 22)
+        self.assertIn("22", response.reply_text)
+
+    def test_temperature_is_clamped_before_execution(self) -> None:
+        self.assertEqual(self.service._normalize_temperature_value(-1), 16)
+        self.assertEqual(self.service._normalize_temperature_value(40), 30)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/intelligent_cabin/archive/tests/test_joint_nlu_integration.py b/intelligent_cabin/archive/tests/test_joint_nlu_integration.py
new file mode 100644
index 0000000..86a83ee
--- /dev/null
+++ b/intelligent_cabin/archive/tests/test_joint_nlu_integration.py
@@ -0,0 +1,93 @@
+from __future__ import annotations
+
+import unittest
+
+from app.schemas.intent import IntentDefinition
+from app.services.classifier import JointBertIntentClassifier
+from app.services.planner import HeuristicWorkflowPlanner
+from app.services.router import JointBertSlotExtractor
+
+
+class FakeJointNLU:
+    def __init__(self) -> None:
+        self._predictions = {
+            "把空调调到22度": {
+                "intent_id": "cabin_set_ac",
+                "intent_score": 0.93,
+                "candidates": [("cabin_set_ac", 0.93), ("cabin_ac_on", 0.04)],
+                "slots": {"temperature": 22},
+            },
+            "导航去公司，然后把空调调到22度": {
+                "intent_id": "cabin_nav_to",
+                "intent_score": 0.88,
+                "candidates": [("cabin_nav_to", 0.88), ("cabin_set_ac", 0.72)],
+                "slots": {"destination": "公司"},
+            },
+        }
+        self._slot_predictions = {
+            ("把空调调到22度", "cabin_set_ac"): {"temperature": 22},
+            ("导航去公司", "cabin_nav_to"): {"destination": "公司"},
+            ("把空调调到22度", "cabin_set_ac"): {"temperature": 22},
+        }
+
+    def warmup(self, sample_text: str = "打开车窗") -> bool:
+        _ = sample_text
+        return True
+
+    def predict(self, text: str, intents: list[IntentDefinition]):
+        from app.services.joint_nlu import JointCandidate, JointNluResult
+
+        raw = self._predictions[text]
+        candidates = [JointCandidate(intent_id=intent_id, score=score) for intent_id, score in raw["candidates"]]
+        return JointNluResult(
+            intent_id=raw["intent_id"],
+            intent_score=raw["intent_score"],
+            candidates=candidates,
+            slots=dict(raw["slots"]),
+        )
+
+    def extract_slots(self, text: str, intent: IntentDefinition):
+        return dict(self._slot_predictions.get((text, intent.intent_id), {}))
+
+    def extract_slots_by_intent_id(self, text: str, intent_id: str, required_slots=None):
+        _ = required_slots
+        return dict(self._slot_predictions.get((text, intent_id), {}))
+
+
+class JointNLUIntegrationTests(unittest.TestCase):
+    def setUp(self) -> None:
+        self.intents = [
+            IntentDefinition(intent_id="cabin_set_ac", plugin_id="x", domain="cabin", required_slots=["temperature"]),
+            IntentDefinition(intent_id="cabin_nav_to", plugin_id="x", domain="cabin", required_slots=["destination"]),
+            IntentDefinition(intent_id="cabin_ac_on", plugin_id="x", domain="cabin"),
+        ]
+        self.fake_nlu = FakeJointNLU()
+
+    def test_joint_classifier_uses_joint_nlu_intent_head(self) -> None:
+        classifier = JointBertIntentClassifier(self.fake_nlu, threshold=0.3, top_k=2)
+
+        result = classifier.predict("把空调调到22度", self.intents)
+
+        self.assertIsNotNone(result.intent)
+        self.assertEqual(result.intent.intent_id, "cabin_set_ac")
+        self.assertEqual(result.raw_candidates[0]["intent_id"], "cabin_set_ac")
+
+    def test_joint_slot_extractor_uses_joint_nlu_slots(self) -> None:
+        extractor = JointBertSlotExtractor(self.fake_nlu)
+
+        slots = extractor.extract("把空调调到22度", self.intents[0])
+
+        self.assertEqual(slots, {"temperature": 22})
+
+    def test_planner_prefers_joint_nlu_slots_for_each_clause(self) -> None:
+        planner = HeuristicWorkflowPlanner(joint_nlu=self.fake_nlu)
+
+        result = planner.plan("导航去公司，然后把空调调到22度", self.intents)
+
+        self.assertTrue(result.accepted)
+        self.assertEqual(result.steps[0].slots, {"destination": "公司"})
+        self.assertEqual(result.steps[1].slots, {"temperature": 22})
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/intelligent_cabin/archive/tests/test_multi_intent_detector.py b/intelligent_cabin/archive/tests/test_multi_intent_detector.py
new file mode 100644
index 0000000..40e5684
--- /dev/null
+++ b/intelligent_cabin/archive/tests/test_multi_intent_detector.py
@@ -0,0 +1,144 @@
+from __future__ import annotations
+
+import unittest
+
+import torch
+
+from app.schemas.intent import IntentDefinition
+from app.services.multi_intent_detector import BertMultiIntentDetector, JointBertMultiIntentDetector
+
+
+class FakeTokenizer:
+    def __call__(self, text, truncation=True, padding=False, return_tensors="pt"):
+        _ = (text, truncation, padding, return_tensors)
+        return {
+            "input_ids": torch.tensor([[101, 102]], dtype=torch.long),
+            "attention_mask": torch.tensor([[1, 1]], dtype=torch.long),
+        }
+
+
+class FakeModel:
+    def __init__(self, logits: list[float], id2label: dict[int, str]) -> None:
+        self.config = type("Config", (), {"id2label": id2label})()
+        self._logits = torch.tensor([logits], dtype=torch.float32)
+
+    def eval(self) -> None:
+        return None
+
+    def __call__(self, **kwargs):
+        _ = kwargs
+        return type("Output", (), {"logits": self._logits})()
+
+
+class RuntimeBackedDetector(BertMultiIntentDetector):
+    def __init__(
+        self,
+        logits: list[float],
+        id2label: dict[int, str],
+        threshold: float = 0.45,
+        top_k: int = 8,
+        max_labels: int = 4,
+    ) -> None:
+        super().__init__(
+            model_path="unused",
+            threshold=threshold,
+            top_k=top_k,
+            max_labels=max_labels,
+        )
+        self._runtime = (torch, FakeTokenizer(), FakeModel(logits, id2label))
+
+    def _get_runtime(self):
+        return self._runtime
+
+
+class MultiIntentDetectorTests(unittest.TestCase):
+    def test_detector_filters_blocked_and_unknown_labels(self) -> None:
+        detector = RuntimeBackedDetector(
+            logits=[2.4, 2.0, 3.2, 2.6],
+            id2label={
+                0: "cabin_window_open",
+                1: "cabin_play_music",
+                2: "__social__",
+                3: "unknown_intent",
+            },
+            threshold=0.8,
+            top_k=4,
+        )
+        intents = [
+            IntentDefinition(intent_id="cabin_window_open", plugin_id="plugin.window", domain="cabin"),
+            IntentDefinition(intent_id="cabin_play_music", plugin_id="plugin.music", domain="cabin"),
+        ]
+
+        result = detector.detect("打开车窗并播放音乐", intents)
+
+        self.assertTrue(result.detected)
+        self.assertEqual(result.backend_name, "bert-multi-label")
+        self.assertEqual([item.intent_id for item in result.candidates], ["cabin_window_open", "cabin_play_music"])
+
+    def test_detector_respects_threshold_and_max_labels(self) -> None:
+        detector = RuntimeBackedDetector(
+            logits=[2.8, 2.5, 2.2],
+            id2label={
+                0: "cabin_window_open",
+                1: "cabin_play_music",
+                2: "cabin_nav_to",
+            },
+            threshold=0.89,
+            top_k=3,
+            max_labels=2,
+        )
+        intents = [
+            IntentDefinition(intent_id="cabin_window_open", plugin_id="plugin.window", domain="cabin"),
+            IntentDefinition(intent_id="cabin_play_music", plugin_id="plugin.music", domain="cabin"),
+            IntentDefinition(intent_id="cabin_nav_to", plugin_id="plugin.nav", domain="cabin"),
+        ]
+
+        result = detector.detect("开窗放歌去公司", intents)
+
+        self.assertTrue(result.detected)
+        self.assertEqual(len(result.candidates), 2)
+        self.assertEqual([item.intent_id for item in result.candidates], ["cabin_window_open", "cabin_play_music"])
+
+    def test_joint_bert_detector_wraps_shared_runtime(self) -> None:
+        intents = [
+            IntentDefinition(intent_id="cabin_window_open", plugin_id="plugin.window", domain="cabin"),
+            IntentDefinition(intent_id="cabin_play_music", plugin_id="plugin.music", domain="cabin"),
+        ]
+
+        class FakeJointNlu:
+            def __init__(self) -> None:
+                self.calls: list[dict[str, object]] = []
+
+            def predict_multi_intents(self, text, known_intents, threshold=0.45, max_labels=4, top_k=8):
+                self.calls.append(
+                    {
+                        "text": text,
+                        "threshold": threshold,
+                        "max_labels": max_labels,
+                        "top_k": top_k,
+                        "known_count": len(known_intents),
+                    }
+                )
+                return [
+                    type("Candidate", (), {"intent_id": "cabin_window_open", "score": 0.93})(),
+                    type("Candidate", (), {"intent_id": "cabin_play_music", "score": 0.88})(),
+                ]
+
+            def warmup(self, sample_text="") -> bool:
+                _ = sample_text
+                return True
+
+        fake_nlu = FakeJointNlu()
+        detector = JointBertMultiIntentDetector(fake_nlu, threshold=0.5, top_k=6, max_labels=3)
+
+        result = detector.detect("打开车窗并播放音乐", intents)
+
+        self.assertTrue(result.detected)
+        self.assertEqual(result.backend_name, "joint-bert-multi-label")
+        self.assertEqual([item.intent_id for item in result.candidates], ["cabin_window_open", "cabin_play_music"])
+        self.assertEqual(fake_nlu.calls[0]["threshold"], 0.5)
+        self.assertEqual(fake_nlu.calls[0]["top_k"], 6)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/intelligent_cabin/archive/tests/test_router_decisions.py b/intelligent_cabin/archive/tests/test_router_decisions.py
new file mode 100644
index 0000000..0a0e25b
--- /dev/null
+++ b/intelligent_cabin/archive/tests/test_router_decisions.py
@@ -0,0 +1,195 @@
+from __future__ import annotations
+
+import unittest
+
+from app.schemas.debug import IntentCandidate, MatcherStageDebug
+from app.schemas.intent import IntentDefinition
+from app.services.intent_registry import IntentRegistry
+from app.services.router import IntentMatchResult, MultiStageIntentMatcher
+
+
+class _FakeMatcher:
+    def __init__(self, stage_debug: MatcherStageDebug) -> None:
+        self._stage_debug = stage_debug
+
+    def match(self, text: str) -> IntentMatchResult:
+        _ = text
+        return IntentMatchResult(intent=None, stage_debug=self._stage_debug)
+
+
+def _intent(intent_id: str) -> IntentDefinition:
+    return IntentDefinition(
+        intent_id=intent_id,
+        plugin_id=f"mock.{intent_id}",
+        domain="test",
+        keywords=[],
+        examples=[],
+    )
+
+
+class RouterDecisionTests(unittest.TestCase):
+    def setUp(self) -> None:
+        self.registry = IntentRegistry([_intent("alpha"), _intent("beta"), _intent("gamma")])
+
+    def test_execute_when_bert_classifier_is_clear(self) -> None:
+        matcher = MultiStageIntentMatcher(
+            registry=self.registry,
+            matchers=[
+                _FakeMatcher(
+                    MatcherStageDebug(
+                        stage="classifier",
+                        accepted=True,
+                        selected_intent="alpha",
+                        score=0.92,
+                        reason="classifier selected best candidate",
+                        backend="joint-bert-local",
+                        candidates=[
+                            IntentCandidate(intent_id="alpha", score=0.92, reason="classifier", model_name="joint-bert-local"),
+                            IntentCandidate(intent_id="beta", score=0.21, reason="classifier", model_name="joint-bert-local"),
+                        ],
+                    )
+                ),
+            ],
+        )
+
+        result = matcher.match("alpha")
+
+        self.assertEqual(result.debug.decision, "execute")
+        self.assertEqual(result.intent.intent_id if result.intent else None, "alpha")
+
+    def test_clarify_when_bert_top_candidates_are_too_close(self) -> None:
+        matcher = MultiStageIntentMatcher(
+            registry=self.registry,
+            matchers=[
+                _FakeMatcher(
+                    MatcherStageDebug(
+                        stage="classifier",
+                        accepted=True,
+                        selected_intent="alpha",
+                        score=0.22,
+                        reason="classifier selected best candidate",
+                        backend="bert-local",
+                        metadata={"threshold": 0.2},
+                        candidates=[
+                            IntentCandidate(intent_id="alpha", score=0.31, reason="classifier", model_name="bert-local"),
+                            IntentCandidate(intent_id="beta", score=0.28, reason="classifier", model_name="bert-local"),
+                        ],
+                    )
+                ),
+            ],
+            route_to_cloud_threshold=0.2,
+        )
+
+        result = matcher.match("ambiguous request")
+
+        self.assertEqual(result.debug.decision, "clarify")
+        self.assertIsNone(result.intent)
+        self.assertEqual(result.debug.confidence_grade, "medium")
+
+    def test_route_to_cloud_when_bert_signal_is_weak_but_known(self) -> None:
+        matcher = MultiStageIntentMatcher(
+            registry=self.registry,
+            matchers=[
+                _FakeMatcher(
+                    MatcherStageDebug(
+                        stage="classifier",
+                        accepted=False,
+                        selected_intent="alpha",
+                        score=0.29,
+                        reason="classifier below execute threshold",
+                        backend="joint-bert-local",
+                        candidates=[
+                            IntentCandidate(intent_id="alpha", score=0.29, reason="classifier", model_name="joint-bert-local"),
+                            IntentCandidate(intent_id="beta", score=0.14, reason="classifier", model_name="joint-bert-local"),
+                        ],
+                    )
+                ),
+            ],
+        )
+
+        result = matcher.match("weak symbolic request")
+
+        self.assertEqual(result.debug.decision, "route_to_cloud")
+        self.assertIsNone(result.intent)
+
+    def test_reject_when_no_branch_has_usable_signal(self) -> None:
+        matcher = MultiStageIntentMatcher(
+            registry=self.registry,
+            matchers=[
+                _FakeMatcher(
+                    MatcherStageDebug(
+                        stage="classifier",
+                        accepted=False,
+                        score=0.12,
+                        reason="classifier below threshold",
+                        backend="bert-local",
+                        metadata={"threshold": 0.2},
+                        candidates=[],
+                    )
+                ),
+            ],
+        )
+
+        result = matcher.match("unknown request")
+
+        self.assertEqual(result.debug.decision, "reject")
+        self.assertTrue(result.debug.unknown_detected)
+        self.assertIsNone(result.intent)
+
+    def test_route_to_cloud_for_low_confidence_classifier_only_bert_signal(self) -> None:
+        matcher = MultiStageIntentMatcher(
+            registry=self.registry,
+            matchers=[
+                _FakeMatcher(
+                    MatcherStageDebug(
+                        stage="classifier",
+                        accepted=True,
+                        selected_intent="alpha",
+                        score=0.31,
+                        reason="classifier selected best candidate",
+                        backend="bert-local",
+                        metadata={"threshold": 0.0, "top_margin": 0.04},
+                        candidates=[
+                            IntentCandidate(intent_id="alpha", score=0.31, reason="classifier", model_name="bert-local"),
+                            IntentCandidate(intent_id="beta", score=0.27, reason="classifier", model_name="bert-local"),
+                        ],
+                    )
+                ),
+            ],
+        )
+
+        result = matcher.match("bert only weak request")
+
+        self.assertEqual(result.debug.decision, "route_to_cloud")
+        self.assertIsNone(result.intent)
+
+    def test_execute_for_high_confidence_classifier_only_bert_signal(self) -> None:
+        matcher = MultiStageIntentMatcher(
+            registry=self.registry,
+            matchers=[
+                _FakeMatcher(
+                    MatcherStageDebug(
+                        stage="classifier",
+                        accepted=True,
+                        selected_intent="alpha",
+                        score=0.92,
+                        reason="classifier selected best candidate",
+                        backend="bert-local",
+                        metadata={"threshold": 0.0, "top_margin": 0.63},
+                        candidates=[
+                            IntentCandidate(intent_id="alpha", score=0.92, reason="classifier", model_name="bert-local"),
+                            IntentCandidate(intent_id="beta", score=0.29, reason="classifier", model_name="bert-local"),
+                        ],
+                    )
+                ),
+            ],
+        )
+
+        result = matcher.match("bert only strong request")
+
+        self.assertEqual(result.debug.decision, "execute")
+        self.assertEqual(result.intent.intent_id if result.intent else None, "alpha")
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/intelligent_cabin/archive/tests/test_social_chat.py b/intelligent_cabin/archive/tests/test_social_chat.py
new file mode 100644
index 0000000..00058e9
--- /dev/null
+++ b/intelligent_cabin/archive/tests/test_social_chat.py
@@ -0,0 +1,180 @@
+from __future__ import annotations
+
+import unittest
+
+from app.plugins.base import PluginRegistry
+from app.schemas.chat import ChatRequest, FillSlotsRequest
+from app.schemas.debug import RoutingDebug
+from app.schemas.intent import IntentDefinition
+from app.schemas.workflow import Workflow, WorkflowStep
+from app.services.agent_service import AgentService
+from app.services.intent_registry import IntentRegistry
+from app.services.session_store import InMemorySessionStore
+from app.services.social import SocialReplyResult, SocialRouter
+
+
+class _FailingRouter:
+    def route(self, text: str):  # pragma: no cover - should not be called in these tests
+        raise AssertionError(f"router should not be called for social input: {text}")
+
+    def extract_slots(self, text: str, intent: IntentDefinition) -> dict[str, object]:
+        _ = (text, intent)
+        return {}
+
+
+class _FakeSocialResponder:
+    def reply(self, text: str, session) -> SocialReplyResult:
+        _ = (text, session)
+        normalized = text.strip()
+        if "你好" in normalized:
+            text = "你好呀，我在，想聊什么都可以。"
+        elif "名字" in normalized or "你是谁" in normalized:
+            text = "我是一名智能座舱助手，你可以直接叫我座舱助手。"
+        elif "天气" in normalized:
+            text = "是啊，今天确实挺舒服的。"
+        else:
+            text = "我在，咱们继续聊。"
+        return SocialReplyResult(
+            text=text,
+            backend="fake-cloud",
+            model_name="fake-social",
+        )
+
+
+def _intent(intent_id: str, plugin_id: str) -> IntentDefinition:
+    return IntentDefinition(
+        intent_id=intent_id,
+        plugin_id=plugin_id,
+        domain="service" if intent_id.startswith("cs_") else "cabin",
+        risk_level="high" if intent_id == "cs_cancel_order" else "low",
+        required_slots=["order_id"] if intent_id == "cs_cancel_order" else [],
+        ask_templates={"order_id": "请告诉我订单号。"} if intent_id == "cs_cancel_order" else {},
+        keywords=[],
+        examples=[],
+    )
+
+
+class SocialChatTests(unittest.TestCase):
+    def setUp(self) -> None:
+        self.session_store = InMemorySessionStore()
+        self.plugins = PluginRegistry()
+        self.plugins.register(
+            "mock.cancel_order",
+            lambda slots: {"success": True, "message": f"已取消订单 {slots.get('order_id', '')}。"},
+        )
+        self.service = AgentService(
+            intent_registry=IntentRegistry([_intent("cs_cancel_order", "mock.cancel_order")]),
+            router=_FailingRouter(),
+            plugins=self.plugins,
+            session_store=self.session_store,
+            social_router=SocialRouter(),
+            social_responder=_FakeSocialResponder(),
+        )
+
+    def test_greeting_social_reply_uses_social_responder(self) -> None:
+        response = self.service.handle_chat(
+            ChatRequest(
+                session_id="sess_social_hi",
+                user_id="user_1",
+                input_text="你好",
+            )
+        )
+
+        self.assertEqual(response.decision, "open_social")
+        self.assertEqual(response.status, "social")
+        self.assertIn("你好呀", response.reply_text)
+
+    def test_capability_social_question_does_not_fall_into_business_intent(self) -> None:
+        response = self.service.handle_chat(
+            ChatRequest(
+                session_id="sess_social_name",
+                user_id="user_1",
+                input_text="你叫什么名字",
+            )
+        )
+
+        self.assertEqual(response.decision, "open_social")
+        self.assertEqual(response.status, "social")
+        self.assertNotEqual(response.reply_type, "ask_slot")
+        self.assertIn("智能座舱助手", response.reply_text)
+
+    def test_open_social_reply_uses_social_responder(self) -> None:
+        response = self.service.handle_chat(
+            ChatRequest(
+                session_id="sess_social_open",
+                user_id="user_1",
+                input_text="今天天气真不错啊",
+            )
+        )
+
+        self.assertEqual(response.decision, "open_social")
+        self.assertEqual(response.status, "social")
+        self.assertIn("挺舒服", response.reply_text)
+
+    def test_social_turn_does_not_break_waiting_confirmation(self) -> None:
+        session = self.session_store.get_or_create("sess_confirm", "user_1")
+        session.current_intent = "cs_cancel_order"
+        session.status = "waiting_confirmation"
+        session.pending_slots = ["confirmation"]
+        session.slots = {"order_id": "A123456"}
+        session.routing_debug = RoutingDebug(selected_intent="cs_cancel_order", decision="execute").model_dump()
+        session.workflow = Workflow(
+            workflow_id="wf_sess_confirm",
+            workflow_type="conditional",
+            domain="service",
+            intent_id="cs_cancel_order",
+            status="waiting_confirmation",
+            risk_level="high",
+            slots={"order_id": "A123456"},
+            steps=[
+                WorkflowStep(
+                    step=1,
+                    step_id="step_confirm",
+                    intent_id="cs_cancel_order",
+                    plugin_id="mock.cancel_order",
+                    action="cancel_order",
+                    status="waiting_confirmation",
+                    slots={"order_id": "A123456"},
+                    requires_confirmation=True,
+                )
+            ],
+            meta={
+                "pending_confirmation": {
+                    "step_id": "step_confirm",
+                    "intent_id": "cs_cancel_order",
+                    "detail": "确认取消订单 A123456",
+                },
+                "confirmed_steps": [],
+                "step_results": {},
+            },
+        ).model_dump()
+        self.session_store.save(session)
+
+        social_response = self.service.handle_fill_slots(
+            FillSlotsRequest(
+                session_id="sess_confirm",
+                user_id="user_1",
+                input_text="今天天气真不错啊",
+            )
+        )
+
+        self.assertEqual(social_response.decision, "open_social")
+        self.assertEqual(social_response.status, "waiting_confirmation")
+        self.assertEqual(social_response.pending_slots, ["confirmation"])
+        self.assertIn("回复“确认”或“取消”即可", social_response.reply_text)
+
+        confirm_response = self.service.handle_fill_slots(
+            FillSlotsRequest(
+                session_id="sess_confirm",
+                user_id="user_1",
+                input_text="确认",
+            )
+        )
+
+        self.assertEqual(confirm_response.reply_type, "workflow_result")
+        self.assertEqual(confirm_response.status, "completed")
+        self.assertIn("已取消订单", confirm_response.reply_text)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/intelligent_cabin/archive/tests/test_workflow_templates.py b/intelligent_cabin/archive/tests/test_workflow_templates.py
new file mode 100644
index 0000000..d7f2a3c
--- /dev/null
+++ b/intelligent_cabin/archive/tests/test_workflow_templates.py
@@ -0,0 +1,184 @@
+from __future__ import annotations
+
+import unittest
+from pathlib import Path
+
+from app.schemas.configuration import WorkflowTemplatesConfig
+from app.services.classifier import ClassificationResult
+from app.services.multi_intent_detector import MultiIntentCandidate, MultiIntentDetectionResult
+from app.services.planner import CompositeWorkflowPlanner, HeuristicWorkflowPlanner, TemplateWorkflowPlanner
+from app.services.intent_registry import IntentRegistry
+
+
+class FakeClauseClassifier:
+    def __init__(self, predictions: dict[str, list[dict[str, float | str]]]) -> None:
+        self._predictions = predictions
+
+    def predict(self, text, intents):
+        _ = intents
+        return ClassificationResult(
+            intent=None,
+            score=0.0,
+            model_name="fake-bert-clause",
+            backend_name="fake-bert-clause",
+            raw_candidates=self._predictions.get(text, []),
+        )
+
+
+class FakeMultiIntentDetector:
+    def __init__(self, predictions: dict[str, list[tuple[str, float]]]) -> None:
+        self._predictions = predictions
+
+    def detect(self, text, intents):
+        _ = intents
+        candidates = [
+            MultiIntentCandidate(intent_id=intent_id, score=score, label=intent_id)
+            for intent_id, score in self._predictions.get(text, [])
+        ]
+        return MultiIntentDetectionResult(
+            detected=len(candidates) >= 2,
+            candidates=candidates,
+            reason="fake detector",
+            backend_name="fake-multi",
+        )
+
+
+class WorkflowTemplateTests(unittest.TestCase):
+    def setUp(self) -> None:
+        self.registry = IntentRegistry.from_json("app/data/intents.json")
+        self.templates = WorkflowTemplatesConfig.model_validate_json(
+            Path("config/workflows.yml").read_text(encoding="utf-8")
+        )
+
+    def test_template_planner_matches_sequence_template(self) -> None:
+        planner = TemplateWorkflowPlanner(self.templates)
+
+        result = planner.plan("打开车窗，然后把空调调到20度", self.registry.list())
+
+        self.assertTrue(result.accepted)
+        self.assertEqual(result.backend, "local-template")
+        self.assertEqual(result.workflow_type, "sequence")
+        self.assertEqual([step.intent_id for step in result.steps], ["cabin_window_open", "cabin_set_ac"])
+        self.assertEqual(result.steps[1].slots.get("temperature"), 20)
+
+    def test_template_planner_matches_conditional_template(self) -> None:
+        planner = TemplateWorkflowPlanner(self.templates)
+
+        result = planner.plan("查一下订单A123456，如果还没发货就取消", self.registry.list())
+
+        self.assertTrue(result.accepted)
+        self.assertEqual(result.workflow_type, "conditional")
+        self.assertEqual([step.intent_id for step in result.steps], ["cs_query_order", "cs_cancel_order"])
+        self.assertEqual(result.steps[1].depends_on, [1])
+        self.assertTrue(result.steps[1].requires_confirmation)
+
+    def test_composite_planner_falls_back_to_heuristic_when_template_misses(self) -> None:
+        planner = CompositeWorkflowPlanner([TemplateWorkflowPlanner(self.templates), HeuristicWorkflowPlanner()])
+
+        result = planner.plan("打开车窗，并且播放轻音乐", self.registry.list())
+
+        self.assertTrue(result.accepted)
+        self.assertIn(result.backend, {"local-template", "local-heuristic"})
+        self.assertEqual(result.workflow_type, "sequence")
+
+    def test_heuristic_planner_parses_ac_then_window_close_sequence(self) -> None:
+        planner = HeuristicWorkflowPlanner()
+
+        result = planner.plan("打开空调，再把窗户降下来", self.registry.list())
+
+        self.assertTrue(result.accepted)
+        self.assertEqual(result.backend, "local-heuristic")
+        self.assertEqual(result.workflow_type, "sequence")
+        self.assertEqual([step.intent_id for step in result.steps], ["cabin_ac_on", "cabin_window_close"])
+
+    def test_planner_metadata_contains_clause_analysis(self) -> None:
+        planner = HeuristicWorkflowPlanner()
+
+        result = planner.plan("打开空调，然后打开车窗", self.registry.list())
+
+        self.assertTrue(result.accepted)
+        self.assertTrue(result.metadata.get("multi_intent_detected"))
+        clause_analysis = result.metadata.get("clause_analysis", [])
+        self.assertEqual(len(clause_analysis), 2)
+        self.assertEqual(clause_analysis[0].get("selected_intent_id"), "cabin_ac_on")
+        self.assertEqual(clause_analysis[1].get("selected_intent_id"), "cabin_window_open")
+
+    def test_heuristic_planner_supports_shared_action_parallel_objects(self) -> None:
+        planner = HeuristicWorkflowPlanner()
+
+        result = planner.plan("打开空调和车窗", self.registry.list())
+
+        self.assertTrue(result.accepted)
+        self.assertEqual(result.workflow_type, "sequence")
+        self.assertEqual([step.intent_id for step in result.steps], ["cabin_ac_on", "cabin_window_open"])
+
+    def test_heuristic_planner_supports_parallel_objects_with_suffix_action(self) -> None:
+        planner = HeuristicWorkflowPlanner()
+
+        result = planner.plan("把车窗和天窗打开", self.registry.list())
+
+        self.assertTrue(result.accepted)
+        self.assertEqual(result.workflow_type, "sequence")
+        self.assertEqual([step.intent_id for step in result.steps], ["cabin_window_open", "cabin_sunroof_open"])
+
+    def test_heuristic_planner_supports_parallel_clause_with_bing_connector(self) -> None:
+        planner = HeuristicWorkflowPlanner()
+
+        result = planner.plan("打开车窗并播放轻音乐", self.registry.list())
+
+        self.assertTrue(result.accepted)
+        self.assertEqual(result.workflow_type, "sequence")
+        self.assertEqual([step.intent_id for step in result.steps], ["cabin_window_open", "cabin_play_music"])
+
+    def test_heuristic_planner_can_use_clause_classifier_to_rescue_semantic_clause(self) -> None:
+        planner = HeuristicWorkflowPlanner(
+            clause_classifier=FakeClauseClassifier(
+                {
+                    "车里太闷了": [
+                        {"label": "cabin_window_open", "intent_id": "cabin_window_open", "score": 0.83},
+                    ],
+                    "来点轻音乐": [
+                        {"label": "cabin_play_music", "intent_id": "cabin_play_music", "score": 0.91},
+                    ],
+                }
+            )
+        )
+
+        result = planner.plan("车里太闷了，然后来点轻音乐", self.registry.list())
+
+        self.assertTrue(result.accepted)
+        self.assertEqual(result.workflow_type, "sequence")
+        self.assertEqual([step.intent_id for step in result.steps], ["cabin_window_open", "cabin_play_music"])
+        clause_analysis = result.metadata.get("clause_analysis", [])
+        self.assertGreater(clause_analysis[0].get("candidates", [])[0].get("model_score", 0.0), 0.8)
+
+    def test_heuristic_planner_can_use_multi_intent_detector_prior(self) -> None:
+        planner = HeuristicWorkflowPlanner(
+            clause_classifier=FakeClauseClassifier(
+                {
+                    "来点轻音乐": [
+                        {"label": "cabin_play_music", "intent_id": "cabin_play_music", "score": 0.91},
+                    ],
+                }
+            ),
+            multi_intent_detector=FakeMultiIntentDetector(
+                {
+                    "顺便开下车窗，再来点轻音乐": [
+                        ("cabin_window_open", 0.87),
+                        ("cabin_play_music", 0.82),
+                    ]
+                }
+            ),
+        )
+
+        result = planner.plan("顺便开下车窗，再来点轻音乐", self.registry.list())
+
+        self.assertTrue(result.accepted)
+        self.assertEqual([step.intent_id for step in result.steps], ["cabin_window_open", "cabin_play_music"])
+        detector_meta = result.metadata.get("multi_intent_detector") or {}
+        self.assertTrue(detector_meta.get("detected"))
+        self.assertEqual(len(detector_meta.get("candidates", [])), 2)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/intelligent_cabin/config/actions.yml b/intelligent_cabin/config/actions.yml
new file mode 100644
index 0000000..a32f0d5
--- /dev/null
+++ b/intelligent_cabin/config/actions.yml
@@ -0,0 +1,38 @@
+{
+  "actions": [
+    { "action_id": "action_cs_query_order", "plugin_id": "plugin.order.query", "risk_level": "low" },
+    { "action_id": "action_cs_query_logistics", "plugin_id": "plugin.logistics.query", "risk_level": "low" },
+    { "action_id": "action_cs_cancel_order", "plugin_id": "plugin.order.cancel", "risk_level": "medium" },
+    { "action_id": "action_cs_transfer_human", "plugin_id": "plugin.service.transfer_human", "risk_level": "low" },
+    { "action_id": "action_cabin_nav_cancel", "plugin_id": "plugin.cabin.navigation.cancel", "risk_level": "low" },
+    { "action_id": "action_cabin_nav_to", "plugin_id": "plugin.cabin.navigation", "risk_level": "medium" },
+    { "action_id": "action_cabin_ac_on", "plugin_id": "plugin.cabin.ac.on", "risk_level": "low" },
+    { "action_id": "action_cabin_ac_off", "plugin_id": "plugin.cabin.ac.off", "risk_level": "low" },
+    { "action_id": "action_cabin_set_ac", "plugin_id": "plugin.cabin.ac_control", "risk_level": "low" },
+    { "action_id": "action_cabin_fan_up", "plugin_id": "plugin.cabin.fan.up", "risk_level": "low" },
+    { "action_id": "action_cabin_fan_down", "plugin_id": "plugin.cabin.fan.down", "risk_level": "low" },
+    { "action_id": "action_cabin_defog_front_on", "plugin_id": "plugin.cabin.defog.front_on", "risk_level": "low" },
+    { "action_id": "action_cabin_defog_rear_on", "plugin_id": "plugin.cabin.defog.rear_on", "risk_level": "low" },
+    { "action_id": "action_cabin_window_open", "plugin_id": "plugin.cabin.window.open", "risk_level": "low" },
+    { "action_id": "action_cabin_window_close", "plugin_id": "plugin.cabin.window.close", "risk_level": "low" },
+    { "action_id": "action_cabin_sunroof_open", "plugin_id": "plugin.cabin.sunroof.open", "risk_level": "low" },
+    { "action_id": "action_cabin_sunroof_close", "plugin_id": "plugin.cabin.sunroof.close", "risk_level": "low" },
+    { "action_id": "action_cabin_lock_doors", "plugin_id": "plugin.cabin.doors.lock", "risk_level": "medium" },
+    { "action_id": "action_cabin_unlock_doors", "plugin_id": "plugin.cabin.doors.unlock", "risk_level": "medium" },
+    { "action_id": "action_cabin_play_music", "plugin_id": "plugin.cabin.music_play", "risk_level": "low" },
+    { "action_id": "action_cabin_pause_music", "plugin_id": "plugin.cabin.music.pause", "risk_level": "low" },
+    { "action_id": "action_cabin_next_track", "plugin_id": "plugin.cabin.music.next", "risk_level": "low" },
+    { "action_id": "action_cabin_previous_track", "plugin_id": "plugin.cabin.music.previous", "risk_level": "low" },
+    { "action_id": "action_cabin_volume_up", "plugin_id": "plugin.cabin.volume.up", "risk_level": "low" },
+    { "action_id": "action_cabin_volume_down", "plugin_id": "plugin.cabin.volume.down", "risk_level": "low" },
+    { "action_id": "action_cabin_volume_mute", "plugin_id": "plugin.cabin.volume.mute", "risk_level": "low" },
+    { "action_id": "action_cabin_lights_on", "plugin_id": "plugin.cabin.lights.on", "risk_level": "low" },
+    { "action_id": "action_cabin_lights_off", "plugin_id": "plugin.cabin.lights.off", "risk_level": "low" },
+    { "action_id": "action_cabin_seat_heat_on", "plugin_id": "plugin.cabin.seat_heat.on", "risk_level": "low" },
+    { "action_id": "action_cabin_seat_heat_off", "plugin_id": "plugin.cabin.seat_heat.off", "risk_level": "low" },
+    { "action_id": "action_cabin_mirror_fold", "plugin_id": "plugin.cabin.mirror.fold", "risk_level": "low" },
+    { "action_id": "action_cabin_mirror_unfold", "plugin_id": "plugin.cabin.mirror.unfold", "risk_level": "low" },
+    { "action_id": "action_cabin_wiper_on", "plugin_id": "plugin.cabin.wiper.on", "risk_level": "low" },
+    { "action_id": "action_cabin_wiper_off", "plugin_id": "plugin.cabin.wiper.off", "risk_level": "low" }
+  ]
+}
diff --git a/intelligent_cabin/config/context_rewrite.yml b/intelligent_cabin/config/context_rewrite.yml
new file mode 100644
index 0000000..a43acd2
--- /dev/null
+++ b/intelligent_cabin/config/context_rewrite.yml
@@ -0,0 +1,76 @@
+# 本地上下文改写引擎配置
+# 用于多轮相对调节的短句改写，如 "再快一点" → "速度设为 85 mm/min"
+# 每个 param_context 对应一类可以相对调节的参数。
+# 部署到不同设备时，只需修改本文件（或切换 AGENT_CONTEXT_REWRITE_CONFIG_PATH）。
+
+param_contexts:
+  # ────────────────────────────────────────────────
+  # 加工速度
+  # ────────────────────────────────────────────────
+  - intent_ids:
+      - wirecut_set_speed
+    slot_name: speed
+    unit: "mm/min"
+    step: 5
+    min_value: 1
+    max_value: 9999
+    default_value: 80
+    up_phrases:
+      - 再快一点
+      - 快一点
+      - 加快
+      - 速度调高
+      - 速度大一点
+    down_phrases:
+      - 再慢一点
+      - 慢一点
+      - 减慢
+      - 速度调低
+      - 速度小一点
+    rewrite_template: "速度设为 {value} mm/min"
+
+  # ────────────────────────────────────────────────
+  # 放电电压（放电码）
+  # ────────────────────────────────────────────────
+  - intent_ids:
+      - wirecut_set_voltage
+    slot_name: voltage
+    unit: ""
+    step: 5
+    min_value: 0
+    max_value: 200
+    default_value: 90
+    up_phrases:
+      - 电压高一点
+      - 电压调高
+      - 放电码高一点
+      - 放电码大一点
+    down_phrases:
+      - 电压低一点
+      - 电压调低
+      - 放电码低一点
+      - 放电码小一点
+    rewrite_template: "电压设为 {value}"
+
+  # ────────────────────────────────────────────────
+  # 放电电流（跟踪值）
+  # ────────────────────────────────────────────────
+  - intent_ids:
+      - wirecut_set_current
+    slot_name: current
+    unit: ""
+    step: 1
+    min_value: 0
+    max_value: 30
+    default_value: 5
+    up_phrases:
+      - 电流大一点
+      - 电流调高
+      - 跟踪值高一点
+      - 跟踪值大一点
+    down_phrases:
+      - 电流小一点
+      - 电流调低
+      - 跟踪值低一点
+      - 跟踪值小一点
+    rewrite_template: "电流设为 {value}"
diff --git a/intelligent_cabin/config/dialog_acts.yml b/intelligent_cabin/config/dialog_acts.yml
new file mode 100644
index 0000000..fbbb929
--- /dev/null
+++ b/intelligent_cabin/config/dialog_acts.yml
@@ -0,0 +1,79 @@
+# Dialog Act 配置
+# act_id     : 行为标识，代码内引用此字段
+# phrases    : 字符串包含匹配词表，匹配到任意一个即命中该 act
+# numeric_patterns : 正则列表，输入文本匹配任意一个则命中该 act（用于数字类 inform）
+# priority   : 可选，数字越小越先检查（默认按声明顺序）
+#
+# 说明：不同设备部署时，修改本文件即可调整对话行为词表，无需改动代码。
+
+acts:
+  - act_id: affirm
+    phrases:
+      - 确认
+      - 好的
+      - 继续
+      - 可以
+      - 确定
+      - 执行
+      - 是
+      - "ok"
+      - "yes"
+
+  - act_id: deny
+    phrases:
+      - 不要
+      - 不行
+      - 否
+      - 不
+      - "no"
+
+  - act_id: cancel
+    phrases:
+      - 取消
+      - 算了
+      - 不用了
+      - 停止
+      - 中止
+      - 放弃
+
+  - act_id: modify
+    phrases:
+      - 改成
+      - 换成
+      - 再低一点
+      - 再高一点
+      - 调大
+      - 调小
+      - 快一点
+      - 慢一点
+
+  - act_id: chitchat
+    phrases:
+      - 你好
+      - 谢谢
+      - 再见
+      - 天气
+      - 真不错
+
+  - act_id: request
+    phrases:
+      - 帮我
+      - 打开
+      - 关闭
+      - 导航
+      - 播放
+      - 查询
+      - 查
+      - 启动
+      - 停止加工
+      - 回零
+      - 加载
+      - 设为
+      - 设置
+
+  # inform：用于用户直接说出数值（作为 slot 填写），如 "85" / "22度" / "0.5"
+  # phrases 留空，完全依赖 numeric_patterns 匹配
+  - act_id: inform
+    phrases: []
+    numeric_patterns:
+      - "\\d+"
diff --git a/intelligent_cabin/config/domain.yml b/intelligent_cabin/config/domain.yml
new file mode 100644
index 0000000..9385ced
--- /dev/null
+++ b/intelligent_cabin/config/domain.yml
@@ -0,0 +1,38 @@
+{
+  "intents": [
+    { "intent_id": "cs_query_order", "domain": "customer_service", "action_id": "action_cs_query_order", "label": "查订单", "required_slots": ["order_id"], "ask_templates": { "order_id": "请提供订单号。" }, "keywords": ["查询订单", "查订单", "订单状态"], "examples": ["帮我查一下订单", "我的订单到哪一步了", "查下订单状态"] },
+    { "intent_id": "cs_query_logistics", "domain": "customer_service", "action_id": "action_cs_query_logistics", "label": "查物流", "required_slots": ["order_id"], "ask_templates": { "order_id": "请提供订单号。" }, "keywords": ["查物流", "物流", "快递"], "examples": ["帮我查一下快递", "看看物流到哪了", "快递什么时候到"] },
+    { "intent_id": "cs_cancel_order", "domain": "customer_service", "action_id": "action_cs_cancel_order", "label": "取消订单", "required_slots": ["order_id"], "ask_templates": { "order_id": "请提供要取消的订单号。" }, "keywords": ["取消订单", "撤销订单"], "examples": ["帮我取消这个订单", "我不想要了取消吧", "撤销刚才的订单"] },
+    { "intent_id": "cs_transfer_human", "domain": "customer_service", "action_id": "action_cs_transfer_human", "label": "转人工", "required_slots": [], "ask_templates": {}, "keywords": ["转人工", "人工客服", "联系客服"], "examples": ["我要人工客服", "帮我转人工", "联系客服"] },
+    { "intent_id": "cabin_nav_cancel", "domain": "cabin", "action_id": "action_cabin_nav_cancel", "label": "结束导航", "required_slots": [], "ask_templates": {}, "keywords": ["取消导航", "结束导航", "停止导航"], "examples": ["把导航关掉", "退出导航", "别导航了"] },
+    { "intent_id": "cabin_nav_to", "domain": "cabin", "action_id": "action_cabin_nav_to", "label": "导航", "required_slots": ["destination"], "ask_templates": { "destination": "请告诉我要导航去哪里。" }, "keywords": ["导航去", "导航到", "带我去"], "examples": ["导航去公司", "带我去机场", "导航到虹桥火车站"] },
+    { "intent_id": "cabin_ac_on", "domain": "cabin", "action_id": "action_cabin_ac_on", "label": "打开空调", "required_slots": [], "ask_templates": {}, "keywords": ["打开空调", "开启空调", "空调打开"], "examples": ["把空调打开", "开空调", "启动空调"] },
+    { "intent_id": "cabin_ac_off", "domain": "cabin", "action_id": "action_cabin_ac_off", "label": "关闭空调", "required_slots": [], "ask_templates": {}, "keywords": ["关闭空调", "关掉空调", "空调关闭"], "examples": ["把空调关掉", "别吹空调了", "空调先关了"] },
+    { "intent_id": "cabin_set_ac", "domain": "cabin", "action_id": "action_cabin_set_ac", "label": "调空调", "required_slots": ["temperature"], "ask_templates": { "temperature": "请告诉我要设置多少度。" }, "keywords": ["空调调到", "温度设成", "设成多少度"], "examples": ["把空调调到22度", "温度设成24度", "空调调到20度"] },
+    { "intent_id": "cabin_fan_up", "domain": "cabin", "action_id": "action_cabin_fan_up", "label": "调大风量", "required_slots": [], "ask_templates": {}, "keywords": ["调大风量", "风量大一点", "风量调高"], "examples": ["把风量调大一点", "空调风再大一点", "风量开大些"] },
+    { "intent_id": "cabin_fan_down", "domain": "cabin", "action_id": "action_cabin_fan_down", "label": "调小风量", "required_slots": [], "ask_templates": {}, "keywords": ["调小风量", "风量小一点", "风量调低"], "examples": ["把风量调小一点", "空调风太大了", "风量关小些"] },
+    { "intent_id": "cabin_defog_front_on", "domain": "cabin", "action_id": "action_cabin_defog_front_on", "label": "打开前挡除雾", "required_slots": [], "ask_templates": {}, "keywords": ["打开前挡除雾", "前挡风除雾", "前窗除雾"], "examples": ["帮我打开前挡除雾", "前挡风玻璃起雾了", "开一下前挡除雾"] },
+    { "intent_id": "cabin_defog_rear_on", "domain": "cabin", "action_id": "action_cabin_defog_rear_on", "label": "打开后挡除雾", "required_slots": [], "ask_templates": {}, "keywords": ["打开后挡除雾", "后挡风除雾", "后窗除雾"], "examples": ["帮我打开后挡除雾", "后挡风玻璃起雾了", "开一下后挡除雾"] },
+    { "intent_id": "cabin_window_open", "domain": "cabin", "action_id": "action_cabin_window_open", "label": "打开车窗", "required_slots": [], "ask_templates": {}, "keywords": ["打开车窗", "开车窗", "车窗打开"], "examples": ["把车窗打开", "帮我开一下车窗", "打开一点车窗"] },
+    { "intent_id": "cabin_window_close", "domain": "cabin", "action_id": "action_cabin_window_close", "label": "关闭车窗", "required_slots": [], "ask_templates": {}, "keywords": ["关闭车窗", "关车窗", "车窗关上"], "examples": ["把车窗关上", "帮我关一下车窗", "车窗全部关闭"] },
+    { "intent_id": "cabin_sunroof_open", "domain": "cabin", "action_id": "action_cabin_sunroof_open", "label": "打开天窗", "required_slots": [], "ask_templates": {}, "keywords": ["打开天窗", "开天窗", "天窗打开"], "examples": ["把天窗打开", "帮我开一下天窗", "天窗打开一点"] },
+    { "intent_id": "cabin_sunroof_close", "domain": "cabin", "action_id": "action_cabin_sunroof_close", "label": "关闭天窗", "required_slots": [], "ask_templates": {}, "keywords": ["关闭天窗", "关天窗", "天窗关上"], "examples": ["把天窗关上", "帮我关一下天窗", "关闭全景天窗"] },
+    { "intent_id": "cabin_lock_doors", "domain": "cabin", "action_id": "action_cabin_lock_doors", "label": "锁车门", "required_slots": [], "ask_templates": {}, "keywords": ["锁车门", "锁门", "车门锁上"], "examples": ["帮我锁车", "把车门锁上", "全部车门上锁"] },
+    { "intent_id": "cabin_unlock_doors", "domain": "cabin", "action_id": "action_cabin_unlock_doors", "label": "解锁车门", "required_slots": [], "ask_templates": {}, "keywords": ["解锁车门", "开锁", "车门解锁"], "examples": ["帮我解锁车门", "把车门打开锁", "全部车门解锁"] },
+    { "intent_id": "cabin_play_music", "domain": "cabin", "action_id": "action_cabin_play_music", "label": "播放音乐", "required_slots": [], "ask_templates": {}, "keywords": ["播放音乐", "来点音乐", "放首歌"], "examples": ["播放轻音乐", "来点歌", "帮我放首歌"] },
+    { "intent_id": "cabin_pause_music", "domain": "cabin", "action_id": "action_cabin_pause_music", "label": "暂停音乐", "required_slots": [], "ask_templates": {}, "keywords": ["暂停音乐", "暂停播放", "音乐暂停"], "examples": ["把音乐暂停", "先别放了", "暂停当前歌曲"] },
+    { "intent_id": "cabin_next_track", "domain": "cabin", "action_id": "action_cabin_next_track", "label": "下一首", "required_slots": [], "ask_templates": {}, "keywords": ["下一首", "切下一首", "换首歌"], "examples": ["帮我切到下一首", "下一首歌", "换一首歌"] },
+    { "intent_id": "cabin_previous_track", "domain": "cabin", "action_id": "action_cabin_previous_track", "label": "上一首", "required_slots": [], "ask_templates": {}, "keywords": ["上一首", "切上一首", "返回上一首"], "examples": ["帮我切到上一首", "上一首歌", "返回刚才那首歌"] },
+    { "intent_id": "cabin_volume_up", "domain": "cabin", "action_id": "action_cabin_volume_up", "label": "调大音量", "required_slots": [], "ask_templates": {}, "keywords": ["调大音量", "音量大一点", "音量调高"], "examples": ["把音量调大一点", "声音太小了", "音量开大些"] },
+    { "intent_id": "cabin_volume_down", "domain": "cabin", "action_id": "action_cabin_volume_down", "label": "调小音量", "required_slots": [], "ask_templates": {}, "keywords": ["调小音量", "音量小一点", "音量调低"], "examples": ["把音量调小一点", "声音太大了", "音量关小些"] },
+    { "intent_id": "cabin_volume_mute", "domain": "cabin", "action_id": "action_cabin_volume_mute", "label": "静音", "required_slots": [], "ask_templates": {}, "keywords": ["静音", "关闭声音", "音量静音"], "examples": ["把声音关掉", "先静音", "音响静音"] },
+    { "intent_id": "cabin_lights_on", "domain": "cabin", "action_id": "action_cabin_lights_on", "label": "打开车灯", "required_slots": [], "ask_templates": {}, "keywords": ["打开车灯", "开灯", "车灯打开"], "examples": ["把车灯打开", "帮我开一下灯", "打开大灯"] },
+    { "intent_id": "cabin_lights_off", "domain": "cabin", "action_id": "action_cabin_lights_off", "label": "关闭车灯", "required_slots": [], "ask_templates": {}, "keywords": ["关闭车灯", "关灯", "车灯关闭"], "examples": ["把车灯关掉", "帮我关一下灯", "关闭大灯"] },
+    { "intent_id": "cabin_seat_heat_on", "domain": "cabin", "action_id": "action_cabin_seat_heat_on", "label": "打开座椅加热", "required_slots": [], "ask_templates": {}, "keywords": ["打开座椅加热", "开座椅加热", "座椅加热打开"], "examples": ["把座椅加热打开", "帮我开一下座椅加热", "打开主驾座椅加热"] },
+    { "intent_id": "cabin_seat_heat_off", "domain": "cabin", "action_id": "action_cabin_seat_heat_off", "label": "关闭座椅加热", "required_slots": [], "ask_templates": {}, "keywords": ["关闭座椅加热", "关座椅加热", "座椅加热关闭"], "examples": ["把座椅加热关掉", "帮我关一下座椅加热", "关闭主驾座椅加热"] },
+    { "intent_id": "cabin_mirror_fold", "domain": "cabin", "action_id": "action_cabin_mirror_fold", "label": "折叠后视镜", "required_slots": [], "ask_templates": {}, "keywords": ["折叠后视镜", "收起后视镜", "后视镜折叠"], "examples": ["把后视镜折叠起来", "帮我收起后视镜", "折叠两侧后视镜"] },
+    { "intent_id": "cabin_mirror_unfold", "domain": "cabin", "action_id": "action_cabin_mirror_unfold", "label": "展开后视镜", "required_slots": [], "ask_templates": {}, "keywords": ["展开后视镜", "打开后视镜", "后视镜展开"], "examples": ["把后视镜展开", "帮我打开后视镜", "展开两侧后视镜"] },
+    { "intent_id": "cabin_wiper_on", "domain": "cabin", "action_id": "action_cabin_wiper_on", "label": "打开雨刷", "required_slots": [], "ask_templates": {}, "keywords": ["打开雨刷", "开雨刷", "雨刷启动"], "examples": ["把雨刷打开", "帮我开一下雨刷", "启动雨刮器"] },
+    { "intent_id": "cabin_wiper_off", "domain": "cabin", "action_id": "action_cabin_wiper_off", "label": "关闭雨刷", "required_slots": [], "ask_templates": {}, "keywords": ["关闭雨刷", "关雨刷", "雨刷停止"], "examples": ["把雨刷关掉", "帮我关一下雨刷", "停止雨刮器"] }
+  ]
+}
diff --git a/intelligent_cabin/config/forms.yml b/intelligent_cabin/config/forms.yml
new file mode 100644
index 0000000..7bc8e18
--- /dev/null
+++ b/intelligent_cabin/config/forms.yml
@@ -0,0 +1,39 @@
+{
+  "forms": [
+    {
+      "intent_id": "cs_query_order",
+      "required_slots": ["order_id"],
+      "ask_templates": {
+        "order_id": "请提供订单号。"
+      }
+    },
+    {
+      "intent_id": "cs_query_logistics",
+      "required_slots": ["order_id"],
+      "ask_templates": {
+        "order_id": "请提供订单号。"
+      }
+    },
+    {
+      "intent_id": "cs_cancel_order",
+      "required_slots": ["order_id"],
+      "ask_templates": {
+        "order_id": "请提供要取消的订单号。"
+      }
+    },
+    {
+      "intent_id": "cabin_nav_to",
+      "required_slots": ["destination"],
+      "ask_templates": {
+        "destination": "请告诉我要导航去哪里。"
+      }
+    },
+    {
+      "intent_id": "cabin_set_ac",
+      "required_slots": ["temperature"],
+      "ask_templates": {
+        "temperature": "请告诉我要设置多少度。"
+      }
+    }
+  ]
+}
diff --git a/intelligent_cabin/config/knowledge/false_weld_alarm.md b/intelligent_cabin/config/knowledge/false_weld_alarm.md
new file mode 100644
index 0000000..8727184
--- /dev/null
+++ b/intelligent_cabin/config/knowledge/false_weld_alarm.md
@@ -0,0 +1,82 @@
+# 虚焊报警处理 — 弯管工序 / 涡流探伤
+
+## 结论
+
+虚焊报警（涡流探伤触发）主要来自**弯管成形形态偏差**，需用激光扫描仪逐一确认模具点位，偏差超限时通过平板 AI 自动调节，再开机生产一小段验证。
+
+---
+
+## 处理步骤
+
+1. **定位报警来源**：确认涡流探伤仪报警段对应的管材来自哪个模具区间。
+2. **激光扫描检测**：使用激光扫描仪逐一扫描弯管工序中每个模具点位。
+3. **偏差判定**：平板 App 自动比对标准形态与当前形态，显示偏差值。
+4. **AI 自动调节**：偏差超出正常误差范围时，在平板上点击「AI 自动调节」。
+5. **验证**：调节完毕后开机生产约 1～2m，重新扫描确认偏差已消除。
+
+---
+
+## 激光扫描仪操作详情
+
+### 准备工作
+
+- 设备：激光轮廓扫描仪（安装在模具出口侧）
+- 前提：停机、管材退出模具区、扫描仪校准完成（上电 30s 后自动完成）
+
+### 扫描步骤
+
+1. 打开平板 App → 选择「弯管检测」→ 点击「开始扫描」
+2. 扫描仪激光线覆盖管材横截面，约 2s 完成单点采集
+3. 移动至下一模具出口，重复扫描
+4. 扫描完成后点击「生成报告」，平板显示差异云图
+
+### 偏差判定标准
+
+| 偏差等级 | 数值范围 | 建议操作 |
+|:---:|:---:|:---:|
+| 正常 | ≤ ±0.15mm | 无需处理 |
+| 警告 | ±0.15～0.30mm | 记录，下次保养时处理 |
+| 超限 | > ±0.30mm | 立即 AI 自动调节 |
+
+### 图片参考
+
+<!-- media: images/laser_scan_setup.jpg -->
+> 📷 **图1**：激光扫描仪安装位置（模具出口侧，红色激光线垂直于管材轴线）
+> 本地路径：`/assets/knowledge/laser_scan_setup.jpg`
+
+<!-- media: images/tablet_deviation_map.jpg -->
+> 📷 **图2**：平板 App 差异云图示意（绿色=正常，黄色=警告，红色=超限）
+> 本地路径：`/assets/knowledge/tablet_deviation_map.jpg`
+
+### 视频教程
+
+<!-- media: videos/laser_scan_tutorial.mp4 -->
+> 🎬 **视频**：激光扫描仪完整操作流程（约 4 分钟）
+> 本地路径：`/assets/knowledge/laser_scan_tutorial.mp4`
+
+---
+
+## AI 自动调节说明
+
+1. 平板 App 偏差报告页，点击「AI 自动调节」
+2. 系统读取偏差向量，计算每个伺服轴的补偿量
+3. 补偿量写入 PLC，各伺服电机自动移动到新位置（约 15s）
+4. 屏幕显示「调节完成，请验证」
+
+> ⚠️ **注意**：调节期间禁止手动移动任何模具；若调节失败（伺服报警），请联系设备工程师。
+
+---
+
+## 相关设备
+
+- 涡流探伤仪型号：EC-500（上海康肽）
+- 激光扫描仪型号：LJ-X8200（基恩士）
+- 控制平板：焊管机 HMI Pro v2.3
+
+---
+
+## 引用文档
+
+- 《焊管机维护手册》第 7 章：弯管工序成形质量管理
+- 《激光扫描仪操作规程》SOP-QC-003 v1.2
+- 《涡流探伤报警处置流程》SOP-QC-007 v2.0
diff --git a/intelligent_cabin/config/responses.yml b/intelligent_cabin/config/responses.yml
new file mode 100644
index 0000000..1d6ce4d
--- /dev/null
+++ b/intelligent_cabin/config/responses.yml
@@ -0,0 +1,30 @@
+{
+  "templates": {
+    "ask_order_id": "请提供订单号。",
+    "ask_cancel_order_id": "请告诉我订单号。",
+    "ask_destination": "请告诉我要去哪里。",
+    "ask_temperature": "请告诉我要设置多少度。",
+    "workflow_failed": "这次没处理成功，请稍后再试。",
+    "workflow_summary_empty": "好的，已经处理完成。",
+    "confirm_retry": "我需要一个明确确认。请回复“确认”继续，或回复“取消”终止。",
+    "confirm_cancelled": "好的，已取消这一步。",
+    "ack_default": "收到，马上处理。",
+    "ack_cabin": "好的，马上处理。",
+    "ack_service": "收到，我来处理。",
+    "reject": "这个我暂时做不了，但我可以帮你查询、控制或转人工。",
+    "short_social_greeting": "你好，我在。",
+    "short_social_thanks": "不客气。",
+    "short_social_goodbye": "好的，有需要再叫我。",
+    "short_social_capability": "我可以帮你查订单、查物流、取消订单、导航、调空调、播放音乐或转人工。",
+    "short_social_default": "我在。",
+    "open_social_fallback": "可以和你聊两句，你也可以继续告诉我想处理什么。",
+    "pending_confirmation_hint": "当前这一步还在等你确认，回复“确认”或“取消”即可。",
+    "pending_slot_order_id": "当前还缺订单号，你继续告诉我订单号就行。",
+    "pending_slot_temperature": "当前还缺温度，你继续告诉我要设置多少度就行。",
+    "pending_slot_destination": "当前还缺目的地，你继续告诉我要去哪里就行。",
+    "pending_slot_default": "当前还缺一个关键信息，你继续补充就行。",
+    "pending_running": "当前任务还在继续，你也可以直接继续下一个指令。",
+    "task_stopped": "好的，已停止当前任务。",
+    "fallback": "我还没完全听懂，你可以换个简短说法，或告诉我是查询、控制还是转人工。"
+  }
+}
diff --git a/intelligent_cabin/config/rules.yml b/intelligent_cabin/config/rules.yml
new file mode 100644
index 0000000..f054b9e
--- /dev/null
+++ b/intelligent_cabin/config/rules.yml
@@ -0,0 +1,43 @@
+{
+  "stop": {
+    "phrases": [
+      "不用了",
+      "算了",
+      "先不要了",
+      "先这样吧",
+      "停一下",
+      "停止",
+      "停止当前任务",
+      "结束这次操作",
+      "别弄了",
+      "不需要了"
+    ]
+  },
+  "confirmation": {
+    "positive_tokens": [
+      "确认",
+      "好的",
+      "是",
+      "继续",
+      "可以",
+      "确定",
+      "yes",
+      "ok"
+    ],
+    "negative_tokens": [
+      "取消",
+      "不用",
+      "不要",
+      "否",
+      "no",
+      "算了",
+      "停止"
+    ],
+    "required_intents": [
+      "cs_cancel_order"
+    ],
+    "required_risk_levels": [
+      "high"
+    ]
+  }
+}
diff --git a/intelligent_cabin/config/voice_aliases.yml b/intelligent_cabin/config/voice_aliases.yml
new file mode 100644
index 0000000..8298856
--- /dev/null
+++ b/intelligent_cabin/config/voice_aliases.yml
@@ -0,0 +1,95 @@
+# voice_aliases.yml
+# 语音点击匹配词表 · 前端阶段 0 / 阶段 1 使用
+#
+# 说明：
+#   - affirm_words / deny_words / cancel_words 用于阶段 0（停止词）和阶段 1a（等待确认状态的 affirm/deny）
+#   - intent_aliases 用于各 Artifact 的 voiceActions 语音别名
+#   - 部署到不同设备时只需替换本文件，无需改动代码
+#
+# TS 构建时由脚本 scripts/generate-voice-aliases.mjs 读取本文件，
+# 自动生成 src/lib/nlu/voice-aliases.gen.ts
+
+affirm_words:
+  - 确认
+  - 好的
+  - 执行
+  - 是
+  - 对
+  - 继续
+  - 好
+  - 可以
+  - 确定
+  - "ok"
+  - "yes"
+
+deny_words:
+  - 不行
+  - 不要
+  - 不
+  - 否
+  - "no"
+
+cancel_words:
+  - 取消
+  - 算了
+  - 不用了
+  - 停止
+  - 中止
+  - 放弃
+
+# ─── 工控设备别名（按 intent_id 分组）───────────────────────────────────────
+# 这里的 key 与 intelligent_cabin 里 IntentDefinition.intent_id 一一对应。
+# 前端 Artifact 创建时会读取这些别名，填入 ArtifactAction.voiceAliases。
+intent_aliases:
+  wirecut_start_run:
+    - 开始
+    - 启动
+    - 加工
+    - 跑起来
+    - 开始加工
+    - 启动加工
+
+  wirecut_stop_run:
+    - 停
+    - 停机
+    - 急停
+    - 停止
+    - 停止加工
+
+  wirecut_pause_run:
+    - 暂停
+    - 变频暂停
+
+  wirecut_resume_run:
+    - 继续
+    - 恢复
+    - 继续加工
+
+  wirecut_home_all:
+    - 回零
+    - 归零
+    - 回原点
+    - 全轴回零
+
+  wirecut_home_x:
+    - X轴回零
+    - x回零
+
+  wirecut_home_y:
+    - Y轴回零
+    - y回零
+
+  wirecut_set_speed:
+    - 设置速度
+    - 调速度
+    - 修改速度
+
+  wirecut_set_voltage:
+    - 设置电压
+    - 调电压
+    - 修改电压
+
+  wirecut_set_tension:
+    - 设置拉力
+    - 调拉力
+    - 修改拉力
diff --git a/intelligent_cabin/config/workflows.yml b/intelligent_cabin/config/workflows.yml
new file mode 100644
index 0000000..cc4f3ad
--- /dev/null
+++ b/intelligent_cabin/config/workflows.yml
@@ -0,0 +1,34 @@
+{
+  "templates": [
+    {
+      "template_id": "window_then_ac_sequence",
+      "workflow_type": "sequence",
+      "intent_sequence": ["cabin_window_open", "cabin_set_ac"],
+      "trigger_keywords": ["车窗", "空调"],
+      "step_overrides": [
+        {},
+        {}
+      ]
+    },
+    {
+      "template_id": "query_then_cancel_if_pending",
+      "workflow_type": "conditional",
+      "intent_sequence": ["cs_query_order", "cs_cancel_order"],
+      "trigger_keywords": ["订单"],
+      "step_overrides": [
+        {},
+        {
+          "depends_on": [1],
+          "condition": {
+            "source_step": 1,
+            "field": "order_status",
+            "operator": "equals",
+            "value": "pending_shipment",
+            "description": "仅在订单未发货时取消"
+          },
+          "requires_confirmation": true
+        }
+      ]
+    }
+  ]
+}
diff --git a/intelligent_cabin/models/local_bert_intent/config.json b/intelligent_cabin/models/local_bert_intent/config.json
new file mode 100644
index 0000000..ad03e66
--- /dev/null
+++ b/intelligent_cabin/models/local_bert_intent/config.json
@@ -0,0 +1,108 @@
+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "__out_of_scope__",
+    "1": "__social__",
+    "2": "cabin_ac_off",
+    "3": "cabin_ac_on",
+    "4": "cabin_defog_front_on",
+    "5": "cabin_defog_rear_on",
+    "6": "cabin_fan_down",
+    "7": "cabin_fan_up",
+    "8": "cabin_lights_off",
+    "9": "cabin_lights_on",
+    "10": "cabin_lock_doors",
+    "11": "cabin_mirror_fold",
+    "12": "cabin_mirror_unfold",
+    "13": "cabin_nav_cancel",
+    "14": "cabin_nav_to",
+    "15": "cabin_next_track",
+    "16": "cabin_pause_music",
+    "17": "cabin_play_music",
+    "18": "cabin_previous_track",
+    "19": "cabin_seat_heat_off",
+    "20": "cabin_seat_heat_on",
+    "21": "cabin_set_ac",
+    "22": "cabin_sunroof_close",
+    "23": "cabin_sunroof_open",
+    "24": "cabin_unlock_doors",
+    "25": "cabin_volume_down",
+    "26": "cabin_volume_mute",
+    "27": "cabin_volume_up",
+    "28": "cabin_window_close",
+    "29": "cabin_window_open",
+    "30": "cabin_wiper_off",
+    "31": "cabin_wiper_on",
+    "32": "cs_cancel_order",
+    "33": "cs_query_logistics",
+    "34": "cs_query_order",
+    "35": "cs_transfer_human"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "__out_of_scope__": 0,
+    "__social__": 1,
+    "cabin_ac_off": 2,
+    "cabin_ac_on": 3,
+    "cabin_defog_front_on": 4,
+    "cabin_defog_rear_on": 5,
+    "cabin_fan_down": 6,
+    "cabin_fan_up": 7,
+    "cabin_lights_off": 8,
+    "cabin_lights_on": 9,
+    "cabin_lock_doors": 10,
+    "cabin_mirror_fold": 11,
+    "cabin_mirror_unfold": 12,
+    "cabin_nav_cancel": 13,
+    "cabin_nav_to": 14,
+    "cabin_next_track": 15,
+    "cabin_pause_music": 16,
+    "cabin_play_music": 17,
+    "cabin_previous_track": 18,
+    "cabin_seat_heat_off": 19,
+    "cabin_seat_heat_on": 20,
+    "cabin_set_ac": 21,
+    "cabin_sunroof_close": 22,
+    "cabin_sunroof_open": 23,
+    "cabin_unlock_doors": 24,
+    "cabin_volume_down": 25,
+    "cabin_volume_mute": 26,
+    "cabin_volume_up": 27,
+    "cabin_window_close": 28,
+    "cabin_window_open": 29,
+    "cabin_wiper_off": 30,
+    "cabin_wiper_on": 31,
+    "cs_cancel_order": 32,
+    "cs_query_logistics": 33,
+    "cs_query_order": 34,
+    "cs_transfer_human": 35
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "transformers_version": "4.57.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 21128
+}
diff --git a/intelligent_cabin/models/local_bert_intent/label_map.json b/intelligent_cabin/models/local_bert_intent/label_map.json
new file mode 100644
index 0000000..e9c5865
--- /dev/null
+++ b/intelligent_cabin/models/local_bert_intent/label_map.json
@@ -0,0 +1,38 @@
+{
+  "LABEL_0": "__out_of_scope__",
+  "LABEL_1": "__social__",
+  "LABEL_2": "cabin_ac_off",
+  "LABEL_3": "cabin_ac_on",
+  "LABEL_4": "cabin_defog_front_on",
+  "LABEL_5": "cabin_defog_rear_on",
+  "LABEL_6": "cabin_fan_down",
+  "LABEL_7": "cabin_fan_up",
+  "LABEL_8": "cabin_lights_off",
+  "LABEL_9": "cabin_lights_on",
+  "LABEL_10": "cabin_lock_doors",
+  "LABEL_11": "cabin_mirror_fold",
+  "LABEL_12": "cabin_mirror_unfold",
+  "LABEL_13": "cabin_nav_cancel",
+  "LABEL_14": "cabin_nav_to",
+  "LABEL_15": "cabin_next_track",
+  "LABEL_16": "cabin_pause_music",
+  "LABEL_17": "cabin_play_music",
+  "LABEL_18": "cabin_previous_track",
+  "LABEL_19": "cabin_seat_heat_off",
+  "LABEL_20": "cabin_seat_heat_on",
+  "LABEL_21": "cabin_set_ac",
+  "LABEL_22": "cabin_sunroof_close",
+  "LABEL_23": "cabin_sunroof_open",
+  "LABEL_24": "cabin_unlock_doors",
+  "LABEL_25": "cabin_volume_down",
+  "LABEL_26": "cabin_volume_mute",
+  "LABEL_27": "cabin_volume_up",
+  "LABEL_28": "cabin_window_close",
+  "LABEL_29": "cabin_window_open",
+  "LABEL_30": "cabin_wiper_off",
+  "LABEL_31": "cabin_wiper_on",
+  "LABEL_32": "cs_cancel_order",
+  "LABEL_33": "cs_query_logistics",
+  "LABEL_34": "cs_query_order",
+  "LABEL_35": "cs_transfer_human"
+}
\ No newline at end of file
diff --git a/intelligent_cabin/models/local_bert_intent/special_tokens_map.json b/intelligent_cabin/models/local_bert_intent/special_tokens_map.json
new file mode 100644
index 0000000..9bbecc1
--- /dev/null
+++ b/intelligent_cabin/models/local_bert_intent/special_tokens_map.json
@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/intelligent_cabin/models/local_bert_intent/tokenizer.json b/intelligent_cabin/models/local_bert_intent/tokenizer.json
new file mode 100644
index 0000000..6b001d5
--- /dev/null
+++ b/intelligent_cabin/models/local_bert_intent/tokenizer.json
@@ -0,0 +1,21292 @@
+{
+  "version": "1.0",
+  "truncation": {
+    "direction": "Right",
+    "max_length": 48,
+    "strategy": "LongestFirst",
+    "stride": 0
+  },
+  "padding": {
+    "strategy": {
+      "Fixed": 48
+    },
+    "direction": "Right",
+    "pad_to_multiple_of": null,
+    "pad_id": 0,
+    "pad_type_id": 0,
+    "pad_token": "[PAD]"
+  },
+  "added_tokens": [
+    {
+      "id": 0,
+      "content": "[PAD]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 100,
+      "content": "[UNK]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 101,
+      "content": "[CLS]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 102,
+      "content": "[SEP]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 103,
+      "content": "[MASK]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    }
+  ],
+  "normalizer": {
+    "type": "BertNormalizer",
+    "clean_text": true,
+    "handle_chinese_chars": true,
+    "strip_accents": null,
+    "lowercase": true
+  },
+  "pre_tokenizer": {
+    "type": "BertPreTokenizer"
+  },
+  "post_processor": {
+    "type": "TemplateProcessing",
+    "single": [
+      {
+        "SpecialToken": {
+          "id": "[CLS]",
+          "type_id": 0
+        }
+      },
+      {
+        "Sequence": {
+          "id": "A",
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\ No newline at end of file
diff --git a/intelligent_cabin/models/local_bert_intent/tokenizer_config.json b/intelligent_cabin/models/local_bert_intent/tokenizer_config.json
new file mode 100644
index 0000000..bb71a3d
--- /dev/null
+++ b/intelligent_cabin/models/local_bert_intent/tokenizer_config.json
@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}
diff --git a/intelligent_cabin/models/local_bert_intent/train_summary.json b/intelligent_cabin/models/local_bert_intent/train_summary.json
new file mode 100644
index 0000000..3459bcc
--- /dev/null
+++ b/intelligent_cabin/models/local_bert_intent/train_summary.json
@@ -0,0 +1,10 @@
+{
+  "base_model": "hfl/chinese-macbert-base",
+  "epochs": 16,
+  "batch_size": 8,
+  "learning_rate": 2e-05,
+  "train_size": 1557,
+  "dev_size": 401,
+  "best_dev_accuracy": 0.9875,
+  "device": "mps"
+}
\ No newline at end of file
diff --git a/intelligent_cabin/models/local_bert_intent/vocab.txt b/intelligent_cabin/models/local_bert_intent/vocab.txt
new file mode 100644
index 0000000..ca4f978
--- /dev/null
+++ b/intelligent_cabin/models/local_bert_intent/vocab.txt
@@ -0,0 +1,21128 @@
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diff --git a/intelligent_cabin/models/local_bert_multi_intent/config.json b/intelligent_cabin/models/local_bert_multi_intent/config.json
new file mode 100644
index 0000000..1216363
--- /dev/null
+++ b/intelligent_cabin/models/local_bert_multi_intent/config.json
@@ -0,0 +1,104 @@
+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "cabin_ac_off",
+    "1": "cabin_ac_on",
+    "2": "cabin_defog_front_on",
+    "3": "cabin_defog_rear_on",
+    "4": "cabin_fan_down",
+    "5": "cabin_fan_up",
+    "6": "cabin_lights_off",
+    "7": "cabin_lights_on",
+    "8": "cabin_lock_doors",
+    "9": "cabin_mirror_fold",
+    "10": "cabin_mirror_unfold",
+    "11": "cabin_nav_cancel",
+    "12": "cabin_nav_to",
+    "13": "cabin_next_track",
+    "14": "cabin_pause_music",
+    "15": "cabin_play_music",
+    "16": "cabin_previous_track",
+    "17": "cabin_seat_heat_off",
+    "18": "cabin_seat_heat_on",
+    "19": "cabin_set_ac",
+    "20": "cabin_sunroof_close",
+    "21": "cabin_sunroof_open",
+    "22": "cabin_unlock_doors",
+    "23": "cabin_volume_down",
+    "24": "cabin_volume_mute",
+    "25": "cabin_volume_up",
+    "26": "cabin_window_close",
+    "27": "cabin_window_open",
+    "28": "cabin_wiper_off",
+    "29": "cabin_wiper_on",
+    "30": "cs_cancel_order",
+    "31": "cs_query_logistics",
+    "32": "cs_query_order",
+    "33": "cs_transfer_human"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "cabin_ac_off": 0,
+    "cabin_ac_on": 1,
+    "cabin_defog_front_on": 2,
+    "cabin_defog_rear_on": 3,
+    "cabin_fan_down": 4,
+    "cabin_fan_up": 5,
+    "cabin_lights_off": 6,
+    "cabin_lights_on": 7,
+    "cabin_lock_doors": 8,
+    "cabin_mirror_fold": 9,
+    "cabin_mirror_unfold": 10,
+    "cabin_nav_cancel": 11,
+    "cabin_nav_to": 12,
+    "cabin_next_track": 13,
+    "cabin_pause_music": 14,
+    "cabin_play_music": 15,
+    "cabin_previous_track": 16,
+    "cabin_seat_heat_off": 17,
+    "cabin_seat_heat_on": 18,
+    "cabin_set_ac": 19,
+    "cabin_sunroof_close": 20,
+    "cabin_sunroof_open": 21,
+    "cabin_unlock_doors": 22,
+    "cabin_volume_down": 23,
+    "cabin_volume_mute": 24,
+    "cabin_volume_up": 25,
+    "cabin_window_close": 26,
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+  "position_embedding_type": "absolute",
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+  "transformers_version": "4.57.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 21128
+}
diff --git a/intelligent_cabin/models/local_bert_multi_intent/label_map.json b/intelligent_cabin/models/local_bert_multi_intent/label_map.json
new file mode 100644
index 0000000..434734d
--- /dev/null
+++ b/intelligent_cabin/models/local_bert_multi_intent/label_map.json
@@ -0,0 +1,36 @@
+{
+  "LABEL_0": "cabin_ac_off",
+  "LABEL_1": "cabin_ac_on",
+  "LABEL_2": "cabin_defog_front_on",
+  "LABEL_3": "cabin_defog_rear_on",
+  "LABEL_4": "cabin_fan_down",
+  "LABEL_5": "cabin_fan_up",
+  "LABEL_6": "cabin_lights_off",
+  "LABEL_7": "cabin_lights_on",
+  "LABEL_8": "cabin_lock_doors",
+  "LABEL_9": "cabin_mirror_fold",
+  "LABEL_10": "cabin_mirror_unfold",
+  "LABEL_11": "cabin_nav_cancel",
+  "LABEL_12": "cabin_nav_to",
+  "LABEL_13": "cabin_next_track",
+  "LABEL_14": "cabin_pause_music",
+  "LABEL_15": "cabin_play_music",
+  "LABEL_16": "cabin_previous_track",
+  "LABEL_17": "cabin_seat_heat_off",
+  "LABEL_18": "cabin_seat_heat_on",
+  "LABEL_19": "cabin_set_ac",
+  "LABEL_20": "cabin_sunroof_close",
+  "LABEL_21": "cabin_sunroof_open",
+  "LABEL_22": "cabin_unlock_doors",
+  "LABEL_23": "cabin_volume_down",
+  "LABEL_24": "cabin_volume_mute",
+  "LABEL_25": "cabin_volume_up",
+  "LABEL_26": "cabin_window_close",
+  "LABEL_27": "cabin_window_open",
+  "LABEL_28": "cabin_wiper_off",
+  "LABEL_29": "cabin_wiper_on",
+  "LABEL_30": "cs_cancel_order",
+  "LABEL_31": "cs_query_logistics",
+  "LABEL_32": "cs_query_order",
+  "LABEL_33": "cs_transfer_human"
+}
\ No newline at end of file
diff --git a/intelligent_cabin/models/local_bert_multi_intent/special_tokens_map.json b/intelligent_cabin/models/local_bert_multi_intent/special_tokens_map.json
new file mode 100644
index 0000000..9bbecc1
--- /dev/null
+++ b/intelligent_cabin/models/local_bert_multi_intent/special_tokens_map.json
@@ -0,0 +1,37 @@
+{
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+}
diff --git a/intelligent_cabin/models/local_bert_multi_intent/tokenizer.json b/intelligent_cabin/models/local_bert_multi_intent/tokenizer.json
new file mode 100644
index 0000000..6b001d5
--- /dev/null
+++ b/intelligent_cabin/models/local_bert_multi_intent/tokenizer.json
@@ -0,0 +1,21292 @@
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diff --git a/intelligent_cabin/models/local_bert_multi_intent/tokenizer_config.json b/intelligent_cabin/models/local_bert_multi_intent/tokenizer_config.json
new file mode 100644
index 0000000..bb71a3d
--- /dev/null
+++ b/intelligent_cabin/models/local_bert_multi_intent/tokenizer_config.json
@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "rstrip": false,
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+      "special": true
+    },
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
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+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}
diff --git a/intelligent_cabin/models/local_bert_multi_intent/train_summary.json b/intelligent_cabin/models/local_bert_multi_intent/train_summary.json
new file mode 100644
index 0000000..c8ed92c
--- /dev/null
+++ b/intelligent_cabin/models/local_bert_multi_intent/train_summary.json
@@ -0,0 +1,56 @@
+{
+  "task_type": "multi_label_intent_detection",
+  "base_model": "hfl/chinese-macbert-base",
+  "epochs": 12,
+  "batch_size": 8,
+  "learning_rate": 2e-05,
+  "threshold": 0.5,
+  "top_k": 4,
+  "train_size": 862,
+  "dev_size": 236,
+  "label_count": 34,
+  "labels": [
+    "cabin_ac_off",
+    "cabin_ac_on",
+    "cabin_defog_front_on",
+    "cabin_defog_rear_on",
+    "cabin_fan_down",
+    "cabin_fan_up",
+    "cabin_lights_off",
+    "cabin_lights_on",
+    "cabin_lock_doors",
+    "cabin_mirror_fold",
+    "cabin_mirror_unfold",
+    "cabin_nav_cancel",
+    "cabin_nav_to",
+    "cabin_next_track",
+    "cabin_pause_music",
+    "cabin_play_music",
+    "cabin_previous_track",
+    "cabin_seat_heat_off",
+    "cabin_seat_heat_on",
+    "cabin_set_ac",
+    "cabin_sunroof_close",
+    "cabin_sunroof_open",
+    "cabin_unlock_doors",
+    "cabin_volume_down",
+    "cabin_volume_mute",
+    "cabin_volume_up",
+    "cabin_window_close",
+    "cabin_window_open",
+    "cabin_wiper_off",
+    "cabin_wiper_on",
+    "cs_cancel_order",
+    "cs_query_logistics",
+    "cs_query_order",
+    "cs_transfer_human"
+  ],
+  "best_dev_metrics": {
+    "micro_precision": 1.0,
+    "micro_recall": 0.9929,
+    "micro_f1": 0.9964,
+    "exact_match": 0.9915,
+    "recall_at_k": 1.0
+  },
+  "device": "mps"
+}
\ No newline at end of file
diff --git a/intelligent_cabin/models/local_bert_multi_intent/vocab.txt b/intelligent_cabin/models/local_bert_multi_intent/vocab.txt
new file mode 100644
index 0000000..ca4f978
--- /dev/null
+++ b/intelligent_cabin/models/local_bert_multi_intent/vocab.txt
@@ -0,0 +1,21128 @@
+[PAD]
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+[UNK]
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diff --git a/intelligent_cabin/models/local_joint_bert_nlu/joint_nlu_config.json b/intelligent_cabin/models/local_joint_bert_nlu/joint_nlu_config.json
new file mode 100644
index 0000000..ed47d38
--- /dev/null
+++ b/intelligent_cabin/models/local_joint_bert_nlu/joint_nlu_config.json
@@ -0,0 +1,57 @@
+{
+  "base_model_name": "hfl/chinese-macbert-base",
+  "intent_task": "multi_label",
+  "intent_labels": [
+    "cabin_ac_off",
+    "cabin_ac_on",
+    "cabin_defog_front_on",
+    "cabin_defog_rear_on",
+    "cabin_fan_down",
+    "cabin_fan_up",
+    "cabin_lights_off",
+    "cabin_lights_on",
+    "cabin_lock_doors",
+    "cabin_mirror_fold",
+    "cabin_mirror_unfold",
+    "cabin_nav_cancel",
+    "cabin_nav_to",
+    "cabin_next_track",
+    "cabin_pause_music",
+    "cabin_play_music",
+    "cabin_previous_track",
+    "cabin_seat_heat_off",
+    "cabin_seat_heat_on",
+    "cabin_set_ac",
+    "cabin_sunroof_close",
+    "cabin_sunroof_open",
+    "cabin_unlock_doors",
+    "cabin_volume_down",
+    "cabin_volume_mute",
+    "cabin_volume_up",
+    "cabin_window_close",
+    "cabin_window_open",
+    "cabin_wiper_off",
+    "cabin_wiper_on",
+    "cs_cancel_order",
+    "cs_query_logistics",
+    "cs_query_order",
+    "cs_transfer_human"
+  ],
+  "slot_labels": [
+    "O",
+    "B-destination",
+    "I-destination",
+    "B-genre",
+    "I-genre",
+    "B-order_id",
+    "I-order_id",
+    "B-song",
+    "I-song",
+    "B-temperature",
+    "I-temperature"
+  ],
+  "max_length": 64,
+  "intent_threshold": 0.4,
+  "multi_intent_threshold": 0.4,
+  "max_multi_intents": 4
+}
\ No newline at end of file
diff --git a/intelligent_cabin/models/local_joint_bert_nlu/special_tokens_map.json b/intelligent_cabin/models/local_joint_bert_nlu/special_tokens_map.json
new file mode 100644
index 0000000..9bbecc1
--- /dev/null
+++ b/intelligent_cabin/models/local_joint_bert_nlu/special_tokens_map.json
@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/intelligent_cabin/models/local_joint_bert_nlu/tokenizer.json b/intelligent_cabin/models/local_joint_bert_nlu/tokenizer.json
new file mode 100644
index 0000000..c5690b0
--- /dev/null
+++ b/intelligent_cabin/models/local_joint_bert_nlu/tokenizer.json
@@ -0,0 +1,21292 @@
+{
+  "version": "1.0",
+  "truncation": {
+    "direction": "Right",
+    "max_length": 64,
+    "strategy": "LongestFirst",
+    "stride": 0
+  },
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+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
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+      "content": "[UNK]",
+      "single_word": false,
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+      "normalized": false,
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+    },
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+      "special": true
+    },
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+      "content": "[SEP]",
+      "single_word": false,
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+      "content": "[MASK]",
+      "single_word": false,
+      "lstrip": false,
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+      "normalized": false,
+      "special": true
+    }
+  ],
+  "normalizer": {
+    "type": "BertNormalizer",
+    "clean_text": true,
+    "handle_chinese_chars": true,
+    "strip_accents": null,
+    "lowercase": true
+  },
+  "pre_tokenizer": {
+    "type": "BertPreTokenizer"
+  },
+  "post_processor": {
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diff --git a/intelligent_cabin/models/local_joint_bert_nlu/tokenizer_config.json b/intelligent_cabin/models/local_joint_bert_nlu/tokenizer_config.json
new file mode 100644
index 0000000..bb71a3d
--- /dev/null
+++ b/intelligent_cabin/models/local_joint_bert_nlu/tokenizer_config.json
@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
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+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
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+  "tokenizer_class": "BertTokenizer",
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+}
diff --git a/intelligent_cabin/models/local_joint_bert_nlu/train_summary.json b/intelligent_cabin/models/local_joint_bert_nlu/train_summary.json
new file mode 100644
index 0000000..806d315
--- /dev/null
+++ b/intelligent_cabin/models/local_joint_bert_nlu/train_summary.json
@@ -0,0 +1,17 @@
+{
+  "train_size": 744,
+  "eval_size": 22,
+  "metrics": {
+    "intent_threshold": 0.4,
+    "intent_micro_precision": 0.8974,
+    "intent_micro_recall": 1.0,
+    "intent_micro_f1": 0.9459,
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+    "intent_label_count": 34.0,
+    "slot_label_count": 11.0,
+    "train_loss": 0.1618
+  }
+}
\ No newline at end of file
diff --git a/intelligent_cabin/models/local_joint_bert_nlu/vocab.txt b/intelligent_cabin/models/local_joint_bert_nlu/vocab.txt
new file mode 100644
index 0000000..ca4f978
--- /dev/null
+++ b/intelligent_cabin/models/local_joint_bert_nlu/vocab.txt
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diff --git a/intelligent_cabin/pyproject.toml b/intelligent_cabin/pyproject.toml
new file mode 100644
index 0000000..757bc52
--- /dev/null
+++ b/intelligent_cabin/pyproject.toml
@@ -0,0 +1,24 @@
+[project]
+name = "intelligent-cabin-agent"
+version = "0.1.0"
+description = "High-response agent service for customer service, aftersales, front desk, and intelligent cabin."
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+  "fastapi>=0.115.0,<1.0.0",
+  "uvicorn[standard]>=0.30.0,<1.0.0",
+  "pydantic>=2.8.0,<3.0.0",
+  "pydantic-settings>=2.3.0,<3.0.0",
+  "PyYAML>=6.0.2,<7.0.0",
+  "torch>=2.11.0,<3.0.0",
+  "transformers>=4.57.0,<5.0.0",
+]
+
+[build-system]
+requires = ["setuptools>=68", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools]
+
+[tool.setuptools.packages.find]
+include = ["app*"]
diff --git a/intelligent_cabin/requirements.txt b/intelligent_cabin/requirements.txt
new file mode 100644
index 0000000..4c2ccb3
--- /dev/null
+++ b/intelligent_cabin/requirements.txt
@@ -0,0 +1,8 @@
+fastapi>=0.115.0,<1.0.0
+uvicorn[standard]>=0.30.0,<1.0.0
+pydantic>=2.8.0,<3.0.0
+pydantic-settings>=2.3.0,<3.0.0
+redis>=5.0.0,<6.0.0
+PyYAML>=6.0.2,<7.0.0
+torch>=2.11.0,<3.0.0
+transformers>=4.57.0,<5.0.0
diff --git a/package-lock.json b/package-lock.json
index 052ad2d..439f8a5 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -8,9 +8,14 @@
       "name": "ai-canvas",
       "version": "0.1.0",
       "dependencies": {
+        "@ai-sdk/openai": "^0.0.60",
+        "@ai-sdk/react": "^0.0.4",
+        "ai": "^3.4.9",
+        "lucide-react": "^1.14.0",
         "next": "16.2.5",
         "react": "19.2.4",
-        "react-dom": "19.2.4"
+        "react-dom": "19.2.4",
+        "zod": "^3.23.8"
       },
       "devDependencies": {
         "@tailwindcss/postcss": "^4",
@@ -23,6 +28,524 @@
         "typescript": "^5"
       }
     },
+    "node_modules/@ai-sdk/openai": {
+      "version": "0.0.60",
+      "resolved": "https://registry.npmjs.org/@ai-sdk/openai/-/openai-0.0.60.tgz",
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+      "license": "Apache-2.0",
+      "dependencies": {
+        "@ai-sdk/provider": "0.0.23",
+        "@ai-sdk/provider-utils": "1.0.19"
+      },
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+      },
+      "peerDependencies": {
+        "zod": "^3.0.0"
+      }
+    },
+    "node_modules/@ai-sdk/provider": {
+      "version": "0.0.23",
+      "resolved": "https://registry.npmjs.org/@ai-sdk/provider/-/provider-0.0.23.tgz",
+      "integrity": "sha512-oAc49O5+xypVrKM7EUU5P/Y4DUL4JZUWVxhejoAVOTOl3WZUEWsMbP3QZR+TrimQIsS0WR/n9UuF6U0jPdp0tQ==",
+      "license": "Apache-2.0",
+      "dependencies": {
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+      },
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+    "node_modules/@ai-sdk/provider-utils": {
+      "version": "1.0.19",
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+      "funding": [
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+          "type": "github",
+          "url": "https://github.com/sponsors/ai"
+        }
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+      },
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+          "url": "https://github.com/sponsors/ai"
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       "version": "0.4.1",
       "resolved": "https://registry.npmjs.org/json-schema-traverse/-/json-schema-traverse-0.4.1.tgz",
@@ -4491,6 +5225,35 @@
         "node": ">=6"
       }
     },
+    "node_modules/jsondiffpatch": {
+      "version": "0.6.0",
+      "resolved": "https://registry.npmjs.org/jsondiffpatch/-/jsondiffpatch-0.6.0.tgz",
+      "integrity": "sha512-3QItJOXp2AP1uv7waBkao5nCvhEv+QmJAd38Ybq7wNI74Q+BBmnLn4EDKz6yI9xGAIQoUF87qHt+kc1IVxB4zQ==",
+      "license": "MIT",
+      "dependencies": {
+        "@types/diff-match-patch": "^1.0.36",
+        "chalk": "^5.3.0",
+        "diff-match-patch": "^1.0.5"
+      },
+      "bin": {
+        "jsondiffpatch": "bin/jsondiffpatch.js"
+      },
+      "engines": {
+        "node": "^18.0.0 || >=20.0.0"
+      }
+    },
+    "node_modules/jsondiffpatch/node_modules/chalk": {
+      "version": "5.6.2",
+      "resolved": "https://registry.npmjs.org/chalk/-/chalk-5.6.2.tgz",
+      "integrity": "sha512-7NzBL0rN6fMUW+f7A6Io4h40qQlG+xGmtMxfbnH/K7TAtt8JQWVQK+6g0UXKMeVJoyV5EkkNsErQ8pVD3bLHbA==",
+      "license": "MIT",
+      "engines": {
+        "node": "^12.17.0 || ^14.13 || >=16.0.0"
+      },
+      "funding": {
+        "url": "https://github.com/chalk/chalk?sponsor=1"
+      }
+    },
     "node_modules/jsx-ast-utils": {
       "version": "3.3.5",
       "resolved": "https://registry.npmjs.org/jsx-ast-utils/-/jsx-ast-utils-3.3.5.tgz",
@@ -4858,6 +5621,15 @@
         "yallist": "^3.0.2"
       }
     },
+    "node_modules/lucide-react": {
+      "version": "1.14.0",
+      "resolved": "https://registry.npmjs.org/lucide-react/-/lucide-react-1.14.0.tgz",
+      "integrity": "sha512-+1mdWcfSJVUsaTIjN9zoezmUhfXo5l0vP7ekBMPo3jcS/aIkxHnXqAPsByszMZx/Y8oQBRJxJx5xg+RH3urzxA==",
+      "license": "ISC",
+      "peerDependencies": {
+        "react": "^16.5.1 || ^17.0.0 || ^18.0.0 || ^19.0.0"
+      }
+    },
     "node_modules/magic-string": {
       "version": "0.30.21",
       "resolved": "https://registry.npmjs.org/magic-string/-/magic-string-0.30.21.tgz",
@@ -5634,6 +6406,12 @@
       "integrity": "sha512-eNv+WrVbKu1f3vbYJT/xtiF5syA5HPIMtf9IgY/nKg0sWqzAUEvqY/xm7OcZc/qafLx/iO9FgOmeSAp4v5ti/Q==",
       "license": "MIT"
     },
+    "node_modules/secure-json-parse": {
+      "version": "2.7.0",
+      "resolved": "https://registry.npmjs.org/secure-json-parse/-/secure-json-parse-2.7.0.tgz",
+      "integrity": "sha512-6aU+Rwsezw7VR8/nyvKTx8QpWH9FrcYiXXlqC4z5d5XQBDRqtbfsRjnwGyqbi3gddNtWHuEk9OANUotL26qKUw==",
+      "license": "BSD-3-Clause"
+    },
     "node_modules/semver": {
       "version": "6.3.1",
       "resolved": "https://registry.npmjs.org/semver/-/semver-6.3.1.tgz",
@@ -5859,6 +6637,18 @@
         "node": ">=0.10.0"
       }
     },
+    "node_modules/sswr": {
+      "version": "2.1.0",
+      "resolved": "https://registry.npmjs.org/sswr/-/sswr-2.1.0.tgz",
+      "integrity": "sha512-Cqc355SYlTAaUt8iDPaC/4DPPXK925PePLMxyBKuWd5kKc5mwsG3nT9+Mq2tyguL5s7b4Jg+IRMpTRsNTAfpSQ==",
+      "license": "MIT",
+      "dependencies": {
+        "swrev": "^4.0.0"
+      },
+      "peerDependencies": {
+        "svelte": "^4.0.0 || ^5.0.0-next.0"
+      }
+    },
     "node_modules/stable-hash": {
       "version": "0.0.5",
       "resolved": "https://registry.npmjs.org/stable-hash/-/stable-hash-0.0.5.tgz",
@@ -6065,6 +6855,33 @@
         "url": "https://github.com/sponsors/ljharb"
       }
     },
+    "node_modules/swr": {
+      "version": "2.2.0",
+      "resolved": "https://registry.npmjs.org/swr/-/swr-2.2.0.tgz",
+      "integrity": "sha512-AjqHOv2lAhkuUdIiBu9xbuettzAzWXmCEcLONNKJRba87WAefz8Ca9d6ds/SzrPc235n1IxWYdhJ2zF3MNUaoQ==",
+      "license": "MIT",
+      "dependencies": {
+        "use-sync-external-store": "^1.2.0"
+      },
+      "peerDependencies": {
+        "react": "^16.11.0 || ^17.0.0 || ^18.0.0"
+      }
+    },
+    "node_modules/swrev": {
+      "version": "4.0.0",
+      "resolved": "https://registry.npmjs.org/swrev/-/swrev-4.0.0.tgz",
+      "integrity": "sha512-LqVcOHSB4cPGgitD1riJ1Hh4vdmITOp+BkmfmXRh4hSF/t7EnS4iD+SOTmq7w5pPm/SiPeto4ADbKS6dHUDWFA==",
+      "license": "MIT"
+    },
+    "node_modules/swrv": {
+      "version": "1.0.4",
+      "resolved": "https://registry.npmjs.org/swrv/-/swrv-1.0.4.tgz",
+      "integrity": "sha512-zjEkcP8Ywmj+xOJW3lIT65ciY/4AL4e/Or7Gj0MzU3zBJNMdJiT8geVZhINavnlHRMMCcJLHhraLTAiDOTmQ9g==",
+      "license": "Apache-2.0",
+      "peerDependencies": {
+        "vue": ">=3.2.26 < 4"
+      }
+    },
     "node_modules/tailwindcss": {
       "version": "4.2.4",
       "resolved": "https://registry.npmjs.org/tailwindcss/-/tailwindcss-4.2.4.tgz",
@@ -6423,6 +7240,15 @@
         "punycode": "^2.1.0"
       }
     },
+    "node_modules/use-sync-external-store": {
+      "version": "1.6.0",
+      "resolved": "https://registry.npmjs.org/use-sync-external-store/-/use-sync-external-store-1.6.0.tgz",
+      "integrity": "sha512-Pp6GSwGP/NrPIrxVFAIkOQeyw8lFenOHijQWkUTrDvrF4ALqylP2C/KCkeS9dpUM3KvYRQhna5vt7IL95+ZQ9w==",
+      "license": "MIT",
+      "peerDependencies": {
+        "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0"
+      }
+    },
     "node_modules/which": {
       "version": "2.0.2",
       "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz",
@@ -6559,15 +7385,23 @@
       }
     },
     "node_modules/zod": {
-      "version": "4.4.3",
-      "resolved": "https://registry.npmjs.org/zod/-/zod-4.4.3.tgz",
-      "integrity": "sha512-ytENFjIJFl2UwYglde2jchW2Hwm4GJFLDiSXWdTrJQBIN9Fcyp7n4DhxJEiWNAJMV1/BqWfW/kkg71UDcHJyTQ==",
-      "dev": true,
+      "version": "3.23.8",
+      "resolved": "https://registry.npmjs.org/zod/-/zod-3.23.8.tgz",
+      "integrity": "sha512-XBx9AXhXktjUqnepgTiE5flcKIYWi/rme0Eaj+5Y0lftuGBq+jyRu/md4WnuxqgP1ubdpNCsYEYPxrzVHD8d6g==",
       "license": "MIT",
       "funding": {
         "url": "https://github.com/sponsors/colinhacks"
       }
     },
+    "node_modules/zod-to-json-schema": {
+      "version": "3.23.2",
+      "resolved": "https://registry.npmjs.org/zod-to-json-schema/-/zod-to-json-schema-3.23.2.tgz",
+      "integrity": "sha512-uSt90Gzc/tUfyNqxnjlfBs8W6WSGpNBv0rVsNxP/BVSMHMKGdthPYff4xtCHYloJGM0CFxFsb3NbC0eqPhfImw==",
+      "license": "ISC",
+      "peerDependencies": {
+        "zod": "^3.23.3"
+      }
+    },
     "node_modules/zod-validation-error": {
       "version": "4.0.2",
       "resolved": "https://registry.npmjs.org/zod-validation-error/-/zod-validation-error-4.0.2.tgz",
diff --git a/package.json b/package.json
index 2e8e34c..eab55c0 100644
--- a/package.json
+++ b/package.json
@@ -3,15 +3,23 @@
   "version": "0.1.0",
   "private": true,
   "scripts": {
+    "generate:voice-aliases": "node scripts/generate-voice-aliases.mjs",
+    "predev": "node scripts/generate-voice-aliases.mjs",
+    "prebuild": "node scripts/generate-voice-aliases.mjs",
     "dev": "next dev",
     "build": "next build",
     "start": "next start",
     "lint": "eslint"
   },
   "dependencies": {
+    "@ai-sdk/openai": "^0.0.60",
+    "@ai-sdk/react": "^0.0.4",
+    "ai": "^3.4.9",
+    "lucide-react": "^1.14.0",
     "next": "16.2.5",
     "react": "19.2.4",
-    "react-dom": "19.2.4"
+    "react-dom": "19.2.4",
+    "zod": "^3.23.8"
   },
   "devDependencies": {
     "@tailwindcss/postcss": "^4",
diff --git a/scripts/generate-voice-aliases.mjs b/scripts/generate-voice-aliases.mjs
new file mode 100644
index 0000000..776988b
--- /dev/null
+++ b/scripts/generate-voice-aliases.mjs
@@ -0,0 +1,98 @@
+#!/usr/bin/env node
+/**
+ * scripts/generate-voice-aliases.mjs
+ *
+ * 读取 intelligent_cabin/config/voice_aliases.yml
+ * 生成 src/lib/nlu/voice-aliases.gen.ts（构建时嵌入，运行时无需 HTTP 请求）
+ *
+ * 用法：
+ *   node scripts/generate-voice-aliases.mjs
+ *   或在 package.json 的 prebuild / predev 中调用
+ */
+
+import { readFileSync, writeFileSync } from "node:fs";
+import { resolve, dirname } from "node:path";
+import { fileURLToPath } from "node:url";
+
+const __dirname = dirname(fileURLToPath(import.meta.url));
+const ROOT = resolve(__dirname, "..");
+
+// ── 极简 YAML 解析（只处理本文件的结构，无需外部依赖）────────────────────
+function parseSimpleYaml(text) {
+  const lines = text.split("\n");
+  const result = {};
+  let currentKey = null;
+  let currentListKey = null;
+  let currentObject = result;
+
+  for (const raw of lines) {
+    const line = raw;
+    const stripped = raw.trimStart();
+    if (!stripped || stripped.startsWith("#")) continue;
+
+    const indent = raw.length - stripped.length;
+
+    // 顶级 key（indent = 0）
+    if (indent === 0 && stripped.endsWith(":")) {
+      currentKey = stripped.slice(0, -1).trim();
+      result[currentKey] = {};
+      currentObject = result[currentKey];
+      currentListKey = null;
+      continue;
+    }
+
+    // intent_aliases 下的子 key（indent = 2）
+    if (indent === 2 && stripped.endsWith(":")) {
+      currentListKey = stripped.slice(0, -1).trim();
+      if (!Array.isArray(currentObject[currentListKey])) {
+        currentObject[currentListKey] = [];
+      }
+      continue;
+    }
+
+    // list items（indent = 2 for top-level lists, indent = 4 for nested）
+    if (stripped.startsWith("- ")) {
+      const value = stripped.slice(2).trim().replace(/^["']|["']$/g, "");
+      if (indent === 2 && !currentListKey) {
+        // top-level list under result key
+        if (!Array.isArray(result[currentKey])) {
+          result[currentKey] = [];
+        }
+        result[currentKey].push(value);
+      } else if (indent === 4 && currentListKey) {
+        currentObject[currentListKey].push(value);
+      }
+      continue;
+    }
+  }
+
+  return result;
+}
+
+// ── 读取 YAML ────────────────────────────────────────────────────────────────
+const yamlPath = resolve(ROOT, "intelligent_cabin/config/voice_aliases.yml");
+const raw = readFileSync(yamlPath, "utf8");
+const parsed = parseSimpleYaml(raw);
+
+const affirmWords = parsed["affirm_words"] ?? [];
+const denyWords = parsed["deny_words"] ?? [];
+const cancelWords = parsed["cancel_words"] ?? [];
+const intentAliases = parsed["intent_aliases"] ?? {};
+
+// ── 生成 TypeScript ───────────────────────────────────────────────────────────
+const ts = `// AUTO-GENERATED — do not edit manually
+// Source: intelligent_cabin/config/voice_aliases.yml
+// Regenerate: node scripts/generate-voice-aliases.mjs
+
+export const AFFIRM_WORDS: readonly string[] = ${JSON.stringify(affirmWords, null, 2)} as const;
+
+export const DENY_WORDS: readonly string[] = ${JSON.stringify(denyWords, null, 2)} as const;
+
+export const CANCEL_WORDS: readonly string[] = ${JSON.stringify(cancelWords, null, 2)} as const;
+
+export const INTENT_ALIASES: Readonly<Record<string, readonly string[]>> = ${JSON.stringify(intentAliases, null, 2)} as const;
+`;
+
+const outPath = resolve(ROOT, "src/lib/nlu/voice-aliases.gen.ts");
+writeFileSync(outPath, ts, "utf8");
+console.log(`✅  Generated: ${outPath}`);
diff --git a/src/app/api/chat/route.ts b/src/app/api/chat/route.ts
new file mode 100644
index 0000000..d5c4d46
--- /dev/null
+++ b/src/app/api/chat/route.ts
@@ -0,0 +1,138 @@
+import { createOpenAI } from "@ai-sdk/openai";
+import { streamText } from "ai";
+import { z } from "zod";
+import {
+  createDeviceActionArtifact,
+  createGuidedProcedureArtifact,
+  createKnowledgeLessonArtifact,
+  createParameterChangeArtifact,
+} from "@/lib/artifacts/mock";
+
+const qwen = createOpenAI({
+  baseURL: "https://dashscope.aliyuncs.com/compatible-mode/v1",
+  apiKey: process.env.DASHSCOPE_API_KEY,
+});
+
+const mockParameterValues: Record<string, string | number> = {
+  主轴转速: 1200,
+  水泵压力上限: 0.6,
+  温度补偿: 0,
+  进给速度: 480,
+};
+
+export const maxDuration = 30;
+
+export async function POST(req: Request) {
+  const { messages } = await req.json();
+
+  const result = await streamText({
+    model: qwen("qwen-plus"),
+    maxSteps: 2,
+    system: [
+      "你是工业生产设备的 AI 交互画布编排助手。",
+      "你不要只输出大段文字；当用户要调参、控制设备、查询设备知识、进入调机流程时，优先调用工具生成 artifact。",
+      "第一阶段只支持文字输入和鼠标点击，不要提 ASR、语音识别或权限分级。",
+      "一次用户请求最多只能调用一个工具。",
+      "工具调用完成后，用一句很短的话说明右侧画布已经生成了什么，绝不能再次调用同一个工具。",
+      "当用户说确认、执行、下一步、已完成等短指令时，如果上文已有当前画布，尽量解释为操作当前画布。",
+    ].join("\n"),
+    messages,
+    tools: {
+      proposeParameterChange: {
+        description: "生成工业参数修改可视化组件，展示当前值、目标值和确认按钮。",
+        parameters: z.object({
+          parameterName: z.string().describe("参数名称，例如 主轴转速、水泵压力上限、温度补偿"),
+          targetValue: z.union([z.string(), z.number()]).describe("目标值"),
+          unit: z.string().optional().describe("单位，例如 rpm、MPa、°C"),
+          deviceName: z.string().optional().describe("设备名称"),
+        }),
+        execute: async ({ parameterName, targetValue, unit, deviceName }) => {
+          const normalizedName = parameterName.includes("转速") ? "主轴转速" : parameterName;
+          const currentValue = mockParameterValues[normalizedName] ?? "未读取";
+          const artifact = createParameterChangeArtifact({
+            parameterName: normalizedName,
+            currentValue,
+            targetValue,
+            unit,
+            deviceName,
+          });
+
+          return {
+            llmSummary: `${normalizedName} 参数修改卡片已生成，当前值 ${currentValue}${unit ?? ""}，目标值 ${targetValue}${unit ?? ""}。`,
+            artifact,
+            executionRecord: {
+              toolName: "proposeParameterChange",
+              args: { parameterName: normalizedName, targetValue, unit, deviceName },
+              resultCode: "MOCK_PREVIEW_CREATED",
+              createdAt: new Date().toISOString(),
+            },
+          };
+        },
+      },
+      controlIndustrialDevice: {
+        description: "生成具象设备控制动画组件，例如打开探照灯、启动水泵、关闭风机。",
+        parameters: z.object({
+          deviceName: z.string().describe("设备名称，例如 探照灯、水泵、除尘风机"),
+          action: z.enum(["open", "close", "start", "stop"]).describe("动作"),
+          deviceKind: z.enum(["light", "pump", "fan", "machine"]).describe("设备类型"),
+        }),
+        execute: async ({ deviceName, action, deviceKind }) => {
+          const artifact = createDeviceActionArtifact({ deviceName, action, deviceKind });
+
+          return {
+            llmSummary: `${deviceName} 的${action}动作卡片已生成，等待用户确认。`,
+            artifact,
+            executionRecord: {
+              toolName: "controlIndustrialDevice",
+              args: { deviceName, action, deviceKind },
+              resultCode: "MOCK_PREVIEW_CREATED",
+              createdAt: new Date().toISOString(),
+            },
+          };
+        },
+      },
+      answerEquipmentKnowledge: {
+        description: "生成设备说明书、报警处理或调机知识的教学式卡片。",
+        parameters: z.object({
+          topic: z.string().describe("知识主题，例如 主轴温度报警处理、冷却泵无流量怎么办"),
+        }),
+        execute: async ({ topic }) => {
+          const artifact = createKnowledgeLessonArtifact(topic);
+
+          return {
+            llmSummary: `${topic} 的教学式知识卡片已生成。`,
+            artifact,
+            executionRecord: {
+              toolName: "answerEquipmentKnowledge",
+              args: { topic },
+              resultCode: "MOCK_KNOWLEDGE_CREATED",
+              createdAt: new Date().toISOString(),
+            },
+          };
+        },
+      },
+      startGuidedProcedure: {
+        description: "生成一个 3 到 5 步的引导式调机流程组件。",
+        parameters: z.object({
+          procedureName: z.string().optional().describe("流程名称"),
+        }),
+        execute: async () => {
+          const artifact = createGuidedProcedureArtifact();
+
+          return {
+            llmSummary: "主轴温度报警调机流程已经生成，当前等待第 1 步反馈。",
+            artifact,
+            executionRecord: {
+              toolName: "startGuidedProcedure",
+              args: {},
+              resultCode: "MOCK_PROCEDURE_CREATED",
+              createdAt: new Date().toISOString(),
+            },
+          };
+        },
+      },
+    },
+  });
+
+  return result.toDataStreamResponse();
+}
diff --git a/src/app/globals.css b/src/app/globals.css
index a2dc41e..4710cfe 100644
--- a/src/app/globals.css
+++ b/src/app/globals.css
@@ -8,19 +8,12 @@
 @theme inline {
   --color-background: var(--background);
   --color-foreground: var(--foreground);
-  --font-sans: var(--font-geist-sans);
-  --font-mono: var(--font-geist-mono);
-}
-
-@media (prefers-color-scheme: dark) {
-  :root {
-    --background: #0a0a0a;
-    --foreground: #ededed;
-  }
+  --font-sans: Arial, "PingFang SC", "Microsoft YaHei", sans-serif;
+  --font-mono: "SFMono-Regular", Consolas, "Liberation Mono", monospace;
 }
 
 body {
   background: var(--background);
   color: var(--foreground);
-  font-family: Arial, Helvetica, sans-serif;
+  font-family: Arial, "PingFang SC", "Microsoft YaHei", sans-serif;
 }
diff --git a/src/app/layout.tsx b/src/app/layout.tsx
index 976eb90..864f575 100644
--- a/src/app/layout.tsx
+++ b/src/app/layout.tsx
@@ -1,20 +1,9 @@
 import type { Metadata } from "next";
-import { Geist, Geist_Mono } from "next/font/google";
 import "./globals.css";
 
-const geistSans = Geist({
-  variable: "--font-geist-sans",
-  subsets: ["latin"],
-});
-
-const geistMono = Geist_Mono({
-  variable: "--font-geist-mono",
-  subsets: ["latin"],
-});
-
 export const metadata: Metadata = {
-  title: "Create Next App",
-  description: "Generated by create next app",
+  title: "工业 AI 交互画布",
+  description: "面向工业设备的 AI Artifact 交互原型",
 };
 
 export default function RootLayout({
@@ -23,10 +12,7 @@ export default function RootLayout({
   children: React.ReactNode;
 }>) {
   return (
-    <html
-      lang="en"
-      className={`${geistSans.variable} ${geistMono.variable} h-full antialiased`}
-    >
+    <html lang="zh-CN" className="h-full antialiased">
       <body className="min-h-full flex flex-col">{children}</body>
     </html>
   );
diff --git a/src/app/page.tsx b/src/app/page.tsx
index 3f36f7c..875f3f0 100644
--- a/src/app/page.tsx
+++ b/src/app/page.tsx
@@ -1,65 +1,607 @@
-import Image from "next/image";
+"use client";
+
+import {
+  Bot,
+  CheckCircle2,
+  Factory,
+  MessageSquareText,
+  Send,
+  User,
+  AlertCircle,
+  BookOpen,
+  Layers,
+  Settings,
+  Thermometer,
+} from "lucide-react";
+
+import type { FormEvent } from "react";
+import { useCallback, useRef, useState } from "react";
+import { ArtifactRenderer } from "@/components/artifacts/ArtifactRenderer";
+import {
+  createActionEvent,
+  matchTextToAction,
+  updateArtifactForAction,
+} from "@/lib/artifacts/reducer";
+import type { ArtifactAction, ArtifactActionEvent, InteractionArtifact } from "@/lib/artifacts/types";
+import {
+  buildVisibleActions,
+  isNluResult,
+  processVoiceInput,
+} from "@/lib/nlu/pipeline";
+import type { NluResult, PipelineSession } from "@/lib/nlu/types";
+import { buildArtifactFromNlu } from "@/lib/artifacts/factory";
+import {
+  buildFalseWeldKnowledgeArtifact,
+  buildDieMaterialAdjustArtifact,
+  buildDieStatusOverviewArtifact,
+} from "@/lib/artifacts/factory";
+
+
+
+// ── 类型 ─────────────────────────────────────────────────────────────────────
+
+type ChatMessage = {
+  id: string;
+  role: "user" | "assistant" | "system" | "error";
+  text: string;
+};
+
+type FlowEvent = {
+  id: string;
+  title: string;
+  detail: string;
+  branch: string;
+  kind: "ux" | "tech" | "action";
+};
+
+type ReferenceTab = {
+  id: string;
+  label: string;
+  kind: "tool" | "knowledge" | "smalltalk";
+  artifact?: InteractionArtifact;
+  text?: string;
+  branch: string;
+};
+
+function uid(prefix: string) {
+  return `${prefix}_${Date.now()}_${Math.random().toString(16).slice(2)}`;
+}
+
+function isGuidedProcedureActive(artifact?: InteractionArtifact) {
+  return artifact?.type === "guided_procedure" && artifact.status !== "success";
+}
+
+// ── 主组件 ───────────────────────────────────────────────────────────────────
 
 export default function Home() {
+  const [inputValue, setInputValue] = useState("");
+  const [activeArtifact, setActiveArtifact] = useState<InteractionArtifact>();
+  const [referenceTabs, setReferenceTabs] = useState<ReferenceTab[]>([]);
+  const [activeReferenceId, setActiveReferenceId] = useState<string>();
+  const [messages, setMessages] = useState<ChatMessage[]>([]);
+  const [isLoading, setIsLoading] = useState(false);
+  const [, setFlowEvents] = useState<FlowEvent[]>([
+    {
+      id: uid("event"),
+      title: "等待输入",
+      detail: "当前没有激活调机流程，系统处于大流程 A：普通对话。",
+      branch: "大流程 A · 普通对话",
+      kind: "ux",
+    },
+  ]);
+
+  // session_id 在页面生命周期内固定，重新刷新页面才重置
+  const sessionId = useRef(uid("session")).current;
+  const userId = useRef("user_001").current;
+
+  const flowMode = isGuidedProcedureActive(activeArtifact) ? "B" : "A";
+  const activeReference =
+    referenceTabs.find((tab) => tab.id === activeReferenceId) ??
+    referenceTabs[referenceTabs.length - 1];
+
+  // 当前可见 Artifact 按钮（供 pipeline 阶段 1b 使用）
+  const visibleActions = activeArtifact
+    ? buildVisibleActions(activeArtifact.artifactId, activeArtifact.actions)
+    : [];
+
+  const pipelineSession = useCallback(
+    (): PipelineSession => ({
+      sessionId,
+      status: activeArtifact?.status ?? "idle",
+      visibleActions,
+    }),
+    [sessionId, activeArtifact, visibleActions],
+  );
+
+  // ── helpers ──────────────────────────────────────────────────────────────────
+
+  function addMessage(role: ChatMessage["role"], text: string) {
+    setMessages((items) => [...items, { id: uid("msg"), role, text }]);
+  }
+
+  function addFlowEvent(event: Omit<FlowEvent, "id">) {
+    setFlowEvents((items) => [...items, { id: uid("event"), ...event }]);
+  }
+
+  // ── NLU 结果处理 ─────────────────────────────────────────────────────────────
+
+  function handleNluResult(result: NluResult) {
+    addFlowEvent({
+      kind: "tech",
+      branch: `${result.decisionGrade} · ${result.routeHint}`,
+      title: `NLU：${result.domain} / ${result.intent}`,
+      detail: `confidence=${result.confidence.toFixed(3)}; routeHint=${result.routeHint}; slots=${JSON.stringify(result.slots)}`,
+    });
+
+    if (isGuidedProcedureActive(activeArtifact)) {
+      handleNluResultDuringProcedure(result);
+      return;
+    }
+
+    // 等待补槽：只回复文字，不渲染 Artifact
+    if (result.pendingSlots.length > 0) {
+      addMessage("assistant", result.replyText);
+      addFlowEvent({
+        kind: "ux",
+        branch: "ask_slot",
+        title: "等待补槽",
+        detail: `pending: ${result.pendingSlots.join(", ")}`,
+      });
+      return;
+    }
+
+    // ── LLM knowledge_search function call 命中 → 直接渲染知识卡片 ──────────
+    if (result.knowledgeContent) {
+      const knowledgeArtifact = buildFalseWeldKnowledgeArtifact();
+      setActiveArtifact(knowledgeArtifact);
+      setReferenceTabs([]);
+      setActiveReferenceId(undefined);
+      addMessage("assistant", result.replyText);
+      addFlowEvent({
+        kind: "ux",
+        branch: "knowledge_query",
+        title: `LLM 知识检索 → Artifact：knowledge_lesson`,
+        detail: `doc=${result.knowledgeDocId ?? "llm_answer"}`,
+      });
+      return;
+    }
+
+    // 尝试从 NLU 结果构造 Artifact
+    const artifact = buildArtifactFromNlu(result);
+    if (artifact) {
+      setActiveArtifact(artifact);
+      setReferenceTabs([]);
+      setActiveReferenceId(undefined);
+      addMessage("assistant", result.replyText);
+      addFlowEvent({
+        kind: "ux",
+        branch: result.routeHint,
+        title: `主画布生成 Artifact：${artifact.type}`,
+        detail: `intent=${result.intent}; artifact=${artifact.artifactId}`,
+      });
+      return;
+    }
+
+    // 无 Artifact：纯文字回复
+    addMessage("assistant", result.replyText);
+    addFlowEvent({
+      kind: "ux",
+      branch: result.routeHint,
+      title: "AI 文字回复",
+      detail: `decision=${result.rawDecision}`,
+    });
+  }
+
+  function handleNluResultDuringProcedure(result: NluResult) {
+    addMessage(
+      "assistant",
+      `当前处于调机流程中。${result.replyText}`,
+    );
+    addFlowEvent({
+      kind: "ux",
+      branch: result.routeHint,
+      title: "临时回答（调机流程中）",
+      detail: "大流程 B：结果进入参考区，不覆盖调机主卡。",
+    });
+
+    const tab: ReferenceTab = {
+      id: uid("ref"),
+      label: result.routeHint === "knowledge_query" ? "知识参考" : "工具结果",
+      kind: result.routeHint === "knowledge_query" ? "knowledge" : "smalltalk",
+      text: result.replyText,
+      branch: result.routeHint,
+    };
+    setReferenceTabs((items) => [...items, tab]);
+    setActiveReferenceId(tab.id);
+  }
+
+  // ── Artifact 动作处理 ────────────────────────────────────────────────────────
+
+  function runArtifactAction(
+    action: ArtifactAction,
+    value?: unknown,
+    source: ArtifactActionEvent["source"] = "click",
+    transcript?: string,
+  ) {
+    if (!activeArtifact) return;
+
+    const event = createActionEvent(activeArtifact.artifactId, action, source, value, transcript);
+    addFlowEvent({
+      kind: "action",
+      branch: "命中当前组件动作",
+      title: `ArtifactActionEvent：${action.actionId}`,
+      detail: transcript ? `source=${source}; transcript=${transcript}` : `source=${source}`,
+    });
+
+    if (
+      action.actionId === "artifact.confirm" &&
+      ["parameter_change", "device_action"].includes(activeArtifact.type)
+    ) {
+      setActiveArtifact({
+        ...activeArtifact,
+        status: "running",
+        actions: activeArtifact.actions.map((item) => ({ ...item, disabled: true })),
+      } as InteractionArtifact);
+      window.setTimeout(() => {
+        setActiveArtifact((current) =>
+          current ? updateArtifactForAction(current, event) : current,
+        );
+      }, 600);
+      return;
+    }
+
+    setActiveArtifact((current) =>
+      current ? updateArtifactForAction(current, event) : current,
+    );
+  }
+
+  function runReferenceAction(tabId: string, action: ArtifactAction, value?: unknown) {
+    const tab = referenceTabs.find((item) => item.id === tabId);
+    if (!tab?.artifact) return;
+
+    const event = createActionEvent(tab.artifact.artifactId, action, "click", value);
+    setReferenceTabs((items) =>
+      items.map((item) =>
+        item.id === tabId && item.artifact
+          ? { ...item, artifact: updateArtifactForAction(item.artifact, event) }
+          : item,
+      ),
+    );
+  }
+
+  // ── 表单提交 ─────────────────────────────────────────────────────────────────
+
+  async function handleSubmit(e: FormEvent) {
+    e.preventDefault();
+    const text = inputValue.trim();
+    if (!text || isLoading) return;
+
+    addMessage("user", text);
+    setInputValue("");
+    setIsLoading(true);
+
+    try {
+      // 阶段 1b：先检查 Artifact 文本命中（本地，同步）
+      if (activeArtifact) {
+        const matchedAction = matchTextToAction(activeArtifact, text);
+        if (matchedAction) {
+          runArtifactAction(matchedAction, text, inferInputSource(text), text);
+          addMessage("system", "命中当前组件动作，未进入新任务识别。");
+          return;
+        }
+      }
+
+      // pipeline（异步，调 intelligent_cabin）
+      const result = await processVoiceInput(text, pipelineSession(), userId);
+
+      if (isNluResult(result)) {
+        handleNluResult(result);
+        return;
+      }
+
+      // PipelineEvent 处理
+      switch (result.type) {
+        case "stop_action":
+          setActiveArtifact((current) =>
+            current ? { ...current, status: "paused" } : current,
+          );
+          addMessage("system", "已停止当前操作。");
+          addFlowEvent({ kind: "action", branch: "停止词", title: "停止操作", detail: result.sourceText });
+          break;
+
+        case "voice_click_event": {
+          if (activeArtifact) {
+            const action = activeArtifact.actions.find(
+              (a) => a.actionId === result.actionId,
+            );
+            if (action) {
+              runArtifactAction(action, undefined, "voice", text);
+            }
+          }
+          break;
+        }
+
+        case "slot_fill_event":
+          addMessage("system", `Slot 填写：${result.value}`);
+          // nluResult 已由 pipeline 内部更新服务端 session，Canvas 此处可选更新 UI
+          if ("nluResult" in result) {
+            handleNluResult((result as { nluResult: NluResult }).nluResult);
+          }
+          break;
+      }
+    } catch (err) {
+      const msg = err instanceof Error ? err.message : String(err);
+      addMessage("error", `NLU 服务错误：${msg}`);
+    } finally {
+      setIsLoading(false);
+    }
+  }
+
+  // ── 渲染 ─────────────────────────────────────────────────────────────────────
+
   return (
-    <div className="flex flex-col flex-1 items-center justify-center bg-zinc-50 font-sans dark:bg-black">
-      <main className="flex flex-1 w-full max-w-3xl flex-col items-center justify-between py-32 px-16 bg-white dark:bg-black sm:items-start">
-        <Image
-          className="dark:invert"
-          src="/next.svg"
-          alt="Next.js logo"
-          width={100}
-          height={20}
-          priority
-        />
-        <div className="flex flex-col items-center gap-6 text-center sm:items-start sm:text-left">
-          <h1 className="max-w-xs text-3xl font-semibold leading-10 tracking-tight text-black dark:text-zinc-50">
-            To get started, edit the page.tsx file.
-          </h1>
-          <p className="max-w-md text-lg leading-8 text-zinc-600 dark:text-zinc-400">
-            Looking for a starting point or more instructions? Head over to{" "}
-            <a
-              href="https://vercel.com/templates?framework=next.js&utm_source=create-next-app&utm_medium=appdir-template-tw&utm_campaign=create-next-app"
-              className="font-medium text-zinc-950 dark:text-zinc-50"
-            >
-              Templates
-            </a>{" "}
-            or the{" "}
-            <a
-              href="https://nextjs.org/learn?utm_source=create-next-app&utm_medium=appdir-template-tw&utm_campaign=create-next-app"
-              className="font-medium text-zinc-950 dark:text-zinc-50"
-            >
-              Learning
-            </a>{" "}
-            center.
-          </p>
-        </div>
-        <div className="flex flex-col gap-4 text-base font-medium sm:flex-row">
-          <a
-            className="flex h-12 w-full items-center justify-center gap-2 rounded-full bg-foreground px-5 text-background transition-colors hover:bg-[#383838] dark:hover:bg-[#ccc] md:w-[158px]"
-            href="https://vercel.com/new?utm_source=create-next-app&utm_medium=appdir-template-tw&utm_campaign=create-next-app"
-            target="_blank"
-            rel="noopener noreferrer"
-          >
-            <Image
-              className="dark:invert"
-              src="/vercel.svg"
-              alt="Vercel logomark"
-              width={16}
-              height={16}
-            />
-            Deploy Now
-          </a>
-          <a
-            className="flex h-12 w-full items-center justify-center rounded-full border border-solid border-black/[.08] px-5 transition-colors hover:border-transparent hover:bg-black/[.04] dark:border-white/[.145] dark:hover:bg-[#1a1a1a] md:w-[158px]"
-            href="https://nextjs.org/docs?utm_source=create-next-app&utm_medium=appdir-template-tw&utm_campaign=create-next-app"
-            target="_blank"
-            rel="noopener noreferrer"
-          >
-            Documentation
-          </a>
+    <div className="min-h-screen w-full bg-[#f0f2f5] text-zinc-950">
+      <header className="sticky top-0 z-20 flex min-h-16 items-center justify-between gap-4 border-b border-zinc-200 bg-white/95 px-5 backdrop-blur">
+        <div className="flex items-center gap-3">
+          <div className="flex h-10 w-10 items-center justify-center rounded-lg bg-zinc-950 text-white">
+            <Factory className="h-5 w-5" />
+          </div>
+          <div>
+            <h1 className="text-base font-extrabold">工业 AI 交互画布</h1>
+            <p className="text-xs text-zinc-500">
+              intelligent_cabin NLU · 大流程 {flowMode} · Artifact 状态机
+            </p>
+          </div>
         </div>
+        <span
+          className={`rounded-full px-3 py-1.5 text-xs font-bold ${
+            flowMode === "A" ? "bg-blue-50 text-blue-700" : "bg-emerald-50 text-emerald-700"
+          }`}
+        >
+          {flowMode === "A" ? "大流程 A · 普通对话" : "大流程 B · 调机流程中"}
+        </span>
+      </header>
+
+      <main className="mx-auto grid w-full max-w-[1800px] gap-4 p-4 xl:grid-cols-[minmax(0,1fr)_420px]">
+        <section className="grid min-w-0 gap-4">
+          <section className="rounded-xl border border-zinc-200 bg-white p-4">
+            <div className="mb-3 flex flex-wrap items-center justify-between gap-3">
+              <div>
+                <div className="text-sm font-extrabold">
+                  {flowMode === "A" ? "大流程 A · 普通对话" : "大流程 B · 调机流程中"}
+                </div>
+                <p className="mt-1 text-xs text-zinc-500">
+                  {flowMode === "A"
+                    ? "无激活调机流程：输入经过四阶段 Pipeline → intelligent_cabin NLU。"
+                    : "GuidedProcedure 激活：短指令优先操作当前步骤，其他结果进入参考区。"}
+                </p>
+              </div>
+            </div>
+
+            {/* ── 演示工具栏（BERT 未训练弯管机语义时直接触发）── */}
+            <div className="mb-3 rounded-lg border border-dashed border-zinc-300 bg-zinc-50/60 p-3">
+              <p className="mb-2 text-[11px] font-semibold uppercase tracking-widest text-zinc-400">
+                演示工具 · 直接触发（无需 NLU）
+              </p>
+              <div className="flex flex-wrap gap-2">
+                <button
+                  type="button"
+                  onClick={() => setActiveArtifact(buildFalseWeldKnowledgeArtifact())}
+                  className="inline-flex items-center gap-1.5 rounded-lg border border-emerald-200 bg-emerald-50 px-3 py-1.5 text-xs font-semibold text-emerald-700 hover:bg-emerald-100 transition-colors"
+                >
+                  <BookOpen className="h-3.5 w-3.5" />
+                  虚焊报警怎么办
+                </button>
+                <button
+                  type="button"
+                  onClick={() => setActiveArtifact(buildDieMaterialAdjustArtifact())}
+                  className="inline-flex items-center gap-1.5 rounded-lg border border-blue-200 bg-blue-50 px-3 py-1.5 text-xs font-semibold text-blue-700 hover:bg-blue-100 transition-colors"
+                >
+                  <Settings className="h-3.5 w-3.5" />
+                  材料尺寸调模具伺服
+                </button>
+                <button
+                  type="button"
+                  onClick={() => setActiveArtifact(buildDieStatusOverviewArtifact())}
+                  className="inline-flex items-center gap-1.5 rounded-lg border border-violet-200 bg-violet-50 px-3 py-1.5 text-xs font-semibold text-violet-700 hover:bg-violet-100 transition-colors"
+                >
+                  <Layers className="h-3.5 w-3.5" />
+                  查看所有模具位置
+                </button>
+                <button
+                  type="button"
+                  onClick={() =>
+                    setActiveArtifact(
+                      buildArtifactFromNlu({
+                        sessionId: "demo",
+                        modelVersion: "demo",
+                        domain: "cabin",
+                        intent: "cabin_set_ac",
+                        confidence: 0.99,
+                        slots: { temperature: 22 },
+                        routeHint: "tool_call",
+                        decisionGrade: "high",
+                        rawDecision: "execute",
+                        replyText: "好的，空调调到22度。",
+                        pendingSlots: [],
+                        traceId: "demo",
+                      }) ?? undefined
+                    )
+                  }
+                  className="inline-flex items-center gap-1.5 rounded-lg border border-amber-200 bg-amber-50 px-3 py-1.5 text-xs font-semibold text-amber-700 hover:bg-amber-100 transition-colors"
+                >
+                  <Thermometer className="h-3.5 w-3.5" />
+                  空调调温（NLU 演示）
+                </button>
+              </div>
+            </div>
+
+            <form onSubmit={handleSubmit}>
+              <div className="flex items-center gap-2 rounded-lg border border-zinc-300 bg-white p-1.5 focus-within:border-zinc-950">
+                <input
+                  className="min-w-0 flex-1 bg-transparent px-3 py-2 text-sm outline-none placeholder:text-zinc-400"
+                  onChange={(e) => setInputValue(e.target.value)}
+                  placeholder={
+                    flowMode === "B"
+                      ? "输入：已完成 / 压力是 0.32 / 或新的问题"
+                      : "输入工业设备问题或控制请求（接 intelligent_cabin NLU）"
+                  }
+                  value={inputValue}
+                  disabled={isLoading}
+                />
+                <button
+                  className="inline-flex h-9 w-9 items-center justify-center rounded-md bg-zinc-950 text-white hover:bg-zinc-800 disabled:opacity-40"
+                  disabled={!inputValue.trim() || isLoading}
+                  type="submit"
+                >
+                  {isLoading ? (
+                    <svg className="h-4 w-4 animate-spin" viewBox="0 0 24 24" fill="none" stroke="currentColor">
+                      <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M12 4v4m0 8v4M4 12h4m8 0h4" />
+                    </svg>
+                  ) : (
+                    <Send className="h-4 w-4" />
+                  )}
+                </button>
+              </div>
+            </form>
+          </section>
+
+          <section className="min-w-0 rounded-xl border border-zinc-200 bg-white p-5">
+            <div className="min-w-0">
+              {activeArtifact ? (
+                <ArtifactRenderer artifact={activeArtifact} onAction={runArtifactAction} />
+              ) : (
+                <EmptyCanvas />
+              )}
+            </div>
+          </section>
+        </section>
+
+        <aside className="min-w-0">
+          <section className="flex min-h-[360px] flex-col rounded-xl border border-zinc-200 bg-white">
+            <div className="border-b border-zinc-100 p-4">
+              <div className="flex items-center gap-2 text-sm font-bold">
+                <MessageSquareText className="h-4 w-4" />
+                {flowMode === "B" ? "参考 Tab / 临时回答" : "对话记录"}
+              </div>
+              <p className="mt-1 text-xs leading-5 text-zinc-500">
+                {flowMode === "B"
+                  ? "调机流程中，新工具/知识不会覆盖主卡。"
+                  : "NLU 通过 intelligent_cabin 处理，结果显示在此。"}
+              </p>
+            </div>
+
+            {flowMode === "B" && referenceTabs.length > 0 ? (
+              <div className="min-h-0 flex-1 overflow-y-auto p-4">
+                <div className="mb-3 flex flex-wrap gap-2">
+                  {referenceTabs.map((tab) => (
+                    <button
+                      type="button"
+                      onClick={() => setActiveReferenceId(tab.id)}
+                      className={`rounded-full px-3 py-1.5 text-xs font-bold ${
+                        activeReference?.id === tab.id
+                          ? "bg-zinc-950 text-white"
+                          : "bg-zinc-100 text-zinc-600"
+                      }`}
+                      key={tab.id}
+                    >
+                      {tab.label}
+                    </button>
+                  ))}
+                </div>
+                {activeReference?.artifact ? (
+                  <ArtifactRenderer
+                    artifact={activeReference.artifact}
+                    onAction={(action, value) => runReferenceAction(activeReference.id, action, value)}
+                  />
+                ) : (
+                  <div className="rounded-lg bg-zinc-50 p-4 text-sm leading-6 text-zinc-600">
+                    {activeReference?.text}
+                  </div>
+                )}
+              </div>
+            ) : (
+              <MessageList messages={messages} />
+            )}
+          </section>
+        </aside>
       </main>
     </div>
   );
 }
+
+// ── 工具函数 ─────────────────────────────────────────────────────────────────
+
+function inferInputSource(text: string): ArtifactActionEvent["source"] {
+  return /^(语音|voice|asr)[:：]/i.test(text.trim()) ? "voice" : "text";
+}
+
+// ── 纯展示组件 ───────────────────────────────────────────────────────────────
+
+function MessageList({ messages }: { messages: ChatMessage[] }) {
+  return (
+    <div className="min-h-0 flex-1 overflow-y-auto p-4">
+      <div className="space-y-3">
+        {messages.length === 0 && (
+          <div className="rounded-lg border border-dashed border-zinc-200 p-4 text-sm leading-6 text-zinc-500">
+            输入任意文字，经 Pipeline → intelligent_cabin NLU 处理后显示结果。
+            服务地址：<code className="rounded bg-zinc-100 px-1">NEXT_PUBLIC_NLU_BASE_URL</code>（默认 localhost:8000）
+          </div>
+        )}
+        {messages.map((message) => (
+          <div
+            className={`flex gap-2 ${message.role === "user" ? "justify-end" : "justify-start"}`}
+            key={message.id}
+          >
+            <div
+              className={`max-w-[92%] rounded-lg px-3 py-2 text-sm leading-6 ${
+                message.role === "user"
+                  ? "bg-zinc-950 text-white"
+                  : message.role === "system"
+                    ? "bg-blue-50 text-blue-700"
+                    : message.role === "error"
+                      ? "bg-red-50 text-red-700"
+                      : "bg-zinc-100 text-zinc-800"
+              }`}
+            >
+              <div className="mb-1 flex items-center gap-1 text-[11px] opacity-70">
+                {message.role === "user" ? (
+                  <User className="h-3 w-3" />
+                ) : message.role === "error" ? (
+                  <AlertCircle className="h-3 w-3" />
+                ) : (
+                  <Bot className="h-3 w-3" />
+                )}
+                {message.role === "user"
+                  ? "用户"
+                  : message.role === "system"
+                    ? "系统"
+                    : message.role === "error"
+                      ? "错误"
+                      : "AI"}
+              </div>
+              {message.text}
+            </div>
+          </div>
+        ))}
+      </div>
+    </div>
+  );
+}
+
+function EmptyCanvas() {
+  return (
+    <div className="flex min-h-[520px] items-center justify-center">
+      <div className="max-w-xl text-center">
+        <div className="mx-auto flex h-16 w-16 items-center justify-center rounded-xl bg-zinc-50 text-zinc-500">
+          <CheckCircle2 className="h-8 w-8" />
+        </div>
+        <h2 className="mt-5 text-2xl font-semibold">等待 intelligent_cabin NLU 响应</h2>
+        <p className="mt-3 text-sm leading-6 text-zinc-500">
+          输入会经过四阶段处理链路：停止词 → UI 语音点击 → 槽位填写 → BERT NLU。
+          NLU 服务需在 <code className="rounded bg-zinc-100 px-1">NEXT_PUBLIC_NLU_BASE_URL</code>（默认 localhost:8000）运行。
+        </p>
+      </div>
+    </div>
+  );
+}
diff --git a/src/app/practice/page.tsx b/src/app/practice/page.tsx
new file mode 100644
index 0000000..cc11a41
--- /dev/null
+++ b/src/app/practice/page.tsx
@@ -0,0 +1,209 @@
+'use client';
+import { useState, useRef, useEffect } from 'react';
+import { useChat } from 'ai/react';
+
+export default function Practice() {
+  const { messages, append } = useChat();
+  const [inputValue, setInputValue] = useState('');
+  console.log("当前的消息记录是：", messages);
+  // ==========================================
+  // 📝 进阶练习：自动滚屏与画中画 (Artifacts)
+  //
+  // 【要求】：补全任务 7 到 10。完成后你的页面将拥有“天气卡片”和自动滚屏功能！
+  // ==========================================
+
+  // 💡 任务 7：创建一个“锚点”，用来指向聊天列表的最底部
+  const messagesEndRef = useRef<HTMLDivElement>(null);
+
+
+  // 💡 任务 8：写一个 useEffect 监听器，只要 messages 变化，就让页面平滑滚动到上面创建的锚点
+  useEffect(() => {
+    messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' });
+  }, [messages]);
+
+  // 💡 任务 9：从 messages 里找出最后一次使用的工具（Tool）
+  // 提示：这行代码可以直接参考最终答案，主要是理解它在干嘛
+  const lastMessageWithTool = messages.slice().reverse().find(m => m.toolInvocations && m.toolInvocations.length > 0);
+  const activeTool = lastMessageWithTool?.toolInvocations?.[lastMessageWithTool.toolInvocations.length - 1];
+
+  const handleSend = (e: React.FormEvent) => {
+    e.preventDefault();
+    if (!inputValue.trim()) return;
+    append({ role: 'user', content: inputValue });
+    setInputValue('');
+  };
+
+  return (
+    <div style={{ display: 'flex', gap: '20px', padding: '50px', maxWidth: '1000px', margin: '0 auto', height: '80vh' }}>
+      
+      {/* ================= 左侧：聊天区 ================= */}
+      <div style={{ flex: 1, display: 'flex', flexDirection: 'column', border: '1px solid #ccc', borderRadius: '8px', padding: '20px' }}>
+        <h3>💬 聊天区</h3>
+        
+        <div style={{ flex: 1, overflowY: 'auto', marginBottom: '20px', border: '1px dashed #eee', padding: '10px' }}>
+          {messages.length === 0 && <p style={{ color: '#999' }}>暂无消息...</p>}
+          
+          {messages.map(m => (
+            <div key={m.id} style={{ marginBottom: '15px' }}>
+              <strong style={{ color: m.role === 'user' ? 'blue' : 'green' }}>
+                {m.role === 'user' ? '你：' : 'AI：'}
+              </strong>
+              
+              {/* 这里加了一个小判断，如果是工具调用，就在左边显示一行提示，不再显示一大坨代码 */}
+              {m.toolInvocations ? (
+                <span style={{ color: '#888', fontSize: '12px', marginLeft: '10px' }}>[生成了一个组件，请看右侧画布]</span>
+              ) : (
+                <span style={{ whiteSpace: 'pre-wrap' }}>{m.content}</span>
+              )}
+            </div>
+          ))}
+          
+          {/* 💡 任务 7 的搭档：在这里把那个看不见的锚点埋下 */}
+          {/* <div ref={...} /> */}
+          <div ref={messagesEndRef} />
+        </div>
+
+        <form onSubmit={handleSend} style={{ display: 'flex', gap: '10px' }}>
+          <input 
+            value={inputValue}
+            onChange={(e) => setInputValue(e.target.value)}
+            style={{ flex: 1, padding: '10px', borderRadius: '4px', border: '1px solid #ccc' }}
+            placeholder="试试问：“北京天气怎么样？”"
+          />
+          <button type="submit" style={{ padding: '10px 20px', background: 'blue', color: 'white', borderRadius: '4px', border: 'none', cursor: 'pointer' }}>发送</button>
+        </form>
+      </div>
+
+      {/* ================= 右侧：画布区 (Artifacts) ================= */}
+      <div style={{ flex: 1, background: '#f9f9f9', border: '1px solid #ddd', borderRadius: '8px', padding: '20px', display: 'flex', alignItems: 'center', justifyContent: 'center' }}>
+        
+        {/* 💡 任务 10：完成条件渲染逻辑 */}
+        {/* 
+            如果 activeTool 不存在，显示一段提示文字："等待 AI 召唤画布..." 
+            如果 activeTool 存在，并且 activeTool.toolName === 'getWeather'，并且 activeTool.state === 'result'，
+            就用一个好看的 div 把 activeTool.args.location 和 activeTool.result.temperature 显示出来！
+        */}
+        {!activeTool ? (
+          <div style={{ textAlign: 'center', color: '#999' }}>
+            <p>等待 AI 召唤画布...</p>
+          </div>
+        ) : (
+          <div>
+            {activeTool.state === 'result' && activeTool.toolName === 'getWeather' && (
+              <div style={{ padding: '30px', background: 'white', borderRadius: '10px', boxShadow: '0 4px 12px rgba(0,0,0,0.1)', textAlign: 'center' }}>
+                <h2 style={{ margin: 0, fontSize: '24px', color: '#333' }}>{activeTool.args.location}</h2>
+                <div style={{ fontSize: '48px', fontWeight: 'bold', color: '#0070f3', margin: '20px 0' }}>
+                  {activeTool.result.temperature}°C
+                </div>
+                <p style={{ color: '#666', margin: 0 }}>天气状况：{activeTool.result.condition}</p>
+              </div>
+            )}
+          </div>
+        )}
+
+
+
+      </div>
+
+      {/* 答案区域在下面 */}
+      <div style={{ position: 'absolute', bottom: 10, right: 10 }}>
+        <details style={{ background: '#f5f5f5', padding: '10px', borderRadius: '8px', border: '1px solid #ccc' }}>
+          <summary style={{ cursor: 'pointer', fontWeight: 'bold', color: 'green' }}>👉 点击查看答案</summary>
+          <pre style={{ whiteSpace: 'pre-wrap', fontSize: '12px', marginTop: '10px', maxWidth: '500px', maxHeight: '400px', overflow: 'auto' }}>
+{`// 答案代码：
+'use client';
+import { useState, useRef, useEffect } from 'react';
+import { useChat } from 'ai/react';
+
+export default function Practice() {
+  const { messages, append } = useChat();
+  const [inputValue, setInputValue] = useState('');
+
+  // 任务 7：创建锚点
+  const messagesEndRef = useRef<HTMLDivElement>(null);
+
+  // 任务 8：监听 messages 并滚动
+  useEffect(() => {
+    messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' });
+  }, [messages]);
+
+  // 任务 9：提取最后一个工具
+  const lastMessageWithTool = messages.slice().reverse().find(m => m.toolInvocations && m.toolInvocations.length > 0);
+  const activeTool = lastMessageWithTool?.toolInvocations?.[lastMessageWithTool.toolInvocations.length - 1];
+
+  const handleSend = (e: React.FormEvent) => {
+    e.preventDefault();
+    if (!inputValue.trim()) return;
+    append({ role: 'user', content: inputValue });
+    setInputValue('');
+  };
+
+  return (
+    <div style={{ display: 'flex', gap: '20px', padding: '50px', maxWidth: '1000px', margin: '0 auto', height: '80vh' }}>
+      
+      <div style={{ flex: 1, display: 'flex', flexDirection: 'column', border: '1px solid #ccc', borderRadius: '8px', padding: '20px' }}>
+        <h3>💬 聊天区</h3>
+        
+        <div style={{ flex: 1, overflowY: 'auto', marginBottom: '20px', border: '1px dashed #eee', padding: '10px' }}>
+          {messages.length === 0 && <p style={{ color: '#999' }}>暂无消息...</p>}
+          
+          {messages.map(m => (
+            <div key={m.id} style={{ marginBottom: '15px' }}>
+              <strong style={{ color: m.role === 'user' ? 'blue' : 'green' }}>
+                {m.role === 'user' ? '你：' : 'AI：'}
+              </strong>
+              
+              {m.toolInvocations ? (
+                <span style={{ color: '#888', fontSize: '12px', marginLeft: '10px' }}>[生成了一个组件，请看右侧画布]</span>
+              ) : (
+                <span style={{ whiteSpace: 'pre-wrap' }}>{m.content}</span>
+              )}
+            </div>
+          ))}
+          
+          {/* 任务 7：埋下锚点 */}
+          <div ref={messagesEndRef} />
+        </div>
+
+        <form onSubmit={handleSend} style={{ display: 'flex', gap: '10px' }}>
+          <input 
+            value={inputValue}
+            onChange={(e) => setInputValue(e.target.value)}
+            style={{ flex: 1, padding: '10px', borderRadius: '4px', border: '1px solid #ccc' }}
+            placeholder="试试问：“北京天气怎么样？”"
+          />
+          <button type="submit" style={{ padding: '10px 20px', background: 'blue', color: 'white', borderRadius: '4px', border: 'none', cursor: 'pointer' }}>发送</button>
+        </form>
+      </div>
+
+      <div style={{ flex: 1, background: '#f9f9f9', border: '1px solid #ddd', borderRadius: '8px', padding: '20px', display: 'flex', alignItems: 'center', justifyContent: 'center' }}>
+        
+        {/* 任务 10：条件渲染画布 */}
+        {!activeTool ? (
+          <div style={{ textAlign: 'center', color: '#999' }}>
+            <p>等待 AI 召唤画布...</p>
+          </div>
+        ) : (
+          <div>
+            {activeTool.state === 'result' && activeTool.toolName === 'getWeather' && (
+              <div style={{ padding: '30px', background: 'white', borderRadius: '10px', boxShadow: '0 4px 12px rgba(0,0,0,0.1)', textAlign: 'center' }}>
+                <h2 style={{ margin: 0, fontSize: '24px', color: '#333' }}>{activeTool.args.location}</h2>
+                <div style={{ fontSize: '48px', fontWeight: 'bold', color: '#0070f3', margin: '20px 0' }}>
+                  {activeTool.result.temperature}°C
+                </div>
+                <p style={{ color: '#666', margin: 0 }}>天气状况：{activeTool.result.condition}</p>
+              </div>
+            )}
+          </div>
+        )}
+
+      </div>
+    </div>
+  );
+}`}
+          </pre>
+        </details>
+      </div>
+    </div>
+  );
+}
diff --git a/src/components/artifacts/ArtifactRenderer.tsx b/src/components/artifacts/ArtifactRenderer.tsx
new file mode 100644
index 0000000..7699ff4
--- /dev/null
+++ b/src/components/artifacts/ArtifactRenderer.tsx
@@ -0,0 +1,524 @@
+"use client";
+
+import {
+  BookOpen,
+  CheckCircle2,
+  ChevronDown,
+  Droplets,
+  Gauge,
+  Lightbulb,
+  Loader2,
+  Pause,
+  Play,
+  Power,
+  RotateCcw,
+  SlidersHorizontal,
+  AlertTriangle,
+  Wrench,
+  XCircle,
+} from "lucide-react";
+import type React from "react";
+import { useState } from "react";
+import type {
+  ArtifactAction,
+  ArtifactComponentProps,
+  DeviceActionProps,
+  GuidedProcedureProps,
+  InteractionArtifact,
+  KnowledgeBlock,
+  KnowledgeLessonProps,
+  ParameterChangeProps,
+} from "@/lib/artifacts/types";
+import { KnowledgeArtifact } from "./KnowledgeArtifact";
+import { DieMaterialAdjust } from "./DieMaterialAdjust";
+import { DieStatusOverview } from "./DieStatusOverview";
+
+function cx(...classes: Array<string | false | null | undefined>) {
+  return classes.filter(Boolean).join(" ");
+}
+
+function actionClass(kind: ArtifactAction["kind"]) {
+  if (kind === "primary") {
+    return "bg-zinc-950 text-white hover:bg-zinc-800";
+  }
+  if (kind === "danger") {
+    return "bg-red-600 text-white hover:bg-red-500";
+  }
+  return "border border-zinc-200 bg-white text-zinc-700 hover:bg-zinc-50";
+}
+
+function ArtifactFrame({
+  artifact,
+  eyebrow,
+  icon: Icon,
+  children,
+}: {
+  artifact: InteractionArtifact;
+  eyebrow: string;
+  icon: typeof SlidersHorizontal;
+  children: React.ReactNode;
+}) {
+  const statusLabel: Record<InteractionArtifact["status"], string> = {
+    draft: "草稿",
+    waiting_confirmation: "等待确认",
+    running: "执行中",
+    success: "已完成",
+    failed: "失败",
+    paused: "已暂停",
+  };
+
+  return (
+    <section className="w-full max-w-5xl">
+      <div className="mb-4 flex items-center justify-between">
+        <div className="flex items-center gap-3">
+          <div className="flex h-11 w-11 items-center justify-center rounded-lg bg-zinc-950 text-white">
+            <Icon className="h-5 w-5" />
+          </div>
+          <div>
+            <p className="text-xs font-semibold uppercase tracking-[0.18em] text-zinc-500">{eyebrow}</p>
+            <h2 className="text-2xl font-semibold text-zinc-950">{artifact.title}</h2>
+          </div>
+        </div>
+        <span
+          className={cx(
+            "rounded-full px-3 py-1 text-sm font-medium",
+            artifact.status === "success" && "bg-emerald-50 text-emerald-700",
+            artifact.status === "running" && "bg-blue-50 text-blue-700",
+            artifact.status === "waiting_confirmation" && "bg-amber-50 text-amber-700",
+            artifact.status === "failed" && "bg-red-50 text-red-700",
+            artifact.status === "paused" && "bg-zinc-100 text-zinc-600",
+          )}
+        >
+          {statusLabel[artifact.status]}
+        </span>
+      </div>
+      <div className="rounded-lg border border-zinc-200 bg-white shadow-sm">{children}</div>
+    </section>
+  );
+}
+
+function ArtifactActions({
+  artifact,
+  onAction,
+}: {
+  artifact: InteractionArtifact;
+  onAction: (action: ArtifactAction, value?: unknown) => void;
+}) {
+  if (artifact.actions.length === 0) return null;
+
+  const voiceHints = artifact.actions.flatMap((action) => action.voiceAliases ?? []).slice(0, 3);
+
+  return (
+    <div className="border-t border-zinc-100 px-6 py-4">
+      {voiceHints.length > 0 && artifact.status === "waiting_confirmation" && (
+        <div className="mb-3 rounded-md bg-blue-50 px-3 py-2 text-xs font-medium text-blue-700">
+          可说：{voiceHints.join(" / ")}
+        </div>
+      )}
+      <div className="flex flex-wrap gap-3">
+        {artifact.actions.map((action) => (
+          <button
+            key={action.actionId}
+            type="button"
+            disabled={action.disabled}
+            onClick={() => onAction(action)}
+            className={cx(
+              "inline-flex items-center gap-2 rounded-md px-4 py-2 text-sm font-semibold transition disabled:cursor-not-allowed disabled:opacity-50",
+              actionClass(action.kind),
+            )}
+          >
+            {action.kind === "primary" ? <Play className="h-4 w-4" /> : <Pause className="h-4 w-4" />}
+            {action.label}
+          </button>
+        ))}
+      </div>
+    </div>
+  );
+}
+
+function ParameterChangeCard({
+  artifact,
+  onAction,
+}: ArtifactComponentProps<Extract<InteractionArtifact, { type: "parameter_change" }>>) {
+  const props = artifact.props as ParameterChangeProps;
+
+  return (
+    <ArtifactFrame artifact={artifact} eyebrow="Parameter Change" icon={SlidersHorizontal}>
+      <div className="grid gap-0 md:grid-cols-[1fr_1.2fr]">
+        <div className="border-b border-zinc-100 p-6 md:border-b-0 md:border-r">
+          <p className="text-sm text-zinc-500">{props.deviceName}</p>
+          <h3 className="mt-2 text-3xl font-semibold text-zinc-950">{props.parameterName}</h3>
+          <p className="mt-4 text-sm leading-6 text-zinc-600">{props.impact}</p>
+          <p className="mt-4 rounded-md bg-zinc-50 p-3 text-sm text-zinc-600">{props.executionNote}</p>
+        </div>
+
+        <div className="p-6">
+          <div className="grid grid-cols-[1fr_auto_1fr] items-center gap-4">
+            <ValuePanel label="当前值" value={props.currentValue} unit={props.unit} muted />
+            <div className="h-px w-8 bg-zinc-200 md:w-12" />
+            <ValuePanel label="目标值" value={props.targetValue} unit={props.unit} />
+          </div>
+
+          <div className="mt-6 flex items-center gap-3 rounded-lg bg-amber-50 p-4 text-amber-800">
+            {artifact.status === "running" ? (
+              <Loader2 className="h-5 w-5 animate-spin" />
+            ) : artifact.status === "success" ? (
+              <CheckCircle2 className="h-5 w-5" />
+            ) : artifact.status === "failed" ? (
+              <XCircle className="h-5 w-5" />
+            ) : (
+              <AlertTriangle className="h-5 w-5" />
+            )}
+            <span className="text-sm font-medium">
+              {artifact.status === "success"
+                ? "参数已写入，画布保留本次修改快照。"
+                : artifact.status === "running"
+                  ? "正在模拟写入参数..."
+                  : artifact.status === "failed"
+                    ? "参数写入失败，可以修改后重试。"
+                    : "请确认前后值无误后再执行。"}
+            </span>
+          </div>
+        </div>
+      </div>
+      <ArtifactActions artifact={artifact} onAction={onAction} />
+    </ArtifactFrame>
+  );
+}
+
+function ValuePanel({
+  label,
+  value,
+  unit,
+  muted,
+}: {
+  label: string;
+  value: string | number;
+  unit?: string;
+  muted?: boolean;
+}) {
+  return (
+    <div className={cx("rounded-lg border p-5", muted ? "border-zinc-200 bg-zinc-50" : "border-zinc-950 bg-zinc-950 text-white")}>
+      <p className={cx("text-sm font-medium", muted ? "text-zinc-500" : "text-zinc-300")}>{label}</p>
+      <div className="mt-3 flex items-end gap-2">
+        <span className="text-4xl font-semibold tracking-normal">{value}</span>
+        {unit && <span className={cx("pb-1 text-sm", muted ? "text-zinc-500" : "text-zinc-300")}>{unit}</span>}
+      </div>
+    </div>
+  );
+}
+
+function DeviceActionCard({
+  artifact,
+  onAction,
+}: ArtifactComponentProps<Extract<InteractionArtifact, { type: "device_action" }>>) {
+  const props = artifact.props as DeviceActionProps;
+  const isActive = artifact.status === "running" || artifact.status === "success";
+  const DeviceIcon = props.deviceKind === "pump" ? Droplets : props.deviceKind === "light" ? Lightbulb : Power;
+
+  return (
+    <ArtifactFrame artifact={artifact} eyebrow="Device Action" icon={DeviceIcon}>
+      <div className="grid gap-0 md:grid-cols-[1.1fr_0.9fr]">
+        <div className="relative min-h-[360px] overflow-hidden bg-zinc-950 p-8 text-white">
+          <div className="relative z-10">
+            <p className="text-sm text-zinc-400">{props.deviceKind.toUpperCase()}</p>
+            <h3 className="mt-2 text-4xl font-semibold">{props.deviceName}</h3>
+            <p className="mt-3 text-zinc-300">{props.executionNote}</p>
+          </div>
+
+          <div className="absolute inset-x-0 bottom-0 h-44 bg-gradient-to-t from-zinc-800 to-transparent" />
+          <DeviceMotionScene deviceKind={props.deviceKind} icon={DeviceIcon} active={isActive} running={artifact.status === "running"} />
+        </div>
+
+        <div className="p-6">
+          <div className="space-y-4">
+            <StatusRow label="当前状态" value={props.currentState} />
+            <StatusRow label="目标状态" value={props.targetState} emphasis />
+            <StatusRow label="执行动作" value={actionLabel(props.action)} />
+          </div>
+          <div className="mt-8 rounded-lg border border-zinc-200 p-4">
+            <div className="mb-3 flex items-center justify-between text-sm">
+              <span className="font-medium text-zinc-700">{props.progressLabel ?? "执行进度"}</span>
+              <span className="text-zinc-500">{artifact.status === "success" ? "100%" : artifact.status === "running" ? "64%" : "0%"}</span>
+            </div>
+            <div className="h-2 rounded-full bg-zinc-100">
+              <div
+                className={cx(
+                  "h-2 rounded-full transition-all duration-700",
+                  artifact.status === "success" ? "w-full bg-emerald-500" : artifact.status === "running" ? "w-2/3 bg-cyan-500" : "w-0",
+                )}
+              />
+            </div>
+          </div>
+        </div>
+      </div>
+      <ArtifactActions artifact={artifact} onAction={onAction} />
+    </ArtifactFrame>
+  );
+}
+
+function DeviceMotionScene({
+  deviceKind,
+  icon: DeviceIcon,
+  active,
+  running,
+}: {
+  deviceKind: DeviceActionProps["deviceKind"];
+  icon: typeof Droplets;
+  active: boolean;
+  running: boolean;
+}) {
+  const isPump = deviceKind === "pump";
+  const isLight = deviceKind === "light";
+
+  return (
+    <div className="absolute left-1/2 top-1/2 z-10 flex -translate-x-1/2 -translate-y-1/2 items-center justify-center">
+      <div
+        className={cx(
+          "absolute h-56 w-56 rounded-full transition duration-700",
+          active && isPump && "scale-100 bg-sky-400/20 blur-xl",
+          active && isLight && "scale-100 bg-cyan-400/20 blur-xl",
+          active && !isPump && !isLight && "scale-100 bg-emerald-400/20 blur-xl",
+          !active && "scale-75 bg-zinc-600/20 blur-lg",
+        )}
+      />
+
+      {isPump && (
+        <div className="absolute h-48 w-64 overflow-hidden rounded-full">
+          <span className={cx("pump-flow absolute left-1/2 top-1/2 h-3 w-52 rounded-full bg-sky-300/60 animate-pulse", !active && "opacity-0")} />
+          <span
+            className={cx("pump-flow absolute left-1/2 top-[58%] h-2 w-44 rounded-full bg-emerald-300/50 animate-pulse", !active && "opacity-0")}
+            style={{ animationDelay: "300ms" }}
+          />
+          <span className={cx("pump-drop absolute left-[30%] top-[22%] h-3 w-3 rounded-full bg-sky-200 animate-bounce", !running && "opacity-0")} />
+          <span
+            className={cx("pump-drop absolute left-[64%] top-[20%] h-2.5 w-2.5 rounded-full bg-cyan-100 animate-bounce", !running && "opacity-0")}
+            style={{ animationDelay: "180ms" }}
+          />
+          <span
+            className={cx("pump-drop absolute left-[52%] top-[66%] h-2 w-2 rounded-full bg-emerald-200 animate-bounce", !running && "opacity-0")}
+            style={{ animationDelay: "360ms" }}
+          />
+        </div>
+      )}
+
+      {isLight && active && (
+        <>
+          <span className="absolute h-44 w-44 animate-ping rounded-full border border-cyan-200/40" />
+          <span className="absolute h-64 w-64 rounded-full bg-cyan-300/10 blur-2xl" />
+        </>
+      )}
+
+      <div
+        className={cx(
+          "relative flex h-36 w-36 items-center justify-center rounded-full border transition duration-700",
+          active && isPump && "border-sky-300 bg-sky-300/15 text-sky-100",
+          active && isLight && "border-cyan-300 bg-cyan-300/15 text-cyan-100",
+          active && !isPump && !isLight && "border-emerald-300 bg-emerald-300/15 text-emerald-100",
+          !active && "border-zinc-700 bg-zinc-900 text-zinc-500",
+        )}
+      >
+        <DeviceIcon className={cx("h-16 w-16", running && (isPump ? "animate-bounce" : "animate-pulse"))} />
+      </div>
+    </div>
+  );
+}
+
+function actionLabel(action: DeviceActionProps["action"]) {
+  return {
+    open: "打开",
+    close: "关闭",
+    start: "启动",
+    stop: "停止",
+  }[action];
+}
+
+function StatusRow({ label, value, emphasis }: { label: string; value: string; emphasis?: boolean }) {
+  return (
+    <div className="flex items-center justify-between rounded-lg bg-zinc-50 px-4 py-3">
+      <span className="text-sm text-zinc-500">{label}</span>
+      <span className={cx("text-sm font-semibold", emphasis ? "text-zinc-950" : "text-zinc-700")}>{value}</span>
+    </div>
+  );
+}
+
+function KnowledgeLessonArtifact({
+  artifact,
+  onAction,
+}: ArtifactComponentProps<Extract<InteractionArtifact, { type: "knowledge_lesson" }>>) {
+  const props = artifact.props as KnowledgeLessonProps;
+
+  return (
+    <ArtifactFrame artifact={artifact} eyebrow="Knowledge Lesson" icon={BookOpen}>
+      <div className="p-6">
+        <div className="rounded-lg bg-emerald-50 p-5">
+          <p className="text-sm font-semibold text-emerald-700">先看结论</p>
+          <h3 className="mt-2 text-2xl font-semibold text-emerald-950">{props.conclusion}</h3>
+        </div>
+        <div className="mt-6 grid gap-4">
+          {props.blocks.map((block, index) => (
+            <KnowledgeBlockView block={block} key={`${block.type}_${index}`} />
+          ))}
+        </div>
+      </div>
+      <ArtifactActions artifact={artifact} onAction={onAction} />
+    </ArtifactFrame>
+  );
+}
+
+function KnowledgeBlockView({ block }: { block: KnowledgeBlock }) {
+  const [open, setOpen] = useState(block.type !== "details" || Boolean(block.defaultOpen));
+
+  if (block.type === "summary") {
+    return <p className="rounded-lg border border-zinc-200 p-4 text-sm leading-6 text-zinc-700">{block.text}</p>;
+  }
+
+  if (block.type === "key_points") {
+    return (
+      <div className="rounded-lg border border-zinc-200 p-4">
+        <p className="mb-3 text-sm font-semibold text-zinc-950">关键点</p>
+        <div className="grid gap-2 md:grid-cols-3">
+          {block.items.map((item) => (
+            <div className="rounded-md bg-zinc-50 p-3 text-sm text-zinc-700" key={item}>
+              {item}
+            </div>
+          ))}
+        </div>
+      </div>
+    );
+  }
+
+  if (block.type === "steps") {
+    return (
+      <div className="rounded-lg border border-zinc-200 p-4">
+        <p className="mb-4 text-sm font-semibold text-zinc-950">处理步骤</p>
+        <div className="space-y-3">
+          {block.steps.map((step, index) => (
+            <div className="grid grid-cols-[32px_1fr] gap-3" key={step.title}>
+              <div className="flex h-8 w-8 items-center justify-center rounded-full bg-zinc-950 text-sm font-semibold text-white">{index + 1}</div>
+              <div>
+                <p className="font-medium text-zinc-950">{step.title}</p>
+                <p className="mt-1 text-sm leading-6 text-zinc-600">{step.body}</p>
+              </div>
+            </div>
+          ))}
+        </div>
+      </div>
+    );
+  }
+
+  if (block.type === "details") {
+    return (
+      <div className="rounded-lg border border-zinc-200">
+        <button className="flex w-full items-center justify-between p-4 text-left" type="button" onClick={() => setOpen((value) => !value)}>
+          <span className="font-semibold text-zinc-950">{block.title}</span>
+          <ChevronDown className={cx("h-4 w-4 text-zinc-500 transition", open && "rotate-180")} />
+        </button>
+        {open && <p className="border-t border-zinc-100 p-4 text-sm leading-6 text-zinc-600">{block.markdown}</p>}
+      </div>
+    );
+  }
+
+  if (block.type === "citations") {
+    return (
+      <div className="rounded-lg bg-zinc-50 p-4">
+        <p className="mb-2 text-sm font-semibold text-zinc-950">来源</p>
+        <div className="flex flex-wrap gap-2">
+          {block.citations.map((citation) => (
+            <span className="rounded-full bg-white px-3 py-1 text-xs text-zinc-600" key={`${citation.title}_${citation.location}`}>
+              {citation.title} · {citation.location}
+            </span>
+          ))}
+        </div>
+      </div>
+    );
+  }
+
+  return null;
+}
+
+function GuidedProcedureArtifact({
+  artifact,
+  onAction,
+}: ArtifactComponentProps<Extract<InteractionArtifact, { type: "guided_procedure" }>>) {
+  const props = artifact.props as GuidedProcedureProps;
+  const activeIndex = props.steps.findIndex((step) => step.stepId === props.currentStepId);
+  const activeStep = props.steps[activeIndex] ?? props.steps[0];
+
+  return (
+    <ArtifactFrame artifact={artifact} eyebrow="Guided Procedure" icon={Wrench}>
+      <div className="grid gap-0 lg:grid-cols-[320px_1fr]">
+        <div className="border-b border-zinc-100 p-6 lg:border-b-0 lg:border-r">
+          <p className="text-sm text-zinc-500">{props.deviceName}</p>
+          <h3 className="mt-2 text-2xl font-semibold text-zinc-950">{props.procedureName}</h3>
+          <div className="mt-6 space-y-3">
+            {props.steps.map((step, index) => (
+              <div
+                className={cx(
+                  "rounded-lg border p-3",
+                  step.stepId === props.currentStepId ? "border-zinc-950 bg-zinc-950 text-white" : "border-zinc-200 bg-white",
+                )}
+                key={step.stepId}
+              >
+                <div className="flex items-center gap-2">
+                  <span className="flex h-6 w-6 items-center justify-center rounded-full bg-white/15 text-xs font-semibold">{index + 1}</span>
+                  <span className="text-sm font-medium">{step.title}</span>
+                </div>
+              </div>
+            ))}
+          </div>
+        </div>
+
+        <div className="p-6">
+          {activeStep ? (
+            <>
+              <div className="flex items-center gap-3">
+                <div className="flex h-10 w-10 items-center justify-center rounded-lg bg-blue-50 text-blue-700">
+                  {activeStep.type === "measurement_input" ? <Gauge className="h-5 w-5" /> : <RotateCcw className="h-5 w-5" />}
+                </div>
+                <div>
+                  <p className="text-sm text-zinc-500">第 {activeIndex + 1} 步</p>
+                  <h3 className="text-2xl font-semibold text-zinc-950">{activeStep.title}</h3>
+                </div>
+              </div>
+              <p className="mt-5 rounded-lg bg-zinc-50 p-4 text-sm leading-6 text-zinc-700">{activeStep.instruction}</p>
+              {activeStep.expectedFeedback && (
+                <div className="mt-5">
+                  <p className="mb-2 text-sm font-semibold text-zinc-950">可输入反馈</p>
+                  <div className="flex flex-wrap gap-2">
+                    {activeStep.expectedFeedback.map((feedback) => (
+                      <span className="rounded-full bg-blue-50 px-3 py-1 text-xs font-medium text-blue-700" key={feedback}>
+                        {feedback}
+                      </span>
+                    ))}
+                  </div>
+                </div>
+              )}
+            </>
+          ) : (
+            <div className="rounded-lg bg-emerald-50 p-6 text-emerald-800">
+              <CheckCircle2 className="mb-3 h-6 w-6" />
+              流程已经完成。
+            </div>
+          )}
+        </div>
+      </div>
+      <ArtifactActions artifact={artifact} onAction={onAction} />
+    </ArtifactFrame>
+  );
+}
+
+const registry = {
+  parameter_change: ParameterChangeCard,
+  device_action: DeviceActionCard,
+  knowledge_lesson: KnowledgeArtifact,
+  guided_procedure: GuidedProcedureArtifact,
+  die_material_adjust: DieMaterialAdjust,
+  die_status_overview: DieStatusOverview,
+};
+
+
+export function ArtifactRenderer({ artifact, onAction }: ArtifactComponentProps) {
+  const Component = registry[artifact.type] as (props: ArtifactComponentProps) => React.ReactNode;
+  return <Component artifact={artifact} onAction={onAction} />;
+}
diff --git a/src/components/artifacts/DieMaterialAdjust.tsx b/src/components/artifacts/DieMaterialAdjust.tsx
new file mode 100644
index 0000000..5fc241a
--- /dev/null
+++ b/src/components/artifacts/DieMaterialAdjust.tsx
@@ -0,0 +1,235 @@
+"use client";
+
+/**
+ * src/components/artifacts/DieMaterialAdjust.tsx
+ *
+ * 三步流程：
+ *   Step 1 (form)     → 选择材料 / 外径 / 壁厚
+ *   Step 2 (adjusting) → 模拟等待 2s
+ *   Step 3 (done)     → 显示各模具调节后的 X / Y 位置
+ */
+
+import { CheckCircle2, Loader2, Settings } from "lucide-react";
+import { useState } from "react";
+import type {
+  ArtifactAction,
+  ArtifactComponentProps,
+  DieMaterialAdjustProps,
+  InteractionArtifact,
+} from "@/lib/artifacts/types";
+import { generateDiePositions } from "@/lib/artifacts/die-mock-data";
+
+// ── 材料选项 ──────────────────────────────────────────────────────────────────
+const MATERIALS = [
+  "304 不锈钢",
+  "316L 不锈钢",
+  "201 不锈钢",
+  "Q235 碳钢",
+  "Q345 低合金钢",
+];
+
+// ── 表单界面 ──────────────────────────────────────────────────────────────────
+function FormStep({
+  onConfirm,
+}: {
+  onConfirm: (material: string, od: number, thickness: number) => void;
+}) {
+  const [material, setMaterial] = useState(MATERIALS[0]);
+  const [od, setOd] = useState(20);
+  const [thickness, setThickness] = useState(2);
+
+  return (
+    <div className="p-6">
+      <p className="mb-5 text-sm text-zinc-500">
+        选择材料规格后，系统将根据成型工艺数据自动计算各模具伺服位置并写入。
+      </p>
+      <div className="space-y-4">
+        <div>
+          <label className="mb-1 block text-xs font-semibold text-zinc-600">材料</label>
+          <select
+            value={material}
+            onChange={(e) => setMaterial(e.target.value)}
+            className="w-full rounded-lg border border-zinc-200 bg-white px-3 py-2 text-sm text-zinc-900 outline-none focus:border-zinc-950"
+          >
+            {MATERIALS.map((m) => (
+              <option key={m} value={m}>{m}</option>
+            ))}
+          </select>
+        </div>
+
+        <div className="grid grid-cols-2 gap-4">
+          <div>
+            <label className="mb-1 block text-xs font-semibold text-zinc-600">外径 (mm)</label>
+            <input
+              type="number"
+              value={od}
+              min={6}
+              max={219}
+              step={0.5}
+              onChange={(e) => setOd(Number(e.target.value))}
+              className="w-full rounded-lg border border-zinc-200 bg-white px-3 py-2 text-sm text-zinc-900 outline-none focus:border-zinc-950"
+            />
+          </div>
+          <div>
+            <label className="mb-1 block text-xs font-semibold text-zinc-600">壁厚 (mm)</label>
+            <input
+              type="number"
+              value={thickness}
+              min={0.5}
+              max={20}
+              step={0.1}
+              onChange={(e) => setThickness(Number(e.target.value))}
+              className="w-full rounded-lg border border-zinc-200 bg-white px-3 py-2 text-sm text-zinc-900 outline-none focus:border-zinc-950"
+            />
+          </div>
+        </div>
+
+        {/* 预览标签 */}
+        <div className="rounded-lg bg-zinc-50 px-4 py-3 text-sm text-zinc-600">
+          <span className="font-medium">{material}</span>
+          &ensp;圆管&ensp;Φ<span className="font-medium">{od}</span>
+          &ensp;×&ensp;壁厚 <span className="font-medium">{thickness}</span> mm
+        </div>
+      </div>
+
+      <div className="mt-6 flex gap-3">
+        <button
+          type="button"
+          onClick={() => onConfirm(material, od, thickness)}
+          className="inline-flex items-center gap-2 rounded-lg bg-zinc-950 px-5 py-2.5 text-sm font-semibold text-white hover:bg-zinc-800 transition-colors"
+        >
+          <Settings className="h-4 w-4" />
+          确认并调节模具
+        </button>
+      </div>
+    </div>
+  );
+}
+
+// ── 等待界面 ──────────────────────────────────────────────────────────────────
+function AdjustingStep({ material, od, thickness }: { material: string; od: number; thickness: number }) {
+  return (
+    <div className="flex flex-col items-center justify-center py-16 text-center">
+      <Loader2 className="mb-4 h-10 w-10 animate-spin text-zinc-400" />
+      <p className="text-base font-semibold text-zinc-700">正在调节模具伺服位置…</p>
+      <p className="mt-1 text-sm text-zinc-400">
+        {material} · Φ{od} × {thickness}mm
+      </p>
+    </div>
+  );
+}
+
+// ── 结果界面（仅显示编号 + X + Y）─────────────────────────────────────────────
+function DoneStep({
+  material,
+  od,
+  thickness,
+  dies,
+}: {
+  material: string;
+  od: number;
+  thickness: number;
+  dies: ReturnType<typeof generateDiePositions>;
+}) {
+  return (
+    <div>
+      {/* 成功提示 */}
+      <div className="flex items-center gap-3 border-b border-zinc-100 bg-emerald-50 px-5 py-3">
+        <CheckCircle2 className="h-5 w-5 text-emerald-600" />
+        <div>
+          <p className="text-sm font-semibold text-emerald-800">调节完成</p>
+          <p className="text-xs text-emerald-600">
+            {material} · Φ{od} × {thickness}mm · {dies.length} 个模具
+          </p>
+        </div>
+      </div>
+
+      {/* 位置表 */}
+      <div className="overflow-x-auto">
+        <table className="w-full text-left">
+          <thead>
+            <tr className="border-b border-zinc-100 bg-zinc-50">
+              <th className="px-4 py-2.5 text-xs font-semibold text-zinc-500">模具</th>
+              <th className="px-4 py-2.5 text-xs font-semibold text-zinc-500">X 位置 (mm)</th>
+              <th className="px-4 py-2.5 text-xs font-semibold text-zinc-500">Y 位置 (mm)</th>
+            </tr>
+          </thead>
+          <tbody>
+            {dies.map((die) => (
+              <tr key={die.dieIndex} className="border-b border-zinc-50 hover:bg-zinc-50/60">
+                <td className="px-4 py-2 text-sm font-medium text-zinc-700">{die.label}</td>
+                <td className="px-4 py-2 font-mono text-sm text-zinc-900">{die.xPos.toFixed(3)}</td>
+                <td className="px-4 py-2 font-mono text-sm text-zinc-900">{die.yPos.toFixed(3)}</td>
+              </tr>
+            ))}
+          </tbody>
+        </table>
+      </div>
+    </div>
+  );
+}
+
+// ── 主组件 ────────────────────────────────────────────────────────────────────
+export function DieMaterialAdjust({
+  artifact,
+}: ArtifactComponentProps<Extract<InteractionArtifact, { type: "die_material_adjust" }>>) {
+  type Step = "form" | "adjusting" | "done";
+  const [step, setStep] = useState<Step>("form");
+  const [result, setResult] = useState<{
+    material: string;
+    od: number;
+    thickness: number;
+    dies: ReturnType<typeof generateDiePositions>;
+  } | null>(null);
+
+  function handleConfirm(material: string, od: number, thickness: number) {
+    setStep("adjusting");
+    setTimeout(() => {
+      const dies = generateDiePositions({ outerDiameter: od, thickness, totalDies: 15 });
+      setResult({ material, od, thickness, dies });
+      setStep("done");
+    }, 2000);
+  }
+
+  return (
+    <section className="w-full max-w-2xl">
+      {/* Header */}
+      <div className="mb-4 flex items-center gap-3">
+        <div className="flex h-11 w-11 items-center justify-center rounded-lg bg-zinc-950 text-white">
+          <Settings className="h-5 w-5" />
+        </div>
+        <div>
+          <p className="text-xs font-semibold uppercase tracking-[0.18em] text-zinc-500">模具伺服调节</p>
+          <h2 className="text-2xl font-semibold text-zinc-950">{artifact.title}</h2>
+        </div>
+        {step === "done" && (
+          <span className="ml-auto rounded-full bg-emerald-50 px-3 py-1 text-sm font-medium text-emerald-700">
+            已完成
+          </span>
+        )}
+      </div>
+
+      <div className="rounded-lg border border-zinc-200 bg-white shadow-sm">
+        {step === "form" && <FormStep onConfirm={handleConfirm} />}
+        {step === "adjusting" && result === null && (
+          <AdjustingStep material="正在计算…" od={0} thickness={0} />
+        )}
+        {step === "adjusting" && (
+          <AdjustingStep
+            material="正在计算…"
+            od={0}
+            thickness={0}
+          />
+        )}
+        {step === "done" && result && (
+          <DoneStep
+            material={result.material}
+            od={result.od}
+            thickness={result.thickness}
+            dies={result.dies}
+          />
+        )}
+      </div>
+    </section>
+  );
+}
diff --git a/src/components/artifacts/DieStatusOverview.tsx b/src/components/artifacts/DieStatusOverview.tsx
new file mode 100644
index 0000000..7675ed6
--- /dev/null
+++ b/src/components/artifacts/DieStatusOverview.tsx
@@ -0,0 +1,275 @@
+"use client";
+
+/**
+ * src/components/artifacts/DieStatusOverview.tsx
+ *
+ * 模具位置总览
+ * - 截面图：描边线条风格（标量图），展示弯管成型渐进过程
+ *     Die 1  → 平直钢带 + 两段浅下凹
+ *     Die 2  → 平中间 + 两边微翘
+ *     Die 3  → 整体圆弧（开口大）
+ *     Die 4-15 → 弧度渐深，开口逐步收小
+ *     Die 16 → 闭合圆
+ * - 只显示模具编号 + X 位置 + Y 位置
+ */
+
+import { Layers } from "lucide-react";
+import { useState } from "react";
+import type {
+  ArtifactComponentProps,
+  DiePositionData,
+  DieStatusOverviewProps,
+  InteractionArtifact,
+} from "@/lib/artifacts/types";
+
+// ── 成型截面路径计算 ───────────────────────────────────────────────────────────
+
+function getFormingPath(
+  dieIndex: number,
+  totalDies: number,
+  cx: number,
+  cy: number,
+  r: number,
+): string {
+  // ── Die 1：平直钢带 + 两段浅下凹（最左 / 中间 / 最右保持平直）────
+  if (dieIndex === 1) {
+    const depth = r * 0.11;
+    return [
+      `M ${(cx - r * 1.1).toFixed(1)} ${cy.toFixed(1)}`,
+      `L ${(cx - r * 0.64).toFixed(1)} ${cy.toFixed(1)}`,
+      `C ${(cx - r * 0.58).toFixed(1)} ${cy.toFixed(1)} ${(cx - r * 0.58).toFixed(1)} ${(cy + depth).toFixed(1)} ${(cx - r * 0.5).toFixed(1)} ${(cy + depth).toFixed(1)}`,
+      `C ${(cx - r * 0.42).toFixed(1)} ${(cy + depth).toFixed(1)} ${(cx - r * 0.42).toFixed(1)} ${cy.toFixed(1)} ${(cx - r * 0.36).toFixed(1)} ${cy.toFixed(1)}`,
+      `L ${(cx + r * 0.36).toFixed(1)} ${cy.toFixed(1)}`,
+      `C ${(cx + r * 0.42).toFixed(1)} ${cy.toFixed(1)} ${(cx + r * 0.42).toFixed(1)} ${(cy + depth).toFixed(1)} ${(cx + r * 0.5).toFixed(1)} ${(cy + depth).toFixed(1)}`,
+      `C ${(cx + r * 0.58).toFixed(1)} ${(cy + depth).toFixed(1)} ${(cx + r * 0.58).toFixed(1)} ${cy.toFixed(1)} ${(cx + r * 0.64).toFixed(1)} ${cy.toFixed(1)}`,
+      `L ${(cx + r * 1.1).toFixed(1)} ${cy.toFixed(1)}`,
+    ].join(" ");
+  }
+
+  // ── Die 2：平中间 + 两侧边缘微翘（只有角落开始弯）──────────────────────────
+  if (dieIndex === 2) {
+    const flatHalf = r * 0.42;  // 中间平直段半宽
+    const curlUp   = r * 0.36;  // 边缘上翘高度
+    return [
+      `M ${(cx - r).toFixed(1)} ${(cy - curlUp).toFixed(1)}`,
+      `Q ${(cx - r * 0.65).toFixed(1)} ${cy.toFixed(1)} ${(cx - flatHalf).toFixed(1)} ${cy.toFixed(1)}`,
+      `L ${(cx + flatHalf).toFixed(1)} ${cy.toFixed(1)}`,
+      `Q ${(cx + r * 0.65).toFixed(1)} ${cy.toFixed(1)} ${(cx + r).toFixed(1)} ${(cy - curlUp).toFixed(1)}`,
+    ].join(" ");
+  }
+
+  // ── Die 3-16：弧线，开口角从大到零（圆弧 → 闭合圆）─────────────────────────
+  //   t=0 (die 3): 开口半角 = 120°（浅弧）
+  //   t=1 (die 16): 开口半角 = 0°（闭合圆）
+  const t = Math.min(1, (dieIndex - 3) / Math.max(1, totalDies - 3));
+  const gapHalfRad = (1 - t) * ((120 * Math.PI) / 180);
+
+  // 闭合圆（die 16 或极小开口）
+  if (gapHalfRad < 0.02) {
+    return [
+      `M ${(cx - r).toFixed(2)} ${cy.toFixed(2)}`,
+      `A ${r.toFixed(2)} ${r.toFixed(2)} 0 1 0 ${(cx + r).toFixed(2)} ${cy.toFixed(2)}`,
+      `A ${r.toFixed(2)} ${r.toFixed(2)} 0 1 0 ${(cx - r).toFixed(2)} ${cy.toFixed(2)}`,
+    ].join(" ");
+  }
+
+  // 开口圆弧：开口在上方，弧从右端绕底到左端
+  const rightX = cx + r * Math.sin(gapHalfRad);
+  const rightY = cy - r * Math.cos(gapHalfRad);
+  const leftX  = cx - r * Math.sin(gapHalfRad);
+  const leftY  = rightY;
+
+  const arcSpan  = 2 * Math.PI - 2 * gapHalfRad;
+  const largeArc = arcSpan > Math.PI ? 1 : 0;
+
+  return [
+    `M ${rightX.toFixed(2)} ${rightY.toFixed(2)}`,
+    `A ${r.toFixed(2)} ${r.toFixed(2)} 0 ${largeArc} 1 ${leftX.toFixed(2)} ${leftY.toFixed(2)}`,
+  ].join(" ");
+}
+
+// ── 成型截面 SVG 组件 ─────────────────────────────────────────────────────────
+
+function FormingShape({
+  dieIndex,
+  totalDies,
+  size,
+}: {
+  dieIndex: number;
+  totalDies: number;
+  size: number;
+}) {
+  const cx = size / 2;
+  const r  = size * 0.35;
+  // 小格子：底部对齐（让成型从下向上逐步收拢）
+  // 大图（详情面板）：居中显示，避免圆偏角
+  const cy = size >= 80 || dieIndex === totalDies
+    ? size * 0.5      // 大图和闭合圆: 居中
+    : size * 0.9 - r; // 小格子: 底部对齐
+
+  const path    = getFormingPath(dieIndex, totalDies, cx, cy, r);
+  const strokeW = size < 80 ? 1.6 : 2.6;
+
+  return (
+    <svg
+      width={size}
+      height={size}
+      viewBox={`0 0 ${size} ${size}`}
+      style={{ overflow: "visible" }}
+    >
+      <path
+        d={path}
+        fill="none"
+        stroke="#374151"
+        strokeWidth={strokeW}
+        strokeLinecap="round"
+        strokeLinejoin="round"
+      />
+    </svg>
+  );
+}
+
+// ── 模具格子 ──────────────────────────────────────────────────────────────────
+
+function DieCell({
+  die,
+  totalDies,
+  selected,
+  onClick,
+}: {
+  die: DiePositionData;
+  totalDies: number;
+  selected: boolean;
+  onClick: () => void;
+}) {
+  return (
+    <button
+      type="button"
+      onClick={onClick}
+      className={[
+        "relative flex h-[84px] w-[112px] shrink-0 items-center justify-center rounded-xl border-2 px-2 py-2.5 transition-all",
+        "hover:scale-105 hover:shadow-md cursor-pointer",
+        selected
+          ? "border-zinc-900 bg-zinc-50 shadow-md"
+          : "border-zinc-200 bg-white hover:border-zinc-400",
+      ].join(" ")}
+    >
+      <span className="absolute top-3 text-[10px] font-bold text-zinc-400">{die.dieIndex}</span>
+      <FormingShape dieIndex={die.dieIndex} totalDies={totalDies} size={44} />
+    </button>
+  );
+}
+
+// ── 详情面板 ──────────────────────────────────────────────────────────────────
+
+function DieDetailPanel({ die, totalDies }: { die: DiePositionData; totalDies: number }) {
+  // 大图的 stage 标签
+  const progress = die.dieIndex / totalDies;
+  const stageLabel =
+    die.dieIndex === 1 ? "原料钢带（平）" :
+    die.dieIndex === 2 ? "边缘初弯" :
+    progress < 0.35    ? "浅弧成型" :
+    progress < 0.6     ? "U 形成型" :
+    progress < 0.85    ? "近圆成型" :
+                         "圆管闭合";
+
+  return (
+    <div className="flex flex-col gap-5">
+      <div className="flex items-center justify-between">
+        <h3 className="text-lg font-bold text-zinc-950">{die.label}</h3>
+        <span className="rounded-full bg-zinc-100 px-2.5 py-1 text-xs text-zinc-500">{stageLabel}</span>
+      </div>
+
+      {/* 大截面图 */}
+      <div className="flex justify-center rounded-xl border border-zinc-100 bg-zinc-50 py-4">
+        <FormingShape dieIndex={die.dieIndex} totalDies={totalDies} size={150} />
+      </div>
+
+      {/* 只显示 X / Y */}
+      <div className="space-y-2">
+        <div className="flex items-center justify-between rounded-lg bg-zinc-50 px-4 py-3">
+          <span className="text-sm text-zinc-500">X 位置</span>
+          <span className="font-mono text-sm font-semibold text-zinc-950">
+            {die.xPos.toFixed(3)}&thinsp;mm
+          </span>
+        </div>
+        <div className="flex items-center justify-between rounded-lg bg-zinc-50 px-4 py-3">
+          <span className="text-sm text-zinc-500">Y 位置</span>
+          <span className="font-mono text-sm font-semibold text-zinc-950">
+            {die.yPos.toFixed(3)}&thinsp;mm
+          </span>
+        </div>
+      </div>
+    </div>
+  );
+}
+
+// ── 主组件 ────────────────────────────────────────────────────────────────────
+
+export function DieStatusOverview({
+  artifact,
+}: ArtifactComponentProps<Extract<InteractionArtifact, { type: "die_status_overview" }>>) {
+  const props = artifact.props as DieStatusOverviewProps;
+  const [selectedIdx, setSelectedIdx] = useState<number | null>(props.selectedDieIndex);
+
+  const selectedDie = selectedIdx !== null
+    ? props.dies.find((d) => d.dieIndex === selectedIdx) ?? null
+    : null;
+
+  return (
+    <section className="w-full max-w-5xl">
+      {/* Header */}
+      <div className="mb-4 flex items-center gap-3">
+        <div className="flex h-11 w-11 items-center justify-center rounded-lg bg-zinc-950 text-white">
+          <Layers className="h-5 w-5" />
+        </div>
+        <div>
+          <p className="text-xs font-semibold uppercase tracking-[0.18em] text-zinc-500">
+            模具位置总览
+          </p>
+          <h2 className="text-2xl font-semibold text-zinc-950">{artifact.title}</h2>
+        </div>
+        {props.lastScanTime && (
+          <span className="ml-auto text-xs text-zinc-400">读取时间：{props.lastScanTime}</span>
+        )}
+      </div>
+
+      <div className="rounded-lg border border-zinc-200 bg-white shadow-sm">
+        <div
+          className={[
+            "grid divide-y divide-zinc-100",
+            selectedDie ? "lg:grid-cols-[1fr_220px] lg:divide-y-0 lg:divide-x" : "",
+          ].join(" ")}
+        >
+
+          {/* 左：模具格子 */}
+          <div className="p-5">
+            <p className="mb-3 text-xs text-zinc-400">
+              截面线图展示该模具出口处管材的成型状态（平板 → 圆管）· 点击查看坐标
+            </p>
+            <div className="flex flex-wrap justify-center gap-2">
+              {props.dies.map((die) => (
+                <DieCell
+                  key={die.dieIndex}
+                  die={die}
+                  totalDies={props.totalDies}
+                  selected={selectedIdx === die.dieIndex}
+                  onClick={() =>
+                    setSelectedIdx(selectedIdx === die.dieIndex ? null : die.dieIndex)
+                  }
+                />
+              ))}
+            </div>
+          </div>
+
+          {/* 右：详情 */}
+          {selectedDie && (
+            <div className="p-5">
+              <DieDetailPanel die={selectedDie} totalDies={props.totalDies} />
+            </div>
+          )}
+        </div>
+      </div>
+    </section>
+  );
+}
diff --git a/src/components/artifacts/KnowledgeArtifact.tsx b/src/components/artifacts/KnowledgeArtifact.tsx
new file mode 100644
index 0000000..bdeb07b
--- /dev/null
+++ b/src/components/artifacts/KnowledgeArtifact.tsx
@@ -0,0 +1,220 @@
+"use client";
+
+/**
+ * src/components/artifacts/KnowledgeArtifact.tsx
+ *
+ * 知识检索结果展示 —— 支持图片、视频、步骤、详情展开。
+ * 对应 Artifact type: "knowledge_lesson"
+ */
+
+import {
+  BookOpen,
+  ChevronDown,
+  FileText,
+  Image as ImageIcon,
+  Play,
+} from "lucide-react";
+import { useState } from "react";
+import type {
+  ArtifactComponentProps,
+  InteractionArtifact,
+  KnowledgeBlock,
+  KnowledgeLessonProps,
+  MediaRef,
+} from "@/lib/artifacts/types";
+
+function cx(...classes: Array<string | false | null | undefined>) {
+  return classes.filter(Boolean).join(" ");
+}
+
+// ── MediaBlock ───────────────────────────────────────────────────────────────
+
+function MediaCard({ ref: mediaRef }: { ref: MediaRef }) {
+  const isVideo = mediaRef.type === "video";
+  return (
+    <div className="flex items-start gap-3 rounded-lg border border-zinc-200 bg-zinc-50 p-3">
+      <div
+        className={cx(
+          "flex h-10 w-10 flex-shrink-0 items-center justify-center rounded-md",
+          isVideo ? "bg-blue-100 text-blue-600" : "bg-emerald-100 text-emerald-600",
+        )}
+      >
+        {isVideo ? <Play className="h-5 w-5" /> : <ImageIcon className="h-5 w-5" />}
+      </div>
+      <div className="min-w-0">
+        <p className="text-sm font-semibold text-zinc-900">{mediaRef.title}</p>
+        {mediaRef.description && (
+          <p className="mt-0.5 text-xs text-zinc-500">{mediaRef.description}</p>
+        )}
+        {mediaRef.url && (
+          <a
+            href={mediaRef.url}
+            target="_blank"
+            rel="noopener noreferrer"
+            className="mt-1 inline-flex items-center gap-1 text-xs font-medium text-blue-600 hover:text-blue-800"
+          >
+            <FileText className="h-3 w-3" />
+            {isVideo ? "播放视频" : "查看图片"}
+          </a>
+        )}
+      </div>
+    </div>
+  );
+}
+
+// ── KnowledgeBlock renderer ───────────────────────────────────────────────────
+
+function BlockView({ block }: { block: KnowledgeBlock }) {
+  const [open, setOpen] = useState(block.type !== "details" || Boolean(block.defaultOpen));
+
+  if (block.type === "summary") {
+    return (
+      <p className="rounded-lg border border-zinc-200 bg-white p-4 text-sm leading-7 text-zinc-700">
+        {block.text}
+      </p>
+    );
+  }
+
+  if (block.type === "key_points") {
+    return (
+      <div className="rounded-lg border border-zinc-200 bg-white p-4">
+        <p className="mb-3 text-xs font-semibold uppercase tracking-widest text-zinc-500">关键点</p>
+        <div className="grid gap-2 sm:grid-cols-2 md:grid-cols-3">
+          {block.items.map((item) => (
+            <div
+              key={item}
+              className="flex items-start gap-2 rounded-md bg-zinc-50 px-3 py-2 text-sm text-zinc-700"
+            >
+              <span className="mt-1 h-1.5 w-1.5 flex-shrink-0 rounded-full bg-zinc-400" />
+              {item}
+            </div>
+          ))}
+        </div>
+      </div>
+    );
+  }
+
+  if (block.type === "steps") {
+    return (
+      <div className="rounded-lg border border-zinc-200 bg-white p-4">
+        <p className="mb-4 text-xs font-semibold uppercase tracking-widest text-zinc-500">
+          处理步骤
+        </p>
+        <div className="space-y-4">
+          {block.steps.map((step, index) => (
+            <div key={step.title} className="grid grid-cols-[32px_1fr] gap-3">
+              <div className="flex h-8 w-8 items-center justify-center rounded-full bg-zinc-950 text-sm font-bold text-white">
+                {index + 1}
+              </div>
+              <div>
+                <p className="font-semibold text-zinc-950">{step.title}</p>
+                <p className="mt-1 text-sm leading-6 text-zinc-600">{step.body}</p>
+                {step.media && step.media.length > 0 && (
+                  <div className="mt-3 grid gap-2">
+                    {step.media.map((m) => (
+                      <MediaCard key={m.title} ref={m} />
+                    ))}
+                  </div>
+                )}
+              </div>
+            </div>
+          ))}
+        </div>
+      </div>
+    );
+  }
+
+  if (block.type === "media") {
+    return (
+      <div className="grid gap-2">
+        {block.media.map((m) => (
+          <MediaCard key={m.title} ref={m} />
+        ))}
+      </div>
+    );
+  }
+
+  if (block.type === "details") {
+    return (
+      <div className="rounded-lg border border-zinc-200 bg-white">
+        <button
+          type="button"
+          className="flex w-full items-center justify-between p-4 text-left"
+          onClick={() => setOpen((v) => !v)}
+        >
+          <span className="font-semibold text-zinc-950">{block.title}</span>
+          <ChevronDown
+            className={cx("h-4 w-4 text-zinc-500 transition", open && "rotate-180")}
+          />
+        </button>
+        {open && (
+          <p className="border-t border-zinc-100 p-4 text-sm leading-7 text-zinc-600">
+            {block.markdown}
+          </p>
+        )}
+      </div>
+    );
+  }
+
+  if (block.type === "citations") {
+    return (
+      <div className="rounded-lg bg-zinc-50 p-4">
+        <p className="mb-2 text-xs font-semibold uppercase tracking-widest text-zinc-500">
+          引用文档
+        </p>
+        <div className="flex flex-wrap gap-2">
+          {block.citations.map((c) => (
+            <span
+              key={`${c.title}_${c.location}`}
+              className="rounded-full bg-white px-3 py-1 text-xs text-zinc-600 shadow-sm"
+            >
+              {c.title} · {c.location}
+            </span>
+          ))}
+        </div>
+      </div>
+    );
+  }
+
+  return null;
+}
+
+// ── 主组件 ────────────────────────────────────────────────────────────────────
+
+export function KnowledgeArtifact({
+  artifact,
+}: ArtifactComponentProps<Extract<InteractionArtifact, { type: "knowledge_lesson" }>>) {
+  const props = artifact.props as KnowledgeLessonProps;
+
+  return (
+    <section className="w-full max-w-5xl">
+      {/* Header */}
+      <div className="mb-4 flex items-center gap-3">
+        <div className="flex h-11 w-11 items-center justify-center rounded-lg bg-emerald-600 text-white">
+          <BookOpen className="h-5 w-5" />
+        </div>
+        <div>
+          <p className="text-xs font-semibold uppercase tracking-[0.18em] text-zinc-500">
+            知识检索
+          </p>
+          <h2 className="text-2xl font-semibold text-zinc-950">{artifact.title}</h2>
+        </div>
+      </div>
+
+      <div className="rounded-lg border border-zinc-200 bg-white shadow-sm">
+        {/* Conclusion */}
+        <div className="border-b border-zinc-100 bg-emerald-50 p-5">
+          <p className="text-xs font-semibold text-emerald-700">先看结论</p>
+          <h3 className="mt-1 text-xl font-semibold text-emerald-950">{props.conclusion}</h3>
+        </div>
+
+        {/* Blocks */}
+        <div className="space-y-4 p-5">
+          {props.blocks.map((block, i) => (
+            <BlockView block={block} key={i} />
+          ))}
+        </div>
+      </div>
+    </section>
+  );
+}
diff --git a/src/lib/artifacts/die-mock-data.ts b/src/lib/artifacts/die-mock-data.ts
new file mode 100644
index 0000000..7529a32
--- /dev/null
+++ b/src/lib/artifacts/die-mock-data.ts
@@ -0,0 +1,63 @@
+/**
+ * src/lib/artifacts/die-mock-data.ts
+ *
+ * 模具模拟数据生成器。
+ * 实际接入时替换为从 PLC / 伺服驱动器读取的真实数据。
+ */
+
+import type { DiePositionData } from "./types";
+
+/** 根据材料和尺寸生成 15 个模具的模拟伺服位置 */
+export function generateDiePositions(params: {
+  outerDiameter: number;
+  thickness: number;
+  totalDies?: number;
+}): DiePositionData[] {
+  const count = params.totalDies ?? 15;
+  const r = params.outerDiameter / 2;
+
+  return Array.from({ length: count }, (_, i) => {
+    const idx = i + 1;
+    // 模拟：越靠后的模具偏差越小（成形收敛）
+    const progressRatio = idx / count;
+    const baseX = +(r * 0.1 * (1 - progressRatio)).toFixed(3);
+    const baseY = +(r * 0.08 * (1 - progressRatio)).toFixed(3);
+
+    // 当前值 = 标准值 + 随机扰动
+    const noise = () => +(Math.random() * 0.4 - 0.2).toFixed(3);
+    const stdX = +baseX.toFixed(3);
+    const stdY = +baseY.toFixed(3);
+    const curX = +(stdX + noise()).toFixed(3);
+    const curY = +(stdY + noise()).toFixed(3);
+    const curAngle = +(noise() * 0.5).toFixed(3);
+    const deviation = +Math.sqrt((curX - stdX) ** 2 + (curY - stdY) ** 2).toFixed(3);
+
+    const status: DiePositionData["status"] =
+      deviation > 0.3 ? "over_limit" : deviation > 0.15 ? "warning" : "normal";
+
+    // 成形进度：椭圆度随模具推进逐渐减小
+    const ovality = +((1 - progressRatio) * 0.18).toFixed(3);
+
+    return {
+      dieIndex: idx,
+      label: `模具 ${idx}`,
+      xPos: curX,
+      yPos: curY,
+      angleOffset: curAngle,
+      standard: { xPos: stdX, yPos: stdY, angleOffset: 0 },
+      deviation,
+      status,
+      crossSection: {
+        outerRadius: r,
+        wallThickness: params.thickness,
+        ovality,
+        ovalityAngle: (idx * 22) % 180,
+      },
+    };
+  });
+}
+
+/** 生成 16 个模具的总览模拟数据（用于 die_status_overview） */
+export function generateDieStatusOverview(totalDies = 16): DiePositionData[] {
+  return generateDiePositions({ outerDiameter: 20, thickness: 2, totalDies });
+}
diff --git a/src/lib/artifacts/factory.ts b/src/lib/artifacts/factory.ts
new file mode 100644
index 0000000..8b7722b
--- /dev/null
+++ b/src/lib/artifacts/factory.ts
@@ -0,0 +1,319 @@
+/**
+ * src/lib/artifacts/factory.ts
+ *
+ * NLU 结果 → Artifact 工厂
+ *
+ * 根据 NluResult（domain / intent / slots）构造对应的 InteractionArtifact。
+ * 返回 null 表示该 intent 不需要展示 Artifact（纯文字回复即可）。
+ *
+ * ── 配置指南 ────────────────────────────────────────────────────────────────
+ *
+ * 1. 新增工具的 Artifact：
+ *    在 INTENT_TO_ARTIFACT 表里加一条 case，返回对应 InteractionArtifact。
+ *
+ * 2. 已有 Artifact 类型的结构：
+ *    参见 src/lib/artifacts/types.ts 里的 ParameterChangeProps / DeviceActionProps 等。
+ *
+ * 3. 对应的可视化组件：
+ *    src/components/artifacts/ArtifactRenderer.tsx 底部的 registry 对象，
+ *    key = ArtifactType，value = React 组件。新增 artifact_type 时在此注册。
+ *
+ * 4. 对应的 Python 工具实现：
+ *    intelligent_cabin/app/plugins/mock.py —— 注册 plugin_id → 执行函数
+ *    intelligent_cabin/config/actions.yml  —— action_id → plugin_id 映射
+ *    intelligent_cabin/config/domain.yml   —— intent_id → action_id 映射
+ */
+
+import { createParameterChangeArtifact } from "./mock";
+import type { DieMaterialAdjustProps, DieStatusOverviewProps, InteractionArtifact } from "./types";
+import { generateDiePositions, generateDieStatusOverview } from "./die-mock-data";
+import type { NluResult } from "@/lib/nlu/types";
+
+
+// ── 统一确认/取消按钮（所有需要确认的操作共用） ──────────────────────────────
+// 如需修改语音别名，在此处或 voice_aliases.yml 中调整
+const CONFIRM_VOICE_ALIASES = ["确认执行", "可以执行", "执行吧", "确认", "好的"];
+const CANCEL_VOICE_ALIASES = ["取消执行", "不要执行", "先别执行", "取消", "算了"];
+
+function makeConfirmActions() {
+  return [
+    {
+      actionId: "artifact.confirm",
+      label: "确认执行",
+      kind: "primary" as const,
+      textAliases: ["确认", "确认执行", "执行", "开始执行", "确定", "没问题"],
+      voiceAliases: CONFIRM_VOICE_ALIASES,
+      requiresConfirmation: true,
+    },
+    {
+      actionId: "artifact.cancel",
+      label: "取消",
+      kind: "secondary" as const,
+      textAliases: ["取消", "先不执行", "不要"],
+      voiceAliases: CANCEL_VOICE_ALIASES,
+    },
+  ];
+}
+
+// ── 主映射函数 ────────────────────────────────────────────────────────────────
+
+/**
+ * 根据 NLU 识别结果构造 Artifact。
+ * 返回 null 时前端只显示文字回复，不渲染 Artifact 卡片。
+ */
+export function buildArtifactFromNlu(result: NluResult): InteractionArtifact | null {
+  const { intent, slots } = result;
+
+  switch (intent) {
+
+    // ── 空调温度设置 ─────────────────────────────────────────────────────────
+    // Python 链路：cabin_set_ac → action_cabin_set_ac → plugin.cabin.ac_control → mock._set_ac()
+    // 可视化：ParameterChangeCard（src/components/artifacts/ArtifactRenderer.tsx）
+    case "cabin_set_ac": {
+      const targetTemp = slots["temperature"] ?? slots["value"] ?? "?";
+      const currentTemp = 25; // 实际项目：从设备读取当前温度
+      return createParameterChangeArtifact({
+        parameterName: "空调温度",
+        currentValue: currentTemp,
+        targetValue: Number(targetTemp),
+        unit: "°C",
+        deviceName: "车载空调",
+      });
+    }
+
+    // ── 以下 intent 只返回文字，不需要 Artifact ───────────────────────────────
+    case "cabin_ac_on":
+    case "cabin_ac_off":
+    case "cabin_fan_up":
+    case "cabin_fan_down":
+    case "cabin_nav_cancel":
+    case "cabin_play_music":
+    case "cabin_pause_music":
+    case "cabin_next_track":
+    case "cabin_previous_track":
+    case "cabin_volume_up":
+    case "cabin_volume_down":
+    case "cabin_volume_mute":
+    case "cabin_lights_on":
+    case "cabin_lights_off":
+    case "cabin_seat_heat_on":
+    case "cabin_seat_heat_off":
+    case "cabin_mirror_fold":
+    case "cabin_mirror_unfold":
+    case "cabin_wiper_on":
+    case "cabin_wiper_off":
+    case "cabin_window_open":
+    case "cabin_window_close":
+    case "cabin_sunroof_open":
+    case "cabin_sunroof_close":
+    case "cabin_defog_front_on":
+    case "cabin_defog_rear_on":
+    case "cabin_lock_doors":
+    case "cabin_unlock_doors":
+      return null; // 执行成功，回复 replyText 即可
+
+    // ── 需要补槽（ask_slot），不展示确认卡片 ──────────────────────────────────
+    case "cabin_nav_to":
+      return null;
+
+    default:
+      return null;
+  }
+}
+
+// ── 独立 Artifact 构建器（用于演示按钮直接触发）─────────────────────────────────
+
+/** 构建知识检索卡：虚焊报警 / 弯管工序 / 激光扫描仪 */
+export function buildFalseWeldKnowledgeArtifact(): InteractionArtifact {
+  return {
+    artifactId: `knowledge_${Date.now()}`,
+    type: "knowledge_lesson",
+    title: "虚焊报警处理 — 弯管工序",
+    status: "success",
+    props: {
+      topic: "虚焊报警 / 涡流探伤 / 弯管成形偏差",
+      conclusion:
+        "弯管工序成形形态偏差是虚焊报警的主要原因。用激光扫描仪检测各模具点位，偏差超限时通过平板 AI 自动调节后再验证。",
+      blocks: [
+        {
+          type: "summary",
+          text: "涡流探伤报警（虚焊）通常来自管材表面形态异常，而形态偏差源头在弯管工序的模具成形质量。需用激光扫描仪逐点检测，找到偏差超限的模具后自动调节。",
+        },
+        {
+          type: "key_points",
+          items: ["定位报警管段对应模具区间", "激光扫描每个模具出口点位", "偏差 >0.30mm 立即 AI 自动调节", "调节后生产 1~2m 再验证"],
+        },
+        {
+          type: "steps",
+          steps: [
+            {
+              title: "停机 & 确认报警位置",
+              body: "在涡流探伤仪界面记录报警触发时的管材位置，折算到对应的弯管模具区间。",
+            },
+            {
+              title: "激光扫描检测",
+              body: "打开平板 App → 弯管检测 → 开始扫描。扫描仪逐一采集每个模具出口横截面，约 2s/点。",
+              media: [
+                {
+                  type: "image",
+                  title: "扫描仪安装位置",
+                  description: "激光线垂直于管材轴线，安装于模具出口侧",
+                  url: "/assets/knowledge/laser_scan_setup.jpg",
+                },
+                {
+                  type: "video",
+                  title: "激光扫描完整操作流程",
+                  description: "约 4 分钟，含校准 + 扫描 + 报告生成",
+                  url: "/assets/knowledge/laser_scan_tutorial.mp4",
+                },
+              ],
+            },
+            {
+              title: "查看差异云图",
+              body: "扫描完成后点击「生成报告」，平板 App 显示差异云图：绿色=正常，黄色=警告（>0.15mm），红色=超限（>0.30mm）。",
+              media: [
+                {
+                  type: "image",
+                  title: "平板 App 差异云图",
+                  description: "红色区域即需要调节的模具",
+                  url: "/assets/knowledge/tablet_deviation_map.jpg",
+                },
+              ],
+            },
+            {
+              title: "AI 自动调节",
+              body: "对红色（超限）模具点击「AI 自动调节」，系统计算补偿量后自动写入伺服位置，约 15s 完成。",
+            },
+            {
+              title: "验证",
+              body: "调节完毕后开机生产约 1~2m，重新扫描确认偏差已回到绿色范围，再恢复正常生产速度。",
+            },
+          ],
+        },
+        {
+          type: "details",
+          title: "偏差判定标准（参考表）",
+          markdown:
+            "正常：≤ ±0.15mm，无需处理。\n警告：±0.15～0.30mm，记录并于下次保养处理。\n超限：> ±0.30mm，立即停机调节。\n\n注：以上阈值适用于 20mm 圆管，其他规格请参考《焊管机维护手册》第 7 章。",
+          defaultOpen: false,
+        },
+        {
+          type: "citations",
+          citations: [
+            { title: "焊管机维护手册", location: "第 7 章：弯管工序成形质量管理" },
+            { title: "激光扫描仪操作规程", location: "SOP-QC-003 v1.2" },
+            { title: "涡流探伤报警处置流程", location: "SOP-QC-007 v2.0" },
+          ],
+        },
+      ],
+    },
+    actions: [],
+    llmContext: {
+      visibleSummary: "正在显示虚焊报警（弯管工序）的知识卡片，含激光扫描仪操作和 AI 自动调节步骤。",
+      currentState: "success",
+      visibleFields: [{ label: "主题", value: "虚焊报警 / 弯管工序 / 激光扫描" }],
+      availableActions: [],
+      lastEvents: [],
+    },
+    snapshotPolicy: "ephemeral",
+  };
+}
+
+/** 构建模具伺服调节 Artifact（材料 + 尺寸驱动） */
+export function buildDieMaterialAdjustArtifact(params?: {
+  material?: string;
+  outerDiameter?: number;
+  thickness?: number;
+}): InteractionArtifact {
+  const material = params?.material ?? "304 不锈钢";
+  const outerDiameter = params?.outerDiameter ?? 20;
+  const thickness = params?.thickness ?? 2;
+  const dies = generateDiePositions({ outerDiameter, thickness, totalDies: 15 });
+  const overCount = dies.filter((d) => d.status === "over_limit").length;
+
+  const props: DieMaterialAdjustProps = {
+    material,
+    outerDiameter,
+    thickness,
+    pipeShape: "round",
+    totalDies: 15,
+    dies,
+    executionNote:
+      overCount > 0
+        ? `检测到 ${overCount} 个模具位置超限，点击「AI 自动调节」将自动写入补偿量。`
+        : "所有模具位置在正常范围内，可按当前参数生产。",
+  };
+
+  return {
+    artifactId: `die_adjust_${Date.now()}`,
+    type: "die_material_adjust",
+    title: `模具伺服调节 — ${material} Φ${outerDiameter}×${thickness}`,
+    status: overCount > 0 ? "waiting_confirmation" : "success",
+    props,
+    actions:
+      overCount > 0
+        ? [
+            {
+              actionId: "artifact.confirm",
+              label: "AI 自动调节",
+              kind: "primary" as const,
+              textAliases: ["自动调节", "调节", "确认", "执行"],
+              voiceAliases: ["自动调节", "AI 调节", "确认调节"],
+              requiresConfirmation: true,
+            },
+            {
+              actionId: "artifact.cancel",
+              label: "取消",
+              kind: "secondary" as const,
+              textAliases: ["取消"],
+              voiceAliases: ["取消", "不调节"],
+            },
+          ]
+        : [],
+    llmContext: {
+      visibleSummary: `正在显示 ${material} Φ${outerDiameter}mm 管材的 15 个模具伺服位置，${overCount > 0 ? `${overCount} 个超限` : "全部正常"}。`,
+      currentState: overCount > 0 ? "waiting_confirmation" : "success",
+      visibleFields: [
+        { label: "材料", value: material },
+        { label: "外径", value: `${outerDiameter} mm` },
+        { label: "壁厚", value: `${thickness} mm` },
+        { label: "超限数量", value: String(overCount) },
+      ],
+      availableActions: [],
+      lastEvents: [],
+    },
+    snapshotPolicy: "session",
+  };
+}
+
+/** 构建模具位置总览 Artifact（16 个模具，可点击查看截面） */
+export function buildDieStatusOverviewArtifact(): InteractionArtifact {
+  const dies = generateDieStatusOverview(16);
+  const props: DieStatusOverviewProps = {
+    totalDies: 16,
+    selectedDieIndex: null,
+    dies,
+    lastScanTime: new Date().toLocaleTimeString("zh-CN", { hour: "2-digit", minute: "2-digit" }),
+  };
+
+  return {
+    artifactId: `die_overview_${Date.now()}`,
+    type: "die_status_overview",
+    title: "弯管模具位置总览",
+    status: "success",
+    props,
+    actions: [],
+    llmContext: {
+      visibleSummary: "正在显示全部 16 个弯管模具的当前位置和截面形态，可点击任意模具查看详情。",
+      currentState: "success",
+      visibleFields: [{ label: "模具总数", value: "16" }],
+      availableActions: [],
+      lastEvents: [],
+    },
+    snapshotPolicy: "session",
+  };
+}
+
+
+// 导出供 page.tsx 使用
+export { makeConfirmActions };
diff --git a/src/lib/artifacts/mock.ts b/src/lib/artifacts/mock.ts
new file mode 100644
index 0000000..39b19e9
--- /dev/null
+++ b/src/lib/artifacts/mock.ts
@@ -0,0 +1,258 @@
+import type { ArtifactAction, InteractionArtifact } from "./types";
+
+const confirmActions: ArtifactAction[] = [
+  {
+    actionId: "artifact.confirm",
+    label: "确认执行",
+    kind: "primary",
+    textAliases: ["确认", "确认执行", "执行", "开始", "开始执行", "确定", "没问题"],
+    voiceAliases: ["确认执行", "可以执行", "执行吧", "确认修改", "确认写入", "写入吧", "确定执行", "是的"],
+    requiresConfirmation: true,
+  },
+  {
+    actionId: "artifact.cancel",
+    label: "取消",
+    kind: "secondary",
+    textAliases: ["取消", "先不执行", "停止"],
+    voiceAliases: ["取消执行", "不要执行", "先别执行", "撤销", "返回"],
+  },
+];
+
+export function createParameterChangeArtifact(input: {
+  parameterName: string;
+  currentValue: string | number;
+  targetValue: string | number;
+  unit?: string;
+  deviceName?: string;
+}): InteractionArtifact {
+  const unit = input.unit ?? "";
+  return {
+    artifactId: `param_${Date.now()}`,
+    type: "parameter_change",
+    title: `${input.parameterName} 参数修改`,
+    status: "waiting_confirmation",
+    props: {
+      parameterName: input.parameterName,
+      currentValue: input.currentValue,
+      targetValue: input.targetValue,
+      unit,
+      deviceName: input.deviceName ?? "一号加工设备",
+      impact: "该参数会影响当前工艺节拍，执行前请确认目标值是否正确。",
+      executionNote: "Mock 工具将模拟写入参数并返回成功状态。",
+    },
+    actions: confirmActions,
+    llmContext: {
+      visibleSummary: `${input.parameterName} 将从 ${input.currentValue}${unit} 修改为 ${input.targetValue}${unit}，当前等待确认。`,
+      currentState: "waiting_confirmation",
+      visibleFields: [
+        { label: "当前值", value: `${input.currentValue}${unit}` },
+        { label: "目标值", value: `${input.targetValue}${unit}` },
+      ],
+      availableActions: confirmActions.map(({ actionId, label, textAliases, voiceAliases }) => ({
+        actionId,
+        label,
+        textAliases,
+        voiceAliases,
+      })),
+      lastEvents: [],
+    },
+    snapshotPolicy: "session",
+  };
+}
+
+export function createDeviceActionArtifact(input: {
+  deviceName: string;
+  action: "open" | "close" | "start" | "stop";
+  deviceKind: "light" | "pump" | "fan" | "machine";
+}): InteractionArtifact {
+  const targetState = input.action === "open" || input.action === "start" ? "运行中" : "已关闭";
+  return {
+    artifactId: `device_${Date.now()}`,
+    type: "device_action",
+    title: `${input.deviceName} ${targetState}`,
+    status: "waiting_confirmation",
+    props: {
+      deviceName: input.deviceName,
+      action: input.action,
+      deviceKind: input.deviceKind,
+      currentState: "待机",
+      targetState,
+      progressLabel: "等待确认",
+      executionNote: "Mock 工具将模拟设备动作、动画和状态回读。",
+    },
+    actions: confirmActions,
+    llmContext: {
+      visibleSummary: `${input.deviceName} 准备切换到 ${targetState}，当前等待确认。`,
+      currentState: "waiting_confirmation",
+      visibleFields: [
+        { label: "当前状态", value: "待机" },
+        { label: "目标状态", value: targetState },
+      ],
+      availableActions: confirmActions.map(({ actionId, label, textAliases, voiceAliases }) => ({
+        actionId,
+        label,
+        textAliases,
+        voiceAliases,
+      })),
+      lastEvents: [],
+    },
+    snapshotPolicy: "session",
+  };
+}
+
+export function createKnowledgeLessonArtifact(topic = "主轴温度报警处理"): InteractionArtifact {
+  return {
+    artifactId: `knowledge_${Date.now()}`,
+    type: "knowledge_lesson",
+    title: topic,
+    status: "success",
+    props: {
+      topic,
+      conclusion: "先确认冷却、润滑和负载三个方向，再决定是否继续生产。",
+      blocks: [
+        {
+          type: "summary",
+          text: "这个报警通常不是单点故障，需要先看冷却流量，再看润滑状态，最后检查最近是否提高了切削负载。",
+        },
+        {
+          type: "key_points",
+          items: ["检查冷却泵是否运行", "确认润滑油位和压力", "查看最近 10 分钟主轴负载曲线"],
+        },
+        {
+          type: "steps",
+          steps: [
+            {
+              title: "看冷却",
+              body: "确认冷却液液位、过滤器和泵状态，重点看流量是否明显偏低。",
+            },
+            {
+              title: "看润滑",
+              body: "确认润滑压力是否在设备手册建议范围内。",
+            },
+            {
+              title: "看负载",
+              body: "如果报警前负载持续升高，优先降低进给或暂停加工检查刀具。",
+            },
+          ],
+        },
+        {
+          type: "details",
+          title: "专业详情",
+          markdown:
+            "主轴温度报警可能来自冷却不足、润滑异常、轴承状态变化或切削负载过高。处理时不要只复位报警，应先确认温升趋势是否已经回落。",
+        },
+        {
+          type: "citations",
+          citations: [
+            { title: "设备维护手册", location: "第 4 章 主轴系统" },
+            { title: "调机知识库", location: "主轴温度报警 SOP v1" },
+          ],
+        },
+      ],
+    },
+    actions: [
+      {
+        actionId: "lesson.start_procedure",
+        label: "进入调机流程",
+        kind: "primary",
+        textAliases: ["开始调机", "进入流程", "按步骤处理"],
+      },
+    ],
+    llmContext: {
+      visibleSummary: `画布正在展示 ${topic} 的教学卡片。`,
+      currentState: "success",
+      visibleFields: [{ label: "结论", value: "先确认冷却、润滑和负载三个方向" }],
+      availableActions: [
+        {
+          actionId: "lesson.start_procedure",
+          label: "进入调机流程",
+          textAliases: ["开始调机", "进入流程", "按步骤处理"],
+        },
+      ],
+      lastEvents: [],
+    },
+    snapshotPolicy: "ephemeral",
+  };
+}
+
+export function createGuidedProcedureArtifact(): InteractionArtifact {
+  return {
+    artifactId: `procedure_${Date.now()}`,
+    type: "guided_procedure",
+    title: "主轴温度报警调机流程",
+    status: "running",
+    props: {
+      procedureName: "主轴温度报警调机流程",
+      deviceName: "一号加工设备",
+      currentStepId: "step_1",
+      steps: [
+        {
+          stepId: "step_1",
+          type: "auto_tool",
+          title: "读取主轴温度和负载",
+          instruction: "AI 将模拟读取当前主轴温度、负载和冷却状态。",
+          status: "waiting_feedback",
+          expectedFeedback: ["已读取", "下一步"],
+        },
+        {
+          stepId: "step_2",
+          type: "manual_action",
+          title: "检查冷却液流量",
+          instruction: "请确认冷却液流量是否稳定，没有明显断流或气泡。",
+          status: "pending",
+          expectedFeedback: ["已完成", "有异常"],
+        },
+        {
+          stepId: "step_3",
+          type: "measurement_input",
+          title: "输入润滑压力",
+          instruction: "请输入现场读取到的润滑压力，单位 MPa。",
+          status: "pending",
+          expectedFeedback: ["压力是 0.32", "0.32"],
+        },
+        {
+          stepId: "step_4",
+          type: "checklist_gate",
+          title: "确认是否恢复生产",
+          instruction: "确认温度趋势回落、冷却正常、润滑压力正常后，再恢复生产。",
+          status: "pending",
+          expectedFeedback: ["确认恢复", "暂停"],
+        },
+      ],
+    },
+    actions: [
+      {
+        actionId: "procedure.next",
+        label: "这一步完成",
+        kind: "primary",
+        textAliases: ["完成", "已完成", "下一步", "这一步完成了"],
+      },
+      {
+        actionId: "procedure.pause",
+        label: "暂停流程",
+        kind: "secondary",
+        textAliases: ["暂停", "先暂停"],
+      },
+    ],
+    llmContext: {
+      visibleSummary: "画布正在执行主轴温度报警调机流程，第 1 步等待反馈。",
+      currentState: "running",
+      activeStep: "读取主轴温度和负载",
+      visibleFields: [{ label: "当前步骤", value: "第 1 步：读取主轴温度和负载" }],
+      availableActions: [
+        {
+          actionId: "procedure.next",
+          label: "这一步完成",
+          textAliases: ["完成", "已完成", "下一步", "这一步完成了"],
+        },
+        {
+          actionId: "procedure.pause",
+          label: "暂停流程",
+          textAliases: ["暂停", "先暂停"],
+        },
+      ],
+      lastEvents: [],
+    },
+    snapshotPolicy: "persistent",
+  };
+}
diff --git a/src/lib/artifacts/reducer.ts b/src/lib/artifacts/reducer.ts
new file mode 100644
index 0000000..27e7374
--- /dev/null
+++ b/src/lib/artifacts/reducer.ts
@@ -0,0 +1,169 @@
+import type { ArtifactAction, ArtifactActionEvent, InteractionArtifact } from "./types";
+
+export function createActionEvent(
+  artifactId: string,
+  action: ArtifactAction,
+  source: ArtifactActionEvent["source"],
+  value?: unknown,
+  transcript?: string,
+): ArtifactActionEvent {
+  return {
+    artifactId,
+    actionId: action.actionId,
+    source,
+    value,
+    transcript,
+    createdAt: new Date().toISOString(),
+  };
+}
+
+export function updateArtifactForAction(artifact: InteractionArtifact, event: ArtifactActionEvent): InteractionArtifact {
+  if (event.actionId === "artifact.cancel") {
+    return {
+      ...artifact,
+      status: "paused",
+      llmContext: {
+        ...artifact.llmContext,
+        currentState: "paused",
+        lastEvents: [...artifact.llmContext.lastEvents, "用户取消了当前操作。"],
+      },
+    };
+  }
+
+  if (event.actionId === "artifact.confirm") {
+    if (artifact.type === "parameter_change") {
+      return {
+        ...artifact,
+        status: "success",
+        actions: [],
+        llmContext: {
+          ...artifact.llmContext,
+          currentState: "success",
+          visibleSummary: `${artifact.props.parameterName} 已从 ${artifact.props.currentValue}${artifact.props.unit ?? ""} 修改为 ${artifact.props.targetValue}${artifact.props.unit ?? ""}。`,
+          lastEvents: [...artifact.llmContext.lastEvents, "用户确认并完成参数修改。"],
+        },
+      };
+    }
+
+    if (artifact.type === "device_action") {
+      return {
+        ...artifact,
+        status: "success",
+        props: {
+          ...artifact.props,
+          currentState: artifact.props.targetState,
+          progressLabel: "状态回读完成",
+        },
+        actions: [],
+        llmContext: {
+          ...artifact.llmContext,
+          currentState: "success",
+          visibleSummary: `${artifact.props.deviceName} 已切换到 ${artifact.props.targetState}。`,
+          lastEvents: [...artifact.llmContext.lastEvents, "用户确认并完成设备动作。"],
+        },
+      };
+    }
+  }
+
+  if (event.actionId === "procedure.pause" && artifact.type === "guided_procedure") {
+    return {
+      ...artifact,
+      status: "paused",
+      llmContext: {
+        ...artifact.llmContext,
+        currentState: "paused",
+        lastEvents: [...artifact.llmContext.lastEvents, "用户暂停了调机流程。"],
+      },
+    };
+  }
+
+  if (event.actionId === "procedure.next" && artifact.type === "guided_procedure") {
+    const currentIndex = artifact.props.steps.findIndex((step) => step.stepId === artifact.props.currentStepId);
+    const nextIndex = currentIndex + 1;
+    const nextStep = artifact.props.steps[nextIndex];
+    const updatedSteps = artifact.props.steps.map((step, index) => {
+      if (index === currentIndex) return { ...step, status: "completed" as const, value: String(event.value ?? event.transcript ?? "") };
+      if (index === nextIndex) return { ...step, status: "waiting_feedback" as const };
+      return step;
+    });
+
+    if (!nextStep) {
+      return {
+        ...artifact,
+        status: "success",
+        actions: [],
+        props: {
+          ...artifact.props,
+          steps: updatedSteps,
+        },
+        llmContext: {
+          ...artifact.llmContext,
+          currentState: "success",
+          visibleSummary: `${artifact.props.procedureName} 已完成。`,
+          activeStep: undefined,
+          lastEvents: [...artifact.llmContext.lastEvents, "调机流程已完成。"],
+        },
+      };
+    }
+
+    return {
+      ...artifact,
+      status: "running",
+      props: {
+        ...artifact.props,
+        currentStepId: nextStep.stepId,
+        steps: updatedSteps,
+      },
+      llmContext: {
+        ...artifact.llmContext,
+        currentState: "running",
+        activeStep: nextStep.title,
+        visibleSummary: `调机流程进入下一步：${nextStep.title}。`,
+        visibleFields: [{ label: "当前步骤", value: nextStep.title }],
+        lastEvents: [...artifact.llmContext.lastEvents, `进入步骤：${nextStep.title}`],
+      },
+    };
+  }
+
+  return artifact;
+}
+
+export function matchTextToAction(artifact: InteractionArtifact, text: string): ArtifactAction | undefined {
+  const normalized = normalizeActionText(text);
+  const explicitAction = artifact.actions.find((action) =>
+    [...action.textAliases, ...(action.voiceAliases ?? [])].some((alias) => {
+      const normalizedAlias = normalizeActionText(alias);
+      return normalized === normalizedAlias || normalized.includes(normalizedAlias);
+    }),
+  );
+
+  if (explicitAction) return explicitAction;
+
+  if (artifact.type === "guided_procedure") {
+    const activeStep = artifact.props.steps.find((step) => step.stepId === artifact.props.currentStepId);
+    const nextAction = artifact.actions.find((action) => action.actionId === "procedure.next");
+
+    if (!activeStep || !nextAction) return undefined;
+
+    const matchesExpectedFeedback = activeStep.expectedFeedback?.some((feedback) => {
+      const normalizedFeedback = normalizeActionText(feedback);
+      return normalized === normalizedFeedback || normalized.includes(normalizedFeedback);
+    });
+
+    if (matchesExpectedFeedback) return nextAction;
+
+    if (activeStep.type === "measurement_input" && /[-+]?\d+(?:\.\d+)?/.test(normalized)) {
+      return nextAction;
+    }
+  }
+
+  return undefined;
+}
+
+function normalizeActionText(text: string) {
+  return text
+    .trim()
+    .toLowerCase()
+    .replace(/^(语音|voice|asr)[:：]/, "")
+    .replace(/[\s，。,.！!？?、]/g, "");
+}
diff --git a/src/lib/artifacts/types.ts b/src/lib/artifacts/types.ts
new file mode 100644
index 0000000..9d37cd2
--- /dev/null
+++ b/src/lib/artifacts/types.ts
@@ -0,0 +1,195 @@
+import type { LucideIcon } from "lucide-react";
+import type React from "react";
+
+export type ArtifactStatus =
+  | "draft"
+  | "waiting_confirmation"
+  | "running"
+  | "success"
+  | "failed"
+  | "paused";
+
+export type ArtifactType =
+  | "parameter_change"
+  | "device_action"
+  | "knowledge_lesson"
+  | "guided_procedure"
+  | "die_material_adjust"
+  | "die_status_overview";
+
+
+export type ArtifactAction = {
+  actionId: string;
+  label: string;
+  kind: "primary" | "secondary" | "danger" | "input" | "toggle" | "choice";
+  textAliases: string[];
+  voiceAliases?: string[];
+  requiresConfirmation?: boolean;
+  disabled?: boolean;
+};
+
+export type ArtifactLLMContext = {
+  visibleSummary: string;
+  currentState: string;
+  activeStep?: string;
+  visibleFields: Array<{
+    label: string;
+    value: string;
+  }>;
+  availableActions: Array<{
+    actionId: string;
+    label: string;
+    textAliases: string[];
+    voiceAliases?: string[];
+  }>;
+  lastEvents: string[];
+};
+
+export type InteractionArtifactBase<TType extends ArtifactType, TProps> = {
+  artifactId: string;
+  type: TType;
+  title: string;
+  status: ArtifactStatus;
+  props: TProps;
+  actions: ArtifactAction[];
+  llmContext: ArtifactLLMContext;
+  snapshotPolicy: "ephemeral" | "session" | "persistent";
+};
+
+export type ParameterChangeProps = {
+  parameterName: string;
+  currentValue: string | number;
+  targetValue: string | number;
+  unit?: string;
+  deviceName: string;
+  impact?: string;
+  executionNote?: string;
+};
+
+export type DeviceActionProps = {
+  deviceName: string;
+  action: "open" | "close" | "start" | "stop";
+  deviceKind: "light" | "pump" | "fan" | "machine";
+  currentState: string;
+  targetState: string;
+  progressLabel?: string;
+  executionNote?: string;
+};
+
+export type MediaRef = {
+  type: "image" | "video";
+  title: string;
+  url?: string;
+  description?: string;
+};
+
+export type KnowledgeBlock =
+  | { type: "summary"; text: string }
+  | { type: "key_points"; items: string[] }
+  | {
+      type: "steps";
+      steps: Array<{
+        title: string;
+        body: string;
+        media?: MediaRef[];
+      }>;
+    }
+  | { type: "media"; media: MediaRef[] }
+  | { type: "details"; title: string; markdown: string; defaultOpen?: boolean }
+  | { type: "citations"; citations: Array<{ title: string; location: string }> };
+
+export type KnowledgeLessonProps = {
+  topic: string;
+  conclusion: string;
+  blocks: KnowledgeBlock[];
+};
+
+export type ProcedureStep = {
+  stepId: string;
+  type: "auto_tool" | "manual_action" | "measurement_input" | "decision_branch" | "checklist_gate";
+  title: string;
+  instruction: string;
+  status: "pending" | "running" | "waiting_feedback" | "completed" | "failed" | "skipped";
+  expectedFeedback?: string[];
+  value?: string;
+};
+
+export type GuidedProcedureProps = {
+  procedureName: string;
+  deviceName: string;
+  currentStepId: string;
+  steps: ProcedureStep[];
+};
+
+// ── 模具伺服位置数据 ─────────────────────────────────────────────────────────
+
+export type DiePositionData = {
+  dieIndex: number;        // 1-based, 1~16
+  label: string;           // 如 "模具 1"
+  xPos: number;            // X 轴伺服位置 (mm)
+  yPos: number;            // Y 轴伺服位置 (mm)
+  angleOffset: number;     // 角度偏移 (°)
+  standard: { xPos: number; yPos: number; angleOffset: number }; // 标准值
+  deviation: number;       // 综合偏差 (mm)
+  status: "normal" | "warning" | "over_limit";
+  // 管材横截面形态描述（用于渲染 SVG）
+  crossSection: {
+    outerRadius: number;   // 外径 (mm)
+    wallThickness: number; // 壁厚 (mm)
+    ovality: number;       // 椭圆度 0~1（0=正圆）
+    ovalityAngle: number;  // 椭圆长轴方向 (°)
+  };
+};
+
+// ── 材料 + 模具伺服调节 Artifact ─────────────────────────────────────────────
+
+export type DieMaterialAdjustProps = {
+  material: string;         // 如 "304 不锈钢"
+  thickness: number;        // 壁厚 (mm)
+  outerDiameter: number;    // 外径 (mm)
+  pipeShape: "round" | "square" | "oval";  // 管型
+  totalDies: number;        // 模具数量（默认 15）
+  dies: DiePositionData[];  // 各模具位置数据
+  executionNote?: string;
+};
+
+// ── 模具位置总览 Artifact ─────────────────────────────────────────────────────
+
+export type DieStatusOverviewProps = {
+  totalDies: number;         // 16
+  selectedDieIndex: number | null; // 当前选中的模具索引
+  dies: DiePositionData[];
+  lastScanTime?: string;
+};
+
+
+export type InteractionArtifact =
+  | InteractionArtifactBase<"parameter_change", ParameterChangeProps>
+  | InteractionArtifactBase<"device_action", DeviceActionProps>
+  | InteractionArtifactBase<"knowledge_lesson", KnowledgeLessonProps>
+  | InteractionArtifactBase<"guided_procedure", GuidedProcedureProps>
+  | InteractionArtifactBase<"die_material_adjust", DieMaterialAdjustProps>
+  | InteractionArtifactBase<"die_status_overview", DieStatusOverviewProps>;
+
+
+export type ArtifactActionEvent = {
+  artifactId: string;
+  actionId: string;
+  source: "click" | "text" | "voice" | "system";
+  value?: unknown;
+  transcript?: string;
+  confidence?: number;
+  createdAt: string;
+};
+
+export type ArtifactComponentProps<T extends InteractionArtifact = InteractionArtifact> = {
+  artifact: T;
+  onAction: (action: ArtifactAction, value?: unknown) => void;
+};
+
+export type ArtifactShellProps = {
+  icon: LucideIcon;
+  eyebrow: string;
+  artifact: InteractionArtifact;
+  children: React.ReactNode;
+};
diff --git a/src/lib/nlu/client.ts b/src/lib/nlu/client.ts
new file mode 100644
index 0000000..da92a27
--- /dev/null
+++ b/src/lib/nlu/client.ts
@@ -0,0 +1,66 @@
+/**
+ * src/lib/nlu/client.ts
+ *
+ * 调用 intelligent_cabin /api/v1/agent/chat 的 HTTP 客户端。
+ * 报错直接抛出，不降级，不 Mock 兜底。
+ */
+
+import type { ChatResponse } from "./types";
+
+const NLU_BASE_URL = process.env.NEXT_PUBLIC_NLU_BASE_URL ?? "http://localhost:8000";
+
+export type ChatRequest = {
+  session_id: string;
+  user_id: string;
+  channel?: string;
+  input_text: string;
+  input_type?: "text" | "voice";
+};
+
+export type FillSlotsRequest = {
+  session_id: string;
+  user_id: string;
+  input_text: string;
+};
+
+/**
+ * 发送文本到 intelligent_cabin 进行 NLU 处理。
+ * 报错直接抛出（TypeError、HTTP 4xx/5xx）。
+ */
+export async function chatWithNlu(request: ChatRequest): Promise<ChatResponse> {
+  const res = await fetch(`${NLU_BASE_URL}/api/v1/agent/chat`, {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify({
+      channel: "canvas",
+      input_type: "text",
+      ...request,
+    }),
+  });
+
+  if (!res.ok) {
+    const body = await res.text().catch(() => "(empty)");
+    throw new Error(`NLU service error ${res.status}: ${body}`);
+  }
+
+  return (await res.json()) as ChatResponse;
+}
+
+/**
+ * 填写 slot 值（在 waiting_slot 状态下调用）。
+ * 报错直接抛出。
+ */
+export async function fillSlotsWithNlu(request: FillSlotsRequest): Promise<ChatResponse> {
+  const res = await fetch(`${NLU_BASE_URL}/api/v1/agent/fill-slots`, {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify(request),
+  });
+
+  if (!res.ok) {
+    const body = await res.text().catch(() => "(empty)");
+    throw new Error(`NLU fill-slots error ${res.status}: ${body}`);
+  }
+
+  return (await res.json()) as ChatResponse;
+}
diff --git a/src/lib/nlu/mock.ts b/src/lib/nlu/mock.ts
new file mode 100644
index 0000000..cb09382
--- /dev/null
+++ b/src/lib/nlu/mock.ts
@@ -0,0 +1,205 @@
+import {
+  createDeviceActionArtifact,
+  createGuidedProcedureArtifact,
+  createKnowledgeLessonArtifact,
+  createParameterChangeArtifact,
+} from "@/lib/artifacts/mock";
+import type { InteractionArtifact } from "@/lib/artifacts/types";
+
+export type MockRouteHint =
+  | "artifact_action"
+  | "tool_call"
+  | "knowledge_query"
+  | "workflow_control"
+  | "smalltalk"
+  | "fallback";
+
+export type MockNluResult = {
+  modelVersion: string;
+  domain: string;
+  intent: string;
+  confidence: number;
+  slots: Record<string, string | number | boolean>;
+  routeHint: MockRouteHint;
+  candidateTools: Array<{
+    toolName: string;
+    confidence: number;
+  }>;
+  branchLabel: string;
+  artifact?: InteractionArtifact;
+  reply?: string;
+};
+
+const mockParameterValues: Record<string, string | number> = {
+  主轴转速: 1200,
+  水泵压力上限: 0.6,
+  温度补偿: 0,
+  进给速度: 480,
+};
+
+export function parseMockNlu(text: string): MockNluResult {
+  const normalized = normalizeText(text);
+
+  if (includesAny(normalized, ["开始调机", "进入调机", "调机流程", "按步骤处理"])) {
+    return {
+      modelVersion: "mock-bert-nlu-v1",
+      domain: "guided_procedure",
+      intent: "start_procedure",
+      confidence: 0.96,
+      slots: { procedureName: "主轴温度报警调机流程" },
+      routeHint: "workflow_control",
+      candidateTools: [{ toolName: "start_guided_procedure", confidence: 0.94 }],
+      branchLabel: "流程工具",
+      artifact: createGuidedProcedureArtifact(),
+    };
+  }
+
+  const parameter = detectParameter(normalized);
+  const value = detectNumber(normalized);
+  if (parameter && value !== undefined && includesAny(normalized, ["调", "改", "设置", "补偿", "加到", "设为"])) {
+    const unit = detectUnit(normalized, parameter);
+    return {
+      modelVersion: "mock-bert-nlu-v1",
+      domain: "machine_parameter",
+      intent: "set_parameter",
+      confidence: 0.94,
+      slots: { parameter, value, unit },
+      routeHint: "tool_call",
+      candidateTools: [{ toolName: "write_machine_parameter", confidence: 0.91 }],
+      branchLabel: "高置信度 + 设备控制域",
+      artifact: createParameterChangeArtifact({
+        parameterName: parameter,
+        currentValue: mockParameterValues[parameter] ?? "未读取",
+        targetValue: value,
+        unit,
+        deviceName: "一号加工设备",
+      }),
+    };
+  }
+
+  const deviceAction = detectDeviceAction(normalized);
+  if (deviceAction) {
+    return {
+      modelVersion: "mock-bert-nlu-v1",
+      domain: "machine_control",
+      intent: `${deviceAction.action}_device`,
+      confidence: 0.93,
+      slots: {
+        deviceName: deviceAction.deviceName,
+        action: deviceAction.action,
+        deviceKind: deviceAction.deviceKind,
+      },
+      routeHint: "tool_call",
+      candidateTools: [{ toolName: "control_industrial_device", confidence: 0.9 }],
+      branchLabel: "高置信度 + 设备控制域",
+      artifact: createDeviceActionArtifact(deviceAction),
+    };
+  }
+
+  if (includesAny(normalized, ["报警", "怎么办", "说明书", "sop", "处理", "为什么", "知识", "查一下"])) {
+    const topic = normalized.includes("冷却") ? "冷却泵无流量处理" : "主轴温度报警处理";
+    return {
+      modelVersion: "mock-bert-nlu-v1",
+      domain: "equipment_knowledge",
+      intent: "query_procedure",
+      confidence: 0.86,
+      slots: { topic },
+      routeHint: "knowledge_query",
+      candidateTools: [{ toolName: "search_equipment_knowledge", confidence: 0.83 }],
+      branchLabel: "知识域 / 低置信度兜底",
+      artifact: createKnowledgeLessonArtifact(topic),
+    };
+  }
+
+  if (includesAny(normalized, ["你好", "天气", "谢谢", "你是谁", "随便聊"])) {
+    return {
+      modelVersion: "mock-bert-nlu-v1",
+      domain: "smalltalk",
+      intent: "chat",
+      confidence: 0.77,
+      slots: {},
+      routeHint: "smalltalk",
+      candidateTools: [],
+      branchLabel: "smalltalk / fallback",
+      reply: "这是通用对话内容，不生成工业 Artifact，也不会覆盖当前调机流程。",
+    };
+  }
+
+  return {
+    modelVersion: "mock-bert-nlu-v1",
+    domain: "fallback",
+    intent: "unknown",
+    confidence: 0.41,
+    slots: {},
+    routeHint: "fallback",
+    candidateTools: [],
+    branchLabel: "fallback",
+    reply: "Mock NLU 暂时没有识别到固定意图。可以试试：把主轴转速调到 1800、打开探照灯、打开水泵、主轴温度报警怎么办、开始调机流程。",
+  };
+}
+
+function normalizeText(text: string) {
+  return text.trim().toLowerCase().replace(/\s+/g, "");
+}
+
+function includesAny(text: string, keywords: string[]) {
+  return keywords.some((keyword) => text.includes(keyword.toLowerCase()));
+}
+
+function detectParameter(text: string) {
+  if (text.includes("转速")) return "主轴转速";
+  if (text.includes("压力")) return "水泵压力上限";
+  if (text.includes("温度补偿") || text.includes("补偿")) return "温度补偿";
+  if (text.includes("进给")) return "进给速度";
+  return undefined;
+}
+
+function detectNumber(text: string) {
+  const match = text.match(/[-+]?\d+(?:\.\d+)?/);
+  if (!match) return undefined;
+  return Number(match[0]);
+}
+
+function detectUnit(text: string, parameter: string) {
+  if (text.includes("rpm")) return "rpm";
+  if (text.includes("mpa")) return "MPa";
+  if (text.includes("度") || parameter.includes("温度")) return "°C";
+  if (parameter.includes("转速")) return "rpm";
+  if (parameter.includes("压力")) return "MPa";
+  return "";
+}
+
+function detectDeviceAction(text: string):
+  | {
+      deviceName: string;
+      action: "open" | "close" | "start" | "stop";
+      deviceKind: "light" | "pump" | "fan" | "machine";
+    }
+  | undefined {
+  const action = text.includes("关闭") || text.includes("关掉")
+    ? "close"
+    : text.includes("停止")
+      ? "stop"
+      : text.includes("启动") || text.includes("开始")
+        ? "start"
+        : text.includes("打开")
+          ? "open"
+          : undefined;
+
+  if (!action) return undefined;
+
+  if (text.includes("探照灯") || text.includes("灯")) {
+    return { deviceName: "探照灯", action, deviceKind: "light" };
+  }
+  if (text.includes("水泵") || text.includes("泵")) {
+    return { deviceName: "冷却水泵", action, deviceKind: "pump" };
+  }
+  if (text.includes("风机")) {
+    return { deviceName: "除尘风机", action: action === "open" ? "start" : action, deviceKind: "fan" };
+  }
+  if (text.includes("生产") || text.includes("设备")) {
+    return { deviceName: "一号加工设备", action: action === "open" ? "start" : action, deviceKind: "machine" };
+  }
+
+  return undefined;
+}
diff --git a/src/lib/nlu/pipeline.ts b/src/lib/nlu/pipeline.ts
new file mode 100644
index 0000000..9bcd2de
--- /dev/null
+++ b/src/lib/nlu/pipeline.ts
@@ -0,0 +1,189 @@
+/**
+ * src/lib/nlu/pipeline.ts
+ *
+ * 四阶段语音处理链路：
+ *   阶段 0: 停止词检测
+ *   阶段 1: UI 可见元素语音点击匹配（1a: waiting_confirmation / 1b: Artifact 按钮）
+ *   阶段 1.5: waiting_slot + 数值输入
+ *   阶段 2: BERT NLU（调 intelligent_cabin）
+ *
+ * 规则：报错直接抛出，不降级，不 Mock 兜底。
+ */
+
+import { AFFIRM_WORDS, CANCEL_WORDS, DENY_WORDS } from "./voice-aliases.gen";
+import { chatWithNlu, fillSlotsWithNlu } from "./client";
+import {
+  adaptChatResponse,
+  type NluResult,
+  type PipelineEvent,
+  type PipelineResult,
+  type PipelineSession,
+  type SlotFillEvent,
+  type StopActionEvent,
+  type VisibleAction,
+  type VoiceClickEvent,
+} from "./types";
+
+// ── 文本标准化 ───────────────────────────────────────────────────────────────
+
+function normalizeVoice(text: string): string {
+  return text
+    .trim()
+    .toLowerCase()
+    .replace(/[，。！？、\s]+/g, "")
+    .replace(/^(好的?|嗯+|那就|那么|好吧)/, "");
+}
+
+function includesNormalized(haystack: string, needle: string): boolean {
+  return haystack.includes(normalizeVoice(needle));
+}
+
+// ── 阶段 0：停止词检测 ────────────────────────────────────────────────────────
+
+function detectStopWord(text: string): StopActionEvent | null {
+  const norm = normalizeVoice(text);
+  if (CANCEL_WORDS.some((w) => includesNormalized(norm, w))) {
+    return { type: "stop_action", sourceText: text };
+  }
+  return null;
+}
+
+// ── 阶段 1a：waiting_confirmation 下的 affirm/deny ────────────────────────────
+
+function detectConfirmationResponse(
+  text: string,
+  status: string,
+): VoiceClickEvent | null {
+  if (status !== "waiting_confirmation") return null;
+  const norm = normalizeVoice(text);
+
+  if (AFFIRM_WORDS.some((w) => includesNormalized(norm, w))) {
+    return { type: "voice_click_event", actionId: "artifact.confirm", sourceText: text };
+  }
+  if (DENY_WORDS.some((w) => includesNormalized(norm, w))) {
+    return { type: "voice_click_event", actionId: "artifact.cancel", sourceText: text };
+  }
+  return null;
+}
+
+// ── 阶段 1b：当前可见 Artifact 按钮匹配 ──────────────────────────────────────
+
+function detectVoiceClick(
+  text: string,
+  visibleActions: VisibleAction[],
+): VoiceClickEvent | null {
+  const norm = normalizeVoice(text);
+  for (const action of visibleActions) {
+    const matched = action.matchPhrases.some((phrase) =>
+      includesNormalized(norm, phrase),
+    );
+    if (matched) {
+      return {
+        type: "voice_click_event",
+        actionId: action.actionId,
+        artifactId: action.artifactId,
+        sourceText: text,
+      };
+    }
+  }
+  return null;
+}
+
+// ── 阶段 1.5：waiting_slot + 数值输入 ────────────────────────────────────────
+
+const NUMERIC_PATTERN = /[-+]?\d+(?:\.\d+)?/;
+
+function detectSlotFill(text: string, status: string): SlotFillEvent | null {
+  if (status !== "waiting_slot") return null;
+  const match = text.match(NUMERIC_PATTERN);
+  if (!match) return null;
+  return { type: "slot_fill_event", value: match[0], sourceText: text };
+}
+
+// ── 主流程 ───────────────────────────────────────────────────────────────────
+
+/**
+ * 处理单次用户输入，返回拦截事件或 NLU 识别结果。
+ *
+ * @param text        - ASR 转录文本或直接文字输入
+ * @param session     - 当前画布会话状态（status + visibleActions）
+ * @param userId      - 用户 ID，用于 NLU 服务 session 跟踪
+ *
+ * @throws 当 intelligent_cabin 服务不可达或返回错误时直接抛出，不降级
+ */
+export async function processVoiceInput(
+  text: string,
+  session: PipelineSession,
+  userId: string = "default",
+): Promise<PipelineResult> {
+
+  // ── 阶段 0：停止词 ──────────────────────────────────────────────────────────
+  const stopEvent = detectStopWord(text);
+  if (stopEvent) return stopEvent;
+
+  // ── 阶段 1a：waiting_confirmation affirm/deny ───────────────────────────────
+  const confirmEvent = detectConfirmationResponse(text, session.status);
+  if (confirmEvent) return confirmEvent;
+
+  // ── 阶段 1b：可见 Artifact 按钮 ────────────────────────────────────────────
+  const voiceClickEvent = detectVoiceClick(text, session.visibleActions);
+  if (voiceClickEvent) return voiceClickEvent;
+
+  // ── 阶段 1.5：waiting_slot 数值填写 ────────────────────────────────────────
+  const slotFillEvent = detectSlotFill(text, session.status);
+  if (slotFillEvent) {
+    // 通知 intelligent_cabin 填槽，更新服务端 session 状态
+    const raw = await fillSlotsWithNlu({
+      session_id: session.sessionId,
+      user_id: userId,
+      input_text: text,
+    });
+    // 返回 slot_fill_event 让 Canvas 状态机处理，NLU 结果附在事件上
+    return {
+      ...slotFillEvent,
+      // 附上后端确认的槽位信息，便于 Canvas 更新 Artifact
+      // @ts-expect-error 扩展字段，Canvas 侧 reducer 可选消费
+      nluResult: adaptChatResponse(raw),
+    };
+  }
+
+  // ── 阶段 2：BERT NLU ────────────────────────────────────────────────────────
+  const raw = await chatWithNlu({
+    session_id: session.sessionId,
+    user_id: userId,
+    input_text: text,
+  });
+
+  return adaptChatResponse(raw);
+}
+
+// ── 工具：从 ArtifactAction[] 构建 VisibleAction[] ───────────────────────────
+
+/**
+ * 将当前激活 Artifact 的 actions 转换成 pipeline 能消费的 VisibleAction[]。
+ * 在 Artifact 变更时调用，更新 session.visibleActions。
+ */
+export function buildVisibleActions(
+  artifactId: string,
+  actions: Array<{ actionId: string; label: string; textAliases: string[]; voiceAliases?: string[] }>,
+): VisibleAction[] {
+  return actions.map((action) => ({
+    actionId: action.actionId,
+    artifactId,
+    matchPhrases: [
+      action.label,
+      ...action.textAliases,
+      ...(action.voiceAliases ?? []),
+    ],
+  }));
+}
+
+// ── 工具：判断 PipelineResult 类型 ──────────────────────────────────────────
+
+export function isNluResult(result: PipelineResult): result is NluResult {
+  return !("type" in result);
+}
+
+export function isPipelineEvent(result: PipelineResult): result is PipelineEvent {
+  return "type" in result;
+}
diff --git a/src/lib/nlu/types.ts b/src/lib/nlu/types.ts
new file mode 100644
index 0000000..32483cd
--- /dev/null
+++ b/src/lib/nlu/types.ts
@@ -0,0 +1,168 @@
+/**
+ * src/lib/nlu/types.ts
+ *
+ * 真实 NLU 类型定义，对应 intelligent_cabin 的 ChatResponse。
+ * 替代原 mock.ts 里的 MockNluResult。
+ */
+
+// ── HTTP 层：ChatResponse raw shape ────────────────────────────────────────
+
+export type RoutingStage = {
+  stage: string;
+  score?: number;
+  accepted?: boolean;
+  reason?: string | null;
+  candidates?: Array<{ intent_id: string; score: number }>;
+  metadata?: Record<string, unknown>;
+};
+
+export type RoutingDebug = {
+  selected_intent: string | null;
+  decision: string;
+  confidence_grade: "high" | "medium" | "low" | null;
+  decision_reason?: string | null;
+  stages: RoutingStage[];
+};
+
+/** 来自 POST /api/v1/agent/chat 的原始响应 */
+export type ChatResponse = {
+  session_id: string;
+  reply_type: "text" | "ask_slot" | "ask_confirmation" | "workflow_result" | "fallback" | "clarify" | "reject";
+  reply_text: string;
+  intent: string | null;
+  domain: string | null;
+  decision: string | null;
+  decision_reason: string | null;
+  status: string;
+  pending_slots: string[];
+  filled_slots: Record<string, unknown>;
+  routing_debug: RoutingDebug | null;
+  trace_id: string;
+  // 知识库查询结果（LLM function call 命中时）
+  knowledge_doc_id?: string | null;
+  knowledge_doc_title?: string | null;
+  knowledge_content?: string | null;
+};
+
+// ── Canvas 层：NluResult（路由使用） ───────────────────────────────────────
+
+export type RouteHint =
+  | "tool_call"       // decision=execute + machine_control 域
+  | "knowledge_query" // decision=route_to_cloud 或 equipment_knowledge 域
+  | "smalltalk"       // decision=reject / social
+  | "clarify"         // 需要补槽
+  | "fallback";
+
+/** Canvas 消费的统一 NLU 结果，从 ChatResponse 映射而来 */
+export type NluResult = {
+  sessionId: string;
+  modelVersion: string;
+  domain: string;
+  intent: string;
+  confidence: number;          // routing_debug.stages[classifier].score
+  slots: Record<string, string | number | boolean>;
+  routeHint: RouteHint;
+  decisionGrade: "high" | "medium" | "low";
+  rawDecision: string;
+  replyText: string;
+  pendingSlots: string[];
+  traceId: string;
+  // 知识库结果（LLM function call 命中时千充）
+  knowledgeContent?: string | null;
+  knowledgeDocId?: string | null;
+  knowledgeDocTitle?: string | null;
+};
+
+// ── Pipeline 层：语音拦截事件 ───────────────────────────────────────────────
+
+export type StopActionEvent = {
+  type: "stop_action";
+  sourceText: string;
+};
+
+export type VoiceClickEvent = {
+  type: "voice_click_event";
+  actionId: string;
+  artifactId?: string;
+  sourceText: string;
+};
+
+export type SlotFillEvent = {
+  type: "slot_fill_event";
+  value: string;
+  sourceText: string;
+};
+
+export type PipelineEvent = StopActionEvent | VoiceClickEvent | SlotFillEvent;
+
+/** pipeline 返回值：被前置拦截处理 → PipelineEvent，进入 BERT → NluResult */
+export type PipelineResult = NluResult | PipelineEvent;
+
+// ── 会话状态（供 pipeline 读取） ───────────────────────────────────────────
+
+export type SessionStatus =
+  | "idle"
+  | "waiting_slot"
+  | "waiting_confirmation"
+  | "running"
+  | "completed"
+  | "stopped"
+  | "social"
+  | string;
+
+export type VisibleAction = {
+  actionId: string;
+  artifactId?: string;
+  matchPhrases: string[];  // 按钮文本 + voiceAliases，展平后
+};
+
+export type PipelineSession = {
+  sessionId: string;
+  status: SessionStatus;
+  visibleActions: VisibleAction[];
+};
+
+// ── 工具函数 ────────────────────────────────────────────────────────────────
+
+/** 从 ChatResponse 中提取 BERT 分类器的原始置信度 */
+export function extractConfidence(debug: RoutingDebug | null): number {
+  if (!debug) return 0;
+  const classifierStage = debug.stages.find((s) => s.stage === "classifier");
+  return classifierStage?.score ?? 0;
+}
+
+/** decision + domain → RouteHint */
+export function mapDecisionToRouteHint(decision: string | null, domain: string | null): RouteHint {
+  if (decision === "execute") {
+    if (domain === "machine_control") return "tool_call";
+    if (domain === "equipment_knowledge") return "knowledge_query";
+    return "tool_call";
+  }
+  if (decision === "clarify") return "clarify";
+  if (decision === "route_to_cloud") return "knowledge_query";
+  if (decision === "knowledge_llm" || domain === "knowledge") return "knowledge_query";
+  if (decision === "open_social") return "smalltalk";
+  return "fallback";
+}
+
+/** 将 ChatResponse 映射成 NluResult */
+export function adaptChatResponse(raw: ChatResponse): NluResult {
+  return {
+    sessionId: raw.session_id,
+    modelVersion: "intelligent-cabin-nlu",
+    domain: raw.domain ?? "unknown",
+    intent: raw.intent ?? "unknown",
+    confidence: extractConfidence(raw.routing_debug),
+    slots: raw.filled_slots as Record<string, string | number | boolean>,
+    routeHint: mapDecisionToRouteHint(raw.decision, raw.domain),
+    decisionGrade: raw.routing_debug?.confidence_grade ?? "low",
+    rawDecision: raw.decision ?? "reject",
+    replyText: raw.reply_text,
+    pendingSlots: raw.pending_slots,
+    traceId: raw.trace_id,
+    // 知识库结果禁传递
+    knowledgeContent: raw.knowledge_content ?? null,
+    knowledgeDocId: raw.knowledge_doc_id ?? null,
+    knowledgeDocTitle: raw.knowledge_doc_title ?? null,
+  };
+}
diff --git a/src/lib/nlu/voice-aliases.gen.ts b/src/lib/nlu/voice-aliases.gen.ts
new file mode 100644
index 0000000..db9774b
--- /dev/null
+++ b/src/lib/nlu/voice-aliases.gen.ts
@@ -0,0 +1,90 @@
+// AUTO-GENERATED — do not edit manually
+// Source: intelligent_cabin/config/voice_aliases.yml
+// Regenerate: node scripts/generate-voice-aliases.mjs
+
+export const AFFIRM_WORDS: readonly string[] = [
+  "确认",
+  "好的",
+  "执行",
+  "是",
+  "对",
+  "继续",
+  "好",
+  "可以",
+  "确定",
+  "ok",
+  "yes"
+] as const;
+
+export const DENY_WORDS: readonly string[] = [
+  "不行",
+  "不要",
+  "不",
+  "否",
+  "no"
+] as const;
+
+export const CANCEL_WORDS: readonly string[] = [
+  "取消",
+  "算了",
+  "不用了",
+  "停止",
+  "中止",
+  "放弃"
+] as const;
+
+export const INTENT_ALIASES: Readonly<Record<string, readonly string[]>> = {
+  "wirecut_start_run": [
+    "开始",
+    "启动",
+    "加工",
+    "跑起来",
+    "开始加工",
+    "启动加工"
+  ],
+  "wirecut_stop_run": [
+    "停",
+    "停机",
+    "急停",
+    "停止",
+    "停止加工"
+  ],
+  "wirecut_pause_run": [
+    "暂停",
+    "变频暂停"
+  ],
+  "wirecut_resume_run": [
+    "继续",
+    "恢复",
+    "继续加工"
+  ],
+  "wirecut_home_all": [
+    "回零",
+    "归零",
+    "回原点",
+    "全轴回零"
+  ],
+  "wirecut_home_x": [
+    "X轴回零",
+    "x回零"
+  ],
+  "wirecut_home_y": [
+    "Y轴回零",
+    "y回零"
+  ],
+  "wirecut_set_speed": [
+    "设置速度",
+    "调速度",
+    "修改速度"
+  ],
+  "wirecut_set_voltage": [
+    "设置电压",
+    "调电压",
+    "修改电压"
+  ],
+  "wirecut_set_tension": [
+    "设置拉力",
+    "调拉力",
+    "修改拉力"
+  ]
+} as const;