From 515eb3f09cddd25a0b6f2ba0cb6a815426596b31 Mon Sep 17 00:00:00 2001
From: xtkuang <87661715@qq.com>
Date: Mon, 20 Jul 2026 16:59:37 +0800
Subject: [PATCH] project init
---
README.md | 421 ++++
configs/smoke.yaml | 41 +
detect_server/README.md | 293 +++
detect_server/pipeline.yaml | 180 ++
docs/architecture.md | 812 ++++++++
pyproject.toml | 91 +
.../generate_cmvr_stubs.cpython-312.pyc | Bin 0 -> 5353 bytes
scripts/bootstrap.sh | 161 ++
scripts/generate_cmvr_stubs.py | 117 ++
.../agv_command_pb2.cpython-310.pyc | Bin 0 -> 5641 bytes
.../agv_command_pb2.cpython-312.pyc | Bin 0 -> 7477 bytes
.../agv_command_pb2_grpc.cpython-310.pyc | Bin 0 -> 811 bytes
.../agv_command_pb2_grpc.cpython-312.pyc | Bin 0 -> 1010 bytes
.../agv_service_pb2.cpython-310.pyc | Bin 0 -> 3090 bytes
.../agv_service_pb2.cpython-312.pyc | Bin 0 -> 3513 bytes
.../agv_service_pb2_grpc.cpython-310.pyc | Bin 0 -> 13824 bytes
.../agv_service_pb2_grpc.cpython-312.pyc | Bin 0 -> 33298 bytes
.../arm_command_pb2.cpython-310.pyc | Bin 0 -> 5929 bytes
.../arm_command_pb2.cpython-312.pyc | Bin 0 -> 7922 bytes
.../arm_command_pb2_grpc.cpython-310.pyc | Bin 0 -> 811 bytes
.../arm_command_pb2_grpc.cpython-312.pyc | Bin 0 -> 1010 bytes
.../arm_service_pb2.cpython-310.pyc | Bin 0 -> 2199 bytes
.../arm_service_pb2.cpython-312.pyc | Bin 0 -> 2622 bytes
.../arm_service_pb2_grpc.cpython-310.pyc | Bin 0 -> 8870 bytes
.../arm_service_pb2_grpc.cpython-312.pyc | Bin 0 -> 21152 bytes
.../biohead_command_pb2.cpython-310.pyc | Bin 0 -> 6585 bytes
.../biohead_command_pb2.cpython-312.pyc | Bin 0 -> 8938 bytes
.../biohead_command_pb2_grpc.cpython-310.pyc | Bin 0 -> 819 bytes
.../biohead_command_pb2_grpc.cpython-312.pyc | Bin 0 -> 1018 bytes
.../biohead_service_pb2.cpython-310.pyc | Bin 0 -> 2208 bytes
.../biohead_service_pb2.cpython-312.pyc | Bin 0 -> 2632 bytes
.../biohead_service_pb2_grpc.cpython-310.pyc | Bin 0 -> 8980 bytes
.../biohead_service_pb2_grpc.cpython-312.pyc | Bin 0 -> 20872 bytes
.../camera_command_pb2.cpython-310.pyc | Bin 0 -> 6303 bytes
.../camera_command_pb2.cpython-312.pyc | Bin 0 -> 8375 bytes
.../camera_command_pb2_grpc.cpython-310.pyc | Bin 0 -> 817 bytes
.../camera_command_pb2_grpc.cpython-312.pyc | Bin 0 -> 1016 bytes
.../camera_service_pb2.cpython-310.pyc | Bin 0 -> 2335 bytes
.../camera_service_pb2.cpython-312.pyc | Bin 0 -> 2759 bytes
.../camera_service_pb2_grpc.cpython-310.pyc | Bin 0 -> 8454 bytes
.../camera_service_pb2_grpc.cpython-312.pyc | Bin 0 -> 19034 bytes
.../__pycache__/common_pb2.cpython-310.pyc | Bin 0 -> 1965 bytes
.../__pycache__/common_pb2.cpython-312.pyc | Bin 0 -> 2565 bytes
.../common_pb2_grpc.cpython-310.pyc | Bin 0 -> 801 bytes
.../common_pb2_grpc.cpython-312.pyc | Bin 0 -> 1000 bytes
.../dexhand_command_pb2.cpython-310.pyc | Bin 0 -> 5196 bytes
.../dexhand_command_pb2.cpython-312.pyc | Bin 0 -> 7039 bytes
.../dexhand_command_pb2_grpc.cpython-310.pyc | Bin 0 -> 819 bytes
.../dexhand_command_pb2_grpc.cpython-312.pyc | Bin 0 -> 1018 bytes
.../dexhand_service_pb2.cpython-310.pyc | Bin 0 -> 2036 bytes
.../dexhand_service_pb2.cpython-312.pyc | Bin 0 -> 2460 bytes
.../dexhand_service_pb2_grpc.cpython-310.pyc | Bin 0 -> 6993 bytes
.../dexhand_service_pb2_grpc.cpython-312.pyc | Bin 0 -> 15003 bytes
.../hlc_command_pb2.cpython-310.pyc | Bin 0 -> 1377 bytes
.../hlc_command_pb2.cpython-312.pyc | Bin 0 -> 1887 bytes
.../hlc_command_pb2_grpc.cpython-310.pyc | Bin 0 -> 811 bytes
.../hlc_command_pb2_grpc.cpython-312.pyc | Bin 0 -> 1010 bytes
.../hlc_service_pb2.cpython-310.pyc | Bin 0 -> 1203 bytes
.../hlc_service_pb2.cpython-312.pyc | Bin 0 -> 1625 bytes
.../hlc_service_pb2_grpc.cpython-310.pyc | Bin 0 -> 3047 bytes
.../hlc_service_pb2_grpc.cpython-312.pyc | Bin 0 -> 4346 bytes
.../microphone_command_pb2.cpython-310.pyc | Bin 0 -> 3682 bytes
.../microphone_command_pb2.cpython-312.pyc | Bin 0 -> 5029 bytes
...icrophone_command_pb2_grpc.cpython-310.pyc | Bin 0 -> 825 bytes
...icrophone_command_pb2_grpc.cpython-312.pyc | Bin 0 -> 1024 bytes
.../microphone_service_pb2.cpython-310.pyc | Bin 0 -> 1913 bytes
.../microphone_service_pb2.cpython-312.pyc | Bin 0 -> 2337 bytes
...icrophone_service_pb2_grpc.cpython-310.pyc | Bin 0 -> 6287 bytes
...icrophone_service_pb2_grpc.cpython-312.pyc | Bin 0 -> 13216 bytes
.../speaker_command_pb2.cpython-310.pyc | Bin 0 -> 3899 bytes
.../speaker_command_pb2.cpython-312.pyc | Bin 0 -> 5332 bytes
.../speaker_command_pb2_grpc.cpython-310.pyc | Bin 0 -> 819 bytes
.../speaker_command_pb2_grpc.cpython-312.pyc | Bin 0 -> 1018 bytes
.../speaker_service_pb2.cpython-310.pyc | Bin 0 -> 1852 bytes
.../speaker_service_pb2.cpython-312.pyc | Bin 0 -> 2276 bytes
.../speaker_service_pb2_grpc.cpython-310.pyc | Bin 0 -> 6243 bytes
.../speaker_service_pb2_grpc.cpython-312.pyc | Bin 0 -> 13260 bytes
.../system_command_pb2.cpython-310.pyc | Bin 0 -> 2865 bytes
.../system_command_pb2.cpython-312.pyc | Bin 0 -> 3859 bytes
.../system_command_pb2_grpc.cpython-310.pyc | Bin 0 -> 817 bytes
.../system_command_pb2_grpc.cpython-312.pyc | Bin 0 -> 1016 bytes
.../system_service_pb2.cpython-310.pyc | Bin 0 -> 1556 bytes
.../system_service_pb2.cpython-312.pyc | Bin 0 -> 1980 bytes
.../system_service_pb2_grpc.cpython-310.pyc | Bin 0 -> 4664 bytes
.../system_service_pb2_grpc.cpython-312.pyc | Bin 0 -> 8830 bytes
.../test_service_pb2.cpython-310.pyc | Bin 0 -> 1248 bytes
.../test_service_pb2.cpython-312.pyc | Bin 0 -> 1750 bytes
.../test_service_pb2_grpc.cpython-310.pyc | Bin 0 -> 3035 bytes
.../test_service_pb2_grpc.cpython-312.pyc | Bin 0 -> 4190 bytes
src/cmvr/api/agv_command_pb2.py | 102 +
src/cmvr/api/agv_command_pb2.pyi | 215 ++
src/cmvr/api/agv_command_pb2_grpc.py | 24 +
src/cmvr/api/agv_service_pb2.py | 38 +
src/cmvr/api/agv_service_pb2.pyi | 6 +
src/cmvr/api/agv_service_pb2_grpc.py | 943 +++++++++
src/cmvr/api/arm_command_pb2.py | 109 +
src/cmvr/api/arm_command_pb2.pyi | 318 +++
src/cmvr/api/arm_command_pb2_grpc.py | 24 +
src/cmvr/api/arm_service_pb2.py | 38 +
src/cmvr/api/arm_service_pb2.pyi | 6 +
src/cmvr/api/arm_service_pb2_grpc.py | 614 ++++++
src/cmvr/api/biohead_command_pb2.py | 125 ++
src/cmvr/api/biohead_command_pb2.pyi | 308 +++
src/cmvr/api/biohead_command_pb2_grpc.py | 24 +
src/cmvr/api/biohead_service_pb2.py | 37 +
src/cmvr/api/biohead_service_pb2.pyi | 5 +
src/cmvr/api/biohead_service_pb2_grpc.py | 581 ++++++
src/cmvr/api/camera_command_pb2.py | 111 +
src/cmvr/api/camera_command_pb2.pyi | 273 +++
src/cmvr/api/camera_command_pb2_grpc.py | 24 +
src/cmvr/api/camera_service_pb2.py | 37 +
src/cmvr/api/camera_service_pb2.pyi | 5 +
src/cmvr/api/camera_service_pb2_grpc.py | 527 +++++
src/cmvr/api/common_pb2.py | 47 +
src/cmvr/api/common_pb2.pyi | 66 +
src/cmvr/api/common_pb2_grpc.py | 24 +
src/cmvr/api/dexhand_command_pb2.py | 99 +
src/cmvr/api/dexhand_command_pb2.pyi | 222 ++
src/cmvr/api/dexhand_command_pb2_grpc.py | 24 +
src/cmvr/api/dexhand_service_pb2.py | 37 +
src/cmvr/api/dexhand_service_pb2.pyi | 5 +
src/cmvr/api/dexhand_service_pb2_grpc.py | 399 ++++
src/cmvr/api/hlc_command_pb2.py | 41 +
src/cmvr/api/hlc_command_pb2.pyi | 27 +
src/cmvr/api/hlc_command_pb2_grpc.py | 24 +
src/cmvr/api/hlc_service_pb2.py | 37 +
src/cmvr/api/hlc_service_pb2.pyi | 5 +
src/cmvr/api/hlc_service_pb2_grpc.py | 97 +
src/cmvr/api/microphone_command_pb2.py | 79 +
src/cmvr/api/microphone_command_pb2.pyi | 127 ++
src/cmvr/api/microphone_command_pb2_grpc.py | 24 +
src/cmvr/api/microphone_service_pb2.py | 37 +
src/cmvr/api/microphone_service_pb2.pyi | 5 +
src/cmvr/api/microphone_service_pb2_grpc.py | 357 ++++
src/cmvr/api/speaker_command_pb2.py | 83 +
src/cmvr/api/speaker_command_pb2.pyi | 154 ++
src/cmvr/api/speaker_command_pb2_grpc.py | 24 +
src/cmvr/api/speaker_service_pb2.py | 37 +
src/cmvr/api/speaker_service_pb2.pyi | 5 +
src/cmvr/api/speaker_service_pb2_grpc.py | 357 ++++
src/cmvr/api/system_command_pb2.py | 63 +
src/cmvr/api/system_command_pb2.pyi | 116 ++
src/cmvr/api/system_command_pb2_grpc.py | 24 +
src/cmvr/api/system_service_pb2.py | 37 +
src/cmvr/api/system_service_pb2.pyi | 5 +
src/cmvr/api/system_service_pb2_grpc.py | 226 ++
src/cmvr/api/test_service_pb2.py | 40 +
src/cmvr/api/test_service_pb2.pyi | 17 +
src/cmvr/api/test_service_pb2_grpc.py | 97 +
.../__pycache__/geometry_pb2.cpython-310.pyc | Bin 0 -> 2670 bytes
.../__pycache__/geometry_pb2.cpython-312.pyc | Bin 0 -> 3424 bytes
.../geometry_pb2_grpc.cpython-310.pyc | Bin 0 -> 811 bytes
.../geometry_pb2_grpc.cpython-312.pyc | Bin 0 -> 1010 bytes
src/cmvr/common/geometry_pb2.py | 52 +
src/cmvr/common/geometry_pb2.pyi | 140 ++
src/cmvr/common/geometry_pb2_grpc.py | 24 +
.../joint_limits_config_pb2.cpython-310.pyc | Bin 0 -> 1492 bytes
.../joint_limits_config_pb2.cpython-312.pyc | Bin 0 -> 2000 bytes
...int_limits_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 833 bytes
...int_limits_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1032 bytes
.../lawba_ik_config_pb2.cpython-310.pyc | Bin 0 -> 1395 bytes
.../lawba_ik_config_pb2.cpython-312.pyc | Bin 0 -> 1819 bytes
.../lawba_ik_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 825 bytes
.../lawba_ik_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1024 bytes
...inocchio_dls_ik_config_pb2.cpython-310.pyc | Bin 0 -> 1712 bytes
...inocchio_dls_ik_config_pb2.cpython-312.pyc | Bin 0 -> 2180 bytes
...hio_dls_ik_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 841 bytes
...hio_dls_ik_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1040 bytes
...pinocchio_qp_ik_config_pb2.cpython-310.pyc | Bin 0 -> 1293 bytes
...pinocchio_qp_ik_config_pb2.cpython-312.pyc | Bin 0 -> 1713 bytes
...chio_qp_ik_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 839 bytes
...chio_qp_ik_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1038 bytes
.../srs_ik_config_pb2.cpython-310.pyc | Bin 0 -> 1225 bytes
.../srs_ik_config_pb2.cpython-312.pyc | Bin 0 -> 1643 bytes
.../srs_ik_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 821 bytes
.../srs_ik_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1020 bytes
...creen_algorithm_config_pb2.cpython-310.pyc | Bin 0 -> 2427 bytes
...creen_algorithm_config_pb2.cpython-312.pyc | Bin 0 -> 2896 bytes
..._algorithm_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 853 bytes
..._algorithm_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1052 bytes
...h_screen_common_config_pb2.cpython-310.pyc | Bin 0 -> 2354 bytes
...h_screen_common_config_pb2.cpython-312.pyc | Bin 0 -> 2994 bytes
...een_common_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 847 bytes
...een_common_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1046 bytes
.../agv_config_pb2.cpython-310.pyc | Bin 0 -> 2037 bytes
.../agv_config_pb2.cpython-312.pyc | Bin 0 -> 2629 bytes
.../agv_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 837 bytes
.../agv_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1036 bytes
src/cmvr/config/agv_config/agv_config_pb2.py | 44 +
src/cmvr/config/agv_config/agv_config_pb2.pyi | 79 +
.../config/agv_config/agv_config_pb2_grpc.py | 24 +
.../arm_config_pb2.cpython-310.pyc | Bin 0 -> 6594 bytes
.../arm_config_pb2.cpython-312.pyc | Bin 0 -> 7810 bytes
.../arm_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 837 bytes
.../arm_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1036 bytes
src/cmvr/config/arm_config/arm_config_pb2.py | 78 +
src/cmvr/config/arm_config/arm_config_pb2.pyi | 260 +++
.../config/arm_config/arm_config_pb2_grpc.py | 24 +
.../aubo_robot_config_pb2.cpython-310.pyc | Bin 0 -> 1226 bytes
.../aubo_robot_config_pb2.cpython-312.pyc | Bin 0 -> 1646 bytes
...aubo_robot_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 865 bytes
...aubo_robot_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1064 bytes
.../aubo_robot_config_pb2.py | 36 +
.../aubo_robot_config_pb2.pyi | 19 +
.../aubo_robot_config_pb2_grpc.py | 24 +
.../biohead_config_pb2.cpython-310.pyc | Bin 0 -> 1602 bytes
.../biohead_config_pb2.cpython-312.pyc | Bin 0 -> 2110 bytes
.../biohead_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 853 bytes
.../biohead_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1052 bytes
.../biohead_config/biohead_config_pb2.py | 40 +
.../biohead_config/biohead_config_pb2.pyi | 43 +
.../biohead_config/biohead_config_pb2_grpc.py | 24 +
.../camera_config_pb2.cpython-310.pyc | Bin 0 -> 2763 bytes
.../camera_config_pb2.cpython-312.pyc | Bin 0 -> 3527 bytes
.../camera_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 849 bytes
.../camera_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1048 bytes
.../config/camera_config/camera_config_pb2.py | 52 +
.../camera_config/camera_config_pb2.pyi | 123 ++
.../camera_config/camera_config_pb2_grpc.py | 24 +
.../cmvr_es_config_pb2.cpython-310.pyc | Bin 0 -> 1383 bytes
.../cmvr_es_config_pb2.cpython-312.pyc | Bin 0 -> 1845 bytes
.../cmvr_es_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 853 bytes
.../cmvr_es_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1052 bytes
.../cmvr_es_config/cmvr_es_config_pb2.py | 38 +
.../cmvr_es_config/cmvr_es_config_pb2.pyi | 24 +
.../cmvr_es_config/cmvr_es_config_pb2_grpc.py | 24 +
.../device_manager_config_pb2.cpython-310.pyc | Bin 0 -> 2231 bytes
.../device_manager_config_pb2.cpython-312.pyc | Bin 0 -> 2783 bytes
...ce_manager_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 881 bytes
...ce_manager_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1080 bytes
.../device_manager_config_pb2.py | 42 +
.../device_manager_config_pb2.pyi | 58 +
.../device_manager_config_pb2_grpc.py | 24 +
.../dexhand_config_pb2.cpython-310.pyc | Bin 0 -> 2424 bytes
.../dexhand_config_pb2.cpython-312.pyc | Bin 0 -> 3062 bytes
.../dexhand_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 853 bytes
.../dexhand_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1052 bytes
.../dexhand_config/dexhand_config_pb2.py | 46 +
.../dexhand_config/dexhand_config_pb2.pyi | 89 +
.../dexhand_config/dexhand_config_pb2_grpc.py | 24 +
.../grpc_server_config_pb2.cpython-310.pyc | Bin 0 -> 1362 bytes
.../grpc_server_config_pb2.cpython-312.pyc | Bin 0 -> 1826 bytes
...rpc_server_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 869 bytes
...rpc_server_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1068 bytes
.../grpc_server_config_pb2.py | 38 +
.../grpc_server_config_pb2.pyi | 24 +
.../grpc_server_config_pb2_grpc.py | 24 +
src/cmvr/config/joint_limits_config_pb2.py | 40 +
src/cmvr/config/joint_limits_config_pb2.pyi | 37 +
.../config/joint_limits_config_pb2_grpc.py | 24 +
src/cmvr/config/lawba_ik_config_pb2.py | 37 +
src/cmvr/config/lawba_ik_config_pb2.pyi | 25 +
src/cmvr/config/lawba_ik_config_pb2_grpc.py | 24 +
.../logger_config_pb2.cpython-310.pyc | Bin 0 -> 1997 bytes
.../logger_config_pb2.cpython-312.pyc | Bin 0 -> 2585 bytes
.../logger_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 849 bytes
.../logger_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1048 bytes
.../config/logger_config/logger_config_pb2.py | 44 +
.../logger_config/logger_config_pb2.pyi | 67 +
.../logger_config/logger_config_pb2_grpc.py | 24 +
.../microphone_config_pb2.cpython-310.pyc | Bin 0 -> 1692 bytes
.../microphone_config_pb2.cpython-312.pyc | Bin 0 -> 2244 bytes
...microphone_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 865 bytes
...microphone_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1064 bytes
.../microphone_config_pb2.py | 42 +
.../microphone_config_pb2.pyi | 41 +
.../microphone_config_pb2_grpc.py | 24 +
.../motor_config_pb2.cpython-310.pyc | Bin 0 -> 2342 bytes
.../motor_config_pb2.cpython-312.pyc | Bin 0 -> 3026 bytes
.../motor_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 845 bytes
.../motor_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1044 bytes
.../config/motor_config/motor_config_pb2.py | 49 +
.../config/motor_config/motor_config_pb2.pyi | 84 +
.../motor_config/motor_config_pb2_grpc.py | 24 +
.../config/pinocchio_dls_ik_config_pb2.py | 39 +
.../config/pinocchio_dls_ik_config_pb2.pyi | 43 +
.../pinocchio_dls_ik_config_pb2_grpc.py | 24 +
src/cmvr/config/pinocchio_qp_ik_config_pb2.py | 36 +
.../config/pinocchio_qp_ik_config_pb2.pyi | 26 +
.../config/pinocchio_qp_ik_config_pb2_grpc.py | 24 +
.../speaker_config_pb2.cpython-310.pyc | Bin 0 -> 1553 bytes
.../speaker_config_pb2.cpython-312.pyc | Bin 0 -> 2105 bytes
.../speaker_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 853 bytes
.../speaker_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1052 bytes
.../speaker_config/speaker_config_pb2.py | 42 +
.../speaker_config/speaker_config_pb2.pyi | 33 +
.../speaker_config/speaker_config_pb2_grpc.py | 24 +
src/cmvr/config/srs_ik_config_pb2.py | 36 +
src/cmvr/config/srs_ik_config_pb2.pyi | 25 +
src/cmvr/config/srs_ik_config_pb2_grpc.py | 24 +
.../task_manager_config_pb2.cpython-310.pyc | Bin 0 -> 1964 bytes
.../task_manager_config_pb2.cpython-312.pyc | Bin 0 -> 2560 bytes
...sk_manager_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 873 bytes
...sk_manager_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1072 bytes
.../task_manager_config_pb2.py | 44 +
.../task_manager_config_pb2.pyi | 52 +
.../task_manager_config_pb2_grpc.py | 24 +
.../touch_screen_algorithm_config_pb2.py | 40 +
.../touch_screen_algorithm_config_pb2.pyi | 50 +
.../touch_screen_algorithm_config_pb2_grpc.py | 24 +
.../config/touch_screen_common_config_pb2.py | 46 +
.../config/touch_screen_common_config_pb2.pyi | 61 +
.../touch_screen_common_config_pb2_grpc.py | 24 +
...uch_screen_task_config_pb2.cpython-310.pyc | Bin 0 -> 5260 bytes
...uch_screen_task_config_pb2.cpython-312.pyc | Bin 0 -> 6225 bytes
...creen_task_config_pb2_grpc.cpython-310.pyc | Bin 0 -> 893 bytes
...creen_task_config_pb2_grpc.cpython-312.pyc | Bin 0 -> 1092 bytes
.../touch_screen_task_config_pb2.py | 63 +
.../touch_screen_task_config_pb2.pyi | 158 ++
.../touch_screen_task_config_pb2_grpc.py | 24 +
.../msgs/__pycache__/agv_pb2.cpython-310.pyc | Bin 0 -> 5304 bytes
.../msgs/__pycache__/agv_pb2.cpython-312.pyc | Bin 0 -> 6730 bytes
.../__pycache__/agv_pb2_grpc.cpython-310.pyc | Bin 0 -> 797 bytes
.../__pycache__/agv_pb2_grpc.cpython-312.pyc | Bin 0 -> 996 bytes
.../can_card_parameter_pb2.cpython-310.pyc | Bin 0 -> 2304 bytes
.../can_card_parameter_pb2.cpython-312.pyc | Bin 0 -> 2944 bytes
...an_card_parameter_pb2_grpc.cpython-310.pyc | Bin 0 -> 827 bytes
...an_card_parameter_pb2_grpc.cpython-312.pyc | Bin 0 -> 1026 bytes
.../__pycache__/canopen_pb2.cpython-310.pyc | Bin 0 -> 3836 bytes
.../__pycache__/canopen_pb2.cpython-312.pyc | Bin 0 -> 4553 bytes
.../canopen_pb2_grpc.cpython-310.pyc | Bin 0 -> 805 bytes
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src/cmvr/msgs/agv_pb2.py | 82 +
src/cmvr/msgs/agv_pb2.pyi | 319 +++
src/cmvr/msgs/agv_pb2_grpc.py | 24 +
src/cmvr/msgs/can_card_parameter_pb2.py | 46 +
src/cmvr/msgs/can_card_parameter_pb2.pyi | 76 +
src/cmvr/msgs/can_card_parameter_pb2_grpc.py | 24 +
src/cmvr/msgs/canopen_pb2.py | 50 +
src/cmvr/msgs/canopen_pb2.pyi | 225 ++
src/cmvr/msgs/canopen_pb2_grpc.py | 24 +
src/cmvr/msgs/error_code_pb2.py | 38 +
src/cmvr/msgs/error_code_pb2.pyi | 35 +
src/cmvr/msgs/error_code_pb2_grpc.py | 24 +
src/cmvr/msgs/motor_pb2.py | 39 +
src/cmvr/msgs/motor_pb2.pyi | 123 ++
src/cmvr/msgs/motor_pb2_grpc.py | 24 +
src/cmvr/msgs/robot_detail_pb2.py | 42 +
src/cmvr/msgs/robot_detail_pb2.pyi | 22 +
src/cmvr/msgs/robot_detail_pb2_grpc.py | 24 +
src/cmvr_edge_ai.egg-info/PKG-INFO | 405 ++++
src/cmvr_edge_ai.egg-info/SOURCES.txt | 207 ++
.../dependency_links.txt | 1 +
src/cmvr_edge_ai.egg-info/entry_points.txt | 2 +
src/cmvr_edge_ai.egg-info/requires.txt | 25 +
src/cmvr_edge_ai.egg-info/top_level.txt | 2 +
src/cmvr_edge_ai/__init__.py | 3 +
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src/cmvr_edge_ai/application.py | 199 ++
src/cmvr_edge_ai/cli.py | 122 ++
src/cmvr_edge_ai/compiler.py | 375 ++++
src/cmvr_edge_ai/config/__init__.py | 45 +
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src/cmvr_edge_ai/config/loader.py | 145 ++
src/cmvr_edge_ai/config/models.py | 363 ++++
src/cmvr_edge_ai/connectors/__init__.py | 42 +
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src/cmvr_edge_ai/connectors/_context.py | 94 +
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src/cmvr_edge_ai/connectors/cmvr_es/agv.py | 230 ++
src/cmvr_edge_ai/connectors/cmvr_es/camera.py | 591 ++++++
src/cmvr_edge_ai/connectors/cmvr_es/errors.py | 13 +
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src/cmvr_edge_ai/contracts/control.py | 47 +
src/cmvr_edge_ai/contracts/events.py | 84 +
src/cmvr_edge_ai/contracts/media.py | 75 +
src/cmvr_edge_ai/core/__init__.py | 62 +
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src/cmvr_edge_ai/core/component.py | 196 ++
src/cmvr_edge_ai/core/edge_queue.py | 232 +++
src/cmvr_edge_ai/core/envelope.py | 139 ++
src/cmvr_edge_ai/core/runtime.py | 667 ++++++
src/cmvr_edge_ai/detection/__init__.py | 38 +
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src/cmvr_edge_ai/detection/annotation.py | 176 ++
src/cmvr_edge_ai/detection/base.py | 50 +
src/cmvr_edge_ai/detection/models/__init__.py | 17 +
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src/cmvr_edge_ai/detection/models/ppe_yolo.py | 96 +
src/cmvr_edge_ai/detection/models/yolo.py | 209 ++
src/cmvr_edge_ai/detection/operator.py | 374 ++++
src/cmvr_edge_ai/detection/plugins.py | 62 +
src/cmvr_edge_ai/detection/registry.py | 154 ++
src/cmvr_edge_ai/detection/rules.py | 661 ++++++
src/cmvr_edge_ai/detection/video.py | 319 +++
src/cmvr_edge_ai/observability/__init__.py | 5 +
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src/cmvr_edge_ai/observability/logging.py | 36 +
src/cmvr_edge_ai/plugins/__init__.py | 23 +
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src/cmvr_edge_ai/plugins/builtin.py | 296 +++
src/cmvr_edge_ai/plugins/registry.py | 166 ++
src/cmvr_edge_ai/transports/__init__.py | 5 +
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src/cmvr_edge_ai/transports/grpc/__init__.py | 16 +
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src/cmvr_edge_ai/transports/grpc/channel.py | 79 +
src/cmvr_edge_ai/transports/http/__init__.py | 5 +
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src/cmvr_edge_ai/transports/quic/__init__.py | 1 +
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src/cmvr_edge_ai/transports/udp/__init__.py | 5 +
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src/cmvr_edge_ai/workers/process.py | 211 ++
src/cmvr_edge_ai/workers/thread.py | 32 +
talk_server/README.md | 15 +
talk_server/pipeline.yaml | 42 +
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tests/conftest.py | 13 +
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tests/integration/test_application_cli.py | 116 ++
tests/integration/test_detection_pipeline.py | 223 ++
.../test_dual_detection_pipeline.py | 349 ++++
tests/integration/test_process_worker.py | 98 +
tests/integration/test_runtime.py | 235 +++
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tests/unit/test_agv_connector.py | 44 +
tests/unit/test_bidi_stream.py | 50 +
tests/unit/test_builtin_plugins.py | 24 +
tests/unit/test_camera_connector.py | 576 +++++
tests/unit/test_compiler.py | 372 ++++
tests/unit/test_config.py | 145 ++
tests/unit/test_detection_annotation.py | 169 ++
tests/unit/test_detection_compile.py | 105 +
tests/unit/test_detection_operator.py | 442 ++++
tests/unit/test_detection_registry.py | 177 ++
tests/unit/test_detection_trigger.py | 692 +++++++
tests/unit/test_edge_queue.py | 86 +
tests/unit/test_http_json_sink.py | 505 +++++
tests/unit/test_safety_gate.py | 106 +
tests/unit/test_video_decoder.py | 445 ++++
tests/unit/test_yolo_model.py | 378 ++++
uv.lock | 1845 +++++++++++++++++
604 files changed, 29171 insertions(+)
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000..43d148d
--- /dev/null
+++ b/README.md
@@ -0,0 +1,421 @@
+# cmvr-edge-ai
+
+`cmvr-edge-ai` 是部署在机器人边缘端的、配置驱动的 AI 流水线运行时。它把上游传感器、AI 算法和下游机器人/平台接口拆成可组合节点，通过 YAML 把节点连接成有向无环图（DAG）。协议只出现在边界连接器中，算法节点使用统一的内部消息，因此以后增加 HTTP、gRPC、UDP 或 QUIC 时不需要改动算法本身。
+
+当前版本已经包含可运行的检测链路和框架基础设施：
+
+- 严格 YAML 配置校验、环境变量展开和 DAG 编译检查；
+- `Source -> Operator -> Sink` 异步运行时；
+- 每条边独立的有界队列和明确的溢出策略；
+- 版本化插件注册表、检测模型注册表和 Python entry point 扩展机制；
+- cmvr-es RGB 相机 gRPC Source（带退避上限的指数重连）、AGV gRPC Sink；
+- PyAV H264/H265 有状态解码、通用检测模型节点和重复命中规则节点；
+- Construction PPE YOLOv8 与六类 PPE YOLOv8n 模型注册，以及按标签、置信度和最大 FPS 的部署配置；
+- 平台 HTTP JSON Sink，支持有限重试、`raise/log_and_drop` 失败策略，并使用告警
+ `event_id` 作为幂等键；
+- `RobotCommand -> ApprovedRobotCommand` 安全门和无重试的类型化 AGV 命令映射；
+- 文本/JSON 日志、共享 gRPC Channel 与 HTTP Client；
+- 可直接执行的 smoke、PPE 检测和对话占位配置。
+
+`detect_server/pipeline.yaml` 当前只启用 Construction PPE 模型，并经过时间窗口规则向
+8081 上报告警。六类 PPE 模型及第二 HTTP 平台的实现仍保留在注册表和连接器中，但不在
+当前 Pipeline 图中实例化，因此不会加载第二份权重、执行第二次推理或访问 8082。
+VAD/ASR/LLM/TTS 尚未内置；`talk_server/pipeline.yaml` 仍使用模拟音频数据，等待
+cmvr-es 音频双向流 proto 落地。
+
+## 架构概览
+
+```mermaid
+flowchart LR
+ A["cmvr-es / 平台
gRPC、HTTP、未来 UDP/QUIC"] --> B["Source 连接器"]
+ B --> C["每条边独立的有界队列"]
+ C --> D["Operator DAG
解码、检测、VAD、ASR、LLM、TTS、策略"]
+ D --> E["Safety Gate"]
+ D --> F["平台 Sink"]
+ E --> G["机器人执行器 Sink"]
+```
+
+内部节点传递 `Envelope[T]`，其中包含载荷、`schema/version`、采集时间、序号、截止时间、`trace_id` 和 `session_id`。连接器负责 protobuf/HTTP JSON 与内部契约之间的转换，AI 插件不应直接依赖 cmvr-es protobuf。
+
+完整设计和配置字段见 [docs/architecture.md](docs/architecture.md)。
+
+## 目录
+
+```text
+cmvr_edge_ai/
+├── .python-version # uv 默认 Python 3.10
+├── uv.lock # 所有 profile 的可复现依赖锁
+├── configs/
+│ └── smoke.yaml # 不依赖外部服务的最小运行验证
+├── detect_server/
+│ └── pipeline.yaml # cmvr-es 相机 -> PPE 告警 -> HTTP 平台
+├── talk_server/
+│ ├── nodes/ # 对话插件预留目录
+│ └── pipeline.yaml # 模拟音频 -> 对话占位 -> 日志
+├── scripts/
+│ ├── bootstrap.sh # 一键创建 uv 环境、生成 bindings 并自检
+│ └── generate_cmvr_stubs.py # 从 cmvr-es proto 生成 Python bindings
+├── src/cmvr_edge_ai/
+│ ├── config/ # 配置模型、加载与环境变量展开
+│ ├── core/ # Envelope、组件接口、队列和 DAG 运行时
+│ ├── contracts/ # 协议无关的图像、音频、AI 和控制契约
+│ ├── detection/ # 模型注册、视频解码、推理与时间窗口规则
+│ ├── plugins/ # 插件注册、发现和内置基础插件
+│ ├── connectors/ # cmvr-es 与平台边界连接器
+│ ├── transports/ # gRPC/HTTP 连接池；UDP/QUIC 扩展位置
+│ ├── workers/ # 显式线程 offload 与常驻进程 Worker 工具
+│ ├── observability/ # 低开销文本/JSON 日志
+│ ├── application.py # 多 Pipeline 与共享网络客户端的所有者
+│ ├── compiler.py # 配置到可执行 DAG 的编译器
+│ └── cli.py # validate/run/plugins/models
+└── tests/
+```
+
+## 快速开始
+
+项目使用 `uv` 管理 Python、`.venv` 和锁定依赖。`.python-version` 默认选择
+Python 3.10，支持范围是 3.10～3.12；首次执行时，uv 会在本机没有合适解释器时
+自动安装。先确认已经安装 uv：
+
+```bash
+uv --version
+```
+
+未安装时可使用 uv 官方安装器：
+
+```bash
+curl -LsSf https://astral.sh/uv/install.sh | sh
+```
+
+只验证框架和模拟对话链路时，一条命令创建最小环境并自检：
+
+```bash
+cd /home/xtkuang/Projects/cmvr/cmvr_edge_ai
+bash scripts/bootstrap.sh --profile core
+```
+
+`uv.lock` 是安装的唯一版本来源；bootstrap 使用 `uv sync --locked`，不会在用户
+机器上重新选择依赖版本。无需 `source .venv/bin/activate`，统一通过
+`uv run --no-sync` 使用已经安装好的环境：
+
+```bash
+uv run --no-sync cmvr-edge-ai validate --config configs/smoke.yaml
+uv run --no-sync cmvr-edge-ai plugins
+uv run --no-sync cmvr-edge-ai run \
+ --config configs/smoke.yaml \
+ --log-level INFO \
+ --log-format text
+```
+
+预期会看到两条日志，内容分别包含 `framework-ready` 和 `bounded-dag-running`。该 Source 是有限数据源，数据处理完后进程会自然退出。
+
+bootstrap 支持以下环境：
+
+| Profile | 安装内容 | 命令 |
+|---|---|---|
+| `core` | 框架核心和模拟 smoke/talk 链路 | `bash scripts/bootstrap.sh --profile core` |
+| `detection-cpu` | gRPC、HTTP、PyAV、Pillow 告警图片和固定版本 CPU YOLO；默认值 | `bash scripts/bootstrap.sh` |
+| `dev` | `detection-cpu` 加测试和 protobuf codegen 工具，并运行完整测试 | `bash scripts/bootstrap.sh --profile dev` |
+
+如果只希望安装、不执行自检，可加 `--skip-check`。完整参数通过以下命令查看：
+
+```bash
+bash scripts/bootstrap.sh --help
+```
+
+不要使用 `uv sync --all-extras`：`yolo` 与 `yolo-cpu` 是为不同 PyTorch 来源准备的
+互斥环境。请使用 bootstrap profile，或显式只选择其中一个 extra。
+告警图片的画框和 JPEG 编码由独立的 `image` extra 提供；默认的
+`detection-cpu`/`dev` profile 已安装它，手动组合检测环境时也必须选择
+`--extra image`。
+
+CLI 的四个子命令如下：
+
+| 命令 | 用途 |
+|---|---|
+| `validate -c FILE [--pipeline ID]` | 加载配置、展开环境变量、构造插件并校验 DAG；不启动 Pipeline |
+| `run -c FILE [--pipeline ID]` | 启动选中的 Pipeline；未指定时启动所有 `enabled: true` 的 Pipeline |
+| `plugins` | 列出内置插件和已安装 entry point 插件 |
+| `models` | 列出检测模型 ID、名称、backend 和注册的全部标签 |
+
+`--pipeline` 可以重复传入。`run` 还支持 `--log-level` 和 `--log-format text|json`。配置错误退出码为 `2`，运行错误为 `1`，键盘中断为 `130`。
+
+## 运行 PPE 检测链路
+
+默认 bootstrap 就是当前 YAML 使用的 CPU 检测环境。它会从相邻的
+`../cmvr-es` 读取 proto、用锁定的 `grpcio-tools` 生成 bindings，然后安装完整
+检测依赖并校验 smoke 和 PPE 配置：
+
+```bash
+cd /home/xtkuang/Projects/cmvr/cmvr_edge_ai
+bash scripts/bootstrap.sh
+```
+
+如果 cmvr-es 不在相邻目录，显式指定路径：
+
+```bash
+bash scripts/bootstrap.sh \
+ --cmvr-es-root /home/xtkuang/Projects/cmvr/cmvr-es
+```
+
+bootstrap 默认使用 portable codegen，所以不要求 cmvr-es 已经编译出 `protoc`。
+要跳过生成（例如部署包已经包含匹配版本的 bindings），使用
+`--skip-codegen`。手动生成时可以执行：
+
+```bash
+uv sync --locked --only-group codegen
+.venv/bin/python scripts/generate_cmvr_stubs.py \
+ --cmvr-es-root /home/xtkuang/Projects/cmvr/cmvr-es \
+ --portable
+```
+
+生成文件默认写入 `src/cmvr/...`，使 `cmvr.api.*_pb2` 可以被连接器导入。
+生成后应再次执行目标 profile 的 `uv sync --locked`，让可编辑安装识别新包；bootstrap
+已经按这个顺序处理。
+
+在 `detect_server/pipeline.yaml` 中配置部署参数：
+
+```yaml
+endpoints:
+ cmvr_es:
+ target: 127.0.0.1:50052
+ platform:
+ base_url: http://127.0.0.1:8081
+
+pipelines:
+ detection:
+ nodes:
+ camera:
+ with:
+ device_id: right_hand_cam
+ stream_log_interval_s: 5
+ detector:
+ with:
+ attach_frame: true
+ inference_log_interval_s: 5
+ model_options:
+ weights: /home/xtkuang/Projects/cmvr/changan_robot/construction-ppe-yolov8/best.pt
+ device: cpu
+ repeat_gate:
+ with:
+ alert_image:
+ enabled: true
+ jpeg_quality: 85
+ platform:
+ with:
+ failure_mode: log_and_drop
+```
+
+然后验证并运行检测链路，不再要求预先导出环境变量：
+
+```bash
+uv run --no-sync cmvr-edge-ai models
+uv run --no-sync cmvr-edge-ai validate \
+ --config detect_server/pipeline.yaml \
+ --pipeline detection
+uv run --no-sync cmvr-edge-ai run --config detect_server/pipeline.yaml \
+ --pipeline detection \
+ --log-level INFO \
+ --log-format json
+```
+
+启动后，detector 会先输出一条 `detection model loaded`，表示对应权重已经成功
+加载。收到解码帧并完成真实 `predict` 后，会立即输出第一条 `detection inference`，之后
+按 `inference_log_interval_s` 聚合输出一次；其中 `window_frames` 是本周期推理帧数，
+`window_detections` 是检测框总数，`hit_labels` 是各标签的检测框累计数。持续只有 loaded 而没有
+inference，说明相机或 decoder 尚未把帧送到模型；inference 中 detection 为 0 只表示
+当前阈值下没有命中。短时调试可设为 `1` 秒，生产环境可设为 `30`～`60` 秒，省略则关闭
+周期推理日志。这里使用标准日志而不是裸 `print`，因此与 `--log-format json` 兼容。
+
+相机连接器会在每次首次连接或重连时先发 `CameraService.StartCamera`，收到成功反馈后
+才建立 `GetRGBImageStream`。终端会依次出现 `camera start requested/succeeded`、
+`camera stream opening`、`camera stream first frame` 和周期性的 `camera stream progress`。
+如果只有 opening 而没有 first frame，progress 中会持续显示
+`first_frame_received=false window_frames=0`，用于区分“RPC 已建立但相机没有出帧”。
+
+运行前需要确认 Construction PPE 权重存在、cmvr-es 已启用 `right_hand_cam`，8081 平台的
+`/v1/detection-alerts` 可访问。默认 `yolo-cpu` profile 将 PyTorch 2.7.0 和
+torchvision 0.22.0 绑定到官方 CPU wheel，并固定 checkpoint 记录的 Ultralytics
+8.4.31。`model_options.device` 在该环境中应保持 `cpu`，`half` 应保持 `false`。
+
+x86 CUDA 和 Jetson/JetPack 的 PyTorch wheel 与驱动强绑定，不能复用这个 CPU
+profile。项目保留了不绑定 CPU index 的 `yolo` extra 作为设备专用环境的基础，但
+GPU 部署前应为目标设备建立单独的 uv source/lock（或使用 NVIDIA 容器），再把 YAML
+中的 `device` 改为 `cuda:0`；不要只改 YAML 就认为 CUDA 环境已经就绪。
+
+`detection.model@1` 根据 `model` 从 `DetectionModelRegistry` 解析模型。`detect_labels` 只选择需要检测的标签，省略时检测注册模型的全部标签；`confidence` 是全局阈值，也可以用 `label_confidence` 为个别标签覆盖。当前内置 `construction-ppe-yolov8@1` 的 19 个标签和 `ppe-6classes-yolov8n@1` 的 6 个标签都可以通过 `cmvr-edge-ai models` 查看。`attach_frame: true` 让检测结果临时携带对应的解码帧，供后续告警节点使用；因此原 detector 到 repeat gate 的队列应保持较小，避免堆积未压缩图像。
+
+六类模型的标签是 `Gloves`、`Vest`、`goggles`、`helmet`、`mask` 和 `safety_shoe`，
+语义是“画面中检测到了该装备”，不是“人员缺少该装备”。它没有 `Person` 或 `No-*`
+类，也没有人员与装备关联能力，因此不能只靠配置推断某个人未佩戴 PPE。该模型当前仅
+注册、未被 `detect_server/pipeline.yaml` 引用；需要恢复第二分支时，应同时配置 detector、
+8082 endpoint、HTTP Sink 和两条关联 edge。
+
+若以后恢复双模型配置，应从 decoder 输出端口 fan-out，让两个 detector 共享同一个相机
+订阅和 PyAV decoder；两个模型仍会分别加载和推理，并共享应用的有界线程池。
+
+`detection.repeat_gate@1` 只在一个规则的 `window_ms` 内看到至少 `min_hits` 个不同帧后生成 `DetectionAlert/v1`。同一帧有多个同类框仍只算一次；触发后进入 `cooldown_ms`，冷却期间不累计，结束后必须重新满足次数。`scope: source` 按相机统计；`scope: track` 按 `track_id` 统计，但当前 YOLO adapter 只做逐帧检测，不产生 `track_id`，因此使用 track 规则前必须增加跟踪/关联节点。
+
+启用 `alert_image` 后，repeat gate 只在规则真正触发时使用 Pillow 对阈值帧画框并编码 JPEG，不会给每一帧都生成图片。告警的 `detections` 和图片中的 bounding boxes 都来自达到 `min_hits` 的阈值帧；窗口内更早帧只参与 `hit_count`、时间范围和最大置信度统计。HTTP JSON 中图片位于 `payload.image`：
+
+```json
+{
+ "image": {
+ "media_type": "image/jpeg",
+ "width": 1280,
+ "height": 720,
+ "encoding": "base64",
+ "data": "..."
+ }
+}
+```
+
+这里的外层对象是 `DetectionAlert` payload 的片段；HTTP JSON Sink 会把内部 JPEG
+`bytes` 转成上述扁平 Base64 图片对象。未启用图片，或运行时因第三方结果未附带
+帧、坏帧等原因渲染失败时，告警仍会发送且 `payload.image` 为 `null`。
+同一阈值帧若同时触发多条规则或多个 track，只编码一次相关框的并集，并让这些
+告警共享同一个不可变 JPEG 对象，以限制边缘端瞬时 CPU 和内存开销。
+
+对话占位链路不依赖音频 proto：
+
+```bash
+uv run --no-sync cmvr-edge-ai validate --config talk_server/pipeline.yaml
+uv run --no-sync cmvr-edge-ai run --config talk_server/pipeline.yaml
+```
+
+## 配置最小示例
+
+```yaml
+api_version: cmvr.edge.ai/v1
+
+runtime:
+ thread_workers: 2
+ shutdown_timeout_s: 5
+
+pipelines:
+ example:
+ enabled: true
+ nodes:
+ source:
+ uses: core.sequence_source@1
+ with:
+ items: [hello]
+ schema_name: TextEvent
+ schema_version: 1
+ sink:
+ uses: core.log_sink@1
+ edges:
+ - from: source.output
+ to: sink.input
+ qos:
+ profile: request
+ capacity: 4
+ overflow: block
+```
+
+配置模型是严格的，多余字段会报错。字符串支持以下环境变量形式：
+
+- `${NAME}`：变量必须存在；
+- `${NAME:-default}`：未设置或为空时使用默认值；
+- `env://NAME`：整个字符串取自必填环境变量。
+
+v1 支持五个 `qos.profile`，并在编译期约束其溢出策略：编码 H264/H265 在解码前必须使用 `video_contiguous`，只能阻塞或拒绝；解码后的完整图像可使用 `realtime_latest` 丢旧帧控制延迟；`audio_contiguous` 和 `request` 只能阻塞或拒绝；`telemetry` 支持全部策略。未填写 QoS 时使用无损的 `request + block + capacity=1`。`reject` 会抛异常，`error` 是它的兼容别名。`max_age_ms`、`put_timeout_ms` 已预留，设置后 `validate` 会拒绝配置。完整矩阵见架构文档。
+
+## 注册新的检测模型
+
+检测模型和 DAG 插件是两层注册：流水线固定使用通用的 `detection.model@1`，具体模型通过 `DetectionModelRegistry` 注册 `DetectionModelSpec`。每个 spec 必须给出版本化 `model_id`、面向运维的 `name`、有序且唯一的 `supported_labels`、`backend` 和 factory。factory 返回实现 `load/predict/close` 的 `DetectionModel`；部署 YAML 中的 `model_options` 原样交给它。第三方模型包可以使用 `cmvr_edge_ai.detection_models` entry point 发布 spec 或注册回调。安装后先执行 `cmvr-edge-ai models`，再让配置引用其中的精确模型 ID。
+
+模型实际输出的标签仍会在通用 Operator 边界二次校验和过滤；模型返回未注册标签会让节点失败。直接相连的重复规则若引用了 detector 没有选择的标签，也会在 `validate` 阶段被编译器拒绝。
+
+## 开发插件
+
+插件必须使用带版本的稳定 ID，例如 `example.text_upper@1`，并声明节点种类、输入/输出端口及 schema。工厂签名固定为 `(node_id, params)`；组件分别继承 `Source`、`Operator` 或 `Sink`。
+
+```python
+from collections.abc import Mapping
+from typing import Any
+
+from cmvr_edge_ai.contracts import TextEvent
+from cmvr_edge_ai.core import Emission, Envelope, Operator
+from cmvr_edge_ai.plugins import PluginKind, PluginRegistry, PluginSpec
+
+
+class UppercaseOperator(Operator):
+ def __init__(self, node_id: str, params: Mapping[str, Any]) -> None:
+ self.node_id = node_id
+
+ async def process(
+ self, envelope: Envelope[Any], input_port: str = "input"
+ ) -> Emission:
+ event = envelope.payload
+ if not isinstance(event, TextEvent):
+ raise TypeError("expected TextEvent")
+ result = TextEvent(text=event.text.upper(), role=event.role, final=event.final)
+ return Emission(
+ "output",
+ envelope.with_payload(result, schema_name="TextEvent", schema_version=1),
+ )
+
+
+def register_plugins(registry: PluginRegistry) -> None:
+ registry.register(
+ PluginSpec(
+ plugin_id="example.text_upper@1",
+ kind=PluginKind.OPERATOR,
+ factory=UppercaseOperator,
+ inputs={"input": "TextEvent/v1"},
+ outputs={"output": "TextEvent/v1"},
+ description="Uppercase text events",
+ )
+ )
+```
+
+在插件包的 `pyproject.toml` 中注册：
+
+```toml
+[project.entry-points."cmvr_edge_ai.plugins"]
+example = "my_cmvr_plugin.plugins:register_plugins"
+```
+
+安装插件包后，用下面的命令确认发现成功：
+
+```bash
+uv run --no-sync cmvr-edge-ai plugins
+```
+
+配置文件不能通过 `module:Class` 任意导入代码；只会使用内置或已安装 entry point 注册的插件。插件完整生命周期、返回值规范和连接器开发约定见 [docs/architecture.md](docs/architecture.md#5-插件开发约定)。
+
+## 机器人控制安全边界
+
+控制链固定为：
+
+```text
+Policy -> RobotCommand/v1
+ -> safety.robot_command_gate@1
+ -> ApprovedRobotCommand/v1
+ -> cmvr.grpc.agv_command_sink@1
+```
+
+AGV Sink 只接受 `ApprovedRobotCommand`，原始 `RobotCommand` 会在运行时被拒绝。配置编译器还要求安全门是执行器的直接前驱；安全门与 actuator 之间不能插入其他节点，也不能增加绕过安全门的输入边。内置安全门负责过期检查和默认 5 秒的最大 TTL、动作/设备白名单、参数范围、最小发送间隔及默认开启的单调序号检查；不合格命令会被丢弃并记录 warning，不会因为一条业务拒绝停止整条 Pipeline。
+
+AGV Sink 必须绑定固定的非空 `device_id`。`set_velocity` 默认禁用；`unsafe_allow_unleased_velocity: true` 只是面向隔离测试场景的显式逃生开关，并且仍要求 Sink 配置 `vx/vy/wz` 的 `velocity_limits`。它只能限制当前客户端发出的数值，并在正常关闭时尽力调用 `stopVelocityControl`；进程崩溃、`SIGKILL`、断电或网络分区时无法保证停车。
+
+真实机器人要启用持续速度控制，必须先在 cmvr-es 服务端实现带过期时间的 lease/deadman：客户端停止续租后，由 cmvr-es 在独立于 edge-ai 进程的安全路径中自动清零速度并停车。客户端的安全门、TTL 和 shutdown hook 不能替代这项服务端保护。
+
+## 当前实现边界
+
+- 音频内部契约 `AudioChunk/v1` 已定义，但 cmvr-es 麦克风/扬声器双向流 proto 和连接器尚未落地。
+- UDP 与 QUIC 目录目前是扩展占位，没有可用传输实现。
+- 运行时 v1 只支持 `execution.mode: async|inline`，两者当前都是单 task、单并发执行。`thread`、`process`、`model_worker` 是保留值；`concurrency != 1`、非空 `max_in_flight/timeout_s`、`ordered: false` 也是保留配置，都会在编译期被拒绝。插件仍可在组件内部显式使用 `workers.run_blocking()`、`PersistentProcessWorker` 或自有模型 Worker，但不能把 `execution` 声明误当作自动调度。
+- v1 对未实现的声明采取 fail-closed：显式设置 `runtime.max_processes/process_start_method/health_bind/reserved_memory_mb`、非默认 pipeline `priority` 或任何非空 `resources` 都会在编译期被拒绝。健康状态和队列统计目前只能通过 Python API 获取。
+- 当前没有配置热更新、配置 overlay、持久化 outbox/spool 或共享内存图像池。HTTP Sink
+ 只有当前进程内的有限重试；默认 `failure_mode: raise` 会终止 Pipeline，当前检测配置的
+ `log_and_drop` 则在重试耗尽后记录 WARNING 并丢弃该告警。两种模式在进程退出或断电时
+ 都可能丢失未上报数据。
+- 相机 Source 可以重连，但 cmvr-es 当前服务端通过 `getLatestEncodedFrame` 获取最新编码数据；如果上游在 edge-ai 收到之前已跳过 H264/H265 参考包，`video_contiguous` 无法补回数据，解码器只能在错误后重置并等待关键帧。正式部署应验证 cmvr-es 输出的是连续 access unit 流，或改为对 AI 友好的原始/JPEG/可检测不连续性的接口。
+- 内置 YOLO adapter 不运行 tracker，所有检测的 `track_id` 都为空；当前示例因此使用 `scope: source`。
+- PPE 权重仓库对权重的许可说明与 Ultralytics runtime/checkpoint 中的 AGPL 信息需要在商业交付前核对，并同时确认训练数据和权重分发许可。
+- 端口 schema 在编译期按字符串匹配；运行时不会自动验证 Python payload 类型，插件必须在边界处主动检查。
+- AGV 执行器不会自动重试。安全门已提供动作/设备白名单、TTL、参数范围、最小间隔和单调序号检查，但设备状态、command ID 去重、来源仲裁、服务端 lease/deadman 和急停优先级仍需按机器人型号补齐。服务端 deadman 未完成前，禁止把 `set_velocity` 用于真实机器人持续运动。
+
+这些限制及推荐演进顺序在 [docs/architecture.md](docs/architecture.md#9-当前限制与演进顺序) 中有更详细说明。
diff --git a/configs/smoke.yaml b/configs/smoke.yaml
new file mode 100644
index 0000000..d148214
--- /dev/null
+++ b/configs/smoke.yaml
@@ -0,0 +1,41 @@
+api_version: cmvr.edge.ai/v1
+
+runtime:
+ thread_workers: 2
+ shutdown_timeout_s: 5
+
+pipelines:
+ smoke:
+ enabled: true
+ nodes:
+ source:
+ uses: core.sequence_source@1
+ with:
+ items:
+ - framework-ready
+ - bounded-dag-running
+ schema_name: TextEvent
+ schema_version: 1
+
+ operator:
+ uses: core.passthrough@1
+
+ sink:
+ uses: core.log_sink@1
+ with:
+ logger: cmvr_edge_ai.smoke
+
+ edges:
+ - from: source.output
+ to: operator.input
+ qos:
+ profile: request
+ capacity: 4
+ overflow: block
+
+ - from: operator.output
+ to: sink.input
+ qos:
+ profile: telemetry
+ capacity: 4
+ overflow: block
diff --git a/detect_server/README.md b/detect_server/README.md
new file mode 100644
index 0000000..7c535fc
--- /dev/null
+++ b/detect_server/README.md
@@ -0,0 +1,293 @@
+# PPE 检测流水线
+
+`pipeline.yaml` 是当前可运行的园区施工安全装备检测链路。当前只启用 Construction
+PPE 模型和 8081 告警平台；六类模型与 8082 模拟平台的实现保留但不实例化：
+
+```text
+cmvr-es CameraService
+ -> H264/H265 ImageFrame/v1
+ -> media.video_decoder.pyav@1
+ -> BGR8 ImageFrame/v1
+ -> construction-ppe-yolov8@1 -> DetectionResult/v1
+ -> detection.repeat_gate@1 -> DetectionAlert/v1
+ -> POST 127.0.0.1:8081/v1/detection-alerts
+```
+
+## 安装与启动
+
+从仓库根目录执行一键安装。默认 profile 安装锁定的 CPU 检测环境、生成 cmvr-es
+bindings，并验证 smoke 与本检测配置：
+
+```bash
+cd /home/xtkuang/Projects/cmvr/cmvr_edge_ai
+bash scripts/bootstrap.sh
+```
+
+cmvr-es 不在相邻的 `../cmvr-es` 时指定实际路径：
+
+```bash
+bash scripts/bootstrap.sh \
+ --cmvr-es-root /home/xtkuang/Projects/cmvr/cmvr-es
+```
+
+环境固定 Python 3.10、Ultralytics 8.4.31、PyTorch 2.7.0 CPU 和 torchvision
+0.22.0，并通过 `image` extra 安装 Pillow 以生成告警图片；所有具体包版本记录在
+仓库的 `uv.lock` 中。GPU/Jetson 需要单独适配与驱动或 JetPack 匹配的 PyTorch
+wheel，不能直接复用 `detection-cpu` profile。手动组合依赖时必须显式增加
+`--extra image`，不能只依赖 YOLO 间接安装 Pillow。
+
+直接编辑 `detect_server/pipeline.yaml` 中的部署参数：
+
+```yaml
+endpoints:
+ cmvr_es:
+ target: 127.0.0.1:50052
+ platform:
+ base_url: http://127.0.0.1:8081
+
+pipelines:
+ detection:
+ nodes:
+ camera:
+ with:
+ device_id: right_hand_cam
+ stream_log_interval_s: 5
+ detector:
+ with:
+ attach_frame: true
+ inference_log_interval_s: 5
+ model_options:
+ weights: /home/xtkuang/Projects/cmvr/changan_robot/construction-ppe-yolov8/best.pt
+ device: cpu
+ repeat_gate:
+ with:
+ alert_image:
+ enabled: true
+ jpeg_quality: 85
+ platform:
+ with:
+ failure_mode: log_and_drop
+```
+
+完成配置后启动，不需要再通过 shell `export` 传入这些值：
+
+```bash
+uv run --no-sync cmvr-edge-ai models
+uv run --no-sync cmvr-edge-ai validate \
+ -c detect_server/pipeline.yaml \
+ --pipeline detection
+uv run --no-sync cmvr-edge-ai run -c detect_server/pipeline.yaml \
+ --pipeline detection \
+ --log-level INFO \
+ --log-format json
+```
+
+运行时会看到类似下面两类 JSON 日志：
+
+```json
+{"level":"INFO","logger":"cmvr_edge_ai.detection.operator","message":"detection model loaded node=detector model=construction-ppe-yolov8@1 ..."}
+{"level":"INFO","logger":"cmvr_edge_ai.detection.operator","message":"detection inference node=detector model=construction-ppe-yolov8@1 total_frames=1 window_frames=1 window_detections=2 hit_labels=No-Helmet:2 ..."}
+```
+
+- `detection model loaded`：checkpoint 已成功加载并完成标签顺序校验；当前 detector
+ 应出现一条；
+- `detection inference`：模型确实收到解码帧并执行了 `predict`；第一帧立即输出，
+ 后续按配置周期聚合；
+- `window_detections=0 hit_labels=none`：模型在工作，但本周期没有高于配置阈值的命中；
+- 只有 loaded、长期没有 inference：优先检查 cmvr-es 相机流、decoder 和关键帧；
+- 短时观察可把 `inference_log_interval_s` 改为 `1` 秒，长期运行建议 `30`～`60` 秒，
+ 省略该字段会关闭周期推理日志。
+
+运行前确认 cmvr-es 已启用 `right_hand_cam`、Construction PPE 权重存在，并且 8081
+平台接受 `POST /v1/detection-alerts`。默认 profile 下 `model_options.device` 应设为
+`cpu`；只有完成设备专用的 CUDA/Jetson PyTorch 环境适配后，才能改为 `cuda:0` 等值。
+
+## 模型与标签
+
+`detection.model@1` 不绑定某一个框架；它通过 `DetectionModelRegistry` 查找配置中的
+`model`。每个 `DetectionModelSpec` 注册版本化模型 ID、模型名称、backend 和有序
+`supported_labels`。注册表当前包含两个内置模型，但 Pipeline 只引用第一个：
+
+- `construction-ppe-yolov8@1`：19 类，包含原分支用于违规告警的 `No-*` 标签；
+- `ppe-6classes-yolov8n@1`：`Gloves`、`Vest`、`goggles`、`helmet`、`mask`、
+ `safety_shoe` 六个正向装备标签。
+
+可用 `cmvr-edge-ai models` 核对 ID、名称、backend 和标签顺序。六类模型只表达
+“检测到某件装备”，不包含 `Person` 或 `No-*` 类，也不执行人员/PPE 关联；所以它
+不能直接判断某个人缺少装备。需要这种语义时，仍应增加人员检测、空间关联和缺失
+判定节点，不能把“没有检测到 helmet”直接当成“人员未戴安全帽”。
+
+检测节点的关键参数：
+
+| 参数 | 语义 |
+|---|---|
+| `model` | 必须是已注册的精确模型 ID |
+| `detect_labels` | 本次部署需要的标签；省略表示模型的全部注册标签 |
+| `confidence` | 所有选中标签的默认置信度阈值 |
+| `label_confidence` | 可选的逐标签阈值覆盖 |
+| `max_fps` | 推理启动频率上限；过密的解码帧会被跳过 |
+| `inference_log_interval_s` | 可选推理摘要周期（秒）；首帧立即输出，省略表示关闭周期日志 |
+| `attach_frame` | 是否让 `DetectionResult` 临时附带对应解码帧；生成告警图片时必须为 `true` |
+| `model_options` | backend 私有选项；当前 YOLO 支持 weights、device、imgsz、iou、half、max_det、agnostic_nms |
+
+配置阶段会拒绝未知模型、模型不支持的 `detect_labels`、重复标签和非法阈值。YOLO 加载 checkpoint 时还会严格比较 checkpoint 的实际标签及顺序与注册信息，避免类别编号静默错位。
+
+## 六类逐推理结果分支
+
+这一分支当前默认关闭。恢复后，六类 detector 不连接 repeat gate，而是把每次完成推理产生的
+`DetectionResult/v1` 直接交给第二个 HTTP Sink。其 POST Envelope 的
+`schema` 为 `DetectionResult/v1`，`payload` 示例为：
+
+```json
+{
+ "detections": [
+ {
+ "label": "helmet",
+ "confidence": 0.91,
+ "box": {"x_min": 120.0, "y_min": 60.0, "x_max": 250.0, "y_max": 220.0},
+ "track_id": null
+ }
+ ],
+ "model_id": "ppe-6classes-yolov8n@1",
+ "inference_ms": 34.2,
+ "model_name": "PPE Detection YOLOv8n (6 Classes)"
+}
+```
+
+该结构刻意不同于原分支的 `DetectionAlert/v1`：它没有 `rule_id`、`hit_count`、
+`event_id` 或 `image`，空检测帧也会以空 `detections` 数组上报。`cooldown_ms` 是
+repeat gate 的内部规则配置，不属于 `DetectionAlert` payload。由于
+payload 没有 event ID，HTTP Sink 的 `Idempotency-Key` 回退使用 Envelope
+`trace_id`。这只是模拟的逐结果接口；若平台要求另一套字段命名或嵌套，需要增加
+平台专用转换节点/Sink，而不是仅修改 endpoint。
+
+## 重复触发规则
+
+`detection.repeat_gate@1` 按规则维护滑动时间窗口。规则字段如下：
+
+| 参数 | 语义 |
+|---|---|
+| `id` | 唯一规则 ID，写入告警 |
+| `labels` | 任一标签匹配即视为该规则在当前帧命中 |
+| `min_confidence` | 规则侧最低置信度，可高于 detector 阈值 |
+| `min_hits` | 触发所需的不同帧数 |
+| `window_ms` | 上述命中必须落入的时间窗口 |
+| `cooldown_ms` | 告警后的静默期；期间不累计，结束后重新计数 |
+| `scope` | `source` 按相机统计；`track` 按相机内的 `track_id` 分别统计 |
+
+一次命中按 `(source_id, sequence)` 的不同视频帧计算：同一帧出现多个相同标签框只算一次，重复投递同一帧也不会增加计数。一个规则即使配置多个标签，同一 scope 在一帧内仍只增加一次。`time_source` 可选 `captured`、`received` 或优先采集时间的 `auto`；当前 cmvr-es 成功帧通常没有 `header.timestamp`，示例显式使用 `received`。
+
+`alert_image.enabled` 默认为 `false`；启用后要求 detector 同时配置
+`attach_frame: true`。`jpeg_quality` 是 `1..95` 的整数，默认 85。repeat gate
+只在规则达到 `min_hits`、准备输出告警时，才用 Pillow 在当前阈值帧上绘制匹配的
+bounding boxes、标签和置信度并编码 JPEG，不会为每个推理结果都渲染。告警里的
+`detections` 和图片框均来自阈值帧；窗口内更早的命中只参与次数、时间范围和
+`max_confidence` 统计。
+
+同一阈值帧同时触发多条规则或多个 track 时，repeat gate 对这些告警相关框取并集，
+只画框并编码一次，然后让本帧产生的告警共享该 JPEG，避免在边缘端重复编码。
+非有限坐标、反向/退化框、非法置信度等 detection 会在规则计数前被忽略，并记录在
+`invalid_detections` 健康指标中，避免 `NaN/Inf` 令整个 HTTP JSON 告警无法发送。
+
+当前 YOLO adapter 是逐帧检测，不执行人员跟踪，输出的 `track_id` 为 `None`。因此示例使用 `scope: source`；如果业务要求“同一个人连续多次违规”，必须在 detector 和 repeat gate 之间加入 tracker 及人员/PPE 关联节点，之后才能使用 `scope: track`。
+
+## 相机启动与视频流日志
+
+cmvr-es 的 `StartCamera` 和 `GetRGBImageStream` 是两个不同阶段：前者打开物理相机，
+后者只启动编码和流式传输。连接器在每个首次连接或重连 session 中严格执行：
+
+```text
+StartCamera -> 检查 feedback.header.success -> GetRGBImageStream -> 等待首帧
+```
+
+`StartCamera` 使用 `cmvr_es.timeout_s`；RPC 异常或 `header.success=false` 都进入同一套
+指数退避重连。正常关闭只取消当前 stream，不自动调用设备级 `StopCamera`，避免影响
+同一相机的其他客户端。
+
+使用 `--log-level INFO --log-format json` 时，按顺序关注：
+
+- `camera source configured`：endpoint、device 和超时配置完成；
+- `camera start requested/succeeded`：StartCamera 已被调用并成功；
+- `camera stream opening`：流请求已创建，正在等待第一帧；
+- `camera stream first frame`：已经收到真实编码帧，包含 codec、尺寸、字节数和远程序号；
+- `camera stream progress`：每 `stream_log_interval_s` 秒输出帧数、FPS、bitrate、关键帧、
+ 最后序号和 `last_frame_age_s`；即使完全没帧也会输出 `first_frame_received=false`；
+- `camera stream disconnected`：包含错误类型、重连次数和退避时间；
+- `camera stream closed`、`camera source stopped`：当前 session 和本地 Source 已清理。
+
+INFO 日志不会打印帧二进制。示例设置 `stream_log_interval_s: 5` 便于联调，正式部署
+可改为 30～60 秒。
+
+## 视频连续性和背压
+
+H264/H265 是有参考关系的编码流，解码前不能任意丢包：
+
+- `camera -> decoder` 使用 `video_contiguous + block`，保持 edge-ai 内部已经接受的数据连续；
+- decoder 在新 session、编码参数变化、sequence gap 或解码错误后释放上下文，等待下一个关键帧；
+- `decoder -> detector` 已经是完整 BGR 图像，使用 `realtime_latest + drop_oldest` 和容量 1，以有限内存换取较低实时延迟；
+- `detector -> repeat_gate` 在 `attach_frame: true` 时携带未压缩图像，示例把队列容量限制为 2，避免排队的 `DetectionResult` 长时间占用大量内存。
+
+该边使用 `drop_oldest` 偏向低延迟；持续过载时，被丢弃的推理结果不会计入
+`min_hits`。如果业务更重视每次推理结果都参与计数，可改为 `overflow: block`，但要
+接受延迟向上游传播，并继续保持很小的队列容量。
+
+恢复第二模型时，应从 decoder 输出端口 fan-out，以避免第二次相机订阅和第二次
+H264/H265 解码；这不会复用模型计算，两个 YOLO 实例仍会分别加载权重并共享应用的
+有界线程池。
+
+这个保证只覆盖 edge-ai 内部。当前 cmvr-es 服务端通过 `getLatestEncodedFrame` 读取最新编码数据；如果它在负载或时序竞争下已经跳过参考包，`video_contiguous` 无法恢复丢失内容，而且当前 edge-ai 的本地 sequence 不能可靠暴露这种上游跳包。此时 PyAV 可能报错，decoder 会重置并等待关键帧。上线前必须用真实摄像头长时间验证连续性；更稳妥的方案是让 cmvr-es 提供连续 access unit 流，或额外提供原始/JPEG/带显式 discontinuity 的 AI 接口。
+
+相机 Source 默认启用指数退避重连；每次重新订阅生成新的 `session_id`，使 decoder 主动重置。`reconnect_initial_s`、`reconnect_max_s` 和可选 `max_reconnect_attempts` 可配置；省略最大次数表示持续重连直到进程关闭。
+
+## HTTP 输出可靠性
+
+每次规则触发会产生带唯一 `event_id` 的 `DetectionAlert/v1`。HTTP Sink 优先把该
+`event_id` 放入 `Idempotency-Key`，没有事件 ID 时才回退到 `trace_id`。它对连接/超时
+错误和配置的临时 HTTP 状态执行有限指数退避，示例最多尝试 3 次。
+
+`failure_mode` 支持两种明确语义：默认 `raise` 在最终投递失败时让 Pipeline 失败；当前
+检测配置使用 `log_and_drop`，最终连接失败、超时或非 2xx 时输出
+`HTTP report dropped ... action=drop` WARNING，丢弃当前告警并继续处理后续视频帧。
+`CancelledError`、序列化错误和未知本地程序异常不会被吞掉。
+
+当前唯一 HTTP 输出将告警发送到
+`http://127.0.0.1:8081/v1/detection-alerts`。8082 六类逐推理结果 endpoint 当前未配置。
+
+启用告警图片后，POST JSON 的 `payload.image` 结构如下：
+
+```json
+{
+ "media_type": "image/jpeg",
+ "width": 1280,
+ "height": 720,
+ "encoding": "base64",
+ "data": "/9j/4AAQSk..."
+}
+```
+
+`width/height` 是 JPEG 的像素尺寸；HTTP JSON Sink 将内部 JPEG `bytes` 特判为
+上述扁平对象，`encoding` 固定为 `base64`。关闭 `alert_image`，或运行时因第三方
+结果未附带帧、坏帧等原因渲染失败时，告警仍会发送且该字段为 `null`。Base64 会
+额外增加约三分之一的体积，平台和反向代理需要配置足够的请求体上限。
+
+这不是持久化 outbox：`log_and_drop` WARNING 表示该告警已经永久丢失，不会自动补发；
+每次告警在放弃前仍会完成有限重试，因此平台离线时 Sink 队列会短暂阻塞。进程崩溃、
+断电或告警仍在内存队列中时也可能丢失。平台必须按幂等键去重；对“不可丢告警”的
+部署，还需要后续增加有界磁盘 outbox、投递确认和恢复发送。
+
+## 部署风险
+
+- 模型 README 报告某些小样本类别存在漏检，实际园区需要按相机角度、光照、遮挡和距离重新标定置信度与 `min_hits`；规则降噪不能补偿模型系统性漏检。
+- 权重目录对权重许可的描述与 Ultralytics runtime/checkpoint 中的 AGPL 信息需要在商业部署前核对；同时确认训练数据来源和权重再分发权利。
+- `.pt` 使用 PyTorch checkpoint loader，只加载可信构建和受控分发的权重，并固定、验证实际 Ultralytics 8.x 版本。
+
+如果检测结果还要驱动机器人，控制路径必须保持：
+
+```text
+DetectionResult -> policy -> RobotCommand/v1
+ -> safety.robot_command_gate@1
+ -> ApprovedRobotCommand/v1
+ -> cmvr.grpc.agv_command_sink@1
+```
+
+编译器要求 actuator 的每个直接前驱都是安全门，安全门和执行器之间不能插入普通变换节点，也不能存在绕过路径。
diff --git a/detect_server/pipeline.yaml b/detect_server/pipeline.yaml
new file mode 100644
index 0000000..2ce7667
--- /dev/null
+++ b/detect_server/pipeline.yaml
@@ -0,0 +1,180 @@
+api_version: cmvr.edge.ai/v1
+
+runtime:
+ thread_workers: 3
+ shutdown_timeout_s: 8
+
+endpoints:
+ cmvr_es:
+ transport: grpc
+ # cmvr-es gRPC address. Change this value for each deployed robot.
+ target: 192.168.0.102:50052
+ tls: false
+ timeout_s: 5
+ options:
+ max_receive_mb: 32
+
+ platform:
+ transport: http
+ # Violation-alert platform HTTP base URL.
+ base_url: http://127.0.0.1:8081
+ timeout_s: 3
+
+pipelines:
+ detection:
+ enabled: true
+ nodes:
+ camera:
+ uses: cmvr.grpc.camera_rgb_stream@1
+ with:
+ endpoint: cmvr_es
+ device_id: wrist_cam
+ pixel_format: BGR8
+ reconnect: true
+ reconnect_initial_s: 0.5
+ reconnect_max_s: 10
+ # Log stream state immediately on first frame and emit a periodic
+ # progress/stall heartbeat without printing every encoded frame.
+ stream_log_interval_s: 5
+
+ decoder:
+ uses: media.video_decoder.pyav@1
+
+ detector:
+ uses: detection.model@1
+ with:
+ model: construction-ppe-yolov8@1
+ # Omitting detect_labels means all registered labels. This example
+ # asks the backend to return only PPE violations used by the rules.
+ detect_labels:
+ - No-Boots
+ - No-Ear-Protection
+ - No-Glass
+ - No-Glove
+ - No-Helmet
+ - No-Mask
+ - No-Vest
+ confidence: 0.50
+ max_fps: 10
+ # Log the first completed inference immediately, then aggregate one
+ # heartbeat every 5 seconds so model activity is visible without
+ # printing every frame. Set to 1 for one-second debugging, or omit to disable.
+ inference_log_interval_s: 5
+ # Keep the decoded threshold frame available to repeat_gate so an
+ # annotated alert image can be rendered only when a rule triggers.
+ attach_frame: true
+ model_options:
+ # Model artifact and inference device are deployment configuration,
+ # not process environment requirements.
+ weights: /home/xtkuang/Projects/cmvr/changan_robot/construction-ppe-yolov8/best.pt
+ device: cpu
+ imgsz: 640
+ iou: 0.70
+ half: false
+ max_det: 100
+
+ repeat_gate:
+ uses: detection.repeat_gate@1
+ with:
+ # cmvr-es currently omits capture timestamps on successful stream
+ # frames, so received time is the deterministic deployment default.
+ time_source: received
+ alert_image:
+ enabled: true
+ jpeg_quality: 85
+ rules:
+ - id: no-boots
+ labels: [No-Boots]
+ min_hits: 3
+ window_ms: 2000
+ cooldown_ms: 30000
+ min_confidence: 0.50
+ scope: source
+ - id: no-ear-protection
+ labels: [No-Ear-Protection]
+ min_hits: 3
+ window_ms: 2000
+ cooldown_ms: 30000
+ min_confidence: 0.50
+ scope: source
+ - id: no-glass
+ labels: [No-Glass]
+ min_hits: 3
+ window_ms: 2000
+ cooldown_ms: 30000
+ min_confidence: 0.50
+ scope: source
+ - id: no-glove
+ labels: [No-Glove]
+ min_hits: 3
+ window_ms: 2000
+ cooldown_ms: 30000
+ min_confidence: 0.50
+ scope: source
+ - id: no-helmet
+ labels: [No-Helmet]
+ min_hits: 3
+ window_ms: 2000
+ cooldown_ms: 30000
+ min_confidence: 0.50
+ scope: source
+ - id: no-mask
+ labels: [No-Mask]
+ min_hits: 3
+ window_ms: 2000
+ cooldown_ms: 30000
+ min_confidence: 0.50
+ scope: source
+ - id: no-vest
+ labels: [No-Vest]
+ min_hits: 3
+ window_ms: 2000
+ cooldown_ms: 30000
+ min_confidence: 0.50
+ scope: source
+
+ platform:
+ uses: platform.http_json_sink@1
+ with:
+ endpoint: platform
+ path: /v1/detection-alerts
+ # Platform outages must not stop camera capture or inference. After
+ # bounded retries, log a WARNING and drop only this report.
+ failure_mode: log_and_drop
+ max_attempts: 3
+ retry_initial_s: 0.25
+ retry_max_s: 2
+
+ edges:
+ # Encoded H264/H265 packets must remain contiguous before decode.
+ - from: camera.frames
+ to: decoder.frames
+ qos:
+ profile: video_contiguous
+ capacity: 8
+ overflow: block
+
+ # Once frames are decoded, keeping only the newest frame bounds latency.
+ - from: decoder.frames
+ to: detector.frames
+ qos:
+ profile: realtime_latest
+ capacity: 1
+ overflow: drop_oldest
+
+ - from: detector.detections
+ to: repeat_gate.detections
+ qos:
+ profile: telemetry
+ # DetectionResult carries a decoded frame when attach_frame is enabled;
+ # keep this queue short so raw image buffers cannot accumulate. Under
+ # sustained overload drop_oldest also means dropped results do not count.
+ capacity: 2
+ overflow: drop_oldest
+
+ - from: repeat_gate.alerts
+ to: platform.input
+ qos:
+ profile: telemetry
+ capacity: 64
+ overflow: block
diff --git a/docs/architecture.md b/docs/architecture.md
new file mode 100644
index 0000000..73b5cb6
--- /dev/null
+++ b/docs/architecture.md
@@ -0,0 +1,812 @@
+# cmvr-edge-ai 架构与开发指南
+
+本文描述当前代码已经实现的架构、配置语义和扩展边界。文中的“当前”指运行时 v1；规划能力会明确标注，避免把配置字段误认为已经具备的调度能力。
+
+## 1. 目标与边界
+
+`cmvr-edge-ai` 负责在单台机器人边缘计算设备上，把三类模块可靠地连接起来：
+
+1. 上游数据：cmvr-es 的相机、未来的麦克风音频流，以及平台下发的数据；
+2. 中间 AI：视频解码、检测、VAD、ASR、LLM、TTS、决策和策略；
+3. 下游输出：cmvr-es 的 AGV/机械臂/扬声器控制，以及平台 HTTP/gRPC 接口。
+
+框架刻意保持轻量：默认不依赖 Kafka、Redis、Celery 或 Kubernetes。一个进程内使用 `asyncio` 管理 I/O 和 DAG；需要 CPU/GPU 隔离的插件自行接入有限线程池或常驻 Worker。这样可以在算力、内存和显存有限的边缘端按需增加复杂度。
+
+核心设计约束：
+
+- **配置驱动**：YAML 只选择已注册插件、连接端口并声明 QoS/资源，不执行任意 Python 导入；
+- **协议隔离**：protobuf、HTTP JSON、未来 QUIC 包只在连接器中出现；
+- **内部契约稳定**：算法通过 `Envelope` 和版本化 schema 交流；
+- **有界资源**：所有边都有固定容量，满载行为必须显式配置；
+- **故障显式**：节点异常默认终止 Pipeline；只有连接器显式配置的有界重试和
+ `log_and_drop` 等降级策略可以改变该行为；
+- **控制失效安全**：执行器只接收已批准命令，且其每个直接前驱都必须是安全门；持续运动还依赖 cmvr-es 服务端 lease/deadman。
+
+## 2. 分层架构
+
+```mermaid
+flowchart TB
+ subgraph External["外部系统"]
+ CE["cmvr-es
gRPC / future QUIC"]
+ PLAT["业务平台
HTTP / gRPC"]
+ end
+
+ subgraph Boundary["协议边界"]
+ SRC["Source Connectors"]
+ SNK["Sink Connectors"]
+ POOL["共享 gRPC Channel / HTTP Client"]
+ end
+
+ subgraph Runtime["单进程 asyncio 运行时"]
+ ENV["Envelope + 内部契约"]
+ QUEUE["每条 Edge 的有界队列"]
+ OPS["Operator DAG"]
+ SAFE["Safety Gate"]
+ LIFE["生命周期 / 健康状态 / 优雅退出"]
+ end
+
+ CFG["严格 YAML + 插件注册表 + DAG 编译器"] --> Runtime
+ CE --> SRC
+ PLAT --> SRC
+ SRC --> ENV
+ ENV --> QUEUE --> OPS
+ OPS --> SAFE --> SNK
+ OPS --> SNK
+ SNK --> CE
+ SNK --> PLAT
+ POOL --- SRC
+ POOL --- SNK
+ LIFE --- SRC
+ LIFE --- OPS
+ LIFE --- SNK
+```
+
+### 2.1 配置层
+
+`config/loader.py` 使用 `yaml.safe_load` 读取文件并递归展开环境变量，随后由 Pydantic 严格模型校验。所有模型均设置 `extra="forbid"`，拼错字段会立即失败。
+
+`compiler.py` 继续完成无法只靠字段类型判断的检查：
+
+- `api_version` 必须等于 `cmvr.edge.ai/v1`；
+- 插件必须存在，工厂创建的对象必须与声明的 Source/Operator/Sink 种类一致；
+- Source 不能有入边，Sink 不能有出边，Operator/Sink 至少有一条入边；
+- 端口必须存在，schema 必须相同或其中一端为通配符 `*`；
+- 图必须无环，不能存在重复边；
+- 带 `transport:` 标签的连接器只能引用相同 transport 的 endpoint；
+- 活跃 Pipeline 中不能包含 `enabled: false` 的节点；
+- 每个带 `actuator` 标签 Sink 的直接前驱都必须是带 `safety_gate` 标签的节点，安全门和执行器之间不能插入其他节点；
+- v1 只接受 `execution.mode: async|inline` 和默认单并发语义；保留的 mode/并发字段会在编译期被拒绝；
+- 显式保留 runtime 字段、非 `normal` priority 和非空 resources 会被拒绝，不会被静默忽略；
+- QoS profile 必须是 v1 支持的五种之一，且 overflow 必须符合该 profile 的语义；
+- 直接连接的 `detection.model@1 -> detection.repeat_gate@1` 会检查规则标签确实包含在 detector 本次选择的标签中；
+- 当前未实现的 `max_age_ms` 和 `put_timeout_ms` 不能设置。
+
+`validate` 会实际调用插件工厂，因此也会检查构造函数所需参数、已注册模型 ID 和标签选择；但不会执行组件的 `setup()`。YOLO 权重是否存在、网络连通性、cmvr protobuf 是否存在、PyAV/Ultralytics/Pillow 可选依赖是否可导入，以及 checkpoint 内部标签是否与注册信息一致，要到 `run` 的 setup 或首个编码帧时才检查；启用 `alert_image` 后 Pillow 会在 repeat gate 的 setup 阶段提前检查。
+
+### 2.2 内部契约层
+
+协议边界进入运行时后统一变为：
+
+```text
+Envelope[T]
+├── payload: T
+├── schema_name + schema_version
+├── source_id + sequence
+├── captured_at_ns + received_at_ns
+├── deadline_ns
+├── trace_id + session_id
+└── attributes
+```
+
+`Envelope` 是冻结 dataclass，`attributes` 被复制为只读 mapping。它只保证浅层不可变：如果 payload 是 NumPy 数组等可变对象，发布后必须把它当作只读数据，才能安全地 fan-out 并为以后零拷贝/共享内存保留空间。
+
+当前稳定内部 payload：
+
+| Schema | Python 类型 | 用途 |
+|---|---|---|
+| `ImageFrame/v1` | `ImageFrame` | 编码视频包或解码后的图像 buffer |
+| `AudioChunk/v1` | `AudioChunk` | 连续音频块，包含格式、采样率和 discontinuity 标记 |
+| `DetectionResult/v1` | `DetectionResult` | 检测框、标签、置信度和推理时间；可临时附带对应解码帧供告警节点使用 |
+| `DetectionAlert/v1` | `DetectionAlert` | 规则 ID、模型、scope、时间窗口、命中数、置信度、阈值帧检测框、可选 JPEG 告警图和唯一 event ID |
+| `TextEvent/v1` | `TextEvent` | ASR/LLM/TTS 链路中的文本事件 |
+| `ChatTurn/v1` | `ChatTurn` | 带 session 和历史的对话输入 |
+| `RobotCommand/v1` | `RobotCommand` | 带 TTL、序号和 command ID 的协议无关控制命令 |
+| `ApprovedRobotCommand/v1` | `ApprovedRobotCommand` | 由可信安全门批准后交给执行器的控制命令 |
+
+`PluginSpec` 中的 schema 是编译期契约字符串。当前运行时不会反射检查 payload 的 Python 类型；连接器和算法插件仍应使用 `isinstance` 或自己的严格模型在边界处失败。
+
+### 2.3 组件层
+
+运行时只有三类组件：
+
+| 组件 | 输入/输出 | 核心方法 |
+|---|---|---|
+| `Source` | 无输入，一个或多个输出端口 | `messages()` 返回 async iterator |
+| `Operator` | 一个或多个输入/输出端口 | `process(envelope, input_port)` |
+| `Sink` | 一个或多个输入端口，无输出 | `consume(envelope, input_port)` |
+
+所有组件共享以下生命周期：
+
+```text
+setup(context) -> start() -> 执行 -> health() -> stop()
+```
+
+建议在 `__init__` 只解析轻量参数，在 `setup` 建立网络连接或加载模型，在 `stop` 释放资源并保证幂等。`ComponentContext` 提供 `pipeline_id`、`node_id`、全局 shutdown event，以及 endpoint、共享连接池和节点配置等 metadata。
+
+Operator 可以返回：
+
+- `None`：过滤该消息；
+- `Envelope`：从默认 `output` 端口发送；
+- `Emission(port, envelope)`：从指定端口发送；
+- 上述对象的同步或异步 iterable：一进多出。
+
+Source 也可以直接 yield `Envelope` 或 `Emission`。返回原始 bytes、模型对象或其他未包装值会在产生该值的节点附近报错。
+
+### 2.4 DAG 运行时
+
+当前每个 Pipeline 在一个 asyncio event loop 中运行，每个节点一个 task。`execution.mode: async` 和 `inline` 在 v1 中都由该 task await 组件方法，Operator 和 Sink 每次只处理一个消息。v1 不会复制节点 task，也不提供配置驱动的并行调度。
+
+每条有向边拥有独立队列。一个输出端口 fan-out 到多条边时，同一个只读 Envelope 会送入每个队列；队列之间的容量和丢弃计数独立。注意：路由会等待所有边的 `put()`，因此任意 fan-out 分支使用 `overflow: block` 且消费变慢时，仍会对共同生产者产生背压。希望“平台慢但控制链不停”时，平台分支必须选择合适的丢弃策略，或在后续加入独立 spool。
+
+多个入边到同一节点时，运行时处理最先就绪的边，不保证不同边之间的全局顺序。单条边内部保持 FIFO（`drop_*` 导致的丢弃除外）。
+
+节点自然结束后，其全部出边会关闭；消费者会读完已接受的数据后结束。收到 SIGINT/SIGTERM 时：
+
+1. 设置全局 shutdown event；
+2. 取消 Source task，并先调用 Source 的 `stop()`；
+3. 让 Operator/Sink 在 `shutdown_timeout_s` 内排空队列；
+4. 超时后取消剩余 task、丢弃未处理消息；
+5. 按 setup 的反序释放组件和共享网络客户端。
+
+任何未被组件显式处理的节点执行异常都会包装为带 node ID 的 `NodeExecutionError`，停止
+Pipeline，并由 CLI 以运行错误退出。相机 Source 和平台 HTTP Sink 内部实现了各自明确、
+有界的重连/重试语义；HTTP Sink 还可显式选择 `log_and_drop`，但框架仍不提供通用节点级
+supervisor 或 retry policy。
+
+## 3. 配置参考
+
+### 3.1 根字段
+
+| 字段 | 必填 | 说明 |
+|---|---:|---|
+| `api_version` | 是 | 当前只能是 `cmvr.edge.ai/v1` |
+| `runtime` | 否 | 进程级运行参数 |
+| `endpoints` | 否 | 命名的外部服务连接信息 |
+| `pipelines` | 是 | 至少一个命名 Pipeline |
+
+endpoint、pipeline 和 node 名称只能包含字母、数字、`_`、`-`，且不能以数字开头。端口引用固定使用 `node.port`。
+
+### 3.2 `runtime`
+
+| 字段 | 默认值 | 当前语义 |
+|---|---:|---|
+| `thread_workers` | `4` | 创建共享 `ThreadPoolExecutor`，并通过 ComponentContext 提供给插件 |
+| `max_processes` | `1` | 保留；v1 配置中必须省略，显式设置（即使写默认值）也会被拒绝 |
+| `process_start_method` | `spawn` | 保留；v1 配置中必须省略 |
+| `shutdown_timeout_s` | `10.0` | 优雅排空的最长等待时间 |
+| `health_bind` | `null` | 保留；v1 配置中必须省略，当前不监听健康检查端口 |
+| `reserved_memory_mb` | `0` | 保留；v1 配置中必须省略，当前不执行内存仲裁 |
+
+v1 可配置的 runtime 字段只有 `thread_workers` 和 `shutdown_timeout_s`。边缘端应保持较小的 `thread_workers`；第三方推理库本身还可能创建线程，应同时限制 OpenMP/MKL/模型运行时线程数，避免过度订阅 CPU。
+
+### 3.3 `endpoints.`
+
+| 字段 | 默认值 | 说明 |
+|---|---:|---|
+| `transport` | 无 | 必填，转为小写；当前连接器支持 `grpc`、`http` |
+| `target` | `null` | gRPC 地址，例如 `127.0.0.1:50052` |
+| `base_url` | `null` | HTTP 基础 URL |
+| `bind` | `null` | 为未来 ingress/server 连接器预留 |
+| `tls` | `false` | 仅用于 gRPC channel 是否使用 TLS；HTTP endpoint 禁止设置该字段 |
+| `timeout_s` | `5.0` | 连接器请求超时；流式相机订阅不使用该 unary timeout |
+| `metadata` | `{}` | gRPC metadata 预留；当前 HTTP 连接器把它作为默认 header |
+| `options` | `{}` | 传输私有参数 |
+
+当前 gRPC options 支持：
+
+```yaml
+options:
+ root_certificates: /path/to/ca.pem
+ max_receive_mb: 32
+ max_send_mb: 8
+ channel_options:
+ grpc.keepalive_time_ms: 20000
+```
+
+HTTP 是否使用 TLS 由 `base_url` 的 `https://` scheme 决定，证书验证默认开启；只有隔离测试环境才可显式设置 `options.verify_tls: false`。当前 gRPC `metadata` 尚未自动附加到 RPC，若需要鉴权应在连接器中显式实现。
+
+### 3.4 `pipelines.`
+
+| 字段 | 默认值 | 说明 |
+|---|---:|---|
+| `enabled` | `true` | 未指定 `--pipeline` 时是否启动 |
+| `priority` | `normal` | v1 必须为 `normal`；`low/high` 或数值优先级会因无调度器而被拒绝 |
+| `nodes` | 无 | 至少一个节点 |
+| `edges` | `[]` | 有向连接列表 |
+
+不要在活跃 Pipeline 中保留 `enabled: false` 节点；当前编译器会直接拒绝。要暂时关闭逻辑，请禁用整个 Pipeline 或从图和配置中移除该节点。
+
+### 3.5 `nodes.`
+
+| 字段 | 默认值 | 说明 |
+|---|---:|---|
+| `uses` | 无 | 版本化插件 ID，例如 `cmvr.grpc.camera_rgb_stream@1` |
+| `with` | `{}` | 原样传给插件工厂的参数；Python 模型内字段名为 `params` |
+| `execution` | 见下表 | 调度意图声明 |
+| `resources` | `{}` | 预留资源需求字段；v1 必须保持为空/默认值 |
+| `enabled` | `true` | 当前只允许活跃 Pipeline 中为 `true` |
+
+`execution` 字段：
+
+| 字段 | 默认值 | 允许值/限制 |
+|---|---:|---|
+| `mode` | `async` | v1 支持 `async`、`inline`；`thread/process/model_worker` 为保留值并被编译器拒绝 |
+| `concurrency` | `1` | v1 必须为 `1`，其他值被编译器拒绝 |
+| `max_in_flight` | `null` | 预留；非空值被编译器拒绝 |
+| `timeout_s` | `null` | 预留；非空值被编译器拒绝 |
+| `ordered` | `true` | v1 必须为 `true`，`false` 被编译器拒绝 |
+
+`async` 与 `inline` 当前没有调度差异，都是 event loop 中的单 task、单 in-flight 调用。配置模型保留其他枚举值是为了后续版本演进，不代表 v1 能执行它们；`validate` 会 fail closed，而不是静默退化到 async。
+
+`resources` 模型预留了 `cpu_cores`、`memory_mb`、`gpu_device`、`gpu_memory_mb`、`exclusive_gpu`，但 v1 无法强制执行资源隔离，因此任何非空/非默认声明都会被编译器拒绝。不要依赖“只写说明但不生效”的资源配置；当前应在部署层和插件实现中限制 CPU/GPU。
+
+### 3.6 `edges[]` 与 QoS
+
+```yaml
+- from: camera.frames
+ to: decoder.frames
+ qos:
+ profile: video_contiguous
+ capacity: 8
+ overflow: block
+- from: decoder.frames
+ to: detector.frames
+ qos:
+ profile: realtime_latest
+ capacity: 1
+ overflow: drop_oldest
+```
+
+| 字段 | 默认值 | 当前语义 |
+|---|---:|---|
+| `profile` | `request` | v1 支持 `realtime_latest`、`video_contiguous`、`audio_contiguous`、`request`、`telemetry` |
+| `capacity` | `1` | 队列最大消息数 |
+| `overflow` | `block` | `drop_oldest`、`drop_newest`、`block`、`reject`；`error` 是 `reject` 的兼容别名 |
+| `max_age_ms` | `null` | 已预留；设置后编译失败 |
+| `put_timeout_ms` | `null` | 已预留；设置后编译失败 |
+
+溢出行为：
+
+| 策略 | 队列满时行为 | 适合场景 |
+|---|---|---|
+| `drop_oldest` | 丢掉最旧消息，再接受新消息 | 实时视频，优先处理最新画面 |
+| `drop_newest` | 保留已有消息，拒收这次新消息但不抛异常 | 需要保留已排队批次的低优先级数据 |
+| `block` | 生产者等待空位 | 必须连续的视频编码包、音频或请求；会传播背压 |
+| `reject` / `error` | 抛出队列满异常，Pipeline 失败 | 不能静默丢失且希望监督器介入的控制链 |
+
+v1 强制执行的 profile/overflow 矩阵：
+
+| profile | 允许的 overflow | 典型用途与注意事项 |
+|---|---|---|
+| `realtime_latest` | `drop_oldest`、`drop_newest` | 已解码的完整视频帧；常用容量 `1..2`，优先限制推理延迟 |
+| `video_contiguous` | `block`、`reject`、`error` | H264/H265 等 inter-frame 编码流在解码前不能静默丢包；此保证不覆盖上游在发送前已经丢失的数据 |
+| `audio_contiguous` | `block`、`reject`、`error` | 连续音频不能静默丢弃；阻塞预算必须明确，拒绝时应重建连续性 |
+| `request` | `block`、`reject`、`error` | 请求/控制；使用有限容量和消息 TTL，禁止执行积压旧动作 |
+| `telemetry` | 所有五种策略 | 平台上报；当前无磁盘 spool，按数据重要性选择阻塞、丢弃或失败 |
+
+队列记录入队、出队、水位、丢弃、拒绝和强制关闭丢弃计数，当前可通过 Python `PipelineRuntime.edge_stats()` 获取，尚未暴露为服务指标。
+
+## 4. 内置插件与连接器
+
+运行 `cmvr-edge-ai plugins` 可查看当前实际注册结果。
+
+### 4.1 基础插件
+
+| 插件 ID | 类型 | 端口 | 用途 |
+|---|---|---|---|
+| `core.sequence_source@1` | Source | `output: *` | 有限模拟数据源，用于 smoke/replay |
+| `core.passthrough@1` | Operator | `input: * -> output: *` | 占位和协议无关透传 |
+| `core.log_sink@1` | Sink | `input: *` | 把 Envelope 摘要和 payload 写日志 |
+| `safety.robot_command_gate@1` | Operator | `RobotCommand/v1 -> ApprovedRobotCommand/v1` | TTL、白名单、参数限值、序号与限频，并建立批准边界 |
+
+安全门参数：
+
+```yaml
+with:
+ allowed_actions: [set_velocity, emergency_stop]
+ allowed_devices: [src1100]
+ min_interval_ms: 100
+ max_ttl_ms: 5000
+ enforce_monotonic_sequence: true
+ argument_limits:
+ set_velocity:
+ vx: {min: -0.3, max: 0.3}
+ vy: {min: -0.2, max: 0.2}
+ wz: {min: -0.5, max: 0.5}
+```
+
+`allowed_actions` 必须非空；`allowed_devices` 为空表示不额外限定设备。`max_ttl_ms` 默认 5000，拒绝有效期异常长的命令。`require_limits_for` 可以列出还必须配置参数范围的 action；`set_velocity` 无条件要求 `argument_limits.set_velocity` 同时定义 `vx/vy/wz`。范围边界必须是有限数且 `min <= max`。过期、过快、重放、乱序、未授权、缺少参数或越界命令都会 fail-closed 丢弃并写审计 warning，不会因一条业务拒绝停止整条控制 Pipeline。通过检查后，安全门把原始 `RobotCommand` 包装为带 `approved_by/approved_at_ns` 的 `ApprovedRobotCommand`。
+
+### 4.2 检测模型、解码与触发规则
+
+检测流水线内置三个模型无关插件：
+
+| 插件 ID | 输入 -> 输出 | 语义 |
+|---|---|---|
+| `media.video_decoder.pyav@1` | `ImageFrame/v1 -> ImageFrame/v1` | 用可选 PyAV 保持 H264/H265 decoder context，输出 packed `BGR8`；stream/session/codec/尺寸变化、sequence gap 或解码错误后重置并等待关键帧 |
+| `detection.model@1` | `ImageFrame/v1 -> DetectionResult/v1` | 加载一个已注册模型，在线程池中推理，按部署标签和阈值二次过滤；可附带对应解码帧 |
+| `detection.repeat_gate@1` | `DetectionResult/v1 -> DetectionAlert/v1` | 按不同帧、时间窗口、scope 和 cooldown 把逐帧检测转换为平台告警，仅在规则触发时按需画框并编码 JPEG |
+
+核心包不会强制安装大型视觉运行时。使用解码器安装 `video` extra；告警图的画框和 JPEG 编码由 Pillow 提供，安装独立的 `image` extra；内置 YOLO adapter 提供通用 `yolo` 和固定 CPU wheel 的 `yolo-cpu` 两个互斥 extra。当前 PPE CPU 链路通过 `bash scripts/bootstrap.sh` 一键生成 cmvr-es bindings，并按 `uv.lock` 安装 `grpc + http + video + image + yolo-cpu`。开发环境使用 `bash scripts/bootstrap.sh --profile dev`。PyAV、Pillow 和 Ultralytics 都延迟导入，因此不运行对应检测能力的对话服务不会加载它们。不要依赖 Ultralytics 间接带入 Pillow；手动组合告警图片环境时必须显式选择 `--extra image`。CUDA/Jetson 必须按目标驱动或 JetPack 建立独立依赖源和锁文件。
+
+具体模型不再各自注册一套 DAG 插件。`DetectionModelRegistry` 保存 `DetectionModelSpec`：
+
+| 字段 | 说明 |
+|---|---|
+| `model_id` | 版本化稳定 ID，例如 `construction-ppe-yolov8@1` |
+| `name` | 日志、CLI 和告警中的可读模型名称 |
+| `supported_labels` | 有序、非空、无重复的标签；顺序必须与 backend 类别编号一致 |
+| `backend` | backend 标识，例如 `ultralytics-yolo` |
+| `factory` | 接收 `model_options` 并返回 `DetectionModel` 的工厂 |
+
+`cmvr-edge-ai models` 输出当前 model registry 的 ID、name、backend 和 labels。第三方包可在 `cmvr_edge_ai.detection_models` entry point 中暴露一个 `DetectionModelSpec` 或注册回调。当前内置 `construction-ppe-yolov8@1`（19 类）和 `ppe-6classes-yolov8n@1`（`Gloves/Vest/goggles/helmet/mask/safety_shoe`）；YOLO adapter 在加载时严格比较 checkpoint `model.names` 与各自注册的标签及顺序，避免错误的类别编号继续运行。
+
+`detection.model@1` 参数：
+
+```yaml
+with:
+ model: construction-ppe-yolov8@1
+ detect_labels: [No-Helmet, No-Vest]
+ confidence: 0.5
+ label_confidence:
+ No-Helmet: 0.6
+ max_fps: 10
+ inference_log_interval_s: 5
+ attach_frame: true
+ model_options:
+ weights: /home/xtkuang/Projects/cmvr/changan_robot/construction-ppe-yolov8/best.pt
+ device: cpu
+ imgsz: 640
+```
+
+- `detect_labels` 省略时选择模型注册的全部标签；显式空列表、重复或未知标签会失败；
+- `confidence` 是全局阈值，`label_confidence` 可逐标签覆盖；backend 接收所有选中标签中的最低阈值，通用 Operator 再逐框做严格后过滤；
+- `max_fps` 是推理启动频率上限，跳过的帧不会产生 `DetectionResult`；
+- `inference_log_interval_s` 省略时不输出周期推理日志；配置后第一帧立即输出，之后按
+ 周期聚合 `window_frames/window_detections/hit_labels/avg_inference_ms`。模型加载成功
+ 始终输出一次 INFO。这样可以区分“权重已加载但没有输入帧”“持续推理但没有命中”与
+ “检测到目标”，同时避免逐帧日志拖慢边缘端；
+- `attach_frame` 默认 `false`；启用时 `DetectionResult` 临时引用对应的解码帧，供 repeat gate 在阈值帧上生成告警图片。由于它携带未压缩图像，detector 到 repeat gate 的队列容量必须保持较小；
+- `model_options` 属于 backend；内置 YOLO 支持 `weights/device/imgsz/iou/half/max_det/agnostic_nms`；
+- YOLO 只接受解码后的 `BGR8/RGB8` packed buffer，并把 `RGB8` 转为 backend 使用的 BGR 顺序；它不运行 tracker，返回的 `track_id` 为 `None`。
+
+示例 detector 到 repeat gate 使用小容量 `drop_oldest` 队列以控制延迟和原始帧内存；
+持续过载时丢弃的推理结果不会参与 `min_hits`。需要每个已完成推理都参与计数时可改
+为 `block`，代价是背压和延迟向上游传播。
+
+如果恢复六类模型，可把同一个 decoder 输出 fan-out 到两个 detector，而不是创建两个
+独立 Pipeline：
+
+```text
+camera -> decoder -> construction detector -> repeat gate -> 8081 alert API
+ `-> six-class detector -----------------> 8082 result API
+```
+
+该可选双分支方案的两条 `decoder.frames` 出边使用独立的
+`realtime_latest + drop_oldest` 容量 1 队列，
+路由的是同一个只读 `ImageFrame` 引用。这样只建立一个相机 gRPC stream 和一个 PyAV
+decoder；如果把两个分支拆成两个 Pipeline，即使 gRPC channel 可以复用，也会创建
+两个订阅 RPC 和两个 decoder 实例。fan-out 只复用输入，两个 YOLO 模型仍分别加载并
+执行推理，共享进程级有界线程池。示例把六类 YOLOv8n 限制为 5 FPS，避免在尚未完成
+目标硬件测量前让两个 CPU 模型都追赶相机帧率。
+
+六类模型只包含正向装备类，没有 `Person` 或 `No-*` 标签；它的
+`DetectionResult/v1` 表示当前推理帧中“检测到了哪些装备”，不能单独推断某个人
+缺少装备。该分支保持 `attach_frame: false`，绕过 repeat gate，把每个完成推理的
+结果直接发送到 `/v1/ppe-detections`，所以不会生成 `DetectionAlert` 的 `rule_id`、
+`hit_count`、event ID 或告警图字段；`cooldown_ms` 只是 repeat gate 的内部规则配置，
+不属于告警 payload。输出边使用容量 16 的 `telemetry + drop_oldest`，模拟
+平台持续变慢时会优先保留较新的结果；要求逐条可靠送达时应改用可接受背压的策略或
+增加持久 outbox。
+
+`detection.repeat_gate@1` 的 `rules` 每项包含 `id/labels/min_hits/window_ms/cooldown_ms/min_confidence/scope`。一个规则/scope 在同一 `(source_id, sequence)` 帧最多增加一次 hit，不按框数量累加；`window_ms` 内达到 `min_hits` 才输出告警。触发后清空命中窗口，`cooldown_ms` 内的帧不累计，冷却结束后重新计数。`time_source` 支持 `captured/received/auto`；`captured` 缺失会失败，`auto` 优先 captured 后回退 received。
+
+告警图片是 repeat gate 的全局配置，不需要在每条 rule 中重复：
+
+```yaml
+with:
+ time_source: received
+ alert_image:
+ enabled: true
+ jpeg_quality: 85
+ rules:
+ - id: no-helmet
+ labels: [No-Helmet]
+ min_hits: 3
+ window_ms: 2000
+```
+
+`alert_image.enabled` 默认 `false`；启用时上游 detector 必须设置
+`attach_frame: true`。`jpeg_quality` 默认 85，只接受 `1..95` 的整数。Pillow 只在
+规则达到阈值并准备发出告警时绘制 bounding boxes、标签和置信度并编码 JPEG，
+不会为每个 `DetectionResult` 产生图片。
+
+`scope: source` 按相机和规则隔离状态；`scope: track` 还按 `track_id` 隔离，同一 track ID 在不同相机之间不会混合。没有 track ID 的检测会被 track 规则忽略。当前 Construction PPE 告警分支必须使用 `source`；要判断同一个人，需要先增加 tracker 和人员/PPE 关联节点。
+
+每次触发创建 `DetectionAlert.event_id`。告警的 `detections` 和图片中的 bounding boxes 只来自达到 `min_hits` 的阈值帧，以控制内存和 HTTP payload；窗口内更早帧只参与 `labels/max_confidence/first_seen/last_seen/hit_count` 的聚合。内部可选图片为 JPEG bytes；HTTP wire 中 `payload.image` 被序列化为扁平对象：
+
+```json
+{
+ "media_type": "image/jpeg",
+ "width": 1280,
+ "height": 720,
+ "encoding": "base64",
+ "data": "/9j/4AAQSk..."
+}
+```
+
+关闭图片，或因第三方结果未附带帧、坏帧等原因渲染失败时，告警仍会发送且
+`payload.image` 为 `null`；证据图失败不能阻断结构化告警。Base64 比原始 JPEG
+额外增加约三分之一体积，平台入口和反向代理必须设置相应的请求体上限。
+同一阈值帧同时触发多条规则或多个 track 时，节点对所有相关告警框取并集并只编码
+一次，再让这些告警共享同一个不可变 JPEG，从而限制瞬时 CPU 与内存开销。
+非有限坐标、反向/退化框、非法置信度或 track ID 的 detection 会在规则计数前被
+忽略并增加 `invalid_detections` 健康计数，防止 `NaN/Inf` 破坏整个 HTTP JSON。
+
+### 4.3 cmvr-es gRPC 连接器
+
+`cmvr.grpc.camera_rgb_stream@1`：
+
+- 参数：`endpoint`、必填 `device_id`、可选 `pixel_format`（默认 `BGR8`）、
+ `stream_log_interval_s`（默认 30 秒），以及
+ `reconnect/reconnect_initial_s/reconnect_max_s/max_reconnect_attempts`；
+- 每个首次连接和重连 session 都先调用 unary `CameraService.StartCamera`，复用 endpoint
+ 的 `timeout_s`，并检查业务反馈 `header.success/error_message`；只有成功后才调用
+ `CameraService.GetRGBImageStream`。这两步不能省略：前者打开物理相机，后者在服务端
+ 启动编码流；
+- 发送一次订阅请求并保持 request side，兼容当前 cmvr-es 行为和未来真正双向协议；
+- 把 `color_frame` 转为 `ImageFrame/v1`，保留 codec、关键帧、内参、序号和时间戳；
+- 默认在 Start RPC、断流、流 RPC 或业务失败后指数退避重连；每次尝试使用新的
+ `session_id`，收到一帧后重置连续失败计数；`max_reconnect_attempts` 省略表示持续尝试；
+- INFO 日志覆盖 source configured、StartCamera 请求/成功、stream opening、首帧、周期
+ progress、stream close 和 source stop；WARNING/ERROR 覆盖业务反馈错误、断流、重连与
+ 重试耗尽。progress 即使尚未收到首帧也会定时输出，并报告帧数、吞吐、关键帧、
+ codec、尺寸和最后一帧年龄，但不会输出图像 bytes；
+- stop 时主动 cancel stream，并且等待重连期间也能被 shutdown event 立即打断。默认
+ 不调用设备级 `StopCamera`，避免中断同一相机的其他客户端；
+- 不做 H264 解码，编码包必须交给有状态解码器插件。
+
+重连不能修复 cmvr-es 生产端已经丢失的编码包。当前 cmvr-es 服务端使用 `getLatestEncodedFrame` 取得最新数据，负载或时序竞争可能在 edge-ai 看到数据之前跳过 H264/H265 参考包；本地 `video_contiguous` 只保护已经入队的数据，而且 camera Source 的本地 sequence 不能可靠表示这种上游跳包。decoder 遇到 FFmpeg 错误会重置并等待关键帧，但上线前仍应验证服务端提供连续 access unit，或增加原始/JPEG/显式 discontinuity 的 AI 接口。
+
+`cmvr.grpc.agv_command_sink@1`：
+
+- 参数：`endpoint` 和固定、非空的 `device_id`；一个 Sink 只绑定一台设备；
+- 输入必须为 `ApprovedRobotCommand/v1`，直接传入原始 `RobotCommand` 会被拒绝；
+- 支持 `emergency_stop`、`clear_fault`、`pause_navigation`、`resume_navigation`、`cancel_navigation`、`stop_velocity_control`、`stop_mapping` 和 `set_velocity`；
+- `set_velocity` 默认禁用；只有显式设置 `unsafe_allow_unleased_velocity: true`，并在 Sink 再配置完整 `velocity_limits.vx/vy/wz` 后才会发出；
+- `set_velocity` 的 `vx/vy/wz` 必须是有限数，并同时通过安全门和 Sink 两层范围校验；
+- 同时检查 gRPC 调用异常与响应 `header.success`，并把 RPC timeout 限制在命令剩余 TTL 内；
+- 不自动重试运动命令；正常关闭时若曾成功发送速度，会尽力调用 `stopVelocityControl`。
+
+下面只展示 actuator 节点的危险参数；该节点仍必须直接连接前述安全门。此开关只应用于架空轮、受控台架等隔离环境：
+
+```yaml
+actuator:
+ uses: cmvr.grpc.agv_command_sink@1
+ with:
+ endpoint: cmvr_es
+ device_id: src1100
+ unsafe_allow_unleased_velocity: true
+ velocity_limits:
+ vx: {min: -0.3, max: 0.3}
+ vy: {min: -0.2, max: 0.2}
+ wz: {min: -0.5, max: 0.5}
+```
+
+`unsafe_allow_unleased_velocity` 必须是 YAML 布尔字面量，字符串 `"true"/"false"` 会被拒绝。该 override 不是 crash-safety：edge-ai 被 `SIGKILL`、崩溃、断电或与 cmvr-es 网络分区时，Python `stop()` 没有机会可靠执行。真实机器人必须由 cmvr-es 服务端持有速度 lease，并在 lease/heartbeat 超时后独立执行清零和停车。
+
+### 4.4 平台 HTTP 连接器
+
+`platform.http_json_sink@1` 接收任意 schema：
+
+- 参数：`endpoint`、`path`（默认 `/`）、`max_attempts`（默认 3）、`retry_initial_s`、`retry_max_s` 和 `retry_statuses`；
+- POST Envelope 元数据、输入端口、attributes 和 payload；
+- bytes 转换为 `{encoding: base64, data: ...}`；
+- `EncodedImage` 特判为 `media_type/width/height/encoding/data` 同层的扁平对象，避免 `payload.image.data` 再嵌套一层；
+- payload 有非空 `event_id` 时以它作为 `Idempotency-Key`，否则使用 `trace_id`；平台仍必须真正实现按键去重；
+- 对连接/超时类错误以及默认 `408/425/429/500/502/503/504` 执行有限指数退避；不在
+ allowlist 的状态不会重试；最终失败由 `failure_mode` 决定：`raise` 抛异常，
+ `log_and_drop` 输出 WARNING、丢弃当前报告并继续；
+- 没有持久化 outbox/spool。有限重试只存在于当前进程内，崩溃、断电或重启不会恢复尚未投递的告警。
+
+同一个通用 Sink 可以按节点配置不同的 endpoint 和 path。当前检测配置只把
+`DetectionAlert/v1` 发到 `http://127.0.0.1:8081/v1/detection-alerts`。若恢复六类模型，
+可把每次推理的 `DetectionResult/v1` 发到独立 endpoint；它与告警复用 Envelope JSON
+外层，但 payload 契约不同，且没有 event ID，幂等键会回退为 `trace_id`。Sink 不支持
+通过 YAML 重命名或重排 payload 字段，如果目标平台要求自定义 wire contract，应增加
+平台专用转换节点或 Sink。
+
+运行时对未处理异常采用 fail-fast。HTTP Sink 默认 `failure_mode: raise`，最终发送失败
+会终止它所在的 Pipeline；当前 8081 告警 Sink 显式使用 `log_and_drop`，所以平台离线只会
+产生 WARNING 并丢弃对应告警，不会停止相机和检测。该模式不是可靠投递机制。
+
+## 5. 插件开发约定
+
+### 5.1 创建组件
+
+插件工厂固定接收 `node_id: str` 与 `params: Mapping[str, Any]`，应在构造阶段检查无需 I/O 的必填参数。下面展示一个最小 Operator：
+
+```python
+from collections.abc import Mapping
+from typing import Any
+
+from cmvr_edge_ai.contracts import TextEvent
+from cmvr_edge_ai.core import Emission, Envelope, Operator
+
+
+class UppercaseOperator(Operator):
+ def __init__(self, node_id: str, params: Mapping[str, Any]) -> None:
+ self._node_id = node_id
+ self._prefix = str(params.get("prefix", ""))
+
+ async def process(
+ self,
+ envelope: Envelope[Any],
+ input_port: str = "input",
+ ) -> Emission:
+ del input_port
+ event = envelope.payload
+ if not isinstance(event, TextEvent):
+ raise TypeError(
+ f"{self._node_id} expected TextEvent, got {type(event).__name__}"
+ )
+ result = TextEvent(
+ text=self._prefix + event.text.upper(),
+ role=event.role,
+ final=event.final,
+ )
+ return Emission(
+ "output",
+ envelope.with_payload(
+ result,
+ schema_name="TextEvent",
+ schema_version=1,
+ ),
+ )
+```
+
+使用 `with_payload()` 可以保留 trace、session、sequence、采集时间和 deadline，只更新载荷与接收时间。除非开始了一次新的独立请求，不要随意生成新的 `trace_id`。
+
+### 5.2 注册 PluginSpec
+
+```python
+from cmvr_edge_ai.plugins import PluginKind, PluginRegistry, PluginSpec
+
+
+def register_plugins(registry: PluginRegistry) -> None:
+ registry.register(
+ PluginSpec(
+ plugin_id="example.text_upper@1",
+ kind=PluginKind.OPERATOR,
+ factory=UppercaseOperator,
+ inputs={"input": "TextEvent/v1"},
+ outputs={"output": "TextEvent/v1"},
+ description="Uppercase a text event",
+ tags=frozenset({"domain:dialogue"}),
+ )
+ )
+```
+
+规则：
+
+- ID 必须包含版本，例如 `@1`；不兼容的端口或参数变更发布新版本；
+- Source 不能声明 input，Sink 不能声明 output；
+- 端口名必须与组件实际发送/接收的端口一致；
+- 使用精确 schema；只在真正协议无关的调试/路由节点使用 `*`；
+- 执行器 Sink 加 `actuator` 标签，并只声明/接受 `ApprovedRobotCommand/v1`；安全节点加 `safety_gate` 标签并完成 `RobotCommand -> ApprovedRobotCommand` 转换；不要为了通过编译给普通变换节点冒充安全标签；连接器加 `transport:grpc` 等标签；
+- 同一个 ID 重复注册会失败。
+
+### 5.3 通过 entry point 发布
+
+第三方插件包的 `pyproject.toml`：
+
+```toml
+[project]
+name = "cmvr-edge-ai-example-plugin"
+version = "0.1.0"
+dependencies = ["cmvr-edge-ai>=0.1,<0.2"]
+
+[project.entry-points."cmvr_edge_ai.plugins"]
+example = "my_cmvr_plugin.plugins:register_plugins"
+```
+
+entry point 可以直接导出一个 `PluginSpec`，也可以像示例一样导出接收 registry 的回调。安装包后，默认 registry 会在 CLI 启动时发现它。只安装可信插件：entry point 是 Python 可执行代码，虽然 YAML 本身不能任意导入模块，已安装插件仍拥有当前进程权限。
+
+### 5.4 阻塞计算与模型
+
+禁止直接在 `process()` 中执行长时间阻塞 I/O 或 Python/模型推理，否则整个 event loop 的 gRPC、HTTP、队列和其他 Pipeline 都会停顿。
+
+框架提供 `workers.run_blocking()`。插件在 `setup()` 中取得应用共享的有界线程池，再显式 offload：
+
+```python
+from cmvr_edge_ai.workers import run_blocking
+
+async def setup(self, context):
+ self._thread_executor = context.metadata["thread_executor"]
+
+async def process(self, envelope, input_port="input"):
+ result = await run_blocking(
+ self._blocking_infer,
+ envelope.payload,
+ executor=self._thread_executor,
+ )
+ return envelope.with_payload(
+ result,
+ schema_name="DetectionResult",
+ schema_version=1,
+ )
+```
+
+线程 offload 适合释放 GIL 的推理库或阻塞 SDK。不要省略 `executor` 后假定 `runtime.thread_workers` 仍然生效；未指定 executor 时会落到宿主 event loop 的默认 executor。
+
+纯 Python CPU 密集任务、大模型或需要故障隔离的模型应由插件维护常驻进程/模型 Worker；不要每帧创建进程或重复加载模型。框架提供 `PersistentProcessWorker` 作为小型基础设施：它使用一个常驻子进程、有界请求/结果队列、串行关联和可选超时，并把子进程异常还原为 `RemoteWorkerError`。请求一旦超时或在途取消，Worker 会标记为 `poisoned` 并拒绝后续 submit，避免迟到结果被误配；插件必须 stop 后新建 Worker。插件仍负责在 `setup/start` 创建和启动它、在 `stop` 回收它，并确保 `spawn` 模式下 factory/payload 可序列化。
+
+在 v1 配置中，节点仍必须使用 `execution.mode: async`（默认）或 `inline`，且保持默认单并发字段。`thread|process|model_worker` 以及非默认并发字段会在编译期被拒绝。上面的线程/进程 helper 是组件内部显式调用的实现细节，框架不会根据 YAML 自动 offload。插件需要自行限制 in-flight 数量并在 `stop()` 回收 Worker。若跨进程发送大图像，优先传编码数据；确认复制成为瓶颈后再实现固定大小共享内存池，并只通过 IPC 传 slot/shape/dtype/时间戳。
+
+### 5.5 开发新协议连接器
+
+新增 gRPC、HTTP、UDP 或 QUIC 支持时，应把 wire format 的解析和错误映射放在 `connectors/`/`transports/`，对 DAG 只暴露内部契约：
+
+```text
+wire message -> 校验 -> 内部 payload -> Envelope -> AI Operators
+AI result -> typed command/event -> wire message -> external service
+```
+
+不要让算法插件导入 `*_pb2`。连接器应：
+
+- 在 `setup` 获取/建立连接，在 `stop` 取消 stream 并释放资源；
+- 为每条消息保留设备 ID、序号、采集时间、trace/session；
+- 区分 transport failure 与业务响应失败；
+- 明确超时、重连、幂等和背压策略；
+- 对流式编码数据保持状态，不能把任意 H264 packet 当作独立图片；
+- 为音频丢包/重连产生 `discontinuity`，不能静默拼接不连续 PCM；
+- 对控制接口使用显式 action 到 RPC 的白名单映射，禁止从配置反射任意方法名。
+- 新增控制 Sink 时只接受 `ApprovedRobotCommand`，并要求安全门成为图上的直接前驱；不要兼容接收原始 `RobotCommand`。
+
+gRPC transport 已提供有界的 `BidiRequestStream` 请求迭代器，可供未来真正的双向音频 RPC 使用。它限制为单消费者，正常 close 会排空已接受请求并原子唤醒被背压阻塞的 producer；若 gRPC 放弃迭代器则丢弃无法再发送的请求。它只解决 request side 的背压与关闭，不包含任何音频 proto、重连、响应解析或扬声器策略；这些仍属于具体连接器。
+
+## 6. 并发和资源模型
+
+当前真实执行关系如下：
+
+```mermaid
+flowchart LR
+ MAIN["一个 Python 进程"] --> LOOP["一个 asyncio event loop"]
+ LOOP --> P1["Pipeline A: 每节点一个 task"]
+ LOOP --> P2["Pipeline B: 每节点一个 task"]
+ LOOP --> TP["共享有界 ThreadPoolExecutor"]
+ P1 -. "插件显式 workers.run_blocking" .-> TP
+ P2 -. "插件显式 workers.run_blocking" .-> TP
+ P1 -. "插件自行实现" .-> MW["可选常驻进程/Model Worker"]
+```
+
+多个 Pipeline 共用同一 event loop、gRPC ChannelPool、HttpClientPool 和应用线程池。同名 endpoint 会复用客户端；同一 endpoint ID 如果请求了冲突的 settings 会报错。线程池只通过 `ComponentContext.metadata["thread_executor"]` 提供；`execution.mode: thread` 在 v1 是非法保留值，不会自动调用线程池。
+
+共享 gRPC channel 不等于共享流式 RPC 或 Source 实例。两个 Pipeline 各自配置同一
+camera node 时会建立两个订阅并重复解码；同一物理输入需要供多个模型使用时，应优先
+在一个 Pipeline 内从 decoder 的输出端口 fan-out。每个 fan-out 分支仍应使用独立的
+小容量队列，避免慢模型把实时图像积压在内存中。
+
+边缘端容量规划建议：
+
+- 优先减少输入帧率、分辨率和队列容量，不用无界缓存换吞吐；
+- 同一 GPU 上尽量复用一个常驻模型实例，避免每 Pipeline 重复加载；
+- 编码视频在 decoder 前使用 `video_contiguous`，不能用 latest-frame 丢包；只有 decoder 输出完整图像后，才能在推理前使用 `realtime_latest` 主动丢旧帧；当前运行时不会根据 deadline 自动丢弃；
+- 音频需要连续性，不能照搬视频的 `drop_oldest`；
+- 平台上报与机器人控制使用不同边，并为非关键遥测选择非阻塞溢出策略；
+- 通过实际端到端延迟、queue high watermark、drop counter 和显存峰值决定是否引入进程/共享内存。
+
+## 7. 控制安全模型
+
+机器人控制链必须保持以下形态：
+
+```text
+AI 结果 -> Policy -> RobotCommand/v1
+ -> Safety Gate
+ -> ApprovedRobotCommand/v1
+ -> 类型化执行器 Sink
+```
+
+这里有四层进程内防线：
+
+1. 编译器要求 `actuator` 的每个直接前驱都带 `safety_gate` 标签；安全门与 Sink 之间不能插入转换/透传节点，也不能增加旁路输入；
+2. 安全门只接收 `RobotCommand/v1`，检查通过后才输出 `ApprovedRobotCommand/v1`；
+3. AGV Sink 的端口 schema 和运行时类型检查都只接受 `ApprovedRobotCommand`；
+4. AGV Sink 只支持明确列出的 action，不会根据配置字符串反射调用任意 gRPC 方法，也不会自动重试运动命令。
+
+`ApprovedRobotCommand` 是进程内类型边界，不是加密签名。只有可信插件可以被安装并赋予 `safety_gate` 标签；恶意或被篡改的 Python 插件仍拥有当前进程权限，因此插件供应链、包版本锁定和文件完整性属于安全模型的一部分。
+
+当前安全门已经检查：
+
+- payload 必须是 `RobotCommand`；
+- Envelope deadline 和 `RobotCommand.valid_until_ns`；
+- action 白名单；
+- 可选 device 白名单；
+- 为 action 配置的每个参数必须存在、是有限数并位于 `[min, max]`；
+- `set_velocity` 必须为 `vx/vy/wz` 配置完整范围；`require_limits_for` 可把同一规则扩展到其他 action；
+- 默认按 `(device, action)` 拒绝 sequence 重放和乱序；
+- 同一 `(device, action)` 的最小发送间隔。
+
+投入真实机器人前仍必须由机器人型号相关 Policy/Safety 插件补齐：
+
+- 除已配置参数范围外的速度、位置、加速度和 jerk 联合约束；
+- 当前模式、故障、急停、定位质量、电量及障碍物状态；
+- command ID 去重和多来源优先级仲裁；
+- 急停独立高优先级路径和权限控制；
+- 审计日志、认证、TLS 和密钥管理。
+
+最重要的边界位于进程外：持续速度命令必须由 cmvr-es 服务端发放短期 lease，并由独立 deadman 在续租/heartbeat 超时后清零速度。当前 Sink 会在调用前检查 TTL，并把客户端 RPC timeout 截断到剩余 TTL，但当前 cmvr-es 请求没有承载 `valid_until/command_id/sequence`，服务端无法据此拒绝网络中迟到的命令。`unsafe_allow_unleased_velocity`、客户端 TTL、安全门以及 Sink `stop()` 都不能覆盖进程崩溃、`SIGKILL`、断电或网络分区。cmvr-es 未实现 server-side lease/deadman 前，`set_velocity` 只允许在隔离测试环境使用，禁止用于真实机器人持续运动。
+
+运动命令默认不重试。只有外部 API 提供明确幂等契约，并完成 command ID 去重后，才能为特定 action 增加有界重试。
+
+## 8. 运维流程
+
+推荐发布/启动顺序：
+
+1. 安装固定版本的核心包和可信插件包；
+2. 如果使用 cmvr-es gRPC 连接器，安装匹配 cmvr-es proto 版本的 `cmvr-api` wheel，或运行 stub 生成脚本；
+3. 在部署 YAML 中配置 endpoint、证书引用、模型路径和推理设备；凭据应通过部署平台的 secret 机制注入；
+4. 执行 `cmvr-edge-ai plugins` 和 `cmvr-edge-ai models`，保存实际插件、模型及标签清单；
+5. 执行 `cmvr-edge-ai validate -c ...`；
+6. 确认 cmvr-es 设备启用、设备 ID 正确、平台接口可达；
+7. 先运行回放/smoke，再连接真实传感器；
+8. 控制链先使用空载/限速/人工急停条件验证；
+9. 使用 SIGTERM 停止并给 `shutdown_timeout_s` 留出排空时间。
+
+示例命令：
+
+```bash
+uv run --no-sync cmvr-edge-ai models
+uv run --no-sync cmvr-edge-ai validate -c detect_server/pipeline.yaml --pipeline detection
+uv run --no-sync cmvr-edge-ai run -c detect_server/pipeline.yaml \
+ --pipeline detection \
+ --log-level INFO \
+ --log-format json
+```
+
+当前日志可输出文本或单行 JSON。日志中不要写入音频原始数据、图像 base64、认证 metadata 或用户隐私内容；生产插件应只记录 trace ID、schema、耗时、尺寸、丢弃计数和经过脱敏的错误信息。
+
+## 9. 当前限制与演进顺序
+
+### 9.1 当前限制
+
+- cmvr-es 音频双向流 proto 尚未实现；框架只有 `AudioChunk` 契约和对话占位配置；
+- 检测链路已经提供 PyAV H264/H265 解码、双 PPE YOLO 注册/推理、单次解码 fan-out 和重复触发规则；VAD、ASR、LLM、TTS 仍需插件提供；
+- UDP/QUIC 只有扩展目录，无 transport/connector；
+- execution v1 只支持 `async/inline` 默认单并发；其他 mode 和非默认并发字段会在编译期拒绝，线程/进程/模型 offload 必须由插件显式实现；
+- 非空 resources、非 `normal` priority 和显式保留 runtime 字段都会被拒绝；`health_bind` 未启动服务，队列指标也未导出；
+- 无热更新、overlay、通用节点级 supervisor/restart、持久化 outbox/spool；相机和 HTTP 的重连/重试是 connector 内部的局部策略；
+- 无自动 deadline 丢弃、`max_age_ms`、`put_timeout_ms`；
+- 无共享内存池；跨进程 Worker 由插件负责；
+- 配置只支持单个 YAML 加环境变量，不支持 include/merge；
+- 当前 cmvr-es 相机成功响应通常未填写 `header.timestamp`，因此连接器的 `captured_at_ns` 可能为 `null`；精确采集时延需要后续在帧协议中增加设备采集时钟，不能用客户端接收时间冒充；
+- cmvr-es 当前通过 `getLatestEncodedFrame` 取得最新编码数据，可能在 edge-ai 收到之前跳过 inter-frame 参考包；本地连续队列和重连无法补回上游丢失内容，必须验证/改造服务端流语义；
+- HTTP Sink 对告警使用 `event_id`、对普通逐帧结果回退使用 `trace_id` 作为幂等键，并
+ 提供有限退避和 `raise/log_and_drop` 终态策略，但没有持久投递；`log_and_drop` 的
+ WARNING 即表示该报告已丢失，进程崩溃或断电也可能丢失内存中的输出；
+- 内置 YOLO adapter 不产生 `track_id`，所以 `scope: track` 需要外部 tracker 及人员/PPE 关联节点；
+- PPE `.pt` 只能来自可信制品源；商业部署还需核对权重说明、Ultralytics runtime/checkpoint 的 AGPL/商业许可，以及训练数据和权重分发权利；
+- AGV `set_velocity` 默认禁用；unsafe override 和正常 shutdown stop 都不具备崩溃安全性，不能替代 cmvr-es server-side lease/deadman、机器人本体限位、急停和功能安全系统。
+
+### 9.2 推荐演进顺序
+
+1. **真实流与模型验收**：用录制数据和目标边缘设备验证 cmvr-es 编码连续性、PyAV 长时间恢复、PPE 精度/FPS/内存/显存和端到端告警；
+2. **可靠告警投递**：在现有 event ID 和有限重试之上增加有界持久 outbox、确认、恢复发送和容量/保留策略；
+3. **同人违规语义**：增加 tracker 与 Worker/PPE 空间关联，验证 ID switch 后再启用 `scope: track`；
+4. **补齐可观测性**：导出 health、队列水位/丢弃、重连/重试、节点延迟和模型资源指标；
+5. **控制安全闭环**：先在 cmvr-es 实现速度 lease/server-side deadman，再补齐设备状态输入、机器人型号限值、优先级与审计，然后才启用真实 AGV/机械臂动作；
+6. **接入音频双向流**：proto 落地后实现麦克风 Source/扬声器 Sink，严格处理 chunk 顺序、背压和 discontinuity；
+7. **按测量结果优化并发**：先使用现有显式 thread offload，再按测量结果增加常驻 model/process worker；确认复制瓶颈后才加入共享内存；
+8. **扩展协议**：用相同内部契约实现 QUIC/UDP connector，不修改算法插件。
+
+每一步都应先通过 `validate`、smoke、录制数据 replay 和资源峰值检查，再接入真实设备。
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..7f2c28d
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,91 @@
+[build-system]
+requires = ["setuptools>=68", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "cmvr-edge-ai"
+version = "0.1.0"
+description = "Configuration-driven AI pipeline runtime for CMVR robot edge devices"
+readme = "README.md"
+requires-python = ">=3.10,<3.13"
+dependencies = [
+ "pydantic>=2.8,<3",
+ "PyYAML>=6,<7",
+]
+
+[project.optional-dependencies]
+grpc = [
+ "grpcio>=1.76,<2",
+ "protobuf>=6.31.1,<7",
+]
+http = [
+ "httpx>=0.27,<1",
+]
+video = [
+ "av>=12,<17",
+]
+image = [
+ "Pillow>=10,<13",
+]
+yolo = [
+ "numpy>=1.24,<3",
+ "ultralytics==8.4.31",
+]
+yolo-cpu = [
+ "numpy>=1.24,<3",
+ "torch==2.7.0",
+ "torchvision==0.22.0",
+ "ultralytics==8.4.31",
+]
+
+[dependency-groups]
+test = [
+ "pytest>=7.4,<9",
+ "pytest-asyncio>=0.23,<1",
+]
+codegen = [
+ "grpcio-tools>=1.76,<2",
+]
+dev = [
+ { include-group = "test" },
+ { include-group = "codegen" },
+]
+
+[project.scripts]
+cmvr-edge-ai = "cmvr_edge_ai.cli:main"
+
+[tool.setuptools]
+package-dir = {"" = "src"}
+
+[tool.setuptools.packages.find]
+where = ["src"]
+
+[tool.uv]
+required-version = ">=0.11.16"
+default-groups = []
+conflicts = [
+ [
+ { extra = "yolo" },
+ { extra = "yolo-cpu" },
+ ],
+]
+required-environments = [
+ "sys_platform == 'linux' and platform_machine == 'x86_64' and python_version == '3.10'",
+]
+
+[tool.uv.sources]
+torch = [
+ { index = "pytorch-cpu", extra = "yolo-cpu", marker = "sys_platform == 'linux' and platform_machine == 'x86_64'" },
+]
+torchvision = [
+ { index = "pytorch-cpu", extra = "yolo-cpu", marker = "sys_platform == 'linux' and platform_machine == 'x86_64'" },
+]
+
+[[tool.uv.index]]
+name = "pytorch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+explicit = true
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+addopts = "-q"
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diff --git a/scripts/bootstrap.sh b/scripts/bootstrap.sh
new file mode 100755
index 0000000..960aee7
--- /dev/null
+++ b/scripts/bootstrap.sh
@@ -0,0 +1,161 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
+PROJECT_ROOT="$(cd -- "${SCRIPT_DIR}/.." && pwd)"
+
+profile="detection-cpu"
+cmvr_es_root="${PROJECT_ROOT}/../cmvr-es"
+skip_codegen=false
+skip_check=false
+
+usage() {
+ cat <<'EOF'
+Usage: bash scripts/bootstrap.sh [options]
+
+Create the locked uv environment, optionally generate cmvr-es Python bindings,
+and run basic health checks.
+
+Options:
+ --profile PROFILE core, detection-cpu (default), or dev
+ --cmvr-es-root PATH cmvr-es checkout (default: sibling ../cmvr-es)
+ --skip-codegen do not generate cmvr-es protobuf/gRPC bindings
+ --skip-check install only; skip validation and tests
+ -h, --help show this help
+
+Profiles:
+ core framework and simulated talk/smoke pipeline only
+ detection-cpu gRPC + HTTP + PyAV + locked CPU YOLO runtime
+ dev detection-cpu plus tests and portable protobuf codegen tools
+EOF
+}
+
+while (($#)); do
+ case "$1" in
+ --profile)
+ if (($# < 2)); then
+ echo "error: --profile requires a value" >&2
+ exit 2
+ fi
+ profile="$2"
+ shift 2
+ ;;
+ --cmvr-es-root)
+ if (($# < 2)); then
+ echo "error: --cmvr-es-root requires a path" >&2
+ exit 2
+ fi
+ cmvr_es_root="$2"
+ shift 2
+ ;;
+ --skip-codegen)
+ skip_codegen=true
+ shift
+ ;;
+ --skip-check)
+ skip_check=true
+ shift
+ ;;
+ -h|--help)
+ usage
+ exit 0
+ ;;
+ *)
+ echo "error: unknown argument: $1" >&2
+ usage >&2
+ exit 2
+ ;;
+ esac
+done
+
+case "${profile}" in
+ core)
+ sync_args=(--locked --no-default-groups)
+ needs_cmvr_bindings=false
+ ;;
+ detection-cpu)
+ sync_args=(
+ --locked
+ --no-default-groups
+ --extra grpc
+ --extra http
+ --extra video
+ --extra image
+ --extra yolo-cpu
+ )
+ needs_cmvr_bindings=true
+ ;;
+ dev)
+ sync_args=(
+ --locked
+ --no-default-groups
+ --group dev
+ --extra grpc
+ --extra http
+ --extra video
+ --extra image
+ --extra yolo-cpu
+ )
+ needs_cmvr_bindings=true
+ ;;
+ *)
+ echo "error: unsupported profile '${profile}'" >&2
+ echo "expected one of: core, detection-cpu, dev" >&2
+ exit 2
+ ;;
+esac
+
+if ! command -v uv >/dev/null 2>&1; then
+ echo "error: uv is not installed or not available on PATH" >&2
+ echo "see https://docs.astral.sh/uv/getting-started/installation/" >&2
+ exit 1
+fi
+
+cd "${PROJECT_ROOT}"
+echo "==> project: ${PROJECT_ROOT}"
+echo "==> profile: ${profile}"
+
+if [[ "${needs_cmvr_bindings}" == true && "${skip_codegen}" == false ]]; then
+ if [[ ! -d "${cmvr_es_root}/protos/cmvr" ]]; then
+ echo "error: cmvr-es protos were not found under: ${cmvr_es_root}" >&2
+ echo "pass --cmvr-es-root PATH or use --skip-codegen with existing bindings" >&2
+ exit 1
+ fi
+
+ echo "==> installing the locked protobuf codegen environment"
+ uv sync --locked --only-group codegen
+ echo "==> generating cmvr-es Python bindings from ${cmvr_es_root}"
+ .venv/bin/python scripts/generate_cmvr_stubs.py \
+ --cmvr-es-root "${cmvr_es_root}" \
+ --portable
+fi
+
+echo "==> installing the locked ${profile} environment"
+uv sync "${sync_args[@]}"
+
+if [[ "${skip_check}" == true ]]; then
+ echo "==> installation complete (checks skipped)"
+ exit 0
+fi
+
+echo "==> validating the smoke pipeline"
+.venv/bin/cmvr-edge-ai validate --config configs/smoke.yaml
+
+if [[ "${needs_cmvr_bindings}" == true ]]; then
+ echo "==> checking detection runtime imports"
+ .venv/bin/python -c \
+ "import av, grpc, httpx, PIL, torch, ultralytics; import cmvr.api.camera_service_pb2_grpc; print(f'torch={torch.__version__} cuda={torch.cuda.is_available()} ultralytics={ultralytics.__version__} pillow={PIL.__version__}')"
+ echo "==> validating the PPE detection pipeline"
+ .venv/bin/cmvr-edge-ai validate \
+ --config detect_server/pipeline.yaml \
+ --pipeline detection
+ .venv/bin/cmvr-edge-ai models
+fi
+
+if [[ "${profile}" == dev ]]; then
+ echo "==> running tests"
+ .venv/bin/pytest
+fi
+
+echo "==> bootstrap complete"
+echo "run commands with: uv run --no-sync cmvr-edge-ai ..."
diff --git a/scripts/generate_cmvr_stubs.py b/scripts/generate_cmvr_stubs.py
new file mode 100644
index 0000000..927f609
--- /dev/null
+++ b/scripts/generate_cmvr_stubs.py
@@ -0,0 +1,117 @@
+#!/usr/bin/env python3
+"""Generate Python protobuf/gRPC bindings from the cmvr-es source tree."""
+
+from __future__ import annotations
+
+import argparse
+import platform
+import subprocess
+import sys
+from pathlib import Path
+
+
+DEFAULT_CMVR_ES_ROOT = Path("/home/xtkuang/Projects/cmvr/cmvr-es")
+
+
+def parse_args() -> argparse.Namespace:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument(
+ "--cmvr-es-root",
+ type=Path,
+ default=DEFAULT_CMVR_ES_ROOT,
+ help="cmvr-es checkout containing protos/ and output/bin/",
+ )
+ parser.add_argument(
+ "--output",
+ type=Path,
+ default=Path("src"),
+ help="output root; generated imports require a top-level cmvr package",
+ )
+ parser.add_argument(
+ "--portable",
+ action="store_true",
+ help="use python -m grpc_tools.protoc instead of cmvr-es bundled tools",
+ )
+ return parser.parse_args()
+
+
+def build_command(args: argparse.Namespace, proto_files: list[Path]) -> list[str]:
+ proto_root = args.cmvr_es_root.resolve() / "protos"
+ output = args.output.resolve()
+ relative_files = [str(path.relative_to(proto_root)) for path in proto_files]
+
+ if args.portable:
+ try:
+ import grpc_tools
+ except ImportError as exc:
+ raise RuntimeError(
+ "portable generation requires grpcio-tools; install the dev extra"
+ ) from exc
+ well_known = Path(grpc_tools.__file__).resolve().parent / "_proto"
+ return [
+ sys.executable,
+ "-m",
+ "grpc_tools.protoc",
+ f"-I{proto_root}",
+ f"-I{well_known}",
+ f"--python_out={output}",
+ f"--pyi_out={output}",
+ f"--grpc_python_out={output}",
+ *relative_files,
+ ]
+
+ protoc = args.cmvr_es_root.resolve() / "output/bin/protoc"
+ grpc_plugin = args.cmvr_es_root.resolve() / "output/bin/grpc_python_plugin"
+ if not protoc.is_file() or not grpc_plugin.is_file():
+ raise FileNotFoundError(
+ "cmvr-es protobuf tools are missing; build cmvr-es first or pass --portable"
+ )
+ well_known = _bundled_well_known_proto_root(args.cmvr_es_root.resolve())
+ return [
+ str(protoc),
+ f"-I{proto_root}",
+ f"-I{well_known}",
+ f"--python_out={output}",
+ f"--pyi_out={output}",
+ f"--grpc_python_out={output}",
+ f"--plugin=protoc-gen-grpc_python={grpc_plugin}",
+ *relative_files,
+ ]
+
+
+def _bundled_well_known_proto_root(cmvr_es_root: Path) -> Path:
+ architecture = (
+ "arm" if platform.machine().lower() in {"aarch64", "arm64"} else "x86"
+ )
+ candidates = (
+ cmvr_es_root / "dependency" / architecture / "third_party/grpc/v1.76.0/include",
+ cmvr_es_root / "dependency/x86/third_party/grpc/v1.76.0/include",
+ cmvr_es_root / "dependency/arm/third_party/grpc/v1.76.0/include",
+ )
+ for candidate in candidates:
+ if (candidate / "google/protobuf/timestamp.proto").is_file():
+ return candidate
+ raise FileNotFoundError(
+ "google/protobuf/timestamp.proto was not found in the cmvr-es toolchain"
+ )
+
+
+def main() -> int:
+ args = parse_args()
+ proto_root = args.cmvr_es_root.resolve() / "protos"
+ if not proto_root.is_dir():
+ raise FileNotFoundError(f"proto root does not exist: {proto_root}")
+
+ proto_files = sorted((proto_root / "cmvr").rglob("*.proto"))
+ if not proto_files:
+ raise FileNotFoundError(f"no cmvr proto files found under {proto_root}")
+
+ args.output.mkdir(parents=True, exist_ok=True)
+ command = build_command(args, proto_files)
+ subprocess.run(command, cwd=proto_root, check=True)
+ print(f"generated {len(proto_files)} proto files into {args.output.resolve()}")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/src/cmvr/api/__pycache__/agv_command_pb2.cpython-310.pyc b/src/cmvr/api/__pycache__/agv_command_pb2.cpython-310.pyc
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