行业视频管理服务 IVM-micro_checkpoint_data 微卡口业务:功能介绍
功能介绍
微卡口业务:在机动车进入智能感知范围时,抓取机动车相关信息进行上报的智能场景。
微卡口业务消息体的message_type值为micro_checkpoint_data。
目前行业视频管理服务会处理以下场景:
itgt_type/target_type枚举值:
- 51 微卡口(摄像机SDC/NVR800开启微卡口/车辆智能下的功能,机动车进去区域且触发违法停车、非机动车占用机动车道、机动车占用非机动车道、逆行/倒车、压线等事件,则会被抓拍且分析出目标行为和特征信息,如:品牌、款式、主/副驾驶的情况,包括有无打电话、有无系安全带、有无遮阳板等)
- 52 微卡口车流量统计(摄像机SDC/NVR800开启微卡口/车辆智能下的交通流量统计功能,机动车进入区域则会被统计分析,最后得到统计分析结果,如:车辆计数、车辆平均速度、车流密度等)
字段名 |
类型 |
说明 |
---|---|---|
device_id |
String |
设备ID,正常情况下不为空,必传 |
channel_id |
String |
通道ID,正常情况下不为空,必传 |
data_id |
String |
数据ID:正常情况下不为空,必传。可用于查询智能图片数据,参考链接:智能图片下载 |
report_time |
String |
上报时间:示例:2021-03-15T16:43:00+08:00 |
data |
Data object |
业务信息 |
字段名 |
类型 |
说明 |
---|---|---|
channel_id |
Int64 |
通道ID |
channel_id_ex |
Int64 |
相机扩展通道ID |
pts |
Int64 |
时间戳 |
sdc_device_id |
String |
主从机设备ID |
sdc_uuid |
String |
摄像机视频源通道号 |
intelligence_type |
Int |
智能类型 |
image_height |
Int |
图片高度 |
image_width |
Int |
图片宽度 |
meta_type_mask |
Int |
元数据类型掩码 枚举值:
|
intelligent_target_index |
Int |
智能目标/业务类型索引 |
target_time_domain_info |
Int |
配合索引使用,标识三层数据时域信息 枚举值:
|
sys_language_type |
Int |
后台系统语言类型 |
target_type |
Int |
target类型,对应微卡口车流量统计类型 |
字段名 |
类型 |
说明 |
---|---|---|
car_pre_brand |
String |
品牌字符:中文字符,例如大众 |
car_pre_brand_index |
Int |
品牌字符索引,当检测到机动车属性时传该值,见附录车款类型 |
car_sub_brand |
String |
子款字符:中文字符,例如明锐 |
car_sub_brand_index |
Int |
子款字符索引 |
car_year_brand |
String |
年款字符:例如2011 |
cur_snap_index |
Int |
当前抓拍序列号 |
device_id |
String |
设备ID |
dir_id |
String |
方向编号 |
data_id |
String |
数据ID:正常情况下不为空,必传。可用于查询智能图片数据,参考链接:智能图片下载 |
feature_frame_flag |
Int |
当前帧是否为关键帧,抠特征图来源帧 |
global_object_id |
Int64 |
智能目标全局ID |
ir_info |
String |
方向信息 |
lane_id |
Int |
车道号 |
mfr_car_pendant |
Int |
挂件 枚举值:
|
mfr_main_belt |
Int |
主驾驶安全带 枚举值:
|
mfr_main_call |
Int |
主驾驶打电话 枚举值:
|
mfr_main_sun_visor |
Int |
主驾驶遮阳板 枚举值:
|
mfr_nap_kin_box |
Int |
纸巾盒 枚举值:
|
mfr_vice_belt |
Int |
副驾驶安全带 枚举值:
|
mfr_vice_exist |
Int |
是否有副驾驶 枚举值:
|
mfr_vice_sun_visor |
Int |
副驾驶遮阳板 枚举值:
|
mfr_year_log |
Int |
年检标 枚举值:
|
panorama_pic |
String |
全景图,已转化为url |
panorama_pic_size |
Int |
全景图大小 |
pic_snapshot_dst_offset |
Int64 |
夏令时偏移时间:单位秒/s |
pic_snapshot_time |
Int |
抓拍时间:单位秒/s |
pic_snapshot_timems |
Int64 |
抓拍时间:单位毫秒/ms |
pic_snapshot_tzone |
Int64 |
抓拍时区:单位毫秒/ms 东区为+ 西区为-,支持夏令时 |
plate_char |
String |
车牌字符 |
plate_color |
Int |
车牌颜色,当检测到机动车属性时传该值,见附录车牌颜色 |
plate_confidence |
Int |
车牌置信度 |
plate_pic |
String |
车牌抠图:已转化为图片url |
plate_pos |
Rect object |
车牌位置万分比 |
plate_pos_abs |
Rect object |
车牌位置绝对坐标 |
plate_pos_com |
Rect object |
车牌位置万分比 |
plate_snapshot_type |
Int |
车牌抓拍触发类型 枚举值:
|
plate_type |
Int |
车牌类型,参考附录车牌类型 |
producer_name |
String |
数据生成者名字 |
roid_id |
String |
道路编号 |
target_type |
Int |
智能业务类型 枚举值:
|
trecord_type |
Int |
告警类型,见附录告警类型 |
vehicle_color |
Int |
车辆颜色,当检测到机动车属性时传该值,见附录车辆颜色 |
vehicle_direction |
Int |
车辆运动方向 枚举值:
|
vehicle_pic |
String |
车辆特写图,已转化为url |
vehicle_pos |
Rect object |
车辆位置 |
vehicle_pos_abs |
Rect object |
车辆位置绝对坐标 |
vehicle_pos_com |
Rect object |
车辆位置相对坐标万分比 |
vehicle_type |
Int |
机非人类型,当检测到机非人属性时传该值,见附录机非人类型 |
vehicle_type_ext |
Int |
机非人扩展类型,当检测到机非人属性时传该值,见附录机非人类型,例如机非人类型为轿车,扩展类型为两厢轿车 |
vlpr_alg_type |
Int |
车牌算法类型 |
microport_traffic_statistics |
Int |
微卡口车流量统计,历史版本遗留字段,为1代表该包为微卡口车流量统计 |
statistics_average_speed |
Int |
平均速度 |
statistics_congestion_degree |
Int |
交通状态 |
statistics_lane_count |
Int |
微卡口车流量统计车道数量 |
statistics_lane_index |
Int |
微卡口车流量统计当前车道 |
statistics_lane_space_used_ratio |
Int |
车道空间占有率 |
statistics_lane_time_used_ratio |
Int |
车道时间占有率 |
statistics_queue_length |
Int |
排队长度 |
statistics_vehicle_car_large_count |
Int |
大型车数量 |
statistics_vehicle_car_med_count |
Int |
中型车数量 |
statistics_vehicle_car_small_count |
Int |
小型车数量 |
statistics_vehicle_count |
Int |
车辆计数 |
statistics_vehicle_density |
Int |
车流密度 |
statistics_vehicle_head_interval |
Int |
车头时间间隔 |
statistics_vehicle_head_space_interval |
Int |
车头空间间隔 |
traffic_statistics_cycle |
Int |
车流量统计周期 |
字段名 |
类型 |
说明 |
---|---|---|
x |
Int |
检测框左上角坐标点x 计算方式,x1 = x *全景图像素宽度/ 10000 |
y |
Int |
检测框左上角坐标点y 计算方式,y1 = y *全景图像素高度/ 10000 |
width |
Int |
检测框宽度 计算方式 widht1 = widht *全景图像素宽度/ 10000 |
height |
Int |
检测框长度 计算方式 height1 = height *全景图像素高度/ 10000 |
示例一、微卡口
{ "message_id": 1676872319771064837, "message_type": "micro_checkpoint_data", "data": { "device_id": "219123456CYP***", "channel_id": "0", "data_id": "167687231972200300350000kcxdq130", "report_time": "2023-02-20T13:51:57+08:00", "data": { "common": { "channel_id": 101, "channel_id_ex": 101, "image_height": 720, "image_width": 1280, "meta_type_mask": 2, "pts": 786519119707, "sdc_uuid": "224440c1-966e-57eb-fd7b-8ca03739be7e", "sys_language_type": 0 }, "targets": [ { "car_pre_brand": "日产", "car_pre_brand_index": 75, "car_sub_brand": "轩逸", "car_sub_brand_index": 574, "car_year_brand": "2009_2012_2016_2018", "cur_snap_index": 0, "data_id": "167687231972200300350000kcxdq130", "device_id": "", "dir_id": "", "feature_frame_flag": 1, "global_object_id": 7200441985172434795, "ir_info": "", "lane_id": 3, "mfr_car_pendant": 0, "mfr_main_belt": 1, "mfr_main_call": 0, "mfr_main_sun_visor": 0, "mfr_nap_kin_box": 0, "mfr_vice_belt": 0, "mfr_vice_exist": 0, "mfr_vice_sun_visor": 0, "mfr_year_log": 0, "panorama_pic": "https://www.example.com/v1/holo/tlv_219123456CYP***_0_20230220_tlv_167687231972200300020000kcxdq130.jpg/static", "panorama_pic_size": 103310, "pic_snapshot_dst_offset": 0, "pic_snapshot_time": 1676872317, "pic_snapshot_timems": 1676872317957, "pic_snapshot_tzone": 28800000, "plate_char": "浙A306B1", "plate_color": 1, "plate_confidence": 97, "plate_pic": "https://www.example.com/v1/holo/tlv_219123456CYP***_0_20230220_tlv_167687231972200300320000kcxdq130.jpg/static", "plate_pos": { "x": 7726, "y": 5027, "width": 726, "height": 694 }, "plate_pos_abs": { "x": 989, "y": 362, "width": 93, "height": 50 }, "plate_pos_com": { "x": 7726, "y": 5027, "width": 726, "height": 694 }, "plate_snapshot_type": 1, "plate_type": 1, "producer_name": "ITGT", "roid_id": "", "target_type": 51, "trecord_type": 36, "vehicle_color": 2, "vehicle_direction": 4, "vehicle_pic": "https://www.example.com/v1/holo/tlv_219123456CYP***_0_20230220_tlv_167687231972200300010000kcxdq130.jpg/static", "vehicle_pos": { "x": 4429, "y": 1361, "width": 4000, "height": 4750 }, "vehicle_pos_abs": { "x": 567, "y": 98, "width": 512, "height": 342 }, "vehicle_pos_com": { "x": 4429, "y": 1361, "width": 4000, "height": 4750 }, "vehicle_type": 1, "vehicle_type_ext": 18, "vlpr_alg_type": 0 } ] } }, "test": false }
示例二、微卡口车流量统计
{ "message_id": 1676874462279656679, "message_type": "micro_checkpoint_data", "data": { "device_id": "219123456CYP***", "channel_id": "0", "data_id": "167687446220900300350000kcxdq130", "report_time": "2023-02-20T14:27:40+08:00", "data": { "common": { "channel_id": 101, "channel_id_ex": 101, "image_height": 720, "image_width": 1280, "meta_type_mask": 2, "pts": 146494760, "sdc_uuid": "224440c1-966e-57eb-fd7b-8ca03739be7e", "sys_language_type": 0, "target_type": 52 }, "targets": [ { "car_pre_brand": "斯柯达", "car_pre_brand_index": 74, "car_sub_brand": "明锐", "car_sub_brand_index": 554, "car_year_brand": "2010", "cur_snap_index": 0, "data_id": "167687446220900300350000kcxdq130", "microport_traffic_statistics": 1, "device_id": "", "dir_id": "", "feature_frame_flag": 1, "global_object_id": 7202244372492976151, "ir_info": "", "lane_id": 3, "mfr_car_pendant": 0, "mfr_main_belt": 1, "mfr_main_call": 0, "mfr_main_sun_visor": 0, "mfr_nap_kin_box": 0, "mfr_vice_belt": 0, "mfr_vice_exist": 0, "mfr_vice_sun_visor": 0, "mfr_year_log": 0, "panorama_pic": "https://www.example.com/v1/holo/tlv_219123456CYP***_0_20230220_tlv_167687446220900300020000kcxdq130.jpg/static", "panorama_pic_size": 98965, "pic_snapshot_dst_offset": 0, "pic_snapshot_time": 1676874459, "pic_snapshot_timems": 1676874459506, "pic_snapshot_tzone": 28800000, "plate_char": "浙A068PN", "plate_color": 1, "plate_confidence": 97, "plate_pic": "https://www.example.com/v1/holo/tlv_219123456CYP***_0_20230220_tlv_167687446220900300320000kcxdq130.jpg/static", "plate_pos": { "x": 5953, "y": 3222, "width": 765, "height": 472 }, "plate_pos_abs": { "x": 762, "y": 232, "width": 98, "height": 34 }, "plate_pos_com": { "x": 5953, "y": 3222, "width": 765, "height": 472 }, "plate_snapshot_type": 1, "plate_type": 1, "producer_name": "ITGT", "statistics_average_speed": 0, "statistics_congestion_degree": 1, "statistics_lane_count": 3, "statistics_lane_index": 1, "statistics_lane_space_used_ratio": 0, "statistics_lane_time_used_ratio": 0, "statistics_queue_length": 0, "statistics_vehicle_car_large_count": 0, "statistics_vehicle_car_med_count": 0, "statistics_vehicle_car_small_count": 0, "statistics_vehicle_count": 0, "statistics_vehicle_density": 0, "statistics_vehicle_head_interval": 0, "statistics_vehicle_head_space_interval": 0, "roid_id": "", "target_type": 52, "traffic_statistics_cycle": 5, "trecord_type": 36, "vehicle_color": 2, "vehicle_direction": 4, "vehicle_pic": "https://www.example.com/v1/holo/tlv_219123456CYP***_0_20230220_tlv_167687446220900300010000kcxdq130.jpg/static", "vehicle_pos": { "x": 3648, "y": 569, "width": 3281, "height": 3625 }, "vehicle_pos_abs": { "x": 467, "y": 41, "width": 420, "height": 261 }, "vehicle_pos_com": { "x": 3648, "y": 569, "width": 3281, "height": 3625 }, "vehicle_type": 1, "vehicle_type_ext": 17, "vlpr_alg_type": 0 } ] } }, "test": false }