micro_checkpoint_data 微卡口业务
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
} 
