The invention discloses a smart city AI big data multi-target information fusion cooperative tracking and positioning method. The method comprises the following steps: checking the urban vehicle ownership according to the vehicle sales condition and the vehicle registration information; importing a city map and inserting a parking lot position; obtaining the parking space number and the vehicle parking number of the parking lot; analyzing the parking space saturation of the parking lot; and checking the urban road congestion condition, providing a relatively convenient parking lot navigation route, and carrying out visual display on an urban map. By providing the parking space saturation of the parking lot, a user can conveniently check the parking condition of each parking lot according to the map, if the number of parked vehicles reaches 95% of the parking space, the parking lot is not selected, congestion near the parking lot is prevented from being aggravated, and the parking lot is more intelligent. When the vehicle parking saturation amount of the selected parking lot is lower than 95%, a navigation which does not cause congestion relatively can be given on the city map, so that the user can drive the vehicle to the parking lot conveniently.
本发明公开了智慧城市AI大数据多目标的信息融合协同跟踪定位方法,包括以下步骤:根据汽车销售状况及汽车登记信息核实城市车辆保有量;导入城市地图并插入停车场位置;获取停车场车位数量及车辆停放数量;分析停车场的车位饱和度;查看城市道拥堵情况,提供相对便捷的停车场导航路线,并在城市地图上进行可视化展示。本发明通过提供停车场的车位饱和度,便于用户根据地图查看每个停车场的停放情况,如若车辆停放数量达到停车位的百分之九十五,则不选择该停车场,也避免了加重该停车场附近的拥堵,当选择的停车场车辆停放饱和量低于百分之九十五,城市地图上可以给出一条相对不会拥堵的导航,便于用户驾车前往停靠。
Smart city AI big data multi-target information fusion cooperative tracking and positioning method
智慧城市AI大数据多目标的信息融合协同跟踪定位方法
2023-12-05
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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