停车车辆智能找回数据

通过对车辆停车数据的深入分析，形成车辆实时位置数据和停车行为偏好数据，此数据能够： 1. 提高执行效率：可基于此数据构建车辆监测查找系统，为司法机关提供即时信息，司法机关能够快速定位被执行人名下的车辆，减少人工搜索和追踪的时间; 2.节约资源：减少因寻找车辆而投入的人力物力，节约司法资源，降低社会成本，可以极大地缩短车辆处置时间，减少车辆处置成本; 3.增强威慑：提高被执行或丢失车辆的找回率，增强法律的威慑力，促进社会秩序的稳定; 4.提升司法判决支持：基于此数据可完善车辆信息数据库，通过车辆信息数据库的检索，司法部门可以获取有关证据，支持相关司法判决; 5.数字化路网模型：此数据可助力对高速路网进行数学建模，形成数字化、可视化、能计算、能分析的数字道路，进而对路网拓扑结构、交通状况和信息流动进行数字化分析，为司法机关提供更精确的车辆路径预测和判断。 综上所述，本数据可助力司法机关等机构快速找寻被执行或丢失的车辆，提高被执行或丢失车辆的找回率。 还增强了执法的透明度和司法判决的准确性，对于提升司法工作效率和公正性具有显著的价值。1、基于结构化的车辆停车数据，将车辆停车数据进行清洗，剔除无效车牌、空车牌、进出场时间异常的数据； 2、根据历史车辆停车数据，计算车辆历史停车次数、平均停车时长、上次停留时间、车辆停车城市特征数据； 3、当车辆进或出停车场时，通过canal工具获取车辆进出场数据，采用Flink技术实时对车辆进出场数据分析并与步骤2计算得出的停车偏好数据融合，形成以加盐加密后的车牌为主键的车辆当前车辆位置数据和历史停车偏好数据。 加盐加密方式：采用SHA-256算法对车牌加盐机密，其中盐值是采用私有算法随机生成。 其中，车辆类型、出/入场类型、出/入场时间、出入车场、出入场地址是车辆本次出入车场的实时计算结果； 历史停车次数、平均停留时长、上次停留时长是通过车辆在本次出/入车场之前，在该车场的近半年的历史停车数据计算得出，计算时不包括当前停车数据； 车辆停车城市是通过车辆在本次出/入车场之前，该车辆近半年的所有车场历史停车数据计算得出，计算时不包括当前停车数据。Through in-depth analysis of vehicle parking data, two types of datasets are generated: real-time vehicle location data and parking behavior preference data. This data can deliver the following benefits: 1. Improve judicial execution efficiency: A vehicle monitoring and locating system can be built based on this data to provide real-time information for judicial authorities. Judicial authorities can quickly locate vehicles registered under the names of persons subject to enforcement, reducing the time spent on manual search and tracking; 2. Save resources: Cut down on the manpower and material resources invested in searching for vehicles, conserve judicial resources and lower social costs. It can significantly shorten the time required for vehicle disposal and reduce related disposal costs; 3. Strengthen deterrence: Raise the recovery rate of vehicles subject to enforcement or lost vehicles, enhance the deterrent effect of law, and promote social order stability; 4. Strengthen support for judicial adjudications: Improve the vehicle information database using this data. Through queries in the vehicle information database, judicial departments can obtain relevant evidence to support corresponding judicial adjudications; 5. Digital road network modeling: This data can support mathematical modeling of expressway road networks, creating a digital road that is digitized, visualizable, computable and analyzable. It then enables digital analysis of road network topology, traffic conditions and information flow, providing judicial authorities with more accurate vehicle route prediction and judgment. In summary, this data can assist institutions such as judicial authorities in quickly locating vehicles subject to enforcement or lost vehicles, and improve the recovery rate of such vehicles. It also enhances law enforcement transparency and the accuracy of judicial adjudications, and holds significant value for improving the efficiency and fairness of judicial work. 1. Based on structured vehicle parking data, clean the dataset by eliminating entries with invalid license plates, empty license plates, and abnormal entry/exit timestamps; 2. Calculate historical parking metrics for each vehicle based on its historical parking data, including total parking times, average parking duration, last stay duration, and urban parking characteristic data of the vehicle; 3. When a vehicle enters or exits a parking lot, retrieve the vehicle's entry/exit data via the Canal tool. Use Flink technology to perform real-time analysis of the entry/exit data, and fuse it with the parking preference data calculated in step 2 to generate real-time vehicle location data and historical parking preference data, using the salted and encrypted license plate as the primary key. Salted encryption approach: The SHA-256 algorithm is applied to salt and encrypt license plates, where the salt value is randomly generated using a proprietary algorithm. Specifically: - Vehicle type, entry/exit type, entry/exit timestamp, entry/exit parking lot, and entry/exit address are real-time calculation results derived from the vehicle's current entry/exit event; - Historical parking times, average stay duration, and last stay duration are calculated using the vehicle's historical parking data in the target parking lot over the six months prior to the current entry/exit event, excluding the current parking record; - The vehicle's parking city is calculated using all historical parking data of the vehicle across all parking lots over the six months prior to the current entry/exit event, excluding the current parking record.