乘用车/商用车 残值分析

汽车残值计算利用人工智能和机器深度学习技术，通过Spearman相关系数挖掘各个特征与价格走势的关联度，使用了特征排序、时间序列分析等技术进行预测，预测未来1-3年的车辆价值，在车辆正常车况的情况下，给出的车辆3年残值预测容错率在7.8%以内。根据残值数据能更好地为金融产品做基础设计和资产回收服务，设计合理的首付比例。 乘用车月度残值接口
入参：车型id，城市、上牌日期、行驶里程、车辆颜色、内饰状况、漆面状况、工况状况
出参：车辆的残值日期和对应的残值价
商用车未来残值接口
入参：车型id，城市、上牌日期、行驶里程、货运类型、轮胎成新
出参：车辆的残值日期、默认车况、不同车况对应的个人交易价、车商零售价、车商收车价

This vehicle residual value calculation framework leverages artificial intelligence and deep learning technologies. It employs the Spearman correlation coefficient to quantify the correlation between individual features and vehicle price trends, and adopts techniques including feature ranking and time series analysis to forecast vehicle values over the subsequent 1 to 3 years. Under normal vehicle conditions, the error rate of the 3-year residual value prediction is within 7.8%. With the obtained residual value data, this system can better support the basic design of financial products, asset recovery services, and the determination of reasonable down payment ratios.

Passenger Vehicle Monthly Residual Value API
Input Parameters: vehicle model ID, city, license plate registration date, mileage, vehicle color, interior condition, paint condition, working condition
Output Parameters: residual value date and corresponding residual value of the vehicle

Commercial Vehicle Future Residual Value API
Input Parameters: vehicle model ID, city, license plate registration date, mileage, freight type, tire newness level
Output Parameters: vehicle residual value date, default vehicle condition, individual transaction price, dealer retail price and dealer purchase price corresponding to different vehicle conditions