Method and equipment for confirming evaluated price of used car

Abstract

The present invention relates to a method and equipment for confirming the evaluation price of a used car. The method includes: S1: according to the model ID corresponding to the vehicle, find the corresponding model type, benchmark price and influence coefficient; S2: according to the type of model and the area where the vehicle is located , to find the corresponding regional coefficient; S3: Calculate the difference coefficient according to the actual license plate time, actual mileage, vehicle age function, mileage function and influence coefficient of the vehicle; S4: Evaluate the price by the formula = base price * regional coefficient * difference coefficient Get an appraisal price. The invention is convenient for human intervention and adjustment, easy to manage, stronger in structure, more saving in storage space, fast in calculation speed, can improve response speed, and can ensure that the output result is controllable, and at the same time, the result output can be guaranteed by controlling the correction function and function coefficient Logically correct.

Description

technical field [0001] The invention relates to the technical field of second-hand car transaction control, in particular to a method and equipment for confirming the evaluation price of a second-hand car. Background technique [0002] As we all know, the opacity of used car price information is an important reason why consumers are discouraged when facing used cars, and it has also become an important factor hindering the healthy development of the used car industry. In the entire used car transaction chain, how to give a fair price reference has become a particularly urgent need in the used car market. However, the current method of using traditional appraisers for valuation and pricing is inefficient, with too many subjective factors and artificial information collection. Limited channels, biased experience pricing and other problems, in this context, many second-hand car valuation platforms have emerged, such as Che 300, Fair Price, Autohome, etc., but second-hand cars a...

AI Technical Summary

Problems solved by technology

In the entire used car transaction chain, how to give a fair price reference has become a particularly urgent need in the used car market. However, the current method of using traditional appraisers for valuation and pricing is inefficient, with too many subjective factors and artificial information collection. Limited channels, biased experience pricing and other problems, in this context, many second-hand car valuation platforms have emerged, such as Che 300, Fair Price, Autohome, etc., but second-hand cars are different from new cars. They have non-standardized attributes, as the number of users continues to rise, the valuation software needs to be able to meet the valuation of specific vehicles under the combination of parameters such as various models, different licensing times, different mileage, and different regions, and needs to meet the interface calls. High concurrency and the logic of the results (for example: when other parameters are the same, the longer the mileage, the lower the price, etc.)

[0003] At present, the commonly used method of evaluating the value of used cars is to calculate the price of used cars in real time by storing parameter variable coefficients. The bottom layer of most used car valuation software stores several parameters such as model, age, mileage, and region. For used cars The weight coefficient of the price influence, when the specific model information is transmitted to the system, will be multiplied with the pre-stored coefficient matrix. The disadvantages of this storage and calculation method are: 1. When there are too many parameter variable coefficients, the coefficient matrix is ​​too large , takes up a lot of storage space; 2. The weight coefficient is too abstract, which is not easy to manage or adjust manually; 3. When the parameters are called online for calculation, the calculation of the coefficient matrix will also be too large, and the response speed will be too fast when there is high concurrency. slow situation