Method and equipment for confirming evaluated price of used car

Abstract

The invention relates to a method and equipment for confirming evaluated price of a used car. The method comprises the steps of S1, finding a corresponding vehicle type, a reference price and an influence coefficient according to a vehicle type ID which corresponds with the car; S2, searching a corresponding area coefficient according to the vehicle type and an area in which the car exists; S3, calculating a difference coefficient according to an actual license registering time, an actual driving mileage, a vehicle age function and an influence coefficient of the vehicle; and S4, obtaining an evaluation cost through a formula of evaluation cost =reference cost*area coefficient*difference coefficient. The method and the equipment have advantages of high convenience in artificial intervention adjustment, convenient management, better structural performance, high storage space saving effect, high calculating speed, effective response speed improvement, controllable output result, and high result output logicality correctness through controlling the correcting function and the function coefficient.

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