Application Of Digital Technology In Electronic Equipment Evaluation And Economic Life Analysis

In recent years,with the rapid development of big data,machine learning and other fields,the digital transformation of traditional industries has become the current research focus.At present,there are many subjective factors of the appraised equipment,low efficiency of the appraisers,and difficulties in the objective and fair appraisal results in the appraisal of fixed assets,which not only damage the interests of the interested parties of the assets,but also bring many adverse effects on the development of the asset appraisal industry.The current market price method is a simple and effective method in asset evaluation.Its reference data is directly from the market.However,in practical application,there are also problems such as unclear data sources and subjective evaluation methods.In view of this,this paper attempts to take the open market transaction data as the source,construct the equipment characteristic price model through extracting the big data to realize the price evaluation of the equipment,and further carry out data mining on this basis to analyze the economic life of the equipment.This paper takes the laptops of some enterprises in a modern smart demonstration zone in the north as the research object,and carries out the following research: First,it carries out a literature review of relevant research methods.According to the equilibrium value theory and the substitution principle of price formation,by analyzing and comparing the applicable scope of several common asset evaluation methods,it selects the market method as the evaluation method of equipment asset value in this paper.Secondly,based on the hedonic price theory and the transaction data of the second-hand e-commerce platform,collect and screen the relevant factors that affect the equipment price.Three first-level indicators and 18 second-level indicators of the equipment are constructed with reference to relevant research.The characteristic price model of the equipment is constructed through data preprocessing and feature screening.Next,the hedonic price model is analyzed by using multiple linear regression,random forest regression,XGBoost regression algorithm and Light GBM regression algorithm respectively.The data set is divided into training set and test set by 7:3.The effects of different algorithms are analyzed by using mean absolute error(MAE),root mean square error(RMSE),coefficient of determination()and other indicators,and the algorithm with better evaluation effect is selected.Finally,this paper analyzes the problems existing in the traditional method of calculating the end-of-year residual value of the economic life.It attempts to take the evaluation value of the equipment at the end of the year as the year-end residual value of the equipment,combine the daily maintenance cost,annual use cost and other data information of the equipment as the data source of the economic life calculation,and use the equipment asset evaluation and management system built by SQL Lite and QT Creator as a tool to assist in the economic life analysis of the equipment.In view of the problems existing in the traditional asset evaluation methods and the economic life analysis process,the equipment price is evaluated more scientifically and reasonably by constructing the characteristic price model of the equipment and based on the machine learning regression algorithm.By giving full play to the role of digital technology in data collection,data analysis,data integration,data mining and other aspects,this paper shows the potential value of big data as an asset,which not only provides new ideas for the evaluation of electronic equipment,but also provides relevant references for similar work.