enterprise asset management system and GA-BP-based enterprise equipment life prediction method

Abstract

An enterprise asset management system and GA-BP-based enterprise equipment life prediction method is disclosed. The invention extracts the factors affecting the life of the equipment from the asset management system by analyzing the data and business of the enterprise asset management system, and extracts relevant data from the database to construct a GA-BP prediction model to predict the life ofthe enterprise equipment. The method also analyzes the business process and data flow of an enterprise asset management system and extracts abstractions that affect the life of the device. The methodextracts data from the database to build a dataset and construct a BP neural network model for prediction. The genetic algorithm is used to optimize the BP model and construct the GA-BP prediction model. According to the experimental results, the GA-BP model is optimized. The GA-BP model is optimized by the fitness function and the crossover operator. The improved genetic algorithm is more evolutionary, and the effect is more obvious in the sense of global optimization. The invention is more suitable for the whole optimization of the BP mode.

Description

technical field [0001] The invention is a method for predicting the service life of enterprise equipment based on an enterprise asset management system and GA-BP, and relates to the technical field of enterprise asset management. Background technique [0002] Enterprise assets are important means of production in the process of enterprise production and operation, the core competitiveness of a group enterprise, and the basis for the existence and development of enterprise productivity. Generally speaking, corporate assets are divided into two categories: tangible assets and intangible assets. Intangible assets include patent rights, non-patented technologies, trademark rights, copyrights, franchises, land use rights, etc., and tangible assets include production equipment, office equipment, Land, housing, transportation, etc. The enterprise asset management system is established on the basis of advanced management concepts such as comprehensive production management and benc...

AI Technical Summary

Problems solved by technology

However, the BP neural network also has the disadvantage that it is easy to fall into a local optimum. The ideas to solve this problem focus on two aspects, one is the self-adjustment of the BP network, and the other is the combination of the BP network and other algorithms. The common ones are genetic algorithm and particle swarm optimization. Algorithms, etc., in which the genetic algorithm has great advantages in global optimization, and the GA-BP, which optimizes the initial weight and threshold of the BP with the genetic algorithm, has become a research hotspot

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