Optimization And Application Of Multi-join Query Algorithm For Grid Asset Management System

In recent years,Chinese rapid economic development and continuous changes in the power grid industry,power grid enterprise asset management system faces a dramatic increase in the amount of data,complex database table relationships,frequent data updates,how to efficiently use the huge data grid asset data has become a popular issue of concern and urgent need to solve the power grid enterprises.The current grid asset management system is characterized by a large volume of data and complex data types,the power grid asset management system Multi-join Query is a more complex operation in query processing,the performance of the database table join operation seriously affects the efficiency of Multi-join Query processing,so the search for the optimal execution plan becomes the key to affect the query efficiency of the power grid asset management system,at this stage The proposed database Multi-join Query algorithm cannot obtain the optimal query plan quickly and comprehensively.The Grey Wolf Optimization algorithm is often used to solve the query NP(Non-deterministic Polynomial)problem of the grid asset management system because of the characteristics of few parameters,easy implementation and simple efficiency.However,due to the limitations of wolf pack search mechanism,the traditional Grey Wolf Optimization algorithm converges slowly and easily into local optimum in the late stage,which affects the query efficiency and data utilization of the grid asset management system.Therefore,a novel Grey Wolf Optimization algorithm based on the traditional Grey Wolf Optimization algorithm is proposed with a multi-factor dynamic weighting strategy and a hybrid simulated annealing algorithm.A multi-factor dynamic weighting strategy is proposed based on the dynamic weighting strategy,which coordinates the search individuals by increasing different proportions of dynamic weights to speed up the convergence of the search mechanism.In addition,the simulated annealing algorithm is introduced into the Grey Wolf Optimization algorithm,using the Metropolis principle of good stochastic burst hopping for balancing the local optimum and the global optimum search ability to avoid falling into the local optimum.The performance of the improved Grey Wolf Optimization algorithm is tested by using 13 benchmark test functions commonly used in swarm intelligence optimization algorithms,and the accuracy of the improved Grey Wolf Optimization algorithm is improved to e-324 by comparing with other improved Grey Wolf Optimization algorithms,and the average convergence speed is improved by 10% compared with the traditional Grey Wolf Optimization algorithm.The improved Grey Wolf Optimization algorithm is applied to the database Multi-join Query optimization of the grid asset management system.By designing and constructing the relational database model of the grid asset management system,the improved Grey Wolf Optimization algorithm is used to filter the query plan to obtain the optimal query path using the query cost model.The database query efficiency is proved to be improved through multiple data queries,and the problems of slow query speed and low query efficiency of the system are solved.The improvement and application of the Grey Wolf Optimization algorithm provide useful reference for future research and development of Multi-join Query for grid asset management system database.