Study On Multi-join Query Optimization For Data Management Model Of Smart Grid

In recent years,computer and communication technology develops rapidly.The smart grid has become the focus attention of the global power industry.Due to the deepening and advancing of smart grid construction,amount of data generated by power grid and monitoring equipment is exponential growing.The environment of big data is gradually forming,which brings many difficulties to the storage and query of smart grid data.To meet real-time data acquisition of smart grid applications and ensure safe operation of the smart grid,the query optimization is important for data management model which combines the acquisition layer,storage layer,management layer and application layer.Then it not only promotes the development of the smart grid,but also provides technical reserves for large smart grid data management.This thesis systematically analyzes the characteristics of smart grid data.It elaborates on relational technologies of smart grid data management model,and deeply studies the challenges which the smart grid management is facing.To deal with the low query efficiency caused by the large amounts of grid data and high security requirements,the thesis focuses on the multi-join query optimization methods under smart grid data management model.The traditional data query methods are easy to fall into local optimum,and the algorithms have the problem of low efficiency.To solve these problems,a method based on genetic algorithm for multi-join query optimization has been proposed.It gives the fitness function which can portray the characteristics of smart grid data and establish the data management model.Therefore,it performs the multi-join query optimization for smart grid data by genetic algorithm,which not only reduces the query time,but also improves the query efficiency.This thesis proposes a hybrid intelligent multi-join query optimization algorithm in order to better satisfy the requirement of smart grid for data query.The Guo Tao algorithm is introduced in the crossover of this suggested algorithm to improve population diversity and avoid premature convergence of genetic algorithm.At the same time the mutation operator involves the particle swarm algorithm to improve global convergence of the algorithm global convergence.The query efficiency and accuracy of the algorithm are significantly higher than the traditional genetic algorithm.