摘要
Abstract
In response to the problems of traditional sluice gate scheduling relying on manual experience and slow response,taking Shuxin Sluice as the research object,a smart sluice gate regulation method based on deep learning algorithms was proposed.By constructing a multi-objective optimization model aiming at maximizing water supply efficiency and stable gate operation,and introducing the improved Particle Swarm Optimization(PSO)algorithm to solve the model,adaptive and precise regulation of gate opening was realized.The results show that the intelligent scheduling system can reduce the average response time from 136.0 s with manual experience to 52.1 s,with an efficiency improvement of 61.7%;the average absolute error of flow regulation is 3.1 m3/s,and most average absolute errors are within 4.6 m3/s.On this basis,a smart sluice regulation system integrating intelligent operation,safety monitoring and data analysis was developed,realizing autonomous optimal operation of the gates.This study provides valuable theoretical methods and technical approaches for the intelligent scheduling of sluices,and is of practical significance for improving the operation efficiency of water conservancy projects and the precise scheduling of water resources.关键词
水闸调度/深度学习/粒子群优化Key words
sluice operation scheduling/deep learning/particle swarm optimization分类
建筑与水利
