摘要
针对温室变量微喷作业中空气湿度传感器部署问题,在经典连续粒子群思想和算法框架基础上,重新定义问题的表示方法和速度-位置规则来优化空气湿度传感器部署;利用时空协同克里金插值方法构建适应度函数。针对适应度函数,利用设计的离散粒子群算法的迭代计算得到传感器位置与数目。通过仿真实验,对最终传感器部署的采样值使用Kriging插值与未优化的数据三维图进行空气湿度场重建精度对比和统计方法验证,验证了该算法的有效性,为温室变量微喷作业奠定基础。
To solve the air humidity sensor deployment in greenhouse variable micro-spray operation,based on the classical continuous particle swarm idea and algorithm framework,the problem representation method and speed-position rule are redefined to optimize the air humidity sensor deployment;The fitness function is constructed by using space-time collaborative Kriging interpolation method.The position and number of sensors are obtained by iterative calculation of discrete particle swarm optimization algorithm for the fitness function of the design.Through the simulation experiment,the accuracy of the air humidity field reconstruction and the statistical method verification are carried out on the sampled values of the final sensor deployment using Kriging interpolation and unoptimized data three-dimensional map.The effectiveness of the algorithm is verified,which lays a foundation for the greenhouse variable micro-spray operation.
作者
付聪
郑世健
刘知贵
FU Cong;ZHENG Shi-jian;LIU Zhi-gui(Southwest University of Science and Technology, Information Engineering College, Mianyang 621000,Sichuan Province,China;China Academy of Engineering Physics,Institute of Electronic Engineering, Mianyang 621000,Sichuan Province,China)
出处
《节水灌溉》
北大核心
2020年第7期69-73,80,共6页
Water Saving Irrigation
基金
四川省科技厅项目“食用、观赏、药用兼用果树新种类费约果(Feijoa)产业化开发应用集成技术研究”(2014HH0053)。
关键词
湿度场
离散粒子群算法
逐步累积和算法
传感器部署
humidity field
discrete particle swarm algorithm
stepwise accumulation and algorithm
sensor deployment
