基于云边端协同架构的智能灌溉管控平台设计与应用研究OA

The informatisation of agricultural irrigation management represents a crucial direction for future sustainable agricultural development.However,existing control platforms suffer from limitations in decision-making model capabilities and insufficient control timeliness.To address this,this study constructs an intelligent irrigation control platform based on a'cloud-edge-end'collaborative architecture.Utilising Kubernetes containerised orchestration technology,it enables flexible deployment and elastic scheduling of models and algorithms.For diverse cultivation scenarios,we developed a water balance decision model for field irrigation and a cumulative photosynthetically active radiation-based irrigation decision model for greenhouses,both utilising deep learning prediction.Concurrently,we designed a closed-loop control module encompassing'water demand monitoring-irrigation decision-making-intelligent control-post-irrigation evaluation,'thereby creating a dynamic management platform covering the entire irrigation lifecycle.Results from regional farmland simulations and practical applications in protected agriculture demonstrate:in field maize simulation validation,the platform's decision-generated irrigation volume reduced by approximately 18.3%compared to experience-based irrigation,while yield increased by 27.3%and irrigation water use efficiency improved by about 56%;In controlled environment lettuce cultivation trials,water savings reached 10.02%,yields increased by 9.38%,and irrigation water use efficiency improved by 21.33%.This irrigation platform provides technical and methodological support for multi-scenario irrigation management.