Rice Field Irrigation Control Using Knowledge Graph Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing intelligent irrigation systems for rice fields fail to adjust irrigation strategies when planting conditions are poor, such as insufficient rainfall and low soil moisture, leading to delayed crop growth despite considering crop growth cycles and weather forecasts.
Innovation Solution
An intelligent irrigation control method using a cloud service platform that monitors crop conditions, constructs water and growth coefficients, matches irrigation strategies from a knowledge graph, optimizes strategies with a trained model, and controls irrigation coefficients to ensure precise and efficient water supply.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the crops are irrigated according to the predetermined irrigation strategy without adjusting the irrigation strategy when planting conditions are poor, then the irrigation system operates automatically, but the irrigation effect is delayed and does not meet expected results
Solution Approach 1:
The system implements feedback by continuously monitoring actual crop growth data and comparing it with expected growth patterns. When deviations are detected (indicating poor planting conditions), the system automatically adjusts irrigation strategies in real-time, ensuring timely corrective action without manual intervention.
Solution Approach 2:
The irrigation strategy transitions from static (predetermined) to dynamic (real-time adjustable). The system dynamically modifies irrigation parameters based on current crop conditions, weather data, and growth stage, allowing the irrigation plan to adapt flexibly to changing conditions and prevent growth delays.
2Adaptability or versatility
If multiple irrigation strategies are matched from the crop irrigation knowledge graph, then the irrigation approach becomes more comprehensive, but the strategy selection complexity increases
Solution Approach 1:
The system introduces an intermediary component (the crop irrigation knowledge graph with pre-defined strategies) that mediates between complex irrigation requirements and simple execution. Multiple irrigation strategies are pre-matched in the knowledge graph based on crop types, growth stages, and conditions, allowing the system to select appropriate strategies without complex real-time decision-making complexity.
Data Source
AI summary
Provided is an intelligent irrigation control method for rice fields based on a cloud service platform and a system thereof. Constructing a planting condition data set and a condition coefficient, monitoring the current water supply environment of crops, and constructing a water supply coefficient. If the water supply coefficient is lower than the water supply threshold, based on the current growth data of crops, a plurality of corresponding irrigation strategies are matched from the pre-constructed crop irrigation knowledge graph, and the target strategy is selected from a plurality of irrigation strategies; constructing an irrigation observation coefficient set, and constructing irrigation coefficient. If the irrigation coefficient is lower than the efficiency threshold, use the trained strategy optimization model to optimize the target strategy, and control the irrigation coefficient based on the optimized target strategy to complete the irrigation of crops in the irrigation area.