Drip irrigation pattern evaluation and optimization method and system based on fusion ecological model

By collecting and standardizing multi-source data, constructing a dynamic collaborative feature matrix, and introducing particle swarm optimization with ecological model constraints, the problem of difficulty in identifying causal coupling relationships in drip irrigation optimization was solved, achieving a balance between high yield and ecological safety and the field application of optimization results.

CN122066309BActive Publication Date: 2026-06-23WATER RESOURCES RES INST OF SHANDONG PROVINCE
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Patent Information

Application Number
CN202610492024.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-04-15
Publication Date
2026-06-23
Estimated Expiration
2046-04-15

AI Technical Summary

Technical Problem

Existing drip irrigation optimization methods cannot effectively handle the causal coupling relationship between irrigation parameters, crop parameters and ecological indicators, making it difficult for optimization models to identify the real correlation between high yield and low ecological risk. Furthermore, they lack robustness for field application and dynamic constraints on ecological processes, making it difficult to implement multi-objective decision-making in practice.

Method used

By collecting multi-source data, standardizing and dynamically aligning it over time, a dynamic collaborative feature matrix is ​​constructed. Ecological model constraints and adaptive inertial weights are introduced, and multi-objective optimization is performed using a particle swarm optimizer. A comprehensive evaluation system is then built, and rolling optimization is performed through an intelligent control platform to achieve dynamic adaptive regulation of the drip irrigation mode.

Benefits of technology

It achieves multi-objective optimization under ecological constraints, improves the robustness and field applicability of drip irrigation mode, can dynamically respond to environmental changes, and ensures a balance between high yield and ecological security.

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Abstract

The present application relates to the technical field of intelligent management and control of agricultural irrigation, and particularly relates to a drip irrigation mode evaluation and optimization method and system fusing an ecological model, and specifically as follows: first, collecting multi-source data of an irrigation plot and completing standardization processing; through dynamic time alignment and adaptive kernel mapping with soil salinity constraints, a dynamic collaborative feature matrix retaining coupling relationship is constructed; a particle swarm optimizer fusing ecological model constraints is constructed to perform multi-objective optimization on drip irrigation mode parameters to obtain a Pareto optimal solution set; a comprehensive evaluation system containing yield marginal benefit and uncertainty penalty is established to screen an optimal scheme; finally, through field correction and rolling optimization, closed-loop management and control is realized. The present application can solve the problems of data coupling relationship damage, ecological risk constraint absence and optimization scheme robustness deficiency in existing drip irrigation optimization methods, realize multi-objective optimization of drip irrigation mode, take into account high yield benefit and ecological safety, and improve scheme robustness and field applicability.
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