A safety monitoring method and intelligent system for operation state of irrigation and drainage engineering

By combining robust scaling and adaptive nonlinear transformation with mutual information theory and gradient boosting decision trees, a deep classification network integrating physical priors is constructed. This solves the problem of data sensitivity and nonlinear coupling relationships that are difficult to capture in traditional methods, and enables accurate monitoring and graded alarm of the operation status of irrigation and drainage projects.

CN121901996BActive Publication Date: 2026-06-19WATER RESOURCES RES INST OF SHANDONG PROVINCE
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
WATER RESOURCES RES INST OF SHANDONG PROVINCE
Filing Date
2026-03-25
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Traditional irrigation district management relies on manual experience and regular inspections, making it difficult to detect progressive safety hazards such as pipeline leaks, gate jamming, and declining pump station efficiency in a timely manner. Existing data normalization methods are sensitive to sensor data and cannot effectively explore the nonlinear coupling relationships of multi-source monitoring data. Furthermore, deep learning methods lack the integration of physical laws, leading to misjudgments and insufficient robustness.

Method used

A robust scaling method based on quantiles and medians is adopted to suppress outlier interference. An adaptive feature interaction mechanism is constructed by combining adaptive nonlinear transformation and mutual information theory. Key features are selected by gradient boosting decision trees. A deep classification network integrating physical priors is constructed. High-order interactive features are generated by using feature importance scores for soft weighting and physical constraints, and then state level classification is performed.

Benefits of technology

It enables accurate monitoring of the operational status of irrigation and drainage projects, suppresses the influence of noise and outliers, captures nonlinear coupling relationships, improves the robustness and interpretability of the model, and ensures effective classification and hierarchical alarms under imbalanced data.

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Abstract

This invention relates to a safety monitoring method and intelligent system for the operational status of irrigation and drainage projects, belonging to the field of artificial intelligence technology. It includes the following steps: collecting and labeling monitoring data of irrigation and drainage project operations through sensors to construct a training dataset; normalizing the data using robust scaling of quantiles and medians combined with adaptive nonlinear transformation; then mining and filtering high-order interactive features using mutual information theory and gradient boosting decision trees; constructing a deep classification network that integrates physical priors and adaptive feature interactions, introducing physical constraints and multi-scale feature fusion, and optimizing the model through adaptive marginal classification loss and physical feature manifold alignment loss; inputting preprocessed real-time data into the model to achieve operational status level classification and graded alarms. This invention can suppress data noise, uncover the coupling relationship of multiple indicators, incorporate physical laws to improve model interpretability and robustness, accurately identify and warn of anomalies in irrigation and drainage projects, and is suitable for intelligent safety monitoring in irrigation districts.
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