Irrigation area water consumption estimation method and system based on adaptive dynamic feature fusion

By using an adaptive dynamic feature fusion method based on a deep LSTM model, the problems of dynamic changes and periodic drift in irrigation water data were solved, achieving high-precision short-term prediction and robust long-term trend capture, thus improving the scientific nature of irrigation scheduling and water use efficiency.

CN122114490APending Publication Date: 2026-05-29SHANDONG HAOZHI INFORMATION TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHANDONG HAOZHI INFORMATION TECHNOLOGY CO LTD
Filing Date
2026-02-12
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

Existing technologies cannot effectively capture the dynamic changes, periodic drift behavior, and non-fixed time-delay response patterns in irrigation water data, resulting in prediction lag, trend misjudgment, and performance degradation after long-term deployment. They cannot simultaneously meet the needs of short-term scheduling and long-term trends, and are difficult to adapt to data distribution drift caused by changes in irrigation facilities or climate anomalies.

Method used

By constructing an adaptive dynamic feature fusion method based on a deep LSTM model, utilizing dynamic lag embedding matrix and periodic feature fusion, combined with multi-scale feature pyramid output and incremental training, the model's adaptability to sudden changes in meteorological response and its long-term adaptability are improved, thus solving the problems of dynamic data changes and irrigation area structure adjustments.

Benefits of technology

It significantly improves the scientific nature and water use efficiency of irrigation scheduling, enables high-precision short-term prediction and robust long-term trend capture, adapts to complex spatiotemporal changes, and enhances the robustness and adaptability of the model.

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Abstract

The present application relates to the field of artificial intelligence and data processing technology, in particular to a method and system for estimating irrigation water consumption based on adaptive dynamic feature fusion, specifically as follows: real-time collection of irrigation water consumption data; preprocessing of the collected water consumption data; constructing a dynamic lag embedding matrix based on historical water consumption information, and then fusing it with periodic features to obtain historical fusion features; constructing an irrigation water consumption estimation model for agricultural irrigation in the irrigation area based on a deep LSTM model, inputting the preprocessed data and historical fusion features into the model for training until the preset condition is met and the training is stopped, obtaining the trained model; incremental training of the trained model; inputting the newly collected irrigation water consumption data and meteorological data into the incrementally trained model to estimate the irrigation water consumption for agricultural irrigation in the irrigation area. The present application can solve the problems of prediction bias and inaccuracy caused by dynamic data, and thus improve the scientificity of irrigation scheduling and the robustness of water use efficiency.
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