Artificial intelligence driven energy-water-carbon chain coupling evaluation method and system

By introducing a hybrid model of evaluation dimensions and deep learning, the problems of insufficient evaluation dimensions and imperfect data processing in existing technologies are solved, achieving more comprehensive resource evaluation and model universality, and supporting evaluation needs in multiple scenarios.

CN121936992BActive Publication Date: 2026-07-03NATIONAL INSTITUTE OF METROLOGY CHINA
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
NATIONAL INSTITUTE OF METROLOGY CHINA
Filing Date
2026-01-20
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

Existing technologies lack assessment dimensions in integrated energy systems and resource and environmental management, have imperfect data processing mechanisms, and insufficient model universality, resulting in inaccurate assessment results that are difficult to use directly for engineering decisions.

Method used

By introducing an evaluation dimension, differentiating and processing two types of virtual data nodes, a deep learning hybrid model with embedded physical constraints is constructed to perform coupled evaluation and dynamic prediction at multiple time scales, providing a multi-objective optimization scheme.

Benefits of technology

It enables more comprehensive resource assessment, improves data quality and assessment accuracy, ensures the physical rationality and universality of model output, and supports assessment needs in multiple scenarios.

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Abstract

The present application relates to the technical field of comprehensive energy system and resource environment management, in particular to an energy-water-carbon chain coupling evaluation method and system based on artificial intelligence driving. The method comprises the following steps: collecting energy, water, carbon and scene correlation data, drawing an evaluation boundary and determining a scene type; screening real attribute nodes and supplementing missing information according to different time scales, and identifying and constructing two types of virtual nodes; based on the combination of physical mechanism and AI algorithm, calculating the correlation weight of real attribute nodes and the core elements of energy-water-carbon, and the influence correction coefficient of virtual nodes; constructing a deep learning hybrid coupling model containing a physical constraint layer and optimizing it; based on the model, carrying out multi-time scale coupling evaluation and dynamic prediction; and outputting a report of the optimized scheme. By introducing the energy dimension and virtual node processing mechanism, and combining the AI model with physical constraints, the present application improves the evaluation accuracy and reliability of the four-dimensional coupling system of energy-water-carbon, and adapts to the decision-making needs of multiple scenes.
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