A method for constructing a multi-level agent of a regional integrated energy system

By performing spatiotemporal alignment and visual encoding on real-time data of regional integrated energy systems, multi-level state semantic images and spatial correlation maps are constructed, solving the problems of data fragmentation and misjudgment of anomaly detection. This enables precise perception and collaborative control of the system's operating status, and improves the decision-making and operational efficiency of intelligent agents.

CN121810130BActive Publication Date: 2026-05-08STATE GRID SHANGHAI MUNICIPAL ELECTRIC POWER CO +1
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Patent Information

Application Number
CN202610283267.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-03-10
Publication Date
2026-05-08
Estimated Expiration
2046-03-10

AI Technical Summary

Technical Problem

In regional integrated energy systems, the data acquisition dimensions and spatial identification of multi-source real-time operation data are inconsistent, making it difficult to achieve efficient spatiotemporal alignment. This results in data fragmentation, making it impossible to accurately reflect the system's operational status. Furthermore, anomaly detection and control measures suffer from missed or incorrect judgments. Control commands lack hierarchical and collaborative design, making it difficult to meet the needs of multi-level collaborative operation.

Method used

By performing spatiotemporal alignment processing on real-time running data and mapping it to specific visual attribute primitives, a multi-level state semantic image is constructed. Visual semantic segmentation and spatial correlation map analysis are then performed. Combined with visual difference comparison and multi-frame tracking verification, collaborative control instructions are generated.

Benefits of technology

It enables visualization and refined analysis of system operation status, improves the efficiency and accuracy of state perception of multi-level intelligent agents, has efficient anomaly detection and collaborative control capabilities, and enhances the autonomous decision-making and collaborative operation level of regional integrated energy systems.

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Abstract

The application belongs to the technical field of energy intelligence, and proposes a multi-level agent construction method for a regional integrated energy system, which comprises: performing space-time alignment processing on real-time operation data to obtain a synchronous data stream; mapping parameters in the synchronous data stream into specific visual attribute graph elements to obtain a multi-level state semantic image; performing visual semantic segmentation on the multi-level state semantic image to obtain a visual semantic unit and constructing a spatial correlation graph between the visual semantic units; comparing the spatial correlation graph with a historical synchronous spatial correlation graph in terms of visual differences to obtain an abnormal visual semantic unit; performing multi-frame tracking verification on the abnormal visual semantic unit, and integrating an abnormal unit identifier and an abnormal type into an abnormal event report; and performing hierarchical decision-making on the abnormal event report and the spatial correlation graph to obtain a collaborative control instruction. The application can improve the efficiency of collaborative management of a regional integrated energy system.
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