Multi-modal data fusion method and system based on energy scheduling and storage medium

Through the multimodal data fusion method, dynamic spatiotemporal adjacency matrix and edge node game equilibrium calculation are constructed, which solves the computational complexity and stability problems of the centralized scheduling model and realizes efficient and reliable energy scheduling decisions.

CN120471377AInactive Publication Date: 2025-08-12WUXI YUNSONG INFORMATION TECH CO LTD
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Patent Information

Application Number
CN202510572932.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-06
Publication Date
2025-08-12
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The existing centralized energy scheduling model is difficult to respond in a timely manner when facing new energy fluctuations and extreme weather, and has high computational complexity and is susceptible to single-point failures and network attacks, resulting in instability in scheduling and poor reliability.

Method used

The multi-modal data fusion method is adopted to build a dynamic spatiotemporal adjacency matrix by obtaining multiple data, perform abnormal fluctuation feature extraction and distributed strategy gradient aggregation, combine edge node game equilibrium calculation and digital twin constraint optimization, and generate an anti-disturbance energy scheduling strategy library to realize global scheduling optimization and bias traceability.

Benefits of technology

It improves the efficiency, reliability and intelligence of energy scheduling, can respond to fluctuations in energy supply and demand in a timely manner, avoid single-point failures and network attacks, and ensure system stability and accuracy.

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Abstract

The invention relates to the technical field of energy scheduling, in particular to a multi-modal data fusion method and system based on energy scheduling and a storage medium. The method comprises the following steps: obtaining multi-modal data, and carrying out abnormal fluctuation feature extraction to obtain a space-time fusion abnormal feature labeling set; deducing a multi-objective optimization path according to the space-time fusion abnormal feature labeling set to obtain a dynamic scheduling decision map; performing edge node game equilibrium calculation according to the dynamic scheduling decision map to obtain a trusted scheduling verification chain; performing digital twinborn constraint optimization on the trusted scheduling verification chain to obtain a closed-loop scheduling digital twinborn body; compiling a dynamic scheduling instruction set based on the closed-loop scheduling digital twin to obtain an anti-disturbance energy scheduling strategy library; and obtaining real-time energy supply and demand data, and performing scheduling deviation tracing on the real-time energy supply and demand data according to the anti-disturbance energy scheduling strategy library to obtain an energy distribution decision. According to the invention, the efficiency and reliability of energy scheduling can be improved.
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