An internet of things device energy consumption optimization method based on a knowledge graph

By constructing a data organization method driven by operational disturbance events and an improved R-GCN model, the modeling challenge of multi-device correlation and influence relationships in energy consumption optimization of IoT devices was solved. This enabled accurate representation of energy consumption propagation relationships and dynamic adaptation of optimization decisions, thereby improving the accuracy and interpretability of energy consumption optimization.

CN122432571APending Publication Date: 2026-07-21YIHUA (SHANGHAI) TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202610587719.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-29
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing energy consumption optimization methods for IoT devices are unable to reflect the interrelationships between multiple devices, and lack unified event-based modeling, multi-source heterogeneous data organization structure, and control action impact path evaluation mechanism, resulting in insufficient accuracy and dynamic adaptability of energy consumption optimization results.

Method used

By constructing a data organization method driven by disturbance events, a disturbance response observation sequence is generated. Then, by combining time window alignment, differential calculation and delay matching, a relational response representation matrix is ​​generated. A response-enhanced device energy consumption knowledge graph is constructed. The improved R-GCN model is used for path evaluation and optimization decision-making, forming a closed-loop dynamic optimization process.

Benefits of technology

It improves the accuracy of energy consumption propagation relationship modeling and the interpretability of control action impact assessment in multi-device interconnected scenarios, and enhances the dynamic adaptability of energy consumption optimization decision-making.

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Abstract

The application discloses a kind of based on knowledge graph's Internet of Things equipment energy consumption optimization method, comprising the following steps: step one: identify target equipment and the running disturbance event of associated equipment, extract corresponding monitoring data, generate disturbance response observation sequence;Step two: carry out time window alignment, difference calculation and delay matching, obtain relationship response representation matrix;Step three: construct response enhancement type device energy consumption knowledge graph;Step four: construct device semantic image and candidate regulation path;Step five: input improved R-GCN model, obtain path evaluation result;Step six: generate energy consumption optimization decision result;Step seven: execute target control action, obtain execution feedback data;Step eight: update knowledge graph and correct model, form closed loop dynamic energy consumption optimization.The application is based on the improved R-GCN model of embedding counterfactual path evaluation mechanism, realizes the accurate evaluation and closed loop optimization of Internet of Things equipment energy consumption regulation path.
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