GIS sensor dynamic networking anti-interference diagnosis method based on K nearest neighbor algorithm

By adopting a dynamic networking anti-interference diagnosis method based on the K-nearest neighbor algorithm, the problems of inaccurate link quality assessment and insufficient self-healing ability of GIS wireless sensor networks in strong electromagnetic interference environments are solved, and high-reliability and low-cost adaptive networking of communication is realized.

CN122131216AActive Publication Date: 2026-06-02GLOBAL SCI & TECH (SHANGHAI) CO LTD
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Patent Information

Application Number
CN202610583429.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-29
Publication Date
2026-06-02
Estimated Expiration
2046-04-29

AI Technical Summary

Technical Problem

Existing GIS wireless sensor networks suffer from inaccurate link quality assessments and an inability to effectively distinguish between environmental interference and node hardware failures under conditions of strong electromagnetic interference and multipath effects, resulting in low communication reliability and difficulty in achieving self-healing.

Method used

A dynamic networking anti-interference diagnosis method based on the K-nearest neighbor algorithm is adopted. By collecting multi-dimensional communication link feature data, performing Z-score standardization processing, and using a dynamic anti-interference training sample library and an improved K-nearest neighbor classification model, combined with time decay factor and Gaussian kernel function, the link status is determined, triggering adaptive frequency selection networking strategy or hierarchical fault warning.

Benefits of technology

It significantly improves the accuracy of link quality assessment and the stability of data transmission, can accurately distinguish between environmental interference and hardware failure, realizes dynamic adaptive anti-interference networking, reduces operation and maintenance costs and extends network life cycle.

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Abstract

The invention provides a GIS sensor dynamic networking anti-interference diagnosis method based on a K nearest neighbor algorithm, and belongs to the technical field of electric power Internet of Things communication. The method comprises the following steps: in a time sliding window with a set size and a sliding step length, collecting multi-dimensional communication link characteristic original data between GIS sensor nodes and carrying out Z-score standardization processing; calling a dynamic anti-interference training sample library with an online incremental learning and forgetting mechanism; the state category of a current communication link is judged in real time by utilizing an improved K neighbor classification model introducing a time attenuation factor, calculating a weighted Euclidean distance and adopting a self-adaptive K value selection strategy based on sample density and a Gaussian kernel function for weighted voting; and finally, triggering a targeted self-adaptive frequency selection networking strategy or graded fault early warning operation according to the judgment category. According to the method, environmental interference and hardware faults in the GIS strong electromagnetic environment can be effectively distinguished, and the communication reliability and the self-healing capability of the wireless sensor network are remarkably improved.
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