With the upgrade of transmission line maintenance technology, the visual remote inspection of transmission line channel is widely used. However, the application of these marked data is currently in the stage of statistical analysis and report form, a deeper data mining work has not been carried out, such as the identification of areas with high incidence of alarm. This paper presents a method of analysis of high risk area of transmission line channel based on historical early warning data, which can be applied to the field of transmission line maintenance. By preprocessing the transmission line visual alarm data, using the improved k-means algorithm, analyze the clustering results, and finding out the series of modeling and data analysis. In addition, when determining the initial points, we propose the maximum and minimum longitude and latitude coordinates of the alarm dataset, divide a certain number of longitude and latitude grid, obtain the candidate data within each grid, and the method of obtaining the initial point set after screening. Based on the initial point set, the alarm area distribution is reasonable, and the regional stability does not drift. This paper can identify the visual alarm data of transmission lines with high alarm incidence areas, and provide effective data support for maintenance personnel to guide the deployment of human resources and ensure the safe operation of transmission lines.
KEYWORDS: Data modeling, Correlation coefficients, Data transmission, Inspection, Data acquisition, Statistical modeling, Artificial intelligence, Data mining, Data processing
This paper proposes a continuous analysis method of transmission line channel based on historical early warning data. It calculates the feature data, the real-time alarm data and the corresponding feature data, and passes the threshold value R0. Determine whether it is a continuous alarm. In this paper, through the extraction of sample data extraction and calculation of Pearson moment correlation coefficient, solved the fireworks, foreign body alarm for low frequency, seasonal periodic, conventional single comparison and mechanical have good dimension reduction analysis cannot effectively identify the problem, for subsequent application scenarios such as alarm level intelligent annotation, AI image recognition model suspected false alarm and omission of sample identification model support, and improve the intelligent level of transmission line shipment inspection.
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