Multi-source data fusion method for electric power wearable equipment

Abstract

The invention relates to the technical field of data fusion, and particularly discloses a multi-source data fusion method for electric power wearable equipment, which comprises the following steps: S10, extracting and classifying data information acquired by a multi-sensor system of the electric power wearable equipment, removing irrelevant data information, and acquiring data information needingto be subjected to data fusion; s20, analyzing and processing data information needing to be subjected to data fusion by constructing a Bayesian prediction model; and S30, analyzing and processing theextracted and classified data information through a weighted fusion algorithm based on variance estimation. The multi-source data fusion method oriented to the electric power wearable device has theadvantages of being efficient and accurate.

Description

technical field [0001] The present invention relates to the technical field of data fusion, in particular to a multi-source data fusion method for power wearable devices. Background technique [0002] Generally, because electric workers have to work in various dangerous electric areas, they often need to wear electric wearable devices such as smart safety helmets and smart gloves. Conventional power wearable devices have many sensors. In order to achieve accurate sensing, it is necessary to perform fusion analysis on the data collected by each sensor. [0003] With the rapid development of big data information technology, information fusion technology is widely used in the field of power communication, and multi-sensor data fusion MSDF (Multi-sensor Data Fusion) technology is becoming more and more mature. For the control systems of power wearable devices, sensor Condition monitoring and fault detection, reliability of diagnosis and compensation methods, etc., all have very...

AI Technical Summary

Problems solved by technology

The main difficulty of the Bayesian method is to find a suitable probability distribution, especially when the data comes from low-end sensors

[0006] At present, when using power wearable devices to collect power field operation data, the data diversity is more and the accuracy of the data required by the staff is high. However, the existing data fusion algorithms are not only complicated but also common, and there is no specific solution for power field operations. The data fusion algorithm of wearable devices, which will lead to the problem that the existing data fusion algorithms are not efficient and accurate enough for the data processing results of electric wearable devices

[0007] Therefore, it is necessary to develop a multi-source data fusion and classification method for electric wearable devices, so as to solve the problems that the existing processing methods are not efficient enough and the processing results are not accurate enough

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