Abstract
In order to improve the ability of software dynamic image fault detection, a software dynamic image fault recovery path detection algorithm based on a fuzzy control algorithm is proposed. A software dynamic image fault signal model of a software dynamic image fault was constructed by adopting an embedded feature extraction and a fuzzy control algorithm, and the dynamic image fault signal of the embedded software under the multi-load was subjected to frequency spectrum decomposition and blind source separation. The method comprises the following steps: (1) carrying out noise reduction processing on a software dynamic image fault signal by adopting a multi-dimensional wavelet decomposition method, (2) carrying out wavelet entropy feature extraction on the software dynamic image fault signal of the noise reduction output, and (3) combining the wavelet structural feature recombination method to carry out the recombination of the software dynamic image fault feature. The high-order spectral characteristic of the software dynamic image fault signal was extracted and the high-order spectral characteristic of the extracted embedded software dynamic image fault recovery path was automatically matched, and the automatic identification and detection of the fault part of the software dynamic image was realized.
Highlights
It is necessary to effectively detect and diagnose software dynamic image faults in embedded software, improve the stable operation ability of embedded software equipment, ensure the output stability of embedded software, and study the software dynamic image fault detection methods used in electric embedded software equipment
Aiming to resolve the above problems, this paper proposes a software dynamic image fault recovery path detection algorithm based on a fuzzy control algorithm
The original signal is collected for software dynamic image fault detection, the laser detection method of embedded software working condition sample in the presence of software dynamic image fault state is used for beamforming processing, and the fault code sensor is used for data acquisition
Summary
It is necessary to effectively detect and diagnose software dynamic image faults in embedded software, improve the stable operation ability of embedded software equipment, ensure the output stability of embedded software, and study the software dynamic image fault detection methods used in electric embedded software equipment. A software dynamic image fault detection algorithm based on an improved correlation rule feature analysis method was proposed in Reference [5], where the high-order spectral characteristic of the embedded software monitoring signal was extracted to realize the detection of the software dynamic image fault characteristic. The method described in this paper has the characteristics of high convergence and good real-time performance It realizes the high-precision detection of a software dynamic image and effectively reduces the error. A software dynamic image fault signal model of a software dynamic image fault is was constructed by using embedded feature extraction and a fuzzy control algorithm, and the multidimensional wavelet decomposition method was used to reduce the noise of the software dynamic image fault signal. Simulation experiments were carried out to demonstrate the superior performance of this method in improving software dynamic image fault detection capability
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