3results about How to "Full excavation" patented technology

The invention relates to the technical field of engineering geological rock recognition and classification, and discloses a coal rock recognition method and system based on audio and vibration signal fusion, and the method comprises the steps: obtaining a vibration signal and an audio signal in a coal mining process; respectively carrying out de-noising processing on the vibration signal and the audio signal; cutting the denoised signal according to a set time interval to obtain a plurality of vibration signal segments and a plurality of audio signal segments; performing normalization processing on all the vibration signal segments and all the audio signal segments; the normalized signal segments are input into the trained coal rock recognition model at the corresponding moment according to the time sequence, the trained coal rock recognition model outputs a coal rock recognition result, and the input end of the third-class hidden layer of the coal rock recognition model at the later moment is connected with the output end of the third-class hidden layer of the coal rock recognition model at the previous moment. The method has the advantages of high automation degree, high identification speed, high lithology identification accuracy, low cost and the like.

The invention relates to the technical field of electrical equipment fault diagnosis, and discloses a method for detecting transformer winding deformation based on a multi-size multi-channel CNN-LSTM model. Firstly, excitation signals, response signals and fault simulation signals of a transformer needing to be monitored under different frequencies and different working conditions are obtained. Afterwards, the signals are subjected to normalization processing, amplitude frequency parameters under different frequencies are obtained according to a formula, an amplitude frequency response curve is drawn, and a frequency response curve database under different working conditions is established; and dividing the database, and training a multi-size multi-channel CNN-LSTM model. And finally, collecting frequency response data of the transformer in actual operation, and obtaining the operation state of the current winding through the trained model. The frequency response generated when the transformer winding deforms is analyzed by using the multi-scale multi-channel CNN-LSTM model, so that the calculation amount of the model is not increased, the characteristics of each level of the image can be expressed, the signal characteristics are comprehensively mined, and the accuracy of winding deformation diagnosis is improved.

This invention provides a fuzzy testing mutation scheduling optimization method based on the mayfly algorithm, belonging to the field of fuzzy testing. It includes: Step 1, constructing a lightweight mayfly population for each seed in the fuzzy testing process, where individuals in the population represent a probability distribution of a set of mutation operators; Step 2, selecting mutation operators for each seed in the queue according to a uniform probability distribution, performing mutation, and simultaneously collecting path coverage and crash detection as feedback information, and calculating fitness scores through normalization and weighting; Step 3, simulating diverse behavioral patterns of the mayfly population based on individual fitness scores, dynamically updating the probability distribution of mutation operators, and optimizing the probability distribution; Step 4, finally applying the optimized probability distribution to the next round of seed mutation to generate more effective test cases, and repeatedly executing the above steps to achieve continuous optimization of the mutation scheduling strategy, improving fuzzy testing coverage and vulnerability discovery capabilities.