87 results about "Cepstrum" patented technology

The invention relates to the field of biological medicine, and discloses a method and system for analyzing cough sound by using disease characteristics to diagnose respiratory diseases, and the method comprises the steps: deploying a six-microphone annular array to achieve the precise positioning and triggering of a sound source; self-adaptive spectral subtraction and Wiener filtering cascade are adopted to enhance the audio; segmenting a cough segment based on energy envelope; fusing the Mel-cepstrum, the linear prediction residual error, the harmonic energy ratio and the transient zero-crossing rate to construct a pathological feature matrix; extracting local, medium-range and global time sequence features through a three-branch parallel convolutional network; inputting a disease specific classifier to discriminate asthma, pneumonia and laryngitis respectively, and applying a feature decoupling regular term to improve interpretability. The system correspondingly realizes the modularized processing flow. According to the method, the cough sound collection quality and the disease subtype recognition accuracy in a complex environment are improved, meanwhile, the thermodynamic diagram is output to assist clinical decision making, and the diagnosis credibility and practicability are enhanced.

The invention relates to a transformer fault identification method based on voiceprint signals, and belongs to the technical field of cepstrum for extracting parameters in audio decoding or coding. The method comprises the following steps: setting a fault type and establishing a fault identification model for training; arranging an acoustic sensor to collect voiceprint signals of the transformer; utilizing a dream optimization algorithm to optimize the penalty factor and a successive variational mode decomposition method to decompose a plurality of mode components, and dividing the mode components into pure components and noisy components; noise reduction is carried out on the noisy component by adopting a designed threshold function in combination with wavelet threshold noise reduction, and the noisy component and the pure component after noise reduction are input into the recognition model to obtain a probability vector; and finally, fusing into a first fusion probability vector and a second fusion probability vector through fuzzy measurement, and taking the fault type corresponding to the maximum second fusion probability as the fault type of the transformer. The method can accurately capture the mapping relation between the acoustic features and the transformer fault state, and accurately identifies the transformer fault type.

The invention relates to the technical field of noise recognition, and discloses an intelligent recognition method and system for Bluetooth headset environment noise, and the method comprises the steps: collecting two paths of original audio of a Bluetooth headset, determining the dynamic evolution characteristics of environment noise components, carrying out the frequency domain cepstrum transformation of the environment noise components, and obtaining the frequency domain noise characteristics; carrying out attention fusion on the dynamic evolution features and the frequency domain noise features to obtain fused audio features, and constructing an association feature matrix of the fused audio features; constructing a noise scene candidate set of the Bluetooth headset, and performing noise type screening on the noise of the Bluetooth headset by using the associated feature matrix and the noise scene candidate set to obtain a noise type candidate list; and performing adversarial enhancement processing on the noise corresponding to each noise type in the noise type candidate list to obtain an enhanced noise set, extracting a dynamic characteristic spectrum of the enhanced noise set, and identifying the environmental noise of the Bluetooth headset. According to the invention, the recognition precision of the environment noise of the Bluetooth earphone can be improved.

The invention discloses an Internet of Things anomaly detection method and system based on quaternion state space diffusion enhancement, and belongs to the technical field of network security and artificial intelligence. The method comprises the following steps: mapping a flow time sequence feature into a quaternion tensor to maintain an internal coupling relationship of a multi-dimensional feature; a double-flow encoder is designed, a quaternion selective state space model is adopted to extract continuous fluid features, and a dynamic hypergraph neural network is adopted to model discrete protocol features; carrying out self-supervised pre-training on a resistance pseudo-anomaly sample by utilizing potential diffusion model generation, and optimizing characterization by combining quaternion cepstrum distance loss; the injected learnable prompt vector is optimized in the small sample fine tuning stage, and a category prototype is corrected by using a semi-supervised expectation maximization algorithm; and calculating a sample anomaly score based on an energy model to realize known attack classification and unknown anomaly judgment. According to the method, the generalization ability of the model under the small sample condition and the unknown threat detection ability are remarkably improved.

The invention relates to the field of voice signal processing and voice recognition, and discloses a voice recognition enhancement method and system based on harmonic model fundamental frequency optimization and RNN noise suppression, and the method comprises the steps: collecting a voice signal in a noise environment in real time, carrying out the framing and windowing of the voice signal, and generating multi-dimensional time-frequency data; estimating the fundamental frequency of each frame of voice through a cepstrum analysis method, and screening effective fundamental frequency frames according to a confidence coefficient threshold to form a fundamental frequency characteristic matrix; dynamically adjusting the noise power spectrum of the Wiener filter under the drive of the fundamental frequency characteristic matrix, and carrying out dislocation fusion on the filtering output and the original spectrum to form an enhanced spectrum first draft; inputting the enhanced spectrum first draft into a recurrent neural network in a framing manner, predicting the gain of each frequency band, calculating an inhibition factor, and generating a multi-frame continuous enhanced spectrum sequence; multiple frames of continuous enhanced spectrum sequences are synthesized into voice signals through inverse short-time Fourier transform, an end-to-end enhanced recognition process is formed, and the method has the advantage of improving accuracy.

Provided are a signal processing device, a diagnostic system, and a signal processing method with which it is possible to improve the accuracy of a determination value for determining a lubrication state in a rolling device. A signal processing device (3), which calculates a determination value for determining a lubrication state in a rolling device on the basis of a measurement signal acquired by a sensor (2), is provided with: a first processing unit (321) which converts the measurement signal into a time-domain signal by performing band limitation and envelope processing on the measurement signal; a second processing unit (322) that converts the time-domain signal into a first frequency-domain signal; a third processing unit (323) that converts the logarithmic spectrum of the first frequency domain signal into a first inverse frequency domain signal; a fourth processing unit (324) that generates a second inverted-frequency-domain signal in which the high-order cepstrum domain, which includes the rotation frequency of the scrolling device, of the first inverted-frequency-domain signal is set to a prescribed value; a fifth processing unit (325) that converts the second inverted frequency domain signal into a second frequency domain signal; and a determination value calculation processing unit (33) that calculates a determination value on the basis of the second frequency domain signal.

The invention belongs to the technical field of buildings, and particularly relates to a cable defect positioning and intelligent identification system and method based on a broadband impedance spectrum. The system comprises a vector network analyzer which can inject a sweep frequency excitation signal into a target cable to be detected, a high-precision receiver which is used for collecting signal reflection / transmission response so as to obtain full-band complex impedance spectrum data, and computer equipment which receives test data. The positioning method based on Nuttall-Kai ser mixed window and cepstrum coupling does not need to depend on an intact cable parameter database, is high in anti-interference capability, can accurately identify defect positions, and is high in positioning precision; the one-dimensional impedance spectrum is converted into the two-dimensional image through the GAF, the automatic identification of the defect type is realized by combining the residual neural network (ResNet), the manual intervention is reduced, and the automation degree is high; the deep feature learning capability of ResNet is utilized, and a CBAM attention module is combined, so that the capture of a fine defect mode is enhanced, and the recognition accuracy is improved.

The invention discloses an automatic audio quality scoring method and system combining MFCC and time domain statistical characteristics, and the method comprises the steps: carrying out the segmentation according to seconds on the basis of reference and to-be-detected signal resampling and time alignment, and further extracting cepstrum and time domain statistical characteristics according to 100 ms sub-segments; the method comprises the following steps: forming 170-dimensional second-level features by performing hierarchical aggregation of mean value + maximum value on sub-segment features, splicing the 170-dimensional second-level features with corresponding reference segments to form 340-dimensional joint features, and inputting the 340-dimensional joint features into a pre-trained support vector machine model to output second-by-second discrete scores from 0 to 5, thereby realizing high-time-resolution automatic quality evaluation and anomaly positioning under the conditions of light calculation power and small samples.

To provide a technique for detecting abnormal sound having a relatively small amplitude in a rotating body such as a motor.SOLUTION: A rotating body inspection device includes a frequency spectrum generation unit, a cepstrum generation unit, an order cepstrum generation unit, and an abnormal sound detection unit. The frequency spectrum generation unit generates a frequency spectrum for each measurement section in time series from vibration data that is data of vibration for each rotation speed of the rotating body. The cepstrum generation unit generates a cepstrum from the frequency spectrum for each measurement section. The order cepstrum generation unit generates an order cepstrum that is data obtained by converting a quefrency of the cepstrum into an order for each measurement section. The abnormal sound detection unit detects abnormal sound of the rotating body based on the order cepstrum for each measurement section.SELECTED DRAWING: Figure 2

The invention discloses an earphone defect detection method and device based on voiceprint feature analysis and a storage medium, and relates to the technical field of electroacoustic quality control. According to the method, human ear hearing characteristics are simulated, Mel-cepstrum voiceprint characteristics are extracted from sound response signals, an adaptive weight mechanism based on an equal-loudness curve is introduced, and a voiceprint abnormal deviation index model conforming to human ear subjective perception is constructed; in order to solve the problem that tiny abnormal noise is difficult to recognize, high-order statistics such as kurtosis and skewness are adopted for feature fusion enhancement, and a support vector machine is combined to achieve accurate capture of transient impulse noise. According to the invention, various defect types such as voice coil friction, imbalance of balance degree, diaphragm damage and the like can be fully automatically distinguished, the problems that a traditional test means is disjointed with subjective hearing sense and the omission ratio of hidden defects is high are effectively solved, and the quality inspection efficiency and accuracy of earphone production lines are remarkably improved.

The invention discloses a fracturing-based water hammer wave velocity inversion method, a readable storage medium and a fracturing-based water hammer wave velocity inversion device, and belongs to the technical field of fracturing. Performing noise reduction processing to obtain a noise reduction signal; converting the noise reduction signal to a reverse frequency domain; establishing a relationship between the cepstrum amplitude and the liquid inlet response time, generating a two-dimensional curve graph, identifying a peak point, and correspondingly obtaining the liquid inlet response time; designing a water hammer wave velocity value, and converting to obtain a liquid inlet depth value; and solving the minimum value of the difference sum between the liquid inlet depth value and the designed depth value. The readable storage medium stores the method. The device is provided with a computer program for storing and executing the method. According to the method, the water hammer wave velocity can be effectively determined under the conditions that the bridge plug slides and the actual bridge plug position is not matched with the designed bridge plug position, the liquid inlet position of the fracturing liquid is promoted to be better matched with the designed perforation position, then fracturing liquid inlet point recognition is guided, a fracturing construction scheme is optimized, and the fracturing transformation effect is improved.

The invention relates to the technical field of rotating machinery fault monitoring, and discloses a method and system for monitoring a torsional vibration fault of a shaft system of a steam turbine generator unit, and the method comprises the steps: obtaining a single-point torsional vibration signal, and converting the single-point torsional vibration signal into an angular domain sequence; extracting a narrow-band modulation component of a specific order band and calculating a torsional vibration logarithmic response spectrum; performing inverse transformation on the logarithmic spectrum to obtain cepstrum data reflecting the periodicity of the shafting structure; delimiting a structure reflection propagation time delay interval according to the geometric distance from the sealing end to the rigid coupling and the material wave velocity; identifying a sealing-coupling boundary reflection peak value and a series structure in the interval, and constructing a series enhancement index; reversely deducing an equivalent reflection intensity and an equivalent structure distance, and generating a health index; and finally, mapping the health index into a binarization result through a step function, and outputting normal or abnormal judgment for the sealed end. According to the invention, the high-sensitivity and deterministic monitoring of the local abnormity of the sealing end is realized by using the echo structure information.

The invention relates to a voice emotion recognition method and system, and belongs to the technical field of intelligent interaction. Comprises: acquiring an original voice signal; calculating a frame-level score of the original voice signal, and dividing the original voice signal into a high-reliability frame signal and a low-reliability frame signal according to the frame-level score; performing feature extraction on the high-reliability frame signal, and determining Mel-frequency cepstrum features; performing feature extraction on the low-reliability frame signal to determine rhythmic features; performing feature fusion on the Mel-frequency cepstrum features and the rhythm features to obtain target features; constructing a voice emotion recognition model; and inputting the target feature into a voice emotion recognition model, and determining an emotion category. According to the invention, the accurate judgment of the emotion is realized, and the accuracy of voice emotion recognition and the stability in a complex environment are remarkably improved.

The application discloses an audio quality automatic scoring method and system combining MFCC and time domain statistical features, comprising the following steps: on the basis of resampling and time alignment of a reference signal and a to-be-detected signal, extracting cepstrum and time domain statistical features according to second segmentation and further 100 ms subsegment extraction, forming 170-dimensional second-level features through mean value+maximum value hierarchical aggregation of subsegment features, splicing the corresponding reference segment into 340-dimensional joint features, inputting the pre-trained support vector machine model to output a second-by-second discrete score of 0 to 5, and realizing high-time-resolution automatic quality evaluation and abnormal positioning under the conditions of light computing power and small samples.

The speech synthesis method, the speech synthesis device, the electronic equipment and the storage medium provided by the embodiment of the present application relate to the technical field of financial technology. The method comprises the following steps: performing spectrum feature extraction on original speech data to obtain original speech mel-frequency cepstrum and original speech linear spectrum; performing feature coding on the original speech mel-frequency cepstrum through a preset speaker encoder to obtain original speaker features; performing posterior coding on the original speech linear spectrum through a preset posterior encoder to obtain speech posterior features; performing content feature extraction according to the original speaker features and the speech posterior features to obtain target speech content features; and performing speech synthesis according to preset target speaker features and the target speech content features to obtain synthesized speech data; wherein the synthesized speech data is used to represent speech uttered by a target speaker object according to original speech content. The embodiment of the present application can guarantee the quality of speech synthesis and can perform speech conversion for any speaker.

The application discloses a bearing composite fault diagnosis method and system, first, a bearing composite fault vibration observation signal is acquired; then, the observation signal is subjected to short-time Fourier transform and cepstrum threshold processing, two-dimensional time-frequency mask blind source separation, inverse short-time Fourier transform is performed on the obtained time-frequency domain separation signal and band-pass filtering is performed, independent time domain estimation signals are obtained; then, the estimation signals are subjected to Hilbert envelope demodulation and Fourier transform, envelope spectra of the filtered signals are obtained; finally, each order characteristic frequency band of a target fault is searched, a harmonic energy impact index is constructed, and an impact pulse value is calculated; the calculated impact pulse value is compared with a threshold value, and whether the bearing has a target type fault is diagnosed.

The present application relates to a kind of data-independent anti-multipath radio frequency fingerprint extraction method and system, for extracting the radio frequency fingerprint of wireless device.The method implementation steps include: sampling and preprocessing to wireless signal, obtain received signal;The time-domain signal of received signal is transformed in frequency domain and logarithm operation to obtain cepstrum domain signal;Select two frames of signals experiencing channel incoherence to do cross-correlation operation and further remove the correlation caused by local signal, as radio frequency fingerprint expression;The cross-correlation value between multiple symbols is superimposed and averaged to enhance fingerprint, while eliminating noise.The method of the present application effectively decouples the radio frequency fingerprint component in wireless signal from wireless multipath channel component, varying transmission data component, while enhancing the representation of radio frequency fingerprint, improving the stability of fingerprint, and suppressing noise, which is an effective data-independent anti-multipath radio frequency fingerprint extraction method for any wireless transmission signal.

The invention provides a method and device for determining a hydraulic fracturing temporary plugging transformation effect and computer equipment. On the basis of the method, a first water hammer wave signal before temporary plugging operation and a second water hammer wave signal after temporary plugging operation of a current transformation well section of a target well are obtained, and the cepstrum response energy proportion before temporary plugging and the cepstrum response energy proportion after temporary plugging of each cluster of cracks in the current transformation well section are determined through cepstrum operation according to the first water hammer wave signal before temporary plugging operation and the second water hammer wave signal after temporary plugging operation; determining the temporary plugging efficiency of each cluster of cracks in combination with a first type of distribution function constructed based on the cepstrum response energy proportion of each cluster of cracks before temporary plugging; meanwhile, according to the cepstrum response energy proportion before and after temporary plugging of each cluster crack in the current transformation well section, in combination with a second type of distribution function constructed based on the cepstrum response energy proportion during well section crack uniform development, determining a temporary plugging efficiency penalty coefficient of each cluster crack; and the temporary plugging efficiency and the temporary plugging efficiency penalty coefficient of each cluster of cracks in the current transformed well section are used in a combined mode, and the temporary plugging transformation effect of the current transformed well section is accurately determined.

The application belongs to the technical field of speech recognition and semantic understanding, and discloses a high-precision speech recognition and semantic understanding method. The method comprises the following steps: collecting a far-field multi-sound-source original speech signal to generate a discrete speech sampling sequence; performing frequency domain transformation on the original speech signal to extract an amplitude-frequency distortion quantization parameter; segmenting the original speech signal to generate a speech frame sequence and calculating a non-steady-state speech speed quantization parameter; extracting an original mel-frequency cepstrum feature and performing distortion correction to obtain a weighted acoustic feature through dynamic weighting; constructing a preset semantic feature library and mapping to generate an initial semantic matching vector; and recursively iterating to calibrate the semantic confidence and screening an optimal term to output a result. The application solves the problem of low far-field speech recognition accuracy in the prior art, realizes high-precision speech recognition and semantic understanding through multi-link collaborative optimization, and improves recognition stability.

The invention relates to the technical field of speech processing, and particularly provides a disguise speech recognition method based on a Transform model and mixed features, which is carried out from two aspects of feature extraction and model establishment. A resonance peak parameter is calculated by adopting a cepstrum method, a cepstrum coefficient (GFCC) is obtained through a Gammatone filter bank, then the resonance peak, the GFCC and a difference coefficient of the GFCC are combined into a mixed characteristic parameter, and complementary correlation between mixed characteristics is mined. From the perspective of model establishment, the mixed features are used as the input of the model, and the Transform network model is used as the acoustic model of the voiceprint recognition system, so that the feature distribution is better fitted, the classification effect is remarkably improved, and the performance of the camouflage voice voiceprint recognition system is effectively improved. The problem of performance degradation caused by feature redundancy and modal noise in a traditional method is solved.

The application discloses an Internet of Things anomaly detection method and system based on quaternion state space diffusion enhancement, and belongs to the technical field of network security and artificial intelligence. The method comprises the following steps: mapping traffic time sequence features into a quaternion tensor to maintain the internal coupling relationship of multi-dimensional features; designing a double-flow encoder, extracting continuous flow features by using a quaternion selective state space model, and modeling discrete protocol features by using a dynamic hypergraph neural network; generating an adversarial pseudo-anomaly sample by using a latent diffusion model to perform self-supervised pre-training, and combining a quaternion cepstrum distance loss to optimize the representation; optimizing the injected learnable prompt vector in the small sample fine-tuning stage, and correcting the class prototype by using a semi-supervised expectation maximization algorithm; calculating sample anomaly scores based on an energy model to realize known attack classification and unknown anomaly determination. The application significantly improves the generalization ability of the model under the condition of small samples and the detection ability of unknown threats.

The invention discloses a lung ultrasonic signal feature identification method based on an RF signal, and relates to the technical field of feature identification, and the method comprises the steps: collecting an original lung ultrasonic radio frequency signal, extracting the local frequency spectrum energy distribution of the original lung ultrasonic radio frequency signal in a frequency sub-band, and forming a frequency-time energy matrix; calculating a resonance period vector of a local energy peak value according to the frequency-time energy matrix, and constructing a lung texture dynamic period sensing map through multi-scale fusion mapping; the method comprises the following steps: performing cepstrum transformation on an original lung ultrasonic radio-frequency signal to generate an original radio-frequency cepstrum spectrum, constructing a multi-path reflection structure model based on the original radio-frequency cepstrum spectrum, and then aligning periodic structure candidate features in a lung texture dynamic periodic sensing spectrum with the original radio-frequency cepstrum spectrum to obtain a time domain alignment error; fusion modeling of a periodic change mode of an original lung ultrasonic radio-frequency signal under a multi-frequency sub-band is realized through a step of constructing a lung texture dynamic periodic sensing map.

The application relates to the field of artificial intelligence and discloses an audio generation method, device and equipment based on artificial intelligence and a storage medium, the method comprising the following steps: obtaining to-be-converted audio data, and obtaining a target field identifier corresponding to expected sound quality and / or expected emotion; performing cepstrum feature conversion based on a speech spectrum on the to-be-converted audio data to obtain cepstrum feature data of the to-be-converted audio data; inputting the cepstrum feature data and the target field identifier into a speech conversion model to encode the cepstrum feature data at an encoding layer, randomly sample the encoded vector at a resampling layer, and decode and reconstruct the vector sampled at a decoding layer based on the target field identifier to obtain target audio data; and on the basis of effectively removing sound rhythm information of a sounder, the application effectively converts the audio data into audio data with expected rhythm style information, thereby improving an audio conversion effect.

The application discloses an audio recognition method, device and equipment and a storage medium. The method comprises the following steps: obtaining to-be-recognized audio; performing mel-frequency cepstrum transformation on the to-be-recognized audio to obtain a mel-frequency cepstrum matrix; inputting the mel-frequency cepstrum matrix into a recognition model to obtain a sound event result, wherein the recognition model is obtained by iteratively training a neural network model by using a target sample set, and the target sample set comprises mel-frequency cepstrum matrix samples and sound event labels corresponding to the mel-frequency cepstrum matrix samples. According to the technical scheme, the model size can be effectively reduced and the recognition efficiency can be improved under the condition of ensuring the recognition accuracy.

This invention belongs to the field of oil and gas extraction technology and discloses a method for detecting the effect of temporary plugging of fracture openings in wellbore, comprising: S1, inducing water hammer phenomenon in the fracturing fluid in the wellbore; S2, detecting the real-time pressure signal value P(t) in the wellbore; S3, analyzing the real-time pressure signal value P(t) and plotting a cepstrum; S4, determining whether there are paired peaks based on the cepstrum; if not, stopping; if so, determining the water hammer period between the two peaks in each pair and proceeding to the next step; S5, calculating the average wave velocity C of the fracturing fluid when the water hammer phenomenon occurs in the wellbore, and multiplying the average wave velocity C by the water hammer period to obtain the specific location of the fracture opening. This method for detecting the effect of temporary plugging of fracture openings in wellbore improves the efficiency and accuracy of construction, achieves real-time feedback, and has low detection cost.

The present application relates to the technical field of artificial intelligence, and is applied to the field of smart government affairs, and provides a Chinese long speech recognition method, device and equipment and storage medium, which are used for improving the accuracy of mandarin low-delay speech recognition in a long time period. The Chinese long speech recognition method comprises the following steps: calculating the mel-frequency cepstrum coefficient of the segmented Chinese long speech to be processed to obtain a spectrogram; performing character recognition on the spectrogram by using a target deep speech recognition model to obtain a text sequence to be processed, the target deep speech recognition model is an end-to-end model based on connection time sequence classification, and the target deep speech recognition model comprises two convolution layers, four bidirectional long short-term memory networks, one look-ahead layer and one fully connected layer; estimating the probability of the text sequence to be processed by using a language model in the target deep speech recognition model, and determining a target recognition text sequence based on score data. In addition, the present application also relates to the technical field of block chain, and the target recognition text sequence can be stored in a block chain.

A keyword spotting method and apparatus based on magnetic tunnel junction arrays. The method comprises: using a statistically-aware training method to train a neural network, so as to obtain weight data of the neural network, mapping the weight data to high and low resistance state data of magnetic tunnel junction arrays, on the basis of an actual network structure of the neural network, selecting a magnetic tunnel junction array of a corresponding size, and pre-programming magnetic tunnel junctions into corresponding resistance states (S101); extracting a Mel-frequency cepstral coefficient (MFCC) feature from a speech keyword signal (S102); and during performing keyword spotting, continuously inputting binarized speech MFCC features into the magnetic tunnel junction array in the form of voltage amplitudes, acquiring a multiply-accumulate operation result by means of measuring an output current of a target column of the magnetic tunnel junction array, and finally outputting a hardware-based keyword spotting result after performing normalization and layer-by-layer inference (S103).

The invention relates to an unmanned aerial vehicle radar detection and identification method and system based on background contrast attention and double-flow micro-Doppler fusion, and belongs to the technical field of radar signal processing and intelligent target identification. According to the method, the problems of low unmanned aerial vehicle detection rate, high false alarm rate, and poor recognition robustness caused by observation angle change and sample scarcity in a low signal-to-noise ratio environment are solved. According to the technical scheme, the method comprises the steps of performing pulse compression on a radar signal to construct a distance Doppler map, realizing rapid positioning of a target by using a full convolution detection network integrated with background contrast attention, generating a time Doppler map and a time cepstrum map, and performing few-sample learning recognition through a double-flow feature fusion network based on Transform. According to the method, the detection precision can be remarkably improved, the recognition robustness is enhanced, the real-time efficiency is optimized, and the method adapts to a few-sample condition.

The invention relates to the technical field of mineral resource development, in particular to a high-frequency pressure real-time crack temporary plugging effect recognition method which comprises the steps that a high-frequency pressure meter collects pressure data in the fracturing period and uploads the pressure data to a cloud end; receiving and storing pressure data during fracturing, and filtering the pressure data; performing cepstrum analysis according to the filtered data, and drawing a cepstrum diagram; according to the drawn cepstrum, clustering the cepstrum to obtain a cepstrum after clustering analysis; and according to the cepstrum diagram after clustering analysis, performing cepstrum analysis before and after temporary plugging to determine a temporary plugging effect. Therefore, the high-frequency pressure crack temporary plugging effect can be accurately identified.

The application discloses a kind of electric fish signal detection methods based on mel cepstrum and multilayer perception machine, belong to electric fish signal detection field, comprising: first, the sound emission signal generated by underwater discharge is collected, pre-processing is carried out by pre-emphasis, framing and windowing to improve signal-to-noise ratio;Subsequently, the mel frequency cepstrum coefficient feature of signal is extracted, and is optimized by first-order and second-order difference calculation, and is fused to form a feature parameter set with static and dynamic characteristics;Finally, the feature parameter and detection voltage value are input into multilayer perception machine neural network for nonlinear fitting, and the high-precision electric signal strength is reconstructed.The application effectively utilizes the sound emission signal feature, and significantly improves the sensitivity and confidence of underwater electric fish signal detection.