267 results about "Wavelet packet decomposition" patented technology

The invention belongs to the technical field of semiconductor testing, and discloses a novel automatic detection system and method for the electrostatic protection performance of a power device. A distributed parasitic parameter network model is constructed by acquiring packaging structure parameters of a to-be-tested power device and physical layout data of a test fixture, key parasitic parameter distribution is identified in combination with a time domain reflection measurement technology, a waveform distortion coefficient is calculated, and a spatio-temporal evolution model of electrostatic pulse propagation is constructed. Based on the model, a compensatory predistortion waveform is generated to counteract the parasitic effect of a transmission path, a device port response signal is collected in real time, an energy dissipation characteristic spectrum is extracted through wavelet packet decomposition, and quantitative evaluation of the electrostatic protection performance is achieved. According to the invention, the test precision and repeatability are improved, and the method is suitable for electrostatic protection tests of various novel power devices, especially high-frequency wide-band gap devices.

The invention relates to the technical field of data processing and analysis, and discloses a parallel sensor data analysis method and system for cable fault detection, and the method comprises the steps: synchronously collecting cable fault transient traveling wave signals through distributed monitoring terminals, and forming a multi-channel signal data set; and carrying out wavelet packet decomposition and reconstruction, and denoising to obtain a denoised data set. The method comprises the following steps: firstly, generating node time sequence data with a timestamp, constructing an undirected weighted graph model, calculating a plurality of candidate fault positions through a double-end positioning formula on the basis of corrected time data, and finally, allocating dynamic weights to effective candidate positions according to signal quality and confidence, and generating a fusion positioning result by adopting a weighted fusion algorithm. And physical correction is carried out based on cable laying constraints, and accurate fault position coordinates are output. According to the method, through multi-level data processing and fusion, the fault positioning precision and the system robustness under complex working conditions are improved.

The invention discloses a hydraulic rammer tamping construction parameter adjusting method based on sensing parameter real-time feedback, and relates to the technical field of hydraulic rammer tamping construction.The hydraulic rammer tamping construction parameter adjusting method comprises the steps that a multi-mode sensing monitoring network is constructed to collect full-amount construction data, a wavelet packet decomposition algorithm is adopted for noise layered suppression, and a multi-mode sensing monitoring network is established; constructing a working condition associated data set in combination with the construction stage labels; based on the working condition associated data set, establishing a dynamic tamping effect evaluation model, and outputting a deviation index moment of time-space distribution; training a parameter adjustment intelligent model based on the deviation index matrix and a transfer learning mechanism, generating a multi-parameter collaborative adjustment strategy, and carrying out working condition adaptation degree scoring and adjustment risk early warning on strategy output; and adjusting the intelligent model based on incremental learning and model distillation technology optimization parameters. According to the method, the multi-modal sensing network, the geological dynamic quantitative model, the improved entropy weight method, the migration and reinforcement learning and the lightweight deployment technology are fused, so that full-chain intelligent dynamic optimization and safe controllable execution of hydraulic rammer construction parameters are realized.

The invention relates to the technical field of civil engineering structure health monitoring, in particular to a concrete bridge crack abnormity intelligent monitoring and early warning method based on GAF image classification and GRU prediction.The method comprises the steps that an intelligent monitoring framework integrating GAF image coding, CNN classification and recognition and GRU time sequence predication is constructed, high-frequency noise is removed through wavelet packet decomposition, and then the GRU time sequence predication is carried out; smoothing the crack-temperature coupling time sequence data; encoding the image into a two-dimensional image through a GAF method, and enabling the CNN to recognize an abnormal mode; and training a GRU model based on the high-quality data set after abnormity elimination, and realizing accurate modeling of crack width evolution under temperature driving. According to the method, residual error approximate normal distribution is predicted, the crack width early warning decision coefficient (R) is stabilized to be more than 0.93, a + / -3 sigma dynamic residual error threshold early warning mechanism is combined, structural damage trends under different disturbance scenes can be identified in a graded mode, and the method is suitable for online monitoring and maintenance decision support of bridge crack diseases in actual engineering.

The invention provides an automatic processing method for real-time observation data of an ocean station, and belongs to the technical field of ocean observation data processing. A wavelet packet decomposition multi-scale noise separation algorithm is established to distinguish environmental noise and real signals, dual-sensor redundancy configuration is combined with Bayesian inference to identify sensor drift, and a calibration coefficient is updated in real time through a recursive least square method. A one-dimensional time sequence is mapped to a high-dimensional phase space by utilizing a phase space reconstruction algorithm to realize high-precision prediction of a chaotic signal, a hierarchical data storage architecture is established, and a data migration strategy is iteratively optimized through a hierarchical correlation degree function; the technical problem that time synchronization signals are difficult to reconstruct accurately according to asynchronous sampling data of multiple sensors of an ocean station is solved.

The invention belongs to the technical field of urban rail vehicle detection and maintenance, and discloses a full-process optimization method and system for polygonal wear of a metro vehicle wheel. The method comprises the following steps: firstly, constructing a digital twin-driven train rigid-flexible coupling dynamic model, carrying out global sensitivity analysis, establishing a sensitivity index model, screening key dynamic performance indexes, and carrying out batch simulation to construct a dynamic response database; feature extraction and classification model training are carried out on the index data, multi-layer wavelet packet decomposition is carried out on the one-dimensional vibration signals, and a multi-channel feature vector is constructed and input into a one-dimensional residual network model; inputting actually acquired data into the trained model, calculating a relative close degree to generate a comprehensive index and a grading result, and generating turning repair suggestions based on grading; meanwhile, multi-source monitoring data are collected, a long-short-term memory network is used for predicting the abrasion evolution trend, finally, turning repair suggestions and trends are integrated, an accounting model and an evaluation system are constructed, and an optimal maintenance decision is generated through a multi-target optimization algorithm.

The invention relates to the technical field of relay protection loop detection, and discloses a relay protection loop fault detection method and system, comprising data acquisition, data processing, feature extraction, fault diagnosis and early warning and alarming. Multi-dimensional state data of current, voltage, temperature and vibration are collected in real time, wavelet filtering, mean or median filtering and normalization preprocessing are performed, comprehensive characteristic parameters are extracted in combination with time domain and frequency domain analysis and wavelet packet decomposition, a fault is accurately diagnosed and positioned through a fault diagnosis model, early warning and alarming are achieved based on graded thresholds, and the fault diagnosis accuracy is improved. The fault key information is synchronously transmitted to the operation and maintenance terminal, database storage and display terminal display are matched, the problems of poor real-time performance, one-sided feature extraction, no graded early warning and the like of traditional detection are effectively solved, the fault detection efficiency, the diagnosis accuracy and the operation and maintenance response speed are greatly improved, the operation risk of a power system is remarkably reduced, and the safety and stability of the power system are guaranteed.

The invention relates to the technical field of fault prediction and health management, in particular to a primary and secondary fusion complete ring main unit fault diagnosis method and system. Comprising the following steps: acquiring three-phase instantaneous voltage and current signals in real time, and converting the signals into digital transient data; performing time window preprocessing on the digital transient data, and executing wavelet packet decomposition to generate a transient feature vector; calculating and analyzing the transient feature vector through a transient zero-sequence power direction method and a support vector machine model to generate a local diagnosis result; when the local diagnosis result is that cooperative positioning needs to be started, a transient current similarity coefficient and a transient waveform intensity difference coefficient are calculated based on the digital transient data, and a comprehensive positioning result is generated; determining a fault line according to the comprehensive positioning result, and generating a remote control command; and executing a fault isolation operation based on the remote control command, and executing a PHM process. According to the method, the double local transient diagnosis and the cross-terminal cooperative positioning are deeply fused, so that the high-precision determination of the boundary of the fault section is realized.

The invention relates to the technical field of data processing, and discloses a building construction quality real-time monitoring method and system based on a sensor network. The method comprises the steps of constructing a sensor grid through hydration heat gradient mapping, recognizing welding defects based on acoustic emission spectrum texture and wavelet packet decomposition, obtaining a quality situation by adopting maintenance age weight time-varying fusion, performing multi-scale anomaly detection by applying a residual attention mechanism, and dynamically adjusting a threshold value to generate an intervention strategy in combination with a working condition switching trigger. And intelligent construction quality monitoring is realized. Through the multi-scale feature extraction and cross-modal data fusion technology, the accuracy and real-time performance of construction quality monitoring are remarkably improved.

The invention relates to the technical field of wind turbine generator state monitoring, in particular to a wind turbine generator state monitoring method and system based on multi-source heterogeneous data fusion, and the system comprises a data collection unit, a dynamic noise processing unit, a damage feature analysis unit and a health state output unit. A data acquisition unit synchronously obtains blade strain, unit rotating speed and environment vibration signals through hardware timestamp alignment, and a dynamic noise processing unit removes rotating speed coupling noise by using a two-parameter coupling model, a temperature-pitch angle three-dimensional correction curved surface and closed-loop feedback in combination with variable step blanking and local band elimination protection. The damage feature analysis unit extracts microcrack features by adopting multi-scale wavelet packet decomposition and kurtosis detection, and performs cross validation by fusing a vibration mode confidence factor, and the health state output unit generates a topological graph containing a crack position, an expansion trend and an alarm confidence level, so that the problems of insufficient multi-source data fusion and false and missing judgment of damage are solved, and the safety of the system is improved. And the monitoring precision is improved.

The invention relates to a GIS partial discharge optical detection method and device, and a medium. The method comprises the following steps: collecting a GIS partial discharge optical signal through a light guide rod, and carrying out the preprocessing of the GIS partial discharge optical signal; self-adaptive wavelet packet decomposition is carried out on the preprocessed signals, noise signals are separated through a self-adaptive threshold value adjustment algorithm, multi-scale time-frequency characteristics are extracted from the signals after noise separation, and according to the self-adaptive wavelet packet decomposition, a wavelet basis function is selected in a self-adaptive mode according to statistical indexes of the extracted signals so as to carry out wavelet packet decomposition; and constructing a lightweight convolutional neural network model, classifying the extracted multi-scale time-frequency features, and identifying a partial discharge signal. Compared with the prior art, the method has the advantages that the anti-interference capability is high, weak partial discharge signals can be effectively extracted, and the real-time requirement is met.

The invention relates to the technical field of intelligent detection, and discloses a building quality evaluation method and system based on concrete nondestructive testing and a storage medium. The method comprises the steps that a piezoelectric ceramic sensor array is arranged to collect micro-vibration response signals, and an original vibration data set is obtained; extracting an energy distribution coefficient of each frequency band by using a wavelet packet decomposition algorithm, and constructing a damage feature vector matrix; establishing a physical constraint neural network model, and outputting a damage variable time sequence; fusing the damage variable with ultrasonic and rebound data, and calculating comprehensive strength and damage degree indexes; and calculating the remaining service life by using a time sequence prediction algorithm, and generating an evaluation report. According to the method, the technical problem that the existing concrete nondestructive testing technology cannot realize microstructure damage evolution dynamic monitoring and residual life prediction is solved, and the accuracy of building quality evaluation and the scientificity of predictive maintenance decision are improved.

The invention provides a structure health early warning method and system based on space-time correlation characteristics and digital twinning, and relates to the technical field of data processing. The method comprises the following steps: acquiring multi-source heterogeneous data of a target structure; performing dynamic sampling alignment and wavelet packet decomposition on the multi-source heterogeneous data to extract energy features to obtain synchronous data, and performing abnormal data filtering on the synchronous data to obtain cleaned fusion data; performing wavelet decomposition on a high-frequency vibration signal in the fused data to obtain a damage impact feature, performing time sequence processing on low-frequency temperature data in the fused data to obtain a temperature time feature, and performing dynamic graph convolutional network processing on strain data in the fused data to obtain a spatial correlation feature; constructing an input vector; calculating a damage degree index; and according to the damage degree indexes, early warning grades are divided, and corresponding control instructions are triggered for different early warning grades. By implementing the technical scheme provided by the invention, the accuracy of structural health early warning can be improved.

The invention relates to the technical field of power equipment operation and maintenance and state monitoring, in particular to an optical fiber transformer health state assessment method and system based on feature fusion. According to the invention, sampling signals of a plurality of optical fiber current transformers are collected and preprocessed; constructing a historical data matrix for principal component analysis, establishing a principal component space and calculating a square prediction error control limit value; constructing a simulated fault data set and projecting the simulated fault data set to a principal component space to calculate a square prediction error time sequence; performing wavelet packet decomposition on the square prediction error time sequence to extract a normalized energy feature vector, and training a one-dimensional convolutional neural network model; projecting the real-time sampling signal to a principal component space to judge whether the real-time sampling signal exceeds the limit or not; if yes, the health state level is output through the trained model. According to the invention, accurate grading and real-time online evaluation of the health state of the optical fiber transformer are realized.

The invention provides a photovoltaic inverter operation state diagnosis method, which belongs to the technical field of photovoltaic inverters, and comprises the following steps: collecting multi-source operation signals of a photovoltaic inverter, carrying out wavelet packet decomposition on the signals to construct a time-frequency characteristic dense matrix, generating a fault characteristic super-sparse representation vector through singular value decomposition and sparse processing, and carrying out fault characteristic super-sparse representation on the fault characteristic super-sparse representation vector. Performing envelope demodulation on sensitive mode components obtained by complete set empirical mode decomposition to extract approaching periodic feature vectors, and inputting three types of complementary features into a weak fault recognition model with a circulation attention mechanism to perform fusion diagnosis. And whether preventive maintenance early warning is triggered or not is judged according to the output determinant characteristic value of the convergence state matrix, and an operation strategy is adjusted. The technical problem that early weak fault characteristics of the photovoltaic inverter are difficult to be accurately identified and early warned in time in a strong noise background is solved.

The invention discloses an acoustic emission signal noise reduction and feature extraction method and system suitable for a deep roadway, and relates to the technical field of safety monitoring of deep mineral resource mining, and the method comprises the specific steps: arranging an acoustic emission sensor array along the deep roadway, collecting multi-source data, converting the multi-source data into digital signals, and storing the digital signals; identifying an interference type through a wavelet packet decomposition and environment correction algorithm; self-adaptive noise reduction is carried out by using a complexity sensitive penalty algorithm; time domain and frequency domain features are extracted, coupling parameters are calculated, and time domain and frequency domain features are obtained through Hilbert-Huang transform; and finally, through principal component analysis dimensionality reduction and mutual information entropy screening, constructing a feature vector and transmitting the feature vector to a safety early warning system. According to the invention, the processing precision and reliability of the acoustic emission signal are improved through the multi-source signal acquisition module, the interference identification module and the adaptive noise reduction module; signal characteristics are comprehensively described through algorithm optimization, key characteristics are output through characteristic optimization, real-time monitoring and early warning are achieved through a dynamic damage vector algorithm, and deep roadway construction safety is guaranteed.

The invention discloses a sawing machine saw blade vibration analysis method. Wavelet packet decomposition (WPD) is adopted to decompose a vibration signal into a plurality of equal-bandwidth sub-bands, energy entropy and kurtosis are extracted as features, a fault sensitive frequency band is screened out, a sensor spatial topological graph is constructed, a spatial dependency relationship is modeled by adopting a graph attention network (GAT), and a gradual change fault trend is captured in combination with slow feature analysis (SFA). And combining a bidirectional LSTM network to predict and reconstruct a residual error and calculate a health threshold, and finally realizing online updating of the model and adaptive adjustment of the health threshold through incremental learning (IL). According to the method, high-precision feature expression of complex vibration signals is realized, and the health state of the saw blade can be adaptively evaluated.

The invention relates to a red light therapeutic instrument real-time calibration method based on multi-modal data fusion, which comprises the following steps: firstly, collecting data such as optical power, target surface temperature, internal temperature, reflection spectrum and light source-target surface distance in a time window, and unifying time base alignment; performing anomaly elimination, de-noising and normalization on each modal data, and constructing a standardized data matrix; extracting multi-modal features through power spectral density, wavelet packet decomposition, exponential moving average and Kalman filtering, inputting the multi-modal features into a fusion network containing a modal special encoder and a cross-modal attention unit, and generating a red light treatment state vector; the driving current, the pulse width, the duty ratio, the emission angle and the aging compensation factor are solved in real time through constrained least square optimization and are issued to the driving control module, and the red light source is smoothly adjusted in a soft start mode; the system monitors the output power, calibration is completed if the output power meets a threshold value, and otherwise, the next iteration is started. According to the method, power closed-loop correction can be realized, and dose consistency and thermal safety are improved.

The invention provides a sensor fusion-based pig feed crushing particle size monitoring method, which comprises the following steps of: synchronously acquiring vibration, acoustic emission and optical signals, carrying out band-pass filtering, normalization, feature extraction and wavelet packet decomposition, and dynamically adjusting the confidence coefficient of each channel in combination with working condition perception and a sensor performance knowledge base so as to monitor the crushing particle size of the pig feed. Weighted fusion and conflict evidence modulation of multi-source signals are achieved, the improved Dempster-Shafer evidence theory is introduced to improve the abnormal granularity recognition accuracy, key parameters of crushing equipment are controlled in a linkage mode through a closed-loop feedback mechanism, high-robustness detection and self-adaptive adjustment of the granularity state are achieved, and the method has the advantages of being high in robustness and high in robustness. And the stability and the automation level of the feed crushing process are improved.

The invention relates to a charging pile automatic detection operation and maintenance method and system based on the Internet of Things and a medium, and belongs to the technical field of intelligent power grid operation and maintenance. The automatic detection operation and maintenance method comprises the steps that multi-source sensor data of each charging pile is collected in real time, and a time-space aligned multi-source data set is generated; performing wavelet packet decomposition processing on the multi-source data set through an edge calculation node, generating a compressed feature vector, inputting the compressed feature vector into a pre-trained residual self-encoder model, calculating a reconstruction error, performing association reasoning in combination with a historical fault knowledge graph, and outputting a fault mode and a confidence coefficient set; and an operation and maintenance strategy instruction is dynamically generated in combination with the equipment health index attenuation rate, the charging pile to be operated and maintained is positioned, the digital twin is called for residual life prediction, a maintenance work order is generated through a path optimization algorithm, and an execution terminal is driven to complete operation and maintenance operation. The reliability and stability of the charging pile equipment can be improved, and the dual requirements of a modern charging system for high availability and low operation and maintenance cost are effectively met.

The invention relates to the technical field of lithium battery manufacturing, and discloses a quality control method for an automatic stamping process of a lithium battery shell. According to the method, a multi-dimensional monitoring system is constructed by collecting a stamping pressure time sequence waveform, plate thickness distribution data and a mold vibration frequency spectrum signal in real time. Analyzing the pressure waveform deviation by using a dynamic time warping algorithm, and forming a coupling characteristic matrix by combining the thickness data; wavelet packet decomposition is adopted to process the vibration signal, a frequency band energy distribution spectrum is generated, and a mold structure stability coefficient is calculated. And fusing the characteristics and the coefficients to generate a quality evaluation index, and dynamically adjusting the control parameters of the stamping equipment according to the quality evaluation index to realize closed-loop control. According to the method, the limitation of single parameter monitoring is broken through, dynamic changes in the stamping process are accurately captured through multi-source information coupling analysis, factors such as plate differences and die state changes can be responded in real time, and the comprehensiveness and timeliness of quality control are improved.

The invention relates to the technical field of ship shafting measurement, and discloses a polar region ship shafting comprehensive measurement system and method, and the system comprises a data collection module, a preprocessing module, a data processing module, and an evaluation module. The data acquisition module acquires a strain signal, a vibration acceleration signal, a temperature signal, an axial displacement signal and a radial displacement signal of a shaft system through a sensor group, and the preprocessing module performs wavelet packet decomposition and adaptive threshold denoising, mean filtering and triple standard deviation criterion abnormal value elimination processing on the signals. The data processing module fuses the data of the same physical quantity based on an improved Kalman filtering algorithm and dynamically adjusts the weight of the fused data to obtain final optimized data, and the improved Kalman filtering algorithm comprises the step of introducing a temperature drift compensation factor into a state transition equation, and the evaluation module predicts potential fault types and development trends through an operation state evaluation model based on the optimization data.

The invention relates to the technical field of temperature early warning, and discloses a JP cabinet bus connection point temperature early warning method and device, and the method comprises the steps: collecting the voltage drop time sequence data of a JP cabinet bus connection point, and carrying out the wavelet packet decomposition of the voltage drop time sequence data, and obtaining a conduction characteristic coefficient; performing contact resistance prediction based on the conduction characteristic coefficient to obtain a contact resistance value, and calculating a transient temperature rise characteristic value based on the contact resistance value; a temperature early warning threshold value is calculated based on the transient temperature rise characteristic value and the current change rate, and a temperature early warning signal is generated based on the temperature early warning threshold value, the change characteristics of the contact interface conduction state on different time scales can be effectively captured, and compared with a simple time domain analysis method, the time domain analysis method has the advantage that the accuracy is high. The identification precision and the anti-interference capability of the weak signal change are improved, the problem of insufficient adaptability of a fixed threshold value method under a dynamic load condition is effectively solved, and the accuracy of temperature early warning is further improved.

The invention relates to the technical field of nondestructive testing, and discloses a wind power casting nondestructive testing method and system, and the method comprises the steps: obtaining three-dimensional surface point cloud data of a to-be-tested wind power casting, employing a sound ray tracking algorithm to calculate a sound path distance and a propagation angle between each imaging point and each array element in an imaging grid, and generating a focusing rule; performing full-matrix data acquisition to obtain an original time domain signal, performing wavelet packet decomposition on the original time domain signal, and determining the peak kurtosis of the original time domain signal; carrying out weighted coherent superposition on any imaging point in the imaging grid based on a focusing rule and an original time domain signal, and reconstructing an image; and identifying a defect indication area in the reconstructed image, calculating an average spatial frequency gradient, determining the number of iterations of a deconvolution algorithm, performing deconvolution processing on the image of the defect indication area, and determining the contour and size of the defect. According to the method, the defect signal can be effectively enhanced, the noise interference is inhibited, and the accuracy of the defect detection result of the wind power casting is improved.

The invention discloses a phase modifier bearing fault diagnosis method, which belongs to the technical field of power equipment fault diagnosis, and comprises the following steps of: analyzing independent components, performing blind source separation on a received phase modifier bearing vibration signal, and extracting three types of independent source signals of impact, abrasion and noise; the gradient driving window length is self-adaptive, and the window length of short-time Fourier transform is dynamically adjusted based on the instantaneous frequency gradient of the independent source signal; wavelet packet frequency band energy screening: performing wavelet packet decomposition on the signal after window length adaptive processing, screening a fault characteristic frequency band based on an energy contribution rate, and reconstructing the signal; and enhancing stochastic resonance, and inputting the reconstructed signal into a stochastic resonance system. According to the method, independent component analysis, gradient driving window length self-adaption, wavelet packet frequency band energy screening and stochastic resonance enhanced fourth-order diagnosis chain are constructed, so that multi-stage cooperative processing of phase modifier bearing faults is realized, and the technical problems of time-frequency resolution contradiction, insufficient feature decoupling and weak generalization ability are effectively solved.

The invention relates to a crack defect detection method, system, equipment and medium in an alloy slab ingot casting process, and the method comprises the following steps: synchronously collecting acoustic emission signals through at least two acoustic emission sensor arrays arranged along the width direction of a slab ingot; adaptive segmentation processing is carried out on the acoustic emission signals, and comprehensive characteristic parameters are extracted through wavelet packet decomposition and Hilbert transform collaborative analysis; processing the comprehensive characteristic parameters based on a pre-trained multi-modal deep forest model to obtain crack category information; and configuring a propagation path weight coefficient corresponding to the category for the identified crack feature information, and outputting crack defect characterization information by combining anisotropic correction of sound wave propagation in the slab ingot according to a time difference and a spatial geometrical relationship when an acoustic emission signal reaches each sensor array. Therefore, through a multi-dimensional perception and intelligent analysis cooperation mechanism, all-directional monitoring and accurate characterization of crack defects in the slab ingot solidification process are achieved.

The invention discloses a small current grounding system single-phase grounding fault line selection method and system based on wavelet packet decomposition and comprehensive criteria, and belongs to the technical field of power system relay protection and fault diagnosis. Comprising the following steps: signal acquisition and preprocessing: acquiring bus zero-sequence voltage and each outgoing line zero-sequence current signal, and carrying out filtering and normalization preprocessing on the signals to eliminate interference; performing wavelet packet multi-scale decomposition, selecting a wavelet as a primary function, and performing wavelet packet decomposition on the preprocessed zero-sequence current signals of each line to obtain decomposition coefficients of different frequency bands; selecting characteristic frequency bands, calculating signal energy of each frequency band after each line is decomposed, and selecting the frequency band with the maximum signal energy; constructing a comprehensive criterion, wherein the comprehensive criterion comprises a polarity criterion and an amplitude criterion; and fault line discrimination: comparing the polarity criterion of each line with the bus voltage polarity, and finding out the line with opposite polarity and the most significant amplitude, namely the fault line. And the line selection accuracy and reliability under complex conditions are improved.

The invention provides a quantitative detection method and system for corrosion of steel bars in concrete, and belongs to the technical field of nondestructive testing and health monitoring of civil engineering structures. According to the method, an alternating weak magnetic field is applied to a reinforced concrete structure through a controllable magnetic field excitation device; a superconducting quantum interference magnetic sensor array is adopted to collect magnetic field response signals, and a space magnetic field gradient data set is obtained; performing wavelet packet decomposition and feature extraction on the data set to obtain a corrosion feature frequency band signal; based on a magnetic diffusion-corrosion coupling physical model, inversely calculating steel bar section corrosion rate distribution; and generating and outputting a three-dimensional corrosion distribution cloud picture and a quantitative evaluation report. The system comprises a controllable magnetic field excitation device, a superconducting quantum interference magnetic sensor array, a data processing and inversion calculation device and a result output device which are sequentially connected and cooperatively work. According to the invention, nondestructive, accurate and quantitative detection of corrosion of steel bars in concrete is realized, and the method has the advantages of strong deep penetration ability, high sensitivity to micro-corrosion, good field applicability and the like.

The invention discloses an intelligent electric meter abnormal electricity consumption detection method and system based on self-supervised learning, and relates to the technical field of power system automation, and the method comprises the steps: obtaining time sequence original electricity consumption data containing an electricity consumption load value and an acquisition timestamp; carrying out preprocessing, carrying out wavelet packet decomposition denoising, and filling missing values by adopting an LSTM interpolation algorithm; constructing a self-supervised learning model containing a feature extraction module and double self-supervised tasks; and constructing an anomaly detection model based on the learned features, providing and judging the features, and outputting a detection result. The invention aims to solve the problem of low detection precision caused by noise, missing values and scarcity of abnormal samples in the data of the intelligent electric meter, and can ensure the data quality, reduce the labeling dependence and improve the anomaly detection precision.