121 results about "Characteristic matrix" patented technology

The invention discloses a powder metallurgy gear hardness detection method and system, and belongs to the technical field of metallurgy gears, and the method comprises the steps: obtaining a to-be-detected gear three-dimensional model, and dividing a hardness evaluation region; a thermal response image is collected, a thermal response time gradient parameter TRTG is extracted, and a thermal diffusion characteristic matrix T is formed; establishing a hardness initial model H1 based on the TRTG and compactness; constructing a finite element stress field model, extracting a stress distribution modulation coefficient SDMC, and forming a stress characteristic matrix S; performing weighted correction on H1 based on T and S, and establishing a comprehensive hardness prediction model H2; outputting a hardness prediction result of the whole gear and the key area, and identifying a potential weak area; according to the method, thermal response and stress modulation parameters are fused, non-destructive and high-resolution hardness distribution prediction is achieved, and the method is suitable for quality evaluation and failure early warning of the powder metallurgy gear of a complex structure.

The invention provides an equipment fault prediction method and system based on deep learning, and relates to the technical field of computers, and the method comprises the steps: obtaining first information, second information and third information; extracting historical dynamic operation characteristics of the equipment according to the third information to obtain an operation state characteristic matrix; performing graph construction processing according to the second information, and respectively constructing to obtain a structure graph, a wiring characteristic graph and a control logic graph; according to the operation state characteristic matrix, the structure diagram, the wiring characteristic diagram and the control logic diagram, performing fusion processing to obtain a comprehensive diagram structure; according to the comprehensive graph structure, using a deep learning algorithm to construct and obtain a fault prediction model; and inputting the first information into the fault prediction model to obtain a prediction result. According to the method, the time domain, frequency domain and time-frequency domain characteristics are extracted from the time sequence of the historical operation data in a segmented manner, a multi-dimensional comprehensive graph structure is constructed in combination with the equipment structure, the signal wiring characteristics and the control logic, and the internal characteristics of the equipment and the incidence relation thereof are fully excavated.

The invention provides a food production information traceability management method and system, and relates to the technical field of food production monitoring. The method comprises the steps of obtaining multiple groups of multi-process production record data of target baked food, performing production process characteristic linkage analysis on the target baked food, and generating a plurality of process joint characteristic matrixes; performing mode clustering based on production process distance perception on the multiple groups of multi-process production record data to generate a plurality of abnormal production mode class clusters; performing process anomaly analysis and back propagation traceability on the to-be-analyzed batch to generate a process anomaly detection result of the to-be-analyzed batch; according to the plurality of production abnormity mode class clusters, mode abnormity tracing is carried out on the to-be-analyzed batch, a production abnormity mode matching result of the to-be-analyzed batch is generated, and a production abnormity tracing analysis result of the to-be-analyzed batch is generated through fusion. According to the invention, the positioning precision of the abnormal source process of the baked food is improved.

The invention discloses a distribution network recording fault accurate positioning method based on big data analysis, and relates to the technical field of power distribution network fault positioning, and the method comprises the steps: deploying a fault indicator; setting a trigger threshold value, and triggering high-precision transient recording to generate recording data when the current or the electric field suddenly changes; carrying out time alignment on the recording data and synchronously acquired current intensity, electric field intensity, cable temperature and environment data, extracting transient characteristics and environment characteristics after wavelet transform denoising, and combining the transient characteristics with the environment characteristics to generate a comprehensive characteristic matrix; binding the comprehensive characteristic matrix and the line topology information, and uploading the information to a monitoring center through encryption communication; formulating a dynamic threshold association rule, and judging a fault type; the monitoring center matches the current transient waveform with a historical database and calculates a fault signal propagation time difference; and calculating a fault distance by combining line topology information, and fusing the fault signal propagation time difference and the fault distance to obtain a fault coordinate.

The invention discloses a micro-seismic signal arrival time pickup method based on fuzzy clustering and variational mode decomposition. The method comprises the following steps: firstly, calculating characteristic functions of attribute characteristics such as a micro-seismic signal mean value, power and kurtosis and normalizing the characteristic functions to obtain a characteristic matrix, then primarily picking up a micro-seismic P-wave initial movement position and a first arrival moment through a fuzzy clustering algorithm, and extracting an effective time window by taking the first arrival moment as a reference point; a variational mode decomposition algorithm is utilized to decompose signals in a time window into K intrinsic mode function components, AIC function values of the components are calculated by means of an akaike information criterion algorithm, a minimum value point is picked up to serve as first arrival time, energy ratios of all the components are calculated, and final micro-seismic P-wave first arrival time is obtained through weighted calculation. The method effectively deals with the low signal-to-noise ratio environment of the underground coal mine, has higher pickup precision and reliability compared with a traditional pickup method, can provide accurate micro-seismic occurrence time and position information for mine safety early warning, and powerfully guarantees the safety production of the coal mine.

The invention belongs to the technical field of power monitoring, and discloses a detection method for monitoring the state of a primary and secondary fusion circuit breaker, which comprises the following steps: acquiring state data of the circuit breaker, including electromagnetic waveform data, harmonic response characteristics and temperature distribution gradient; obtaining circuit breaker electric field distribution fingerprints through nonlinear segmented harmonic analysis, constructing a multi-dimensional permeation characteristic matrix, and establishing a micro-scale electrothermal coupling map; performing impedance distortion analysis to obtain a nonlinear region situation table, performing multi-frequency response analysis to obtain a harmonic penetration curve, and constructing a high-frequency harmonic penetration path spectrum; a micro-region diagnosis algorithm is established, nonlinear impedance calibration is carried out, and a multi-level protection instruction sequence is generated; adjusting the operation state of the circuit breaker to obtain a circuit breaker medium steady-state index, and performing risk analysis to obtain a harmonic penetration early warning report; according to the invention, accurate identification and early warning of the abnormal-state area in the circuit breaker are realized, and the safe and stable operation capability of the primary and secondary fusion circuit breaker is improved.

The invention discloses a mucky soil foundation intelligent monitoring method based on multi-source data fusion, and particularly relates to the technical field of foundation monitoring and early warning. The method comprises the following steps: constructing an original soil body state data set of a mucky soil foundation, and carrying out anomaly elimination and time calibration based on a rheological constitutive model to generate a foundation state characteristic matrix; performing dynamic causal association tracking analysis on the feature matrix in a sliding window mode to generate a water-soil coupling drift parameter set; extracting a stress historical dependency coefficient set by using a nonlinear scale analysis method; and according to the water-soil coupling drift parameter set and the stress historical dependency coefficient set, the stability risk level of the mucky soil foundation is judged, and corresponding early warning information is selected from a preset graded early warning instruction database and output, so that the accuracy and adaptability of anomaly recognition and risk early warning are improved, and the accuracy and the adaptability of abnormal recognition and risk early warning are improved. The method is suitable for long-period stability monitoring and intelligent risk prevention and control of high-soft foundations or silt stratums.

The invention provides a feed mixing process uniformity dynamic evaluation and parameter adjustment method, and relates to the technical field of data processing, and the method comprises the steps: building a material characteristic matrix according to raw material characteristic data; correspondingly storing the material characteristic matrix and initial operation parameters of the mixing equipment to generate first mixing control data; in the mixing process, collecting local vibration amplitudes and temperature rise rates at different spatial positions, and combining the local vibration amplitudes and the temperature rise rates with the first mixing control data; calculating the vibration amplitude distribution difference in adjacent sampling periods in the feed mixing treatment process to obtain a spatial fluctuation index; when the spatial fluctuation index is continuously reduced and the temperature rise rate is periodically and suddenly increased, generating local accumulation indication data, and triggering short-time acceleration and inversion operation; executing feed mixing treatment, and generating a mixing termination signal when the spatial fluctuation index change rate of three continuous sampling periods is lower than a preset fluctuation threshold value; according to the invention, autonomy and accuracy of feed mixing uniformity adjustment are improved.

The invention provides a time-space evolution data processing method and system applied to tunnel deformation analysis, and the method comprises the steps: obtaining a time sequence monitoring sequence of different monitoring dimensions in a tunnel construction process, constructing a time-space evolution correlation model of tunnel deformation based on the time sequence monitoring sequence, and analyzing a dynamic coupling relation between structure response data and geological environment action data, generating a space-time correlation feature matrix, generating a feature evolution sequence of tunnel deformation based on the space-time correlation feature matrix, and performing nonlinear mutation feature extraction on the feature evolution sequence to obtain a mutation feature index set; according to the sudden change characteristic index set and a preset accumulated deformation rate threshold value, performing dynamic division of tunnel deformation safety levels, generating a safety level division result, and based on the safety level division result and the spatial distribution data of the tunnel construction section, executing spatial positioning and boundary contour extraction of the potential risk damage area. And obtaining a risk damage area distribution map. According to the invention, the tunnel construction safety risk can be accurately evaluated and effectively controlled.

The invention discloses a multi-layer heterogeneous sequential network feature alignment and clustering method and device based on tensor self-representation. The method comprises the steps that sequential feature matrix sequences {},..., {} of data views in a multi-view heterogeneous network in time windows are obtained; based on the matrix sequence of the first time window, constructing a Laplacian regular term by introducing a self-representation learning mechanism, modeling multiple views by adopting a tensor structure and introducing a Schatten p-norm regularization mode to construct an optimization objective function for optimization, and representing shared feature representation of each data view in the first time window; and solving the function and carrying out clustering processing on the obtained function, wherein the clustering result of each time window is used for carrying out characteristic analysis on nodes in the network. According to the method, on the premise that the original structure and semantics of the network are guaranteed, unified modeling can be carried out on the incomplete multi-view heterogeneous network, and feature expression is aligned.

The invention provides a penaeus vannamei quality prediction and grade evaluation method and system, and belongs to the field of penaeus vannamei cold-chain transportation process quality prediction, and the method comprises the steps: S1, constructing a penaeus vannamei data set, and carrying out the dynamic time-varying coupling correlation graph generation, convection-diffusion cooperative information propagation, multi-task joint optimization and end-to-end MLP prediction to obtain a penaeus vannamei data set; obtaining a prediction result of the quality of the penaeus vannamei; s2, splicing is performed according to the prediction result and the actual quality data, and a feature matrix is constructed; clustering the feature matrix by using a rule fusion K-means + + clustering method to obtain an optimal clustering center set; and S3, based on the prediction result and the clustering center set, carrying out dynamic prediction and evaluation on the quality of the penaeus vannamei in the refrigeration state in a period of time in the future. According to the invention, an intelligent solution is provided for quality monitoring in cold-chain logistics, and economic loss caused by quality deterioration is significantly reduced.

The invention provides a blasting vibration monitoring and influence range prediction method for a long and large tunnel in an environment sensitive area, and relates to the technical field of blasting safety monitoring and vibration control in tunnel engineering, and the method comprises the steps: constructing a three-dimensional dynamic monitoring network, collecting multi-source data based on the three-dimensional dynamic monitoring network, carrying out the preprocessing of the multi-source data, obtaining a blasting vibration feature matrix, and carrying out the prediction of the influence range. Building a prediction model based on the improved LSTM model, training the prediction model based on a preset blasting vibration characteristic matrix and blasting parameters and geological conditions corresponding to the preset blasting vibration characteristic matrix to obtain a trained prediction model, and inputting a to-be-detected blasting vibration characteristic matrix and blasting parameters and geological conditions corresponding to the to-be-detected blasting vibration characteristic matrix into the trained prediction model. And obtaining a prediction result. According to the method, the precision and the real-time performance of blasting vibration monitoring and prediction are remarkably improved, the vibration control problem in blasting construction of the long and large tunnel in the environment sensitive area is effectively solved, the prediction precision is high, the real-time performance is high, the adaptability is high, and the data utilization efficiency is high.

The invention discloses a safety check system and method based on a hidden danger database model, and the method comprises the following steps: S1, building a hidden danger table, a check record table and a rectification record table, and constructing a hidden danger rule base; s2, collecting hidden danger data, and storing the hidden danger data into a hidden danger database model after standardization processing; s3, constructing a hidden danger knowledge graph, and generating a feature matrix; s4, inputting the data and the feature matrix to the time sequence model, and generating a risk scoring matrix; s5, based on the risk score and the knowledge graph, optimizing hidden danger grading and updating the rule base; s6, inputting a multi-modal hidden danger recognition model, and classifying hidden dangers; s7, optimizing task distribution, updating a hidden danger state and storing the hidden danger state into a database; and S8, integrating the data to generate a dynamic characteristic label, optimizing the model, and outputting a heat map and a trend report. Through multi-modal data fusion and dynamic analysis, intelligent management of hidden danger detection, risk assessment, task allocation and model optimization is realized, and the safety check efficiency and accuracy are greatly improved.

The invention discloses a circuit breaker vibration signal composite feature space construction method based on multi-channel fusion, which comprises the following steps of: 1) acquiring a multi-channel circuit breaker operating mechanism vibration signal, and constructing a noise-free reference signal and a standard signal-to-noise ratio test signal; 2) performing CFA-MVMD decomposition on the multi-channel circuit breaker vibration signal to obtain a fusion mode component and a frequency spectrum characteristic thereof; 3) performing segmentation operation on the obtained fusion mode component, performing segmentation time-frequency feature extraction, and constructing a segmentation time-frequency feature matrix; 4) extracting global time-frequency features, and constructing a global time-frequency feature matrix; combining segmentation and global time-frequency feature extraction to construct a multi-channel fused circuit breaker vibration signal composite feature space; and 5) inputting the multi-channel vibration signal composite characteristics of the circuit breaker into the CFA-KELM fault diagnosis model to obtain an early fault state of the circuit breaker. According to the invention, a high-precision and low-cost solution is provided for real-time monitoring of early mechanical faults of the circuit breaker.

The invention relates to the technical field of data processing, and discloses a cross-scale spectrum and chromatographic data associated pollutant conversion evaluation method and system. The method comprises the following steps: collecting reaction parameters through multi-point sensing, and normalizing spectrum and chromatographic data to obtain a characteristic matrix; decomposing and fusing data of different scales by utilizing a multi-layer association tensor to form a structure-activity relation graph; based on the map, constructing a corresponding rule by using a deep self-attention neural network, and generating a degradation efficiency predictor; and the optimal scheme report card is formed according to the predictor evaluation processing parameters. According to the application, accurate prediction and optimization of the degradation process of the organic pollutants in the coal gangue / persulfate system are realized, the number of experiments is reduced, and the scientificity and high efficiency of treatment scheme design are improved.

The invention relates to the technical field of ore raw material analysis, and discloses an intermediate fusion strategy ore sample classification method based on random model combined data, and the method comprises the following steps: S1, collecting LIBS spectrum and Raman spectrum data of a manganese ore sample; s2, preprocessing the spectral data; s3, performing dimensionality reduction on the preprocessed LIBS and Raman data by adopting principal component analysis; s4, splicing the feature matrixes after dimension reduction to construct an intermediate fusion data set; s5, training a random forest classification model based on the intermediate fusion data set; and S6, carrying out classification and identification on the manganese ore samples by utilizing the trained model. According to the method, metal element characteristic spectral lines captured by the LIBS spectrum and molecular vibration characteristics identified by the Raman spectrum are organically integrated through an intermediate fusion strategy, two-dimensional identification is achieved, redundant information is effectively eliminated while main characteristics of the spectrum are reserved, and the accuracy and reliability of manganese ore sample classification are remarkably improved.

A generator fault identification method, device, product and medium relate to the technical field of generator fault category identification. The method comprises the following steps: acquiring a vibration signal and a noise signal; performing multi-mode decomposition processing on the vibration signal and the noise signal to obtain a first sub-mode component and a second sub-mode component; determining a vibration signal sequence based on each first sub-mode component and the vibration signal; determining a noise signal sequence based on each second sub-mode component and the noise signal; determining a vibration characteristic matrix based on the vibration signal sequence; determining a noise feature matrix based on the noise signal sequence; determining a first recognition result and a first confidence coefficient based on the vibration feature matrix; determining a second recognition result and a second confidence coefficient based on the noise feature matrix; when the identification results are inconsistent, determining a fault identification result according to the confidence coefficient; and when the identification results are consistent, determining the first identification result as a fault identification result. The method is advantaged in that generator fault identification accuracy is improved.

The invention relates to the field of data mining, in particular to a method and device for detecting abnormal nodes in the industrial internet, and the method comprises the steps: a given attribute graph is embedded through a multi-layer perceptron to calculate a characteristic matrix of network topology of the industrial internet, and then a comparison positive sample and a comparison negative sample which are composed of nodes and neighbor nodes are constructed; and obtaining a local abnormal difference degree of each node as an attribute abnormal score by using a comparative learning technology. Updating the feature matrix based on a discretization graph diffusion technology, optimizing a new multi-layer perceptron to reconstruct a diffused matrix structure, and using the reconstruction loss as the structure anomaly score of each node; and finally, selecting a node with a relatively high abnormal score as a detected abnormal node. Therefore, the problem that the performance of abnormal node detection is affected due to the fact that only attribute comparison between the target node and the nodes in the local neighborhood of the target node is considered and semantic information of a complex global structure of network topology is ignored in the related technology is solved.

The invention discloses a photovoltaic array multi-working-condition mode recognition method based on Stacking and interpretability analysis. The method comprises the following steps: constructing a multi-dimensional photovoltaic array operation characteristic matrix; constructing a multi-dimensional photovoltaic array operation characteristic matrix and carrying out normalization processing; performing data set division on the normalized feature matrix and the corresponding tag set; the method comprises the following steps: constructing a Stacking learning framework based on a K-fold cross validation mechanism, selecting LightGBM, RF and XGBoost as base learners and LR as a meta learner, and constructing a multi-working-condition mode recognition model; and on the basis of the constructed multi-working-condition mode recognition model, taking F1 score maximization as an optimization target, and adopting an Optuna Bayesian search algorithm to carry out joint optimization on key hyper-parameters and fusion layer weights of all base learners. According to the method, deep derivation is carried out on the original monitoring quantity on the feature engineering level, a tree model-based Stacking architecture is constructed, and global and local interpretation mechanisms are introduced, so that the influence of environmental fluctuation and sample imbalance on an identification result is weakened, and the accuracy and reliability of photovoltaic array working condition identification are improved.

The invention discloses a bolt pre-tightening force prediction method based on ultrasonic echo time-frequency characteristics, and the method comprises the steps: S1, carrying out the ultrasonic excitation of a target bolt, obtaining an ultrasonic echo signal generated by the bolt, and carrying out the time-frequency analysis, and forming an initial time-frequency characteristic matrix; s2, according to a bolt material characteristic database, determining characteristic parameters of ultrasonic echo propagation corresponding to a target bolt material, and performing weighting processing on the initial time-frequency characteristic matrix to obtain a weighted time-frequency characteristic matrix; s3, extracting a time-frequency characteristic index through the weighted time-frequency characteristic matrix, and forming a target characteristic vector; and S4, inputting the target feature vector into the trained double-branch prediction model, outputting a pre-tightening force energy feature value and a pre-tightening force state change value, inputting the pre-constructed neural network model, and outputting a bolt pre-tightening force prediction value. According to the method, the bolt pre-tightening force can be predicted with high precision and high reliability, and the technical problems of low prediction precision and great environmental influence are solved.

The invention discloses a geological exploration data analysis system and method based on multi-source data fusion, and relates to the technical field of data analys.The geological exploration data analysis method comprises the steps that a multi-source sensor is used for collecting general characteristic data in geology, and real-time characteristic values are calculated to construct an environment characteristic matrix; calculating the similarity between the real-time environment characteristic matrix and each type of geological data in the knowledge base, and selecting and judging to obtain a real-time survey geological type; respectively calculating data fusion weights of the real-time survey geological types by using the quality score and the relevancy; performing weighted fusion on the real-time data flow during geological survey by using the calculated data fusion weight; according to different types of geological historical survey data, abnormal thresholds of different types of geology are calculated and standardized, and an abnormal threshold database is generated; and extracting an abnormal threshold of the type of the real-time surveyed geology from the abnormal threshold database, judging the fused data by using the abnormal threshold, obtaining whether the real-time surveyed geology is abnormal or not, and performing early warning.

The invention provides an oil and gas geology virtual well construction method based on multi-modal rock debris digitization, which comprises the following steps: performing multi-dimensional physical and chemical characteristic detection on a rock debris sample of a target well to obtain characteristic parameters of internal composition and structure of the rock debris sample; standardizing the characteristic parameters to generate a digital characteristic matrix; inputting the digital characteristic matrix to a rock debris characteristic-logging parameter conversion model, and calculating a conversion relation between rock debris characteristics and logging data to obtain a pseudo-measurement curve; and acquiring logging data of an adjacent well, fusing the logging data and the pseudo-measurement curve by adopting a Kalman filtering method, and performing error correction on the pseudo-measurement curve to generate a virtual well. According to the invention, through multi-modal rock debris data acquisition and processing, in combination with the machine learning model and the Kalman filtering algorithm, accurate construction from the rock debris sample to the virtual well is realized, and the defect of insufficient construction precision of the virtual well is overcome.

The invention discloses an electrical load anomaly detection method and system based on a multi-granularity fuzzy rough set, and relates to the technical field of power data analysis, and the method comprises the steps: obtaining a high-dimensional time sequence feature matrix from to-be-detected electrical load time sequence data through employing a long-short term memory network; gathering the high-dimensional time sequence characteristic matrix into a plurality of multi-granularity pellets through a multi-granularity pellet generation method; calculating a multi-granularity fuzzy relationship among the multi-granularity pellets, constructing a multi-granularity fuzzy rough set model, and calculating the fuzzy rough density of each multi-granularity pellet and the multi-granularity fuzzy entropy of each attribute based on the model; calculating an abnormal score of each multi-granularity particle ball, mapping the abnormal scores of the multi-granularity particle balls to corresponding samples in the electrical load time sequence data, and performing abnormal load judgment on the corresponding samples based on the abnormal scores; the multi-granularity information of the load data can be effectively utilized, the anti-noise capability is enhanced, and the accuracy of anomaly detection is improved by processing the uncertainty of the data.

The invention provides a dissolved organic matter pedigree network construction method and system, and the method comprises the steps: obtaining a feature matrix and a molecular formula set of a dissolved organic matter sample; clustering the various samples according to the feature matrix to obtain a plurality of initial clusters; according to the feature matrix and the molecular formula set, determining genetic scores of various samples, including homogeneous family scores and heterogeneous family scores, and determining re-classified candidate samples according to the genetic scores; re-classifying the candidate samples, and iteratively confirming the re-classification of new candidate samples to obtain a plurality of target clusters; and according to the element change path between the internal samples of each target cluster, constructing a pedigree network of the dissolved organic matters. According to the method, pedigree affiliation identification and heterogeneous correlation analysis of the soluble organic matters are realized from two aspects of spatial distance distribution and molecular structure generality, and a pedigree network is constructed according to an element change rule, so that the reliability of the pedigree network is improved, and the resolution and stability of pedigree affiliation judgment are ensured.

The invention relates to the technical field of high-speed rail operation and maintenance, and provides a high-speed rail overhead line system part falling prevention method and system based on 4C detection, and the method comprises the steps: recognizing original image data through a target detection network model, and obtaining key part data of high-speed rail overhead line system parts; performing feature extraction on the key component data through a fine-grained detection network model to obtain key component feature data; based on the key component feature data, anti-loose state judgment is conducted on the key component, and anti-loose detection data of the key component is obtained; constructing a spatial-temporal characteristic matrix based on the anti-loosening detection data and the historical state data, and calculating a falling risk coefficient of the key component through a risk assessment model; and carrying out anti-falling early warning based on the falling risk coefficient, generating an anti-falling early warning report containing the positioning information, the abnormal type and the maintenance suggestion, and sending the anti-falling early warning report to a manager. According to the invention, the high-precision loosening state monitoring of the key anti-loosening component of the high-speed rail overhead line system is realized, and the falling risk can be early warned in advance.

The invention provides a metal pitting defect degree evaluation method and system based on artificial intelligence, and the method comprises the steps: obtaining microscopic porosity distribution data of a metal plate in a forming process, and an internal fluctuation signal of the metal plate under the action of a directional stress wave; based on the propagation attenuation rate of the internal fluctuation signal, generating geometric parameters of the structure abnormal area corresponding to the micro porosity distribution data; acquiring current density distribution data of the structure abnormal region; inputting the microscopic porosity distribution data, the geometric parameters of the structure abnormal region and the current density distribution data into an artificial intelligence model so as to associate the microscopic porosity distribution data with the current density distribution data, and generating a pore current coupling characteristic matrix of the metal plate; according to the pore current coupling characteristic matrix, generating a quantitative evaluation result of the internal pitting defect degree of the metal plate; according to the technical scheme provided by the invention, dynamic quantitative evaluation on the degree of the pitting corrosion defect in the metal is realized, and the technical bottleneck of a traditional single-index detection method in defect evolution mechanism analysis and quantitative precision is broken through.

The application provides a kind of metal pitting defect degree evaluation method and system based on artificial intelligence, by obtaining the micro porosity distribution data of metal plate in the forming process, and the internal fluctuation signal of metal plate under the action of directional stress wave;Based on the propagation attenuation rate of internal fluctuation signal, the geometric parameters of structure abnormal area corresponding to micro porosity distribution data are generated;Obtain the current density distribution data of structure abnormal area;Micro porosity distribution data, geometric parameters of structure abnormal area and current density distribution data are input into artificial intelligence model to associate micro porosity distribution data with current density distribution data, generate the pore current coupling characteristic matrix of metal plate;According to pore current coupling characteristic matrix, the quantitative evaluation result of internal pitting defect degree of metal plate is generated;The technical scheme provided by the application realizes the dynamic quantitative evaluation of the degree of metal internal pitting defect, and breaks through the technical bottleneck of traditional single index detection method in defect evolution mechanism analysis and quantitative precision.

The present invention discloses a wire quality detection method based on wire shielding performance, comprising the following steps: collecting electromagnetic interference source data and wire structure data; performing a matrix construction operation to obtain a magnetic interference source characteristic matrix; performing a matrix value extraction operation to obtain a first energy impact factor and a second energy impact factor; performing an interference parameter construction operation to obtain an initial interference degree sequence; performing a sequence update operation to obtain an interference degree distance sequence; performing a cluster region extraction operation to obtain a sensitive shielding region; performing a calculation to obtain a sensitive shielding factor; performing a calculation to obtain a wire shielding value; when the wire shielding value is less than a preset wire shielding performance threshold, determining that the wire shielding performance does not meet the requirements; and when the wire shielding value is greater than or equal to the preset wire shielding performance threshold, determining that the wire shielding performance meets the requirements. This method reflects the shielding performance of the wire by combining electromagnetic interference source and wire structure data, thereby improving the accuracy of wire quality detection.

The application relates to the technical field of titanium alloy detection, and discloses a titanium alloy wire performance detection method, equipment and system. The method collects real-time force value displacement data of a tensile test and echo signal data of ultrasonic detection, obtains smooth force value displacement data and frequency spectrum characteristic parameters after processing, fuses yield strength and tensile strength with peak frequency and amplitude attenuation rate in a characteristic layer, and constructs a first multi-dimensional characteristic matrix; the method inputs a pre-trained deep confidence network model, predicts mechanical properties such as elongation and hardness value, compares with a preset threshold value, marks unqualified items, and generates a detection report. The method improves the comprehensiveness and accuracy of detection through multi-source data fusion and advanced algorithm processing, realizes efficient evaluation of titanium alloy wire performance, and is suitable for strict detection requirements of titanium alloy wire quality in the fields of aerospace, medical devices and the like.

The invention discloses a defect identification method and system for a photoelectric detection image of power transmission and transformation equipment, and relates to the technical field of defect identification, and the method comprises the steps: collecting photoelectric detection data of target power transmission and transformation equipment; carrying out coupling analysis in combination with an equipment topology connection relationship, and drawing up a feature matrix; meanwhile, identifying a defect type mode based on a device topology connection relation and a change trend of a gray scale deviation coefficient in a plurality of detection periods; based on the characteristic matrix, combining the defect type mode and the photoelectric detection data to configure a defect confidence factor; and determining a defect severity level by using the defect confidence factor and carrying out graded reminding. The technical problems that in the prior art, electric transmission and transformation equipment photoelectric detection data integration is insufficient, the defect recognition precision is low, and operation and maintenance response lacks pertinence are solved, and the technical effects that integration analysis of multiple types of photoelectric detection data of the electric transmission and transformation equipment is achieved, and the defect recognition accuracy and the operation and maintenance response pertinence are improved are achieved.