147 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 provides a pressure pipeline corrosion on-line monitoring method and system, and relates to the technical field of pipeline monitoring, and the method comprises the steps: obtaining three-dimensional point cloud data, obtaining reduced sampling point cloud data through an octree filtering algorithm, constructing a geometric feature scattering matrix, inputting a multi-scale fusion twin network, and learning feature differences through a contrast loss function. Generating a corrosion probability distribution diagram; on the basis of an iterative nearest point algorithm, registering the corrosion probability distribution diagram, extracting a corrosion overrun area, performing overlay analysis on the corrosion overrun area and the pipeline stress concentration area, and determining a high-risk corrosion area; the method comprises the following steps: arranging a sensor array around a high-risk corrosion area, acquiring sensor corrosion signals, performing variational mode decomposition, extracting intrinsic function components related to corrosion characteristics, performing sparse representation on the intrinsic function components, constructing a dispersion compensation matrix, and determining a corrected sparse characteristic matrix; and inputting into a multi-core extreme learning machine discriminator to judge whether leakage occurs in the high-risk corrosion area.

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 discloses an under-timing variable working mode parameter identification method and device based on statistical characteristic variable window length, and the method comprises the steps: obtaining and processing a response signal sequence of structural vibration under the excitation of a set environment, and obtaining a processed response signal subsequence in a current sliding window, extracting a time-frequency characteristic matrix from the processed response signal sub-sequence in the current sliding window, extracting a principal component matrix from the time-frequency characteristic matrix by adopting principal component analysis, and analyzing and determining a hysteresis period by adopting an autocorrelation function; according to the time-frequency characteristic matrix, the hysteresis period and the principal component matrix of the current sliding window, estimating to obtain modal parameters of the current sliding window; calculating to obtain the statistical characteristics of the current sliding window and designing a self-adaptive window length adjustment strategy to obtain the window length of the next sliding window; and taking the next sliding window as the current sliding window, and repeating the steps. According to the method, the under-timing variable working mode parameters can be accurately identified.

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 provides a welding seam detection method, medium and system for welding of a floating ship plate of an oil storage tank, belongs to the technical field of electric digital data processing, and realizes comprehensive and accurate monitoring through six key matrixes. The method comprises the following steps: starting from a floating ship plate motion characteristic matrix, accurately capturing height, attitude and weld joint displacement data, establishing a comprehensive detection matrix containing ultrasonic flaw detection, X-ray imaging and surface topography through a multi-source detection technology, and comprehensively depicting weld joint microscopic characteristics. Dynamic stress-strain analysis is deeply carried out, a complex stress field distribution model considering floating influence is constructed, multi-dimensional feature matrixes are combined through weights to form a weld quality comprehensive evaluation matrix, and a self-adaptive detection parameter updating mechanism is established. And finally, a welding seam state vector and a safety threshold matrix are constructed, real-time monitoring and early warning are achieved, and the technical problems that in the prior art, only the internal quality of the welding seam can be statically evaluated, and the actual stress-strain state of the welding seam under the dynamic working condition cannot be comprehensively reflected are solved.

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 bad domain name identification method and system, and the method comprises the steps: obtaining domain name data, extracting a time sequence feature from the domain name data, generating a node feature matrix based on the time sequence feature, obtaining an attribute value of a domain name node in the domain name data, the multi-modal hypergraph is constructed, a multi-modal hypergraph association matrix is obtained based on the multi-modal hypergraph, and the multi-modal hypergraph is an association mode of the domain name node in different modals; and inputting the node feature matrix and the multi-modal hypergraph incidence matrix into a constructed classification model so as to identify the bad domain name based on the classification model. According to the method, by constructing the multi-modal hypergraph, enhancing explanation of the graph structure and aggregating the node feature matrix of the time information of the domain name nodes, the domain name information is used more comprehensively, and the recognition accuracy of bad domain names can be improved.

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 insulation paste testing, and discloses an insulation paste testing method and device, and the method comprises the steps: synchronously collecting the multi-dimensional performance parameters of to-be-tested insulation paste, extracting the feature vector of an initial data set, and carrying out the dynamic loading experiment of the to-be-tested insulation paste based on a performance feature matrix, constructing a time sequence curve of the dynamic response data set, performing multi-dimensional evaluation on the degradation parameter set based on information entropy to obtain a comprehensive score, and performing anomaly detection on the comprehensive score; by synchronously collecting the multi-dimensional performance parameters of the to-be-tested insulation paste, the performance characteristic matrix is constructed, so that the test data can comprehensively reflect the performance of the insulation paste in different dimensions; the comprehensive score is analyzed based on anomaly detection, potential quality hazards can be effectively identified, misjudgment caused by local index anomaly is avoided, quality grades are divided through iteration, it is ensured that a quality evaluation result can more reasonably guide model selection and application of the insulation paste, and the reliability of the insulation paste is improved.

The embodiment of the invention provides a method for detecting the cutter state of a numerical control machine tool, electronic equipment and a storage medium, relates to the technical field of machine tool machining, and can realize real-time detection of the cutter state under the condition that material machining is not affected, so that sudden cutter breaking events can be found in time, and the yield of machined materials is ensured. The method for detecting the cutter state of the numerical control machine tool comprises the steps that previous vibration time sequence data and current vibration time sequence data generated by a current cutter of the numerical control machine tool in the machining process are obtained; according to the previous vibration time sequence data and the current vibration time sequence data, respectively obtaining vibration characteristic matrixes corresponding to the previous vibration time sequence data and the current vibration time sequence data; according to the previous vibration time sequence data and the vibration characteristic matrix corresponding to the current vibration time sequence data, the characteristic difference degree of the current tool between the previous machining process and the current machining process is obtained; and determining the current state of the current cutter based on a comparison result of the feature difference degree and the current difference degree threshold value.

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.

The invention relates to the technical field of cement grinding control, in particular to a cement grinding parameter adjusting method and system based on grain composition, which can improve the quality control capability and efficiency in the cement production process and reduce the production cost and environmental influence. The method comprises the following steps: collecting a cement grinding production sample in real time, and carrying out a screening test on the production sample to obtain real-time grain composition data of cement grinding; calculating an error between the real-time grain composition data and preset target grain composition data to obtain a grain composition error vector; obtaining a plurality of grain composition error vectors according to a preset frequency; aligning the error values of the same grain size at different time points in the plurality of grain size distribution error vectors to the same column, aligning the error values of each grain size at the same time point to the same row, and arranging according to a time sequence to obtain a cement grinding particle error characteristic matrix; and inputting the cement grinding particle error characteristic matrix into a pre-constructed particle grading error analysis model.

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 cable fault feature extraction method and device based on an improved GAN algorithm, and a medium, and the method comprises the steps: firstly collecting the three-phase current of a cable, and carrying out the optimization of an input potential variable of a training generator based on permutation entropy and approximate entropy; importing the potential variables into a training generator to obtain synthetic data, and expanding the small sample fault data set; importing the real data and the synthetic data into a training discriminator for discrimination, and correcting network parameters of the discriminator and the generator according to a discrimination result; when the discriminator cannot distinguish the synthetic data from the real data, outputting sample data sets with the same quantity in different states; feature values of the three-phase current of the cable in the time domain, the frequency domain and the time-frequency domain under the healthy and fault working conditions are taken, and a feature matrix is established; according to the technical scheme, the problem that the number of samples is unbalanced in existing cable fault diagnosis can be solved, the stability and accuracy of synthetic data are greatly improved, the sample expansion process is accelerated, and convenience is provided for extraction of fault features.

The invention discloses a BIM (Building Information Modeling) technology-based detection model optimization method and system. A collision detection matrix is generated by setting collision importance and subsystem type and grade information, a collision type and a reference value are automatically set, and a report is exported. And the system final assembly BIM model automatically reads the matrix, screens the collision detection group, operates the detection process, and sends the result to the user. And the user corrects the model according to the result and circularly detects until the standard is reached. And the system records detection and correction data, generates a report and counts correction times in real time. And when the correction frequency reaches the expectation, the system analyzes the collision characteristics before and after the correction period, forms a characteristic matrix, evaluates the correction effect and generates early warning information. According to the method, the collision detection efficiency and accuracy are effectively improved, the correction efficiency of professionals is improved, and the overall quality of a construction project is further improved.

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.