204 results about "Principal component analysis algorithm" patented technology

The invention provides a battery pack fault diagnosis method and system based on multi-domain characteristics, and relates to the technical field of battery management, and the method comprises the steps: obtaining a median voltage curve of a battery pack and a single voltage curve of a single battery in the battery pack; decomposing the monomer voltage curve by taking the median voltage curve as a decomposition reference; optimizing key parameters of a variational mode decomposition algorithm through a grey wolf optimization algorithm by taking the minimum sum of envelope entropies of decomposition components of the single voltage curve obtained through decomposition as a target; decomposing each monomer voltage curve according to the optimized variational mode decomposition algorithm to obtain a high-frequency fault information component; extracting features of the high-frequency fault information components; performing dimension reduction fusion on the time domain features and the frequency domain features through a principal component analysis algorithm to generate a comprehensive fault coefficient; and according to the time domain feature, the frequency domain feature and the comprehensive fault coefficient, a local gravitation algorithm is combined to identify a fault single battery. According to the invention, the fault diagnosis capability can be improved, and early warning is facilitated.

The invention discloses a transformer fault diagnosis method based on multi-sensor multi-domain feature fusion, and the method comprises the following steps: 1, obtaining a vibration signal data set of a transformer box, and carrying out the decentralization of a collected signal; 2, decomposing the decentralized transformer vibration signal by adopting a dung beetle optimized ICEEMD method, calculating a Pearson correlation coefficient between an IMF component and an original signal, and selecting a strongly correlated component to reconstruct the transformer vibration signal, so as to filter and denoise the transformer vibration signal; step 3, extracting time domain, frequency domain and space domain features in the transformer vibration signals by adopting an MVAR model, VMD-BIS decomposition and Riemannian manifold, and constructing a joint feature vector; 4, performing fusion dimension reduction by adopting a kernel principal component analysis algorithm to obtain a fusion dimension reduction feature vector; and a fifth step of constructing a kernel extreme learning machine model, training parameters of the kernel extreme learning machine model in combination with the time-frequency-space fusion features, and optimizing hyper-parameters of the kernel extreme learning machine model in combination with a Harris eagle optimization algorithm so as to obtain an optimal kernel extreme learning machine classification model. According to the method, the problems of misjudgment, missed judgment and low diagnosis rate caused by unbalanced distribution due to nonlinear characteristics of vibration signals in transformer fault diagnosis are effectively solved.

The invention relates to the technical field of cross-modal data fusion analysis of soil component detection and improvement agent preparation, in particular to a preparation method of a soil improvement agent based on component detection, which comprises the following steps: acquiring soil component data through a near infrared spectrometer, a high performance liquid chromatograph and an inductively coupled plasma mass spectrometer; an incremental principal component analysis algorithm is combined with an oscillation suppression function to update a covariance matrix in real time, a dynamic defect factor priority list is generated, a multi-objective optimization model improved based on NSGA-II is guided to integrate the soil pH value, humidity and organic matter content to construct a dose response curved surface, and a Pareto optimal solution set is solved. A sensor array collects environment feedback data, drives a transfer learning algorithm to calibrate parameters, a dynamic incidence matrix attenuation rate and optimal weight adaptive adjustment, iteratively outputs a modifier synergistic effect solution set through a closed-loop control mechanism, and solves the problems of difficult data fusion, principal component weight offset and component synergistic deviation. And the proportioning accuracy and stability are improved.

The invention relates to the technical field of lithium battery safety management, and particularly discloses a lithium battery thermal runaway early warning and protection method based on an adaptive neural network, which comprises the following steps: acquiring temperature, internal resistance and voltage data of a lithium battery in real time, extracting multi-dimensional features based on discrete wavelet transform, singular value decomposition and principal component analysis algorithms, and performing early warning and protection on the lithium battery thermal runaway. The influence of internal resistance and voltage on abnormal temperature fluctuation is quantified; constructing a feature vector through the comprehensive influence coefficient, inputting the feature vector into an adaptive fuzzy neural network prediction model, outputting a temperature anomaly fluctuation coefficient, and accurately predicting the thermal runaway risk of the lithium battery; according to the lithium battery thermal runaway prediction method, multi-level protection measures such as early warning response triggering, system automatic charging and discharging strategy adjusting, power output reducing and heat dissipation system starting are taken based on the prediction result, development of the thermal runaway risk is restrained, multi-dimensional data fusion and an intelligent algorithm are combined, and the lithium battery thermal runaway prediction accuracy and the risk control real-time performance are improved.

The invention discloses an automatic three-dimensional reconstruction method and system based on power transmission corridor point cloud. The method comprises the following steps: acquiring data and exporting point cloud data; noise points, outliers and ground points are removed from the acquired point cloud data of the power transmission corridor, and down-sampling is performed on the point cloud with a large data volume; performing clustering segmentation on the preprocessed point cloud of the power transmission corridor, positioning the position of a tower, determining a point cloud range of a power line, and removing point clouds of other objects on the ground; aiming at the structural characteristics of a power line and an insulator in the power transmission line, extracting point clouds of the power line, the insulator and a tower by adopting a geometric constraint principal component analysis (PCA) algorithm; performing automatic geometric model reconstruction on the tower, insulator and power line point clouds according to different reconstruction rules to generate each module grid model; and transforming the grid model of each module, restoring the grid model to the original position of the point cloud, and generating a power transmission line digital twin model conforming to the real size. According to the method, automatic segmentation, classification and real-time reconstruction can be accurately carried out on the point cloud of the power transmission corridor.

The invention discloses a method and system for predicting the service life of a fire valve, and relates to the field of prediction maintenance, and the method comprises the steps: obtaining a multi-dimensional monitoring data matrix which comprises the sealing performance data, operation torque data and corrosion degree data of the fire valve continuously monitored in a preset time period; determining a parameter incidence matrix according to the multi-dimensional monitoring data matrix; determining a plurality of principal component vectors and corresponding characteristic values according to the parameter incidence matrix through a principal component analysis algorithm, and determining a current health state score of the fire valve according to the characteristic values corresponding to the plurality of principal component vectors; determining a degradation law function of the fire-fighting valve according to the historical operation record of the fire-fighting valve; and predicting the service life of the fire valve according to the current health state score and the degradation law function. According to the scheme, the accuracy of life prediction of the fire valve is improved, and the problem that the accuracy of life prediction of the fire valve is low is solved.

The invention relates to the technical field of precision manufacturing nondestructive testing and machine vision image processing, in particular to a titanium alloy ring piece surface microcrack defect detection system based on machine vision, which comprises an image acquisition module for acquiring a to-be-processed image data set; the manifold calibration module is used for acquiring a main direction field of background textures and converting the to-be-processed image data set into a standard space image with aligned texture flow; the sparse decomposition module is used for acquiring a shear wave coefficient and decomposing the shear wave coefficient into a low-rank component matrix and a sparse component matrix; the reconstruction judgment module is used for generating a microcrack defect distribution diagram, obtaining a residual image and generating a final defect distribution diagram; the self-adaptive feedback module is used for adjusting the sparsity constraint weight in the robust principal component analysis algorithm; according to the method, the problem of signal aliasing caused by frequency overlapping of cracks and background textures is effectively solved, and the detection sensitivity under the strong texture background is remarkably improved.

The invention discloses a transformer sound signal adaptive wavelet optimization and feature extraction method and system, and the method comprises the steps: calling a Pywt wavelet analysis library, decomposing an original signal according to a decomposition layer number J, and obtaining a multi-layer detail signal; for each layer of detail signals, the following steps are executed: introducing an M estimator to improve a noise variance calculation model, and calculating a standard deviation and a unified monitoring threshold value; constructing a dynamic threshold value based on a denoising signal approximation error minimization criterion; designing a correction factor; correcting the wavelet coefficient of each layer of detail signal; reconstructing a pure signal by using an inverse decomposition method; dividing the pure signal into a plurality of short-time signals; extracting an MFCC feature vector of each short-time signal; weighting and screening MFCC feature vectors by adopting a support vector machine recursive feature elimination method; and compressing the dimension of the feature vector in combination with a principal component analysis algorithm to generate a final feature matrix. According to the method, the problems of contradiction between noise suppression and signal fidelity and low recognition rate caused by high-dimensional feature redundancy in a traditional method are solved.

The invention discloses a tea fertilizer preparation method based on microbial fermentation, and particularly relates to the field of tea compost, tea waste is crushed and wetted, and a nitrogen source and trace elements are added to optimize the carbon nitrogen ratio, so that a foundation is laid for the fermentation process; then, selected microbial strains are proliferated and purified, and the treated waste materials are uniformly inoculated with the selected microbial strains; in the fermentation process, the stability of fermentation conditions is ensured by controlling the temperature and the humidity; the tannin concentration and the pH value are monitored in real time, when the tannin concentration exceeds a preset value and the pH deviates from a normal range, the abnormal state of tannin degradation is evaluated and identified through a reaction activity index, an interference source is positioned through a sparse principal component analysis algorithm, and noise signals are removed; and after the interference source is positioned, the fermentation condition is adjusted in a targeted manner, so that the tannin degradation efficiency reaches the expectation, and the compost is maturely fermented and meets the fertilizer use standard.

The invention discloses an electric power tower point cloud registration method and device fused with normal vector clustering, and relates to the technical field of point cloud registration or other related fields, and the method comprises the steps: scanning an electric power tower to obtain point cloud data, dividing the point cloud data into different voxels, and carrying out the point cloud registration; calculating normal vectors of all the point cloud data in each voxel by adopting a principal component analysis algorithm, dividing all the point cloud data in each voxel into different clusters by adopting a clustering algorithm, determining representative point clouds of each cluster, selecting target point clouds and source point clouds from the representative point clouds, and carrying out point cloud clustering on the target point clouds and the source point clouds; the method comprises the steps of determining a matching point cloud of each source point cloud in target point clouds by adopting a binary tree search algorithm to obtain a plurality of matching pairs, calculating transformation parameters of the matching pairs by adopting an iterative nearest point algorithm, calculating a registration error based on the matching pairs and the transformation parameters, and merging the source point clouds and the target point clouds under the condition that the registration error reaches a preset convergence error range. According to the invention, the technical problems of low point cloud registration precision and poor algorithm convergence in the prior art are solved.

The invention discloses an intelligent disk monitoring system based on multi-modal data fusion and a fault early warning method, and relates to the technical field of power plant management, and the method comprises the steps: collecting the real-time operation data of equipment; performing dimension reduction processing on the operation data by using a principal component analysis algorithm; inputting the data subjected to dimension reduction into a long short-term memory network model, modeling the time sequence dependence of the data by using the long short-term memory network model, and predicting the future operation state of the equipment; based on the prediction result of the operation state, performing feature extraction on the data through a convolutional neural network; according to an output result of the convolutional neural network, alarm signals of different levels are generated according to set threshold levels; and according to the grade of the alarm signal, adjusting operation parameters of the equipment through a feedback control mechanism. According to the intelligent disk monitoring system based on multi-modal data fusion, the accuracy, the real-time performance and the intelligent level of equipment monitoring are improved, and the fault early warning precision and the system response speed are remarkably improved.

The invention relates to the field of computer vision, in particular to an image feature recognition method based on computer vision, which comprises the following steps of: preprocessing an input image, removing image noise and correcting image gray deviation to obtain a preprocessed image; performing multi-scale feature extraction on the preprocessed image, obtaining image local texture features and global contour features under different scales, performing dynamic weight fusion, calculating dynamic fusion weight based on a feature response value and a local complexity factor, and performing dimension reduction processing on fusion features through a principal component analysis algorithm to obtain a target feature set; and matching the reference features, calculating the Euclidean distance between the target feature set and the reference features, determining an adaptive matching threshold, judging a matching result, and completing image feature recognition. According to the method, through adaptive filtering and gray level equalization preprocessing, Gaussian pyramid multi-scale feature extraction and dynamic fusion and adaptive threshold matching, feature extraction accuracy, characterization capability and recognition robustness are improved.

The invention discloses a multi-source heterogeneous learning path planning method based on personalized constraints, which solves the problems of single path, data staticization and the like in the prior art, and comprises the following steps: acquiring multi-source behavior data of a learner, carrying out feature modeling, and constructing a high-dimensional behavior portrait; according to the high-dimensional behavior portrait, utilizing a neural collaborative filtering algorithm to predict the interest and mastering probability of a learner to any knowledge point, and generating a preliminary learning path; carrying out dominant and implicit evaluation on the knowledge mastering state of the learner by adopting a cognitive diagnosis model, and carrying out dynamic correction on the preliminary learning path to obtain a corrected learning path; performing dimension reduction on the multi-source behavior data by adopting a principal component analysis algorithm to extract a core factor, and forming an evaluation basis for path optimization; and constructing a multi-objective path function model, and obtaining an optimal dynamic learning path by adopting a multi-objective optimization path algorithm in combination with the corrected learning path and the core factor. The method has high practical value and popularization value in the technical field of learning path planning.

The invention belongs to the technical field of crop growth prediction, and particularly relates to a crop growth prediction method and system based on multispectral unmanned aerial vehicle monitoring. The method comprises the following steps: acquiring multi-dimensional data of real crops in different growth stages under different soil water contents and disease and insect pest states, determining the contribution degree of each vegetation index to crop growth through a factor analysis algorithm, carrying out dimension reduction on hyperspectral data by using a principal component analysis algorithm, fusing with the multi-spectral data, constructing a multi-spectral resolution characteristic space, and carrying out multi-spectral analysis on the hyperspectral data. The method comprises the following steps: firstly, obtaining a real plant height growth fitting function of crops through analogue simulation by combining LiDAR data and vegetation indexes, thirdly, calculating a real growth vegetation index space and obtaining a real growth state space of a standard staged growth period, and finally, inputting the calculated space and function into a model constructed by a reinforcement learning algorithm for training, and accurate prediction of the crop growth state is realized.

The invention discloses a water quality monitoring method and system based on big data analysis, and the method comprises the steps: obtaining water quality parameter data collected by multiple types of sensors, carrying out the format unification processing of the water quality parameter data through a standardization algorithm, and carrying out the elimination through a median filtering algorithm if abnormal data points are detected, thereby obtaining a standardized data set; aiming at the standardized data set, performing feature extraction on the chemical parameters, the spectral features and the biological indexes by adopting a principal component analysis algorithm to obtain feature vectors, and if the variance contribution rate of the feature vectors exceeds a preset threshold value, retaining the feature vectors and generating a dimension reduction feature data set; according to the dimension reduction feature data set, a random forest algorithm is adopted to construct a pollution evaluation model, a pollution index is calculated, if the pollution index exceeds a preset threshold value, a high pollution state mark is generated, and a pollution evaluation result is output. According to the invention, real-time monitoring, evaluation, early warning and traceability of water quality pollution are realized, and comprehensive technical support is provided for water environment protection.

The invention provides an auxiliary pathological analysis method for a microscope system, and belongs to the technical field of pathological analysis, and the method comprises the steps: firstly carrying out the gray normalization, denoising and other preprocessing of an obtained pathological section image, and carrying out the illumination equalization processing through a region adaptive histogram equalization algorithm; then, color features of the image are extracted in an LAB color space, a color representation center is calculated, a feature mapping matrix is established, and meanwhile texture features are extracted and fused with the color features; and carrying out dimension reduction on the fused feature vector by using a principal component analysis algorithm, and reserving important feature information. And then, establishing a convolutional neural network model, inputting the feature vectors after dimension reduction, performing training and cross validation by using a labeling data set, and improving the model performance through iterative optimization. According to the invention, the technical problem that the diagnosis and judgment of a doctor are seriously influenced due to inconsistent color expression caused by the difference of shooting conditions of pathological section images observed by a microscope is solved.

The invention relates to a circuit board on-line defect detection method and system, and the method comprises the steps: carrying out the synchronous collection and structural integration of multi-source technological parameters such as production line environment temperature and humidity, equipment operation states, material batches and the like, and defect detection images, and achieving the construction of large-sample original data in a production process; through standardization and de-noising preprocessing, multi-modal features are fused, and a distribution mapping model of process and defect features is established by using algorithms such as mutual information analysis and principal component analysis. Based on a feature distribution model and real-time data, a dynamic anomaly detection threshold is adaptively generated, Bayesian inference and evidence reasoning are combined, multi-level confidence levels and risk response suggestions are output, and self-learning evolution of the model and the threshold is realized through a closed-loop feedback mechanism. According to the scheme, the accuracy of anomaly detection, the response timeliness and the risk disposal intelligent level are improved, the method adapts to complex working conditions and batch changes, and the closed-loop optimization and safety control capability of the production process is remarkably enhanced.

The invention provides an electromechanical equipment fault prediction method and system based on multi-source information fusion, and the method comprises the steps: collecting operation data, including vibration data, temperature data and current data, during the operation of electromechanical equipment; performing feature extraction on the operation data based on a principal component analysis algorithm to obtain a fusion feature vector; inputting the fusion feature vector into a fault prediction model based on a deep belief network, and outputting a prediction result; wherein the deep belief network adopts a small-batch stochastic gradient descent algorithm combined with an adaptive learning rate adjustment strategy during training; and judging whether the electromechanical equipment has a fault hidden danger or not according to the prediction result. According to the method, the relevance between different types of data is mined, and the defect of low prediction precision is overcome.

The invention relates to the field of gait analysis, and discloses a gait analysis method and system based on intelligent shoes, and the method comprises the steps: obtaining pressure distribution data captured by a plantar pressure sensor, dividing gait stages according to a pressure mode, and recognizing gait phases; the method comprises the following steps: acquiring three-dimensional acceleration, angular velocity and magnetic field data of an inertial measurement unit, and converting the three-dimensional acceleration data from an IMU coordinate system to an earth coordinate system; performing integral calculation on the three-dimensional acceleration data based on the earth coordinate system to obtain speed data; describing the time stage of the gait through the pressure mode of the pressure distribution data, detecting to obtain a zero speed moment, correcting the speed data by applying a zero speed updating algorithm according to the zero speed moment to obtain the corrected speed data, and further performing integral calculation to obtain the motion trail of the foot; through a principal component analysis algorithm, decoupling the displacement in the step length direction and the step width direction in the foot motion trail so as to obtain step length and step width parameters; the plantar pressure distribution data cloud picture and the foot movement track are combined and visualized, visual reproduction of the foot posture in the gait process is completed, and a convenient mode is provided for a patient to monitor and evaluate the gait health condition of the patient.

The invention provides a coal face mining auxiliary method based on visual and point cloud data fusion modeling, and the method comprises the steps: collecting the visual image data of a coal face, collecting the three-dimensional point cloud data through a point cloud sensor, and obtaining the motion state information through an inertial sensor; based on internal and external parameters and IMU data of joint calibration, mapping point cloud data to an image plane, establishing a geometric mapping relationship between three-dimensional points and pixels, generating an attribute point cloud model containing geometry, color and semantics, and generating a high-precision global three-dimensional digital twinning model; and identifying and tracking equipment instances in the digital twinborn model, and resolving motion parameters of key components based on a URDF kinematics model and a principal component analysis algorithm to support remote control decisions. According to the invention, high-precision real-time sensing and three-dimensional visualization of the working space size, the equipment connection relation and the movement position of the coal face can be realized, and the remote control precision and the response efficiency of unmanned mining are remarkably improved.

The invention relates to the technical field of cycloidal gear finishing, in particular to a grinding finishing method, equipment and device applied to the cycloidal gear machining process, and the method comprises the steps that the difference between the energy of all cutting force data in the main frequency neighborhood in the frequency domain and the total energy within the preset duration before all moments and the change trend of all temperature data are analyzed, and the cutting force data in the main frequency neighborhood in the frequency domain is obtained; high-frequency vibration heat risk degree; based on all the high-frequency vibration heat risk degrees and all the vibration data in a preset time period before the current moment, a modal decomposition algorithm is adopted to construct a first feature value; and on the basis of all the state data and all the high-frequency vibration heat risk degrees in a preset time period before the current moment, a second feature value is constructed by adopting a principal component analysis algorithm, a comprehensive feature value is determined by combining the first feature value, and the finishing speed and the feeding amount are adjusted. According to the method, the problem of self-adaptive lag of the trimming parameters is solved, and distortion of the cutting edge contour in the machining process of the cycloidal wheel due to multi-physics field interference is eliminated.

The invention relates to the technical field of reactive power compensation of a power system, in particular to a stable voltage compensation control method applied to a micro-grid cluster, which specifically comprises the following steps of: analyzing principal components of all items of grid data of each node through a principal component analysis algorithm, extracting the first three principal components, and performing data weight distribution through an entropy weight method to obtain a data weight distribution result; constructing reactive compensation priorities of the nodes; the future reactive compensation priority of the node is predicted through the historical reactive compensation priority of the node, the fluctuation rate of prediction data is analyzed, and the dynamic compensation urgency of the node is constructed in combination with the clustering condition of the node at the current moment; a hierarchical resource allocation strategy is implemented according to the dynamic compensation urgency degree, the calculation process is updated in real time according to the reactive compensation feedback result, a self-optimization closed loop is formed, the problems that a traditional method is insufficient in quantization precision and poor in real-time performance are solved, the reactive demand matching precision and the voltage maintaining stable effect are improved, and the method is suitable for large-scale popularization and application. And the communication load and the calculation complexity are obviously reduced.

The invention discloses a substation primary and secondary equipment communication prefabricated optical cable online monitoring method and system, and belongs to the technical field of communication prefabricated optical cable monitoring. The system comprises an optical cable parameter acquisition module, a communication quality evaluation module, a physical parameter analysis module, a first-aid repair path planning module and a fault database. The method comprises the following steps: optical cable parameter acquisition: acquiring data in real time, and converting high-dimensional data into low-dimensional data through a principal component analysis algorithm; communication quality evaluation: identifying communication characteristics in the low-dimensional signal parameter matrix based on a fuzzy comprehensive evaluation algorithm, and outputting a communication quality score; the physical parameter analysis module extracts physical deep features of the optical cable by using a deep belief network DBN, and inputs an improved Bayesian classifier to output a state classification result; according to the first-aid repair path planning, OTDR detection parameters are dynamically adjusted, and an optimal first-aid repair path is planned in combination with the position of the inspection robot; according to the invention, optical cable communication quality evaluation, accurate physical state classification and efficient fault repair planning are realized.

The invention provides a processing method and system for scoliosis detection based on point cloud features. According to the system, a depth camera is used for monitoring the dynamic state of a subject through a point cloud data acquisition module, and back three-dimensional point cloud information is obtained; the scoliosis area acquisition module acquires a clustered two-dimensional diagram by using a density-based spatial clustering algorithm and a point cloud reprojection algorithm, and acquires an area of interest by using an image morphological processing algorithm and a polynomial curve fitting method; the point cloud registration module obtains symmetrical point clouds by using an iterative reweighted least square method and a principal component analysis algorithm, performs coarse registration by using a Rodrigues rotation matrix and then executes a point cloud registration algorithm; and the transverse asymmetry analysis module obtains a symmetry plane by using the registered point cloud, and obtains a transverse depth difference point cloud by using a surface reconstruction algorithm. The invention provides a transverse asymmetry analysis method based on a point cloud registration algorithm and a surface reconstruction algorithm, scoliosis screening is performed through the depth camera, and popularization of scoliosis screening is facilitated.

The invention relates to the technical field of image analysis, in particular to a metal composite material surface defect intelligent detection method based on machine vision, which comprises the following steps: acquiring an original image and converting the original image into an observation matrix; decomposing the matrix into a low-rank matrix and a sparse matrix through a robust principal component analysis algorithm; utilizing a nuclear norm constraint low-rank matrix to establish a background statistical model, and utilizing a norm constraint sparse matrix; positioning a defect area in the sparse matrix and extracting a gray level co-occurrence matrix feature vector; performing connected domain analysis on the sparse matrix, calculating geometrical morphology parameters and evaluating a stress concentration coefficient; and establishing a self-adaptive decision model to carry out fusion judgment so as to judge the physical damage. According to the method, through low-rank sparse matrix decoupling and multi-dimensional statistical geometric feature fusion analysis, accurate stripping of weak defects and quantitative evaluation of physical damage attributes under a complex background are realized.

The invention provides a sensor-based cold chain equipment fault detection method and system. Belongs to the technical field of cold chain equipment fault detection. The method comprises the following steps: collecting multi-dimensional data of the operation state of the cold chain equipment in real time through a multi-dimensional sensor; performing dimension reduction processing on the standardized multi-dimensional data set by adopting a principal component analysis algorithm, extracting main feature components in the data, and constructing a high-dimensional feature vector set; performing time sequence feature extraction on the multi-dimensional data set by using an LSTM model to obtain a multi-dimensional time sequence feature matrix; fusing the high-dimensional feature vector set with the multi-dimensional time sequence feature matrix to obtain a fused feature matrix; a method of combining principal component analysis and a long short-term memory network is adopted, main features and time sequence features of data are extracted respectively, and the main features and the time sequence features are organically combined through a feature fusion strategy, so that the operation state of the cold chain equipment is described more comprehensively, and the accuracy of fault feature extraction is improved.

The invention relates to the technical field of network traffic classification, in particular to a network traffic data classification method, device and system for a data center, and the method specifically comprises the steps: constructing a feature data matrix based on all types of feature information of all traffic in a data set, obtaining principal components in the matrix through a principal component analysis algorithm, screening representative principal components based on the variance contribution rates of the principal components and the correlation between the variance contribution rates; calculating a dynamic flow interference degree index of the data set based on the Shannon entropy of the representative principal component and the density clustering characteristic; a dynamic threshold is set, a trigger condition of incremental learning is set in combination with a dynamic traffic interference degree index, and after the incremental learning is triggered, a bidirectional GRU network in an RNN model is dynamically adjusted through an elastic weight solidification method, so that traffic classification is carried out, historical knowledge is reserved, and meanwhile, the method adapts to a new traffic mode. The system is ensured to quickly respond to the traffic mode change, the performance degradation is avoided, and the accuracy of network traffic data classification is improved.

The invention relates to the technical field of quality detection, in particular to a quality detection analysis method used in the grinding wheel production process. The method comprises the following steps: acquiring a target surface image of a grinding wheel to be detected and a surface feature value vector corresponding to each pixel point on the target surface image; according to a principal component analysis algorithm and the surface feature value vector, obtaining scatter points in a mapping space corresponding to the target surface image and pixel points corresponding to the scatter points; obtaining an initial bandwidth of a mean shift clustering algorithm, and clustering scattered points in the mapping space according to the initial bandwidth to obtain each target cluster; and obtaining an abnormal cluster according to the distance between the scatter points in the target cluster, obtaining an uneven area on the target surface image according to the pixel points corresponding to the scatter points in the abnormal cluster, and obtaining a quality detection result of the grinding wheel to be detected according to the uneven area. According to the invention, the accuracy of quality detection of the to-be-detected grinding wheel can be improved.

The invention relates to a parameter sensing and fault processing method for an electric energy measuring instrument verification system, in particular to a multi-parameter sensing and fault processing method for an automatic verification system, which is characterized by comprising the following steps of: collecting and analyzing signals, acquiring audio and video information of a verification line body by utilizing inspection equipment, and designing important inspection points and inspection periods. Fault information of a verification line body is collected in real time, classification and installation of sensors are carried out, and a sensor network is formed; accumulating a fault knowledge base and marking fault marks, and establishing an assembly line panoramic intelligent sensing model by using a digital twinning technology; carrying out fusion analysis on a multi-parameter sensing model taking digital twinning as a carrier, and carrying out principal component analysis on fault influence factors; performing data noise reduction and dimension reduction processing by using a principal component analysis algorithm; carrying out target detection by adopting an ensemble learning algorithm; and a time sequence event model of a self-attention mechanism is introduced to carry out fault early warning and defect elimination guidance, and the potential fault occurrence probability of the key equipment is predicted.

The invention provides an intelligent control method and system for a full-automatic coating line of automobile parts, and relates to the field of control systems. The method comprises the following steps: acquiring three-dimensional point cloud data of an automobile part; based on the three-dimensional point cloud data, determining a high-complexity area on the surface of the automobile part as an area to be coated; calculating a covariance matrix of a normal vector in the to-be-coated area by adopting a principal component analysis algorithm, and extracting a principal direction feature vector through the covariance matrix; determining a rotation coordinate value of the rotation axis based on the main direction feature vector; and the sprayer is controlled to move to the autorotation coordinate value, and the automobile parts are coated. By the adoption of the technical scheme, when the surfaces of the automobile parts with different geometrical characteristics are sprayed, the geometrical characteristics of the automobile parts are combined, the rotation coordinate values are determined, when the sprayer is located at the rotation coordinate values, the uniform pattern layer can be completely sprayed to the surfaces of the automobile parts as much as possible, and the spraying effect is guaranteed.