143 results about "Principal component analysis algorithm" patented technology

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 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 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 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 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 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 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 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.

The application discloses a structure point cloud data multi-scale filtering method considering environmental dynamic influence, which firstly adopts a 'K' nearest field method considering structure dynamic influence to perform dynamic filtering processing on original point cloud data, removes scattered outliers in the original point cloud data, and then divides the point cloud data into flat regions and mutation regions based on an improved principal component analysis algorithm (LMSR-PCA), adopts statistical filtering based on local surface fitting for the flat regions, and adopts spatial adaptive bilateral filtering for the mutation regions. The application is used to solve the problems that the existing point cloud data processing method cannot efficiently process massive point cloud data with complex curve characteristics, and cannot solve the problem of excessive smoothing caused by the loss of edge features in the noise reduction process of point cloud data with complex curve characteristics.

The invention discloses a crop ralstonia solanacearum disease prediction method based on an integrated learning model, and the method comprises the following steps: S1, collecting a published 16s rRNA gene sequence related to solanaceae crop bacterial wilt, and carrying out the preprocessing of original sequencing data based on an EasyAmplicon standardized process; s2, performing data dimension reduction by using a principal component analysis algorithm, and retaining 95% of variance; s3, performing hyper-parameter search based on 5-fold cross validation and grid search on the Light GBM model, the CatBoost model and the XGBoost model respectively, and selecting three groups of optimal hyper-parameters of each model; s4, constructing a model according to three groups of optimal hyper-parameters of each selected model, performing prediction, analyzing model result difference based on a Pearson correlation coefficient, and retaining Pearson correlation coefficient mean < lt > with other eight model prediction values; a model of 0.8; and S5, inputting the screened model prediction result into a second-layer element learner RF for integrated learning to obtain a final prediction result.

The invention discloses an underground pipe gallery microcrack detection and measurement method and device, equipment, a medium and a product, and relates to the technical field of underground pipe gallery body crack detection, and the method comprises the steps: inputting an RGB image of a pipe gallery into a target detection model, and obtaining a plurality of regions containing cracks; carrying out edge detection and contour extraction operation on each crack-containing area to obtain a contour diagram of each crack; obtaining three-dimensional point cloud data of the pipe gallery based on the depth map and the RGB image, and sequentially carrying out point cloud density analysis and local curvature calculation on the three-dimensional point cloud data of the pipe gallery; constructing a three-dimensional point cloud model of the cracks based on the three-dimensional point cloud data of the pipe gallery, the contour map of each crack, the point cloud density analysis result and the curvature of the three-dimensional point cloud data; and processing the three-dimensional point cloud model of the crack through a principal component analysis algorithm to obtain the size of each crack. According to the invention, the accuracy of micro-crack detection and size measurement results can be improved.

The invention discloses a flammable and explosive gas anti-explosion safety early warning method and system based on artificial intelligence. The method comprises the following steps: acquiring a data set acquired by a multi-modal sensor; performing dimension reduction processing on the data set by adopting a principal component analysis algorithm to obtain a target feature set; extracting an independent component set related to the gas concentration from the target feature set to generate an initial concentration feature value; classifying environmental parameter fluctuations by adopting a support vector machine algorithm according to the initial concentration characteristic value, and outputting a classification result; and judging whether the classification result exceeds a preset environment fluctuation threshold value or not, and if the classification result exceeds the preset environment fluctuation threshold value, adjusting the initial concentration characteristic value through a self-adaptive filter to generate a corrected concentration characteristic value. The method effectively eliminates the influence of environmental fluctuation on gas concentration measurement, improves the measurement precision and reliability, and is suitable for gas concentration monitoring scenes in various complex environments.

The invention discloses an anti-interference method suitable for a satellite navigation signal, and belongs to the field of satellite navigation signal anti-interference, and the method comprises the steps: collecting multi-channel satellite navigation signal data through a receiver array, processing an initial signal through a self-adaptive filtering algorithm to preliminarily reduce noise interference, and obtaining a preliminarily purified signal; extracting a spatial correlation matrix according to the preliminary purification signal, decomposing the matrix by adopting a principal component analysis algorithm to identify dominant spatial features, and determining an interference direction vector; obtaining the spectrum distribution of the direction suppression signal, further separating the spectrum overlapping part by adopting a principal component analysis algorithm, and judging the residual interference residue; through fusion of the direction suppression signal and the residual interference residue, an adaptive filtering algorithm is adopted to iteratively optimize the weight so as to enhance the navigation signal strength, and an optimized enhanced signal is obtained; and calculating a positioning parameter according to the optimized enhanced signal, judging whether the parameter stability meets a preset threshold value, and obtaining a final stable signal.

In order to overcome the defects of an existing low-rank model in non-stationary trend, pseudo-periodic mode and transient anomaly processing, the invention provides a time series prediction method based on self-adaptive low-rank representation, original time series data is decomposed into a low-rank component (L) and a sparse component (S) through robust principal component tracking (PCP) and principal component analysis (PCA) algorithms, and the low-rank component (L) and the sparse component (S) are subjected to low-rank prediction. And adaptive separation of a trend-periodic component and a residual (abnormal / transient) component is realized. According to the method, an orthogonal transformation matrix (A) is constructed, a data-driven orthogonal basis (B) and a Fourier orthogonal basis (UF, VF) are fused in the matrix, and time sequence data are mapped to a low-rank potential space with higher characterization capacity. The method adopts a convolution kernel norm minimization (CNNM) frame for prediction, a learnable dynamic search window is established under the frame, the size (wx, n) of the window is adaptively adjusted according to a sequence local feature (| un |), and through fusion of fast Fourier transform (FFT) decomposition and introduced motion vector information, the motion vector information of the motion vector is obtained. And accurate acquisition of multi-scale time sequence characteristics (such as periodic modes of different frequencies) is realized. Under the low-rank constraint, the method provided by the invention can effectively process the complex dynamic characteristics of high-dimensional sensing data, and meanwhile, the prediction precision of a non-stationary sequence is remarkably improved through a parameter adaptive mechanism.

The invention relates to the technical field of deep surrounding rock large deformation prediction, and particularly discloses a deep surrounding rock large deformation rapid prediction method and system based on a PCA algorithm, and the method comprises the steps: selecting a prediction index from a plurality of indexes affecting surrounding rock deformation; obtaining the measured value of each prediction index and the corresponding maximum relative deformation according to the prediction indexes, and taking the measured value and the corresponding maximum relative deformation as a data set; extracting a principal component vector from the measured value of each prediction index through a principal component analysis algorithm, and constructing a deformation prediction model in combination with a corresponding principal component coefficient; performing least square regression modeling on the deformation prediction model by using the data set, determining the principal component coefficient, and obtaining a trained deformation prediction model; and performing deformation prediction on a to-be-predicted item according to the trained deformation prediction model. According to the method, historical data of a large deformation project can be fully utilized, multiple indexes are comprehensively considered, the weight of each index does not depend on artificial subjective judgment, and the accuracy of large deformation prediction is greatly improved.

The invention provides a turbine blade wear-resistant layer welding path planning method based on point cloud processing. The method comprises the steps that firstly, a structured light camera and a welding robot tail end coordinate system and a conversion matrix of the welding robot tail end and a world coordinate system are determined through calibration; scanning the turbine blade by using a structured light camera to obtain original three-dimensional point cloud data of the turbine blade, and recording coordinates of a welding gun under a tail end coordinate system of a welding robot; performing point cloud filtering, Euclidean clustering and plane segmentation on the original three-dimensional point cloud data to obtain point cloud data of a to-be-welded surface; the point cloud of the surface to be welded is cut through a principal component analysis algorithm to obtain a preliminary path, a complete path can be obtained through straight line fitting and path logic sorting processing, and then coordinates of a welding gun in a coordinate system at the tail end of the welding robot and the complete path are substituted into a coordinate transformation matrix; coordinates of all the path points in the world coordinate system can be obtained, so that path planning is completed. According to the method, automatic path planning can be realized, and production efficiency is improved.

The invention discloses a laser cutting end surface quality control method, which comprises the following steps: S1, data acquisition: acquiring the thickness, material spectrum, surface roughness, laser power and auxiliary gas pressure data of a workpiece by using a multi-mode sensing module, performing dimension reduction on hyperspectral data through a principal component analysis algorithm, extracting a material feature vector, and calculating the material feature vector; and generating an initial process parameter request in combination with the thickness information. Through a multi-parameter dynamic collaborative optimization mechanism, a coupling rule among parameters in the laser cutting process is deeply excavated, parameters such as laser power, cutting speed, focus position and auxiliary gas pressure are brought into a unified regulation and control system, and through a parameter coupling relation model and a multivariable collaborative regulation strategy, compared with a traditional single-parameter regulation mode, the adjustment efficiency is greatly improved. The notch width consistency is improved by 65%, the fluctuation range is controlled within + / -0.02 mm, the end face roughness is reduced by 52%, the Ra value can reach 3.2 [mu] m or below, and the problems of thick plate slag residue, thin plate overburning deformation and the like are effectively solved.

The application relates to a three-dimensional facial median sagittal plane construction method based on an intelligent registration algorithm, which has the following steps: (1) constructing a variable graph structure neural network algorithm for registration of a three-dimensional facial data ontology and mirror image point cloud, and the algorithm steps are as follows: 1) constructing feature vectors of key points in the ontology and mirror image point cloud data X and Y; 2) obtaining the corresponding relationship of the key points in the point cloud X and Y based on the feature vectors; and 3) calculating the rotation and translation matrix R and t of the mirror image data through singular value decomposition; (2) constructing a three-dimensional facial median sagittal plane through a principal component analysis algorithm; and the application can realize accurate and efficient construction of a three-dimensional facial median sagittal plane, and provides a new median sagittal plane construction solution for digital diagnosis and treatment in the oral clinic.

The invention discloses a mining data acquisition method and system based on the Internet of Things, and the method comprises the steps: obtaining multi-source data streams outputted by mine Internet of Things acquisition equipment, carrying out the classification of different parameter types, and obtaining an initial data set; according to the physical characteristics of each parameter in the initial data set, an adaptive threshold segmentation algorithm is adopted to determine the collection precision range of each type of parameters, and data groups with standardized precision are obtained; acquiring sliding window features of the time sequence from the filtered data sequence, and judging an abnormal value by adopting an isolated forest algorithm to obtain a data set after abnormities are eliminated; according to the data set with the exception removed, aiming at the timestamps and the spatial positions of the multi-source data, a weighted average algorithm is adopted to carry out normalization processing, and a data matrix with consistency integration is obtained; and obtaining a dynamic change trend of each parameter from the consistency integrated data matrix, and extracting key features by adopting a principal component analysis algorithm to obtain a feature data set after dimension reduction.

The invention relates to a sole glue spraying track generation method based on point cloud data, and belongs to the field of computer vision and industrial automation. The method comprises the following steps: calculating a global absolute phase by adopting an improved three-wavelength phase shift profilometry, and generating a sole three-dimensional point cloud through a monocular coding structured light system; generating a glue spraying track based on a three-coordinate scanning weighted principal component analysis algorithm: segmenting the point cloud along the length direction of the sole to extract an upper edge point set, setting an offset for the upper edge point, performing inward offset to obtain a transition point, and optimizing the transition point for a high-curvature region by adopting polynomial interpolation to obtain a glue spraying track; generating a smooth trajectory by using a third-order non-uniform rational B-spline curve interpolation; and calculating a normal vector of the smooth path through weighted principal component analysis, and finally converting the normal vector into a quaternion pose to control glue spraying operation of the six-degree-of-freedom robot. The shoe sole glue spraying track of the robot can be effectively generated, the path precision is high, the glue line uniformity is good, and the method is suitable for automatic glue spraying of various types of shoe soles.