78 results about "Principal component analysis algorithm" patented technology

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 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 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 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 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 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.

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 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.

The invention discloses a target travel direction estimation method, system and device and a storage medium, and relates to the technical field of automatic driving. The target travel path direction estimation method comprises the following steps: acquiring a first movement track of a target, and converting the first movement track into a second movement track; determining a dynamic track and a track length of the target according to the second motion track and a preset adaptive track length determination algorithm; and determining the travel path direction of the target according to the dynamic trajectory, the trajectory length and a preset principal component analysis algorithm. According to the target travel path direction estimation method provided by the invention, the dynamic trajectory of the target is constructed based on the preset adaptive trajectory length determination algorithm, and the trajectory length of the target is dynamically adjusted, so that system errors caused by instantaneous sudden change (such as violent steering) of the target travel path direction are reduced; and the path direction vector of the target is extracted from the motion trail of the target by using a principal component analysis method, so that the accuracy of the path direction estimation of the target is improved.

The embodiment of the invention provides a robot technological process data processing method and device, and the method comprises the steps: carrying out the dimension reduction fusion of multi-source sensor data according to a preset principal component analysis algorithm, obtaining a multi-source time sequence feature, and carrying out the structural modeling of technological path execution data based on an STEP standard, and obtaining a technological path feature; the method comprises the following steps: grouping multi-source time sequence features according to sensor types, constructing three-dimensional feature tensors, performing feature extraction on the three-dimensional feature tensors of each group according to a parallel CNN network to obtain a spatial-temporal feature sequence, performing feature fusion on the spatial-temporal feature sequence and process path features, and inputting the fused features into a bidirectional LSTM network to perform time sequence prediction. Obtaining a time sequence prediction value of the process quality index; and when the time sequence predicted value of the process quality index exceeds a preset parameter control limit, an abnormal alarm is triggered, so that the robot carries out process path correction according to the abnormal alarm, and the application efficiency and accuracy of the robot can be improved.

The invention discloses an integrated energy system fault identification method and system, and relates to the field of integrated energy. The integrated energy system fault identification method comprises the following steps: step 1, constructing a digital twin model of an integrated energy system; 2, simulating operation states of the integrated energy system under different fault types based on a digital twinborn model; 3, performing feature extraction on the fault sample data set; 4, constructing a fusion fault recognition model based on a deep belief network and a support vector machine; 5, real-time operation data of the integrated energy system are collected in real time; and step 6, inputting the real-time fault feature vector into the trained fusion fault recognition model. According to the comprehensive energy system fault identification method and system, the fault identification precision can be improved, feature extraction is carried out through an improved principal component analysis algorithm, a self-adaptive weight factor is introduced to highlight features highly related to fault types, and interference of irrelevant features is reduced.

The application provides a low-quality welding image enhancement method for preserving structure and balancing brightness, and the specific steps are as follows: inputting a low-quality welding original image, forming an initial illumination component image output through a principal component analysis algorithm; processing the initial illumination component image by using a relative total variation model to form an illumination component image output with optimized structure characteristics; dividing the illumination component image with optimized structure characteristics into two paths, one path is directly substituted into a Retinex model together with the original image to obtain a reflection component image, and the other path is processed by a logarithmic transformation enhancement algorithm to obtain an illumination component image with optimized brightness; fusing the reflection component image and the illumination component image with optimized brightness according to the Retinex model to obtain a reconstructed image; and introducing a guide filter to take the initial illumination component image as a guide image to obtain a final enhanced image. The image enhanced by the method has uniform and natural brightness distribution, good structure characteristic information preservation, no artifact or block effect, and clearer image details.

The invention discloses an aviation aluminum plate wave field image damage monitoring method based on non-convex total variation regularization RPCA. The method comprises the following steps: acquiring Lamb wave field image data of a to-be-detected aviation aluminum plate by using a sensor array; decomposing the mapped Lamb wave field image data by using a non-convex total variation regularization robust principal component analysis algorithm, and solving sub-problems by using an alternating direction multiplier method to obtain a low-rank matrix representing background wave field information and a sparse matrix representing damage abnormal values; performing post-processing including sparse response normalization processing, adaptive threshold segmentation, connected region filtering and morphological repair on the sparse matrix to extract a damaged region; according to the extracted damage area, the damage condition of the aviation aluminum plate to be detected is evaluated and positioned. According to the invention, the accuracy and real-time performance of damage identification can be improved.

The application relates to the technical field of underwater communication and signal processing, and provides an anti-interference method for underwater power carrier communication, which comprises the following steps: acquiring a power carrier time-frequency signal received by an SRM and synchronously collecting a control instruction stream, wherein the power carrier time-frequency signal is an OFDM signal; determining a state perception weight matrix based on a relay action time window, wherein the relay action time window is determined based on the control instruction stream; determining a joint optimization problem provided with a phase distortion correction operator based on the power carrier time-frequency signal, the state perception weight matrix and a frequency domain confidence matrix in combination with a double-weighted robust principal component analysis algorithm, wherein the frequency domain confidence matrix is determined based on the high-frequency attenuation characteristics of an underwater umbilical cable; and solving the joint optimization problem based on a linearized alternating direction multiplier method to determine a target power carrier signal. The application solves the problem that reliable communication cannot be realized under the double constraints of strong interference and severe phase jitter in the related art.

Embodiments of the present disclosure disclose a method, device and medium for optimizing data query based on quantum technology. In the method, query text data of a user at a current time and in a historical time window is collected, and a bag-of-words model is constructed after preprocessing the query text data. Feature vectors in the bag-of-words model are quantum state encoded, and a dominant feature vector of the query text data is extracted through a quantum principal component analysis algorithm. Based on the dominant feature vector, a plurality of data items that satisfy a similarity condition with the dominant feature vector are retrieved from a target database to generate a preloaded data set. The preloaded data set is loaded into a head position of an LRU cache linked list in order of similarity with the dominant feature vector. Embodiments of the present disclosure enable faster extraction of data features and identification of data features when facing large amounts of data and high-dimensional data, and faster finding of data with similar features.

The invention provides a vehicle end picture dimension reduction method, device and system based on principal component analysis, and a medium. The method comprises the following steps: acquiring an image in an RGB format acquired by an intelligent vehicle camera; the image in the RGB format is converted into a first three-dimensional array, the first dimension of the first three-dimensional array is the height of the image, the second dimension of the first three-dimensional array is the width of the image, and the third dimension of the first three-dimensional array is an R color channel, a G color channel and a B color channel; separating pixel data of R, G and B color channels in the first three-dimensional array to obtain R, G and B pixel matrixes; carrying out dimensionality reduction on the R, G and B pixel matrixes by adopting a principal component analysis method to obtain R, G and B pixel matrixes after dimensionality reduction; and combining the R, G and B pixel matrixes after dimension reduction into a second three-dimensional array, and generating a dimension reduction image based on the second three-dimensional array. According to the invention, dimension reduction based on the principal component analysis algorithm can be independently carried out on each color channel in the RGB image, cross-channel interference is avoided, and independent optimization of color information is ensured.

The invention discloses an effective reservoir prediction method and device, equipment and a storage medium, and the method comprises the steps: determining a plurality of control indexes which affect the formation of an effective reservoir in a target geological region; determining an identification standard of the effective reservoir according to the plurality of control indexes; performing single-parameter inversion on each control index meeting the identification standard to obtain a corresponding single-parameter data volume; performing matrix conversion and dimensionless processing on all the single-parameter data volumes to obtain a target matrix; performing data extraction and fusion on the target matrix by using a principal component analysis algorithm to obtain a fusion feature parameter data volume; and determining a reservoir corresponding to the fused feature parameter data volume in the target geological area as an effective reservoir. Through the technical scheme provided by the invention, the effective reservoir can be predicted by using the plurality of control indexes, the prediction error of the effective reservoir is reduced, and the prediction precision of the effective reservoir is improved.

The application relates to a kind of parallel robot work space efficient solving method based on seed space segmentation and principal component analysis, the method uses Monte Carlo and stratification method as the basis of solving algorithm, through the fusion of both, in addition to the seed space segmentation for boundary space screening and the principal component analysis algorithm for the stratification direction of boundary seed space, the design of overall solving algorithm is realized.The method can segment the larger work space of parallel robot, and the rough boundary of work space is quickly and independently screened out by the ratio of reversible point and non-reversible point in the segmented space, the range of overall solving space is reduced, and the optimal stratification direction of boundary seed space is selected by the principal component analysis method, the number of stratification layers in the direction with large gradient change is increased, the feature loss is reduced, and the solving accuracy of overall work space boundary is improved.

The invention discloses a power equipment fault diagnosis method and system based on multi-source data fusion. The method comprises the following steps: extracting linear features of data sources from preprocessed multi-source data through an improved principal component analysis (PCA) algorithm; fusing the linear features of the multiple data sources through a weighted fusion strategy, and constructing a fault diagnosis model in which a bidirectional long-short-term memory network BiLSTM and a convolutional neural network CNN are mixed based on an attention mechanism; and the pre-trained fault diagnosis model is used to carry out power equipment fault diagnosis, and a fault type identification result and a fault degree evaluation value are output. According to the invention, through multi-source data acquisition, hierarchical fusion and a hybrid diagnosis model, accurate and real-time diagnosis of power equipment faults and quantitative evaluation of fault degrees are realized; according to the method, the BiLSTM and CNN hybrid model based on the attention mechanism is adopted, the spatial information and the time sequence information of the features are captured at the same time, key features are highlighted, and the accuracy of fault diagnosis is improved.

The application discloses a motor imagery ability grading method and device based on multi-band coupling features, and relates to the field of data processing, which comprises the following steps: obtaining original electroencephalogram signals collected by a to-be-evaluated person in motor imagery and rest stages, and performing pretreatment and data screening to obtain processed electroencephalogram signals; constructing multi-band coupling features based on the processed electroencephalogram signals; adopting a principal component analysis algorithm on the multi-band coupling features, and performing dimension reduction with the optimal principal component number to obtain corresponding dimension reduction features; inputting the dimension reduction features into a trained motor imagery ability grading model to obtain a motor imagery ability grading probability, wherein the motor imagery ability grading probability is the probability that the to-be-evaluated person belongs to a high motor imagery ability level or a low motor imagery ability level, and the motor imagery ability level to which the to-be-evaluated person belongs is determined based on the motor imagery ability grading probability. The application solves the problems of difficulty in accurately grading motor imagery ability and high-dimensional data redundancy.

The application discloses an object recognition and pose estimation method for a grabbing robot, and comprises the following steps: collecting a 2D color image and a 3D depth image of a region to be recognized, and obtaining a point cloud image according to the 2D color image and the 3D depth image; constructing a YOLOv7-tiny-CBAM deep learning neural network model and training the same, performing target detection and recognition on the 2D color image through the trained YOLOv7-tiny-CBAM deep learning neural network model, and obtaining a target object positioning frame; processing the point cloud image and combining the obtained target object positioning frame to obtain each target object in the region to be recognized; calculating each target object through an improved principal component analysis (PCA) algorithm to obtain pose information of each target object; constructing a grabbing priority function, performing grabbing priority calculation according to the pose information of each target object, and obtaining a priority grabbing result. The object recognition and pose estimation method improves the robustness and multifunctionality of the grabbing robot and enhances the environmental adaptability of the grabbing robot.

The invention discloses a rail transit traction power supply system locomotive combination condition identification method and system, and the method comprises the steps: carrying out the dimension reduction processing of first power quality monitoring data of a power supply arm through employing a principal component analysis algorithm, and enabling the features after dimension reduction to retain the main energy and change trend in original data; noise and redundancy characteristics can be effectively suppressed, and a Gaussian mixture model clustering algorithm introducing a time sequence continuity constraint is used for clustering the dimension-reduced first electric energy quality monitoring data, so that the problem of insufficient self-adaptability caused by dependence on fixed power or current threshold division in a traditional method is solved; performing feature extraction on second power quality monitoring data of the power supply side of the traction transformer by using a feature extraction method based on a sliding time window to ensure a better model training effect, and training an extreme gradient boosting tree classification model by using a combined working condition label and a feature vector; therefore, the working condition of the locomotive of the rail transit traction power supply system can be accurately identified in a non-intrusive manner.

The application relates to the technical field of network catering services, in particular to a catering takeout platform scoring method based on XGBoost-PCA, which comprises the following steps: constructing a modular scoring system; performing normalization processing on the data of each module; calculating the original module scores of each module based on an XGBoost algorithm; performing dimension reduction on the original module scores by adopting a PCA principal component analysis algorithm; and performing weighted summation on the module dimension reduction scores to obtain a final platform score; the application constructs multiple decision trees by XGBoost, calculates the gain of different features at a split node, identifies the index with the largest contribution to the prediction result and eliminates the redundant weak features, and further combines PCA to analyze the linear correlation between the features, eliminates the linear redundancy by maximizing the variance, and obtains the dimension reduction comprehensive score, which removes the repeated potential factors in the original data, avoids repeated calculation of these factors in subsequent evaluation, and thus ensures the objectivity of the evaluation.

The invention discloses a frequency band switching method and device, a server, a system, a medium and a product. The frequency band switching method comprises the steps of cleaning to-be-identified data based on a scene adaptive random forest model to determine features of each scene; performing standardization processing on the to-be-identified data through a scene adaptive principal component analysis algorithm to determine weights of features in each scene; determining the scene corresponding to the to-be-identified data according to the weighted distance of the features between the to-be-identified data and the sample data of each scene; and performing frequency band switching based on a scene corresponding to the to-be-identified data. According to the technical scheme, the features and weights in the scene are determined through the adaptive random forest model improved for the scene and the principal component analysis algorithm, the scene to be recognized is determined by calculating the weighting distance, the scene where the terminal is located can be accurately recognized in real time, the corresponding frequency band is automatically switched, and the real-time performance and reliability of frequency band switching are improved.

The invention provides a network intrusion detection method and system based on an improved jellyfish search algorithm. The method comprises the following steps: during data preprocessing, performing dimension reduction on data features by adopting a principal component analysis algorithm, so that the calculation complexity of a model is reduced, and the detection performance is improved; and a jellyfish search algorithm is improved, and then the jellyfish search algorithm is used for parameter optimization of a support vector machine. According to the scheme provided by the invention, the accuracy of intrusion detection can be improved.

The invention discloses a mine pipe gallery information acquisition and early warning method based on 5G communication, and the method comprises the steps: deploying a multi-source sensor in a mine pipe gallery, and transmitting monitoring data to an edge calculation node through a 5G network; after the edge node receives and caches the data, a recursive principal component analysis algorithm is adopted to carry out dynamic dimension reduction processing, and a covariance matrix is dynamically updated through a forgetting factor; the early warning module reads dimension reduction data from the real-time data buffer area, dynamically adjusts an early warning threshold value based on principal component score distribution, and constructs a closed-loop processing flow of early warning information generation, hierarchical pushing and equipment linkage control through a workflow engine; and finally, the early warning information is sent to a terminal user through a 5G network, and equipment response is triggered. According to the method, the data transmission real-time performance, the algorithm environment adaptability and the early warning accuracy are remarkably improved.