511 results about "Feature description" patented technology

The invention relates to an intelligent vacuum coating quality monitoring method based on artificial intelligence, which comprises the following steps: collecting process parameters in real time through a coating equipment sensor, dynamically monitoring vacuum degree change, deposition rate deviation and temperature gradient, and standardizing data flow to obtain a quantitative trend curve of process parameter fluctuation; combining the defect microcosmic feature description set with gas flow fluctuation and power supply voltage jitter to obtain a correlation analysis result of process parameters and defect forms, and calibrating defect types according to a defect feature library to obtain an updated defect classification basis; and inputting real-time new surface image data through the updated defect classification basis, performing defect detection for illumination reflection differences and stripe deflection angles, obtaining a preliminary defect classification result, and adjusting classification weight parameters.

The invention relates to the technical field of visual identification, and discloses a belt tearing detection method, device and equipment based on visual identification, and the method comprises the steps: carrying out the dual-channel image collection and preprocessing of the surface of a conveying belt, and obtaining a multi-channel preprocessing image; extracting a thermal difference feature and a texture structure feature of the multi-channel preprocessed image through a double-flow feature network, and inputting the thermal difference feature and the texture structure feature into a quaternion material deformation analysis model for deformation gradient tensor and invariant parameter calculation to obtain a tear feature description vector; performing hierarchical progressive identification and three-dimensional reconstruction analysis to obtain target tearing feature data; and risk assessment is carried out based on the target tear feature data to obtain a tear grade classification result, the interference of ambient temperature drift and non-uniform illumination is effectively eliminated, the calculation efficiency and accuracy of tear detection are improved, and false alarms and missing alarms are reduced.

The invention belongs to the technical field of three-dimensional measurement, and discloses a 3D laser line scanning system based on a point cloud positioning algorithm and a control method. The method comprises the following steps: acquiring environment initial point cloud data and scanning target information; carrying out feature extraction, constructing an environment feature description model, carrying out three-dimensional space partitioning processing, carrying out regional reflectivity feature analysis, and constructing a space reflection feature model; adaptive optimization configuration is carried out on the laser scanning parameters, and a scanning strategy is generated; scanning path dynamic planning is carried out on the scanning target information, scanning density distribution calculation is carried out, and a scanning path is generated; performing laser line scanning control by using the scanning path and the scanning strategy, and performing scanning data quality real-time monitoring to obtain scanning quality evaluation data; scanning parameter iterative optimization is carried out on the scanning quality evaluation data, point cloud data real-time fusion reconstruction is carried out, a high-precision three-dimensional model is generated, the modeling precision is improved, and meanwhile the system resource utilization efficiency is optimized.

The invention relates to the technical field of model construction, in particular to a method and a system for establishing a parameterized tunnel model based on cloud computing. The method comprises the following steps: acquiring a tunnel section type, and extracting structural semantics to obtain a section feature description set; constructing a parameter dictionary of the tunnel section based on the section feature description set to obtain a dynamic parameter dictionary data set; and modeling a tunnel section geometric structure based on the dynamic parameter dictionary data set to obtain an initial three-dimensional tunnel geometric model. Through dynamic parameter management, a self-correction mechanism and a Latin hypercube sampling technology, efficient automatic modeling and precise simulation optimization of the tunnel section three-dimensional geometric model are achieved, meanwhile, through unified packaging and authority control, safe and efficient management and collaborative sharing of cloud model resources are achieved, and the method is suitable for large-scale popularization and application. And the intelligent level and the engineering efficiency of tunnel model construction and application are comprehensively improved.

The invention relates to the technical field of intelligent detection, and discloses an intelligent detection method, device and equipment for a plastic container, and the method comprises the following steps: carrying out multi-angle image acquisition on preforming, blank forming, multi-layer co-extrusion and final blowing forming stages of a plastic container blow molding process to obtain an original image data set; performing targeted preprocessing on the images in each forming stage to obtain an enhanced image data set, and performing feature calculation to obtain multilayer forming feature description data; establishing a feature mapping relationship among different forming stages to obtain defect position and type data; according to the blow molding equipment control system and method, full-process monitoring of the whole blow molding process is achieved, closed-loop control over defect detection and production parameter adjustment is achieved, and the production efficiency is improved. The technical problem that interface defects of all layers in the multi-layer forming process cannot be monitored in real time through a traditional detection method is solved, and the qualification rate of plastic containers is increased.

The invention provides a visible light and infrared fusion-based river channel fish banning ship monitoring method and system, and relates to the technical field of computer vision, and the method comprises the steps: firstly obtaining a visible light image sequence and an infrared image sequence of a river channel monitoring region; ship visual feature extraction and thermal feature extraction are carried out on the visible light image sequence and the infrared image sequence respectively to obtain a visible light ship feature set and an infrared ship feature set, and cross-source feature fusion processing is carried out on the two feature sets to generate a cross-source fusion feature set; and calling a pre-trained fish forbidding ship identification model to analyze the cross-source fusion feature set, generating a ship monitoring result including the ship type, the position change track and the suspected fish forbidding behavior identifier, and finally generating fish forbidding early warning information including the real-time position and behavior feature description of the suspected fish forbidding ship based on the ship monitoring result. And measures can be quickly taken to stop the fishing forbidding behavior.

The invention relates to the technical field of data analysis, provides an intelligent factory automatic monitoring method and system based on knowledge base enhancement, and realizes more accurate anomaly analysis and more effective process adjustment of an intelligent factory. The method comprises the steps of performing knowledge enhancement fusion processing on an obtained real-time monitoring data set of an intelligent factory through a pre-constructed process knowledge base and a pre-constructed monitoring rule base, and generating a process knowledge graph; performing abnormal mode recognition processing on the process knowledge graph based on a semantic matching strategy, extracting feature description of an abnormal event and a semantic association path with a historical monitoring text, and generating an abnormal mode analysis result containing abnormal root cause inference; according to the abnormal mode analysis result and the dynamic incidence relation in the process knowledge graph, an automatic monitoring report containing root cause priority ranking and optimization operation guidance is generated, and the automatic monitoring report is fed back to the intelligent factory control terminal to trigger process adjustment operation.

The invention relates to an underwater topographic measurement method based on laser radar and visual fusion, which comprises the following steps: synchronously acquiring underwater point cloud, images and physical environment sensing data, and endowing a unified space-time label to realize the space-time consistency of multi-source data; according to the method, the data quality of different modes is improved by means of preprocessing, denoising, scale normalization, feature enhancement and the like, an environment interference weight matrix is constructed in combination with an environment sensing model, feature extraction and matching algorithm parameters are adaptively adjusted according to different environment states, and multi-scale feature description, spatial consistency and physical constraint criteria are established, so that the multi-modal data quality is improved. According to the scheme, high-reliability feature matching between the point cloud and the image is achieved, finally, through environment-driven iterative optimization and fusion, the robustness and precision of space registration are improved, high-precision multi-modal data automatic registration can be stably achieved in the underwater dynamic environment, adaptability is high, and environment perception and space measurement quality is effectively improved.

The invention discloses a two-stage infrared and visible light image registration method, system and device. The method comprises the following steps of image preprocessing, contour extraction, angular point detection, feature description and matching, affine transformation estimation, multi-scale optical flow estimation, optical flow constraint and loss function, reverse resampling and fusion and error evaluation. According to the invention, rough registration is carried out by using contour angular point features, so that a preliminary alignment result can be quickly obtained; refined alignment is carried out in combination with an unsupervised optical flow network, and sub-pixel-level registration precision is achieved. The contour angular points are based on shape information of an image target, are natural and are not influenced by spectral differences, and the matching stability is enhanced through main direction and angle features. The unsupervised depth optical flow model estimates a pixel displacement field by learning consistency characteristics of an input image, and does not need to depend on annotation data. The combination can effectively eliminate the difference between infrared light and visible light, and improves the robustness and adaptability of registration.

The invention relates to a point cloud registration method based on improved ISS-TOLDI features in combination with ICP, and belongs to the technical field of laser point cloud application. The method comprises the steps of obtaining a source point cloud and a target point cloud, and performing preprocessing; extracting feature points from the point cloud data by using an internal shape descriptor ISS algorithm; performing feature description on the extracted feature point set by using an improved TOLDI algorithm; performing coarse registration on the point cloud by using a sampling consistency initial registration algorithm; and performing fine registration on the coarsely registered point cloud data by using an iterative closest point ICP algorithm to complete point cloud registration. According to the method, the TOLDI algorithm is improved, so that the calculation complexity is reduced, the feature integrity is ensured, and the point cloud registration precision is higher.

The invention discloses a test method of a server system, a storage medium, electronic equipment and a program product, and relates to the technical field of computers, and the method comprises the following steps: firstly, obtaining an equipment feature vector of a to-be-tested server system; performing fuzzy matching on the equipment feature vector and a feature description vector capable of performing server system testing in a driver library; if the matching is successful, collecting hardware characteristic parameters of the server system to be tested, and dynamically determining a test threshold value of the hardware state of the server system to be tested according to the environment parameters monitored in real time; and then testing the hardware state of the to-be-tested server system based on the hardware characteristic parameters and the dynamically determined test threshold. Compared with the prior art, automatic identification and adaptation of multi-manufacturer hardware equipment are realized, the time of manual intervention and the complexity of drive configuration are remarkably reduced, the test cost is further reduced, the test efficiency is improved, and meanwhile, the test accuracy and flexibility are also improved.

Provided are a multimodal image matching method and system, terminal device, and storage medium. The method includes: performing self-supervised feature extraction on an optical image and a synthetic aperture radar (SAR) image to obtain a repetitive feature point between the optical image and the SAR image; segmenting the optical image and the SAR image into a first image block sequence based on the repetitive feature point, and performing feature extraction on the first image block sequence through a dual-branch network to obtain feature description vectors of the optical image and the SAR image respectively, where the dual-branch network includes a first branch network for extracting a global feature and a second branch network for extracting a local feature; and performing feature matching on the optical image and the SAR image based on the feature description vectors to obtain a matching point pair between the optical image and the SAR image.

The invention relates to the technical field of unmanned aerial vehicle geological survey, and discloses a path planning method and system for unmanned aerial vehicle geological survey, and the method comprises the steps: obtaining the image, spectrum and topographic data of a remote mountainous area through a multispectral sensor and a laser radar, and recognizing potential vein abnormal points through data fusion and geological feature analysis; and constructing a geological interestingness weight map and planning a flight path. Dense sampling flights are performed in a high interestingness region, high resolution data are processed in real-time to identify key reconnaissance targets, a weight map is updated, and a flight trajectory is dynamically adjusted. Through the closed-loop self-adaptive exploration process, accurate positioning and feature description of the hidden vein are realized, the mineral exploration efficiency is improved, the labor cost and the safety risk are reduced, and an innovative solution is provided for mineral resource exploration in remote areas.

The invention relates to the technical field of augmented reality application, and discloses an augmented reality system and method for immersive experience. The method comprises the following steps: collecting dynamic spatial data of a physical environment in real time, and synchronously obtaining an interactive behavior data stream of a user terminal; performing environment semantic modeling on the dynamic spatial data to generate multi-level feature description of a physical environment, and determining a rendering priority sequence of virtual-real superposition content according to the multi-level feature description; extracting behavior pattern characteristics in historical interaction data of the user, performing immersion correlation analysis on the behavior pattern characteristics to obtain environment cognitive preference parameters of the current user, and generating a virtual-real interaction event sequence in combination with the environment cognitive preference parameters and the interaction behavior data flow; and dynamically compensating the virtual-real synchronization error in the immersive experience process according to the rendering priority sequence and the virtual-real interaction event sequence. According to the method, the accuracy and naturalness of virtual-real fusion can be improved, the personalized requirements of the user are met, and the overall immersive experience effect is enhanced.

The invention discloses a multi-modal remote sensing image matching method, particularly relates to the technical field of remote sensing image processing, and is used for solving the problem of multi-modal image matching. The method mainly comprises the following steps of: 1, improving a phase consistency model, and constructing a phase-moment weighted joint direction feature in combination with a maximum moment and a minimum moment to replace the feature expression of the traditional image gradient; 2, implementing a point product fusion strategy on the phase-amplitude characteristics extracted by the phase consistency model and the maximum moment, and constructing phase-moment weighted joint amplitude characteristics; and 3, on the basis of the steps 1 and 2, identifying the direction of the feature points and screening local peak values to determine the main direction. Three values adjacent to a peak value are selected, and the peak value position is interpolated through parabola fitting so as to improve the matching precision; and step 4, constructing a logarithm polar coordinate descriptor based on regularization non-uniform partition to generate a feature description vector. Through the mode, high-precision and high-efficiency matching of the multi-mode remote sensing image can be realized.

The invention discloses a cultural heritage digital reconstruction system based on deep learning, and the system comprises a data collection module, a data preprocessing module, a feature description module, a modeling module, and a model optimization module. The feature description module is used for acquiring data processed by the data preprocessing module, feature extraction is performed on point cloud data through a point cloud processing network in deep learning, the data preprocessing module is used for data preprocessing, the data preprocessing comprises correction, denoising, color correction and cutting, and the modeling module is used for modeling the point cloud data according to the extracted features. Three-dimensional reconstruction of the cultural heritage is carried out, and a model optimization module is used for smoothing a generated cultural heritage model, filling the deficiency and eliminating noise; the cultural heritage digital reconstruction method has the advantages that the digital system has rich details and sense of reality, people can conveniently visit and learn, and the efficiency and precision of cultural heritage digital reconstruction are improved.

The invention belongs to the technical field of intelligent clothing pattern making, and relates to a text-driven intelligent clothing pattern generation method, which specifically comprises the following steps of: inputting a user-defined clothing pattern feature description text into a trained intelligent clothing pattern generation model to generate clothing pattern data; jointly inputting the generated garment template data and a user-defined action feature description text into a trained learning-based three-dimensional virtual fitting model, and setting parameters of the inelastic fabric to obtain garment template data of the inelastic fabric; the real elastic fabric parameters are adopted to optimize the garment template data of the inelastic fabric, and garment template data of the real elastic fabric are generated; and finely adjusting and storing the garment template data of the real elastic fabric according to individual requirements. According to the method, the generation efficiency and quality of the garment template are remarkably improved, the fitness and comfort of the garment in a dynamic scene are effectively improved, and an innovative solution is provided for intelligent garment pattern making.

The invention provides a satellite fault diagnosis method and system based on multi-source information fusion. The satellite fault diagnosis method comprises the steps that firstly, a multi-source monitoring data set of platform system state data, load system working data and measurement and control link interaction data is generated in the satellite testing process based on an emergency fast response satellite testing system; then performing multi-source information association extraction processing on the multi-source monitoring data set to generate an association feature set reflecting a coupling relationship between systems; then inputting the associated feature set into a pre-trained satellite fault diagnosis model for fault mode analysis processing to obtain a fault feature description containing a fault trigger node and an influence link; a preset fault knowledge base is matched based on the fault feature description, and a fault diagnosis result containing a fault type identifier and a propagation path is generated; and finally, generating a satellite fault handling strategy according to a fault diagnosis result. The method can comprehensively and accurately diagnose the satellite fault and improve the satellite reliability.

According to the movie and television play table book extraction method and device, the storage medium and the computer equipment provided by the invention, after an audio and video file of a movie and television play is split into a video file and an audio file, feature recognition is performed on the video file to obtain a subtitle text, speaker face information and a video understanding text; performing voice understanding on the audio file to obtain a voice transcription text and a voice understanding text; wherein the voice transcription text can be corrected into the standard transcription text with high accuracy through the subtitle text. Therefore, based on the face information of the speaker, the line segment of each speaker in the standard transcriptional text and the audio and video file is aligned, so that speaker information with accurate segmentation and semantic coherence can be obtained; and then, through combination with a character side-writing text generated by side-writing analysis on the speaker based on the video, the voice understanding text and the speaker information, table book information is constructed, and related feature description of the character can be covered on the basis of containing the line content, so that the content and depth of the table book are enriched.

The invention discloses a remote sensing fine-grained ship detection method for multi-scale context feature enhancement, and the method comprises the steps: enabling an output feature of a backbone network to pass through a deep separable convolution layer through a constructed attention feature enhancement module, carrying out the blocking processing, obtaining a local feature, and carrying out the attention enhancement of feature description through a channel. Then, through a constructed multi-scale context feature fusion module, extracting context information of ships with different appearance scales from multi-scale features output by the feature pyramid through hole convolution operation with different expansion rates; after target ship edge information is captured through a constructed feature refining fusion module, the target ship edge information is fused with output features of a context feature fusion module, the identification capability of a network model on ship details and structural features is improved, and the problem that an existing method cannot achieve self-adaptive multi-scale feature enhancement and fusion, and the ship detail and structural feature recognition efficiency is improved is solved. Therefore, the problem of loss of detail features beneficial to positioning and classification is solved.

Embodiments of the present application provide a fingerprint anti-counterfeiting neural network training method, a fingerprint anti-counterfeiting method and device, comprising: obtaining multiple sets of training data, each set of training data including: first native domain data, second native domain data and third native domain data; using the multiple sets of training data to train an initial classification network to obtain a target classification network, the initial classification network including a fusion subnetwork and a classification subnetwork, for each set of training data, the fusion subnetwork is used to generate a first fingerprint matching pair based on the feature description matrix of the first native domain data and the second native domain data, and generate a second fingerprint matching pair based on the feature description matrix of the first native domain data and the third native domain data, and the classification subnetwork is used for fingerprint classification and identification based on the first fingerprint matching pair and the second fingerprint matching pair. Through the present application, the problem of low identification accuracy of true and false fingerprints in related technologies is solved, and the effect of improving the identification accuracy of true and false fingerprints is achieved.

The invention discloses a deep learning-based mixed-shooting image correction method and system, and relates to the field of computer vision, and the method comprises the steps: carrying out the illumination normalization processing of a mixed-shooting image group, and constructing a Gaussian pyramid image group; inputting the Gaussian pyramid image group into a ResNet-34 network to extract a multi-scale feature map, and generating spatial position and channel statistical information; and converting the spatial position and the channel statistical information into a DNA sequence fragment through a DNA base mapping rule. Illumination normalization is achieved through Retinex decomposition and Gaussian filtering, the exposure difference of different devices is effectively eliminated, the input quality of follow-up processing is guaranteed, traditional feature description is converted into biological sequence comparison through DNA base coding, geometric parameters are optimized in combination with quantum annealing, and the accuracy of the method is improved. The matching precision of a low-texture area and the correction effect of a large-view-angle-difference scene are remarkably improved, and the sub-pixel-level matching precision is achieved based on weighted RANSAC and multi-scale pyramid fusion.

Disclosed in the present invention are a multi-modal image matching method and system, and a terminal device and a storage medium. The method comprises: performing self-supervised feature extraction on an optical image and an SAR image, so as to obtain repeatable feature points between the optical image and the SAR image; on the basis of the repeatable feature points, segmenting the optical image and the SAR image into a first image block sequence, and performing feature extraction on the first image block sequence by means of a double-branch network, so as to respectively obtain a feature description vector of the optical image and a feature description vector of the SAR image, wherein the double-branch network comprises a first branch network used for extracting global features and a second branch network used for extracting local features; and on the basis of the feature description vectors, performing feature matching on the optical image and the SAR image, so as to obtain matching point pairs between the optical image and the SAR image. In the present invention, a two-stage training method is used, in which method training is performed first so as to obtain robust SAR-optical image feature description vectors, and learning is then performed in view of the feature description vectors so as to obtain feature points having high matching performance.

The invention discloses a knowledge network construction and recommendation method for artificial intelligence teaching, and relates to the technical field of education informatization, and the method comprises the steps: collecting structured course database data, semi-structured teaching material data and unstructured audio and video resources; executing cross-modal knowledge analysis by adopting a bidirectional sequence labeling model, and extracting an artificial intelligence teaching knowledge subject object and a multi-dimensional feature description thereof; constructing a knowledge unit, and adopting a model of'knowledge subject-feature dimension-feature value 'and'knowledge subject-association relationship-association knowledge subject'; vectorized semantic calibration is realized through an embedded space mapping technology, and heterogeneous semantic unit fusion is completed in combination with a dynamic similarity threshold mechanism; and screening high-quality knowledge units based on a credibility verification engine to construct an artificial intelligence teaching knowledge network. According to the invention, the organization efficiency and personalized service accuracy of artificial intelligence education resources are effectively improved, and the problem of knowledge acquisition efficiency bottleneck caused by discretization of multi-source teaching resources is solved.

The invention relates to the technical field of computers, in particular to an AGV task allocation method and system based on multi-objective optimization, and the method comprises the steps: obtaining a task demand set and a state information set of AGV equipment in a current operation scene, and the task demand set comprises a plurality of task units; performing feature extraction processing on the task demand set and the state information set to obtain task feature description of each task unit and capability feature description of each AGV device; performing joint optimization matching processing according to the task feature description and the capability feature description to generate a matching association relationship set of the task and the AGV equipment, wherein the matching association relationship set comprises a corresponding relationship between the task unit and the AGV equipment and a task execution sequence relationship; and generating a task allocation scheme based on the matching association relationship set, wherein the task allocation scheme comprises a to-be-executed task list of each AGV device and execution time sequence arrangement information of each task. Therefore, task requirements and equipment states can be accurately grasped, and efficient and matched innovative task distribution processing is realized.

The invention provides an elevated warehouse cargo visual identification checking method and related equipment, and the method comprises the steps: obtaining a cargo box region image, carrying out the gray transformation and noise reduction, obtaining a label local information image sequence, recognizing the focus blurring and light refraction caused by projection angle deviation and distance error, and generating a cargo box label image set; determining a texture feature set according to the texture details, analyzing character strokes and pattern distribution according to the texture feature set, and forming a label local feature description set; performing feature comparison on the label local feature description set and a label template database to obtain a feature matching degree, and if the feature matching degree exceeds a preset threshold value, performing feature weight adjustment according to projection light refraction to determine a candidate label matching list; generating a final label candidate set according to the candidate label matching list, and determining complete contents of labels; a mapping relation is established according to the complete content of the label, an inventory checking result is determined, and the inventory identification accuracy is improved; the method can be applied to the technical field of information.

The invention provides a centralized multi-robot collaborative SLAM (Simultaneous Localization and Mapping) method based on an FPGA (Field Programmable Gate Array) platform. The method comprises the following steps: acquiring a color image and IMU (Inertial Measurement Unit) data of a surrounding environment by using a motion camera The color image is input into an FPGA platform of the single robot and converted into a grey-scale map, image pyramid scaling, corner detection and feature description are carried out, and the image is transmitted to a processing system of the single robot; the processing system generates observation information and further generates an image frame; generating a key frame and sending the key frame to a central server; the central server side carries out loopback detection on the key frame, carries out map fusion on local maps established by different single robots to obtain a global map, and optimizes the global map; and constructing a three-dimensional dense point cloud map. According to the method, cooperative positioning and three-dimensional mapping of multiple robots can be carried out in a large-scale and complex environment scene, the problem that computing resources and a detectable range of a single robot in the complex scene are limited is solved, and the scale, the precision and the robustness of map construction are improved.

The invention discloses an obstacle sensing method and system based on laser radar data analysis, and relates to the technical field of radar data obstacle analysis, and the method comprises the steps: scanning a surrounding environment through a multi-mode sensor, obtaining sensor data, synchronously collecting the data of the multi-mode sensor, and carrying out the preprocessing, an obstacle target is obtained from a camera image, sensor data are integrated by using a data fusion algorithm, and obstacle features are extracted by using a segmentation threshold function based on density clustering in combination with an obstacle local feature descriptor and global feature description to form complete obstacle feature description. The laser radar can provide high-resolution distance data, so that the system can accurately identify and position obstacles in the surrounding environment, algorithm processing is combined, the method can complete scanning and analysis of the environment in a short time, rapid response to the obstacles in the dynamic environment is achieved, and the system can be widely applied to the field of dynamic environment monitoring. And the accuracy and reliability of obstacle detection are greatly improved.

The invention belongs to the technical field of optical three-dimensional scanning, and relates to a real-time registration method for high-density point clouds, which comprises the following steps: acquiring two groups of sparse point cloud data generated by three-dimensional scanning in an oral cavity, inputting the point cloud data into a constructed efficient registration model, extracting multi-dimensional feature descriptions of a source point cloud and a target point cloud, and constructing a registration model; the point cloud registration method comprises the following steps of: obtaining a point pair set, calculating a feature distance to form an initial point pair set, performing spatial consistency analysis on the initial point pair set, rejecting outlier pairs to obtain a cluster point pair subset, and finally performing point cloud registration calculation on the cluster point pair subset to complete point cloud registration. The invention further relates to a high-density point cloud real-time registration device, scanning equipment and a storage medium. According to the technical scheme provided by the invention, the registration precision and stability can be improved, and meanwhile, the requirement of the intraoral scanner on the real-time performance is ensured.

The invention discloses a command detection and processing method and device, equipment and a storage medium, and relates to the technical field of network security, and the method comprises the steps: carrying out the global identifier adding operation of an original operation log of target detection equipment, and obtaining an identified operation log; processing the identified operation log to obtain structured data, and establishing a threat knowledge graph based on an attack command of attack feature description information obtained from a preset vulnerability library and the sensitive path; generating detection rules by using a command mode obtained by analyzing the command information based on the threat knowledge graph and a historical attack frequency of the command information, and screening the detection rules to obtain an effective rule set; processing the command information to obtain a syntax tree, and obtaining a target risk score based on a preset risk scoring rule and the structured data scoring syntax tree; and determining a response rule in the effective rule set based on the risk score grade of the target risk score, and processing the command of the command information by using the response rule. Therefore, the command detection and processing efficiency can be improved.