9062results about How to "Improve detection efficiency" patented technology

The invention provides a network attack risk mapping assessment method and system, and relates to the technical field of network attack risks, and the method comprises the steps: collecting a network flow data packet to extract a multi-dimensional attack feature vector, employing a deep neural network to carry out feature learning to obtain an attack type recognition result and an attack intensity assessment value, and constructing an attack feature standard model; based on the attack type identification result and the attack strength evaluation value, combining an asset weight coefficient, a vulnerability hazard level and a vulnerability utilization difficulty coefficient to construct an asset vulnerability evaluation model; and performing association analysis on the real-time attack feature data and the asset vulnerability assessment score, constructing a risk mapping matrix, calculating a comprehensive risk score, and generating risk early warning information. According to the invention, accurate identification, dynamic assessment and timely early warning of the network attack risk are realized, and the accuracy and real-time performance of risk assessment are improved.

The invention provides a method and system for enhancing wafer detection in a semiconductor manufacturing process, and relates to the technical field of wafer detection, and the method comprises the steps: transmitting dual-frequency laser to the surface of a wafer to form a controllable overlapping region, and collecting a beat frequency signal to obtain initial height information; carrying out adaptive filtering processing on the beat frequency signal, reconstructing a three-dimensional contour of the surface of the wafer, and generating an initial defect distribution diagram; and determining a secondary detection area according to the initial defect distribution diagram to perform orthogonal scanning, identifying and rejecting pseudo defect signals, extracting defect features, and determining defect cause types. According to the invention, the accuracy and efficiency of wafer defect detection are improved, and the semiconductor manufacturing quality can be improved.

The invention discloses a multi-modal data fusion-based high dam underwater defect measurement method, which comprises the following steps of: obtaining water flow velocity data, water pressure data and water quality turbidity data, performing preprocessing by adopting a self-adaptive median filtering algorithm, and constructing a hydrodynamic field feature model; then generating an initial trajectory based on the hydrodynamic field feature model and detection target area data, and performing trajectory optimization and stability evaluation in combination with dynamic constraint conditions; adjusting sonar parameters according to the turbidity influence and the trajectory parameters to perform data acquisition; carrying out space-time alignment through extended Kalman filtering, and carrying out feature fusion and optimization by adopting an attention mechanism; and finally, performing multi-scale consistency verification and accurate positioning mapping to obtain a final defect detection result. According to the invention, the problems of defect detection precision and reliability in a complex underwater environment are solved.

The invention provides a textile blending proportion defect detection method, which comprises the following steps: carrying out continuous image acquisition on a blended textile in a vibration state, and adjusting the exposure time and frame rate of camera equipment according to vibration frequency and amplitude characteristics to obtain a textile surface image sequence; the obtained textile surface image sequence is preprocessed, and warp and weft areas are segmented according to the surface texture features of the blended textile; based on the abnormal degree evaluation model, constructing a blending proportion flaw classifier, and training the classifier to identify different types of blending proportion flaws by using the warp and weft abnormal features, the fiber distribution features and the texture features as input; according to a blending proportion defect classifier, in combination with defect positioning requirements in a vibration environment, performing real-time tracking on detected different types of defect proportion areas, compensating image displacement caused by vibration, and ensuring that a defect detection result corresponds to an actual textile position.

The invention discloses a near infrared spectrum rapid detection system and a real-time analysis method, and the system comprises an environmental parameter and spectrum information collection module, a data processing module, an intelligent evaluation model construction and optimization module, a real-time analysis and evaluation module, and a result feedback and optimization module. The intelligent evaluation model construction and optimization module is used for constructing an intelligent evaluation model by fusing a neural network and a support vector machine, taking the characteristic vector information which is obtained after processing and represents environmental parameters and is associated with infrared spectrum information as input, and taking an expected evaluation result which is associated with the infrared spectrum information of a sample as output; training the intelligent evaluation model; and the real-time analysis and evaluation module is used for inputting the environment parameter information and the infrared spectrum information which are acquired in real time into the trained intelligent evaluation model to obtain an evaluation result of the infrared spectrum information. The system and the method have the advantages of high detection speed, high accuracy and the like.

The invention discloses a PPY-YOLO-based steel surface defect detection method and a PPY-YOLO-based steel surface defect detection system. The PPY-YOLO-based steel surface defect detection method comprises the following steps: step 1, preprocessing a steel surface defect image; 2, a neck structure Faster-Neck, a deep learning module C3K2Faster and a spatial pyramid pooling module SPPFSim are introduced into the YOLOv11 network, and a PPY-YOLO target detection model is obtained; step 3, training a PPY-YOLO target detection model; and 4, acquiring a steel image, and inputting the steel image into the trained PPY-YOLO target detection model to obtain the category and positioning information of the steel surface defects. A new model is constructed, the backbone network and the neck network are improved, the backbone network is formed by connecting the input end of the C2PSA module with the output end of the SPPFSim module, and an up-sampling group and a down-sampling group are added to the neck network, so that the model has a relatively good detection effect.

The invention provides an LED product surface defect detection method, which comprises the following steps: carrying out defect feature extraction on a preprocessed LED surface image by adopting a multi-scale morphological algorithm, and respectively carrying out morphological opening operation and closing operation on surface recesses and fine scratches by setting structural elements with different sizes and shapes to obtain candidate regions of the recesses and the scratches; according to the geometric dimensions, morphological characteristics and spatial distribution detail attributes of the recesses and scratches, constructing defect feature vectors, and performing classification and severity evaluation on defect candidate areas through a support vector machine algorithm to obtain the types, positions and quantitative description parameters of defects; and updating the light source parameter database and the defect feature database according to a quality detection result of the LED product, analyzing an association rule between defects and light source parameters through a data mining algorithm, and optimizing a light source parameter configuration strategy and a defect detection model.

The invention belongs to the field of hollowing detection, and provides an outer wall hollowing detection method and system, and the method comprises the steps: carrying out the multi-mode detection of an outer wall, and obtaining an infrared thermal imaging image and an ultrasonic detection signal; performing image enhancement processing on the infrared thermal imaging image; respectively carrying out feature extraction on the infrared thermal imaging image and the ultrasonic echo image by using a convolutional neural network; extracting and fusing high-dimensional feature vectors, and aligning an infrared thermal imaging image with an ultrasonic echo image through an image registration algorithm and a spatial transformation model; and classifying the fused feature vectors through a multi-layer sensor according to the data aligned in the step, and outputting the specific position, size and severity of the hollowing area. According to the scheme, the outer wall hollowing detection can be efficiently carried out.

The invention relates to the technical field of tea leaf detection, and discloses a tea leaf quality grade detection method and system.The method comprises the steps that a tea leaf sample is collected and pretreated; extracting chemical component indexes and physical properties of the pretreated tea leaves, and performing fusion and analysis; and establishing a tea quality grade classification model, inputting characteristics of a tea sample to be detected, outputting a detection result, and generating a tea quality grade report. Through combination of the support vector machine, the gradient boosting decision tree and the improved logistic regression algorithm, the precision of the tea quality classification model is optimized. The dynamic threshold function and the segmented excitation function are introduced to further solve the boundary fuzzy problem of the high-grade tea leaves and the low-grade tea leaves, and accurate judgment of tea leaf grade classification is achieved. And data restoration is carried out through weighted compensation correction and a recheck process, so that the output tea grade report is ensured to have high accuracy and reliability.

The invention relates to the technical field of path planning, in particular to a pipeline robot path planning and detecting method based on multi-data fusion, which comprises the following steps: collecting data from vision, ultrasound, magnetic flux leakage and laser radar, performing timestamp alignment and space coordinate correction on the data, performing data fusion and checking the consistency of the data, an environmental data set is generated. According to the method, by constructing the three-dimensional pipeline model, not only is visual representation of the pipeline provided, but also the understanding of the internal structure of the pipeline is enhanced, so that a maintenance team can make a repair strategy more effectively, and the robot is allowed to check in the most effective sequence through the access priority determined through cost-benefit analysis and the optimized access path; the detection time is shortened, the operation cost is reduced, instant fault diagnosis and quick response are ensured, the detection accuracy and the operation efficiency of the robot are remarkably improved, the pipeline detection efficiency and accuracy are remarkably improved, manual intervention is reduced, and the maintenance cost is reduced.

The invention relates to the technical field of defect detection, in particular to a precise copper material surface defect identification method and system based on optical online detection.The method comprises the steps that a plurality of light source devices are arranged on the surface of a precise copper material, and a multi-light-source illumination system is constructed; dividing the surface of the precise copper material into a plurality of detection areas, and installing an industrial camera in each detection area; collecting surface images of the precise copper material through a plurality of industrial cameras, and adding a timestamp to each surface image to obtain a copper material surface image; the collected copper material surface image is transmitted to a data processing center for feature processing, and a copper material surface feature image is obtained; and constructing a surface defect identification model, and inputting the copper material surface feature image into the surface defect identification model to obtain a copper material surface defect identification result. According to the invention, the hysteresis and inconvenience of off-line detection are effectively solved, the detection precision and efficiency are greatly improved, and the risks of missing detection and false detection are effectively reduced.

The invention provides a bonding wire defect detection method based on global-local importance feature fusion, and solves the problems that in an existing method, structure defects cannot be effectively detected, the detection precision is insufficient, and three-dimensional bonding wire defects with rough edges cannot be segmented. And the sensitive area of the input feature and the weak link in the detection process cannot be revealed. The process comprises the following steps: acquiring a depth image of a PCB, converting a bonding wire depth image point column into point cloud data through matrix operation, performing data preprocessing on bonding wire point cloud by using Cloud Compare software to obtain simplified and optimized bonding wire data, and making a marked point cloud bonding wire data set; after the data set is divided, a three-dimensional point cloud bonding wire defect detection model based on global-local importance feature fusion is trained, finally, normal bonding wire detection, radian abnormity detection, wire bending detection and the like are carried out on a segmented bonding wire plane, and instance segmentation is carried out on the eight bonding wire targets.

The invention relates to the technical field of fan blade detection, and provides a wind power plant fan blade detection method and system based on three-dimensional point clouds, and the method comprises the following steps: carrying out the multi-angle scanning of a fan blade through a laser scanner, and obtaining the three-dimensional point cloud data of the fan blade; performing registration processing on the three-dimensional point cloud data, and unifying the three-dimensional point cloud data into the same coordinate system to obtain registered point cloud data; performing block acceleration processing on the registered point cloud data by adopting an octree partitioning algorithm to obtain block acceleration point cloud data; performing feature extraction and feature segmentation on the block acceleration point cloud data to obtain a key feature region of the fan blade; performing defect identification analysis on the key feature region based on a point cloud network enhancement model to obtain a defect detection result, the defect detection result comprising a defect position and a defect type; and generating a detection report containing the defect position and the defect type, and visually displaying the detection report. According to the invention, the integrity of data acquisition and the precision of defect identification are improved, and the efficiency of fan blade detection is improved.

The invention provides a visible light and thermal infrared image target fusion detection method for the visual angle of an unmanned aerial vehicle, and belongs to the technical field of image processing, and the method comprises the steps: carrying out the preprocessing and data enhancement of a data set of visible light and thermal infrared images of a detection region obtained by the unmanned aerial vehicle, and obtaining a plurality of initial images; initial images under different scales are analyzed based on a multi-scale dynamic feature pyramid network, fusion processing is carried out on the initial image under a visible light mode and the initial image under a thermal infrared mode based on an attention fusion mechanism, a candidate area is locked, and then highlighting processing is carried out on a tiny target, a long-distance target and the candidate area; and splitting all the target samples to obtain a training set and a verification set, training a preset network model based on the training set to obtain an initial detection model, and performing model performance evaluation and model optimization on the initial detection model based on the verification set to obtain a target detection model. The problem of target detection challenge of the unmanned aerial vehicle in a complex environment is solved.

The invention discloses a multi-class defect detection method and device based on a variational auto-encoder and a denoising network, and the method comprises the steps: obtaining an industrial product surface defect image, carrying out the preprocessing, and constructing a training set and a verification set; the training process of the method is divided into two stages: in the first stage, a preprocessed training image is input into a variational auto-encoder for training, feature distribution of the image is learned, and features of a normal image are captured by reconstructing the image; in the second stage, a denoising network and a feature guiding module are used for conducting secondary reconstruction on image features in the feature space of the variational auto-encoder, and therefore the reconstruction capacity and the multi-class reconstruction capacity of the model are enhanced; in the reasoning stage, multi-scale features of an original image and a reconstructed image are extracted through a feature extractor, feature comparison is carried out, and an abnormal scoring graph is generated. According to the method, normal information is reserved while the reconstruction capability of the variational auto-encoder is enhanced, and defect missing detection caused by reconstruction failure is effectively reduced.

The embodiment of the invention relates to the technical field of sealing detection, and discloses an artificial intelligence-based sealing detection method, which comprises the following steps of: determining corresponding control parameter information according to the volume of a detected object and a deformation parameter of the detected object of detected object information; according to the control parameter information, the working states of the air source assembly and the inflation proportional valve are controlled to inflate the tested object; if the pressure conversion information is smaller than a first set threshold value, it is determined that the airtightness of the corresponding measured object meets the requirement, and if the pressure conversion information is larger than the first set threshold value, the next step is executed; and obtaining an infrared detection image of the detected object shot by the infrared camera, inputting the infrared detection image into the air tightness detection model for identification so as to determine the air tightness state of the corresponding detected object, and outputting the corresponding air tightness state. According to the scheme of the embodiment of the invention, through accurate inflation control and an intelligent detection process, the method can improve the detection efficiency while ensuring the detection precision.

The invention provides a tunnel quality safety detection system and method based on laser radar data, and the method achieves the fusion and complementation of crack detection based on point cloud density change and crack detection based on curvature change, and achieves the quick recognition and precise detection of a crack region. The potential crack area is rapidly positioned through point cloud density change detection, and the boundary of the crack is further accurately positioned in combination with curvature change detection, so that the accuracy and integrity of crack detection are improved. For tiny deformation which is very tiny in change in point cloud density and curvature and is difficult to directly detect, a projection plane is further determined on the basis of the identified crack, and after point cloud data is projected to a two-dimensional plane, the characteristics of the tiny crack in the image are highlighted through image enhancement, so that the crack is identified. And then an edge detection algorithm is used to identify the micro crack so as to make up for the defects of a previous point cloud density and curvature detection method in the aspect of micro crack detection, and the accuracy and integrity of crack detection are further improved.

The invention discloses a pollutant diffusion inspection and tracing method based on a U-Net gas diffusion model, and the pollutant diffusion model based on the U-Net can quickly calculate a pollutant diffusion dynamic graph of any release point source and any wind direction, carries out the precision evaluation, predicts the diffusion range and concentration distribution of pollutants under different environment conditions, and improves the accuracy of the pollutant diffusion inspection and tracing. And a reliable basis is provided for subsequent path planning. Through the MPC algorithm, the output change of the process in a period of time in the future is predicted by using the process model, so that the control effect which should be applied to the process at the current moment is determined according to a certain optimization target. Through prediction and optimization of the MPC and a prediction model and feedback correction combined with the MPC, the control strategy can be adjusted at each sampling moment according to the current system state and environment information, and the diffusion trend of pollutants can be predicted more accurately, so that the layout and detection frequency of detection points are optimized, and the detection precision and efficiency are improved.

The invention provides a security risk detection method and device for text graph large model generation content, and the method comprises the steps: selecting a test category according to a test parameter based on a preset test database, and matching a corresponding data set from the test database according to the test category; inputting the keywords in the data set into the tested large model in batches according to the format of forward cue words; performing content security detection on a corresponding picture or video image generated by the detected large model according to the input cue word; performing watermark identification detection on a corresponding picture or video image generated by the detected large model according to the input cue word; and analyzing to obtain a detection report according to the content security detection result and the watermark identification detection result. According to the test database, the risk of the large model system is externally assessed based on the characteristics of the input prompt word, the content annotation and the output video content, and the accuracy and effectiveness of the detection and assessment method are improved.

The invention relates to the technical field of digital simulation, and discloses an emergency rescue method and system based on unmanned aerial vehicles and an urban three-dimensional platform, and the method comprises the steps: calling an emergency rescue region model through the urban three-dimensional platform, driving an unmanned aerial vehicle formation to go to an emergency region to obtain real-time region information, and employing a pre-trained accident condition image recognition algorithm to recognize an accident condition image; accident condition characteristics such as smoke and fire, fire fighting access blockage, emergency exit blockage, urban inland inundation, dangerous slopes, dangerous building detection, illegal building of building roofs, river channel floating object recognition and high-voltage electric tower inclination monitoring are identified, and the accident condition characteristics are substituted into the regional three-dimensional model to generate an accident simulation model. Real-time accident condition feedback is provided through the accident simulation model, rescue workers are assisted to make scientific rescue decisions, through comprehensive analysis and accurate simulation of an emergency rescue area, the detection efficiency of the unmanned aerial vehicle is improved, the timeliness and accuracy of rescue are enhanced, and the problem that in the prior art, the efficiency is low through manual collection of the current accident condition is solved.

The embodiment of the invention relates to the technical field of visual inspection, and discloses a surface defect detection method based on machine vision, which comprises the following steps: performing parameter analysis on an image quality parameter corresponding to a casting surface image to determine an image accuracy index of the casting surface image; if the image precision index of the casting surface image is not less than a precision index threshold; if the area characteristic parameters are smaller than a preset area standard characteristic threshold value, defect detection is conducted on the corresponding casting area image, and if the area characteristic parameters are not smaller than the preset area standard characteristic threshold value, the corresponding casting area image is marked as a to-be-detected defect image; and comparing the regional defect parameter with a surface defect standard index preset in a casting detection library. According to the surface defect detection method based on machine vision in the embodiment of the invention, the casting surface image is divided into a plurality of areas, and feature extraction is performed on each area, so that the surface condition of the casting can be analyzed more meticulously, and the detection accuracy is improved.

The invention discloses a wafer detection scattered light collection objective lens, and relates to the technical field of optical imaging. The scattered light collecting lens is applied to wafer defect detection, the scattered light collecting lens sequentially comprises a first lens group, a second lens group, a third lens group and a fourth lens group from an object plane to an image plane in the optical axis direction of the lens, twelve lenses and an aperture diaphragm, and the twelve lenses and the aperture diaphragm are coaxially arranged; the first lens group is a catadioptric lens group, has positive focal power, and is used for imaging an object plane to an intermediate image plane through two times of reflection. The numerical aperture of the acquisition objective lens provided by the invention is equal to 0.9, the field of view is equal to 2.6 mm, the acquisition objective lens has the characteristics of large field of view, large numerical aperture and small center shielding, the capability of detecting scattered light of defects can be greatly improved, chromatic aberration is corrected by adopting a refraction and reflection mode, and a wider laser bandwidth can be realized compared with a general refraction mode of combining CaF2 and fused quartz.

The invention discloses an automatic single-yarn strength tester and a test method, and relates to the technical field of textile detection equipment, the automatic single-yarn strength tester comprises a single-yarn strength tester used for testing breaking strength, breaking elongation and breaking strength indexes of a single yarn and fiber pure spinning or blended yarn; and the translation mechanism is used for translating a plurality of distributed yarns and carrying out continuous high-efficiency test operation. According to the automatic single-yarn strength tester, a connecting piece is pulled through a tension spring, then a fastening piece and a limiting block are driven to conduct limiting installation on a limiting groove in a rubber pad, the rubber pad can be rapidly replaced after being abraded, the yarn clamping stability is ensured, and the testing accuracy is ensured; the good friction force of the clamping face can be recovered by replacing the rubber pad, it is ensured that the single yarn is tightly fixed in the clamping mechanism, the single yarn cannot move due to unstable clamping in the tensile test process, it is ensured that the test can be conducted smoothly, and the result is accurate.

The invention discloses a power marketing knowledge graph construction method, system and device and a storage medium, and relates to the technical field of power system data management. At present, integration and application of multi-source heterogeneous data are difficult, data anomaly detection and treatment efficiency is low, and data circulation is unstable. The method comprises the following steps: collecting data of a plurality of sources, and carrying out data cleaning and standardization processing; classifying the data according to structuralization, semi-structuralization and non-structuralization, and forming feature vectors through a feature extraction method; extracting knowledge units from the feature vectors by using natural language processing and machine learning technologies, fusing knowledge from different sources through rules or models, and constructing a unified knowledge base; generating a knowledge graph in a graph database by using a knowledge base, and constructing a graph structure of nodes and edges; and carrying out real-time monitoring on data flow in the knowledge graph, and analyzing path efficiency. According to the technical scheme, the data management efficiency is improved, data circulation is monitored in real time, and the business process is optimized.

The invention belongs to the technical field of welding seam detection, and particularly relates to a laser welding seam defect detection machine which comprises support plates, adjusting assemblies arranged between every two adjacent support plates, driving assemblies installed on the support plates and balancing assemblies arranged on one sides of the support plates. Wherein three groups of support plates are arranged in a circumferential array, a front-end support is fixedly mounted on one side of the long edge of each group of support plates, an ultrasonic detection probe, a dot matrix laser and a camera are sequentially mounted on the three groups of front-end supports, an electric control module is mounted on the top surface of each support plate, and a power supply module, a data analysis processing module and a control system are arranged in each electric control module. The device can automatically move on a reducing pipeline, perform positioning detection on a welding seam, and perform circumferential uninterrupted detection in the detection process, so that the detection precision is higher, a mode of combining laser detection and ultrasonic detection is utilized, displacement deviation is automatically corrected after reducing, the error probability is greatly reduced, unnecessary workload is avoided, and the detection efficiency is improved. The detection efficiency is improved.

The invention discloses a roadway deformation real-time detection method and system based on binocular vision, and the method comprises the steps: firstly, installing and debugging a mining industrial binocular camera, and training an anchor rod end target detection algorithm; then real-time acquisition and preprocessing of images are carried out, and environmental noise is removed by using a self-adaptive median filter; and based on the preprocessed image, identifying and positioning the end of the anchor rod by using a trained target detection algorithm. A binocular vision technology is utilized to calculate three-dimensional coordinates of the end of the anchor rod, and feature matching and parallax calculation between images are carried out. And finally, comparing the three-dimensional coordinates of the ends of the anchor rods at different time points, and inputting the three-dimensional coordinates into a roadway deformation calculation module to obtain a deformation result of the roadway so as to realize real-time deformation monitoring and early warning of the roadway.

The invention belongs to the technical field of visual inspection, and particularly relates to a PCB defect detection method and device based on 2D and 3D image visual combination, and the method comprises the steps: collecting the 2D information and 3D information of a PCB, and carrying out the defect marking; the 2D information subjected to defect labeling serves as a training set to be input into a pre-constructed yolov10 target detection network, and a yolov10 model is obtained after training is conducted through a training strategy of double label distribution and consistent matching measurement; converting the 3D information subjected to defect marking into voxel features and coordinates by using a pre-constructed seond target detection network, performing feature extraction on the voxel features and coordinates by using a sparse convolutional neural network, and classifying object directions to obtain a trained seond model; and determining the defect type of the target PCB according to the yov10 model and the seond model. By adopting the deep learning algorithm and the 3D vision technology, the detection accuracy and efficiency can be greatly improved, the maintenance cost is reduced, and the detection robustness and stability are improved.

The invention discloses a tunnel crack tiny defect detection method based on a generative adversarial network. The method comprises the following steps: S1, acquiring a space-time crack data set containing tunnel surface tiny crack information; s2, generating a preprocessed space-time fracture data set; s3, generating a crack expansion image data set; s4, designing a generative adversarial network model which comprises a generator and a discriminator; s5, inputting the preprocessed space-time crack data set into the generative adversarial network model; s6, deploying the trained generative adversarial network model into a tunnel monitoring system; s7, performing classification according to the form, size and expansion trend of the crack, and dividing the crack into different risk grades; and S8, according to the crack detection result and the expansion trend thereof. The tunnel crack detection accuracy and the crack propagation prediction capability are improved, the automation level of the system is remarkably improved, and efficient and accurate crack detection and monitoring can be realized in a complex environment.

The invention provides a pesticide residue detection method and device combined with mass spectrometry, and relates to the technical field of pesticide residue detection.The method comprises the steps that a sample image is recognized, and a sample type and sample morphology characteristics are determined; matching and determining a pretreatment process and a sample interference matrix; detecting according to a pretreatment process; inputting the sample type, the sample morphology characteristics and the detection data set into a pesticide component analysis channel for identification, and outputting a pesticide residue component set; and performing interference correction on the plurality of pesticide concentrations according to the experimental equipment attribute characteristics, the interference matrix type set and the matrix content set, outputting a plurality of corrected pesticide concentrations, and generating a pesticide residue detection report. The technical problem that the accuracy and reliability of pesticide residue detection are poor due to the fact that matrix interference of different types of products cannot be handled in a traditional method can be solved, the interference problem of pesticide residue detection under a complex matrix can be effectively solved, and the accuracy and reliability of pesticide residue detection are remarkably improved.

The invention relates to a malicious script detection method and device, equipment and a storage medium, and the scheme not only can generate a static analysis result through the analysis of a script text, but also can generate a dynamic analysis result according to an operation event of a target script in a running process. And a comprehensive risk assessment result is determined through a machine learning model and a heuristic rule set. Through the mode, malicious behaviors executed by the target script in the running process can be discovered, so that the scheme can effectively detect the novel malicious script; in addition, according to the scheme, the target script is operated through the sandbox container, and the detection efficiency of the malicious script can be improved.