5596 results about "Video image" patented technology

The invention provides a smoke and fire detection method and system based on deep reinforcement learning and electronic equipment, and relates to the technical field of fire fighting. The method comprises the following steps: acquiring thermal imaging data of a forest area through a thermal imager, obtaining temperature distribution conditions of different positions, and determining position coordinates, the number and specific temperature values of temperature abnormal points for an area with abnormal temperature rise; if the temperature value of the temperature abnormal point exceeds a preset temperature threshold value, judging that the temperature abnormal point is a suspected fire point, and collecting a visible light video image of the area through a visible light camera according to the position of the suspected fire point; smog detection processing is carried out on the collected visible light video image, whether smog exists in the image is judged by analyzing the color, texture, motion and other characteristics of the image, and the concentration, morphological characteristics and diffusion speed of the smog are obtained; the meteorological parameters such as the wind speed and the wind direction of a fire occurrence area are obtained through the meteorological sensor, the meteorological parameters are input into the fire spreading model, and the spreading direction and the spreading range of the fire under the current wind field condition are simulated.

The invention relates to a multi-defense-area intelligent linkage alarm method based on an AIoT gateway and related equipment, and the method comprises the following steps: fusing the environment data of an Internet of Things sensor of a plurality of defense areas with a video image stream to generate three-dimensional reconstruction data of a defense area scene; and monitoring and analyzing the data, and extracting an environment feature sequence and a trajectory analysis sequence. And when it is detected that the threat levels of the multiple defense areas exceed a threshold value, causal inference is carried out in combination with gateway historical alarm data, and a cross-defense-area association event chain is generated. According to the method and the system, the AIoT gateway is used as a core, then a cooperative response strategy preset by the AIoT gateway is matched, and the SP voice call module is linked through a 4G / 5G network, so that multi-channel alarm pushing is realized, and the technical problem that comprehensive understanding and dynamic modeling of a defense area scene are difficult to form due to the lack of deep fusion and cooperative analysis means between video image data and environment sensor data is solved.

The invention discloses a video transmission method and system based on streaming processing, and relates to the technical field of video transmission, and the method comprises the steps: collecting a video stream, carrying out the preprocessing, extracting a video frame, packaging the video frame into a picture stream, carrying out the data fragmentation, distributing an initialization path for fragmented data, and calculating an initial traffic proportion; and optimizing path selection and adjusting path flow distribution according to network feedback to obtain a path optimization result, and transmitting the fragmented data according to the path optimization result. Through multi-level feature extraction and key frame selection of video frames, fragmented packaging of video data is realized, a dynamic path optimization technology is combined, path selection and flow distribution are dynamically adjusted based on a Q learning algorithm and a luminescence firefly algorithm, the transmission efficiency is improved, video image processing and a dynamic network optimization technology are fully combined, and the video image processing efficiency is improved. And a brand new solution is provided for efficient and stable transmission of high-definition video streams.

The present disclosure relates to the field of computer vision and discloses a three-dimensional dynamic scene reconstruction method and apparatus, and a storage medium. The three-dimensional dynamic scene reconstruction method comprises: acquiring synchronized videos of a plurality of viewpoints of a dynamic scene; computing matching points between video images of different viewpoints, and estimating intrinsic and extrinsic parameters of each camera; obtaining a Gaussian splatting point set {p0} on the basis of a sparse point cloud constructed according to the depth of each matching point; for the first image frame of each video, using {p0} to perform static training thereon, to obtain a Gaussian splatting point set {p}; for the remaining image frames, dividing {p} into a static point set {S} and a dynamic point set {D}, performing dynamic training on {D}, and constructing a dynamic Gaussian splatting point set {P} from {p}, {S}, and the final {D}; and, in view of the intrinsic and extrinsic parameters of each camera, rendering {P} using a Gaussian splatting rendering pipeline, to obtain rendered images at different moments from new viewpoints.

The invention is suitable for the technical field of machine rooms, and provides a machine room environment management and control method based on a digital twinning technology, which comprises the following steps: constructing a digital twinning machine room model based on a three-dimensional structure of a machine room and a machine room equipment layout; real-time load data of each IT device, real-time power data of each cooling device and environmental parameter data of each type of sensor are obtained, thermal load distribution of the machine room is predicted, a preset environmental control strategy is combined, corresponding working conditions are simulated and generated for the machine room in a digital twin machine room model, PUE values of the machine room under the working conditions are obtained through calculation, and the PUE values of the machine room under the working conditions are obtained. Dynamically adjusting an environment control strategy; running state parameters of the IT equipment and video image data in the machine room are obtained in real time, the running state parameters and the video image data are analyzed, and potential abnormal equipment and abnormal areas are determined in the digital twin machine room model. According to the invention, comprehensive optimization of machine room energy efficiency, operation and maintenance efficiency and environment resource configuration is realized, and the intelligent level of machine room management and control is improved.

The invention relates to the technical field of remote monitoring, in particular to a remote monitoring system and method based on a cloud platform. The method comprises the following steps: acquiring multispectral video image data of an industrial site; analyzing the difference characteristics of visible light and infrared spectrum based on the original data of the multispectral video image, identifying the characteristic mode of electromagnetic interference, and generating spectrum difference interference fingerprint data; identifying an uninterfered area according to the spectrum difference interference fingerprint data to obtain spectrum reliability distribution data; extracting non-interfered spectrum section information based on the spectrum reliability distribution data; performing complementary fusion on the non-interfered spectrum section information and the interfered area to obtain texture enhanced video data; and analyzing the time-varying characteristic of electromagnetic interference by using the texture enhanced video data to obtain interference time sequence characteristic data. According to the invention, dynamic calibration of interference is realized by analyzing the deformation characteristic of the calibration frame, and the adaptive capacity of the system to a changing electromagnetic environment is remarkably improved.

The invention discloses a photovoltaic field area unmanned aerial vehicle inspection system, which relates to the field of unmanned aerial vehicle inspection, and specifically comprises a data acquisition and preprocessing module, a data fusion module, a data processing module, a data storage module, a data processing module and a data processing module, and is characterized in that the data acquisition and preprocessing module is used for acquiring environmental parameter data and carrying out dynamic data cleaning and preprocessing to obtain preprocessed environmental parameter data; carrying out weight adjustment on the basis of the preprocessed environmental parameter data, and carrying out cross-modal attention mechanism processing and synchronous fusion processing on the basis of the environmental parameter data subjected to weight adjustment to obtain cross-modal fusion features and synchronous fusion features; by introducing adaptive adjustment of environmental parameter data, cross-modal data fusion, anomaly detection and a self-learning optimization module, the high-precision inspection capability in a complex environment is realized, a data acquisition strategy is dynamically adjusted based on different environmental states, weight distribution and time synchronization fusion are performed on video images, thermal imaging and audio data, and the accuracy of inspection is improved. And the fusion precision of the multi-modal data and the accuracy of anomaly detection are effectively improved.

Systems and methods for video summary generation are disclosed. A chat and video conference provider generates a text summary for a meeting video and an audio summary based on the text summary. The chat and video conference provider determines a first set of correspondences between a first set of video portions from the meeting video and portions of the text summary based on a transcript of the meeting video. The chat and video conference provider determines a second set of correspondences between a second set of video portions from the meeting video and the portions of the text summary based on image data of the meeting video. The chat and video conference provider selects a plurality of video frames from the first set of the correspondences and the second set of correspondences to generate a video summary of the meeting video based on the audio summary.

The invention, which relates to the technical field of fire protection and security management, discloses an intelligent fire protection and security integrated big data platform system comprising a data acquisition module, a data storage and preprocessing module, an intelligent analysis module and a linkage control module. The data acquisition module integrates fire-fighting and security sensors and equipment, and transmits data to the convergence unit in a wired and wireless communication mode, so that one-stop acquisition of fire-fighting and security basic data is realized; according to the invention, the intelligent analysis module can carry out fire prediction probability evaluation in combination with security video image features, carries out comprehensive analysis on security videos and time series data through CNN and LSTM technologies, analyzes the internal relation of fire protection and security data through an association rule mining algorithm, and generates a risk early warning report; according to the intelligent analysis method, the accuracy and timeliness of safety early warning are improved, potential safety threats can be found and processed in time, and safety accidents such as fire disasters and the like are effectively prevented.

The invention discloses an insurance claim settlement-oriented multi-modal image video evidence analysis method and system. The method comprises the following steps of: acquiring video / image and multi-source data such as metadata, audio, IMU (Inertial Measurement Unit), GPS (Global Positioning System), OBD (On-Board Diagnostic) and the like; calculating content Hash of the video and the audio according to frames, connecting the content Hash with time information in series to form chained Hash, and adding a verification digital signature and a credible timestamp; realizing cross-modal time sequence alignment based on self-adaptive time anchor-attitude coupling; tampering detection is carried out in combination with PRNU fingerprints, noise field consistency, dual compression, copy-movement and the like; multi-view geometry and monocular depth are fused, IMU scale constraint and micro rendering are introduced, three-dimensional reconstruction and re-projection optimization are completed, and collision dynamics verification is carried out; and constructing an event cause and effect graph, judging responsibility in combination with traffic rules, outputting a confidence coefficient vector and a structured report, and generating a verifiable evidence packet. The scheme has the advantages of high efficiency and traceability in the aspects of space-time restoration and interpretable responsibility judgment.

The invention discloses a network edge monitoring and early warning method based on video image AI analysis, and the method comprises the following steps: S1, obtaining video data, processing the video data, and generating an image frame sequence; s2, analyzing an image frame sequence, and extracting space and time features; s3, taking the target as a hypergraph node, constructing a hyperedge based on space and time features, and dynamically adjusting hyperedge connection by using a genetic algorithm; s4, constructing a multi-layer hypergraph Transform network, extracting multi-scale spatial-temporal characteristics, and calculating a semantic relationship between nodes; s5, setting a butterfly optimization algorithm initial population, and dynamically optimizing model parameters through global and local search; s6, constructing an anomaly detection model, identifying an abnormal behavior, and feeding back a result to optimize model parameters and hyperedge selection; and S7, deploying the model at an edge node, triggering early warning when an abnormal behavior is detected, and pushing information to a management platform. According to the invention, through video image AI analysis, accurate detection and real-time early warning of abnormal behaviors in a network edge scene are realized.

The invention discloses an expressway tunnel monitoring method, system and device and a medium, and relates to the technical field of tunnel monitoring. Tunnel environment data are collected in real time through a multi-source heterogeneous sensor array, and the data at least comprise video images, millimeter wave radar point cloud, laser radar three-dimensional coordinates, temperature and humidity and CO concentration parameters. According to the system, an improved YOLOv7 algorithm is used for carrying out target real-time detection, an optical flow method is combined to predict the motion trail of a target object, a tunnel dynamic characteristic spectrum is constructed, real-time monitoring of the tunnel environment is achieved, by deploying a risk level assessment engine, the system can extract space-time correlation characteristics, and the risk level assessment efficiency is improved. A comprehensive risk index is generated in combination with a fuzzy logic decision tree, and when the risk index exceeds a dynamic threshold value, the system triggers a grading early warning mechanism, links tunnel emergency equipment, synchronously generates an emergency plan and pushes the emergency plan to an operation and maintenance terminal to guarantee tunnel operation safety.

The invention discloses a classroom teaching quality auxiliary evaluation method, device and system based on cloud edge collaboration, and the method comprises the steps: obtaining video image data, collected by a camera, of students in a classroom in real time, and transmitting the video image data to an edge device; and in the edge device, performing student classroom behavior identification and tracking on the received video image data by using a pre-trained student classroom behavior identification model, and fusing the identification results of adjacent multiple frames with the identification result of the current frame in the tracking process to obtain the final identification result of the current frame. Evaluating the classroom teaching quality according to the student classroom behavior recognition result, wherein the student classroom behavior recognition model is obtained by training based on a multi-label target detection algorithm and by adopting a knowledge distillation structure in advance; and sending a student classroom behavior identification result and an evaluation result to a cloud video server for storage. The method has the advantages of being simple in implementation method, low in cost, high in real-time performance and precision, high in robustness and the like.

The invention discloses a long video multi-modal understanding and question-answering method and system based on large model and retrieval enhancement generation. The method comprises the following steps: 1) a multi-modal feature extraction module; 2) a multi-modal synchronization and alignment mechanism; 3) constructing a structured memory pool; 4) querying a drive generation mechanism; 5) incremental updating and memory compression strategy; and 6) unifying the multi-modal representation space. The invention provides a long video multi-mode understanding method fusing a large language model and retrieval enhancement generation, and aims to break through the limitation of a traditional method in the aspects of single-mode processing and semantic fragmentation. According to the method, video image features are extracted through a visual model (such as YOLO and ViT), voice transcription and environment voice description are obtained in combination with an audio model (such as Whisper and Qwen-Audio), and unified coding of vision, voice and audio in a long video is achieved. Then, a structured memory pool is constructed through semantic consistency segmentation and timestamp alignment technologies to store time slice data of different modalities.

The invention discloses a security area intrusion early warning method and system based on video images, and belongs to the technical field of intrusion early warning. Video image data are collected in real time through monitoring cameras distributed in a security area, track recording is carried out on an intrusion target by using a preset target detection algorithm, the motion direction, speed change and stay time of the intrusion target are analyzed, a dynamic motion track is generated, and the scene layout in the area is combined, so that the security of the intrusion target is improved. Speculating an intrusion path by using the trajectory prediction model; evaluating danger levels of different potential paths, and sorting the paths according to priorities; according to the path priority, the early warning sensitivity of the monitoring area is adjusted in real time, and if the path analysis result is a high-risk area, an early warning system is triggered, and alarm information is sent out. According to the method, the high-risk path is effectively identified through comprehensive risk assessment and dynamic path sorting; and on the basis of a sensitivity dynamic adjustment strategy and a multi-level early warning mechanism, the real-time performance and the intelligence of the monitoring system are remarkably improved.

The invention provides a large recreation facility operation monitoring method and device, electronic equipment and a storage medium. The method comprises the following steps: constructing a facility three-dimensional model of a recreation facility; establishing a basic failure model of the recreation facility; based on the model and the three-dimensional model, identifying a key risk part; generating a multi-dimensional health assessment framework; key risk parts are detected, and operation parameters and structure health conditions are monitored; the recreation facility is automatically patrolled, and video images are collected; carrying out deep fusion on the nondestructive testing data, the operation parameters, the structure health condition data and the video images to obtain monitoring data; monitoring data are analyzed, and automatic fault recognition, diagnosis and prediction are achieved; evaluating the remaining service life of the key risk part of the recreation facility, and performing trend analysis in combination with historical data; and performing quantitative evaluation on the health state of the facility, generating dynamic risk early warning and maintenance suggestions, and making a maintenance plan. According to the scheme, the safety and the maintenance efficiency of the equipment are remarkably improved.

The embodiment of the invention discloses a bridge crack width monitoring method. The method comprises the following steps: acquiring a continuous video image of a bridge surface; generating a pixel-level segmentation mask image of the crack by using the target detection model; when the target detection model is pre-trained, optical flow features between adjacent frames in the first data set are extracted through an optical flow calculation algorithm, and deep fusion is performed on the optical flow features and the image features; contour pixel points of the crack are extracted based on the crack segmentation mask pattern, and the width change information and the width change rate of the crack are obtained; according to the width change information and the width change rate, performing trend prediction on the crack width change by using a time sequence prediction model to obtain a prediction result of the crack width change; in the training process of the time sequence prediction model, a basic model and a complexity capture module are designed, and the weight of a main model is optimized by using a weighted fusion strategy. According to the invention, efficient and accurate crack width monitoring is realized through crack identification and prediction.

The invention provides a field operation data analysis method and system, and belongs to the technical field of information, and the method comprises the steps: inputting the power distribution equipment type, equipment installation position and planned operation time basic information of an operation task into a unified to-be-examined operation database according to the to-be-examined operation work order information of the power distribution field operation; collecting an examination work video image, and analyzing the operation standardability of an operator; evaluating the work efficiency and quality, and generating rectification suggestions; and comparing historical data to identify anomalies, and optimizing an operation process to realize effective integration and analysis of power distribution field operation data.

A vehicular driver monitoring system includes an interior rearview mirror assembly including a mirror head that accommodates a mirror reflective element, a video display screen, and a driver monitoring camera that views at least a driver's head region of the cabin of the vehicle. The vehicular driver monitoring system, based at least in part on processing of image data captured by the driver monitoring camera, determines a gaze direction of the driver. Based at least in part on determination that the gaze direction of the driver is directed is toward the mirror assembly, the video display screen is electrically operated to display video images at a first brightness, and responsive to determination that the gaze direction of the driver is not directed toward the mirror assembly, the video display screen is electrically operated to display video images at a second brightness that is less than the first brightness.

The invention discloses a driver fatigue driving state recognition method based on deep learning, and relates to the technical field of computer vision recognition methods, and the method comprises the following steps: S01, multi-modal data collection, S02, data preprocessing, S03, data enhancement, S04, multi-modal feature fusion, S05, deep learning model construction, S06, fatigue driving state recognition, and S07, real-time feedback and optimization. S08, a fatigue driving state recognition result is output, the fatigue characteristics of the driver can be captured from multiple dimensions by fusing multi-modal data including video images, vehicle driving data, environment data, voice signals and physiological signals, compared with the prior art, the scheme can more comprehensively analyze the fatigue state of the driver, and the fatigue driving state recognition result is obtained. Therefore, traffic accidents caused by fatigue driving are reduced.

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 discloses a three-dimensional attitude high-precision acquisition and reconstruction system for exercise training, which relates to the technical field of three-dimensional attitude acquisition and reconstruction and comprises a camera calibration module, an image acquisition module, a key point extraction module, a three-dimensional reconstruction module, an attitude optimization module, a data fusion module and a feedback generation module. The camera calibration module is used for performing internal and external parameter calibration on a multi-view camera assembly by adopting a self-adaptive joint calibration method to obtain a projection matrix parameter and a transformation matrix of each camera; the image acquisition module is used for synchronously acquiring multi-view video images of the athlete in the training process based on the calibration parameters, and performing timestamp alignment processing to obtain an original image sequence; and the key point extraction module is used for detecting and identifying a human body image by adopting a deep learning model, extracting two-dimensional coordinates of key human body joint points of each frame from an original image sequence, and obtaining a two-dimensional key point set under each view angle.

The invention discloses a visible light and infrared image fusion target tracking method based on visual cue learning, and relates to the field of extended target tracking. Comprising the steps of inputting video image data, converting an image through a block embedding module, extracting multi-scale information through scale perception attention, extracting each modal feature through a double-branch backbone network, performing specific modal visual prompt learning and inter-modal visual prompt interaction, and performing cross fusion of an adapter and a prediction head. According to the method, a more reliable and efficient target tracking system is realized by introducing a multi-scale information extraction mechanism, enhancing visual prompt learning and interaction capability and developing a more advanced multi-modal fusion strategy, and through the improvements, the performance of target tracking in a complex environment can be remarkably improved, and the requirements of practical application are met.

The invention discloses a distribution network multi-parameter data fusion fault positioning method and system, and the method and system achieve the omnibearing monitoring of a power distribution network fault through the collection of multiple types of data, such as electrical signals, video images, meteorological information and environmental parameters. Compared with a traditional fault positioning method of a single information source, the system can reflect the fault condition more truly, and the positioning accuracy and reliability are remarkably improved. Machine learning or a deep learning model is utilized to analyze fusion data, fault types and positions are automatically identified, intelligent diagnosis is realized, manual intervention is reduced, the operation and maintenance cost is reduced, and the processing efficiency and accuracy are improved. The real-time data acquisition and transmission technology guarantees the system to monitor in real time and rapidly alarm potential faults. According to the method, historical data and an expert knowledge base are combined, fault causes are deeply analyzed, accurate information and maintenance suggestions are provided for maintenance personnel, operation and maintenance decisions are optimized, and the maintenance efficiency and quality are enhanced. The system provides an efficient and intelligent solution for distribution network fault positioning.

The invention relates to the technical field of engineering construction supervision, and discloses a building engineering construction supervision system based on big data analysis, and the system comprises a multi-modal data collection module which is used for collecting multi-source data of a construction site, and the multi-source data comprises structure sensor data, environment monitoring data, video image data, construction log data and building information model (BIM) state data; performing standardization processing and time synchronization on the data to generate a construction state data sequence; and the construction event modeling module identifies key events in the construction process based on the construction state data sequence and constructs a construction event graph, and the construction event graph is composed of event nodes representing construction events and event edges representing event collaboration or time correlation. By introducing an event atlas construction mechanism based on multi-source construction data driving, structured expression and semantic association mapping of key behavior units of a construction site are realized, and the problem of insufficient non-structured information processing capability in construction monitoring is overcome.

The invention discloses a river water surface two-dimensional flow field reconstruction method based on effective tracer detection and tracking, and the method comprises the steps: carrying out the distortion correction of an original video image sequence, and setting an ROI region according to the water level detection river water surface range; the method comprises the following steps: collecting a multi-scene floating object data set, screening floating objects with high relative brightness and clear outlines in different scenes, and training a YOLO model to detect the floating objects on the surface of a river; using an OCSORT matching tracking method to track adjacent frames of targets to obtain a trajectory, and filtering according to the length, angle and variance of the trajectory to obtain a stable trajectory; according to the method, object information obtained through target detection is utilized, according to the area, the area change rate and the length-width ratio of a floating object on an image, the following performance and stability of the floating object are evaluated, a track fusion weight is calculated, and the influence of unreliable floating objects is reduced; and gridding the river surface area after orthographic correction, and for a grid with a plurality of tracks, calculating the average flow velocity of the grid according to the confidence coefficient of each track to obtain a gridding two-dimensional flow velocity vector field. The method can accurately identify the floating object which meets the requirements of visibility, following performance and stability on the river surface as an effective water flow tracer, and is suitable for measuring the flow velocity of the river surface in complex scenes such as night, storm, flare, shadow and the like.

The embodiment of the invention provides a GIS (Geographic Information System)-based metropolitan area safety video monitoring method and system. The method is applied to the technical field of image processing and comprises the following steps: acquiring a video picture of a coverage area in real time through a camera; performing real-time analysis on the video picture by using a target detection algorithm, and identifying a target in the video picture; converting the position of the target in the video picture into an actual geographic coordinate based on coordinate system conversion; continuously tracking the target in different camera coverage areas through the geographic coordinates; based on perspective transformation, converting the video image into an aerial view angle, and splicing aerial views to form a panoramic view; and visualizing the geographic coordinates of the target in the aerial view. In this way, the efficiency and accuracy of the city monitoring system can be improved, and therefore the requirement for city safety monitoring can be better met.

The invention discloses a high-precision instrument assembly fault backtracking method and system, belongs to the field of precision manufacturing, and aims to solve the problems that in a traditional backtracking method, assembly data are scattered and unreliable, fault root positioning is fuzzy, and new scene adaptation depends on a large amount of data. The method comprises the following steps: collecting assembly structured data, video images and environment data in a multi-source manner, filtering out low-quality images, and distributing unique identifiers for products; fusing the multi-modal data to generate a depth feature matrix; constructing an anomaly detection model to output a risk score and a label; hashing the data and then storing the data into a product exclusive private block chain; when a fault occurs, extracting data on the chain through a unique identifier, reconstructing an assembly process by using a graph neural network, and comparing a standard positioning root; and based on the fault report incremental training model, parameters are optimized in combination with meta-reinforcement learning. According to the method, the data authenticity is guaranteed, the fault backtracking precision and efficiency are improved, a new scene is quickly adapted, the production rework rate is reduced, and the stable assembly quality is maintained.

The invention discloses an abnormal behavior security early warning system based on multi-video image analysis, and relates to the field of security early warning, and the system comprises a path construction module which constructs a security path based on the installation position of video collection equipment, and constructs an abnormal behavior comparison library; the feature comparison module is used for constructing a security comparison model by using an algorithm, identifying abnormal behaviors of personnel, performing quantitative analysis to obtain a comprehensive risk index, and determining abnormal security personnel; the personnel judgment module is used for grading the security paths and determining security risk personnel; the personnel analysis module is used for judging the risk judgment value change condition of security risk personnel and determining a priority troubleshooting path; the regional analysis module is used for determining a dangerous region and security and protection high-risk personnel through clustering analysis; and the model optimization module is used for checking high-risk suspicious personnel to obtain false marks and missing personnel, and optimizing a security comparison model. The security early warning capability can be improved, and the method is suitable for various security scenes.

The invention relates to the technical field of data processing, in particular to a smart factory monitoring data dynamic detection method, system and device and a storage medium, and the method is applied to dynamic detection of production monitoring data of production line equipment in a smart factory. Performing anomaly detection, and extracting abnormal equipment in the production line; for each abnormal device, performing quantitative analysis on the abnormal device by using a pre-constructed device abnormality evaluation model to obtain a device abnormality index; acquiring real-time video image information of the abnormal equipment, and extracting operator information of the abnormal equipment based on the real-time video image information; correcting the equipment abnormality index of the abnormal equipment by considering the working experience of the operator information to obtain a corrected equipment abnormality index; according to the invention, comprehensive monitoring of the whole production process can be realized, and problems caused by improper cooperative operation of a plurality of devices can be found.