263results about How to "Improve safety management level" patented technology

The invention provides a multi-dimensional data authority management and privacy protection method for an electric power information network, and the method comprises the steps: dynamically selecting the type of a watermark according to the leakage risk level of a screenshot event, and enabling the watermark type to comprise a simple character watermark, a character and image mixed watermark, an invisible watermark, and a two-dimensional code watermark; user screenshot content factors are added in a screenshot event distribution curve of user historical data to judge the risk level of a user, the risk level of a user group is updated, and when the screenshot frequency, time interval, screenshot mode, distribution mode and curve volatility of the user are all normal and screenshot content only aims at sensitive data screenshot, the risk level is improved; a screen capture auditing and monitoring mechanism is established, full-life-cycle recording and tracking are carried out on screen capture behaviors of a user, and screen capture events, watermark embedding conditions and abnormal behavior information in a system are monitored in real time through a visual instrument panel and a report.

The invention discloses a substation network security defense system based on artificial intelligence. The system comprises a data collection module for collecting transformer substation network data; the anomaly detection module is used for identifying an abnormal mode and an intrusion behavior; the threat intelligence analysis module is used for identifying potential threat and vulnerability information; the intelligent security monitoring and response module is used for evaluating the network security state; the malicious code detection module is used for detecting, analyzing and isolating malicious software; the intelligent security prediction module is used for predicting a network security event and carrying out early warning prompt; the self-adaptive security policy module is used for automatically adjusting or updating an optimal security policy; the system updating and maintaining module is used for updating and maintaining system modules and functions; the user interface and alarm module is used for providing safety state display and alarm for a user; the data backup and recovery module is used for backing up and recovering key data; and a compliance auditing module which makes operation and configuration accord with security standards. According to the invention, hierarchical, intelligent and active protection of the transformer substation network is realized.

The invention provides a bridge structure risk prediction method and system based on digital twinning, and relates to the technical field of bridge structure risk prediction, and the method comprises the steps: obtaining bridge stress node displacement data, environment humidity data, and bridge visual and acoustic monitoring data through the system; the system constructs a digital twinborn model, simulates stress and deformation of a bridge under different load conditions, and calculates a stress deformation potential risk coefficient according to a maximum stress value and an average deformation value. The monitoring risk coefficient is calculated by extracting the total crack length in the visual monitoring data and the frequency characteristics of the acoustic data. And calculating a final risk coefficient by combining the stress deformation potential risk coefficient, the monitoring risk coefficient and the humidity data so as to judge the safety condition of the bridge. According to the method, the accuracy and reliability of bridge safety assessment in a complex environment are enhanced.

The invention discloses a factory operator-oriented posture recognition management system and method, and relates to the technical field of factory operation safety management, and the system comprises a video image collection module, a target detection module, a behavior recognition module, an illegal behavior detection module, an alarm module, a recording module and a QT5 integration module which run in sequence. The application scene of the whole management system identifies and manages operators and automation equipment in a factory. The technical key points are as follows: aiming at the problem that in the prior art, false alarm is easy to generate in behavior recognition in a factory scene, the invention provides a system for detecting factory human-object interaction violation behaviors, the system integrates target detection and behavior recognition algorithms, the interaction condition between factory operators and equipment can be monitored in real time, and the system has high practicability. Possible illegal behaviors can be accurately identified, and an alarm signal can be timely sent out, so that the safety management level of a factory can be improved.

The invention relates to the technical field of water gate maintenance, and discloses a water gate engineering safety risk assessment and early warning system which integrates five modules, namely a data analysis module, a real-time monitoring module, a state comparison module, a risk assessment module and an early warning notification module. The future safety state of the sluice project is predicted through a machine learning model, key safety parameters are monitored in real time, real-time data and predicted data are compared and analyzed, and a curve graph is drawn and visually displayed. And the risk assessment module calculates the difference degree of the two groups of curves and identifies risk points. And the early warning notification module comprehensively evaluates the safety state of the sluice project according to the risk point and the safety evaluation rule, and timely sends an evaluation result and early warning information to a manager so as to quickly take countermeasures. According to the system, the intelligent level of safety management of the water gate engineering is improved, early discovery and early warning of safety risks are realized, and safe operation of the water gate engineering is effectively guaranteed.

The invention provides a substation operation risk analysis method, and relates to the technical field of information, and the method comprises the steps: recognizing whether a person correctly wears a protection device or not in real time through a radio frequency identification reader on the protection device, triggering an alarm if the person does not wear the protection device, and synchronizing the alarm information to a block chain node; the block chain is used for carrying out whole-process recording on the operation behavior of personnel, the operation state of equipment and the disposal condition of a security event to form auditing evidence of security operation, and meanwhile, according to the behavior of the personnel and the state of the equipment, a block chain node dynamically optimizes a security management strategy and an emergency plan to form closed-loop management, so that the security accident is effectively prevented, and the security management efficiency is improved. And the overall safety management level of the transformer substation is improved.

The invention provides a digital twinborn model and method for a coal mine disaster scene based on multi-modal data. The method comprises the following steps: acquiring original data in a mine; performing cleaning, normalization and de-noising preprocessing on the original data; fusing different types of preprocessed original data by using a data fusion algorithm, and constructing a comprehensive digital twinborn model; establishing a virtual mirror image of the mine by using a digital twinning technology, and mapping the mine in the real world into a digital twinning model; real-time mapping and simulation analysis are carried out based on the established digital twinborn model, and the disaster development trend is predicted by monitoring the internal environment and the equipment state of the mine; and according to a model analysis result, optimizing prevention and control measures of coal mine disasters, establishing a real-time monitoring and early warning system of the disasters, and taking measures in time when abnormal conditions are found. The system comprises a data acquisition module, a model construction module and a model analysis module. The pertinence and effectiveness of coal mine disaster prevention and control can be improved.

The invention is suitable for the technical field of health monitoring, and provides a health monitoring method and system based on time sequence Transform and data fusion, and the method comprises the following steps: collecting multi-modal biosensor data; carrying out asynchronous time processing and normalization processing on the acquired multi-mode biosensor data; extracting features from the processed multi-modal biosensor data by using TimesNet, forming an independent input channel by the data of each modal, performing feature extraction on the data of each modal, extracting time sequence features of different scales and abstract levels, and performing feature extraction on the extracted time sequence features of different scales and abstract levels; the extracted features are input into a time sequence Transform network for feature fusion, time sequence prediction and anomaly monitoring; carrying out online platform display on the obtained prediction data, and giving an alarm when abnormity exists in the prediction data; through the time sequence Transform model, the health state of the worker can be monitored in real time, potential abnormal behaviors can be found and diagnosed in time, and the safety management level is improved.

The invention discloses an operation site safety management and control method and system based on a multi-modal large model, and the method comprises the steps: collecting site violation data, and carrying out the preprocessing, and obtaining multi-modal data; constructing a multi-modal large model; carrying out training and optimization on the multi-modal large model by utilizing the preprocessed multi-modal data; and using the optimized multi-modal large model to realize operation site safety management and control. According to the invention, a visible light image, a safety violation knowledge base and other modal data are fused, and a comprehensive safety monitoring and management platform is constructed by using a large model and a big data processing technology, so that real-time monitoring, early warning and intelligent response of potential safety hazards of a complex operation site are realized, and the safety management level of the operation site is improved.

The invention provides a linkage early warning method based on security situation assessment and application thereof. The linkage early warning method comprises the following steps: data preprocessing and feature extraction; an abnormal evaluation model and a normal evaluation model are constructed, and training and parameter adjustment are carried out; evaluating the data acquired in real time by using a safety evaluation function, judging the current safety state of the hydraulic power plant, and outputting a safety parameter; constructing a decision function, and determining whether the current state is abnormal or not based on the safety parameters and environmental factors EnvFactors; dynamically selecting and determining to select an abnormal evaluation model and a normal evaluation model for real-time evaluation, and judging whether the current state needs early warning or not; when early warning is needed, an emergency command system and early warning information are linked to carry out early warning and emergency response, and possible safety risks of the hydraulic power plant are prompted. According to the invention, by constructing the security evaluation function and the decision function and combining the neural network model, the problem that the existing system lacks enough flexibility and adaptability and cannot effectively cope with variable network security threats is solved.

The invention provides a sparse space wind vector field abnormity early warning method based on multi-source heterogeneous spatio-temporal data. Comprising the following steps: S1, obtaining multi-source heterogeneous wind vector data and carrying out space-time standardization; s2, constructing a multi-scale geographic spatio-temporal weighted autoregression model and updating the weight; s3, decomposing a vector field and constructing a regional early warning pilot feature set; s4, boundary flux calculation and PINN-GPR fitting of a sparse space connected domain are carried out; s5, four-dimensional spatio-temporal data variational assimilation and sparse space boundary flux updating are carried out; and S6, abnormal wind field early warning based on regional early warning pilot feature matching. According to the method, the abnormal early warning of the sparse space wind vector field and the adaptive updating of the early warning library can be realized.

The invention discloses a tunnel construction quality evaluation system and method. The evaluation system comprises a data acquisition module, a quality evaluation standard system, an early warning and notification module and a data management module. Wherein the data acquisition module is used for acquiring construction vehicle data, construction site sensors and site photographing; the quality evaluation standard system comprises drilling quality evaluation, back break evaluation, primary support thickness evaluation, anchor rod construction quality evaluation, waterproof and drainage pipe quality evaluation, steel bar binding quality evaluation, secondary lining quality evaluation and primary support flatness evaluation; the early warning and notification module integrates early warning signals from all the evaluation modules; and the data management module is responsible for storing data generated by each independent module and executing cross-module data analysis. According to the tunnel construction quality evaluation system, by integrating advanced monitoring equipment, an automatic algorithm and comprehensive data analysis, the quality control effect in the tunnel construction process is remarkably improved.

The invention provides a crane hoisting monitoring method and system based on multi-normal-form visual fusion and a medium, and relates to the technical field of computer vision. Acquiring an image of the target acquisition area through an image acquisition device, and obtaining a real-time image acquisition video stream; preprocessing the real-time image acquisition video stream to obtain real-time image frame data; performing real-time image frame data identification based on the visual detection model to generate detection information; inputting the detection information into a visual tracking module, and tracking the identification object to obtain tracking information; acquiring a fixed dangerous area or a dynamic dangerous area based on the detection information and the tracking information; and identifying a dangerous condition in the video stream according to the dynamic dangerous area or the fixed dangerous area, obtaining dangerous condition accumulated data and triggering an alarm. The technical problems that in the prior art, the monitoring means is single, and dynamic dangers are difficult to accurately recognize are solved, and the technical effect of accurately recognizing potential dangers of hoisting operation in real time is achieved.

The invention relates to the technical field of coal mine safety supervision, and provides a coal mine safety supervision method, which comprises the following steps of data acquisition, data transmission, data analysis, artificial intelligence judgment, risk early warning, safety decision and event recording. According to the comprehensive coal mine safety supervision method and system, the safety management level of a coal mine is greatly improved, the occurrence probability of accidents is reduced, and the sense of safety and the working efficiency of miners are improved. In addition, through continuous data collection and analysis, the system can be continuously self-optimized to adapt to the change of a coal mine environment, and long-term safety production is realized. The popularization and application of the system undoubtedly bring revolutionary improvement to the safety management of the whole coal mine industry.

The invention discloses an aging deformation mechanism analysis method for large-scale excavation of a high slope under an ultra-deep unloading condition, belongs to the technical field of reservoir bank slope deformation analysis, and obtains key parameters such as the volume, the sliding direction and the water level change of an accumulation body by integrating technical means such as geological exploration, hydrological monitoring and displacement measurement. By utilizing the parameters, a slope stability index, an energy function and a water-force coupling equation are constructed, and a set of complete analysis system is formed. According to the method, the accuracy and the real-time performance of slope stability evaluation are improved, and deep understanding of a slope deformation mechanism is realized through numerical simulation and an optimization algorithm. In addition, the scheme also provides prevention and control measures based on an evaluation result, and active prevention of risk management is realized. Through application of the scheme, the safety and management efficiency of the reservoir bank slope can be remarkably improved, and powerful support is provided for sustainable development of hydropower engineering.

The invention relates to the technical field of information technology and artificial intelligence application, and discloses a campus anti-bullying system based on voice recognition and big data analysis. Comprising a front-end device, a central processing unit, a voice communication real-time talkback module, a prevention reminding module, a disposal reminding module and a user report interface, wherein the front-end device further comprises an AI voice recognition terminal device, a microphone and a sensor module and is used for monitoring environment sound in a monitoring blind area in a campus in real time. By integrating AI voice recognition, a deep learning algorithm and a voiceprint recognition technology, voice features related to campus bullying events can be accurately captured and recognized in a monitoring blind area, and by combining an Ai artificial intelligence large model analysis platform, the system can effectively analyze the grade of the bullying events, intelligently trigger event prevention or event disposal notification pushing, and improve the safety of the bullying events. And the historical event behaviors are subjected to data archiving, so that the accuracy of bullying event identification is greatly improved.

The invention relates to the technical field of tumor patient management, in particular to a tumor patient hospitalization full-period management system, which acquires and manages personal information, physiological data and treatment data of tumor patients through a data management module and ensures the real-time performance and integrity of the data. The data processing module improves the accuracy of the data by supplementing missing values and screening out abnormal values, optimizes the calculation efficiency through dimension reduction processing, retains main information features, and provides high-quality data input for intelligent prediction. The intelligent prediction module is based on a neural network algorithm, the future physiological parameter change trend of the patient is accurately predicted, the physiological anomaly occurrence probability is calculated, the prediction accuracy and timeliness are remarkably improved, and medical staff are helped to recognize potential risks in advance and provide personalized treatment. And the full-cycle visualization module displays patient data through a timeline and generates a risk object attention set, so that key monitoring of high-risk patients is realized, and the safety management level during hospitalization is enhanced.

The invention discloses a special equipment potential safety hazard management and control big data monitoring system and method, and relates to the technical field of special equipment monitoring, and the method achieves the comprehensive monitoring of the elevator shaft environment, the operation state and the internal passenger behavior through the steps 1-5. Through combination of shaft environment monitoring, running state early warning and elevator internal behavior collection, a comprehensive monitoring view angle is provided, and potential safety hazards can be found in time. An environment degradation degree coefficient HJlh, an elevator state variation index Pwd, an elevator internal environment state factor BhvPyz, a behavior deviation factor DevRunz and an abnormity early warning index AlarmIdx are constructed. And comprehensively evaluating to obtain a risk evaluation level, and taking corresponding control measures. The system can identify and prevent various possible potential safety hazards in advance. According to the comprehensive safety guarantee system, the running reliability of the elevator is improved, the fault occurrence probability is reduced, and the safety management level of special equipment is improved.

The invention relates to the technical field of intelligent fire-fighting processing, in particular to an intelligent fire-fighting safety pre-judgment linkage management method and platform. The method comprises the following steps: collecting circuit data, and carrying out circuit abnormal characteristic analysis according to the circuit data to generate circuit abnormal characteristic data; collecting environment infrared spectrum monitoring data; combustible gas leakage characteristic analysis is carried out based on environment infrared spectrum monitoring, and combustible gas leakage characteristic data are generated; collecting water testing operation monitoring data of the fire hydrant; fire hydrant abnormal characteristic analysis is carried out based on the fire hydrant water test operation monitoring data, and fire hydrant abnormal characteristic data are generated; performing data linkage processing on the circuit abnormal characteristic data, the combustible gas leakage characteristic data and the fire hydrant abnormal characteristic data to generate abnormal fire fighting linkage data; and performing fire-fighting safety pre-judgment demand evaluation on the abnormal fire-fighting linkage data to generate fire-fighting safety pre-judgment demand data. According to the invention, through linkage analysis of fire-fighting data, efficient fire-fighting safety pre-judgment management is realized.

The utility model is suitable for the technical field of mine side slope monitoring equipment, and provides mine side slope monitoring equipment based on radar and GNSS, comprising a bottom plate, the top of which is provided with a vertical plate and two side plates; the rotating shaft is rotationally mounted on the sides, close to each other, of the two side plates and is provided with a rotating seat; the supporting column is fixedly mounted on the rotating seat; the shell is fixedly mounted at the top end of the supporting column; the leveling mechanism is arranged on the bottom plate and used for adjusting the angle of a shell of the supporting column; the monitoring alarm mechanism is arranged on the shell and is used for monitoring the landslide displacement of the mine slope; and the power supply mechanism is arranged on the shell and is used for supplying power to the equipment. The mine side slope monitoring equipment based on the radar and the GNSS can monitor and alarm landslide displacement of the mine side slope, is suitable for mine side slopes with different gradients, and has a function of cleaning the photovoltaic panel, so that black dust attached to the photovoltaic panel is prevented from influencing the power generation efficiency of the photovoltaic panel.

The invention discloses a power distribution room remote monitoring and fire-fighting inspection system based on a cloud platform, and relates to the technical field of fire-fighting safety monitoring. The system comprises a remote monitoring module, a fire safety monitoring module and an inspection task scheduling module. And the remote monitoring module acquires and stores the running state data of the power distribution equipment in real time to realize continuous monitoring. The fire safety monitoring module obtains environment data of a power distribution room through an environment sensing unit, establishes a fire prediction model through a random forest algorithm in combination with an equipment operation state, and generates a fire prediction index. And the early warning unit simulates a fire spreading process based on the fire prediction index and predicts a fire spreading direction and an influence area. And the inspection task scheduling module intelligently distributes inspection tasks and adjusts task execution strategies in real time according to the fire risk assessment result in combination with the geographic position and the working load of the inspection personnel. The fire risk is accurately predicted and the inspection task is dynamically scheduled, so that the fire-fighting potential safety hazard of the power distribution room can be timely found and eliminated.

The invention relates to the technical field of intelligent chemical engineering, and discloses an intelligent chemical engineering construction safety and quality detection method based on big data, and the method comprises the following steps: 1, installing a monitor and a sensor; step 2, intelligent analysis; step 3, predictive maintenance; step 4, risk assessment; step 5, quality control; step 6, safety management; 7, accident prevention; 8, remote real-time management is carried out; step 9, decision support; step 10, intelligent reporting; and step 11, displaying an interface. According to the intelligent chemical engineering site construction safety and quality detection method and system based on big data, through real-time monitoring and risk assessment, the intelligent chemical engineering site system can improve the safety management level of a construction site and reduce accidents, and the system can predict potential safety risks and take measures to avoid the potential safety risks by utilizing predictive maintenance and accident prevention technologies; and by using big data and an intelligent optimization algorithm, the resource allocation and the construction process are optimized, and the construction efficiency is improved.

The invention relates to the technical field of construction site safety, and discloses an engineering construction dangerous area intelligent identification method and system based on a generative large model, and the method comprises the steps: obtaining the real-time monitoring data of each area of a construction site and the record of a safety event, and constructing an incidence matrix of the safety event and the monitoring data; for the incidence matrix, obtaining an incidence mode and rule between the security event and the monitoring data, and determining a key monitoring data set influencing the security level; and performing back test verification on the security level and the management and control measures regularly, and performing iterative optimization on setting of the security level by adopting an incremental learning algorithm by comparing and analyzing related data and actual effect data of the management and control measures under the historical security level. According to the invention, intelligentization and automation of construction site safety management are realized, the risk of safety accidents is effectively reduced, and the safety management efficiency and accuracy are improved.

The invention provides a safety management and control system for an intelligent lock of a transformer substation, and the system comprises an intelligent wearable device which is used for monitoring the physiological data of a wearer in real time; the environment monitoring module is used for monitoring the environment risk of the working area in real time; the video monitoring module is used for monitoring the in-out dynamic state and the behavior standard of the personnel; the intelligent lock linkage management module is used for automatically locking an intelligent lock and giving an alarm according to monitoring results of the intelligent wearable device, the environment monitoring module and the video monitoring module when abnormity is monitored; the risk assessment module is used for establishing a risk assessment model and dynamically adjusting the operation authority of the intelligent lock; and the situation awareness control module is used for changing the operation authority of the intelligent lock according to the situation evaluation result. The invention further discloses a corresponding method. According to the invention, the safety and management efficiency of electric power facilities such as a transformer substation are improved.

The invention provides an intelligent laboratory environment safety monitoring system, which comprises a region division unit, an environment monitoring point, a partition environment data management unit, a data recording alarm unit and a visual display background, and is characterized in that the region division unit firstly performs gridding processing on a laboratory space to form a plurality of laboratory partitions; the environment monitoring points are arranged in the laboratory subareas, monitoring layers are divided in each laboratory subarea, at least two sets of environment monitoring points are arranged in the corresponding monitoring layers in each laboratory subarea, environment data collected by the environment monitoring points are sent to the subarea environment data management unit, and the subarea environment data management unit summarizes the data in all the environment monitoring points and sends the summarized data to the environment monitoring points. And a time data relation curve is drawn according to the environment data of the corresponding monitoring layer in each laboratory partition, and the data recording and alarming unit records the time data relation curve and sets limiting intervals in different time periods.

The invention discloses a data acquisition management method and system based on an electronic fence, particularly relates to the field of construction safety management, is used for solving the problem of real-time monitoring of constructors, and is characterized in that risk areas are divided through a GIS (Geographic Information System), the electronic fence is arranged, the position information of the constructors is collected in real time, and individual and collective behavior characteristics are extracted; and calculating the difference degree between the individual and the collective behavior mode, and evaluating the consistency and coordination degree of collective behaviors. Constructing an interaction event matrix, analyzing the interaction strength between constructors, calculating an interaction risk index, identifying a high-frequency interaction group through a graph theory algorithm, and evaluating the group risk level. And the hidden risk index is combined with the main component projection score and the interaction-synergy isomerism index, so that a manager can accurately identify high-risk personnel and formulate targeted management and prevention measures according to an electronic fence area, and comprehensive data support and decision basis are provided for safety management of a construction site.

The invention relates to the technical field of tunnel environment monitoring, in particular to a tunnel environment real-time monitoring and evaluation system which comprises an environment monitoring module, a video monitoring module, a data transmission module, a data processing module, a ventilation adjusting module, a traffic signal control module, an information board issuing module and a user interaction module. The environment monitoring module collects tunnel environment data in real time, the video monitoring module obtains tunnel video data, the data transmission module transmits the data to the data processing module, and the data processing module processes the received data and evaluates the environment safety condition in the tunnel. The ventilation adjusting module automatically adjusts a ventilation system in the tunnel according to an evaluation result, the traffic signal control module automatically controls traffic lights, the information board issuing module issues early warning information through an electronic information board, and the user interaction module provides a visual interface; according to the invention, comprehensive, real-time and intelligent monitoring and adjustment of the environment in the tunnel can be realized, and the safety management level and the operation efficiency of the tunnel are improved.

The present invention discloses a posture recognition management system and method for factory operators, and relates to the technical field of factory operation safety management. The system comprises a video image acquisition module, a target detection module, a behavior recognition module, a violation detection module, an alarm module, a recording module and a QT5 integration module which are operated in sequence. The application scenario of the whole management system is to identify and manage operators and automation equipment in the factory. The technical key points are as follows: in view of the problem that the behavior recognition in the factory scenario in the prior art is prone to false alarms, the present invention proposes a system for detecting violation behaviors of human-object interaction in the factory. The system integrates target detection and behavior recognition algorithms, can monitor the interaction between factory operators and equipment in real time, accurately identify possible violation behaviors, and send out alarm signals in time to improve the safety management level of the factory.

The invention discloses a trusted DCS implementation method and system fusing point-to-point and server architectures, and relates to the technical field of industrial control systems, and the method comprises the steps: building a data link of a calculation part and a protection part on a terminal of a trusted DCS; establishing a data link between the trusted upper computer and the trusted management platform on the trusted upper computer; releasing trusted data of the trusted DCS in the point-to-point architecture of the DCS; and credible state prompting and alarming functions are added in a diagnosis page of the DCS, and credible item starting and stopping, credible abnormity alarming and alarming processing are carried out. According to the invention, by establishing a trusted data link, realizing data intercommunication and introducing a dynamic credibility evaluation mechanism, real-time data release and transmission of trusted information are realized in a point-to-point architecture of the DCS, so that the DCS system can know trusted state change in real time and give an alarm, the ability of the system to resist security threats is remarkably improved, and the reliability of the system is improved. And meanwhile, the compatibility with the existing DCS architecture is kept.

The invention relates to the technical field of portrait analysis, and discloses a space-time portrait analysis method for personnel and places based on perceptual data, which comprises the following steps: summarizing perceptual data captured by a camera and access control perceptual equipment; preprocessing the grabbed sensing data; behavior characteristics in portrait and vehicle track data in the perception data are extracted and classified, sorted and summarized, and a related database is established; according to the extracted data, setting association relationships among personnel, vehicles and places in different dimensions in a period; aggregating association relationships between the personnel and the places; and space-time portraits of personnel and places related to the business are constructed. According to the method for analyzing the space-time portraits of the personnel and the places based on the perceptual data, complete space-time tracks can be extracted, continuous and complete space-time information can be formed, the behavior modes and activity rules of the personnel can be reflected more accurately, deeper behavior analysis ability is provided, and the accuracy and comprehensiveness of the relation portraits are improved.