69 results about "Safety behaviour" patented technology

The invention discloses an underground personnel unsafe behavior rapid analysis and recognition method based on machine vision, and relates to the technical field of intelligent safety monitoring, and the method comprises the steps: configuring an environment sensing unit to monitor underground environment parameters in real time, and dynamically adjusting the threshold parameter of an image recognition and posture estimation model according to the monitoring data; a behavior prediction model is constructed, the behavior prediction model is input based on the time sequence data to predict the future unsafe behavior trend of the underground personnel, and early warning is achieved; visual, sound and other non-visual sensor data are integrated, a multi-modal fusion recognition system is constructed, and the recognition capability in a complex environment is improved through synchronous processing and fusion of modal data; the real-time behaviors of the underground personnel are comprehensively analyzed, and once unsafe behaviors are recognized, an early warning mechanism is started immediately; model parameters are dynamically adjusted through the environment sensing unit, and the adaptability of the system to environment changes is enhanced.

The invention discloses an artificial intelligence identification method for unsafe behaviors of mine underground personnel, and the method comprises the steps: constructing a classification system of the unsafe behaviors, and determining the specific expression forms of all types of behaviors; the unsafe behaviors comprise article type unsafe behaviors, action type unsafe behaviors and area type unsafe behaviors; acquiring a to-be-processed target data set according to the processing flow of the optimized data set; wherein the optimized processing flow comprises data acquisition diversification and pertinence processing, data format conversion and parameter adjustment processing, data set augmentation processing, data set denoising processing and data set labeling processing; constructing a recognition model according to the target data set; and identifying the to-be-identified data according to the identification model, and determining the unsafe behavior of the underground personnel of the mine. According to the embodiment of the invention, unsafe behaviors of underground coal mine personnel can be quickly and effectively identified, the safety production level of a coal mine is improved, accidents are reduced, and the method can be widely applied to the technical field of artificial intelligence.

The invention discloses a comprehensive identification method for unsafe behaviors of construction site personnel, and particularly relates to the field of construction site image analysis and identification, and the method comprises the steps: constructing a boundary line segment connection diagram through edge detection and Hough transformation, and generating a risk mask region based on a plane projection track and a buffer zone; performing instance segmentation and attitude estimation on the personnel, extracting joint points and calculating a gravity center point, screening key points according to operation types, and constructing a relative vector sequence; extracting an included angle between a main attitude axial direction of continuous frames and a boundary normal, and generating a direction consistency matrix and a camber behavior mark; and finally, identifying a resident section in the risk mask area and outputting a constriction violation approaching behavior label. A space compression judgment mechanism based on image feature recognition is achieved, and it is ensured that edge approaching type unsafe behaviors existing when constructors conduct different types of work are recognized in advance and subjected to risk early warning.

The invention discloses an AI-based unsafe behavior identification method and system, and the method comprises the steps: employing a lightweight online modeling method of incremental learning based on the current state information of a worker, and integrating a situation element to dynamically update an individual behavior model; heterogeneous situation data are collected and classified, and key situation features are extracted and dimensionality reduction is carried out; constructing a behavior risk analysis-oriented situational feature system, and performing weight distribution according to the correlation between each situational element and the unsafe behavior to obtain a situational feature weight; in an individual behavior model updating process, according to a change trend of employee behaviors and an identification feedback result, carrying out real-time evaluation on importance of each situation element, dynamically adjusting weights of situation features, and adaptively optimizing a feature fusion strategy; and detecting an abnormal behavior by adopting an abnormal behavior detection method of a self-adaptive threshold to obtain an unsafe behavior identification result. The method can effectively improve the real-time recognition accuracy of unsafe behaviors in the industrial environment, and reduces the false alarm rate.

The invention relates to a method, a system and equipment for intelligently identifying unsafe behaviors of coal mine operating personnel and a medium. The method comprises the following steps: extracting individual trajectory data from an underground video stream through a multi-target tracking algorithm, decomposing continuous actions into an atomic behavior sequence with a space-time mark by using attitude estimation and a space-time diagram convolutional network, and capturing space-time relevance of a behavior chain through an attention mechanism enhanced long and short term memory network to generate a feature vector; risk reasoning is carried out in combination with the coal mine safety knowledge graph to predict the risk event type and probability, and finally early warning information is generated based on a multi-level early warning strategy. According to the scheme, the crossing from isolated action recognition to behavior chain risk prediction is realized, and the early warning capability of potential safety risks in coal mine operation is remarkably improved through fusion of space-time correlation analysis and domain knowledge of the behavior sequence.

The invention discloses an unsafe behavior monitoring system, and the system comprises a real-time monitoring module which is used for obtaining a video image of a construction site scene in real time; the early warning module is used for detecting unsafe behaviors of the video picture and performing early warning on the unsafe behaviors; the personnel safety file management module is used for identifying identity information of construction personnel and recording working states and unsafe behaviors of the construction personnel; the site safety management module is used for calculating a coverage area and a blind area of the camera according to the planar graph information of the construction site, the position of the camera and the visual field; the conversational query module is used for analyzing the query request content of the user, retrieving the related video content according to the query request content and outputting a retrieval result meeting the query requirement; and the safety education training module is used for generating education training contents according to the detected unsafe behaviors. The monitoring efficiency is improved; and meanwhile, analysis of video contents based on different fine granularities can be completed, so that the safety monitoring precision and the monitoring effect are improved.

The invention discloses a safety management system for a water conservancy and hydropower construction enterprise, and relates to the technical field of water conservancy and hydropower construction management.When the system operates, position information and behavior data of workers are obtained in real time through a personnel behavior collection module, behavior specifications of the workers are quantified, and the safety of the workers is evaluated through a safety behavior scoring index S and a risk staying factor T; and a safety risk assessment coefficient R is further fitted, and when the safety risk assessment coefficient R exceeds a preset threshold value Rth, a safety early warning notification is triggered to inform related personnel of potential risks in time, so that the possibility of accidents is effectively reduced. And specific construction operation safety suggestions are provided, and workers are timely informed of execution, so that the workers can take proper safety measures according to real-time feedback, and the problems that the behaviors of the workers cannot be comprehensively monitored, timely early warning cannot be performed and effective intervention measures cannot be formulated in a traditional safety management mode are solved. And the management frequency of high-risk areas and the intervention frequency of high-risk workers are improved.

The invention discloses an unsafe behavior detection method, device and equipment for capital construction personnel, and relates to the field of image detection. The method comprises the following steps: forming a constructor state data set; the protective equipment of the constructor comprises a safety helmet and a reflective vest; a YOLOv8-E model is constructed; the YOLOv8-E model adopts a YOLOv8 model structure, uses an HGStem module and an HGBlock module as feature extraction modules, uses depth separable convolution to perform up-sampling, uses a VoVGSCSP module as an attention unit, and has four layers of feature interaction structures; training a YOLOv8-E model by utilizing the state data set of the construction personnel to obtain an unsafe state recognition model; and inputting the monitoring image of the construction personnel on the infrastructure construction site, which is acquired in real time, into the unsafe state identification model, and outputting an identification result of the wearing state of the protective equipment of the construction personnel. According to the invention, the detection precision of the unsafe states of multiple types of protection equipment of constructors can be improved.

The invention discloses a method and system for automatically detecting unsafe behaviors of workers based on machine vision, and relates to the field of industrial safety, and the method comprises the steps: collecting and preprocessing the behavior data of the workers, the behavior data of the workers comprising position information, equipment state, certificate state and motion trail; performing unsafe behavior detection on the preprocessed operator behavior data to generate a behavior target detection frame; inputting the behavior target detection frame into a target tracking algorithm to perform multi-target tracking, and generating a personnel movement track data set; performing multivariate statistical analysis on the behavior feature vector by using a quantitative theory to generate an unsafe behavior reaction matrix; and evaluating a risk level in the unsafe behavior reaction matrix through a fuzzy analytic hierarchy process, and performing safety early warning notification according to the risk level. According to the method, multivariate statistical analysis is carried out on the behavior feature vectors through a quantification theory, and deep quantification and comprehensive evaluation of the unsafe behavior features of the workers are realized.

The invention discloses an unsafe behavior risk portrait generation method based on multi-modal data fusion and dynamic visualization, and relates to the technical field of safety management and risk assessment, and the method comprises the following steps: continuously collecting original data from different modal data sources in real time through network communication; and it is ensured that information dependent on the whole fusion process has freshness and integrity. According to the method, the high-frequency low-value redundant data is effectively identified by sensing and dynamically regulating and controlling the data source information value in real time, the fusion result is prevented from being interfered by the redundant mode, and the dominant position of the key mode information in the risk portrait is improved. The dynamic adjustment of the contribution degree and a self-adaptive recovery mechanism are fused to enhance the response capability of the system to data change, so that the generated unsafe behavior risk portrait is more accurate, dynamic and targeted, and the accuracy and reliability of overall safety monitoring and accident early warning are improved.

The invention provides an intelligent monitoring method and system for unsafe behaviors of personnel in thermal power plant operation, and relates to the technical field of intelligent monitoring and safety management, and the method comprises the steps: carrying out the risk feature recognition of personnel in a first monitoring image set, and carrying out the evaluation to obtain a plurality of real-time risk coefficients; determining a plurality of marked persons and a plurality of video coverage devices, and calculating to obtain a plurality of risk coefficient deviations; frequency compensation coefficients and personnel identification precision are determined according to risk coefficient deviation analysis, image extraction is performed based on a plurality of frequency compensation coefficients, and monitoring is performed according to a plurality of personnel identification precision configuration optimization feature extraction channels. The technical problems that a traditional safety monitoring method lacks a dynamic adjustment mechanism and cannot adapt to a complex and changeable boiler operation environment, consequently, monitoring resource configuration is unreasonable, and delay, missing detection and false alarm are prone to occurring can be solved, the rationality and scientificity of monitoring resource configuration can be improved, and efficient, flexible and accurate safety monitoring is achieved.

The invention discloses an unsafe behavior recognition method and system based on 5G and artificial intelligence, and relates to the technical field of unsafe behavior recognition, and the method comprises the steps: building a reaction matrix through multi-dimensional feature correlation analysis based on an unsafe behavior motion feature sequence with confidence score, and obtaining a feature sequence of the unsafe behavior; predicting the contribution degree of the unsafe behavior action characteristics with confidence scores to the unsafe behaviors, distributing action weights through FAHP, and dynamically dividing the risk levels of the unsafe behaviors; and a multi-agent cooperation framework is constructed based on a dynamic cooperation optimization strategy and a task decomposition and distribution mechanism, detection is performed according to the risk level, and an unsafe behavior identification report is generated. According to the method, the reaction matrix is constructed through multi-dimensional feature correlation analysis, the weight is dynamically adjusted in combination with the FAHP, and the risk levels are divided, so that adaptive risk assessment and efficient decision are realized, and the recognition precision and the response efficiency are improved.

The invention relates to the technical field of chemical industry park safety risk monitoring and management, in particular to a chemical industry park safety risk monitoring and management system based on the Internet of Things, and the system comprises a multi-dimensional risk data collection unit which is used for collecting dynamic operation data; the individual comprehensive risk resolving unit is used for resolving to obtain an individual comprehensive risk index; the regional risk situation assessment unit is used for generating a safety state, a first-level early warning signal or a second-level early warning signal; the safety behavior deviation monitoring unit is used for constructing an individual safety behavior data set; calculating a safety performance index; the risk closed-loop correction control unit is used for calculating a risk correction factor; the corrected individual comprehensive risk is obtained; triggering message notification management, and sending a warning message to the positioning terminal; a safe evacuation instruction is started, and the large-screen monitoring center is linked to start real-time tracking and video monitoring; according to the method, differentiated risk upgrading management of individuals with bad safety habits is realized, and the problem of disjunction between instantaneous risks and long-term behavior trends is effectively solved.

The invention relates to the field of monitoring systems, and particularly discloses a work site safety behavior monitoring system based on AI video monitoring, which comprises a data acquisition module used for acquiring a high-definition video stream of a work site in real time; the edge calculation module is used for preprocessing the video stream; the behavior recognition module is used for detecting and classifying safety behaviors in the video stream through a deep learning model and outputting behavior types and coordinates; and the dynamic weight library comprises a weight configuration unit, an environment factor acquisition unit, a dynamic adjustment unit and a weight output unit. According to the operation site safety behavior monitoring system based on AI video monitoring, environmental factors are fused in real time through the dynamic weight library, the safety behavior risk weight is automatically adjusted, the risk assessment precision in a complex scene is remarkably improved, and false alarm and missing alarm caused by environmental interference are reduced.

The invention belongs to the field of oil depot management, and discloses an intelligent application management system and device, and the intelligent application management system comprises a personnel positioning management module, an intelligent inspection module, a video intelligent analysis module and a mobile application module. According to the method, different alarm levels are divided, response is carried out according to the emergency degree and potential risk of the situation, it is ensured that resources and attention are focused on the most needed place, abnormal behavior recognition based on deep learning and emotional state monitoring based on sound features can accurately evaluate the behavior and psychological state of personnel, and the accuracy of the abnormal behavior recognition is improved. Early identification of factors which may cause errors; by continuously updating the position of the target object in the continuous frames, movement of the object can be monitored in real time, it is ensured that the position of the object can be accurately known at any moment, potential unsafe behaviors or abnormal activities can be recognized in real time through tracking data, and early warning is given out in time.

The invention discloses a latent variable guided unsafe behavior intervention system and a latent variable guided unsafe behavior intervention method for building workers. A decision laboratory-interpretation structure model is used for analyzing the mutual relation between unsafe behavior factors such as unsafe psychology, unsafe physiology, safety ability and unsafe motivation of the building workers and safety culture, safety atmosphere and safety education. And determining an action path of the intervention measures on the influence factors. And constructing an unsafe behavior occurrence mechanism and intervention mechanism model in combination with the structural equation model. And then simulation research and analysis of the action effect of the intervention strategy are carried out by using system dynamics, and the internal occurrence mechanism and intervention effect of the unsafe behaviors of the building workers are deeply analyzed. The method has the advantages that the relation between unsafe behavior occurrence and intervention paths of workers is studied statically, the unsafe behavior occurrence and intervention process is studied dynamically, the safety management level in the building construction process is improved, and safety accidents are reduced.

The present invention discloses a method and system for correcting the behavior safety management of construction workers based on AI technology, including a cloud computing center, a regional edge computing center and a front-end sensing device; the platform for the training function of the unsafe behavior recognition model in the cloud computing center imports the video image data set of unsafe behavior instances collected by the front-end sensing device from the construction site, repeatedly trains the unsafe behavior algorithm to obtain the unsafe behavior model, and then directly loads the model to the regional edge computing center, thereby performing artificial intelligence analysis on the video image data set, intelligently identifying the trained unsafe behaviors and classifying and storing them; and performing sample annotation on the identified images, on the one hand, providing them to the artificial intelligence model training platform for training, and on the other hand, providing the required case samples to the platform for the precise intervention function of behavior correction. The system effectively reduces the unsafe behaviors of workers in the production process and strengthens behavioral safety.

The invention discloses a community monitoring system. According to the invention, the behavior identification module can identify various abnormal behaviors in real time, such as fighting, suspicious wandering, crowd gathering and the like, and immediately triggers an alarm to notify a manager to deal with the abnormal behaviors, so that the occurrence of safety accidents is effectively prevented. And the behavior modeling module can identify potential criminal behaviors by establishing normal and abnormal behavior models, so that the crime rate is further reduced. And meanwhile, the behavior trajectory analysis module can track personnel trajectories in real time and quickly position personnel positions in an emergency, so that the emergency response capability is improved, and the safety of residents is ensured. And the behavior analysis module can effectively improve the management efficiency of the cell. Through automatic behavior analysis, the behavior analysis module can reduce the manual workload, and managers can concentrate on key tasks, thereby improving the management efficiency. A behavior analysis report is provided for managers, the managers are helped to know the safety condition of the cell, and a more effective safety management strategy is formulated.

The invention discloses a high plateau airport operator unsafe behavior monitoring and early warning method and device, and relates to the technical field of plateau operation monitoring. According to the method, algorithm such as YOLOv8 and feature fusion are combined to collect physical and behavior data from multiple sources and extract features, then a bimodal model is constructed by SVM and logistic regression, and a comprehensive fatigue level is generated by matching with an evidence theory, so that the limitation of single-modal data is avoided; meanwhile, a fuzzy Bayesian dynamic fusion model is introduced, and a fatigue score is corrected in combination with specific environmental data (low pressure, low temperature and the like) of the high plateau, so that risk judgment is more adaptive to physiological-environmental collaborative risk characteristics of the high plateau scene; and finally, the graded early warning is triggered based on the comprehensive risk grade of fatigue, environment and space dimensions, so that the accuracy of risk identification is realized, the timeliness of high-plateau operation safety management is improved through differentiated intervention, and the potential safety hazard of high-plateau operation is effectively reduced.

The invention provides a method for constructing an unsafe behavior monitoring data set for live-line work of a 10kV boom lorry, and relates to the technical field of electric power safety, and the method comprises the steps: collecting video data of unsafe behaviors in the live-line work process of the 10kV boom lorry from multiple perspectives; training a first target detection model by using the obtained first basic data set, and performing target identification on the first basic data set by using the model; performing label correction on the first basic data set according to an identification result of the first target detection model and a corresponding label; training a second target detection model by using the obtained second basic data set; carrying out framing processing on the first basic data set by taking each frame as an interval and then sending the processed data into a second target detection model to carry out target identification so as to obtain a third basic data set formed by key frame image data; and merging the second basic data set and the third basic data set to obtain a monitoring data set. According to the scheme, the rich and effective monitoring data set can be obtained, so that the reliability of the trained monitoring model is improved.

The utility model belongs to the field of safety of coal mine workers, particularly relates to equipment for monitoring unsafe behaviors of coal mine workers, and aims to solve the problems of low sponge falling speed, poor simulation authenticity, single simulation form, low collapse scene reduction degree, high safety and the like in the prior art that a fixed rod is rotated to drive a connecting rope to enable a sponge to fall off. In order to solve the problems that in the prior art, in the prior art, behaviors of workers in an unsafe scene are difficult to effectively monitor, according to the scheme, a clamping assembly is used for containing a sponge block, when the sponge block is suddenly opened, the scene of coal mine collapse can be modeled, meanwhile, recovery and re-release of the sponge block are achieved through a winding roller, and the situation that the sponge block is damaged is avoided. The device has the advantages that the simulation effect of collapse is more real, vibration in coal mine operation, coal mine collapse, poisonous gas leakage and various sounds in a collapse site are simulated, the simulation scene is more real, and the authenticity of physiological response and psychological change data of coal mine workers in an unsafe behavior environment is effectively improved.

The present invention discloses a comprehensive identification method for unsafe behaviors of construction site personnel, which specifically relates to the field of construction site image analysis and identification, including: constructing a boundary segment connection diagram through edge detection and Hough transform, and generating a risk mask area based on plane projection trajectory and buffer zone; performing instance segmentation and posture estimation on personnel, extracting joint points and calculating center of gravity points, screening key points according to operation type and constructing a relative vector sequence; extracting the angle between the main posture axis and the boundary normal of continuous frames, generating a direction consistency matrix and an outward-inclined behavior mark; finally, identifying the resident section within the risk mask area and outputting an oppressive illegal approach behavior label; realizing a "spatial oppression" judgment mechanism based on image feature recognition, ensuring early identification and risk warning of unsafe edge approach behaviors that exist when construction personnel perform different types of operations.

The application discloses a safety education content pushing system and method for a construction site, and belongs to the technical field of safety education of the construction site. The safety education content pushing system comprises the following steps: listing education materials in a tree structure to generate a safety education library, and creating at least one dimension of identification tags for each education material to obtain an identification tag set; performing real-name authentication and numbering on workers to create work information files about the workers; creating a priority pushing model, obtaining pushing keywords required by the workers based on the work information files; mapping the pushing keywords to the identification tag set, matching identification tags having a mapping relationship with the pushing keywords in the identification tag set to obtain a temporary tag set; calling education materials corresponding to the temporary tag set in the safety education library to obtain to-be-pushed education materials, and pushing the to-be-pushed education materials to corresponding workers. The safety education content pushing system achieves efficient and high-precision education content pushing effect based on safety awareness and safety behaviors of the workers, greatly reduces learning time of the workers, and can also make up for part of defects of the workers.

The invention relates to the technical field of construction site safety, and discloses a GAN-based construction site personnel unsafe behavior detection method and system, and the method comprises the following steps: 1, carrying out the literature analysis and summarization, and selecting an application scene with representative personnel unsafe behaviors; 2, collecting and constructing a construction site personnel behavior video data set containing normal and unsafe behaviors; 3, designing a generative adversarial network model framework of unsupervised learning; 4, training the proposed model on the self-built data set, evaluating the performance of the model, and comparing the model with a baseline model; 5, eliminating each pseudo-anomaly generation module through an ablation experiment, and evaluating the contribution of each pseudo-anomaly generation module to the model performance; the method is executed in the system. According to the invention, intelligent detection of unsafe behaviors of workers can be realized by using normal behavior data training, and safety management is carried out by using an informatization technology, so that the safety production level is improved.

The invention discloses a pilot safety risk analysis method and system based on neural computing. The method comprises the following steps: identifying safety risk factors; establishing a mapping relation between the safety risk factors and the unsafe behaviors of the pilot; establishing a structure model of a cortex-basal nucleus-thalamus nerve circuit; the method comprises the following steps: respectively establishing AMPA and GABA nerve synapse calculation models based on a double-exponential synapse theory and an alpha-beta synapse theory; establishing a calculation model of a cortex-basal nucleus-hill nerve circuit; calculating thalamus discharge rates under the influence of different safety risk factors, and simulating a regulation mechanism of dopamine and acetylcholine in a safety risk formation process. According to the method, safety analysis is carried out from the perspective of pilot neurodynamics, and key physiological basis and theoretical support are provided for improving a risk analysis model and optimizing system design by analyzing a neural regulation mechanism of acetylcholine and dopamine in a safety risk forming process.

The invention relates to the field of production safety management, in particular to a tipping paper production safety monitoring method and system, and the method comprises the steps: calculating the abnormal score of a person behavior in each frame of real-time image; and generating an abnormal behavior judgment result of the personnel according to the abnormal score, and for the personnel judged to be abnormal, obtaining identity information of the personnel so as to supervise safety behaviors of all personnel in the production place. The method and the device have the effect of improving the smoking behavior recognition accuracy in the production process of personnel.

The utility model provides limited space networking equipment based on unsafe behaviors of a power plant. The limited space networking equipment comprises a mounting platform, a shell, a first sealing part and a second sealing part, the mounting platform is a platform with a certain thickness; the shell is a hemispherical transparent shell, and the shell and the end face of the installation platform are arranged in a separable mode. The first sealing part is arranged on the end surface of the mounting platform and is arranged along the side line of the end surface; the second sealing part is arranged at the edge of the shell; wherein the inner wall of the shell and the end surface of the mounting platform form an accommodating space, and the accommodating space is used for accommodating monitoring equipment; and the first sealing part is matched with the second sealing part, so that the accommodating space is in a sealed state isolated from the outside. According to the application, the shell is arranged on the mounting platform, the sealed accommodating space is formed between the shell and the mounting platform through the sealed matching of the shell and the mounting platform, particularly through the tight fitting of the first sealing part and the second sealing part, and the monitoring equipment is mounted in the accommodating space for moisture-proof and waterproof sealed protection.

The invention discloses a yolov5-based driver drinking and fatigue driving comprehensive detection system. According to the invention, through the comprehensive monitoring system based on the YOLOv5 target detection algorithm, whether the driver is in an unsafe state such as drinking, fatigue driving or smoking can be identified and judged in real time. By integrating various sensor data, such as an alcohol sensor and a smoke sensor, and an image recognition technology of deep learning, the system can realize multi-dimensional state monitoring of a driver, and gives an alarm in time to prevent potential risks. Therefore, the driving safety can be remarkably improved, and particularly, unsafe behaviors such as drinking, fatigue and smoking can be monitored in a complex driving environment. Experimental results show that the system can maintain a high response speed on the basis of high precision, and can adapt to various driving environments. By integrating various sensors and a deep learning algorithm, the system can realize comprehensive monitoring of driver behaviors, and traffic accidents are reduced.

This invention discloses a network security abnormal behavior early warning management method in the field of network security technology, including the following steps: S1: constructing a security behavior architecture diagram of the target network; S2: inputting security monitoring behaviors into the security behavior architecture diagram and analyzing the target security behaviors; S3: setting an early warning evaluation interval; S4: judging abnormal monitoring behaviors and analyzing the behavior early warning methods of the abnormal monitoring behaviors; S5: analyzing the architecture layer anomaly coefficient of the behavior architecture layer and analyzing the abnormal behavior architecture layer and its architecture layer early warning method; S6: analyzing the network anomaly coefficient of the target network and analyzing the network early warning method of the target network; and performing abnormal behavior early warning of the target network based on the behavior early warning method, architecture layer early warning method, and network early warning method. This invention improves the comprehensiveness and accuracy of target network abnormal behavior early warning management.