413 results about "Industrial security" patented technology

The invention provides a methane leakage anomaly detection system and method based on dual-light fusion, and belongs to the field of industrial safety detection. According to the system, a double-light-path holder type laser detector is arranged in a monitoring area, and methane concentration and image data are synchronously acquired through a 1653nm near-infrared laser module and a visible light image module. Time features (mean values, trends and the like) are extracted from the concentration data through time sequence analysis, and spatial features (edges, textures and the like) are extracted from the image data through deep learning; the system fuses time and space features to generate a comprehensive feature vector, reduces the calculation complexity through a dimension reduction technology, and retains core features at the same time. And constructing an anomaly detection model based on an unsupervised learning algorithm, identifying anomaly features in real time, and triggering an alarm. The system realizes real-time and accurate detection of methane leakage, and enhances the reliability and efficiency of industrial safety.

The invention discloses a power plant specific area personnel behavior identification and early warning system, and relates to the technical field of industrial safety intelligent monitoring, the power plant specific area personnel behavior identification and early warning system comprises a data acquisition module, an edge calculation module and a central processing platform, the edge computing module carries out lightweight processing and localized decision making on original data, the load of the central processing platform is reduced, and the central processing platform realizes full-process safety management and control of a high-risk area through multi-modal data fusion, dynamic authority management and trajectory analysis. According to the invention, lightweight AI models are embedded in various cameras of a work card authentication terminal and a visual perception unit, preliminary reasoning is directly completed at the work card authentication terminal and the cameras, so that the work card authentication terminal and the cameras have edge computing capability, and independent edge servers or industrial personal computers are deployed near data sources of various regions of a power plant. And serving as a regional edge computing node.

The invention relates to the technical field of industrial safety, and discloses an electrolytic aluminum short circuit port operation safety early warning system based on multi-parameter collaborative awareness and intelligent diagnosis, and the system comprises a parameter collaborative awareness module, a dynamic diagnosis module, an early warning decision module, and an execution feedback module. By constructing a multi-dimensional parameter collaborative sensing mechanism, fusing temperature field distribution, current balance degree and insulation state multi-source data in real time and dynamically capturing early abnormal symptoms of a short circuit port, the hysteresis problem of traditional single-parameter threshold monitoring is solved, conversion from passive response to active defense is achieved, and the comprehensiveness and timeliness of operation state monitoring are improved; and meanwhile, based on a historical fault database and a real-time evolution model, a health index is generated and a fault path is predicted, so that maintenance personnel can pre-judge a development trend and a time window of potential risks in advance, and sudden equipment accidents are avoided.

The invention relates to the field of computer vision and industrial safety monitoring, and discloses a pipe gallery pipeline key connection point leakage monitoring system and method based on AI vision, and the system comprises a data collection module, an intelligent analysis module, a 3D modeling module, a space-time verification module and an alarm prediction module. Real-time detection and three-dimensional accurate positioning of a leakage area are realized in combination with neural network optimization driven by physical simulation parameters; further through optical flow tracking and fluid mechanics verification, false alarm events of non-physical rules are screened out; and based on Bayesian decision and diffusion equation prediction, generating a leakage risk heat map and triggering graded alarm. The method solves the technical problems of low leakage detection precision, high false alarm rate and inaccurate positioning in a complex pipe gallery scene, has the advantages of high environmental adaptability, high anti-interference capability and prospective risk pre-judgment, and is suitable for intelligent safety monitoring of underground pipe galleries and petrochemical engineering pipelines.

The invention relates to the technical field of industrial safety management, in particular to an intelligent security monitoring and responding method for a power generation enterprise. The method comprises the following steps: acquiring environmental data of a power generation enterprise; performing enterprise abnormal event detection on the power generation enterprise environment data to obtain enterprise environment abnormal event data; performing multi-source video linkage monitoring on the enterprise environment abnormal event data to obtain multi-angle video data; and performing abnormal event analysis according to the enterprise environment abnormal event data and the multi-angle video data to obtain abnormal event risk assessment data so as to perform intelligent security monitoring and response auxiliary operation of the power generation enterprise. According to the method, the abnormal event detection and analysis capability is improved, and the safety and the operation efficiency of an enterprise are also remarkably improved.

An industrial security system leverages generative artificial intelligence (AI) to automate the process of identifying software or hardware insecurities on industrial assets, generate recommendations for mitigating these vulnerabilities, and, where appropriate, deploy countermeasures to the vulnerable assets. By leveraging automated asset discovery, real-time asset and network monitoring, and generative AI-assisted vulnerability detection and remediation, the system can reduce the amount of time spent by security administrators in identifying and closing security vulnerabilities within their plant environments, and can alert administrators of potential security issues before those issues become critical.

The invention provides an industrial safety risk monitoring and early warning system and method for an autonomous planning intelligent agent, relates to the technical field of industrial safety production, and solves various limitation problems still existing in an existing safety risk monitoring scheme. In the system, an environment sensing layer is used for collecting multi-source sensing data in an industrial production environment in real time; the edge calculation layer is used for carrying out primary processing on the multi-source sensing data and executing data compression and key information extraction so as to generate key analysis data; the cloud analysis layer is used for performing deep intelligent analysis on the key analysis data, executing security risk assessment and risk diffusion prediction, and dynamically generating and adjusting an early warning decision through an autonomous planning agent; and the action execution layer is used for executing a corresponding safety action instruction according to the early warning decision and feeding back an execution result to the cloud analysis layer to realize system optimization. According to the invention, the risk early warning accuracy and the emergency disposal timeliness can be improved finally.

The invention is suitable for the field of industrial safety monitoring, and provides an industrial control safety monitoring method and system, and the method comprises the steps: collecting the network flow data of an industrial control network in real time based on an industrial protocol analysis engine; synchronously acquiring operation state parameters of the key equipment by adopting an edge computing node; constructing a coupling relation quantitative model of the network flow and the equipment operation state, and calculating a dynamic correlation coefficient between the network flow and the equipment operation state through the coupling relation quantitative model; constructing reference correlation coefficient distribution in a normal production state based on historical data, and dynamically updating a safety threshold value; when the dynamic correlation coefficient exceeds a safety threshold value, triggering a multi-stage early warning mechanism; according to the method and the system, deep fusion monitoring of the industrial control network and the equipment operation state is realized, and the limitation of traditional split type monitoring is broken through. Through dynamic quantification of a coupling relationship, adaptive threshold adjustment and multi-level early warning, the timeliness and accuracy of abnormal risk identification are significantly improved, and network attacks and equipment fault hazards are effectively reduced.

The invention relates to the technical field of machine vision, in particular to a system and a method for realizing abnormal behavior detection and intelligent analysis in an industrial scene based on a machine vision technology. According to the system, workshop image data are collected through a high-precision camera, and the action and posture of a worker and the operation state of equipment are analyzed through an image recognition algorithm. In order to ensure accurate detection, a multivariate feature model is constructed to mine behavior features, and a deep learning neural network is adopted to optimize recognition accuracy and speed. The method is characterized in that the image quality is improved through data preprocessing, accurate behavior positioning and recognition are realized through fusion of multiple algorithms, and an intelligent analysis module is designed to generate a report to assist decision making. In industrial production, the system can timely discover abnormal conditions such as illegal operation, deviation from a safe area, fatigue operation and the like, give an alarm and provide an analysis report, so that accidents are avoided, and production benefits are improved. Through testing, the abnormal behavior detection accuracy is high, the system response is rapid, and industrial safety management and production process optimization can be effectively assisted.

The invention relates to the technical field of industrial safety, and provides a rupture disk service life prediction method based on intelligent perception, which comprises the following steps: constructing a multi-mode intelligent sensor array, and collecting data in the running process of a rupture disk in real time; establishing a data transmission network, and transmitting data to a data processing center in real time; preprocessing the collected real-time data to form a standardized data set; and constructing a deep learning model, calculating the fatigue damage cumulant of the rupture disk according to the feature data output by the deep learning model based on a fatigue damage accumulation theory, predicting the residual life of the rupture disk, and generating life early warning information. According to the method, the running data of the rupture disk are collected in real time through the multi-mode intelligent sensor array, the life prediction model is constructed based on the fatigue damage theory, dynamic evaluation and early warning are achieved in combination with a three-level early warning mechanism, multi-source data are fused, the prediction precision is improved, the risk and cost are reduced, and it is guaranteed that industrial production is safe and reliable.

The invention discloses a network security protection management system and method based on big data, and relates to the technical field of network security management, and the method comprises the steps: collecting industrial protocol layer data, physical sensor data and process parameters, extracting network behavior features, physical features and process correlation features, and constructing an industrial security feature vector; according to the security weight coefficient and the industrial security feature vector of the current process stage, calculating a threat score of the equipment, and dynamically adjusting a threat level through a three-level verification mechanism; constructing an equipment dependency graph, performing risk diffusion calculation based on the adjacent matrix and the attenuation coefficient, and generating a risk diffusion priority list to adjust an isolation strategy; an improved multi-target genetic algorithm is adopted to solve a Pareto optimal solution set, an equipment recovery sequence and a process parameter adjustment scheme are determined, and a recovery instruction is issued to realize safe recovery, so that the dynamic perception and accurate defense capability for industrial network threats is improved, and the safety and stability of industrial production are guaranteed.

The invention relates to the technical field of industrial safety, in particular to an intelligent machine tool fire extinguishing system which comprises a fire detection module, a data processing module, a fire type recognition module, a fire extinguishing control module, a man-machine interaction module and a power failure guarantee module. Wherein the fire detection module is used for monitoring multi-dimensional data of a working environment of a machine tool in real time; the data processing module is used for identifying different types of fire features; the fire type identification module is used for identifying the specific type of the fire; the fire extinguishing control module automatically selects a corresponding fire extinguishing medium; the man-machine interaction module is used for sending an alarm signal to an operator when a fire disaster occurs; and the power-off guarantee module is used for automatically cutting off a machine tool power supply when a fire occurs. According to the invention, through the technologies of intelligent fire identification, accurate fire source positioning and timely power cut-off, the fire extinguishing efficiency is effectively improved, early fire control is ensured, and secondary disasters are prevented.

The invention relates to the field of data processing, in particular to a security agent construction system and method based on a large model. Constructing a low-temperature liquid leakage knowledge base; collecting normal operation condition data, and training the leakage sensing model in an unsupervised mode; configuring a large model with a tool calling interface as a security agent; multi-modal data are synchronously collected in real time, a preliminary abnormal alarm is generated through a perception model, and associated information is retrieved; fusing the multi-modal information by the intelligent agent to carry out leakage prediction analysis and comprehensive research and judgment; and for the confirmed leakage event, a professional tool is automatically called to carry out quantitative consequence simulation, and a scenario disposal scheme is generated. According to the invention, the dependence on scarce leakage samples is effectively overcome, and the detection reliability is improved; through knowledge enhancement and multi-modal fusion, the research and judgment accuracy is greatly improved; full-process automation from early perception, intelligent analysis to consequence severity division is realized, and the intelligent level and emergency response efficiency of industrial safety monitoring are remarkably improved.

The invention provides a multi-modal fusion causal analysis method and platform for industrial safety production, and the method comprises the steps: carrying out the preprocessing of multi-modal key data from an industrial site, and generating a standardized time series data matrix; based on the standardized time sequence data matrix, combining industrial physical rules and operation process semantics to construct an industrial safety causal atlas; based on a self-supervised learning mechanism, optimizing the structure and edge weight of the industrial safety causal atlas by using the standardized time sequence data matrix to obtain an optimized causal reasoning model; and performing path-level risk activation judgment and accident causal traceability by using a real-time standardized time sequence data matrix constructed by the causal reasoning model and real-time inflow multi-modal key data to generate a risk alarm and traceability report. According to the method, the perspectiveness, the accuracy and the interpretability of industrial safety monitoring are remarkably improved.

The invention relates to the field of industrial safety monitoring, provides a real-time risk identification and active safety control method and system for industrial operating personnel in order to improve the accuracy and efficiency of a safety monitoring technology, and realizes real-time acquisition and edge preprocessing of human-machine-environment data by integrating a physiological / environmental / equipment multi-mode sensor. An FPN network is utilized to fuse spatial-temporal characteristics, Temporal-PC causal inference and a GBDT integration algorithm are combined, an interpretable risk propagation model is constructed, risk indexes are calculated, three-level early warning is divided, and hierarchical intervention is executed for different risk levels, including local early warning, environment regulation and control, emergency evacuation and multi-department emergency linkage. Through LSTM trend prediction and reinforcement learning dynamic parameter adjustment, closed-loop optimization of an intervention strategy is realized, the problems of information splitting, response lag and the like of a traditional monitoring system are solved, the risk identification precision and emergency disposal efficiency of a chemical high-risk scene are remarkably improved, and the method is suitable for operation safety control in high-temperature, high-pressure, toxic and harmful environments.

The invention provides a full-link detection and trusted industrial control method and system based on a domestic chip, and relates to the technical field of industrial automation and information security. The system comprises a security calculation module which is constructed by integrating a TPM (Trusted Platform Module) in a domestic chip; the special hardware encryption acceleration module is designed based on an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array) architecture and is integrated in a domestic chip; the trusted execution environment module is constructed based on a security isolation technology; the anomaly detection module is constructed based on a pulse neural network; the block chain auditing module is constructed based on a block chain framework; the dynamic security policy engine is used to automatically trigger a security response policy. The invention constructs an integrated industrial safety control system based on a domestic chip, and the system is especially suitable for intelligent manufacturing and high-safety-level industrial scenes. The problems that in the prior art, full-link safe starting cannot be achieved in a localized chip environment, an efficient encryption and trusted execution environment is lacked, and the real-time anomaly detection and log auditing capacity is insufficient are solved.

The invention discloses a vision-based industrial safety production full-cycle monitoring method and system, and belongs to the technical field of industrial safety, and the method comprises the steps: collecting the visual state data and interaction information of a production scene entity in real time, and generating an initial risk conduction link in combination with a pre-constructed working condition rhythm safety risk topological map; based on the initial risk conduction link, adopting a hidden Markov algorithm, a forward diffusion model and a Bayesian model to respectively obtain forward and reverse risk path sequences; inputting the forward and reverse risk path sequences into a risk monitoring map, and outputting risk probability, type and path characteristics through analogue simulation and risk threshold judgment; positioning a core risk node according to the path characteristics, and calling a matched initial prevention and control strategy; generating a secondary prevention and control strategy through an analogue simulation strategy effect, and carrying out iterative optimization until the risk probability is reduced to a safety threshold value; according to the invention, dynamic perception and accurate traceability of industrial production risks are realized, and the real-time performance and reliability of safety production monitoring are improved.

The invention discloses a fuel gas safety linkage control method and system based on a multistage alarm threshold value, and relates to the technical field of industrial safety control, and the method comprises the following steps: obtaining the operation data of a to-be-detected target, forming an operation feature vector, and carrying out the dynamic correction of a standard explosion lower limit concentration threshold value based on the operation feature vector; comparing the real-time gas concentration with the corrected concentration threshold value, and generating an initial grading judgment result in combination with the concentration change rate and the duration parameter; trend prediction is carried out according to time sequence data in the operation feature vector, and an initial grading judgment result is corrected; performing spatial fusion on the corrected grading judgment result, performing cross validation in combination with the running state and the credibility of the monitoring unit, and generating a final risk grade; according to the final risk level, a progressive linkage control action is triggered; according to the invention, through multi-level threshold value dynamic correction and trend prediction, the problems of missed report, false report and response lag in gas safety monitoring are solved.

The invention belongs to the technical field of industrial safety monitoring, and provides a thermal power plant field operation safety identification alarm system, which comprises image acquisition equipment, network transmission equipment, an image identification unit and monitoring terminal equipment, the image acquisition equipment is used for acquiring video data of field operation; the image recognition unit is used for converting the video data into to-be-input image data and extracting key image data in the to-be-input image data; inputting the key image data into a target detection model to obtain an image recognition result; if the image recognition result is that the personnel violation behavior and / or the environment hidden danger state are included, determining an abnormity type, occurrence time, an occurrence place and image evidence; generating alarm information, and sending the alarm information to a monitoring terminal device; and the monitoring terminal equipment is used for displaying the alarm information and forwarding the alarm information to the mobile terminal equipment of the remote management personnel. According to the system provided by the invention, the identification precision, efficiency and convenience of the field operation safety of the thermal power plant are improved.

The invention relates to the technical field of alarms, and discloses an infrastructure catastrophe sound-light alarm system with a fault self-checking capability, and the system comprises a data collection module which is configured to synchronously collect a sound signal, an optical signal, an electrical parameter and an environmental parameter of an alarm; the data preprocessing module is configured to perform noise suppression, normalization and abnormal value elimination on the acquired data; the feature extraction module is configured to extract a sound time-frequency domain feature, an optical signal fluctuation feature, a circuit stability feature and an environment correlation feature; the fault diagnosis module is configured to perform fault type judgment by adopting a multi-modal fusion model and a predefined rule base; and the grading processing module is configured to execute emergency response, maintain early warning or self-adaptive adjustment strategies according to the fault grades. According to the audible and visual alarm fault self-checking system, multi-modal data fusion, self-calibration, mobile detection and graded response are integrated, and the reliability of industrial safety early warning is improved through multi-dimensional data collaborative analysis and dynamic adjustment.

The invention provides an encryption access control method and platform for industrial security data, and relates to the technical field of data encryption access, and the method comprises the steps: carrying out the attribute classification and code identification of an industrial system data set; performing security level division on the industrial system data set according to the data security quantitative index system; performing matching analysis on the multi-level industrial security data set based on the data encryption algorithm list; encrypting and storing the industrial system data set based on the industrial data identification code set by adopting a multi-stage data encryption algorithm to generate an industrial encrypted data warehouse; and establishing a fine-grained access mechanism and a decryption interaction mechanism according to the user role library, and performing access authority distribution and encryption access control on the industrial encrypted data warehouse based on the fine-grained access mechanism and the decryption interaction mechanism. According to the invention, the technical problem that the comprehensive requirements of high safety, high precision and high flexibility in a modern industrial system are difficult to meet in the prior art can be solved.

The invention discloses a classification and grading alarm system based on multi-source data, relates to the technical field of industrial safety and workplace monitoring, and can solve the typical problems that in the prior art, alarm logic is rigid and single, data sources are static and isolated, an intelligent alarm verification mechanism is lacked, and intelligent analysis and management processes are separated. Through multi-source sensing equipment, an edge-cloud cooperative computing architecture, a multi-source event triggering engine, an innovative classification and grading alarm engine and an AI large model rechecking mechanism, event automatic handling measure execution and alarm information distribution are realized through an automatic linkage and notification subsystem; and finally, event closed-loop is completed by the event handling and closed-loop platform, so that efficient, accurate and automatic monitoring, alarming and closed-loop management of industrial and various working place security events are realized.

The invention relates to an industrial safety health risk prediction method and device based on machine learning, and a medium. The method comprises the following steps: obtaining industrial scene description, generating structured data through a large language model, inputting the structured data into a trained industrial safety health risk prediction model, and outputting a predicted potential risk level. Wherein the industrial safety health risk prediction model comprises a feature extraction module, a feature enhancement module and a feature fusion module, and the feature extraction module is used for extracting text features and attribute features; the feature enhancement module comprises an attribute feature enhancement network based on a Transform architecture, a text feature enhancement network based on a Bi-LSTM architecture and a cross attention mechanism, and an accident level embedded network, and is used for performing feature enhancement; and the feature fusion module is used for splicing the output of the feature enhancement module to obtain a comprehensive feature vector. Compared with the prior art, the method can further improve the prevention and coping capacity of safety production accidents.

The invention discloses an industrial internet risk analysis system and method based on a knowledge graph, and the method comprises the steps: collecting data, and defining a multi-source original data flow and the knowledge graph; performing data preprocessing, unifying sample data, and performing mapping splicing on each mode in the multi-source original data stream; constructing, storing and updating a knowledge graph; carrying out risk modeling based on attention, and introducing an attention mechanism and a causal relationship model; carrying out risk scoring and inference; and performing risk early warning and visualization, outputting an early warning list, a propagation path diagram and key hub nodes, and performing butt joint with an existing industrial safety platform for display through an analysis report interface. The invention provides an industrial internet risk analysis scheme which not only can fuse the structural modeling capability of the knowledge graph, but also can utilize a self-attention mechanism to improve the key risk feature recognition precision.

The invention discloses an agent-based security risk assessment and operation management integrated method and system, and relates to the technical field of industrial security production and artificial intelligence, and the method comprises the steps: obtaining enterprise security production operation data, carrying out the preprocessing of the data, obtaining a standardized data set, and carrying out the deep transfer learning through an agent, and extracting scene risk features; calculating a propagation entropy value of the risk mapping data and constructing a propagation path, and performing dynamic time sequence fusion to generate a risk assessment result; generating a scene control instruction based on group game operation and performing optimization to obtain a baseline check instruction; operating data are collected, the conduction probability is calculated, a safety response rule is generated, linkage optimization is carried out, and a cooperative defense strategy is formed. According to the invention, risk feature extraction, propagation path analysis and cooperative prevention and control are realized through the intelligent agent, and the intelligent management level of enterprise safety production is improved.

The invention relates to the technical field of safety assessment methods, in particular to an industrial scene safety assessment method and system based on a generative adversarial network, and the method comprises the steps: obtaining the historical data of an industrial scene; obtaining current industrial environment operation data to obtain a multi-source data set; generating an adversarial network based on historical data and training conditions to obtain a trained generator and discriminator; generating abnormal data of the industrial scene by using a generator based on the multi-source data set; judging the trueness of the abnormal data by using a discriminator, and adjusting the input data of the generator according to the trueness; real and effective abnormal data are obtained; key frames are extracted from real and effective abnormal data, and each frame of data is converted into nodes; generating an industrial safety risk map based on the nodes; converting the industrial safety risk map into a safety risk map by using a dynamic multi-source map reasoning module; and the protection strategy generator is used for extracting protection measures and outputting a protection scheme, so that the recognition capability of the system on unknown risks is enhanced.

The present invention relates to a system and a control method for a USB communication-based smart padlock used for industrial safety and access management purposes. A smart padlock according to the present invention can substitute for existing padlocks of a manual key type to solve issues such as inefficiency of a padlock key distribution procedure, a loss of the padlock key, an accident due to arbitrary transmission to a third party, and a facility operation delay due to noncompliance with a scheduled entry / exit time under an industrial site environment in which numerous padlocks are operated, and to efficiently perform real-time monitoring on an operation status of the padlock and entry / exit workers.

The invention relates to the technical field of industrial safety, and discloses an electrolytic aluminum short circuit port key operation parameter online monitoring and explosion safety protection system which comprises a parameter real-time monitoring module, a threshold activation judgment module, a digital twin modeling module, a risk dynamic prediction module, a protection decision analysis module and an intervention scheme execution module. By constructing a multi-source signal cooperative verification mechanism, when short-circuit parameters are monitored in real time, a self-adaptive digital filtering algorithm is adopted to dynamically match magnetic field intensity and temperature disturbance characteristics, noise stripping and baseline calibration are carried out on original voltage and current signals, the monitoring accuracy of core parameters such as short-circuit resistance is ensured, false alarm triggering is avoided, and real-time monitoring of the short-circuit parameters is realized. And meanwhile, by establishing a parameter credibility scoring model, when signal distortion exceeds a tolerance threshold, a standby sensing channel is automatically switched, so that the risk of production interruption caused by noise pollution is further reduced, and the anti-interference capability and the operation stability of the system are improved.

The invention relates to the technical field of industrial safety monitoring, in particular to a fallen object accurate identification FOD calculation method based on a fixed tower and application of the fallen object accurate identification FOD calculation method. Comprising the following steps: synchronously acquiring multi-source heterogeneous sensing data; carrying out multi-modal data preprocessing and feature extraction; performing cross-modal feature fusion and representation construction; modeling and reasoning a time-space diagram neural network; and performing multi-dimensional threat quantification and decision output. According to the design of the invention, multi-modal sensor groups are deployed at the tower bottom, the tower middle and the tower top of a fixed tower in a layered manner, and a high-definition multispectral camera, a high-density laser radar and a distributed acoustic sensor are integrated, so that 7 * 24-hour full-automatic data acquisition is realized; according to the design, the time and environment limitation of manual inspection is eliminated, and the complex weather detection capability is improved by 80%; meanwhile, the sensor synchronously triggers the mechanism and the grading detection process, the single-frame data processing delay is less than or equal to 50ms, and the real-time monitoring requirement of an aircraft take-off and landing high-frequency scene is met.

The invention discloses an industrial safety detection method combining time alignment and motion consistency, and belongs to the technical field of industrial safety monitoring. According to the method, through seven steps of multi-camera data acquisition and preprocessing, target feature extraction, coordinate projection, instantaneous speed estimation, multi-dimensional similarity fusion, soft matching optimization and model training and target trajectory generation, the problem of space-time inconsistency during multi-camera cooperative detection in an industrial scene is solved. A camera clock offset parameter is innovatively introduced to correct a cross-camera time difference, appearance, geometry and time prior multi-dimensional similarity are fused, a multi-loss function optimization model is constructed, and cross-camera target accurate matching and long-time trajectory tracking are realized. High-precision technical support is provided for personnel safety monitoring and equipment abnormal behavior detection in an industrial scene, and the industrial production safety protection capability is improved.