67 results about "Accident prevention" patented technology

The invention provides a harbor district oil depot control system, belongs to the technical field of harbor district oil depot management and control, and particularly relates to a harbor district oil depot control system which comprises a multi-source data acquisition module, a data processing module, an index generation module, a production scheduling module, a safety management module, an equipment protection module and a control module. Universe data collection of the storage tank area and the loading and unloading area is achieved through the multi-source monitoring equipment, a scheduling, safety and equipment dynamic index system is formed in combination with the AI algorithm, and the data value density is improved; loading and unloading task allocation, four-level safety early warning and equipment health intervention measures are dynamically generated based on a threshold value, and automatic closed-loop management of'monitoring-analysis-execution 'is achieved; through a multi-stage early warning mechanism, full-process plan triggering from daily maintenance to emergency response is supported, and the accident prevention efficiency is improved; a three-dimensional visual billboard and a data cockpit are provided through the integrated management and control platform, cross-department collaborative decision making is achieved, and the emergency response speed is increased.

The invention discloses a steel structure construction safety risk dynamic assessment method and system based on artificial intelligence. The method comprises the steps of obtaining a multi-dimensional data set in real time; based on the multi-dimensional data set, a deep learning algorithm is adopted to fuse an image and a sensor signal, a structure defect is accurately identified, and a defect feature vector is extracted; when the defect feature vector exceeds a preset threshold value, a machine learning classifier is introduced to construct a risk assessment model, classification quantification of defects is realized, and an initial risk level is output; for the initial risk level, analyzing the space-time fusion data by using a probability state transition model, simulating the dynamic evolution trend of the risk and judging the stability of the risk; a mapping model is constructed based on a virtual simulation technology, dynamic simulation deduction is carried out on risk changes, and a visual visualization report and a graded early warning signal level are generated. According to the invention, full-chain intelligent closed loop from defect perception to risk prospective early warning is realized, and the safety management and control level and accident prevention capability of a construction site are greatly improved.

The invention discloses a road accident and illegal parking identification early warning method and system based on intelligent traffic, and relates to the technical field of intelligent traffic, and the method comprises the steps: analyzing a preprocessed road monitoring video stream in real time, and sensing multi-dimensional accident features and multi-modal vehicle state features; constructing a parallel accident identification model and an illegal parking detection model, constructing an adaptive threshold value based on camera parameters and environment features, and identifying an accident scene and an illegal parking scene by using multi-dimensional accident features and multi-modal vehicle state features in combination with the adaptive threshold value; the influence range of the accident scene and the illegal parking scene is estimated, and a multi-level early warning response mechanism is combined to be linked with a sound-light alarm, navigation pushing and rescue platform. According to the invention, through combination of intelligent accident and illegal parking identification and a multistage early warning linkage mechanism, a more efficient and more accurate highway accident prevention and control system is constructed, and travel safety is guaranteed.

The embodiment of the invention discloses a safety assessment method and device for a receiving station, equipment and a medium, and the method comprises the steps: determining a risk event according to a hazard source in a to-be-assessed receiving station, and enabling the risk event to represent a hazard source out-of-control event; determining a safety index of the to-be-evaluated receiving station according to historical data associated with the risk event of other receiving stations and the technological process of the to-be-evaluated receiving station; a comprehensive influence matrix is determined according to the index scores of the safety indexes, a safety evaluation graph of the to-be-evaluated receiving station is determined based on the comprehensive influence matrix, and the safety evaluation graph is used for investigation operation of risk events of the receiving station and characterization of the causal relationship and the hierarchy of the safety indexes. Based on the safety assessment diagram provided by the embodiment of the invention, accident cause factors, causal relationships and levels can be determined, the accuracy of risk disposal can be improved, and meanwhile, through the safety indexes and the safety assessment diagram, the accident occurrence mechanism can be known, the accident prevention capability can be improved, and the occurrence of accidents can be avoided.

The invention provides a method for quickly identifying topological structure change of a gas pipe network. The method comprises the following steps: acquiring data of gas pressure, flow and temperature; modeling the topological structure of the gas pipe network based on the data of gas pressure, flow and temperature; the method comprises the following steps: judging data of gas pressure, flow and temperature through a real-time data monitoring system, and performing feature extraction on historical data and real-time data of an abnormal pipe section and related areas around the abnormal pipe section after judging that the abnormal pipe section is abnormal; embedding a hydraulic equilibrium constraint in the fitness function; performing optimization search on the topological structure of the gas pipe network based on a genetic algorithm; and identifying and adjusting the topological structure of the gas pipe network according to the optimization result of the genetic algorithm. According to the method, the operation characteristics of the gas pipe network under different working conditions are researched, and a basis is provided for optimization design, operation management and accident prevention and treatment of the pipe network.

The invention provides a graph theory-based shipping man-machine coupling failure risk assessment method and system, and relates to the technical field of shipping risk assessment, and the method comprises the steps: extracting structured data with a timestamp, generating an effective data set, screening the effective data set, and generating a key risk factor set; constructing a weighted undirected network through a graph theory based on the key risk factor set, performing sliding segmentation on the effective data set through setting time windows with fixed lengths and a preset step length, recalculating a standardized mutual information value between nodes in each time window, updating an edge weight, and generating a time-varying weighted undirected network topology sequence; performing global efficiency analysis on the time-varying weighted undirected network topology sequence, calculating a network global efficiency decline rate after each node is removed, and sorting the key risk factors according to the decline rate to determine the priority of the key risk factors; and outputting corresponding accident prevention suggestions and sailor training schemes based on the key risk factor priorities.

The present invention relates to an intelligent safety accident prevention system and an intelligent safety accident prevention method using same. An intelligent safety accident prevention system according to one aspect of the present invention is characterized by comprising: a panel unit having an image capturing module for capturing an image of a worker before work on an object is approved; and a monitoring unit which receives the image captured by the image capturing module from the panel unit, analyzes the image to determine whether the worker is wearing safety equipment in a manner meeting predetermined criteria, and approves the work when the worker is wearing the safety equipment in a manner meeting the predetermined criteria.

Method (600), performed by a control unit (100) configured to be contained in a vehicle (120), for notifying geographically adjacent vehicles (128, 129) of door information, the method comprising: Receiving (610) feature data that displays the features of the vehicle (120), Determining (620) door information using feature data, wherein the door information is determined by: Determining a risk level from several risk levels that indicates a risk that one or more doors of the vehicle (120) will open, by determining that the feature data satisfy one or more predetermined conditions that are associated with a specific risk level, Notify (630) the multiple vehicles (128, 129) of the door information by transmitting a wireless signal that includes the door information.

To provide a device for suppressing a stepping error accident, which is easily attached to a vehicle.SOLUTION: The erroneous accident-suppressing device 1 is provided with a speaker 4 for converting a pulse generated at a positive electrode 13a of an on-vehicle battery 13 connected to an ignition coil 12 in the vehicle 10 into a voice.SELECTED DRAWING: Figure 1

The invention discloses a quick-response multi-worker dangerous action prediction and real-time early warning method and system, and belongs to the technical field of computer vision and deep learning, and the method comprises the steps: obtaining video frames of workers and an environment, carrying out the preprocessing, carrying out the real-time detection and tracking of a plurality of workers in the video frames through employing YOLOv5, generating and screening a high-confidence bounding box, and carrying out the cutting; extracting two-dimensional coordinates of the joints of the worker based on attitude estimation and generating a skeleton graph; carrying out feature modeling on a nonlinear relation between skeleton joints by adopting a KAN (Karan Area Network) so as to capture subtle attitude changes, and obtaining a high-dimensional feature vector of an action; time sequence modeling is carried out, future potential dangerous actions are predicted, danger scores are output, when the scores exceed a threshold value, early warning is triggered, and advanced intervention and real-time alarm of high-risk actions are achieved. According to the method, mutual interference in a multi-worker scene can be effectively reduced, the recognition sensitivity and real-time response capability of fine-grained action abnormity are improved, and the method is suitable for industrial production safety monitoring and accident prevention.

The application discloses a method for generating a safety inspection report of an offshore oil production ship shape of a multimodal large model, relates to the technical field of intelligent generation of ship safety inspection, and first collects an offshore oil production ship image, matches a corresponding safety inspection report, and constructs a data set; subsequently, a multimodal report generation model is constructed, the offshore oil production ship image and the corresponding safety inspection report are input into the model to generate a safety inspection report of an offshore oil production ship shape; subsequently, the model is trained, and after multiple rounds of training, a final multimodal report generation model is obtained; finally, the offshore oil production ship image is input into the final model, and a safety inspection report of an offshore oil production ship shape is output. The application enhances the controllability and pertinence of the content generated by the large language model, improves the accuracy and compliance of the shape safety inspection report, and can be widely applied to scenes such as daily operation management of offshore oil production ships, safety guarantee of ocean navigation, compliance review of ports and ship classification societies, and accident prevention and subsequent maintenance disposal.

The invention discloses a crew fatigue identification method based on PSO-ACLSTM, which comprises the following steps: carrying out discrete wavelet transform on a preprocessed electroencephalogram signal, obtaining a prediction probability of an electroencephalogram signal sample by using an electroencephalogram classification algorithm model based on the transformed electroencephalogram signal, further obtaining a cross entropy error, and then adopting an improved particle swarm optimization algorithm module to identify the crew fatigue of the crew. And optimizing hyper-parameters in the electroencephalogram classification algorithm model, optimizing the electroencephalogram classification algorithm model, and further identifying the electroencephalogram signals of the crew needing fatigue identification based on the optimized electroencephalogram classification algorithm model. According to the method, relevance between channels and relevance between different samples can be extracted, the space and time characteristics of signals are further enriched, the accuracy of recognizing the fatigue state of the crew is improved, and crew fatigue state detection and safety accident prevention are facilitated.

This utility model belongs to the field of tobacco technology and discloses an exhaust device, including an exhaust hood, a sensor, and a control module. The exhaust hood is positioned above a vibrating trough and is equipped with a lifting device that drives the exhaust hood to move vertically, ensuring that the exhaust hood has at least two states: raised and lowered. The sensor is located on the bottom end of the exhaust hood facing the vibrating trough. The control module is electrically connected to both the sensor and the lifting device. By installing the sensor on the bottom end of the exhaust hood, where the operator is easily squeezed, when the exhaust hood descends due to misoperation, the sensor at the bottom of the exhaust hood comes into contact with the operator during the cleaning process. The sensor converts the pressure into a change in resistance and sends a signal to the control module. The control module then sends a signal to the lifting device to stop descending and resume ascending, preventing the operator from being squeezed. This proactive protection of stopping upon contact significantly improves the safety of the exhaust device, resulting in a high accident prevention rate.

The invention discloses a monitoring system and method for coal mine safety management, and belongs to the technical field of coal mine safety monitoring, and the method comprises the steps: constructing a coal mine safety system model according to coal mine project engineering approval parameters, and building a pre-action influence factor library based on a pre-action analysis principle, then, a data acquisition device is used for acquiring safety management parameters of a coal mine to be detected and part images acquired by additionally arranging camera equipment, and the safety management parameters, a first output image and correlation factors in a pre-acting influence factor library are input into a coal mine safety system model; and calculating in combination with a pre-action analysis algorithm to obtain a safety management result and a risk evolution trend. According to the method, the pre-acting influence factor library is established, the potential influence factors in the accident causal chain are analyzed, and the trend prediction algorithm is combined, so that the potential risk can be identified and the evolution trend of the potential risk can be predicted before the accident occurs, the transformation from post-event alarm to advanced early warning is realized, and precious time is won for accident prevention.

The invention discloses a probability model analysis method for a secondary accident of an over-limit transport vehicle, and the method comprises the following steps: obtaining traffic accident related data, including numerical data and classification data; inputting the data into a trained over-limit transport vehicle secondary accident probability model, wherein the model is constructed by combining a random forest model after Bayesian optimization with a logistic regression model; and carrying out influence analysis on a model prediction result through an SHAP interpretation framework. According to the method, the interpretability of logistic regression and the nonlinear modeling capability of the random forest are fused, and the model precision is improved by combining Bayesian optimization, so that accurate prediction of the secondary accident probability of the over-limit transport vehicle and quantitative analysis of key influence factors are realized, the blank of model research in the field is filled up, and the method has a wide application prospect. And a scientific basis is provided for traffic safety management and accident prevention.

The explosion-proof lamp capable of detecting the environmental parameters comprises an explosion-proof lamp body, an LED lamp, a heat dissipation plate, a control panel shell, an installation base, a supporting frame and a state indicating lamp unit, the LED lamp is arranged at the bottom of the explosion-proof lamp body, the state indicating lamp unit is arranged on the side portion of the explosion-proof lamp body, and the heat dissipation plate is arranged at the top of the explosion-proof lamp body. The heat dissipation plate is connected with one side of the control panel shell, the supporting frame is installed on the other side of the control panel shell, and the installation base is installed on the supporting frame. A control panel is arranged in the control panel shell and comprises an explosion-proof lamp controller, a Bluetooth module, a combustible gas sensor, a dust sensor, a temperature sensor, a voltage sampling unit, a current sampling unit and a PWM control module, the Bluetooth module, the combustible gas sensor, the dust sensor, the temperature sensor, the voltage sampling unit, the current sampling unit and the PWM control module are electrically connected with the explosion-proof lamp controller, the PWM control module is electrically connected with the LED lamp, and the explosion-proof lamp controller is further electrically connected with the state indicator lamp unit. According to the utility model, the explosion-proof function is realized, and the working environment parameters can be detected so as to realize safety accident prevention.

The system according to this embodiment aims to analyze traffic accident records and provide accident prevention measures and countermeasures. [Solution] The system according to the embodiment comprises an anonymization unit, an analysis unit, an extraction unit, and a provision unit. The anonymization unit anonymizes the traffic accident records. The analysis unit analyzes the traffic accident records anonymized by the anonymization unit. The extraction unit extracts accident patterns and risk factors from the records analyzed by the analysis unit. The provision unit provides accident prevention measures and countermeasures based on the risk factors extracted by the extraction unit.

The invention belongs to the field of industrial safety and artificial intelligence, and discloses a chemical accident risk analysis method and system based on text mining and GNN-Apriori association rules. The method comprises the following steps: cleaning and structuring a chemical accident text, fusing TextRank, BM25 and BERT algorithms, and extracting a high-quality keyword set through a weighted model; constructing a graph structure based on a keyword co-occurrence relationship, and learning node semantic embedding by using GNN; inputting the embedded vector into an improved Apriori algorithm, and mining a semantic enhanced association rule in combination with semantic similarity, support degree, confidence and lifting degree; and finally, constructing a chemical accident causal complex network, and evaluating key nodes by using node centrality by taking the improvement degree as an edge weight to realize risk propagation path analysis and visual display. According to the method, chemical accident causes can be intelligently identified, semantic association is mined, a causal network is modeled, and interpretable decision support is provided for accident prevention and the like.

The present application relates to the technical field of square cabin structure, aims to solve the problem of water accumulation on the top of the square cabin in the prior art, and cannot solve the problems of fire prevention, disaster prevention and accident prevention of the square cabin, easy corrosion of the square cabin, high maintenance cost and easy safety accidents on the top of the square cabin, and provides a square cabin top anti-water accumulation structure, which comprises a cabin body; the top of the cabin body is in a trapezoidal structure, and the top of the cabin body is composed of a top cabin plate, an inner corner piece, an outer corner piece, a covering edge and a covering corner; the longitudinal edges of adjacent top cabin plates are inclined and spliced with each other, the inner corner piece is welded or riveted at the inner side splicing position of the adjacent top cabin plates, and the outer corner piece is welded or riveted at the outer side splicing position of the adjacent top cabin plates; the covering edge is welded or riveted at the outer side splicing position of the adjacent top cabin plates and covers the outer corner piece, and the covering corner is welded or riveted at the outer side corner position of the adjacent top cabin plates.The present application has the beneficial effects that the top of the square cabin does not accumulate water, can solve the problems of fire prevention, disaster prevention and accident prevention of the square cabin, the square cabin is well sealed and not easy to corrode, the maintenance cost is low, and safety accidents are not easy to occur during the work of the top of the square cabin.

This utility model relates to the field of forklift AGV technology, specifically a safety stop triggering device for forklift AGVs, including a fixed base plate, side blocks, guide baffles, detection handle blocks, connecting frames, detection sensors, detection sensor mounting brackets, and detection plates. When the detection handle block is activated and moves close to the fixed base plate, the detection plate, driven by the connecting frame, moves away from the detection end of the detection sensor. Upon detecting the detection plate's misalignment, the detection sensor sends a signal to the corresponding forklift AGV controller, causing the controller to initiate an emergency stop, preventing further accidents. The device is simple in structure and reliable in use. Furthermore, by adding a laser scanning sensor, this utility model can further enhance safety protection for the forklift AGV's forklift movement and improve its accident prevention capabilities.

The invention provides a ship-bridge collision detection method and system, computer equipment and a storage medium, and belongs to the field of bridge structure health monitoring and safety alarming, and the method comprises the steps: collecting the displacement response of a bridge structure key position under ship collision disturbance; the method comprises the following steps: firstly, expanding acquired response data in a frequency domain by using discrete wavelet transform, extracting a low-frequency approximate signal to carry out co-integration test to obtain a multi-scale co-integration equation, and carrying out online real-time estimation on a co-integration coefficient in each scale equation by using a Kalman filtering recursive algorithm; when the co-integration coefficient changes and exceeds a safety limit, automatic alarm is realized; alarm results of co-integration coefficients of multiple scales are synthesized, and when 75% of co-integration combined alarm is set to be exceeded, a ship and bridge collision alarm decision is made. According to the method, the Kalman filtering algorithm is combined, so that the change of the co-integration coefficient can be predicted on line, and the actual engineering requirements are met. On the basis, multi-scale wavelet decomposition and co-integration improve the robustness and precision of alarm, a plurality of alarm results are provided for comprehensive decision making, and scientific basis and technical support are provided for bridge structure health monitoring and accident prevention and control.

The utility model discloses a flow guide type thermos capable of preventing boiling overflow, which comprises a thermos body, the thermos body comprises a thermos body and a thermos cover covering an opening at the upper end of the thermos body, a heating chamber is arranged in the thermos body, and a sealing component is arranged on the side wall, close to a water outlet, in the heating chamber. According to the hot water kettle, the water outlet is automatically kept in a closed state when the whole structure is in a boiling water state, boiling water is prevented from overflowing from the water outlet, and meanwhile, after water is boiled, even if the water accidentally topples over, the water can be prevented from overflowing from the water outlet. A physical barrier formed by the sealing plate and the kettle cover can also prevent hot water from leaking, the potential safety hazard that children make contact with high-temperature liquid is particularly reduced, and through the synergistic effect of mechanical linkage, temperature control response and a flow guide structure, systematic safety improvement is achieved in the aspects of operation convenience, automatic protection reliability and accident prevention.

The invention relates to the technical field of semiconductor device processing, in particular to wafer crushing processing equipment and a processing method.In the equipment, a controller controls an electric control butterfly valve to discharge crushed wafer chippings to a material receiving bag in a timed and quantitative mode, the situation that the material receiving bag is too full is prevented, and continuity and stability of the production process are ensured; the negative pressure supply mechanism forms stable negative pressure in the material collecting barrel, dust generated during crushing is effectively prevented from escaping into workshop air, the health of workers is protected, and the requirements for clean production and environmental protection are met; the controller serves as a center and uniformly coordinates actions of the electric control butterfly valve, weight monitoring, operation of the negative pressure supply mechanism and alarm prompt to form a closed-loop control system, so that the intelligent level of equipment is improved, the dependence on artificial experience is reduced, the accident prevention capability is enhanced through automatic alarm, and the safety of the equipment is improved. Functional modules such as crushing, collecting, weighing and dust removing are integrated on the workbench, the layout is reasonable, the equipment tends to be small, and occupied space is reduced.

The invention relates to the technical field of intelligent electric meters, power distribution internet of things and edge computing, in particular to an intelligent electric meter data collaborative regulation and control system and method, and the method comprises the steps: obtaining the electrical time sequence data and environment perception data of a power distribution circuit through an edge computing module of an intelligent electric meter; timestamp alignment is carried out, and electrical fluctuation characteristics containing current data are extracted; inputting an electric heating environment multi-physics field coupling twinborn model, and calculating a recessive heat accumulation amount and an insulation aging rate; calculating a remaining safety time based on the two; generating a cooperative regulation and control strategy in combination with the preset priority of the electric equipment in the local area network; and when the residual safety time is greater than the safety threshold, a flexible load reduction instruction is sent, and when the residual safety time is less than or equal to the safety threshold, a power-off control instruction is generated, so that local risk prediction and closed-loop disposal are realized, and the continuous power supply capability and the accident prevention and control capability are improved.

To provide an assessment system for plant accident countermeasures capable of enabling effects brought by accident countermeasures to be known.SOLUTION: An assessment system for plant accident countermeasures includes: first acquisition means for acquiring a plurality of risk assessment means for assessing risk of accidents with individually different contents, and accident countermeasure information indicating the contents and countermeasures of the accidents; second acquisition means for acquiring pre-countermeasure plant information and post-countermeasure plant information that indicate respectively states of a plant before and after taking the countermeasures indicated by the accident countermeasure information; individual risk acquisition means for acquiring, for each of the risk assessment means, pre-countermeasure individual risk information indicating an assessment result of the risk based on the pre-countermeasure plant information and post-countermeasure individual risk information indicating an assessment result of a risk based on the post-countermeasure plant information; and individual comparison and determination means for comparing the risk indicated by the post-countermeasure individual risk information and the risk indicated by the pre-countermeasure individual risk information, and determining whether or not there are any post-countermeasure individual risk information having changed in the risk from the pre-countermeasure individual information.SELECTED DRAWING: Figure 1

Techniques for object identification and accident prevention for vehicles in hazardous (e.g., low visibility) conditions are provided. In an example, a method comprises detecting, by a system onboard a vehicle comprising a processor, presence of vehicles and determining respective location, size, trajectory or speed of the detected vehicles. The method can further comprise detecting, by the system, presence of objects or pedestrians and determining respective location, size, trajectory or speed of the detected objects or pedestrians. The method can further comprise analyzing, by the system, the determined respective location, size, trajectory or speed of the detected vehicles, objects or pedestrians and predicting risk of collision. The method can further comprise issuing, by the system, a warning to the respective detected vehicles or pedestrians as a function of the predicted risk of collision.

The application discloses a small-scale visual hydrogen explosion monitoring system and a monitoring method, and belongs to the field of hydrogen explosion monitoring.The application comprises an explosion pipeline system, a gas distribution system, an ignition system, a test and data acquisition system.Through forming sodium chloride crystals in the pipeline, based on the flame color reaction of sodium, the invisible hydrogen flame in the ultraviolet wave stage is colored, further, the sodium lamp is used to deepen the flame color, the capture ability of a high-speed camera on the flame form and position is improved, the hydrogen flame propagation speed can be calculated more accurately, the relationship between the hydrogen flame form and the flame propagation speed is analyzed, and the propagation law of pressure and flame is quantitatively characterized; based on a semiconductor laser, the ignition and initiation at different positions in the pipeline can be realized by using the high-thermal-conductivity characteristics of copper, and the dynamic change characteristics of the bidirectional flame propagation with time are analyzed.The application can predict the space-time evolution law of hydrogen combustion and explosion in a confined space, and provide support for safety design, accident prevention and control and the like of hydrogen-related industries.

The application provides an automatic driving accident prevention simulation scene generation technology based on swarm intelligence, which can realize random sampling in a road accident area, design an algorithm for splitting a lane to establish a KD tree, effectively reduce the time for searching a nearest neighbor lane, quickly determine the direction of a generated accident vehicle, and greatly reduce the search time compared with a method without optimization. Then, the constraint relationship of participants in the automatic driving accident prevention simulation scene is analyzed, an evaluation function is designed, a lane trajectory and a normal direction are searched along a lane in the road accident area based on a firework algorithm, and a suitable vehicle generation area is gradually approached. A sensor object is created through an interface provided by a Carla simulator, and an accident image is generated according to simulation data received by the sensor object. Compared with an existing scheme, the application can effectively improve the speed of automatic driving accident prevention simulation scene generation.

The application provides an accident prevention service data construction method and system based on a double prevention mechanism, which establishes an industry and field double prevention service related knowledge base through network crawling data, maintains a risk evaluation level and risk control level mapping relationship in a risk matrix template, imports an industry and field risk control hidden danger investigation list, forms an enterprise risk management basic knowledge base, formulates a risk grading control plan, allocates and implements a risk grading control task, formulates a hidden danger investigation and management plan, investigates hidden danger points in the plan, accepts hidden danger states after management, forms a Deming closed loop, displays safety risk and hidden danger information distribution according to geographical position, risk level and control level and other multi-dimensional conditions, and exchanges and shares double prevention service related risk assessment reports through a data gateway and other modes. The application solves the problems of high technical threshold, great supervision difficulty and difficult data construction of a risk knowledge base in existing production safety prevention service cooperation technology.

The invention discloses an immersive teaching and training system based on safety accident prevention, and relates to the technical field of safety teaching and training. The system comprises a teaching and training management module, a scene experience module, a behavior supervision module and an effect evaluation module. The teaching and training management module constructs a cross-scene accident chain through multi-source data acquisition and parameter adaptation and adopts a Bayesian network model to quantify risks; the scene experience module is connected with the function scene unit through a standardized interface, and realizes multi-scene dynamic linkage based on accident chain logic; the behavior supervision module synchronously collects physiological and operation data, establishes a correlation model and executes hierarchical intervention; and the effect evaluation module integrates the multi-dimensional data to generate an evaluation report, and reversely optimizes teaching and training parameters. According to the system, through the technical means of multi-module cooperation, a whole-process closed loop of data acquisition, risk assessment, resource allocation, behavior supervision and effect optimization is realized, the practical training performance, accuracy and systematicness are improved, and effective support is provided for safety accident prevention.