130 results about "Accident prevention" patented technology

The invention discloses a coal mine rock burst risk assessment system which comprises a data acquisition module, an energy accumulation rate calculation module, a roadway network modeling module, a risk assessment module and an early warning regulation and control module. The method comprises the following steps of: firstly, calculating an energy accumulation rate of a roadway, then constructing a roadway network diagram, identifying a high stress conduction path, calculating a dynamic risk index and delimiting a risk area by integrating factors such as a historical impact event and an energy index, and automatically triggering corresponding early warning and regulation measures by a system according to a risk level so as to realize accurate early warning and dynamic regulation. And mine operation decision making and accident prevention are assisted.

The invention relates to the technical field of coal mine accident prevention, in particular to a coal mine accident scenario prediction method, device, equipment, medium and product, and the method comprises the steps: collecting coal mine accident cases and classifying coal mine accident scenarios, building a coal mine accident scenario library, determining the accident type and accident feature information in the coal mine accident scenario library, and obtaining a coal mine accident scenario prediction result. After the accidents are classified, the characteristic parameter indexes of different accident scenes are analyzed, and the accident scenes possibly occurring in the coal mine are predicted through the change of the characteristic parameter indexes based on the multi-model fusion neural network, so that the efficiency and the accuracy of the accident scene prediction model are effectively improved; therefore, coal mine accidents are prevented, and the accident handling capacity is improved.

The invention relates to the technical field of intelligent transportation, and particularly discloses a multi-sensor data fusion method based on adaptive Kalman filtering, which is suitable for vehicle trajectory tracking in a tunnel scene. The method comprises the steps of sensor deployment and data acquisition, data preprocessing and anomaly detection, multi-target data association, adaptive filtering fusion and state estimation. By introducing an anomaly detection mechanism and dynamically adjusting a filtering estimation error covariance, the influence of abnormal data such as a multipath effect and electromagnetic interference is effectively suppressed, and the tracking precision and stability of the position, speed and trajectory of the vehicle are improved. The method is remarkably superior to a traditional filtering algorithm in a complex tunnel environment, has the technical advantages of high-precision tracking, high robustness, high adaptability, high real-time performance, comprehensive functions and the like, and can remarkably improve the reliability and performance of a tunnel vehicle monitoring system; the method is suitable for multiple key fields of tunnel safety early warning, accident prevention, traffic flow optimization, emergency response and the like.

The invention relates to a gas accident analysis system and method based on a dynamic Bayesian network. The method comprises the following steps: constructing a dynamic Bayesian network model; gas equipment operation state data and environment parameters are obtained; processing data, and performing gas accident risk assessment and accident evolution path analysis by using an inference algorithm; and generating an accident analysis result and outputting the accident analysis result through a visual tool. According to the gas accident analysis system and method based on the dynamic Bayesian network, multiple risk factors of a gas accident are comprehensively analyzed through the dynamic Bayesian network technology, the possibility of accident occurrence can be effectively predicted, the severity of the accident can be effectively evaluated, and reasonable prevention and response measures are put forward. Compared with a traditional risk assessment method, the method is higher in accuracy, real-time performance and flexibility, can provide more scientific safety management decision support for gas enterprises, remarkably improves the gas accident prevention and emergency processing capability, and has a wide application prospect.

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 relates to the technical field of mine safety, in particular to a coal mine personnel positioning and safety early warning method, which comprises the following steps: acquiring position information of coal mine personnel; processing the position information to obtain high-precision positioning data; analyzing the high-precision positioning data through a clustering algorithm, and marking potential safety hazard data points; performing anomaly detection on the potential safety hazard data points by adopting an isolated forest algorithm to obtain abnormal behavior characteristics and potential safety hazards; and performing risk assessment based on the abnormal behavior characteristics and the potential safety hazards to obtain a risk assessment value, comparing the risk assessment value with a preset risk threshold, and triggering safety early warning according to a comparison result. According to the invention, accurate positioning, real-time monitoring and abnormal behavior early warning of the coal mine personnel can be realized, and the coal mine safety management level and the accident prevention capability are improved.

The invention provides an intelligent supervision system and method for safety production liability insurance, and is applied to the technical field of supervision science and technology. The system comprises an enterprise port, an insurance institution port, a supervision department port and a background port, and integrates four modules: an automatic safety insurance data matching module synchronizes insurance buying information in real time and generates a supervision form; the safety production state monitoring module collects enterprise data to support risk assessment and provide accident prevention service; the accident prevention service evaluation module judges validity through data comparison before and after service and tracks and rectifies the data; and the data statistical analysis module realizes multi-dimensional query, dynamic ranking and visualization. According to the method, data collection, state monitoring, effect evaluation and rectification closed-loop management are realized through multi-port deployment, and a supervision mechanism of enterprise evaluation and supervision reward and punishment linkage is established. According to the invention, through cross-department data intercommunication, the problems of information isolated island and supervision lagging of the traditional responsibility insurance are effectively solved, and the supervision efficiency of the responsibility insurance is remarkably improved.

The invention discloses a mountain area double-lane road driving dynamic risk assessment method based on an artificial potential field theory. The method comprises the following steps: S1, extracting trajectory data with potential conflicts in vehicle natural driving data; s2, constructing an obstacle risk field, and quantifying driving risks caused by dynamic and static obstacles on a road; s3, constructing a road line risk field, and quantifying potential risks caused by driving of the vehicle deviating from the center line; s4, constructing a road boundary risk field, and quantifying the risk generated when the vehicle approaches the road boundary; s5, integrating the risk field models of the three driving risk sources to construct a comprehensive driving risk field model; and S6, visualizing the driving risk of the specific target vehicle in the form of a risk map. According to the method, a mountain area double-lane road driving dynamic risk assessment model based on the artificial potential field theory is constructed, and the mountain area double-lane road driving dynamic risk is scientifically, reasonably and accurately described and discriminated from the angle of accident prevention.

The invention relates to the technical field of traffic management systems, in particular to a traffic incident alarm, early warning and rescue rapid processing method and system, and the method comprises the following steps: S1, configuring hardware facilities; s2, in the hardware facility, the server carries a traffic operation and maintenance platform; according to the intelligent delineator, through hardware setting of the intelligent delineator, the distance between the intelligent delineator and a vehicle is detected through an infrared distance measuring probe, a flashing light is started for early warning when the intelligent delineator reaches a designated range, the designated range can be adjusted based on fog amount, sunlight and other data, and vibration data generated by collision and rubbing are collected through a displacement sensor and a vibration sensor; the automatic alarm function is achieved, based on the two-dimensional code label manual code scanning alarm or the pressing mode of the pressing sensor, quick alarm is achieved, the accident prevention function is achieved, convenient alarm is achieved, the precise coordinate transmission function is achieved, guarantee is provided for timeliness of rescue work, burst flashing is called for road section burst flashing warning, and serial accidents are avoided.

The invention discloses an old people accident prevention comprehensive alarm system based on a multi-modal artificial intelligence model. The system comprises a data acquisition module, a multi-modal fusion module, an artificial intelligence analysis module, an alarm module and a background management module. The data acquisition module is used for acquiring multi-modal data such as video image data, wearable equipment data and environment data of old people; the multi-modal fusion module carries out fusion processing on the data to generate a multi-modal feature vector; the artificial intelligence analysis module analyzes the feature vectors based on a multi-modal artificial intelligence model, and outputs the probability and confidence of occurrence of various accident risks; when the alarm condition is met, the alarm module is started and sends alarm information to related personnel; the background management module carries out all-around management on the system. Various modal data are fused, the artificial intelligence technology is utilized to realize early warning and timely alarm of various accident risks of the elderly, related personnel can be prompted to take measures in time, the occurrence probability of accidents is reduced, the life safety of the elderly is guaranteed, and the system is suitable for various old-age care institutions and old-age living places. The method has important safety protection value.

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 invention is applicable to the technical field of automobiles, and provides an accident prevention-based steering mode adaptive adjustment method, which comprises the following steps of: respectively acquiring environmental information data around a vehicle under a natural driving condition and a narrow space scene driving condition; comprehensively analyzing the input environment information, and judging whether the driving mode needs to be adjusted in the environment defined by the environment information or not; and S2, setting two driving modes including a standard mode and a precise mode, and adjusting the driving mode of the vehicle based on a result output in the step S2. According to the method, the driving safety risk can be avoided from the subjective angle of the driver and the input angle of the operation amount of the driver.

The invention discloses a drowning visual identification method fusing infrared temperature difference features. The method comprises the following steps: S1, image acquisition and feature extraction; s2, extracting an edge feature map; s3, extracting a shape feature map; s4, extracting a color feature map; s5, feature map fusion and segmentation; and S6, identifying a drowning person instance and triggering an alarm. According to the invention, infrared thermal imaging and visible light images are combined, the accuracy and real-time performance of drowning detection are improved by using a multi-feature fusion technology, and particularly, a drowning person is effectively identified in a low-light environment; the system rapidly transmits drowning person information through intelligent alarm and accurate positioning, shortens rescue response time, and improves rescue efficiency. Besides, by adopting an advanced neuron impulse response network and a convolutional neural network, features of drowners are extracted more accurately in a complex background, the method is suitable for various water area environments, and an innovative solution is provided for drowning accident prevention.

The invention belongs to the technical field of ship navigation risk management, and particularly relates to a ship navigation risk control method. Comprising the following steps: acquiring a ship prepared standard data file through a ship navigation service system; determining corresponding elements under the accident type; sorting the comprehensive weight and the influence weight to form a risk accident element graph model; extracting historical navigation accident data according to element types in the risk accident element graph model; establishing a graph form sample database; a multi-layer network classification model is adopted to carry out classification training on the navigation map form samples; and matching ship risk accident features to be analyzed to carry out risk management and control. The method is used for completing risk accident feature classification and identification of different types of ships in the sailing process by analyzing various element features in the historical data of the ship sailing risk accidents, performing classification prediction on the to-be-analyzed ship sailing risk accidents, and determining the sailing accident features so as to take targeted accident prevention measures.

The invention discloses a data analysis method for road traffic accident cause risk coupling, which comprises the following steps: collecting road traffic accident data, checking variable predictive force, constructing a road traffic accident severity cause analysis model by adopting a cumulative logistic model to mine risk factors, and constructing a coupling degree model to carry out risk coupling calculation. And the coupling strength among the risk factors is analyzed, the risk coupling effect of multiple factors is quantified, and risk decoupling suggestions are provided for road traffic safety. According to the method, key risk factors of different types of road traffic accidents can be effectively mined, the coupling effect of different forms of risk factors is quantified, and a decision basis is provided for a traffic management department to make accident prevention countermeasures.

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 embodiment of the invention provides a coal mine gas explosion accident reason analysis method and system, and belongs to the technical field of gas explosion accident prevention. The method comprises the following steps: acquiring report information of historical gas explosion accidents, and performing structured processing on the report information based on a predetermined reason classification framework to obtain structured information; constructing a corresponding accident three-dimensional network model based on the structured information, and determining attribute information of each node in the accident three-dimensional network model; analyzing the occurrence reason of the corresponding gas explosion accident based on the attribute information of each node to obtain the analysis result of each analysis index; and constructing a corresponding explosion accident self-inspection prevention list based on the analysis result of each analysis index, and pushing the explosion accident self-inspection prevention list to a user side. According to the scheme, real-time updating and accurate implementation of accident prevention measures are achieved, and efficient and feasible technical support is provided for coal mine safety management.

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 discloses a hoisting safety detection method and system based on artificial intelligence, and belongs to the technical field of hoisting safety management, and the method comprises the steps of panoramic view perception, target extraction, spatial-temporal trajectory modeling and safety risk assessment. According to the invention, a multi-modal structured target extraction method is adopted for target extraction, RGB images and depth information are integrated, the defect that the recognition rate of a single camera is reduced under shielding and illumination changes is overcome, the three-dimensional position and the motion state of the target can be obtained, and the interaction relationship between the target and a restricted area can be judged; according to the method, spatial-temporal trajectory modeling is carried out by adopting a multi-class target trajectory modeling method, the position change trend of various types of targets is accurately predicted, and the future possible moving region range of the targets is depicted through trajectory distribution modeling, so that prospective support is provided for risk identification; safety risk assessment is carried out by adopting a risk scoring method fusing double risk factors, the comprehensive safety risk is comprehensively assessed, and the accident prevention level in the lifting operation environment is remarkably improved.

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.

The invention relates to an intelligent charging pile with an accident prevention function, which comprises a charging pile body, a pre-embedded box is arranged outside the charging pile body, a supporting plate is mounted at the inner bottom of the pre-embedded box and matched with the charging pile body, and a storage mechanism for lifting the charging pile body is arranged in the pre-embedded box. The upper end of the charging pile body is provided with a fire extinguishing mechanism, the charging pile body is provided with a charging gun, and the charging pile body is provided with a separation mechanism used for emergency separation of the charging gun. And the storage mechanism comprises two lifting screws rotationally connected with the inner wall of the pre-buried box, the two lifting screws are both sleeved with lifting blocks in a threaded mode, and the two lifting blocks are both slidably connected with the inner wall of the pre-buried box. Compared with the prior art, the charging pile can be stored, the charging pile can be protected, fire can be extinguished in time, damage to the charging pile is reduced, rigid pulling of a vehicle to a cable is avoided, the charging pile is protected, and economic losses are reduced.

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.