195 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 safety evaluation, in particular to an ultra-deep soft soil foundation pit safety evaluation method and system, and the method comprises the following steps: obtaining the geological data of an ultra-deep soft soil foundation pit, recording the soil type, soil density, underground water level and temperature data, carrying out the data arrangement and formatting, and generating a geological data record sheet. According to the method, the detailed geological data of the ultra-deep soft soil foundation pit are acquired and arranged, the accuracy and the applicability of the basic data are enhanced, so that subsequent analysis is carried out according to clear actual conditions, and the timeliness and the accuracy of risk prediction are ensured by directly associating prediction of mechanical behaviors with risk assessment; the risk assessment model can be dynamically adjusted, the effectiveness and accuracy of early warning are improved, the risk dependency relationship model and the monitoring scheme are integrated, theoretical and actual operations are effectively linked, the systematicness and adaptability of construction safety management are effectively improved, and the accident prevention capability is remarkably improved.

The invention provides an underground tunnel driving risk evaluation method based on a dynamic Bayesian network. The method comprises the following steps: constructing an underground tunnel driving risk evaluation index system; determining Bayesian network nodes according to the underground tunnel driving risk evaluation index system, and constructing an underground tunnel driving risk dynamic Bayesian network model; obtaining a driving risk assessment result according to the dynamic Bayesian network model of the driving risk of the underground tunnel; and performing driving improvement according to the driving risk assessment result. The method can effectively identify and predict the driving risk probability of different road sections of the underground tunnel, provides risk early warning for drivers, and provides data support for a traffic management department to optimize an induction system and an accident prevention strategy, thereby improving the driving safety and traffic efficiency in the tunnel.

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

The invention relates to the technical field of 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 provides a construction method of a foundation pit construction accident knowledge graph based on text mining. According to the method, related information, accident root causes, affected factors and other elements of foundation pit accidents are associated and connected, the occurrence mechanism and the affected factors of the accidents are comprehensively understood, and experience and guidance are provided for safety management and risk control of foundation pit construction. According to the method, the foundation pit accident knowledge graph with a complete structure and rich contents is constructed, elements such as accident related information, accident root causes and affected factors are associated and connected in a portrait representation mode, reliable reference is provided for decision makers, the accident prevention and management capability is improved, and the accident prevention and management efficiency is improved. And a positive promotion effect is achieved for promoting development and safety management of the foundation pit construction industry.

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 relates to the technical field of power grid data processing, and discloses a power grid accident prevention scheduling method and system under typhoon disasters, and the method comprises the steps: constructing a typhoon disaster anticipated fault set and an N-1 anticipated fault set, and carrying out the preventive control of the simultaneous connection and disconnection of lines in a dense power transmission channel affected by typhoons on the basis of the N-1 safety criterion of a power grid, thereby achieving the prevention scheduling of power grid accidents under typhoon disasters. An optimization objective function is constructed by taking minimization of the sum of unit output adjustment amount absolute values in a ground state as an optimization objective, and the power grid operation ground state, the N-1 anticipated fault state and the disaster anticipated fault state are taken as constraint conditions, so that the calculation scale of the prevention and control problem is reduced, the calculation efficiency is improved, and the method is suitable for large-scale popularization and application. The accident prevention scheduling optimization model of the disaster state is directly established, the model is optimized and solved, and the power grid accident prevention scheduling optimization scheme is obtained according to the optimal solution, so that the problems that the number of cut-off elements is large and anticipated fault analysis is extremely easy to cause non-convergence of power flow are solved, and the power supply reliability under the typhoon disaster is favorably improved.

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 present application relates to a lane intersection accident prevention warning method and system, the method comprising: obtaining main road driving data of the main road; comparing the number of main road vehicles with the preset number of vehicles to obtain a first prompt information that needs to be displayed to the current auxiliary road vehicle on the auxiliary road, and sending the first prompt information to a prompt device; obtaining the auxiliary road vehicle speed of the current auxiliary road vehicle on the auxiliary road; judging whether the current auxiliary road vehicle will collide with the main road vehicle based on the auxiliary road vehicle speed of the current auxiliary road vehicle, the main road vehicle speed, the first distance information, and the second distance information; if so, generating a second prompt information that needs to be displayed to the current auxiliary road vehicle on the auxiliary road, and sending the second prompt information to the prompt device. The present application reduces the possibility of traffic accidents between auxiliary road vehicles entering the main road and main road vehicles, reduces the interference of auxiliary road vehicles entering the main road with main road vehicles driving normally on the main road, and improves the safety of auxiliary road vehicles entering the main road.

The invention discloses a highway construction safety risk intelligent management system, and particularly relates to the technical field of highway construction safety risk management. Comprising a risk base number module, an emergency prevention and control module, a safety toughness module, a statistical analysis module and a safety toolbox module, wherein the risk base number module is used for collecting and presenting engineering information, risk point location distribution and risk grading of a highway under construction in real time; and the safety toolbox module provides real-time inquiry of information such as a current highway, laws and regulations, special action information, a special scheme and a duty table. The expressway construction safety risk intelligent management system integrates the functions of engineering point location, risk hidden danger investigation, risk management, emergency plan and the like, and is tightly combined with each key link of risk management work, so that the effect and timeliness of risk management are improved, and the risk management efficiency is improved. The overall efficiency and capability of hidden danger elimination, accident prevention and risk management in the highway construction process are improved.

To provide a system.SOLUTION: A system is provided, comprising generative AI means of learning past accident information, means of filtering functions to be released using generative AI, and means of enabling effective accident prevention at low cost thereby.SELECTED DRAWING: Figure 1

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 invention relates to the technical field of special equipment accident prevention, and discloses a special equipment accident prevention optimization potential analysis method and system based on a TRIZ functional model. Comprising a data collection module, a function analysis module, an optimization potential analysis module, a decision support module, a system operation module, a user interface module, a data storage module, a system maintenance module and a user management module. A data collection module, a function analysis module, an optimization potential analysis module, a decision support module, a system operation module, a user interface module, a data storage module, a system maintenance module and a user management module are integrated, so that potential accident risks of the special equipment can be effectively identified and prevented, and the safety of the special equipment can be improved; the risk of accident occurrence is reduced, a scientific basis can be provided for continuous improvement of equipment by optimizing potential analysis, and the efficiency and effect of accident prevention are improved, so that the safety management level of the whole industry is improved.

The invention belongs to the technical field of motor control, and provides a blower and a blower accident prevention method, and the method comprises the steps: firstly, obtaining the motion data, collected by a sensor of the blower in a current time range, of the blower at all moments by the blower; then based on the motion data, motion change data of the blower is obtained; when the motion change data meets the preset range threshold condition, it is indicated that the blower is about to have an accident, and finally the blower is controlled according to the preset mode, so that the blower can take certain protection measures before the blower officially has an accident falling or collision event, and the safety of the blower is improved. Therefore, faults of the motor of the blower caused by falling or collision of the blower can be avoided.

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.