98 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 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 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 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 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.

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 relates to a 5G AeroMACS beam pointing method based on multi-time multi-spectrogram target perception, belongs to the technical field of airport scene communication, and aims to update dynamic information, including distance, speed and angle, of a vehicle in real time by using high-precision measurement capability provided by a radar integrated with a 5G-AeroMACS base station, and ensure the stability and rapidity of communication service at the same time. On the premise that the deployment position of the base station and the coverage range of the radar signal are guaranteed, the base station can be ensured to effectively serve the vehicle and provide required positioning and speed information, so that the intelligent level of the whole airport scene traffic system is improved, and a new solution is provided for traffic flow management and 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.

To provide an overhang prediction system and an overhang accident prevention system in which an occurrence of overhang can be predicted at a timing before a vehicle starts turning.SOLUTION: An overhang prediction unit 17 of a large vehicle on-board system 1 mounted on a large vehicle includes: when an approach to an intersection for turning right is detected, checking whether it is possible to enter a road into which the vehicle is turning right depending on the state of a traffic signal using a monitoring sensor 12; if the entry is possible, predicting a driving trajectory when turning right and an overhang amount toward left when a self vehicle turns right; if the maximum value of the overhang amount is larger than a threshold level, predicting a period of right-turn operation according to the presence or absence of an oncoming vehicle etc.; and calculating an overhang period, during which the overhang amount is a hindrance to driving of another vehicle, from the period of right-turn operation.SELECTED DRAWING: Figure 1

The present invention discloses a method for visually identifying drowning by integrating infrared temperature difference features, comprising the following steps: S1, image acquisition and feature extraction; S2, edge feature map extraction; S3, shape feature map extraction; S4, color feature map extraction; S5, feature map fusion and segmentation; S6, drowning person instance identification and alarm triggering. The present invention combines infrared thermal imaging with visible light images and utilizes multi-feature fusion technology to improve the accuracy and real-time performance of drowning detection, effectively identifying drowning persons in low-light environments in particular; the system rapidly transmits drowning person information through intelligent alarms and precise positioning, shortening rescue response time and improving rescue efficiency; in addition, it uses advanced neuron impulse response networks and convolutional neural networks to more accurately extract features of drowning persons in complex backgrounds, making it applicable to a variety of water environments and providing innovative solutions for drowning accident prevention.

To provide an accident prevention system that minimizes occurrence of an accident even when the posture of an operator is disturbed.SOLUTION: An accident prevention system S is a system used in a forklift and comprises a posture specifying unit 303, a speed setting unit 301, and a change command unit 310. The posture specifying unit 303 specifies whether or not the posture of an operator is disturbed. The speed setting unit 301 sets, on the basis of the posture of the operator, a first operation speed that is an operation speed of the forklift at a normal time, or a second operation speed that is an operation speed when the posture of the operator is disturbed and is slower than the first operation speed. The change command unit 310 sets the operation speed of the forklift to the second operation speed when the posture of the operator is disturbed.SELECTED DRAWING: Figure 4

The invention relates to the technical field of intelligent management and control scheme design for accidents of labor dispatchers, in particular to an intelligent management and control method and system for accidents of labor dispatchers. The method comprises the following steps: fusing multi-party data (vehicle speed specification degree and aging pressure) through secure multi-party calculation and a block chain; dynamically calculating a personnel risk value by using the interpretable model and generating a cause report; when the risk value exceeds a threshold value or events such as tumble triggering / helmet-free wearing are triggered, multi-link response is automatically executed according to a grading rule: positioning coordinates are sent to 120 / 110, the nearest hospital rescue is scheduled, insurance case reporting is started, and an alarm is pushed to a distribution platform and a dispatch unit; and dynamically displaying regional risk distribution through a GIS thermodynamic diagram, and automatically locking a track / video / risk value data link to generate an evidence storage packet when an accident occurs. According to the invention, the risk collaborative management and control of the whole distribution link is realized, and the accident prevention and disposal efficiency is remarkably improved.

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.