986 results about "Accident risk" patented technology

The invention discloses a perception intelligent driving complex traffic scene dynamic risk prediction method, and relates to the technical field of risk prediction, and the method comprises the steps: obtaining the multi-source traffic dynamic data of a target region in real time; performing space-time alignment processing on the multi-source traffic dynamic data; inputting the space-time coupling feature matrix into a pre-trained depth space-time prediction model to generate a risk thermodynamic map; calculating a dynamic risk index of each traffic sub-region, and generating a risk level distribution sequence; and triggering a self-adaptive early warning response mechanism according to the risk level distribution sequence, and dynamically adjusting operation parameters of the variable information sign and the traffic signal controller. The technical problems of frequent traffic congestion and high accident risk caused by inaccurate traffic risk prediction and difficulty in dynamic adjustment according to the real-time traffic condition in the prior art are solved, and the technical effects of accurately predicting the traffic risk and improving the safety and traffic efficiency in a complex traffic scene are achieved.

The invention discloses an intelligent road alarm early warning patrol rescue linkage system and a method thereof, and relates to the technical field of road safety. An intelligent hectometer board, an unmanned aerial vehicle cluster and a mobile terminal are used for collecting accident alarm information and road multi-source data; performing accident target detection classification and abnormal condition classification based on the preprocessed data by utilizing edge calculation, and sending to the cloud; importing real-time road multi-source data into a digital twinborn model to obtain traffic situation spatial-temporal characteristics, and combining accident target detection classification and abnormal condition classification analysis to obtain an accident risk thermodynamic diagram for emergency decision making; and according to the accident risk thermodynamic diagram, generating alarm early warning information of different accident types and different risk levels, according to an emergency decision, generating a linkage mechanism of different departments, and sending the linkage mechanism to different department systems to realize automation of a disposal process. According to the invention, a full-chain linkage system of perception-analysis-early warning-linkage is constructed, rapid response and rescue are realized, the management cost is reduced, and the traffic efficiency is optimized.

The invention discloses a built environment and traffic accident causal effect analysis method based on bi-robust learning, and belongs to the field of intelligent traffic safety and traffic accident risk assessment. By integrating satellite remote sensing, interest point distribution and traffic accident data, a spatial-temporal feature enhanced environment index system is constructed; realizing cross-regional data collaborative analysis by adopting federal learning, and guaranteeing data compliance in combination with differential privacy encryption; a generative adversarial network is introduced to generate a high-fidelity anti-fact scene, and a dual robust learning framework is coupled to accurately quantify a condition average processing effect of a built environment on accident risks. According to the scheme, a technical tool with both theoretical preciseness and practical feasibility is provided for urban traffic planning and safety management, and a scientific basis is provided for optimizing and building environment design and reducing traffic accident risks through a multi-source data fusion and privacy cooperation mechanism; and the blank in the aspects of causal robustness, insufficient anti-fact generation precision, privacy protection and multi-source data fusion in the prior art is filled.

The invention provides a traffic accident prediction method fusing multi-source features and a self-adaptive structure, and the method comprises the steps: extracting spatial features such as a geographic position, traffic flow and interest point distribution, combining the time features such as traffic flow change trend, periodicity and anomaly detection, and the external features such as weather and signal lamp density, and carrying out the prediction of a traffic accident. Node multi-dimensional feature representation is comprehensively constructed, a static adjacency matrix and a dynamic adjacency matrix are respectively constructed, geographic distance and node feature similarity information are fused, a self-adaptive adjacency matrix is generated by utilizing learnable parameters, and road network structure changes are dynamically described. Finally, traffic accidents are modeled and predicted based on a graph convolutional neural network, and accurate identification and early warning of accident risks in a complex traffic environment are realized. According to the method, the modeling capability of the prediction model for nonlinear and strong space-time correlation characteristics of traffic data is effectively improved, the accuracy and robustness of traffic accident prediction are remarkably improved, and the method has wide engineering application prospects and popularization value.

The invention discloses an intelligent safety inspection system for a construction site, and relates to the technical field of construction inspection, multiple data sources of the construction site are obtained through a data acquisition module, and a data fusion and processing module dynamically optimizes heterogeneous data through time synchronization, format standardization and noise filtering in combination with a self-adaptive weighted fusion algorithm, so that the construction site safety is improved. The intelligent analysis and early warning module evaluates the data difference degree based on machine learning, adopts clustering analysis to mine an abnormal mode for high-difference data, carries out safety risk identification on low-difference data, and dynamically adjusts an early warning strategy. The terminal interaction module pushes the inspection result and the early warning information to the mobile terminal APP, the monitoring center display screen and the voice broadcast device, so that a manager can remotely check the construction condition and quickly make a decision, the intelligent level of construction site safety inspection can be remarkably improved, efficient and accurate potential safety hazard recognition and dynamic early warning are achieved, and the safety inspection efficiency is improved. And the safety accident risk is effectively reduced.

The invention discloses an expressway dynamic speed limit method based on multi-source data, and belongs to the technical field of expressway speed limit, and the method comprises the steps: obtaining accident data, constructing an accident risk degree prediction model, and generating an accident risk degree trend chart; acquiring real-time traffic flow data, constructing a traffic flow prediction model, and generating a traffic flow tendency chart; obtaining meteorological data, and generating a local meteorological state distribution diagram; performing space alignment and data fusion on the traffic flow tendency chart and the local meteorological state distribution diagram to generate a road section comprehensive risk index; performing time alignment on the accident risk degree trend chart and the comprehensive risk degree index of the road section to generate a real-time risk degree score of the current road section; smoothly adjusting the speed limit value by adopting a linear interpolation algorithm on the basis of the real-time risk degree score in combination with a preset speed limit adjustment rule; and comparing the current vehicle speed with the adjusted speed limit value, and early warning over-speed. According to the invention, accurate speed limit control based on multi-dimensional data is realized, and the road safety is effectively improved.

The invention discloses a tunnel hydrogen energy vehicle leakage and explosion risk prediction and automatic ventilation emergency disposal method, and belongs to the technical field of hydrogen leakage accident risk prediction and emergency disposal. According to the method, the potential leakage point of each component is determined by obtaining hydrogen-related component information of the hydrogen fuel cell vehicle. An accident tree is used to identify a root cause that may cause hydrogen leakage in the system, and then different accident scenarios are defined. And according to the leakage scenes of different hydrogen-related parts, determining the leakage frequency and the complete fracture frequency, and determining the disaster category. And finally, according to the leakage probability, the ignition probability and the consequences of different accident situations, calculating the comprehensive probability of disaster occurrence. And in combination with a hydrogen concentration sensor array arranged in the tunnel, the longitudinal ventilation air speed and the transverse exhaust air rate are automatically analyzed and adjusted, and the optimal economical, safe and efficient combined ventilation array is obtained according to risk indexes. The method can effectively predict the disaster-pregnant risk of the hydrogen storage vehicle in different environments, provides an economic and safe emergency disposal scheme, and can provide a theoretical basis for the safety technology development of a hydrogen storage system.

The invention relates to the technical field of traffic control, and provides a regional green wave coordination control method and system for traffic safety early warning. The method comprises the following steps: traversing a target traffic area to collect traffic data, and constructing a regional dynamic traffic characteristic matrix; performing green wave coordination analysis based on the matrix, and generating a sub-region division scheme; traffic flow characteristics of the subareas are extracted, and green wave coordination control parameters are calculated; formulating and executing an initial control scheme according to parameters, and collecting feedback data; and compensating the optimization parameters according to a feedback result to obtain a final regional coordination control scheme. The technical problem that efficiency and safety are difficult to cooperate due to the fact that traditional green wave control only statically optimizes traffic efficiency and cannot dynamically respond to traffic safety risks is solved, deep coupling of safety early warning and green wave control is achieved through real-time traffic data feedback and dynamic parameter compensation, and the traffic safety is improved. And the traffic efficiency is ensured, and the accident risk is obviously reduced.

The invention discloses an intelligent identification and grading early warning method based on mine production abnormity, and a system and equipment thereof. The method comprises the following steps: obtaining space-time evolution data, earth surface deformation data and environment monitoring data of rock mass stress in a target area; performing abnormal mode detection on the data by using a space-time convolutional network to obtain accident risk probability distribution data; using an ultra wide band positioning system to track the movement track of the person entering the target area in real time to obtain the movement track data of the person; and constructing a three-dimensional early warning index system in combination with the accident risk probability distribution data in the target area and a dynamic early warning mechanism, performing dynamic early warning grading on different positions in the target area by using the three-dimensional early warning index system, and generating a grading response strategy matched with the personnel activity trajectory data. According to the invention, the problems of incapability of tracking a personnel movement track in real time and low timeliness of mine production abnormity early warning are solved.

The invention belongs to the field of automobile evaluation, and provides an automobile live risk evaluation system and method based on a large model, and the method comprises the steps: receiving voice input information of an evaluator in a vehicle on-site inspection process; extracting component state information of the target vehicle based on the structured check item text; acquiring historical maintenance record data of the target vehicle, and performing association fusion on the historical maintenance record data and the component state information; based on a large language model, performing joint reasoning on a currently input inspection label and the historical maintenance record, constructing a causal chain related to an accident risk, and generating at least one subsequent to-be-inspected item; generating voice guide content and feeding back the voice guide content to the evaluator so as to prompt the evaluator to continue to check the target item of the target vehicle; gradually perfecting the risk check path of the target vehicle until the risk chain closed loop is completed; and calling an estimation formula to calculate a residual estimation value of the target vehicle, and outputting a structured risk assessment report.

The invention relates to a method (50) and system (10) for assessing situations presenting an accidentology risk to a vehicle driving on a road network in a determined environment, the method comprising: - monitoring (51) the progress of the vehicle on the road network and in the environment into which the road network is integrated and through which the vehicle is moving; - detecting (52) a risky situation presenting a plurality of accidentology risk factors (13); -warning (53) the driver when a risky situation is recognized; - analysing (54) data recorded before detection (52) of the risky situation, in order to determine an origin of the risky situation; and - producing (55) driving advice depending on determined contextual parameters (16), the driving advice identifying the detected risky situation and recommending a driving behaviour in order to reduce the risks run by the vehicle when confronting the situation. The invention also relates to a vehicle in which the system (10) is embedded, and executing the method (50).

A system for accidentology-based measuring of accident risk-indexing score values for a motor vehicle to be tested providing a measured accident probability value for an occurrence of an accident event having a physical impact to the tested motor vehicle. The system includes a driving scenario module, a test setting module, and a scoring module. The driving scenario module is configured for determining various driving scenarios for the motor vehicle by defining a set of measurable scenario characteristics, which include at least one ADAS variable. The test setting module is configured for determining a test setting in form of a multi-dimensional test matrix, which includes testing protocols for each of the measurable scenario characteristics for providing measured values of measurable scenario characteristics of the various driving scenarios. The scoring module is configured for generating the risk-indexing score by receiving the multi-dimensional test result signal and a historical data information signal.

The invention provides a dam life dynamic probability prediction method and device. According to the dam life dynamic probability prediction method provided by the embodiment of the invention, the dynamic probability prediction of the residual life of the dam can be realized, the early warning timeliness and the scientificity of the maintenance decision are improved, and the full-life-cycle maintenance cost and the accident risk are effectively reduced.

The invention provides an engineering safety prediction method and system based on the Internet of Things. An Internet of Things sensor and a camera are used for collecting multi-source data of an engineering structure, an environment, equipment and the like, and after cleaning and feature extraction, a multi-modal fusion prediction model combining a convolutional neural network and a long-short-term memory network is constructed. Through a dynamic weighted fusion method, image and time sequence feature fusion weights are adjusted according to engineering stages and environmental conditions, and an attention mechanism is used to reinforce a fusion effect. After a data set training model is divided, real-time data can be analyzed to predict security risks. Once the risk is found, the system can give an early warning in time and display response suggestions on the management platform. The method makes up the defects of a traditional monitoring mode, improves the accuracy and timeliness of prediction, provides an efficient solution for engineering safety management, can effectively reduce the accident risk, and has important application value in various engineering fields.

The invention discloses a road traffic information monitoring system and method, and the method comprises the steps: deploying a video monitor, a sensor, an unmanned plane and a satellite device to collect real-time road traffic data, and protecting the data safety through encryption transmission and multilayer identity authentication; the flow is monitored in real time by combining image recognition and sensor data, congestion is analyzed, traffic violation is recognized, and an alarm is given; future flow is predicted through machine learning and big data analysis, and the signal lamp period is intelligently adjusted; through combination of real-time and historical data, accident risk points are identified and early warning is carried out, and early warning is automatically issued and adjustment and control are carried out for severe weather. The AI technology automatically identifies accidents and notifies a management center, dispatches emergency resources, optimizes signal lamps and lane traffic, and dynamically adjusts surrounding flow; according to the road traffic information monitoring system and method, an automatic emergency processing system is established, accidents are automatically detected and alarms are triggered under the conditions of traffic accidents and emergencies, and emergency resources are quickly dispatched.

A computer generates an accident risk definition model to estimate a probability of hazard occurrence as an accident risk by inputting first in-vehicle sensor data collected in the past and hazard occurrence data having information on hazard occurrence from the first in-vehicle sensor data preset therein, generates accident risk estimation data by inputting second in-vehicle sensor data collected in the past to the accident risk definition model and estimating the probability of the hazard occurrence, generates an accident risk prediction model to predict the accident risk after a predetermined time by inputting first biological index data corresponding to the second in-vehicle sensor data and the accident risk estimation data, calculates second biological index data from second biological sensor data by acquiring the second biological sensor data of a driver, and predicts the accident risk after the predetermined time by inputting second biological index data to the accident risk prediction model.

The invention relates to the technical field of chemical safety and emergency management, and discloses an emergency team informatization management system and method.The emergency team informatization management method comprises the steps that a multi-level Bayesian network model is constructed, and chemical accident evolution chain event node condition dependence is shown; establishing an uncertainty propagation analysis model, and quantifying an accident risk transmission rule; multi-dimensional node influence evaluation is carried out, and structural importance, propagation characteristics and intervention feasibility are comprehensively considered; constructing an intervention scheme by combining resource constraints according to an optimal intervention point identified by evaluation; through intelligent simulation evaluation, the intervention effect is optimized, and an emergency team iteration strategy is assisted; according to the method, systematic and dynamic analysis of complex chemical accidents is realized, key intervention nodes of'taking small as big 'can be found under the condition of limited resources, and the emergency disposal efficiency and the resource utilization rate are improved.

The invention provides a chemical ship cabin intelligent coating leakage positioning and early warning control method and system, and relates to the technical field of intelligent monitoring, and the method comprises the steps: collecting data through a multi-class sensor array, constructing a parameter distribution map, extracting features through a convolutional neural network, carrying out the time sequence analysis through a long and short term memory network, and carrying out the early warning. The variational auto-encoder constructs a leakage feature fingerprint database, collects data in real time, then carries out feature extraction and fusion processing, matches the data with the fingerprint database, determines a leakage position and a leakage rate, divides a monitoring area, and executes prevention and control. The leakage monitoring precision and the response speed can be improved, and the chemical accident risk is reduced.

The invention relates to the technical field of hoisting construction dynamic safety assessment, and particularly discloses a hoisting construction dynamic safety assessment method, which comprises the following steps of: acquiring operation parameters of a hoisting system in real time through a multi-modal sensor array, and generating a hoisting dynamic parameter set; the lifting dynamic parameter set comprises pressure distribution data of each lifting point, lifting appliance attitude angle data and load space position data; based on the hoisting dynamic parameter set, force system dynamic balance analysis is executed, and the force system unbalance degree of the crane is obtained through analysis; and when the force system unbalance degree of the crane is larger than a set threshold value, an unbalance active correction mechanism is triggered, and unbalance correction is conducted on the crane through adjustment of the electro-hydraulic proportional valve. And the safety and the efficiency of hoisting operation can be obviously improved. The automatic unbalance correction mechanism not only reduces the accident risk, but also improves the operation flexibility and response speed, and provides powerful technical support for modern hoisting operation.

The invention discloses a method and a system for identifying traffic conflicts in a road interleaving area, particularly relates to the field of traffic management, is used for solving the problems of low identification precision and early warning delay of the traffic conflicts in the interleaving area, and is used for accurately capturing risk events and filtering non-intentional behavior interference based on a composite trigger mechanism of turn light activation and transverse displacement rate; a behavior feature vector is matched with a conflict type template library to realize self-adaptive interception of an asymmetric time window, and risk evolution cycles of scenes such as sharp cut-in and slow lane change are completely covered; introducing a dynamic window expansion mechanism driven by a conflict energy gradient to ensure the whole-process capture of a risk accumulation stage; multi-scale spatio-temporal features are extracted in combination with a convolutional network to generate a thermodynamic diagram, and refined positioning of risk spatio-temporal distribution is achieved; an early warning signal grading mechanism and a closed loop feedback optimization system are established, the system adaptability is continuously improved, real-time and accurate decision support is provided for an intelligent traffic management system, the secondary accident risk is effectively restrained, and the road network traffic efficiency is optimized.

The invention relates to a hydrogen energy system risk factor identification and accident severity prediction method fusing XGBoost-SHAP and LSTM, and belongs to the technical field of hydrogen energy safety. The method comprises the following steps: marking a plurality of hydrogen energy accident cause factors and hydrogen energy accident consequence grades of a hydrogen energy accident report data set; an XGBoost algorithm is adopted to construct a risk factor importance evaluation model, and a core risk factor is obtained in combination with an SHAP interpretability analysis method; an LSTM hydrogen energy accident risk severity prediction model is established, and rolling prediction of a hydrogen energy system risk level is realized according to core risk factor time sequence data; and judging whether the predicted risk level of the hydrogen energy system exceeds a threshold value or not, and if so, triggering graded early warning and outputting a key cause factor interpretation report. According to the method, the subjective deviation of traditional qualitative analysis is remarkably reduced, the capability of capturing the risk factor time sequence dependency relationship is enhanced, and technical support is provided for real-time risk early warning.

The invention discloses a multi-sensor fusion unmanned vehicle detection method suitable for a complex environment. The method comprises the following steps: obtaining road surface data through a laser radar and an infrared thermal imaging camera; detecting rain and snow weather conditions in real time, and calculating weather environment factors based on rainfall data and snowfall data; dynamically adjusting data weight coefficients of the laser radar and the infrared thermal imaging camera based on the weather environment factors; using a computer vision algorithm to identify an obstacle area, and extracting the distance and height of the obstacle according to the corresponding data weight coefficient; and the obstacle type is analyzed, the minimum safe distance or the minimum torque between the unmanned vehicle and the obstacle is calculated, and a driving decision is provided for the unmanned vehicle. According to the invention, the self-adaptive capability and safety of the unmanned vehicle in a complex environment are improved, the accident risk caused by sudden obstacles is effectively reduced through an intelligent early warning and intervention mechanism, and a solid safety foundation is laid for the wide application of an unmanned driving technology.

The invention is applicable to the technical field of petroleum drilling engineering, and provides a drilling overflow early recognition method based on deep reinforcement learning, which comprises the following steps of: firstly, collecting and preprocessing data, and then constructing and optimizing a drilling overflow early recognition model based on a near-end strategy optimization (PPO) algorithm, and finally, collecting data in real time in drilling operation, preprocessing the data, inputting the data into the model, analyzing the change of the characteristic quantity according to a PPO algorithm, judging whether an overflow sign exists according to an overflow drilling process flow, and if so, early warning according to a Reward function strategy. According to the method, a traditional threshold value method and machine learning are combined, a deep reinforcement learning algorithm is used for optimizing drilling parameter threshold values, characteristic quantity is monitored in real time to capture changes before overflow, historical data are self-learned to recognize an overflow mode, and dependence on field operators is reduced; meanwhile, model decision logic is displayed, understanding and trust of engineers are enhanced, more reliable overflow monitoring guarantee is provided for drilling operation, accident risks are reduced, and wellbore safety is guaranteed.

The invention discloses an elastic speed limiting and vehicle distance cooperative regulation and control method and system in an expressway agglomerate fog scene, particularly relates to the technical field of intelligent traffic control, and is used for solving the problem that a secondary accident risk is induced by neglecting a traffic flow phase change characteristic in an existing dynamic speed limiting method. The method comprises the following steps: collecting traffic density, vehicle spacing and visibility space-time distribution data in real time; performing weighted amplification on the traffic density based on the consistency of the visibility gradient direction and the traffic flow direction to generate corrected traffic density; calculating a phase change margin index representing the flow state stability by combining the corrected traffic density and the optical safety deviation degree; when the phase change margin is lower than a threshold value, dynamically constraining the maximum value of the speed-limiting decreasing amplitude according to the agglomerate fog moving trend, and keeping the positive correlation between the maximum value and the phase change margin; generating a target speed limit value based on the constraint decreasing amplitude value, and calculating a safe vehicle distance instruction by fusing the visibility data; and issuing an instruction to the vehicles in the agglomerate fog dynamic influence area to realize collaborative optimization of traffic system stability and single vehicle safety.

The invention discloses an intelligent alarm method for driving intervention after a disaster, and the method comprises the steps: collecting a visible light image and an infrared thermal imaging image, and scanning the coordinate data of a three-dimensional space at the same time; inputting the visible light image into a convolutional neural network model to perform road obstacle type identification, and generating three-dimensional contour data of the obstacle in combination with the three-dimensional space coordinate data; when the height of the obstacle exceeds a height threshold value and the distance between the obstacle and the vehicle is smaller than a distance threshold value, uploading a feature data set containing the GPS coordinates, the three-dimensional contour data and the obstacle type to a cloud server through a cellular network; the cloud server matches the feature data set with a pre-stored disaster area digital elevation model based on the three-dimensional space coordinate data, and calculates the ratio of the projection area of the obstacle on the current lane cross section to the lane width; and when the ratio exceeds a ratio threshold value, generating an alarm instruction. According to the invention, the accident risk caused by misjudgment of obstacles or improper path planning after disasters can be effectively reduced.

The invention, which relates to the technical field of traffic safety, discloses an intelligent AI highway traffic safety early warning system comprising the following components: a data acquisition module, a data analysis and prediction module, an optimal route planning module, a scene simulation module and a coping strategy generation module. According to the invention, through integrating data prediction and scene simulation functions, the early warning capability of highway traffic safety is significantly improved, and the data acquisition module widely collects historical traffic data, real-time traffic flow data and meteorological condition data; the data analysis and prediction module performs accurate analysis and prediction on the multi-source data by using a deep learning algorithm, and can output congestion probabilities and accident risk probabilities of a specific road section in different time periods in real time, and the scene simulation module performs scene simulation according to hypothetical conditions of a traffic management department in combination with current traffic data and prediction results. A traffic flow simulation model is constructed for scene simulation, and the simulation result truly reflects the influence of different traffic events on the road traffic capacity.

The invention discloses a processing method and device for sensing the position of an elevator, and relates to the technical field of elevator position sensing, which comprises the following steps: running in a full stroke under no-load and rated load conditions respectively, recording pulse signal data of a magnetic induction sensor when the elevator stops on each floor and the maximum value of a pulse signal when the elevator passes through each floor, establishing a dual-mode reference database; elevator regular no-load running full-stroke detection is carried out, and elevator real-time stopping deviation is analyzed; when the elevator is powered off or breaks down, counting of the pulse counters corresponding to the current top sensor and the current bottom sensor is obtained, elevator power-off or fault stopping floors are determined, elevator power-off or fault stopping deviation is analyzed, elevator power-off or fault actual stopping deviation is obtained after elevator real-time stopping deviation is subtracted, and the position of the elevator is determined. And the elevator trapped floor and the position of the elevator trapped floor in the hoistway can be quickly positioned, the response time is shortened, blind operation is avoided, and the secondary accident risk is reduced.

The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to an intelligent aerial 3D printing and spraying system and method based on an unmanned aerial vehicle cluster, and the system comprises a spatial three-dimensional laser ranging scanning modeling module for generating a real-size scene model, a three-dimensional positioning network module, and a three-dimensional positioning network composed of a plurality of laser radar arrays. Wherein the laser radar or the wireless positioning beacon array is arranged on a working plane to construct a three-dimensional space coordinate reference system; the printing module is integrated on the unmanned aerial vehicle, compared with traditional high-altitude operation, the construction efficiency is improved, the constraint of tedious procedures such as scaffold building and manual high-altitude operation is thoroughly eliminated, the unmanned aerial vehicle cluster can be rapidly deployed in a short time, and operation tasks such as large-area single-color or full-color spraying, repairing, modeling and printing are efficiently completed with high precision; the project construction period is greatly shortened, the comprehensive construction cost is reduced, the safety construction level is improved, the engineering income is improved, and the construction accident risk is reduced.

The invention relates to the technical field of automatic warehousing, in particular to a warehousing method and system for intelligent logistics, and the method comprises the following steps: collecting warehouse-in and warehouse-out time, storage duration, access times and storage area data of goods, recording the moving quantity of goods in unit time in a storage area, counting the change amplitude of the access times, and storing the goods in the storage area; and adjusting the layout mode between the goods shelves to obtain a goods flow monitoring result. According to the method, by adjusting the space between the goods shelves and the arrangement sequence of the goods shelves, the driving distance of carrying equipment is avoided, the running route is optimized, path intersection points are avoided, warehouse operation is more efficient, meanwhile, the accident risk caused by path intersection is reduced, the storage direction and the space are adjusted by observing and evaluating the form of the goods, and the storage efficiency is improved. The utilization rate of the storage space is increased, the goods are protected against damage caused by uneven pressure distribution, and the overall operation efficiency and goods safety of a warehouse are remarkably improved.

The embodiment of the invention relates to the technical field of vehicles, and discloses a vehicle accident early warning method and device, electronic equipment and a storage medium, the method comprises the following steps: obtaining running state data in a vehicle based on a preset duration, the running state data being determined based on vehicle data obtained by at least one bus source in the vehicle; determining target time sequence characteristic data in the operation state data, wherein the target time sequence characteristic data indicates a change trend, a periodic rule and a sudden change condition of the operation state data in a time dimension and a dependency relationship among the data; and inputting the target time sequence characteristic data into a risk assessment model to obtain an assessment result of the accident risk of the vehicle after the preset duration. By applying the technical scheme of the invention, more accurate vehicle early warning can be realized, and the safety driving requirement of the vehicle can be better met.