793 results about "Accident risk" patented technology

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.

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 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 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 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, 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 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 provides an off-site full-range radiation risk evaluation method and system under a severe accident of a nuclear power plant, and the method comprises the steps: S1, building a radioactive accident risk evaluation basic model architecture, adapting to a model demand, and building a corresponding basic database; s2, constructing radioactive smoke plume characteristics after the accident; s3, calculating accident site space gridding and smoke plume transmission discretization; s4, sampling meteorological conditions, and simulating smoke plume transmission; s5, calculating the radiation dose in the early stage of the grid domain, and carrying out risk assessment; s6, performing risk assessment on the health effect in the early stage of the grid domain; s7, calculating the medium-and-long-term stage radiation dose in the grid domain, and performing dose risk assessment; and S8, performing health effect risk assessment and accident consequence assessment in the medium-and-long-term stage. According to the method, the whole transportation process of the radioactive smoke plume under a serious accident is simulated, loss effects such as dry and wet settlement and radioactive decay of the smoke plume are considered, and the activity of the radionuclide deposited on the ground and retained in the air in the off-site space range is determined.

The invention provides a multi-source heterogeneous data fusion method and device. Equipment state data of a computer monitoring system, equipment ledger and historical operation and maintenance data of a production management system and environmental parameter data of a meteorological system are accessed in real time through a distributed data bus; performing standardization processing on the three types of heterogeneous data based on a unified spatio-temporal index model, and establishing a dynamic association model including a time dimension, a space dimension, an equipment dimension and a working condition dimension; performing causal relationship analysis on the cross-system data by using the dynamic association model to generate a multi-dimensional data association result related to the current accident; and according to a data association result, dynamically generating an emergency disposal scheme and pushing the emergency disposal scheme to a remote centralized control center so as to support real-time decision and disposal operation. According to the method, second-level fusion and intelligent correlation analysis of cross-system heterogeneous data can be realized, the diagnosis efficiency and emergency disposal accuracy of hydroelectric accidents are remarkably improved, the response time is effectively shortened, and the risk of repeated accidents is reduced.

The invention belongs to the field of safety of energy storage batteries, and particularly relates to a method and a device for detecting lithium precipitation of a lithium iron phosphate battery. The method comprises the following steps: acquiring a central wavelength signal of a grating sensor pre-embedded in a negative electrode of a lithium iron phosphate battery; determining the strain of the negative electrode of the lithium iron phosphate battery based on the central wavelength signal; and comparing the strain of the negative electrode of the lithium iron phosphate battery with a strain threshold value to obtain a lithium precipitation detection result of the lithium iron phosphate battery. The strain condition of the negative electrode of the lithium iron phosphate battery can be detected on line through the pre-embedded optical fiber, and the lithium precipitation condition of the lithium iron phosphate battery can be reflected based on the strain of the negative electrode of the lithium iron phosphate battery; and early warning can be carried out in advance, safety risks caused by battery short circuit and thermal runaway are avoided in time, and the accident risk of the lithium iron phosphate battery cabin is reduced.

The invention specifically relates to a remote safety officer allocation system and method for a pilotless automobile, and relates to the technical field of pilotless automobiles, and the system comprises a vehicle state monitoring and data collection module; a communication and network guarantee module; a security officer resource management module; the task scheduling and dynamic coordination module is configured to perform intelligent task allocation based on vehicle requirements and safety officer states to realize optimal resource allocation; and an emergency response and decision support module. According to the invention, the vehicle state monitoring and data acquisition module fuses data of multi-source sensors such as a laser radar and a camera and high-precision positioning information, realizes no-dead-corner environment perception, and improves the integrity of environment information compared with a traditional scheme; after the edge computing node preprocesses the original data, the cloud data transmission quantity is reduced, satellite double-channel communication redundancy is combined, it is ensured that the emergency instruction transmission delay is reduced, and the response speed is increased compared with a single network scheme; and the accident risk caused by data delay or interruption is reduced.

The invention discloses a traffic operation risk real-time identification method based on a low-altitude unmanned aerial vehicle, relates to the technical field of unmanned aerial vehicle traffic monitoring, and solves the technical problem that unmanned aerial vehicle resources are wasted or the coverage of data acquisition is insufficient due to unreasonable arrangement of unmanned aerial vehicles in the prior art, so that the comprehensive identification efficiency of traffic risks is reduced. Comprising the steps that road section basic data of all road sections in a to-be-monitored area are acquired, and the road section basic data comprise road section types, historical accident data and historical traffic flow data; setting a road section danger score of each road section based on the road section basic data; setting an unmanned aerial vehicle distribution scheme based on the road section risk score; acquiring monitoring video data of the unmanned aerial vehicle, and analyzing based on the monitoring video data to obtain an accident risk score of each traffic subject of the road section; giving an alarm based on the accident risk score; by performing targeted analysis on different road sections and setting the number of the unmanned aerial vehicles used for monitoring the different road sections, the comprehensive efficiency of traffic risk identification is improved.

The invention provides a new energy second-hand vehicle battery safety monitoring and early warning method and system, and the method comprises the steps: S1, obtaining battery operation data, collecting voltage, current and temperature parameters in real time through a sensor, generating a battery state data set in combination with historical charging and discharging records, and obtaining the preliminary judgment of the aging degree; s3, abnormal features are extracted from the potential overcharge and overdischarge risks, the abnormal features are matched with a pre-established fault mode library through an early warning system, whether safety accident potential exists or not is judged, and a risk early warning signal is generated; and S9, extracting a long-term operation trend from the safety state feedback data, analyzing the relevance between the battery aging and the safety accident through a trend prediction algorithm, judging a prevention and control measure adjustment direction, and generating an optimization suggestion data set. According to the invention, the safety monitoring and early warning of the whole life cycle of the battery are realized, and the safety accident risk is effectively reduced.

The invention relates to the technical field of unmanned aerial vehicle nests, and particularly discloses a traffic accident intelligent restoration system and method based on an unmanned aerial vehicle nest, and the system is provided with a response priority division module, a selected unmanned aerial vehicle nest matching module, an optimal route planning module, an accident vehicle intelligent search module and an accident scene restoration map generation module. The problems that a traditional processing mode needs long road closing time and affects traffic capacity, vehicles, personnel, scattered objects and the like exist in accident sites and are prone to being damaged, and the traditional mode mostly aims at local two-dimensional scenes, accident information is difficult to collect in an omnibearing mode, accurate identification and responsibility division requirements cannot be met, traffic jam is prone to being caused, and the like are solved. And the secondary accident risk is also increased.

The invention discloses a real-time monitoring and early warning system for an abnormal driving state. The real-time monitoring and early warning system is applied to safe driving of road vehicles. The real-time monitoring and early warning system is provided with a vehicle-mounted data acquisition module, a cloud / edge calculation and analysis module and a non-positive driving behavior early warning and intervention module. The invention further discloses a monitoring and early warning method of the system. By adopting the technical scheme, multi-modal data fusion is adopted, so that the control is more accurate; angry or aggressive driving states are captured through real-time identification and behavior data; early warning intervention is performed to remind a driver or a traffic control department in time, so that accident risks are reduced; the control threshold value is dynamically adjusted, and false alarms are reduced; and through data analysis, a behavior pattern research basis is provided for traffic management.

The invention discloses a monitoring video transmission method for downhole fracture acidification field command, particularly relates to the field of video coding adaptive adjustment, and is used for solving the problem of weak intelligent response capability of monitoring video transmission under the existing remote well site condition. The method comprises the following steps: acquiring the position of a monitoring device and establishing a binding relationship between a monitoring video channel and an operation well; a fracturing accident identification model is trained based on the historical pumping pressure curve, and the accident risk of the operation well is monitored in real time; selecting a corresponding video channel and initiating link bandwidth tilt scheduling for the identified accident risk well; acquiring a video frame sequence of the channel, and judging a video coding guide type corresponding to the accident type through inter-frame motion analysis; a matched code reconstruction instruction is generated according to the guiding type and is issued to a corresponding channel; and uploading the reconstructed video to a remote command end, and recovering the bandwidth configuration and coding parameters of the video channel after the accident risk is removed.

The invention provides an abnormal target detection method and apparatus, an edge computing device and a storage medium. The method comprises the steps of constructing a comprehensive data set based on multi-source image data; training a target detection pre-training model through the comprehensive data set to obtain a target detection training model; and inputting field video monitoring data into the target detection training model, and outputting abnormal information when an abnormal target is detected. According to the technical scheme provided by the embodiment of the invention, the abnormal target is intelligently detected through the edge detection equipment provided with the target detection training model, the real-time monitoring of the working site can be realized without the need of the inspection personnel to arrive at the gas working site for inspection, the real-time performance is high, the labor cost is saved, the accident risk in the inspection work is reduced, and the working efficiency is improved. And the abnormal target detection efficiency is improved.

The invention discloses an underground packer coupling integrity diagnosis system and a digital twinborn life prediction method. The system comprises a data acquisition layer, an edge computing layer, a cloud analysis layer with a built-in L4-level digital twin system and an application layer which are connected in sequence. According to the method, vibration, corrosion and other parameters are collected through a sensor, after preprocessing of an edge layer, a cloud calls a three-field coupling dynamic model, multi-field parameters, the failure probability and the remaining life are calculated in combination with an LSTM and Bayesian fusion algorithm, and an application layer outputs diagnosis results and maintenance suggestions and forms a closed loop. The problems that traditional methods depend on experience, multi-field coupling is difficult to quantify and the digital twinborn level is low are solved, the maintenance cost can be reduced, the well control accident risk is reduced, and a key technical support is provided for predictive maintenance of an oil and gas well.

The invention belongs to the field of expressway traffic data analysis, and relates to an ETC system-based expressway traffic data analysis method, which comprises the following steps of: acquiring original vehicle passing data streams which are acquired by all ETC gantries in a preset time period of a monitored road section and comprise vehicle identifiers, passing timestamps, portal position codes and vehicle type codes; analyzing the original vehicle passing data flow to generate vehicle speed individual dynamic features and vehicle type group static features, constructing a multi-dimensional risk data set with road segment identifiers and time windows as indexes, and inputting the multi-dimensional risk data set into a pre-trained deep learning model, and outputting an accident potential probability value result, executing early warning state judgment based on a probability gradient judgment rule, generating an accident risk thermodynamic diagram of the monitored road section according to the early warning state, and feeding back the accident risk thermodynamic diagram, thereby effectively solving the problems of insufficient prospective prediction and low regional accident potential assessment precision in the prior art. And the timeliness and the accuracy of traffic risk assessment are obviously improved.

The invention relates to the technical field of control system data processing, in particular to a ship intelligent workshop crane hoisting path planning method, which comprises the following steps: collecting crane coordinates, obstacle distribution, load swing angle and material weight, and inputting a physical constraint model to generate a structured environment state data set; initializing wolf pack algorithm parameters based on the structured environment state data set, calculating a compressed detection wolf walking step length according to an obstacle distance, generating a global task queue by an industrial cloud platform according to a dynamic parameter set, and outputting an execution state data set by a crane end; counting emergency braking times from an execution state data set to optimize a walk step length function, comparing task time to optimize a wolf investigation proportion, analyzing historical swing data to establish a step length and swing mapping library, and optimizing a parameter set to feed back and update a physical constraint model. According to the invention, cooperation of environment perception, dynamic path planning and closed-loop verification is realized, and accident risks and production delay in a ship workshop narrow space dynamic obstacle scene are reduced.

The invention discloses an infusion detection method, belongs to the technical field of medical monitoring, and particularly relates to an infusion detection method and system based on computer vision, and the infusion detection method comprises a data acquisition module, a data processing module and a data display module, transfusion target single-frame images and continuous single-frame images are collected at fixed time intervals through an industrial camera, and then material, capacity information, residual dose information and drip chamber speed information detection is conducted on the infusion target single-frame images and the continuous single-frame images; and finally, uploading the material, capacity information, residual medicine amount information and dripping bucket speed information of the infusion target to a medical care terminal, the image of the infusion bag / bottle can be captured through the camera, the liquid level position is automatically identified in combination with a target detection algorithm, and the residual medicine amount is accurately calculated through an image segmentation technology. Abnormal conditions can be fed back in real time, and medical accident risks are reduced.

The invention relates to the technical field of intelligent ship driving, and discloses an intelligent ship driving assistance system based on multi-source information fusion and AI decision, which comprises a multi-source information acquisition module, an information data processing module, an information fusion processing module, an AI decision calculation module and a man-machine interaction module, according to the ship intelligent driving assistance system based on multi-source information fusion and AI decision making, perception blind areas of scenes with low visibility and the like are eliminated through a multi-source information collection and vision enhancement technology, and a target is accurately recognized; through data processing and three-level fusion, multi-source information is efficiently integrated, and the manual analysis pressure is reduced; the AI decision calculation module provides accurate guarantee for berthing, stranding, collision avoidance and other scenes, and accident risks are reduced; the system can also optimize the route and speed in combination with data such as weather, reduce energy consumption, shorten the navigation time, take into account the safety and economy of ship driving, and greatly improve the intelligent level of shipping.

The invention discloses a road traffic safety intelligent early warning method and device based on environment characteristics, and relates to the technical field of municipal roads, and the early warning steps are as follows: collecting road environment data based on a sensor, including road surface condition data and motor vehicle traffic flow data; performing size judgment on the traffic flow data, establishing a congestion threshold value, dividing and marking road sections with traffic flows greater than the congestion threshold value, and uploading the road sections to an unmanned aerial vehicle system; the unmanned aerial vehicle carries a meteorological sensor, constructs a meteorological monitoring network, carries out flight path planning based on the divided and marked congested road sections, collects meteorological environment characteristic data of the congested road sections, and carries out traffic safety early warning on related vehicles. According to the method, in the aspects of traffic management and safety assurance, the congested road sections are marked to assist a traffic department in planning and dispersion in advance, the road passing efficiency is improved, early warning is conducted in time to remind vehicle owners of prevention, accident risks are reduced, personnel and property losses are reduced, and long-term effective operation is achieved. And the road traffic safety management level is comprehensively improved.

The invention discloses a multimodal transport cargo transportation state monitoring system based on a standardized intelligent tray group. The system comprises a modular tray group composed of a main control tray and at least one functional tray; the main control tray is integrated with an edge calculator, an RFID reader-writer, a temperature and humidity sensor and a power supply module; the functional tray comprises a strain sensor and an acceleration sensor; the tray groups are rigidly connected through electromagnetic lock catches, and a voltage sensor is arranged to monitor the connection state; the tray group transmits data through a mixed communication channel; LoRa wireless communication is adopted by default, and magnetic attraction wired connection is switched when the packet loss rate is greater than 1%; after being preprocessed by the edge calculator, the data are encrypted by the Beidou system and uploaded to the background server; the background server executes overload and unbalanced load analysis, vibration grade evaluation, environmental risk early warning and lock catch state monitoring; and triggering a user side to give an alarm when an exception is detected. The safety monitoring problem of goods transportation is effectively solved, the transportation safety is improved, and the accident risk is reduced.

The invention provides an open-pit mine multi-step space-time cooperative efficient mining method, and relates to the technical field of open-pit mine mining, and the method comprises the following four steps: constructing a dynamic three-dimensional geologic model based on multi-source data, carrying out multi-step space-time cooperative pre-planning, carrying out multi-step dynamic scheduling and cross-operation cooperation, and carrying out multi-parameter coupling grading early warning. According to the method, real-time mapping of a physical mine and a virtual model is realized by integrating total element data of geology, equipment and environment through a digital twin platform, further, real-time problems such as equipment conflicts and road closing are solved through pre-planning and optimizing step space layout and time sequence and dynamic scheduling, efficient operation of main mining equipment can be guaranteed, and the real-time performance of the main mining equipment is improved. Then a multi-parameter coupling grading early warning mechanism is introduced, key indexes such as slope stability and dust concentration are monitored in real time, the accident risk is reduced through grading response, and multiple targets of improving the working efficiency, reducing the operation cost, controlling the safety risk and flexibly utilizing resources can be achieved.

The invention provides a multi-source information fusion system and method for intelligent scheduling of road rescue. The method comprises the following steps: determining an optimal rescue path corresponding to each rescue service point; for an optimal rescue path corresponding to any rescue service point, acquiring a rescue return path corresponding to the optimal rescue path, and determining path traffic influence characteristics corresponding to each path section in the rescue return path according to rescue vehicle information of the rescue service point and path driving information of the rescue return path; acquiring a rescue service type, performing traffic influence prediction according to the rescue service type and the path traffic influence characteristics, and determining a secondary accident rate of a rescue return path; and according to the secondary accident rate, rescue service intelligent scheduling is carried out on the to-be-rescued vehicle, secondary accident rate prediction can be carried out according to the traffic influence condition of the rescue return path, intelligent scheduling of different rescue service points is carried out, and the secondary accident risk in the rescue process is reduced.