500 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 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 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 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 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 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 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 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 belongs to the technical field of artificial intelligence and power system monitoring, particularly relates to a transformer fault intelligent diagnosis and life prediction system based on deep learning, and aims to solve the problems that transformer fault recognition is lagged, early defects are difficult to capture, life prediction precision is low and multi-source data fusion is insufficient. The system synchronously collects data of oil chromatography, partial discharge, vibration and the like through multi-mode sensing, feature extraction and attention mechanism fusion are performed through a deep neural network, and fine-grained diagnosis of 12 types of faults is realized; tracking a fault evolution trend in combination with a dynamic Bayesian network, constructing a probabilistic life prediction model based on a physical mechanism and a gating cycle unit, and outputting residual life distribution; cloud edge cooperation and online incremental learning are supported, the model adaptability and prediction precision are improved, and the power grid accident risk is reduced.

The invention provides an urban low-lying road section emergency supervision Internet of Things large model system and method and a storage medium, and belongs to the field of road monitoring. The emergency supervision management platform is configured to obtain driving data of a low-lying area through the emergency supervision object platform; determining ponding data based on the driving data; determining a traffic risk based on the ponding data; in response to the traffic risk greater than a first threshold, determining a drainage parameter; determining scheduling parameters based on the drainage parameters; and sending a scheduling packet containing the drainage parameters to the unmanned vehicle cluster through the heterogeneous communication network, controlling the unmanned vehicle carrying the drainage device to run to the low-lying area, and controlling the water pumping power of the drainage device to be adjusted to the water pumping power of the drainage parameters. The method can effectively reduce the traffic obstruction and accident risk caused by water accumulation, and improves the emergency response efficiency of urban low-lying road sections during heavy rainfall.

The invention relates to the technical field of intelligent traffic control, and discloses a variable speed limiting method considering accident risks in extreme weather. The method comprises the steps of collecting multi-source real-time traffic flow and meteorological data of a target road in extreme weather to form an original data flow; the data stream is injected into an accident risk feature extraction pipeline, the pipeline screens and transforms the data layer by layer according to a risk mode extracted by a historical extreme weather accident case library, and a dynamic accident risk portrait formed by risk quantitative indexes with timestamps is generated; and finally, driving the generation of a variable speed limit control strategy based on the portrait. According to the method, the risk characteristics in the real-time data are deeply mined by using the historical accident mode, and the quantifiable risk portrait is constructed, so that the limitation that a traditional method depends on a fixed threshold value is overcome, the accuracy and the foresight of risk assessment are improved, a more scientific and adaptive speed limiting instruction is generated, and the road safety level in extreme weather is improved.

The invention discloses a design parameter collaborative management method and device for a BIM platform, and relates to the technical field related to BIM, and the method comprises the steps: importing a forward design scheme of a thermal power plant into the BIM platform, and generating a design model; performing multi-dimensional accident tracing according to the design model to obtain an accident association map; obtaining a design adjustment space based on the demand and the map adjustment scheme; performing multi-dimensional risk prediction based on the BIM platform and the risk model to generate a risk prediction map; optimizing the adjustment space based on the prediction map and the risk constraint; through disturbance injection optimization, a final forward design optimization result is obtained, so that the technical problems of low design efficiency and difficulty in guaranteeing design quality caused by dispersed design parameters and insufficient collaboration of the thermal power plant in the forward design process of the traditional thermal power plant are solved, and multi-dimensional accident fitting tracing, risk prediction, optimization and other means are realized. The collaborative management level and design quality of the forward design parameters of the thermal power plant are improved, and the accident risk is reduced.

The invention relates to the technical field of construction management, in particular to a BIM (Building Information Modeling)-based engineering supervision management system and method.According to the BIM-based engineering supervision management system and method, spatial positions and states of construction machinery, vehicles and personnel are captured in real time through a sensor network deployed on site, and a core dynamic spatial conflict calculation module is used for calculating the safety of the construction machinery, the vehicles and the personnel according to a preset safety rule base; and millisecond-level conflict detection and prediction are carried out on the real-time dynamic safety envelope lines of the moving objects. And after potential risks are found, the system sends early warning of different levels from prompting to forced intervention to related personnel, so that a complete management closed loop from active perception and intelligent prediction to co-processing is constructed. Therefore, through pre-virtual simulation and in-process real-time intervention, the risk of major safety accidents such as collision and overturning caused by space conflicts is greatly reduced, and life safety is guaranteed. And by optimizing the construction space and logistics planning, shutdown and rework caused by conflicts are reduced, and the construction efficiency and the project management refinement level are improved.

The invention belongs to the technical field of vehicle risk assessment, and discloses a vehicle accident risk prediction method and device, a storage medium and computer equipment. The method comprises the following steps: acquiring operation parameters of a plurality of vehicles, including load data, operation road type, gradient data, vehicle speed, driving behavior data, night driving time proportion, active safety configuration data, operation weather data, accident frequency and fault frequency; determining a plurality of risk assessment features; taking the risk assessment features as a training set, training a random forest model, and taking the random forest model as an accident risk prediction model when the performance of the random forest model reaches a preset requirement; and inputting the risk assessment features of the to-be-predicted vehicle into the accident risk prediction model, obtaining a survival function output by multiple trees of the accident risk prediction model, and determining the accident occurrence probability of the to-be-predicted vehicle within a preset duration according to the survival function. And the accident risk of the vehicle can be accurately predicted by using the multi-dimensional operation data of the vehicle.

The invention provides a vehicle distance control method and system for agglomerate fog weather, and relates to the technical field of vehicle control, and the method comprises the steps: carrying out the target detection of a target road when the target road is in the agglomerate fog weather, and determining a target vehicle; performing multi-mode monitoring on the target vehicle to obtain a vehicle monitoring sequence; performing safety risk prediction on the target vehicle according to the vehicle monitoring sequence to obtain a vehicle safety risk coefficient; generating a vehicle distance control strategy based on the vehicle safety risk coefficient; and performing control optimization management on the target vehicle according to the vehicle distance control strategy. The technical problem that in the prior art, the safety distance of the vehicle is not accurately controlled in the agglomerate fog weather, and consequently the accident risk in the vehicle driving process is high can be solved, and the technical effects that the occurrence rate of safety accidents such as rear-end collision is reduced, and the road driving safety and traffic stability in the agglomerate fog environment are improved are achieved.

The invention relates to the technical field of vehicle-road cooperative communication, and discloses a vehicle-road cooperative communication emergency response management method and system, and the method comprises the steps: recognizing micro-friction abnormal events through monitoring vehicle operation data, carrying out the time-space aggregation of the micro-friction abnormal events, and deducing the abnormal risk of regional road surface friction force. And generating high-priority early warning information and an emergency resource scheduling strategy according to the risk. The method effectively solves the problems of emergency response lagging and resource mismatching caused by difficulty in identifying the deep relevance of a large amount of fragmented and low-severity-level alarm information when the existing vehicle-road cooperation system faces the large amount of fragmented and low-severity-level alarm information. According to the invention, potential large-scale accident risks caused by hidden environmental factors such as black ice can be timely and accurately identified, so that high-priority early warning can be sent out at the early stage of an accident, targeted emergency resource scheduling is started, the expansion of the accident scale and the aggravation of traffic congestion are effectively avoided, and the accident safety is improved. And the traffic safety and the emergency response efficiency are obviously improved.

The invention provides an intelligent driving safety control method based on multi-source data fusion, and the method comprises the steps: collecting multi-source data such as high-precision map data, vehicle-mounted sensor data and Internet of Vehicles data, carrying out the fusion analysis of the multi-source data to generate fusion environment information, calculating a dynamic risk level based on the information, and carrying out the calculation of the dynamic risk level. And executing a grading response control strategy according to the risk grade. Through multi-source data fusion, the sensing limitation of a single sensor under severe weather or complex road conditions can be overcome, and the accuracy and robustness of environment sensing are improved; the dynamic risk level calculation integrates the lane occupancy rate, visibility and other multi-dimensional factors, and realizes the accurate assessment of real-time risks; the grading response control strategy is gradually upgraded from early warning to active intervention according to the risk level, the problem of response lag or overreaction of a traditional scheme is avoided, and therefore the driving safety and the system adaptability are remarkably improved. The method is suitable for intelligent driving vehicles with different configurations, and the accident risk can be effectively reduced.

The invention discloses a flight safety warning method based on pilot operation intention recognition, which comprises the following steps: S1, collecting EEG data of a subject in each stage of simulated flight through an electroencephalogram signal data collection device; s2, carrying out preprocessing operation on the collected EEG data; s3, extracting and fusing the electroencephalogram signals by constructing a convolutional gradient attention network, and mapping fused features to an operation intention of a pilot; s4, calculating safety risk factors according to the deviation between the flight state and the preset flight envelope, the pilot operation intention and the external environment factors; s5, comparing the risk factor with a preset risk threshold, and dividing the risk into a high level, a middle level and a low level; and S6, the cockpit touch alarm system triggers multi-mode alarm in the cockpit according to the risk level, and adjusts the flight state in real time. The flight accident risk caused by human factors can be reduced, and the aviation safety is improved.

The utility model relates to a kind of sintering conveying devices with online automatic cleaning bulk material, apply in sintering material conveying technical field, solve the problem that large block, ore or iron block possibly occurs plugging accident on belt conveyor into chute;Including support frame and conveyer belt, further including bearing frame, detection component, cleaning component and lower chute, detection component is used to detect whether there is bulk material on conveying belt;Cleaning component is used to clean bulk material on conveyer belt to the both sides of the conveyer belt width direction;Lower chute is provided with two and oppositely arranged at the both sides of the cleaning component, and lower chute is used to discharge bulk material cleaned by the cleaning component;With the effect of realizing online automatic cleaning bulk material, it can solve the problem that bulk material enters into sintering chute with conveyer belt, blocks sintering chute discharge port, avoids the risk of production shutdown accident, and improves the effect of unmanned automatic control ability.

The invention relates to the technical field of battery testing, in particular to a battery testing system and a battery testing method. The battery testing system comprises a water tank, the top of the water tank is provided with an opening, and fire extinguishing water is stored in the water tank; the tray is detachably mounted at the opening of the water tank, and the tray is used for bearing a battery to be tested; the battery state monitoring assembly is used for monitoring the working state of the to-be-tested battery and driving the battery testing system to be switched between a normal testing state and a high-risk state according to the working state of the to-be-tested battery; and when the battery testing system is in a high-risk state, the tray is separated from the opening of the water tank and falls into the water tank together with the battery to be tested. The battery thermal runaway risk is recognized in real time through the battery state monitoring assembly, the tray and the to-be-detected battery are automatically immersed in the fire extinguishing water in the high-risk stage, early-stage accurate early warning and automatic fire extinguishing of a fire are achieved, and extinguishing delay and secondary accident risks of a traditional post-event processing mode are effectively avoided.

The utility model discloses a locking mechanism for a meat grinder, which comprises a machine head, a machine body, a locking component and a handle, and the machine head is provided with a first inserting part; the machine body is provided with a second inserting part, and the machine head is installed on the machine body through inserting connection of the first inserting part and the second inserting part. One of the first inserting part and the second inserting part is an inserting block, and the other one of the first inserting part and the second inserting part is an inserting groove; the locking assembly comprises a first locking piece arranged on the machine head and a second locking piece arranged on the machine body. When the handle is pulled, the first locking piece and the second locking piece can be driven to be mutually locked or unlocked. Therefore, the first locking piece and the second locking piece can be driven to be mutually locked or unlocked by pulling the handle, the operation is simple and convenient, the stable connection between the machine head and the machine body is ensured, the problem that the machine head and the machine body are loosened or separated during working is avoided, the use safety and stability of the meat grinder are improved, the accident risk is reduced, and the safety of the meat grinder is improved. And the use safety of the user is ensured.

The invention discloses a mountainous area expressway secondary accident active prevention and control method and system, and the method comprises the steps: carrying out the global monitoring of a mountainous area expressway continuous slope road section, extracting traffic scene features, and identifying an accident scene; in combination with the current traffic flow state, the road static characteristics, the environmental conditions and the vehicle behaviors, estimating the initial accident occurrence risk based on an accident risk estimation model of risk coupling; the method comprises the following steps: predicting whether a secondary accident of the continuous slope bending of the expressway occurs or not based on a Bayesian optimization random forest model, if so, predicting the occurrence time and place of the secondary accident, and then performing active prevention and control through a warning means and a forced control means. According to the invention, through air-ground integrated monitoring and intelligent control, rapid detection of a primary traffic accident and accurate assessment and active prevention control of a secondary accident risk are realized, so that the traffic accident frequency and damage degree of a high-risk road section are greatly reduced.

The invention provides an elevator safety circuit fault positioning method and system based on distributed current sensing, and relates to the technical field of elevator safety, and the method comprises the steps: arranging a current sensor at a safety node to collect signals, executing wavelet transformation on a current oscillogram, and extracting features; the node dependence directed graph is constructed based on the fault probability distribution, the fault propagation path is determined, the fault positioning report is generated and sent to the maintenance terminal, the accuracy and efficiency of fault positioning of the elevator safety circuit are improved, the maintenance time is shortened, and the elevator safety accident risk is reduced.

The invention relates to the technical field of unmanned aerial vehicle propellers, and discloses an unmanned aerial vehicle engine propeller which comprises an engine body and a propeller body assembled on the engine body. The output end of the engine body is connected with a rotating shaft, and a shaft rod is fixed to the rotating shaft. The propeller body comprises a rotor shaft and blades fixed to the periphery of the rotor shaft, the rotor shaft is arranged on the shaft rod in a sleeving mode and abuts against the rotating shaft, and the shaft rod is sleeved with a first nut pressing the rotor shaft in a threaded mode. Two loading channels are symmetrically formed in the rotor shaft, loading cylinders are installed in the two loading channels, and elastic pressing pieces extending into the loading cylinders are loaded on the loading cylinders. Through multiple limiting structures of the elastic pressing piece, the elastic inserting piece, the electromagnetic block and the magnet frame, in cooperation with magnetic connection of the electromagnetic rod and the magnet block and auxiliary limiting and real-time monitoring of the jacking sensing piece and the pressure sensor, the propeller body is effectively prevented from loosening and falling off, the safety and stability of the unmanned aerial vehicle under the complex working condition are improved, and the safety of the unmanned aerial vehicle is improved. And the maintenance cost and the flight accident risk are reduced.

This traffic safety support system includes: a warning device equipped with an alarm and mounted on a light vehicle; and a traffic safety support device. The traffic safety support system is configured as follows. The traffic safety support device can access big data including traffic volume information associated with a position on a map. The warning device is capable of directly or indirectly communicating with the traffic safety support device, and issues a warning from the alarm, according to the position information of the light vehicle on which said warning device is mounted, said position information being transmitted to the traffic safety support device, and to the risk level obtained from the big data. Thus, using a warning device that can be mounted on a light vehicle such as a bicycle makes it possible not only to notify the driver of said light vehicle of the risk that an accident that may occur nearby, but also to reduce the risk of an accident involving said light vehicle.

The utility model relates to the technical field of building construction, in particular to height-adjustable hanging basket equipment which comprises a hanging device, the hanging device comprises a cross beam structure and a support structure arranged below the cross beam structure, and a height adjusting component is arranged between the support structure and the cross beam structure. The height of the beam structure on the support structure can be adjusted through a height adjusting component, the hanging basket equipment further comprises a reinforcing component, the reinforcing component is provided with a limiting hole, the height adjusting component comprises a front adjusting rod assembly, and the reinforcing component is fixed to a fixing structure of a building. The outer side of the front adjusting rod assembly is sleeved with the limiting hole, the connecting mode allows the front adjusting rod assembly to move up and down in the vertical direction in a limited mode, meanwhile, the front adjusting rod assembly is firmly connected with a building through the reinforcing component, swing or displacement caused by high-altitude wind pressure or misoperation can be effectively resisted through the design, the accident risk is reduced, and the service life of the building is prolonged. And the overall stability of the hanging basket in the using process is obviously enhanced.

The present application relates to the technical field of driving safety, and in particular to a driver behavior evaluation method and system based on driving scenarios, which includes breaking through the limitations of a single data source by integrating multi-dimensional heterogeneous data such as weather, roads, facilities, etc., improving the comprehensiveness and accuracy of accident risk prediction; through dynamic calibration of driving scenario labels, the model can adapt to complex and variable actual driving environments, enhancing the generalization ability; through the division of traffic states and feature quantization, the subjectivity of artificial threshold setting is reduced, which helps to discover the implicit features ignored by traditional methods; through optimization of the scene-specific driver behavior evaluation model, driver behavior optimization suggestions under multiple constraints are generated, reducing traffic flow fluctuations caused by excessive speed limits, while reducing the probability of accidents in high-risk scenarios, achieving the coordinated optimization of safety and efficiency.

The utility model relates to a scrapped motor vehicle disassembling platform. A platform body; each group of moving assembly comprises a group of horizontal moving mechanism and a group of vertical moving mechanism; a tool plate is fixedly arranged on the front side or the rear side of the portion, between the two moving assemblies, of the platform body, and a plurality of mounting grooves which are formed at intervals and used for mounting an electric wrench, a gas spray gun and a water spray gun are fixedly formed in the top of the tool plate. A plurality of hanging rods used for hanging hydraulic shears, hammers and awls are fixedly arranged at the bottom of the tool plate. By integrating the horizontal moving mechanism and the vertical moving mechanism, the platform can flexibly adjust the height and position of the scrapped motor vehicle, so that the disassembly operation is more convenient and efficient, workers do not need to frequently bend down or climb, the labor intensity is reduced, and meanwhile, the safety accident risk caused by misoperation is reduced.

In one aspect, an example method includes: (a) collecting sensor data from one or more vehicles operating within a geographic region and environmental data for the geographic region; (b) generating an accident risk model using one or more machine learning models; (c) receiving a request for navigating between a first geographic position and a second geographic position; (d) identifying attributes of one or more routes between the first geographic position and the second geographic position and through the geographic region, wherein the identified attributes of the one or more routes are based on at least the generated accident risk model; and (e) transmitting an instruction that causes a mobile computing device to display a graphical indication of: (i) the one or more routes; and (ii) the identified attributes of the one or more routes.