1387 results about "Road traffic safety" patented technology

The invention belongs to the technical field of vehicle trajectory prediction, and discloses a multi-scale interactive perception highway vehicle trajectory prediction method, which comprises the following steps: jointly modeling short-term burst features and long-term evolution trends through a convolutional neural network and a bidirectional gating cycle unit, and introducing a time sequence attention mechanism to improve the perception ability for key time slices; in combination with a dynamic graph attention mechanism including physical edge features such as relative position, relative speed and relative acceleration, a vehicle interaction relationship is updated in real time so as to improve spatial modeling precision and interpretability; in the decoding stage, the guide vector and the semantic information of the lane are fused, so that the predicted trajectory conforms to the geometric structure of the road in space and keeps smooth and continuous in time. According to the method, the robustness and adaptability of the model in the sparse adjacent vehicle environment of the expressway can be improved while the prediction precision is ensured, a more stable and reliable trajectory prediction result is provided for an intelligent traffic system, and powerful technical support is provided for traffic safety management and operation scheduling of the expressway.

The invention discloses a road safety early warning method and system based on a mixed precision quantification visual large model, and the method comprises the steps: collecting road traffic safety videos and pictures, and carrying out the preprocessing, data enhancement and marking, thereby forming a diversified data set; a pre-trained visual large model is selected as a teacher model, after fine tuning, output layer and middle layer knowledge is extracted, key features are weighted, and meanwhile, a lightweight neural network is taken as a student model, same input is received, and prediction and middle feature maps are output. And inputting data into the two models and the student model to carry out mixing precision quantification forward propagation, constructing a total loss function containing tasks, knowledge distillation and quantification learning loss, and updating parameters through back propagation. And after training is completed, exporting a quantitative model, and deploying the quantitative model to an edge computing platform to realize safety early warning. The lightweight model can realize rapid reasoning on edge equipment such as a vehicle-mounted road side, and the problem that performance and efficiency are difficult to consider in a traditional model compression method is solved.

The invention relates to the field of intelligent traffic safety, in particular to a hazardous chemical substance transportation path dynamic risk prevention and control system and method based on space-time fusion, and provides a method for calculating a multi-dimensional risk index matrix through a risk index quantification module and combining a space-time environment sensitivity prediction matrix generated by an environment sensitivity analysis module. According to a risk spreading probability cloud picture of the risk spreading prediction module, accurate evaluation of the dynamic risk of the road section is realized; the system establishes a collaborative risk factor assessment model, adjusts a safety threshold according to historical accident data, and generates a space-time road network risk scoring matrix; the risk prevention and control decision module determines an early warning level, selects prevention and control measures, optimizes a transportation path, formulates an emergency response plan for a high-risk road section, and outputs an intelligent decision instruction set; according to the system, comprehensive quantification of hazardous chemical substance characteristics is realized, and the comprehensiveness of risk assessment and the intelligence of prevention and control decision making are improved.

The invention relates to the technical field of traffic safety, in particular to a beforehand prevention and control strategy generation method for traffic safety risks of a highway network in a mountainous area. Comprising the following steps: risk diagnosis and data preparation: collecting road basic information, historical traffic flow data, meteorological data and accident-prone point data of a mountainous area expressway network; constructing a risk assessment model: selecting road alignment, traffic flow, weather and environmental toughness indexes, determining index weights by adopting an improved analytic hierarchy process, constructing the risk assessment model through a fuzzy comprehensive evaluation method, and calculating the traffic safety risk level of each road section; a prevention and control strategy is generated; and strategy verification and iteration. According to the method, the total factor evaluation model is constructed by integrating the road network topological structure, the traffic flow characteristics and the meteorological sensitive section data, so that the problem of one-sided risk identification caused by single factor analysis in the prior art is solved, and systematic description of the mountainous area highway composite risk scene is realized.

The invention relates to the technical field of safety early warning, and particularly discloses a vehicle driving safety early warning method fused with meteorological data, which comprises the following steps: acquiring real-time multi-modal data of meteorological, traffic flow and vehicle state of a target road area, performing exception handling, space-time alignment and standardization to form a standardized data sequence, then constructing a multi-modal fusion tensor, and finally performing data fusion on the multi-modal fusion tensor. Extracting each modal dynamic mode, fusing cross-modal features, outputting a joint feature vector, inputting the joint feature vector into a safety risk prediction model to calculate a dynamic safety risk value, combining digital twin simulation risk conduction, generating graded early warning according to a preset threshold value, and performing management and control through vehicle-road collaborative network publishing and high-risk scene linkage traffic facilities. And finally, collecting feedback data evaluation effects, associating decision data to generate hash records, recording the hash records in the block chain, and carrying out federated learning incremental training optimization model based on feedback. According to the invention, accurate early warning under multi-factor coupling can be realized, data privacy is guaranteed, closed-loop optimization is formed, and road traffic safety and stability are improved.

The invention belongs to the technical field of traffic safety monitoring technologies, and discloses a defogging method and equipment based on infrared light and visible light image fusion. The method comprises the following steps: image preprocessing: denoising and enhancing a foggy image; inputting the preprocessed picture into a defogging model based on infrared light and visible light image fusion to obtain a defogged image; the defogging model based on infrared light and visible light image fusion carries out the following processing on an input picture: processing an infrared light image, enhancing the penetration effect of the infrared light image in a fog environment, and highlighting the contour information of a target; and processing the visible light image, and recovering the color and texture details of the visible light image in the fog environment. According to the method, the definition and the visual effect of the foggy day image can be remarkably improved, the target is prominent, the color is natural, the contrast ratio is effectively improved, the detail information of the image is reserved, rapid defogging processing is realized, and the accuracy of fog concentration estimation is enhanced.

The invention relates to a railway bridge post-earthquake traffic safety probability evaluation method and device, which are applied to the technical field of traffic safety, and the method comprises the steps: obtaining an earthquake-induced damage value set of each component through a probability distribution function of different material parameters of a railway track-bridge system; the method comprises the following steps: acquiring a mapping relation between the earthquake-induced damage of a key component and track irregularity through a balance differential equation of a bridge and railway track structural mechanical model, and acquiring earthquake-induced track random irregularity samples of different components based on an earthquake-induced damage value set of each component and the mapping relation between the earthquake-induced damage of the key component and track irregularity; constructing a power spectrum of the track irregularity caused by vibration of different components; establishing a rapid prediction model of the driving performance indexes on the axle after the earthquake through the earthquake-induced track irregularity sample and the coupling dynamic response result; through a Monte Carlo method, based on the earthquake-induced track irregularity power spectrum and the rapid prediction model, the overrun probability and the confidence interval of the driving safety on the axle after the earthquake are rapidly and accurately obtained.

The invention, which relates to the technical field of intelligent traffic and illumination control, discloses a street lamp vehicle-road cooperative control system comprising a data acquisition module, a data processing module, a control execution module and a decision management module. The data acquisition module is used for acquiring traffic signal lamp phase, vehicle position and speed, street lamp working state and environment illumination intensity data; according to the invention, through a cross-system collaborative algorithm, the traffic flow direction is predicted in combination with the phase of the traffic signal lamp, the brightness of the street lamp illumination area is adjusted in advance, the energy distribution according to needs is realized, the energy consumption is effectively reduced, and through a vehicle-lamp-cloud three-level decision-making mechanism, the computing power priority is dynamically distributed according to the traffic condition. Traffic signal lamp control and vehicle driving path planning are optimized, the road passing efficiency is improved, traffic congestion is relieved, by pushing the lighting optimization path to the vehicle, the vehicle is driven under the proper lighting condition, the emergency brake risk is reduced, and the road traffic safety level is remarkably improved.

The invention discloses a road defect detection method based on an improved RT-DETR-R18 model. A backbone network adopts a CSPNet architecture. According to the invention, innovative improvement is carried out on an original C2f module, and Bottleneck in the original C2f module is replaced by DynamicIncMixerBlock to form a C2fDCMB module. The DynamicIncMixerBlock is characterized in that a DynamicIncMixerBlock is fused with a DynamicInceptionMixer component and a ConvolutionalGLU component, and the DynamicIncMixerBlock and the ConvolutionalGLU component are fused with each other. A dynamic Inception deep convolution structure is adopted by the DynamicInception Mixer, and features of different scales and directions are adaptively captured through dynamic kernel weight distribution; according to the method, AIFI (intra-scale feature interaction) in a high-efficiency hybrid encoder is improved, a module is combined with an EfficentAdditiveAttach and a feedforward network structure to form TransformerEncoder LayerEfficentAdditiveAttach, a RepC3 module is replaced by a RetBlockC3 module in a cross-scale feature fusion module (CCFM) through a multi-head attention mechanism and a nonlinear activation function, the RetBlockC3 is improved based on the RepC3, RetBlock and RelPos2d are introduced, and the RetBlock and RelPos2d are introduced into the RetBlockC3 module to form a multi-scale feature fusion module. According to the method, the accuracy, recall rate and detection speed of road defect detection are obviously superior to those of a traditional detection model, and a more accurate and efficient technical solution is provided for maintenance of road infrastructures and traffic safety guarantee.

The invention discloses an automatic driving safety key simulation scene generation method based on adversarial generation and co-evolution. The method comprises the following steps: receiving a basic traffic scene described by a natural language, generating an antagonistic element scene containing security threats by using a large language model in combination with a traffic safety knowledge base, and analyzing the antagonistic element scene into an executable scene script; constructing a multi-agent confrontation collaboration diagram based on the meta-scene, and recognizing a key background vehicle through a cross-timing attention mechanism in combination with a time mask and time decay mechanism; and performing disturbance optimization on the key background vehicle trajectory to generate an automatic driving test scene. According to the method, a scientific and systematic solution with engineering operability is provided for safety verification of the automatic driving system when the automatic driving system faces real traffic challenges such as multi-source intervention and dynamic collaborative threat, and the method has wide adaptation capability and important industrial popularization value.

The invention provides a vehicle-road cloud collaborative mixed traffic flow optimization method, system and device, and relates to the technical field of intelligent traffic, and the method comprises the steps: extracting a mixed traffic flow feature set containing a road environment, a vehicle state and a driver behavior through obtaining and synchronously fusing the multi-source traffic data of a cloud end, a road end and a vehicle end; according to the method, the intention of a driver is predicted by using a double-layer LSTM model, a vehicle trajectory is predicted in combination with an RNN-LSTM model, confidence fusion analysis of the intention and the trajectory is performed on this basis, accurate prediction of traffic conflict events is realized, and global optimization of traffic flow is supported. According to the method, the intention recognition and trajectory prediction precision in a complex mixed traffic environment is effectively improved, the intelligent decision-making capability of a traffic management system is enhanced, and an efficient technical means is provided for relieving traffic congestion, improving the traffic efficiency and guaranteeing traffic safety.

A Level IV fuel-saving robot system for heavy-duty trucks (HDT) focuses on the minimization of actual fuel consumption (L / 100 km) for long-haul freight based on an electrical power split device and a mixed hybrid powertrain architecture. The Level IV fuel-saving robot has an L4 autonomous driving function within the Operational Design Domain (ODD) of expressways, operates in a “shadow mode” or “Disengagement Mode,” automatically generates a discrepancy report or detachment report, completes the “3R” (Real Vehicle, Real Road, Real Payload) batch validation for an L4 system on a billion mile scale quickly with high performance to cost ratio under the condition of ensuring the traffic safety of existing road users and reduces the total validation expense by more than 65% compared with the modern HDT with internal combustion engine equipped with the L4 system, promoting the early commercialization of the fuel-saving robot.

The invention aims to provide a speed anomaly detection and fraud identification method and system based on trajectory data, and belongs to the technical field of road traffic safety, the method realizes abnormal trajectory detection and fraud identification through multi-stage data processing: firstly, preprocessing original trajectory data, and removing invalid data; noise points are filtered based on a DBSCAN algorithm; track segments are divided according to vehicle speed changes; matching the moving track segment to a map road through a map matching algorithm; calculating an error ratio between the calculation speed and the equipment uploading speed, and identifying an abnormal track segment; and finally, analyzing the speed distribution of the abnormal track section, and identifying a counterfeit behavior. The system comprises a data preprocessing module, a trajectory noise filtering module, a trajectory division module, a map matching module, an abnormal speed detection module and a speed verification and forgery identification module. According to the method, the accuracy of speed anomaly detection is improved, the recognition capability of a hidden speed forgery behavior is enhanced, and the authenticity and credibility of trajectory data are improved.

The invention relates to an intelligent intersection AI collaborative decision-making method and system. The method comprises the following steps: acquiring multi-source traffic perception data, and performing feature fusion processing on the multi-source traffic perception data to generate fused feature information; according to the fused feature information, traffic participant behavior prediction is carried out by using a space-time diagram convolutional network to obtain predicted behavior data; based on the predicted behavior data, signal timing optimization processing is carried out through multi-agent reinforcement learning and collaborative decision, and an optimized signal timing scheme is generated; the optimized signal timing scheme is used for adjusting passing parameters of the intelligent intersection. By adopting the method, the traffic efficiency of the intersection can be obviously improved, the traffic jam is reduced, the traffic safety is enhanced, and the intelligent, dynamic and collaborative control of the traffic signal is realized.

The invention relates to an AI identification road traffic safety illegal behavior image evidence obtaining method and device based on an unmanned aircraft, equipment and a medium, and the method comprises the steps: calling a no-parking region identification model, so as to determine a no-parking region mask in a to-be-inspected region image, calling a potential violation vehicle detection model to identify potential violation vehicles in the no-parking area mask so as to determine bounding boxes of the potential violation vehicles and vehicle types corresponding to the bounding boxes; and calling a target tracking algorithm to perform target tracking on the potential violation vehicle, and determining the potential violation vehicle as a target violation vehicle when detecting that the intersection-to-union ratio between the bounding box and the no-parking area mask in the plurality of continuous image frames exceeds a preset threshold value and the potential violation vehicle is in a static state. And sending the violation evidence packet corresponding to the target violation vehicle to a traffic management system. According to the invention, the efficiency of the monitoring system is improved, and accurate and verifiable violation evidences are provided for the traffic management system.

The invention provides a vehicle safety early warning method and system for a highway network, relates to the field of traffic safety management and control, and solves the technical problem of inaccurate early warning in the prior art. The method comprises the following steps: acquiring environment data of a highway; dividing the expressway into a plurality of areas, and calculating an initial risk value of each area according to the environment data; calculating a risk propagation value of each region based on a dynamic risk propagation equation according to the initial risk value, and determining a risk level of each region according to the risk propagation value; and triggering early warning measures of each area according to the risk level. The method is used in the real-time safety management and control process of the highway network in severe weather, accurate prediction and graded early warning of risks can be realized through multi-dimensional data fusion and dynamic risk propagation modeling, and the safety and traffic efficiency of the highway network under complex meteorological conditions are improved.

The invention relates to the field of traffic control, in particular to an intelligent network connection inductive control platform for road traffic safety facilities. The traffic data acquisition module is used for acquiring a data flow of a traffic sensor and outputting traffic data with a traffic data source identifier through an identification technology; the traffic data processing module is used for obtaining standardized traffic parameters according to a traffic data source identifier adaptive analysis protocol; evaluating a data reliability index according to the index system; the situation fusion module is used for forming a vehicle driving track through a graph neural network, generating a global traffic situation map by using a data reliability index, and constructing a traffic network digital twinborn model; and the decision and control module is used for analyzing traffic states from the traffic network digital twin model, predicting traffic events and collision risks and generating traffic control instructions. According to the platform, through the equipment fingerprint and protocol reverse technology, an information island is broken, the comprehensiveness and high credibility of a data source are ensured, and the road traffic safety and passing efficiency are remarkably improved.

The invention discloses a hydrogen leakage accident holographic perception and disaster situation dynamic prediction system in a tunnel scene, and belongs to the technical field of hydrogen energy traffic safety and intelligent risk management. According to the system, multi-modal information such as environmental parameters, hydrogen concentration, heat source temperature, leakage acoustic characteristics and vehicle states is collected in real time through a multi-modal sensing unit; abnormal detection, time synchronization and space registration are carried out through the information fusion module to generate fusion data in a unified format; the hazard source analysis module locates a leakage source, estimates a leakage rate, locates an ignition source and extracts disaster characteristics based on the fused data; the disaster dynamic prediction module predicts spatio-temporal evolution of diffusion, combustion and explosion by using an agent model and calculates disaster levels; the feedback early warning unit synchronously outputs the prediction information to the tunnel monitoring platform and issues early warning information; and the data storage and self-optimization module uniformly stores historical data and optimizes the prediction model through incremental learning. The system realizes real-time monitoring, rapid evaluation and intelligent disposal of tunnel hydrogen leakage accidents.

The invention relates to the technical field of road traffic safety and intelligent networking, in particular to a networking communication system for road traffic safety facilities. According to the system, a local traffic digital twinborn model is constructed by edge computing nodes and maintained in a federated synchronization mode; traffic data generated by an edge computing node is packaged in a data container with an embedded hash chain and a digital signature encryption traceability log, so that the integrity, primitiveness and traceability of the data are ensured; the vehicle-mounted terminal submits zero-knowledge proof as a service voucher, and the edge computing node verifies and authorizes the service on the premise of not obtaining the original sensitive data. According to the invention, efficient traffic state synchronization is realized by constructing the federated digital twinborn model, the credibility and safety of data exchange are guaranteed by using the data container, the terminal privacy is protected by means of the zero-knowledge proof technology, and the communication efficiency, the data safety and the privacy protection level of the vehicle-road cooperation system are comprehensively improved.

The invention relates to the technical field of intelligent traffic and road safety monitoring, and discloses a 4D imaging millimeter wave radar-based slope deformation monitoring and identification method, which comprises the following steps of: acquiring a continuous point cloud; carrying out voxel modeling on the point cloud in a three-dimensional space; selecting three continuous frames of point clouds, and identifying mutant voxels by calculating the point number variation of each voxel in adjacent frames; performing spatial clustering on the mutant voxels to obtain an obstacle region; performing feature extraction on the obstacle area, wherein the extracted features comprise the height difference, the average reflection intensity and the centroid drift of the obstacle area; carrying out linear weighted fusion on the extracted features, and constructing a landslide mutation index; and judging whether an obstacle target exists in an obstacle area or not according to a preset condition, and performing risk grading identification according to the landslide sudden change index. According to the invention, all-weather, structured and high-reliability perception of major traffic potential safety hazards such as landslide / rockfall is realized, and a new basic capability is provided for a traffic safety system.

The invention discloses a traffic safety early warning method and device for a dangerous cargo transport vehicle, and the method comprises the steps: S1, monitoring the operation state of the dangerous cargo transport vehicle in real time, and collecting environment data in real time; and S2, based on the collected data, establishing a risk assessment model by using a convolutional neural network algorithm, analyzing the influence of extreme weather and complex terrains on the safety of the dangerous cargo transport vehicle, developing a risk assessment algorithm, and according to a risk assessment result, the risk assessment model established by using the convolutional neural network algorithm in the invention can be used for evaluating the safety of the dangerous cargo transport vehicle. And specific influences of extreme weather and complex terrains on vehicle safety can be deeply analyzed. The risk assessment model can learn various risk factors in a complex environment and comprehensively assess the risk factors, so that the accuracy and reliability of risk assessment are improved. The system can continuously monitor the change condition of the risk, timely adjust the early warning strategy according to the actual condition, and continuously improve the adaptive capacity and the early warning effect.

The invention relates to the technical field of artificial intelligence, and discloses an event prediction method and system based on multi-modal fusion, and the method comprises the steps: constructing a knowledge graph encoder, and converting domain expert knowledge into learnable vector representation; constructing a multi-granularity feature extraction network, and extracting features from different microcosmic, mesoscopic and macroscopic scales; realizing a knowledge-guided attention mechanism, and dynamically adjusting feature scale importance; constructing a prototype learning module, and establishing prototype representation of the abnormal category; a knowledge migration mechanism is constructed, and the generalization ability of the model to novel anomalies is enhanced; and multi-granularity abnormal event detection and early warning are realized, and a detection result and interpretable analysis are output. According to the method, through combination of knowledge guidance and multi-scale feature learning, efficient identification and early warning of social abnormal events under the condition of data scarcity are realized, and the method is suitable for the fields of public place safety monitoring, urban traffic safety management, large-scale activity safety guarantee and the like.

The invention discloses a vehicle collision detection, description and early warning system and method based on a driving video, and belongs to the technical field of artificial intelligence and intelligent traffic safety. According to the system, on the basis of a vision-language model, vehicle-mounted videos such as an automobile data recorder are automatically analyzed, and detection, severity grading, natural language description generation and real-time early warning of vehicle collision events are achieved. The system extracts high-dimensional semantic features of video frames through a CLIP model, focuses key information through an attention weighting network, inputs the key information into a deep classification network composed of a plurality of full connection layers, a batch normalization layer and an activation function, and outputs a multi-level accident severity classification result. Integrating target detection and environment information, and generating a structured accident description text by using a fine tuning BART model; a sliding window and time sequence modeling mechanism is adopted, and recognition and early warning of the pre-collision state are achieved. The method effectively solves the technical problems of lack of semantic understanding, inaccurate severity judgment, incapability of early warning and the like of a traditional method, has high accuracy, high interpretability and real-time response capability, and is suitable for intelligent driving assistance and traffic safety monitoring scenes.

A method, device and system for active management and control of road traffic safety are disclosed. The method includes acquiring traffic data of a target road in real time, wherein the target road includes management and control sections; for each management and control section, judging whether there is a traffic accident according to the acquired traffic data; if so, formulating an emergency management and control strategy; if not, extracting traffic flow data from the traffic data, and generating predicted traffic flow data according to the traffic flow data by a traffic flow prediction model; generating a risk level according to the predicted traffic flow data by a risk prediction model; determining the current active management and control strategy of the management and control section according to the risk level and the predicted traffic data; and issuing the corresponding management and control strategy of each of the management and control sections.

The invention belongs to the field of traffic safety management, and discloses a traffic accident severity influence factor analysis method based on local cascade integration, which comprises the following steps: acquiring an accident data set D1; processing the accident data set D1, removing part of redundant attributes, dividing the accident data set D1 into a training set and a test set according to a proportion, and balancing the number of accidents with different severity degrees in the training set through an SMOTENC algorithm to obtain an accident data set D2; importing the accident data set D2 into a local cascade integration model, adjusting hyper-parameters of a local model through a Hyperpt method, and selecting an optimal hyper-parameter combination by using k-fold cross validation; drawing a confusion matrix according to a training result, and selecting indexes to evaluate model performance; and visualizing the model by applying a machine learning output explanation tool SHAP, and analyzing accident severity influence factors according to the visualized model. By adopting the SMOTENC resampling technology, the number of various accidents in the training set is balanced and the model training effect and the classification performance are improved on the premise of considering discrete and continuous variable differences.

The invention relates to the technical field of drunk driving detection, and discloses a non-contact drunk driving detection method based on deep learning, which comprises the following steps: acquiring continuous video stream data through a multispectral camera, extracting a facial region image sequence after inter-frame alignment and noise suppression, and extracting a facial region image sequence; feature extraction and fusion are carried out by using a space-time convolutional neural network, a time sequence decomposition algorithm and the like; and finally, a drunk driving detection result is obtained through a cascaded deep learning classifier and a dynamic threshold segmentation algorithm. The system comprises a data acquisition module, a preprocessing module, a feature extraction module, a classification decision module and a model optimization module. According to the invention, non-contact detection is realized, the risk of cross infection is avoided, and the detection convenience is improved. Various advanced algorithms adopted by the method can accurately extract characteristics related to alcohol metabolism, optimize model performance, improve detection precision and reduce misjudgment caused by environmental factor interference, and the method also has the functions of data enhancement, multi-task learning and online incremental learning, adapts to different environments and guarantees road traffic safety.

The invention discloses an automatic driving automobile obstacle avoidance trajectory planning method based on space-time corridor dynamic search in a curve scene, and relates to the field of intelligent driving and traffic safety, and the method comprises the steps: S1, information collection and reference line fitting; s2, coordinate system conversion of the vehicle state parameters; s3, generating an obstacle space in the three-dimensional space-time map S-D-T; s4, generating a subsection passable space-time corridor; s5, generating feasible track points in the space-time corridor area; and S6, generating an optimal track point. According to the method, track point sudden change and sharp turning conditions are avoided, and safety and efficiency are brought to driving.

The invention provides a dangerous road section intelligent early warning method and device based on image acquisition and identification, and relates to the technical field of intelligent traffic safety early warning. The method comprises the following steps: acquiring a road image, a bridge image and a tunnel image acquired by image acquisition equipment and an unmanned aerial vehicle; respectively inputting the road image, the bridge image and the tunnel image into corresponding anomaly recognition models to generate anomaly recognition results; generating early warning information based on the abnormal recognition result, and automatically sending the early warning information to a display terminal, a user terminal and a management terminal along the road; and controlling the unmanned aerial vehicle to go to the corresponding abnormal area in response to the fact that the abnormal recognition result meets a preset condition, and performing field audio warning and traffic guidance by using the unmanned aerial vehicle. According to the scheme, the accuracy of road, bridge and tunnel anomaly recognition can be improved, and the response efficiency and the field handling capacity of traffic anomalies are improved.

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 provides a traffic control decision-making method, device and equipment based on data analysis, and aims to solve the technical problems of high subjectivity, insufficient data value mining, disjunction of strategy and practical application and lack of continuous learning optimization capability in the traditional traffic control decision-making. Through standardized fusion processing of multi-source traffic data and reverse optimization configuration of a traffic feature library, in combination with a progressive effect evaluation and reverse deduction verification mechanism, a multi-time scale effect tracking and strategy evolution trajectory acquisition system is innovatively established, and a decision cycle mechanism with autonomous learning and dynamic adjustment capabilities is constructed. The association relationship between the traffic state evolution rule and the control strategy is systematically analyzed, and finally an intelligent traffic control decision framework based on operation state data driving is formed; scientific and reliable decision support and technical basis are provided for application scenes such as urban traffic fine management, intelligent traffic system optimization control, traffic jam treatment and traffic safety guarantee.