Traffic event intelligent identification and early warning method and device

Abstract

The embodiment of the application provides a traffic event intelligent identification and early warning method and device, a multi-source data fusion mechanism is innovatively constructed, time stamp alignment and feature extraction are carried out, and comprehensive analysis of image, geomagnetic, radar and meteorological data is realized. A clustering model based on space-time features is designed, combined with density clustering and rule matching, an event type identification strategy is established. An influence range evaluation mechanism is introduced, through vehicle trajectory analysis and position mapping, accurate early warning information pushing is realized. The method effectively solves the shortcomings of traditional technology in data fusion, event identification and early warning release, and significantly improves the accuracy and real-time performance of traffic event identification and early warning.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, in particular to a traffic event intelligent identification and early warning method and device. BACKGROUND

[0002] The existing traffic event identification and early warning method has obvious defects. The traditional system lacks systematicness in multi-source data processing, and it is difficult to effectively integrate camera image, geomagnetic sensor, microwave radar and weather station data, affecting the comprehensiveness of feature extraction.

[0003] In addition, the existing technology has bottlenecks in event identification. Most systems fail to fully utilize the advantages of spatio-temporal feature clustering models, lack event analysis mechanisms based on density clustering, and the event type identification accuracy is not ideal.

[0004] The existing system has technical shortcomings in early warning publishing. It lacks the ability to accurately assess the impact range of the event, and it is difficult to achieve targeted early warning through vehicle trajectory analysis, affecting the early warning effect. Solving these problems is of great significance to improve the traffic event identification and early warning level. SUMMARY

[0005] In view of the problems in the prior art, the present application provides a traffic event intelligent identification and early warning method and device, which can effectively solve the deficiencies of traditional technology in data fusion, event identification and early warning publishing, and significantly improve the accuracy and real-time performance of traffic event identification and early warning.

[0006] In order to solve at least one of the above problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a traffic event intelligent identification and early warning method, comprising:

[0008] Collecting multi-source data of traffic scenes, acquiring road traffic image data stream collected by front-end cameras, vehicle flow data collected by road surface geomagnetic sensors, vehicle speed data collected by microwave radars, and weather data collected by weather stations, aligning the time stamps of the multi-source data and cleaning the data, extracting vehicle passing number, vehicle speed, vehicle position distribution, vehicle stop duration, road occupancy rate, and weather condition to construct a feature parameter set, calculating the time sequence change of the feature parameter set using a sliding window method, and constructing a feature vector matrix;

[0009] Based on the feature vector matrix, a space-time feature clustering model is constructed, a similarity coefficient between each feature vector in the feature vector matrix is calculated, a density clustering algorithm is used for clustering analysis on the feature vector matrix, an event clustering result is generated, an event type recognition rule library is constructed, the event clustering result is matched with the event type recognition rule library, a traffic event type is recognized, key features of the traffic event type are extracted, an event feature description vector is generated, the event feature description vector is compared with a preset multi-dimensional threshold, and traffic event warning data is generated;

[0010] The traffic event warning data is associated with geographic location information of the front-end camera, an event influence range matrix is constructed, an event location coordinate is calculated, an event influence area boundary point is determined, a snapshot device is screened in the event influence area, real-time vehicle passing data is obtained, a vehicle movement trajectory is constructed, a vehicle-position mapping table is established, a vehicle archive database is queried based on the vehicle-position mapping table, a vehicle owner contact method is obtained, the traffic event warning data and the event location coordinate are packaged as warning information, and the warning information is sent to the vehicle owner through a short message gateway.

[0011] Further, it also includes: collecting road traffic image data stream in real time through the front-end camera, starting the road surface geomagnetic sensor to collect vehicle flow data, starting the microwave radar speed measurement module to collect vehicle speed data, connecting the weather station data interface to collect weather data, marking the road traffic image data stream, the vehicle flow data, the vehicle speed data and the weather data according to the data collection time, constructing a multi-source data collection matrix, and establishing a data quality evaluation model based on the multi-source data collection matrix;

[0012] The multi-source data collection matrix is subjected to data integrity check, data reliability score is calculated based on the data quality evaluation model, data with a data reliability score lower than a preset threshold is removed, the time stamps of each data source in the multi-source data collection matrix are converted into a unified time format, the time interval of adjacent sampling points is calculated, the sampling periods of different data sources are aligned based on a linear interpolation algorithm, and a time-aligned multi-source data matrix is generated.

[0013] Further, it also includes: extracting six parameters of vehicle passing number, vehicle speed, vehicle position distribution, vehicle stagnation duration, road surface occupancy rate, and weather condition from the time-aligned multi-source data matrix, normalizing the vehicle passing number and vehicle speed, converting the vehicle position distribution into a spatial density matrix, calculating the cumulative probability distribution of the vehicle stagnation duration, converting the road surface occupancy rate into a saturation index, and numerically quantifying the weather condition to generate a feature parameter set;

[0014] The feature parameter sampling window is set based on a sliding window method, the feature parameter set is input into the sampling window in a time sequence, the mean, variance, skewness and kurtosis of each feature parameter in the sampling window are calculated, a time sequence statistical feature matrix is constructed, a time sequence change value is calculated from the time sequence statistical feature matrix and an adjacent sampling window, and a feature vector matrix is generated by combining the time sequence change value and the time sequence statistical feature matrix.

[0015] Further, the method further comprises: extracting spatial features and temporal features from the feature vector matrix, performing grid processing on the spatial features to construct a spatial correlation matrix, performing time sequence decomposition on the temporal features to construct a time correlation matrix, combining the spatial correlation matrix and the time correlation matrix to construct a space-time feature clustering model, calculating a distance matrix of each feature vector in the feature vector matrix based on the Euclidean distance, and mapping the distance matrix to the space-time feature clustering model to generate a feature vector similarity coefficient;

[0016] A clustering center point is calculated based on the feature vector similarity coefficient, a density clustering parameter set is constructed, the density clustering parameter set is input into a density clustering algorithm, a local density value and a minimum distance value of each feature vector are calculated, a clustering core point is determined, the clustering core point is used as a clustering center for feature vector distribution, and an event clustering result is generated.

[0017] Further, the method further comprises: constructing a traffic event basic feature library, combining feature items in the basic feature library to form a feature rule set, classifying and labeling the feature rule set according to event types, establishing a mapping relationship between the feature rule and the event type, generating an event type identification rule library, performing feature matching on the event clustering result and the event type identification rule library, identifying a traffic event type based on a matching degree, and constructing a feature description vector by extracting key features from the traffic event type;

[0018] The feature description vector is standardized, a multi-dimensional threshold judgment matrix is constructed, the standardized feature description vector is input into the multi-dimensional threshold judgment matrix for numerical comparison, a triggering state of each dimension threshold is calculated, an event early warning level is set based on the triggering state, and traffic event early warning data is generated by combining the traffic event type, the feature description vector and the event early warning level.

[0019] Further, the method further comprises: performing space-time registration on event features in the traffic event early warning data and geographical position information of the front-end camera, constructing a geographical information mapping matrix, calculating longitude and latitude coordinates of an event occurrence position based on the geographical information mapping matrix, converting the longitude and latitude coordinates into event position coordinates in a plane rectangular coordinate system, setting an influence radius parameter according to the event early warning level, and constructing an event influence range matrix with the event position coordinates as the center.

[0020] Calculate the geometric boundary of the influence area based on the event influence range matrix, generate the event influence area boundary point set using the minimum circumscribed polygon algorithm, superimpose the boundary point set with electronic map data, extract the road topology structure within the influence area, determine the layout position of the snapshot device according to the road topology structure, and screen out the snapshot device list located within the influence area.

[0021] Further, it also includes: reading real-time vehicle passing data from the snapshot device list, extracting vehicle feature information and passing time, sorting the vehicle feature information according to time sequence, calculating the vehicle moving direction and speed based on the vehicle feature information of adjacent snapshot points, constructing a vehicle moving trajectory model, establishing a mapping relationship between the license plate number and the position information in the vehicle moving trajectory model, generating a vehicle-position mapping table, and querying the vehicle owner's contact information from the vehicle archive database according to the license plate number in the vehicle-position mapping table;

[0022] Structurally process the traffic event warning data, convert the event location coordinates into road names and kilometer post numbers, construct a warning information template, fill the warning data and location information into the warning information template to generate warning information content, establish a short message sending task queue, write the warning information content and the vehicle owner's contact information into the short message sending task queue, and send the warning information through a short message gateway interface according to the task queue.

[0023] In a second aspect, the application provides a traffic event intelligent identification and warning device, comprising:

[0024] A multi-source data processing module is configured to collect multi-source data of a traffic scene, acquire road traffic image data stream collected by a front-end camera, vehicle flow data collected by a road surface geomagnetic sensor, vehicle speed data collected by a microwave radar, and weather data collected by a weather station, align time stamps and clean data of the multi-source data, extract a feature parameter set including vehicle passing number, vehicle speed, vehicle position distribution, vehicle stagnation duration, road occupancy rate, and weather condition, calculate time sequence variation of the feature parameter set using a sliding window method, and construct a feature vector matrix.

[0025] The traffic event determination module is configured to construct a space-time feature clustering model based on the feature vector matrix, calculate similarity coefficients between each feature vector in the feature vector matrix, perform clustering analysis on the feature vector matrix by using a density clustering algorithm, generate an event clustering result, construct an event type identification rule library, match the event clustering result with the event type identification rule library, identify a traffic event type, extract key features of the traffic event type, generate an event feature description vector, compare the event feature description vector with a preset multi-dimensional threshold, and generate traffic event warning data.

[0026] The identification and warning module is configured to associate the traffic event warning data with geographic position information of the front-end camera, construct an event influence range matrix, calculate an event position coordinate, determine an event influence area boundary point, select a snapshot device within the event influence area, obtain real-time vehicle passing data, construct a vehicle movement trajectory, establish a vehicle-position mapping table, query a vehicle archive database based on the vehicle-position mapping table, obtain a vehicle owner contact method, encapsulate the traffic event warning data and the event position coordinate into warning information, and send the warning information to the vehicle owner through a short message gateway.

[0027] In a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the traffic event intelligent identification and warning method when executing the program.

[0028] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, wherein the computer program is executable on a processor to implement the steps of the traffic event intelligent identification and warning method.

[0029] In a fifth aspect, the present application provides a computer program product, comprising a computer program / instruction, wherein the computer program / instruction is executable on a processor to implement the steps of the traffic event intelligent identification and warning method.

[0030] As can be seen from the above technical solutions, the present application provides a traffic event intelligent identification and warning method and device, which realizes comprehensive analysis of image, geomagnetic, radar and weather data through innovative construction of a multi-source data fusion mechanism, time stamp alignment and feature extraction. A clustering model based on space-time features is designed, density clustering and rule matching are combined, and an event type identification strategy is established. An influence range evaluation mechanism is introduced, vehicle trajectory analysis and position mapping are performed, and precise warning information is pushed. The method effectively solves the deficiencies of traditional technologies in data fusion, event identification and warning release, and significantly improves the accuracy and real-time performance of traffic event identification and warning. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0032] Figure 1 The flowchart of the traffic event intelligent identification and early warning method in the embodiments of the present application;

[0033] Figure 2 The structural diagram of the traffic event intelligent identification and early warning device in the embodiments of the present application;

[0034] Figure 3 The structural diagram of the electronic device in the embodiments of the present application.

[0035] Reference signs:

[0036] Electronic device 9600, central processor 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver program storage unit 9144, antenna 9111, speaker 9131, microphone 9132. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0038] The acquisition, storage, use, processing and the like of data in the technical solutions of the present application all conform to the relevant provisions of national laws and regulations.

[0039] In view of the problems in the prior art, the application provides a traffic event intelligent identification and early warning method and device, which innovatively constructs a multi-source data fusion mechanism, realizes comprehensive analysis of image, geomagnetic, radar and weather data through timestamp alignment and feature extraction, designs a clustering model based on space-time features, combines density clustering and rule matching, establishes an event type identification strategy, introduces an influence range evaluation mechanism, realizes accurate early warning information pushing through vehicle trajectory analysis and position mapping, and effectively solves the deficiencies of traditional technologies in data fusion, event identification and early warning publishing, and significantly improves the accuracy and real-time performance of traffic event identification and early warning.

[0040] In order to effectively solve the deficiencies of traditional technologies in data fusion, event identification and early warning publishing, and significantly improve the accuracy and real-time performance of traffic event identification and early warning, an embodiment of a traffic event intelligent identification and early warning method is provided, as shown in Figure 1 , which specifically includes the following contents:

[0041] Step S101: Collecting traffic scene multi-source data, acquiring road traffic image data stream collected by a front-end camera, vehicle flow data collected by a road surface geomagnetic sensor, vehicle speed data collected by a microwave radar, and weather data collected by a weather station, performing timestamp alignment and data cleaning on the multi-source data, extracting vehicle passing number, vehicle driving speed, vehicle position distribution, vehicle stagnation duration, road surface occupancy rate, and weather condition to construct a feature parameter set, calculating time sequence variation of the feature parameter set by using a sliding window method, and constructing a feature vector matrix;

[0042] Optionally, the embodiment innovatively designs a feature construction scheme based on multi-source data fusion in view of the problems of incomplete data collection and insufficient feature extraction in urban road traffic event intelligent identification. The embodiment first constructs a data collection framework to realize comprehensive perception of a traffic scene through multi-level sensing devices. The system designs a data quality evaluation formula: Quality_Score= α Completeness + β Timeliness - γ×Noise_Factor, where Completeness represents data completeness, Timeliness represents data timeliness, Noise_Factor represents a noise factor, and α, β, γ are dynamic adjustment coefficients. In an urban traffic scene, this multi-dimensional quality evaluation method can effectively guarantee the reliability of data.

[0043] This embodiment deeply optimizes the data collection strategy. In view of the complexity of the traffic scene, a collection mechanism based on multi-source sensing is designed. Through the real-time video stream of the front-end camera, continuous monitoring of the vehicle motion state is realized; through the pavement geomagnetic sensor array, the vehicle flow change is accurately obtained; through the microwave radar speed measurement, the vehicle driving speed is accurately captured; through the weather station data interface, the environmental parameters are obtained in real time. Special attention is paid to the synchronization of data, and when the collection anomaly is detected, the system will supplement the collection through the backup channel. For example, when processing the traffic monitoring of the urban trunk road, through multi-source collection, key information such as vehicle density, driving state, and weather influence can be obtained at the same time, which is crucial for timely detection of traffic anomalies.

[0044] This embodiment innovatively realizes the time alignment mechanism. In view of the sampling characteristics of different data sources, the system constructs a timestamp-based alignment framework. Through time series analysis, unified organization of multi-source data is realized. Special attention is paid to the accuracy of alignment, and through the design of interpolation strategy, the time sequence consistency of data is ensured. This timestamp-based alignment method can effectively ensure the reliability of feature extraction. This embodiment uses the feature change amount calculation formula: wherein represents the current feature value, represents the feature value at the previous moment, represents the time interval, is a smoothing factor.

[0045] This embodiment deeply optimizes the feature extraction strategy. The system constructs a feature extraction framework based on multi-dimensional parameters, and realizes accurate calculation of time series features through sliding window. Special attention is paid to the representativeness of features, and through the design of adaptive window mechanism, the accuracy of feature extraction is improved. For example, when extracting the vehicle stall duration feature, through the analysis of the residence time distribution of vehicles in the detection area, the potential traffic congestion state can be effectively identified; when calculating the road occupancy rate, through the evaluation of the occupancy degree of vehicles to the road space, the change of road traffic capacity can be accurately reflected.

[0046] This embodiment realizes the unified expression of features through matrix construction. The system constructs a vector matrix based on multi-dimensional features, and combines time series changes for comprehensive modeling. Special attention is paid to the integrity of the matrix, and through the establishment of data supplement mechanism, effective coverage of the feature space is realized. This systematic construction scheme provides high-quality data support for subsequent analysis.

[0047] The innovative design of this embodiment not only solves the data collection problem in traditional methods, but also establishes a sustainable optimization feature learning framework. Through multi-level data processing and feature extraction, the system can capture key information from complex traffic scenes. This multi-source fusion-based processing mechanism ensures that the system always maintains effective perception ability when facing complex traffic scenes. In urban traffic monitoring, this intelligent processing scheme significantly improves the accuracy of abnormal event identification.

[0048] This embodiment realizes intelligent monitoring upgrade for traffic events by establishing a complete data processing link. The system can dynamically adjust the processing strategy based on real-time data quality, avoiding the limitations of traditional fixed rule schemes. Through multi-dimensional feature extraction and fusion, the quality and reliability of event identification are significantly improved, providing reliable decision support for traffic management. This intelligent processing mechanism shows strong adaptability and optimization effect in urban traffic monitoring.

[0049] This embodiment not only improves the accuracy of feature extraction, but also establishes a continuously evolving monitoring system through continuous strategy optimization and effect analysis. This real-time feedback-based optimization mechanism ensures that the system can continuously improve as traffic patterns change, providing increasingly accurate feature representations for subsequent analysis. In practical applications, this self-optimization mechanism significantly improves the long-term service quality and monitoring effect of the system, providing reliable technical support for urban traffic management.

[0050] Step S102: Based on the feature vector matrix, a spatio-temporal feature clustering model is constructed, the similarity coefficients between each feature vector in the feature vector matrix are calculated, a density clustering algorithm is used for clustering analysis of the feature vector matrix, an event clustering result is generated, an event type recognition rule library is constructed, the event clustering result is matched with the event type recognition rule library, the traffic event type is identified, the key features of the traffic event type are extracted, an event feature description vector is generated, the event feature description vector is compared with a preset multi-dimensional threshold, and traffic event warning data is generated;

[0051] Optionally, this embodiment innovatively designs an event recognition scheme based on spatio-temporal clustering to address the problems of unclear patterns and complex feature correlations in urban traffic event recognition. This embodiment first constructs a feature clustering framework to achieve accurate modeling of traffic events through multi-level feature analysis. The system designs a similarity evaluation formula: Similarity= α Spatial_Correlation + βTemporal_Correlation - γ × Feature_Distance, where Spatial_Correlation represents spatial correlation, Temporal_Correlation represents temporal correlation, Feature_Distance represents feature distance, and α, β, γ are dynamic adjustment coefficients. In urban traffic event analysis, this multi-dimensional similarity evaluation method can effectively improve the accuracy of clustering.

[0052] This embodiment deeply optimizes the clustering model construction strategy. A dual-dimensional clustering mechanism is designed based on the spatio-temporal characteristics of traffic events. Through spatial dimension analysis, the geographical distribution characteristics of events are accurately described; through time dimension analysis, the evolution law of events is accurately captured. Special attention is paid to the relevance of features, and when feature anomalies are detected, the system will optimize the model through adaptive adjustment method. For example, in processing traffic congestion events on urban roads, spatio-temporal feature clustering can effectively distinguish different types of congestion modes, including fixed road congestion, temporary construction caused congestion and other scenarios, which is crucial for accurate event recognition.

[0053] This embodiment innovatively realizes the density clustering mechanism. A density-based clustering framework is constructed based on the distribution characteristics of event features. Through local density calculation, accurate identification of event clusters is realized. Special attention is paid to the stability of clustering, and through the design of density threshold strategy, the reliability of clustering results is ensured. This density-based clustering method can effectively handle abnormal events. This embodiment adopts the clustering evaluation formula: Cluster_Score = (ρi × δi) / (ρmax × δmax), where ρi represents the local density of point i, δi represents the distance to the high-density point, and ρmax and δmax are the corresponding maximum values.

[0054] This embodiment deeply optimizes the rule matching strategy. The system constructs a rule library framework based on event features, and realizes accurate identification of event types through multi-dimensional matching. Special attention is paid to the integrity of rules, and through the design of hierarchical matching mechanism, the accuracy of identification is improved. For example, in identifying traffic accident events, through analyzing the feature combination of sudden vehicle stop and abnormal traffic flow change, the accident type can be quickly located; in judging traffic control events, through evaluating traffic flow mutation and speed change pattern, the control situation can be accurately identified.

[0055] This embodiment realizes the standardized expression of events through feature description. The system constructs a description vector based on key features, and combines multi-dimensional threshold for early warning judgment. Special attention is paid to the comprehensiveness of description, and through the establishment of feature screening mechanism, effective extraction of event features is realized. This systematic description scheme provides reliable data support for early warning generation.

[0056] The innovative design of this embodiment not only solves the problem of event recognition in traditional methods, but also establishes a sustainable optimization early warning framework. Through multi-level feature clustering and rule matching, the system can identify key events from complex traffic data. This identification mechanism based on spatio-temporal analysis ensures that the system always maintains effective early warning capability when facing diverse traffic scenarios. In urban traffic management, this intelligent identification scheme significantly improves the accuracy of event warning.

[0057] This embodiment realizes intelligent early warning upgrade for traffic events by establishing a complete identification processing link. The system can dynamically adjust the identification strategy based on real-time clustering results, avoiding the limitations of traditional fixed rule schemes. Through multi-dimensional feature analysis and rule matching, the quality and reliability of early warning are significantly improved, providing reliable decision support for traffic management. This intelligent identification mechanism shows strong adaptability and optimization effect in urban traffic monitoring.

[0058] This embodiment not only improves the accuracy of event recognition, but also establishes a continuously evolving early warning system through continuous strategy optimization and effect analysis. This optimization mechanism based on real-time feedback ensures that the system can continuously improve as traffic patterns change, providing increasingly accurate early warning services for traffic management. In practical applications, this self-optimization mechanism significantly improves the long-term service quality and early warning effect of the system, providing reliable technical support for urban traffic management.

[0059] Step S103: associate the traffic event warning data with the geographic location information of the front-end camera, construct an event impact range matrix, calculate the event location coordinates, determine the event impact area boundary points, select the snapshot device within the event impact area, obtain real-time vehicle data, construct vehicle movement trajectories, establish a vehicle-location mapping table, query the vehicle archive database based on the vehicle-location mapping table, obtain the vehicle owner contact information, encapsulate the traffic event warning data and the event location coordinates into early warning information, and send the early warning information to the vehicle owner through the SMS gateway.

[0060] Optionally, this embodiment innovatively designs a set of early warning push scheme based on location association to address the problems of unclear impact range and inaccurate early warning push in urban traffic event warning. This embodiment first constructs a location association framework to achieve accurate assessment of event impact through multi-level spatial analysis. The system designs an impact range assessment formula: Impact_Range = α Event_Severity + βRoad_Connectivity - γ × Traffic_Flow, where Event_Severity represents event severity, Road_Connectivity represents road connectivity, Traffic_Flow represents traffic flow, and a, b, g are dynamic adjustment coefficients. In urban traffic warning, this multi-dimensional influence evaluation method can effectively improve the pertinence of warning.

[0061] This embodiment deeply optimizes the position association strategy. For the spatial characteristics of traffic events, a geographic information-based association mechanism is designed. Through the position data of the front-end camera, accurate positioning of the event occurrence position is achieved; through road topology analysis, the event impact range is accurately evaluated; through device layout analysis, key monitoring points are accurately screened. Special attention is paid to the accuracy of spatial association. When a position anomaly is detected, the system will correct the position through backup positioning methods. For example, in handling traffic accidents on urban viaducts, the impact range of the accident can be accurately determined through position association, including the related sections of the on-ramp, off-ramp, and parallel auxiliary road, which is crucial for precise early warning.

[0062] This embodiment innovatively realizes the influence range calculation mechanism. For the event propagation characteristics, the system constructs a matrix-based range calculation framework. Through the spatial diffusion model, the impact area is accurately defined. Special attention is paid to the rationality of the range, and through the design of adaptive boundary strategy, the reliability of the impact evaluation is ensured. This matrix-based range calculation method can effectively predict the impact of events. This embodiment adopts the boundary determination formula: Boundary_Distance = Base_Range × (1 + Impact_Factor), where Base_Range represents the basic impact distance, and Impact_Factor represents the impact factor, which is determined by the event type and severity.

[0063] This embodiment deeply optimizes the vehicle trajectory construction strategy. The system constructs a trajectory analysis framework based on snapshot data, and realizes accurate tracking of vehicle motion through space-time mapping. Special attention is paid to the continuity of the trajectory, and through the design of the point supplement mechanism, the accuracy of trajectory reconstruction is improved. For example, in tracking possible affected vehicles, by analyzing the vehicle's historical travel records and real-time position, it can accurately predict whether it will enter the event impact area; in determining the warning target, by evaluating the driving direction and speed of the vehicle, it can accurately screen the vehicle owners that need to be warned.

[0064] The embodiment realizes the accurate push of early warning through information packaging. The system constructs an early warning packaging framework based on standard templates, and integrates information in combination with location description. Special attention is paid to the practicality of early warning, and through the establishment of a hierarchical push mechanism, effective distribution of early warning information is realized. This systematic push scheme provides reliable implementation support for early warning services.

[0065] The innovative design of the embodiment not only solves the problem of early warning push in traditional methods, but also establishes a sustainable optimization service framework. Through multi-level spatial analysis and trajectory tracking, the system can identify key early warning objects from complex traffic scenes. This push mechanism based on location association ensures that the system always maintains effective early warning capability when facing diversified traffic events. In urban traffic management, this intelligent early warning scheme significantly improves the accuracy of services.

[0066] The embodiment realizes intelligent service upgrade for traffic events by establishing a complete early warning processing link. The system can dynamically adjust the push strategy based on real-time location association, avoiding the limitations of traditional fixed range schemes. Through multi-dimensional spatial analysis and trajectory tracking, the quality and reliability of early warning are significantly improved, providing reliable travel support for traffic participants. This intelligent early warning mechanism exhibits strong adaptability and optimization effect in urban traffic services.

[0067] The embodiment not only improves the accuracy of early warning push, but also establishes a continuously evolving service system through continuous strategy optimization and effect analysis. This optimization mechanism based on real-time feedback ensures that the system can continuously improve as traffic patterns change, providing more and more accurate early warning services for traffic participants. In practical applications, this self-optimization mechanism significantly improves the long-term service quality and early warning effect of the system, providing reliable technical support for urban traffic management.

[0068] From the above description, it can be seen that the traffic event intelligent identification and early warning method provided by the embodiment can innovatively construct a multi-source data fusion mechanism, realize comprehensive analysis of image, geomagnetic, radar and meteorological data through time stamp alignment and feature extraction. Design a clustering model based on spatio-temporal features, combine density clustering and rule matching to establish an event type identification strategy. Introduce an influence range evaluation mechanism, realize accurate early warning information push through vehicle trajectory analysis and location mapping. This method effectively solves the shortcomings of traditional technologies in data fusion, event identification and early warning publishing, significantly improving the accuracy and real-time performance of traffic event identification and early warning.

[0069] In an embodiment of the traffic event intelligent identification and early warning method of the present application, the following contents can be specifically included:

[0070] Step S201: Real-time acquisition of road traffic image data stream by front-end camera, starting road surface geomagnetic sensor to collect vehicle flow data, opening microwave radar speed measurement module to collect vehicle speed data, connecting weather station data interface to collect weather data, marking the road traffic image data stream, the vehicle flow data, the vehicle speed data and the weather data according to the data acquisition time, constructing a multi-source data acquisition matrix, and establishing a data quality evaluation model based on the multi-source data acquisition matrix;

[0071] Step S202: Data integrity check on the multi-source data acquisition matrix, calculating data reliability score based on the data quality evaluation model, eliminating data with data reliability score lower than the preset threshold, converting the time stamp of each data source in the multi-source data acquisition matrix into a unified time format, calculating the time interval of adjacent sampling points, aligning the sampling periods of different data sources based on linear interpolation algorithm, and generating a time-aligned multi-source data matrix.

[0072] Optionally, the embodiment innovatively designs a data acquisition scheme based on multi-source fusion to solve the problems of heterogeneous data sources and unstable acquisition quality in urban traffic monitoring. The embodiment first constructs a data acquisition framework, and comprehensively monitors the traffic scene through multi-level sensing devices. The system designs a data reliability evaluation formula: Reliability_Score = α Completeness + β Consistency + γ Accuracy - δ Noise_Level, where Completeness represents data completeness, Consistency represents data consistency, Accuracy represents data accuracy, Noise_Level represents noise level, and α, β, γ, δ are dynamic adjustment coefficients. In urban traffic monitoring, this multi-dimensional reliability evaluation method can effectively guarantee the quality of data.

[0073] The embodiment deeply optimizes the data acquisition strategy. A multi-source collaborative acquisition mechanism is designed according to the dynamic characteristics of the traffic scene. The front-end camera captures the motion state of vehicles on the road in real time through high-definition video stream, supports vehicle detection and tracking; the road surface geomagnetic sensor array accurately counts the vehicle flow through the change of magnetic field, realizes vehicle counting and occupancy rate measurement; the microwave radar speed measurement module accurately measures the vehicle speed through the Doppler effect, ensuring the reliability of the speed data; the weather station monitors the weather conditions in real time through professional sensors, and obtains environmental parameters such as visibility and precipitation. Special attention is paid to the synchronization of acquisition. When an abnormality is detected, the system will supplement data through a backup channel. For example, in the processing of traffic monitoring on urban trunk roads, multi-source acquisition can simultaneously obtain vehicle motion characteristics, road conditions, weather influences and other key information, which is crucial for comprehensive traffic state perception.

[0074] The embodiment innovatively realizes a data labeling mechanism. A timestamp-based labeling framework is constructed for the time sequence characteristics of multi-source data. Through precise time synchronization, unified management of different data sources is realized. Special attention is paid to the accuracy of labeling, and through the design of clock calibration strategy, the time sequence consistency of data is ensured. This timestamp-based labeling method can effectively ensure the orderliness of data organization. The data quality evaluation formula used in the embodiment is: Quality_Index = w1 T1 + w2 T2 + w3 T3 + w4 T4, where T1 to T4 represent the quality indicators of camera, geomagnetic, radar and weather data respectively, and w1 to w4 are the corresponding weights.

[0075] The embodiment deeply optimizes the data processing strategy. The system constructs a data organization framework based on multi-dimensional matrix, and realizes accurate evaluation of data quality through integrity check. Special attention is paid to the reliability of data, and through the design of threshold screening mechanism, the effectiveness of data is improved. For example, in evaluating data reliability, through analyzing the continuity, consistency and accuracy of data, abnormal data can be effectively identified; in time alignment, through evaluating the uniformity of sampling interval, the synchronization state of data can be accurately judged.

[0076] The embodiment realizes the accurate alignment of sampling period through linear interpolation. The system constructs an interpolation framework based on time sequence, and combines data characteristics for interpolation calculation. Special attention is paid to the rationality of interpolation, and through the establishment of boundary check mechanism, effective control of interpolation results is realized. This systematic alignment scheme provides high-quality data support for subsequent analysis.

[0077] The innovative design of the embodiment not only solves the data acquisition problem in traditional methods, but also establishes a sustainable optimization data processing framework. Through multi-level data acquisition and quality control, the system can obtain reliable monitoring data from complex traffic scenes. This multi-source fusion-based acquisition mechanism ensures that the system always maintains effective data acquisition capability when facing complex traffic scenes. In urban traffic monitoring, this intelligent acquisition scheme significantly improves the reliability and integrity of data.

[0078] The embodiment realizes intelligent data acquisition upgrade for traffic monitoring by establishing a complete data processing link. The system can dynamically adjust the acquisition strategy based on real-time data quality, avoiding the limitations of traditional fixed sampling schemes. Through multi-dimensional data processing and alignment, the quality and reliability of data are significantly improved, providing reliable data support for traffic monitoring. This intelligent acquisition mechanism shows strong adaptability and optimization effect in urban traffic monitoring.

[0079] This embodiment not only improves the accuracy of data collection, but also establishes a continuously evolving collection system through continuous strategy optimization and effect analysis. This optimization mechanism based on real-time feedback ensures that the system can continuously improve as the traffic scene changes, providing increasingly accurate data support for subsequent analysis. In practical applications, this self-optimization mechanism significantly improves the long-term service quality and monitoring effect of the system, providing reliable technical support for urban traffic management.

[0080] In an embodiment of the traffic event intelligent identification and early warning method of the present application, the following content can also be specifically included:

[0081] Step S301: Extract six parameters of vehicle passing number, vehicle speed, vehicle position distribution, vehicle stagnation duration, road surface occupancy rate, and weather condition from the time-aligned multi-source data matrix. Normalize the vehicle passing number and vehicle speed, convert the vehicle position distribution into a spatial density matrix, calculate the cumulative probability distribution of the vehicle stagnation duration, convert the road surface occupancy rate into a saturation index, and quantitatively analyze the weather condition to generate a feature parameter set.

[0082] Step S302: Set a feature parameter sampling window based on the sliding window method, input the feature parameter set into the sampling window according to the time sequence, calculate the mean, variance, skewness, and kurtosis of each feature parameter in the sampling window, construct a time series statistical feature matrix, calculate the time series change value of the time series statistical feature matrix and the adjacent sampling window, and combine the time series change value and the time series statistical feature matrix to generate a feature vector matrix.

[0083] Optionally, the present embodiment innovatively designs a parameter construction scheme based on multi-dimensional features to address the problems of incomplete feature extraction and unclear time sequence correlation in urban traffic monitoring. The present embodiment first constructs a feature extraction framework to accurately describe the traffic state through multi-level parameter analysis. The system designs a feature normalization formula: Normalized_Feature = (X - μ) / (σ + ε), where X represents the original feature value, μ represents the mean, σ represents the standard deviation, and ε is a smoothing factor. In urban traffic feature analysis, this multi-dimensional feature processing method can effectively improve the comparability of parameters.

[0084] This embodiment deeply optimizes the feature extraction strategy. Aiming at the multi-dimensional characteristics of traffic scenes, a comprehensive index-based extraction mechanism is designed. Through vehicle passing number analysis, accurate statistics of traffic flow are realized, reflecting road load conditions; through vehicle driving speed evaluation, traffic operation efficiency is accurately captured; through vehicle position distribution calculation, spatial occupation characteristics are accurately described; through vehicle stagnation duration monitoring, abnormal residence conditions are effectively identified; through road surface occupancy rate calculation, road use efficiency is accurately evaluated; through weather condition quantification, environmental impact factors are fully considered. Special attention is paid to the representativeness of the features, and when data anomalies are detected, the system will optimize the features through data correction methods. For example, when processing traffic monitoring of urban expressways, multi-dimensional feature extraction can simultaneously obtain traffic flow characteristics, road use efficiency, environmental impact and other key information, which is crucial for comprehensive state assessment.

[0085] This embodiment innovatively realizes the spatial density calculation mechanism. Aiming at the vehicle distribution characteristics, the system constructs a grid-based density calculation framework. Through spatial division, accurate quantification of vehicle distribution is realized. Special attention is paid to the continuity of density, and through the design of smoothing strategy, the rationality of distribution expression is ensured. This density-based distribution expression method can effectively reflect the traffic congestion situation. This embodiment adopts the time sequence feature evaluation formula: Feature_Score = w1 Mean + w2 Var +w3 Skew + w4 Kurt, where Mean, Var, Skew, Kurt represent mean, variance, skewness, kurtosis, and w1 to w4 are corresponding weights.

[0086] This embodiment deeply optimizes the window analysis strategy. The system constructs a time series analysis framework based on sliding window, and realizes accurate characterization of parameter changes through statistical feature calculation. Special attention is paid to the time-varying nature of the features, and through the design of adaptive window mechanism, the accuracy of the analysis is improved. For example, in analyzing traffic flow changes, through statistical feature combination, traffic fluctuation patterns can be effectively captured; in evaluating congestion evolution, through time series difference calculation, congestion development trend can be accurately identified.

[0087] This embodiment realizes the unified expression of features through matrix construction. The system constructs a vector matrix based on multi-dimensional features, and combines time sequence changes for comprehensive modeling. Special attention is paid to the integrity of the matrix, and through the establishment of feature supplement mechanism, effective coverage of feature space is realized. This systematic construction scheme provides high-quality data support for subsequent analysis.

[0088] The innovative design of this embodiment not only solves the feature extraction problem in traditional methods, but also establishes a sustainable optimization feature learning framework. Through multi-level parameter processing and time series analysis, the system can extract effective state features from complex traffic data. This multi-dimensional feature-based processing mechanism ensures that the system always maintains effective feature expression ability when facing complex traffic scenarios. In urban traffic monitoring, this intelligent feature scheme significantly improves the accuracy of state recognition.

[0089] This embodiment realizes intelligent analysis upgrade for traffic monitoring by establishing a complete feature processing link. The system can dynamically adjust the processing strategy based on real-time feature quality, avoiding the limitations of traditional fixed feature schemes. Through multi-dimensional feature extraction and time series analysis, the quality and reliability of the features are significantly improved, providing reliable analysis support for traffic monitoring. This intelligent feature mechanism shows strong adaptability and optimization effect in urban traffic analysis.

[0090] In an embodiment of the traffic event intelligent identification and early warning method of the present application, the following contents can be specifically included:

[0091] Step S401: Extracting spatial features and temporal features from the feature vector matrix, performing grid processing on the spatial features to construct a spatial correlation matrix, performing time series decomposition on the temporal features to construct a time correlation matrix, combining the spatial correlation matrix and the time correlation matrix to construct a spatio-temporal feature clustering model, calculating the distance matrix of each feature vector in the feature vector matrix based on the Euclidean distance, and mapping the distance matrix to the spatio-temporal feature clustering model to generate a feature vector similarity coefficient;

[0092] Step S402: Calculating a clustering center point based on the feature vector similarity coefficient, constructing a density clustering parameter set, inputting the density clustering parameter set into a density clustering algorithm, calculating the local density value and the minimum distance value of each feature vector, determining a clustering core point, assigning the clustering core point as a clustering center for feature vectors, and generating an event clustering result.

[0093] Optionally, this embodiment innovatively designs a clustering optimization scheme based on spatio-temporal features to solve the problems of complex feature association and fuzzy clustering boundary in urban traffic event identification. This embodiment first constructs a feature processing framework to achieve accurate modeling of event patterns through multi-level feature analysis. The system designs a similarity evaluation formula: Similarity_Score = α Spatial_Correlation + β Temporal_Correlation - γ Feature_ Distance + δDensity_Factor, where Spatial_Correlation represents spatial correlation, Temporal_Correlation represents temporal correlation, Feature_Distance represents feature distance, Density_Factor represents density factor, and a, b, g, d are dynamic adjustment coefficients. In urban traffic event analysis, this multi-dimensional similarity evaluation method can effectively improve the accuracy of clustering.

[0094] This embodiment deeply optimizes the feature extraction strategy. A double-dimensional feature processing mechanism is designed based on the spatio-temporal characteristics of traffic events. Through grid processing of spatial features, the geographical distribution of events is accurately described; through sequence decomposition of time features, the evolution law of events is accurately captured. Special attention is paid to the correlation of features. When feature anomalies are detected, the system will optimize the features through adaptive adjustment method. For example, in processing the traffic congestion event of urban road, through spatial grid processing, the spatial range and distribution characteristics of congestion area can be accurately located, and through time sequence decomposition, the development trend and periodic characteristics of congestion can be accurately identified, which is crucial for accurate event clustering.

[0095] This embodiment innovatively realizes the spatio-temporal feature clustering mechanism. A clustering model framework based on combination is constructed for the distribution characteristics of feature vectors. Through matrix operation, the similarity of features is accurately calculated. Special attention is paid to the stability of clustering. Through the design of mapping strategy, the reliability of similarity calculation is ensured. This clustering method based on spatio-temporal fusion can effectively process complex events. This embodiment uses the local density calculation formula: Local_Density = ∑exp(-(dij / dc)^2), where dij represents the distance between points i and j, and dc is the cut-off distance.

[0096] This embodiment deeply optimizes the clustering center determination strategy. The system constructs a center point calculation framework based on similarity coefficient, and realizes accurate identification of clustering core through density analysis. Special attention is paid to the representativeness of the center point. Through the design of evaluation mechanism, the accuracy of center determination is improved. For example, in identifying traffic accident events, the local density distribution of feature vectors can be accurately located; in determining the clustering center of congestion events, the abnormal degree of traffic flow parameters can be accurately identified to form the core area of congestion.

[0097] This embodiment realizes accurate classification of events through density clustering. The system constructs a density-based clustering framework, and combines local density and minimum distance to identify core points. Special attention is paid to the integrity of clustering. Through the establishment of allocation mechanism, effective classification of feature vectors is realized. This systematic clustering scheme provides reliable analysis support for event identification.

[0098] The innovative design of this embodiment not only solves the clustering optimization problem in traditional methods, but also establishes a sustainable optimization analysis framework. Through multi-level feature processing and density analysis, the system can identify key event patterns from complex traffic data. This clustering mechanism based on spatiotemporal features ensures that the system always maintains effective recognition ability when facing diverse traffic scenarios. In urban traffic management, this intelligent clustering scheme significantly improves the accuracy of event recognition.

[0099] This embodiment realizes intelligent analysis upgrade for traffic events by establishing a complete clustering processing link. The system can dynamically adjust the analysis strategy based on real-time clustering results, avoiding the limitations of traditional fixed parameter schemes. Through multi-dimensional feature analysis and density clustering, the recognition quality and reliability are significantly improved, providing reliable decision support for traffic management. This intelligent clustering mechanism exhibits strong adaptability and optimization effect in urban traffic monitoring.

[0100] This embodiment not only improves the accuracy of event recognition, but also establishes a continuously evolving clustering system through continuous strategy optimization and effect analysis. This real-time feedback-based optimization mechanism ensures that the system can continuously improve as traffic patterns change, providing increasingly accurate analysis results for traffic management. In practical applications, this self-optimization mechanism significantly improves the long-term service quality and recognition effect of the system, providing reliable technical support for urban traffic management.

[0101] In an embodiment of the traffic event intelligent identification and early warning method of the present application, the following content can be specifically included:

[0102] Step S501: Construct a traffic event basic feature library, combine the feature items in the basic feature library to form a feature rule set, classify and label the feature rule set according to event types, establish a mapping relationship between feature rules and event types, generate an event type identification rule library, perform feature matching between the event clustering results and the event type identification rule library, identify traffic event types based on matching degree calculation, extract key features from the traffic event types to construct a feature description vector;

[0103] Step S502: Standardize the feature description vector, construct a multi-dimensional threshold judgment matrix, input the standardized feature description vector into the multi-dimensional threshold judgment matrix for numerical comparison, calculate the trigger state of each dimension threshold, set the event warning level based on the trigger state, combine the traffic event type, the feature description vector, and the event warning level to generate traffic event warning data.

[0104] Optionally, the embodiment is directed to the problem of inaccurate rule definition and inaccurate early warning judgment in urban traffic event identification, and a set of event identification scheme based on feature mapping is innovatively designed. The embodiment first constructs a rule processing framework, and realizes accurate identification of event types through multi-level feature analysis. The system designs a rule matching evaluation formula: Match_Score = α Feature_Similarity + β Rule_Coverage - γ × Conflict_Factor, wherein Feature_Similarity represents feature similarity, Rule_Coverage represents rule coverage, Conflict_Factor represents conflict factor, and α, β, γ are dynamic adjustment coefficients. In urban traffic event identification, this multi-dimensional matching evaluation method can effectively improve the accuracy of identification.

[0105] The embodiment deeply optimizes the feature library construction strategy. A hierarchical feature organization mechanism is designed for the diversity of traffic events. Through basic feature extraction, comprehensive coverage of event attributes is realized; through feature combination analysis, event discrimination rules are accurately constructed; and through mapping relationship establishment, event type features are accurately defined. Special attention is paid to the integrity of the rules, and when the rules are missing, the system will supplement the rules through adaptive learning method. For example, in processing traffic accidents on urban roads, feature combination can accurately describe the typical feature mode of accident occurrence, including sudden deceleration, vehicle stasis, and abnormal traffic flow in multiple dimensions, which is crucial for accurate event identification.

[0106] The embodiment innovatively realizes the rule matching mechanism. In view of the complexity of event features, the system constructs an identification framework based on multi-dimensional matching. Through similarity calculation, accurate judgment of event types is realized. Special attention is paid to the reliability of matching, and through the design of weight strategy, the accuracy of identification results is ensured. This rule-based identification method can effectively handle complex events. The embodiment adopts a warning level evaluation formula: Warning_Level = ∑(wi × Ti) / ∑wi, wherein Ti represents the threshold trigger state of the i-th dimension, and wi is the corresponding weight.

[0107] The embodiment deeply optimizes the threshold discrimination strategy. The system constructs a warning judgment framework based on multi-dimensional matrix, and realizes accurate evaluation of feature description through standardization processing. Special attention is paid to the comprehensiveness of discrimination, and through the design of grading mechanism, the accuracy of early warning is improved. For example, in evaluating traffic congestion events, by analyzing the threshold trigger state of multiple dimensions such as traffic density, travel speed, and stasis duration, the severity of congestion can be accurately judged; in determining the warning level of accident events, by evaluating the influence range and duration of the accident, the warning level can be accurately set.

[0108] The embodiment realizes accurate early warning of events through early warning data generation. The system constructs a combination-based early warning framework, integrates information in combination with event types and early warning levels. Special attention is paid to the practicality of early warning, and effective expression of early warning information is realized through the establishment of a description mechanism. This systematic early warning scheme provides reliable decision support for traffic management.

[0109] The innovative design of the embodiment not only solves the problem of event recognition in traditional methods, but also establishes a sustainable optimization early warning framework. Through multi-level rule matching and threshold judgment, the system can identify key events from complex traffic data. This feature mapping-based identification mechanism ensures that the system always maintains effective early warning capability when facing diverse traffic scenarios. In urban traffic management, this intelligent early warning scheme significantly improves the accuracy of event handling.

[0110] The embodiment realizes intelligent early warning upgrade for traffic events by establishing a complete recognition processing link. The system can dynamically adjust the early warning strategy based on real-time recognition results, avoiding the limitations of traditional fixed rule schemes. Through multi-dimensional feature matching and threshold judgment, the quality and reliability of early warning are significantly improved, providing reliable decision support for traffic management. This intelligent early warning mechanism exhibits strong adaptability and optimization effect in urban traffic monitoring.

[0111] In an embodiment of the traffic event intelligent identification and early warning method of the present application, the following contents can be specifically included:

[0112] Step S601: Time and space registration of event features in the traffic event early warning data and geographic location information of the front-end camera is performed to construct a geographic information mapping matrix. The longitude and latitude coordinates of the event occurrence position are calculated based on the geographic information mapping matrix. The longitude and latitude coordinates are converted into event position coordinates in a planar rectangular coordinate system. The influence radius parameter is set according to the event early warning level. The event influence range matrix is constructed with the event position coordinates as the center;

[0113] Step S602: The geometric boundary of the influence area is calculated based on the event influence range matrix. The minimum circumscribed polygon algorithm is used to generate a set of event influence area boundary points. The boundary point set is superimposed and analyzed with electronic map data. The road topology structure within the influence area is extracted. The layout position of the snapshot device is determined according to the road topology structure. The snapshot device list located within the influence area is screened out.

[0114] Optionally, the embodiment is directed to the problem of unclear impact range and inaccurate device selection in urban traffic incident warning, and a set of impact assessment scheme based on spatial analysis is innovatively designed. The embodiment first constructs a spatial analysis framework to achieve accurate assessment of event impact through multi-level location mapping. The system designs an impact range assessment formula: Impact_Range = α Event_Level + β Road_Density - γ Traffic_Flow + δ Network_Connectivity, where Event_Level represents the event level, Road_Density represents the road density, Traffic_Flow represents the traffic flow, Network_Connectivity represents the network connectivity, and α, β, γ, δ are dynamic adjustment coefficients. In urban traffic warning, this multi-dimensional impact assessment method can effectively improve the accuracy of range definition.

[0115] The embodiment deeply optimizes the location registration strategy. According to the spatial characteristics of traffic incidents, a time-space-based registration mechanism is designed. Through event feature analysis, accurate positioning of the event location is achieved; through camera position matching, the monitoring coverage is accurately determined; through coordinate system conversion, the event impact area is accurately mapped. Special attention is paid to the accuracy of registration, and when location anomalies are detected, the system will correct the location through multi-source verification methods. For example, in handling traffic accidents on urban expressways, the time-space registration can accurately determine the location of the accident and its impact on the surrounding road network, including the main road, ramp and other related areas, which is crucial for precise early warning.

[0116] The embodiment innovatively realizes the impact range calculation mechanism. According to the event propagation characteristics, the system constructs a matrix-based range calculation framework. Through radius parameter adjustment, accurate definition of the impact area is achieved. Special attention is paid to the rationality of the range, and through the design of adaptive strategy, the reliability of impact assessment is ensured. This matrix-based range calculation method can effectively predict the impact of events. The embodiment uses the boundary generation formula: Boundary_Points = Min_Polygon(Impact_Matrix, θ), where Impact_Matrix represents the impact range matrix, and θ is the polygon fitting parameter.

[0117] The embodiment deeply optimizes the boundary determination strategy. The system constructs a boundary calculation framework based on the influence matrix, and realizes accurate description of the influence area through the minimum enclosing polygon algorithm. Special attention is paid to the integrity of the boundary, and the accuracy of the boundary generation is improved through the design of optimization mechanism. For example, when determining the congestion influence range, the spread boundary of congestion can be accurately defined by analyzing the road network structure and traffic flow propagation characteristics; when screening monitoring devices, the devices that need to be monitored can be accurately determined by evaluating the spatial relationship between the device location and the influence area.

[0118] The embodiment realizes accurate screening of devices through topological analysis. The system constructs a topological analysis framework based on the road network, and combines electronic maps for device positioning. Special attention is paid to the rationality of screening, and an effective selection of snapshot devices is realized by establishing a coverage evaluation mechanism. This systematic screening scheme provides reliable device support for early warning monitoring.

[0119] The innovative design of the embodiment not only solves the range definition problem in traditional methods, but also establishes a sustainable optimization evaluation framework. Through multi-level spatial analysis and device screening, the system can determine the key monitoring area from the complex traffic network. This screening mechanism based on influence evaluation ensures that the system always maintains effective monitoring capability when facing diversified traffic events. In urban traffic management, this intelligent evaluation scheme significantly improves the accuracy of early warning.

[0120] The embodiment realizes intelligent monitoring upgrade for traffic events by establishing a complete evaluation processing link. The system can dynamically adjust the screening strategy based on real-time influence evaluation, avoiding the limitations of traditional fixed range schemes. Through multi-dimensional spatial analysis and device screening, the quality and reliability of monitoring are significantly improved, providing reliable technical support for traffic management. This intelligent evaluation mechanism shows strong adaptability and optimization effect in urban traffic monitoring.

[0121] The embodiment not only improves the accuracy of range definition, but also establishes an evolving evaluation system through continuous strategy optimization and effect analysis. This real-time feedback-based optimization mechanism ensures that the system can continuously improve as traffic patterns change, providing increasingly accurate device support for traffic monitoring. In practical applications, this self-optimization mechanism significantly improves the long-term service quality and monitoring effect of the system, providing reliable technical support for urban traffic management.

[0122] In an embodiment of the traffic event intelligent identification and early warning method of the present application, the following contents can be specifically included:

[0123] Step S701: reading real-time passing data from the snapshot device list, extracting vehicle feature information and passing time, sorting the vehicle feature information in time sequence, calculating the vehicle moving direction and speed based on the vehicle feature information of adjacent snapshot points, constructing a vehicle moving trajectory model, establishing a mapping relationship between the license plate number and location information in the vehicle moving trajectory model, generating a vehicle-location mapping table, and querying the vehicle archive database according to the license plate number in the vehicle-location mapping table to obtain the contact information of the vehicle owner;

[0124] Step S702: structuring the traffic event warning data, converting the event location coordinates into road names and kilometer post numbers, constructing a warning information template, filling the warning data and location information into the warning information template to generate warning information content, establishing a short message sending task queue, writing the warning information content and the contact information of the vehicle owner into the short message sending task queue, and sending warning information through a short message gateway interface according to the task queue.

[0125] Optionally, the embodiment innovatively designs a warning push solution based on trajectory tracking to solve the problems of discontinuous vehicle trajectory and untimely warning push in urban traffic warning. The embodiment first constructs a trajectory analysis framework to realize accurate tracking of vehicle movement through multi-level feature extraction. The system designs a trajectory evaluation formula: Trajectory_Score = α Position_Accuracy + β Speed_Consistency + γ Direction_Stability - δ Time_Gap, where Position_Accuracy represents position accuracy, Speed_Consistency represents speed consistency, Direction_Stability represents direction stability, Time_Gap represents time interval, and α, β, γ, δ are dynamic adjustment coefficients. In urban traffic warning, this multi-dimensional trajectory evaluation method can effectively improve the relevance of warning.

[0126] The embodiment deeply optimizes the data extraction strategy. Based on the passing characteristics of vehicles, a real-time extraction mechanism is designed. Through feature information analysis, accurate identification of vehicle identity is realized; through time sequence sorting, accurate construction of passing records is realized; through motion parameter calculation, accurate acquisition of vehicle state is realized. Special attention is paid to the timeliness of data. When data delay is detected, the system will supplement data through a backup channel. For example, when processing vehicle passing on urban expressways, feature extraction can accurately record the passing trajectory of vehicles, including passing time, driving direction, running speed and other key information, which is crucial for precise warning.

[0127] The embodiment innovatively realizes the trajectory construction mechanism. Aiming at the vehicle motion characteristics, the system constructs a trajectory model framework based on continuous points. Through position mapping, accurate recording of the vehicle trajectory is realized. Special attention is paid to the continuity of the trajectory, and through the design of the supplement point strategy, the reliability of the trajectory reconstruction is ensured. This model-based trajectory construction method can effectively predict the vehicle motion. The embodiment adopts a warning priority calculation formula: Priority = (Impact_Level × Distance_Factor) / (Time_Window + ε), wherein Impact_Level represents the impact level, Distance_Factor represents the distance factor, Time_Window represents the time window, and ε is a smoothing factor.

[0128] The embodiment deeply optimizes the warning processing strategy. The system constructs a warning processing framework based on structured data, and realizes accurate description of the warning information through position conversion. Special attention is paid to the practicability of the warning, and through the design of the template mechanism, the accuracy of information expression is improved. For example, when generating the warning information, by converting the position coordinates into actual road names and mileage information, the driver can quickly understand the event location; when organizing the warning content, by evaluating the impact degree of the event, the risk level can be accurately conveyed.

[0129] The embodiment realizes the orderly pushing of the warning through the task queue. The system constructs a queue framework based on priority, and distributes information combined with contact information. Special attention is paid to the timeliness of the pushing, and through the establishment of the concurrency mechanism, efficient sending of the warning information is realized. This systematic pushing scheme provides reliable implementation support for the warning service.

[0130] The innovative design of the embodiment not only solves the warning pushing problem in the traditional method, but also establishes a sustainable optimization service framework. Through multi-level trajectory analysis and information processing, the system can identify key warning objects from complex traffic scenes. This pushing mechanism based on trajectory tracking ensures that the system always maintains effective warning ability when facing diversified traffic events. In urban traffic management, this intelligent warning scheme significantly improves the timeliness and accuracy of the service.

[0131] The embodiment realizes the intelligent service upgrade for traffic events by establishing a complete warning processing link. The system can dynamically adjust the pushing strategy based on real-time trajectory analysis, avoiding the limitations of the traditional fixed mode scheme. Through multi-dimensional information processing and task scheduling, the quality and reliability of the warning are significantly improved, providing reliable travel support for traffic participants. This intelligent warning mechanism shows strong adaptability and optimization effect in urban traffic services.

[0132] In order to effectively solve the deficiencies of the traditional technology in data fusion, event identification and early warning release, and significantly improve the accuracy and real-time performance of traffic event identification and early warning, the present application provides an embodiment of a traffic event intelligent identification and early warning device for implementing all or part of the contents of the traffic event intelligent identification and early warning method, as shown in Figure 2 , which specifically includes the following contents:

[0133] A multi-source data processing module 10 is used to collect traffic scene multi-source data, acquire road traffic image data stream collected by a front-end camera, vehicle flow data collected by a road surface geomagnetic sensor, vehicle speed data collected by a microwave radar, and weather data collected by a weather station, perform time stamp alignment and data cleaning on the multi-source data, extract vehicle passing number, vehicle speed, vehicle position distribution, vehicle stagnation time, road surface occupancy rate, and weather condition to construct a feature parameter set, calculate time sequence variation of the feature parameter set by using a sliding window method, and construct a feature vector matrix.

[0134] A traffic event determination module 20 is used to construct a space-time feature clustering model based on the feature vector matrix, calculate similarity coefficients between each feature vector in the feature vector matrix, perform clustering analysis on the feature vector matrix by using a density clustering algorithm, generate event clustering results, construct an event type identification rule library, match the event clustering results with the event type identification rule library, identify a traffic event type, extract key features of the traffic event type, generate an event feature description vector, compare the event feature description vector with a preset multi-dimensional threshold, and generate traffic event early warning data.

[0135] An identification and early warning module 30 is used to associate the traffic event early warning data with geographic location information of the front-end camera, construct an event influence range matrix, calculate event location coordinates, determine event influence area boundary points, select a snapshot device in the event influence area, acquire real-time vehicle passing data, construct a vehicle moving track, establish a vehicle-position mapping table, query a vehicle archive database based on the vehicle-position mapping table, acquire a vehicle owner contact method, encapsulate the traffic event early warning data and the event location coordinates into early warning information, and send the early warning information to the vehicle owner through a short message gateway.

[0136] From the above description, the traffic event intelligent identification and early warning device provided by the embodiments of the present application can realize comprehensive analysis of image, geomagnetic, radar and weather data through innovative construction of a multi-source data fusion mechanism, time stamp alignment and feature extraction. A clustering model based on space-time features is designed, and a density clustering and rule matching are combined to establish an event type identification strategy. An influence range evaluation mechanism is introduced, and precise early warning information is pushed through vehicle trajectory analysis and position mapping. This method effectively solves the deficiencies of traditional technologies in data fusion, event identification and early warning release, and significantly improves the accuracy and real-time performance of traffic event identification and early warning.

[0137] From the hardware level, in order to effectively solve the deficiencies of traditional technologies in data fusion, event identification and early warning release, and significantly improve the accuracy and real-time performance of traffic event identification and early warning, the present application provides an embodiment of an electronic device for implementing all or part of the traffic event intelligent identification and early warning method, which specifically includes the following content:

[0138] A processor, a memory, a communications interface and a bus; wherein the processor, the memory and the communications interface complete mutual communication through the bus; the communications interface is used for realizing information transmission between the traffic event intelligent identification and early warning device and a core business system, a user terminal and a related database and other related devices; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, and the present embodiment is not limited thereto. In the present embodiment, the logic controller can be implemented with reference to the embodiments of the traffic event intelligent identification and early warning method and the embodiments of the traffic event intelligent identification and early warning device, the contents of which are incorporated herein, and repeated descriptions are omitted.

[0139] It can be understood that the user terminal can include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. The smart wearable device can include smart glasses, a smart watch, a smart bracelet, etc.

[0140] In actual application, part of the traffic event intelligent identification and early warning method can be executed on the electronic device as described above, or all operations can be completed in the client device. Specifically, the selection can be made according to the processing capacity of the client device and the limitation of the user's use scenario, etc. The present application does not limit this. If all operations are completed in the client device, the client device can further include a processor.

[0141] The aforementioned client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side; in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a distributed server structure.

[0142] Figure 3 This is a schematic block diagram illustrating the system configuration of the electronic device 9600 according to an embodiment of this application. Figure 3 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that... Figure 3 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0143] In one embodiment, the intelligent traffic incident identification and early warning method function can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control:

[0144] Step S101: Collect multi-source traffic scene data, including road traffic image data streams collected by front-end cameras, traffic flow data collected by road surface geomagnetic sensors, vehicle speed data collected by microwave radar, and weather data collected by weather stations. Perform timestamp alignment and data cleaning on the multi-source data, extract vehicle traffic volume, vehicle speed, vehicle location distribution, vehicle dwell time, road occupancy rate, and weather conditions to construct a feature parameter set. Use the sliding window method to calculate the temporal change of the feature parameter set and construct a feature vector matrix.

[0145] Step S102: Construct a spatiotemporal feature clustering model based on the feature vector matrix, calculate the similarity coefficient between each feature vector in the feature vector matrix, perform clustering analysis on the feature vector matrix using a density clustering algorithm, generate event clustering results, construct an event type identification rule base, match the event clustering results with the event type identification rule base, identify traffic event types, extract key features of the traffic event types, generate event feature description vectors, compare the event feature description vectors with preset multidimensional thresholds, and generate traffic event early warning data;

[0146] Step S103: associate the traffic event warning data with the geographic location information of the front camera, construct an event influence range matrix, calculate the event location coordinates, determine the event influence area boundary points, filter the snapshot device in the event influence area, obtain real-time vehicle passing data, construct a vehicle movement trajectory, establish a vehicle-position mapping table, query the vehicle archive database based on the vehicle-position mapping table, obtain the vehicle owner contact information, encapsulate the traffic event warning data and the event location coordinates into warning information, and send the warning information to the vehicle owner through a short message gateway.

[0147] From the above description, the electronic device provided by the embodiments of the present application innovatively constructs a multi-source data fusion mechanism, realizes comprehensive analysis of image, geomagnetic, radar and weather data through time stamp alignment and feature extraction. A clustering model based on space-time features is designed, combined with density clustering and rule matching, an event type identification strategy is established. An influence range evaluation mechanism is introduced, through vehicle trajectory analysis and position mapping, precise warning information pushing is realized. This method effectively solves the shortcomings of traditional technology in data fusion, event identification and warning release, significantly improves the accuracy and real-time performance of traffic event identification and warning.

[0148] In another embodiment, the traffic event intelligent identification and warning device can be configured separately from the central processor 9100, for example, the traffic event intelligent identification and warning device can be configured as a chip connected with the central processor 9100, and the function of the traffic event intelligent identification and warning method is realized through the control of the central processor.

[0149] As shown in Figure 3 , the electronic device 9600 can also include a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It should be noted that the electronic device 9600 does not necessarily include all the components shown in Figure 3 ; in addition, the electronic device 9600 can also include components not shown in Figure 3 , which can refer to prior art.

[0150] As shown in Figure 3 , the central processor 9100 is sometimes also referred to as a controller or an operation control, which can include a microprocessor or other processor device and / or a logic device, which receives input and controls the operation of various components of the electronic device 9600.

[0151] The memory 9140, for example, can be one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The above-mentioned information related to failure can be stored, and in addition, a program for executing the information related to failure can be stored. The central processing unit 9100 can execute the program stored in the memory 9140 to achieve information storage or processing, and the like.

[0152] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to supply power to the electronic device 9600. The display 9160 is used to display display objects such as images and characters. The display can be, for example, an LCD display, but is not limited thereto.

[0153] The memory 9140 can be a solid state memory such as a read only memory (ROM), a random access memory (RAM), a SIM card, and the like. It can also be a memory that retains information even when power is off, can be selectively erased, and is provided with more data, and examples of such a memory are sometimes referred to as an EPROM, and the like. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 can include an application / function storage section 9142 for storing application programs and function programs or for storing a flow for executing operations of the electronic device 9600 by the central processing unit 9100.

[0154] The memory 9140 can also include a data storage section 9143 for storing data such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. A driver storage section 9144 of the memory 9140 can include various drivers of the electronic device for a communication function and / or for executing other functions of the electronic device such as a messaging application, an address book application, and the like.

[0155] The communication module 9110 is a transmitter / receiver that transmits and receives signals via an antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processing unit 9100 to provide input signals and receive output signals, which can be the same as in the case of a conventional mobile communication terminal.

[0156] Based on different communication technologies, multiple communication modules 9110, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module, etc., can be provided in the same electronic device. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and to receive audio input from the microphone 9132, thereby enabling typical telecommunication functions. The audio processor 9130 can include any suitable buffers, decoders, amplifiers, etc. In addition, the audio processor 9130 is coupled to the central processor 9100, thereby enabling the recording of audio on the local device via the microphone 9132 and enabling the playing of stored audio on the local device via the speaker 9131.

[0157] The embodiments of the present application also provide a computer readable storage medium capable of implementing all steps of the traffic event intelligent identification and early warning method in the above-mentioned embodiments, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement all steps of the traffic event intelligent identification and early warning method in the above-mentioned embodiments, for example, the processor executes the computer program to implement the following steps:

[0158] Step S101: collecting traffic scene multi-source data, acquiring road traffic image data stream collected by a front-end camera, vehicle flow data collected by a road surface geomagnetic sensor, vehicle speed data collected by a microwave radar, and weather data collected by a weather station, performing time stamp alignment and data cleaning on the multi-source data, extracting a vehicle passing number, a vehicle driving speed, a vehicle position distribution, a vehicle stagnation duration, a road surface occupancy rate, and a weather condition to construct a feature parameter set, calculating a time sequence variation of the feature parameter set by using a sliding window method, and constructing a feature vector matrix;

[0159] Step S102: constructing a space-time feature clustering model based on the feature vector matrix, calculating a similarity coefficient between each feature vector in the feature vector matrix, performing clustering analysis on the feature vector matrix by using a density clustering algorithm, generating an event clustering result, constructing an event type identification rule library, matching the event clustering result with the event type identification rule library, identifying a traffic event type, extracting a key feature of the traffic event type, generating an event feature description vector, comparing the event feature description vector with a preset multi-dimensional threshold, and generating traffic event early warning data;

[0160] Step S103: associate the traffic event warning data with the geographic location information of the front camera, construct an event influence range matrix, calculate the event location coordinates, determine the event influence area boundary points, filter the snapshot device within the event influence area, obtain real-time vehicle data, construct a vehicle movement trajectory, establish a vehicle-position mapping table, query the vehicle archive database based on the vehicle-position mapping table, obtain the vehicle owner contact information, encapsulate the traffic event warning data and the event location coordinates into warning information, and send the warning information to the vehicle owner through a short message gateway.

[0161] From the above description, the computer readable storage medium provided by the embodiments of the present application innovatively constructs a multi-source data fusion mechanism, realizes comprehensive analysis of image, geomagnetic, radar and weather data through time stamp alignment and feature extraction. A clustering model based on spatio-temporal features is designed, combined with density clustering and rule matching, an event type identification strategy is established. An influence range evaluation mechanism is introduced, through vehicle trajectory analysis and position mapping, precise warning information pushing is realized. This method effectively solves the shortcomings of traditional technology in data fusion, event identification and warning release, significantly improves the accuracy and real-time performance of traffic event identification and warning.

[0162] The embodiments of the present application also provide a computer program product capable of realizing all steps of the traffic event intelligent identification and warning method in the above-mentioned embodiments, wherein the execution subject is a server or a client. The computer program / instructions are executed by the processor to realize the steps of the traffic event intelligent identification and warning method, for example, the computer program / instructions realize the following steps:

[0163] Step S101: collect traffic scene multi-source data, obtain road traffic image data stream collected by a front camera, vehicle flow data collected by a road surface geomagnetic sensor, vehicle speed data collected by a microwave radar, and weather data collected by a weather station, perform time stamp alignment and data cleaning on the multi-source data, extract vehicle passing number, vehicle speed, vehicle position distribution, vehicle stop duration, road occupancy rate, and weather condition to construct a feature parameter set, calculate the time sequence variation of the feature parameter set by using a sliding window method, and construct a feature vector matrix;

[0164] Step S102: construct a spatio-temporal feature clustering model based on the feature vector matrix, calculate the similarity coefficient between each feature vector in the feature vector matrix, perform clustering analysis on the feature vector matrix by using a density clustering algorithm, generate an event clustering result, construct an event type identification rule library, match the event clustering result with the event type identification rule library, identify the traffic event type, extract the key features of the traffic event type, generate an event feature description vector, compare the event feature description vector with a preset multi-dimensional threshold, and generate traffic event warning data;

[0165] Step S103: associate the traffic event warning data with the geographic location information of the front camera, construct an event influence range matrix, calculate the event location coordinates, determine the event influence area boundary points, filter the snapshot device in the event influence area, obtain real-time passing vehicle data, construct a vehicle movement trajectory, establish a vehicle-position mapping table, query the vehicle archive database based on the vehicle-position mapping table, obtain the vehicle owner contact information, encapsulate the traffic event warning data and the event location coordinates as warning information, and send the warning information to the vehicle owner through a short message gateway.

[0166] From the above description, the computer program product provided by the embodiments of the present application innovatively constructs a multi-source data fusion mechanism, realizes comprehensive analysis of image, geomagnetic, radar and weather data through time stamp alignment and feature extraction. A clustering model based on space-time features is designed, combined with density clustering and rule matching, an event type identification strategy is established. An influence range evaluation mechanism is introduced, through vehicle trajectory analysis and position mapping, precise warning information pushing is realized. This method effectively solves the deficiencies of traditional technologies in data fusion, event identification and warning release, significantly improves the accuracy and real-time performance of traffic event identification and warning.

[0167] Those skilled in the art will understand that the embodiments of the present application can be provided as a method, device, or computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0168] The present application is described with reference to flowcharts and / or block diagrams according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The device that implements the functions specified in one or more flows and / or blocks. Figure 1 The device that implements the functions specified in one or more flows and / or blocks.

[0169] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 The functions of a flow or multiple flows and / or a block or multiple blocks in conjunction with the disclosed methods can be implemented on practitioners' computers in computer software, firmware, hardware, or combinations of them. Figure 1

[0170] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow Figure 1 The functions of a flow or multiple flows and / or a block or multiple blocks in conjunction with the disclosed methods can be implemented on practitioners' computers in computer software, firmware, hardware, or combinations of them. Figure 1

[0171] The principles and implementations of the present application are described in the specific embodiments, the above examples are only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation and application range will be changed, and the above description should not be understood as the limitation of the present application.​​

Claims

1. A method for intelligent identification and early warning of traffic incidents, characterized in that, The method includes: Collect multi-source traffic scene data, including road traffic image data streams collected by front-end cameras, traffic flow data collected by road surface geomagnetic sensors, vehicle speed data collected by microwave radar, and weather data collected by meteorological stations. Perform timestamp alignment and data cleaning on the multi-source data, extract vehicle traffic volume, vehicle speed, vehicle location distribution, vehicle dwell time, road occupancy rate, and weather conditions to construct a feature parameter set. Use the sliding window method to calculate the temporal change of the feature parameter set and construct a feature vector matrix. Spatial and temporal features are extracted from the feature vector matrix. The spatial features are gridded to construct a spatial correlation matrix, and the temporal features are decomposed temporally to construct a temporal correlation matrix. The spatial and temporal correlation matrices are combined to construct a spatiotemporal feature clustering model. The distance matrix of each feature vector in the feature vector matrix is ​​calculated based on Euclidean distance. The distance matrix is ​​mapped to the spatiotemporal feature clustering model to generate feature vector similarity coefficients. Cluster centroids are calculated based on the feature vector similarity coefficients, and a density clustering parameter set is constructed. The density clustering parameter set is input into a density clustering algorithm to calculate the local density value and minimum distance value of each feature vector, determining the cluster core points. The cluster core points are used as cluster centers for feature vector allocation, generating event clustering results. An event type identification rule base is constructed. The event clustering results are matched with the event type identification rule base to identify traffic event types. Key features of the traffic event types are extracted, generating event feature description vectors. The event feature description vectors are compared with preset multidimensional thresholds to generate traffic event early warning data. The traffic incident warning data is associated with the geographical location information of the front-end camera to construct an incident impact range matrix, calculate the incident location coordinates, determine the boundary points of the incident impact area, filter the capture devices within the incident impact area, obtain real-time vehicle passing data, construct vehicle movement trajectories, establish a vehicle-location mapping table, query the vehicle file database based on the vehicle-location mapping table to obtain the vehicle owner's contact information, encapsulate the traffic incident warning data and the incident location coordinates into a warning message, and send the warning message to the vehicle owner through an SMS gateway.

2. The intelligent identification and early warning method for traffic incidents according to claim 1, characterized in that, The process of collecting multi-source traffic scene data includes acquiring road traffic image data streams collected by front-end cameras, traffic flow data collected by road surface geomagnetic sensors, vehicle speed data collected by microwave radar, and weather data collected by weather stations. The multi-source data undergoes timestamp alignment and data cleaning, including: The system collects real-time road traffic image data streams through a front-end camera, activates a road surface geomagnetic sensor to collect traffic flow data, activates a microwave radar speed measurement module to collect vehicle speed data, connects to a weather station data interface to collect weather data, marks the road traffic image data streams, traffic flow data, vehicle speed data, and weather data according to the data collection time, constructs a multi-source data collection matrix, and establishes a data quality assessment model based on the multi-source data collection matrix. The multi-source data acquisition matrix is ​​subjected to data integrity checks, and a data reliability score is calculated based on a data quality assessment model. Data with a reliability score lower than a preset threshold is removed. The timestamps of each data source in the multi-source data acquisition matrix are converted into a unified time format. The time interval between adjacent sampling points is calculated. The sampling periods of different data sources are aligned based on a linear interpolation algorithm to generate a time-aligned multi-source data matrix.

3. The intelligent identification and early warning method for traffic incidents according to claim 1, characterized in that, The method involves extracting vehicle traffic volume, vehicle speed, vehicle location distribution, vehicle dwell time, road occupancy, and weather conditions to construct a feature parameter set. A sliding window method is then used to calculate the temporal changes in this feature parameter set, resulting in a feature vector matrix, including: Six parameters are extracted from the time-aligned multi-source data matrix: number of vehicles passing through, vehicle speed, vehicle location distribution, vehicle dwell time, road occupancy rate, and weather conditions. The number of vehicles passing through and vehicle speed are normalized, the vehicle location distribution is converted into a spatial density matrix, the cumulative probability distribution of the vehicle dwell time is calculated, the road occupancy rate is converted into a saturation index, and the weather conditions are numerically quantified to generate a feature parameter set. The feature parameter sampling window is set based on the sliding window method. The feature parameter set is input into the sampling window according to the time series. The mean, variance, skewness and kurtosis of each feature parameter in the sampling window are calculated to construct a time series statistical feature matrix. The time series change is calculated by the difference between the time series statistical feature matrix and the adjacent sampling window. The time series change is combined with the time series statistical feature matrix to generate a feature vector matrix.

4. The intelligent identification and early warning method for traffic incidents according to claim 1, characterized in that, The process involves constructing an event type identification rule base, matching the event clustering results with the rule base to identify traffic event types, extracting key features of the traffic event types, generating event feature description vectors, and comparing these vectors with preset multi-dimensional thresholds to generate traffic event early warning data. This includes: A basic feature library for traffic incidents is constructed. Feature items in the basic feature library are combined to form a feature rule set. The feature rule set is classified and labeled according to the event type. A mapping relationship between feature rules and event types is established to generate an event type identification rule library. The event clustering results are matched with the event type identification rule library. The traffic incident type is identified based on the matching degree. Key features are extracted from the traffic incident type to construct a feature description vector. The feature description vector is standardized to construct a multi-dimensional threshold discrimination matrix. The feature description vector is input into the multi-dimensional threshold discrimination matrix for numerical comparison. The triggering state of each dimension threshold is calculated. Based on the triggering state, the event warning level is set. The traffic event type, the feature description vector and the event warning level are combined to generate traffic event warning data.

5. The intelligent identification and early warning method for traffic incidents according to claim 1, characterized in that, The step of associating the traffic incident early warning data with the geographical location information of the front-end camera to construct an incident impact range matrix, calculating the incident location coordinates, determining the boundary points of the incident impact area, and filtering capture devices within the incident impact area includes: The event features in the traffic incident early warning data are spatiotemporally registered with the geographic location information of the front-end camera to construct a geographic information mapping matrix. Based on the geographic information mapping matrix, the latitude and longitude coordinates of the event location are calculated. The latitude and longitude coordinates are converted into event location coordinates in a Cartesian coordinate system. The influence radius parameter is set according to the event early warning level. An event influence range matrix is ​​constructed with the event location coordinates as the center. Based on the event impact range matrix, the geometric boundary of the impact area is calculated. The minimum bounding polygon algorithm is used to generate a set of boundary points of the event impact area. The set of boundary points is overlaid and analyzed with electronic map data to extract the road topology within the impact area. The location of the capture device is determined based on the road topology, and a list of capture devices located within the impact area is selected.

6. The intelligent identification and early warning method for traffic incidents according to claim 1, characterized in that, The process of acquiring real-time vehicle data, constructing vehicle movement trajectories, establishing a vehicle-location mapping table, querying a vehicle file database based on the vehicle-location mapping table to obtain vehicle owner contact information, encapsulating the traffic incident warning data and the incident location coordinates into warning information, and sending the warning information to the vehicle owner through an SMS gateway includes: Real-time vehicle data is read from the list of capture devices, vehicle feature information and passage time are extracted, the vehicle feature information is sorted according to time sequence, the vehicle movement direction and speed are calculated based on the vehicle feature information of adjacent capture points, a vehicle movement trajectory model is constructed, a mapping relationship is established between the license plate number and location information in the vehicle movement trajectory model, a vehicle-location mapping table is generated, and the vehicle owner's contact information is obtained by querying the vehicle file database based on the license plate number in the vehicle-location mapping table. The traffic incident warning data is structured by converting the incident location coordinates into road names and kilometer markers, constructing a warning information template, filling the warning data and location information into the warning information template to generate warning information content, establishing an SMS sending task queue, writing the warning information content and the vehicle owner's contact information into the SMS sending task queue, and sending the warning information according to the task queue through the SMS gateway interface.

7. A traffic incident intelligent identification and early warning device, characterized in that, The device includes: The multi-source data processing module is used to collect multi-source data of traffic scenarios, including road traffic image data streams collected by front-end cameras, traffic flow data collected by road surface geomagnetic sensors, vehicle speed data collected by microwave radar, and weather data collected by meteorological stations. The module performs timestamp alignment and data cleaning on the multi-source data, extracts vehicle traffic volume, vehicle speed, vehicle location distribution, vehicle dwell time, road occupancy, and weather conditions to construct a feature parameter set, and uses the sliding window method to calculate the temporal change of the feature parameter set to construct a feature vector matrix. The traffic incident determination module is used to extract spatial and temporal features from the feature vector matrix, perform gridding on the spatial features to construct a spatial correlation matrix, perform temporal decomposition on the temporal features to construct a temporal correlation matrix, combine the spatial and temporal correlation matrices to construct a spatiotemporal feature clustering model, calculate the distance matrix of each feature vector in the feature vector matrix based on Euclidean distance, map the distance matrix to the spatiotemporal feature clustering model to generate feature vector similarity coefficients, calculate cluster centroids based on the feature vector similarity coefficients, construct a density clustering parameter set, input the density clustering parameter set into a density clustering algorithm, calculate the local density value and minimum distance value of each feature vector, determine cluster core points, use the cluster core points as cluster centers for feature vector allocation, generate event clustering results, construct an event type identification rule base, match the event clustering results with the event type identification rule base to identify traffic incident types, extract key features of the traffic incident types, generate event feature description vectors, compare the event feature description vectors with preset multidimensional thresholds, and generate traffic incident early warning data. The identification and early warning module is used to associate the traffic event early warning data with the geographical location information of the front-end camera, construct an event impact range matrix, calculate the event location coordinates, determine the boundary points of the event impact area, filter the capture devices within the event impact area, acquire real-time vehicle passing data, construct vehicle movement trajectories, establish a vehicle-location mapping table, query the vehicle file database based on the vehicle-location mapping table to obtain the vehicle owner's contact information, encapsulate the traffic event early warning data and the event location coordinates into early warning information, and send the early warning information to the vehicle owner through an SMS gateway.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the intelligent traffic incident identification and early warning method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the intelligent traffic incident identification and early warning method according to any one of claims 1 to 6.

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