Danger intelligent early warning identification avoiding system based on road patrol electronic traffic police

Abstract

The invention discloses a road patrol electronic traffic police-based danger intelligent early warning identification avoiding system, which is characterized in that S1, vehicle dynamic parameters, road static characteristics, environmental interference factors and traffic participant information are synchronously acquired through multi-source data, and an electronic traffic police terminal is integrated with a high-definition camera unit and a millimeter wave radar; the environment sensor group covers a plurality of monitoring devices; s2, preprocessing data by an edge node, removing invalid information and preliminarily judging danger; S3, encrypting and transmitting the data to a cloud through a 5G slice; s4, performing deep coupling analysis to calculate a danger coefficient, and triggering early warning when the danger coefficient exceeds a threshold value; and S5, response is executed according to the grade, and V2X vehicle-road cooperative pushing of an avoidance path is included. According to the system, by means of multi-source data fusion and intelligent analysis, danger identification timeliness and accuracy are improved, the accident rate is reduced through hierarchical response, reliable operation of the system is guaranteed through self-calibration and traceability analysis, traffic management optimization is assisted, and powerful support is provided for intelligent road traffic safety management.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation technology, specifically to a hazard intelligent early warning, identification, and avoidance system based on electronic traffic police for road patrols. Background Technology

[0002] The background technology of the hazard intelligent early warning, identification and avoidance system based on road patrol electronic traffic police stems primarily from the increasingly severe road traffic safety situation and the limitations of traditional traffic management models. With the acceleration of urbanization, the number of motor vehicles has surged, and road traffic flow has continued to rise. Various traffic violations, sudden accidents (such as vehicle scrapes and rear-end collisions), and sudden road conditions (such as collapses and obstacles) are frequent safety hazards, seriously threatening the safety of pedestrians and vehicles. Traditional traffic management relies on manual patrols and fixed monitoring equipment for capture, which has problems such as limited coverage, delayed response, high labor costs, and insufficient ability to provide dynamic hazard warnings. It is difficult to perceive and deal with road risks in real time and comprehensively, and there is an urgent need to break through the management bottleneck through intelligent means.

[0003] On the other hand, the rapid development of related technologies has provided solid support for system construction. In recent years, technologies such as artificial intelligence (especially computer vision and deep learning algorithms), the Internet of Things (sensors and vehicle-road cooperative communication), big data (massive traffic data processing and analysis), and smart hardware (high-definition cameras, millimeter-wave radar, and edge computing devices) have matured. For example, image recognition technology can accurately identify targets such as vehicles, pedestrians, and road defects; IoT devices can realize real-time acquisition and transmission of multi-source data; and big data analysis can uncover traffic risk patterns. The integrated application of these technologies makes real-time perception, intelligent identification, and rapid early warning of road hazards possible, laying the technical foundation for the research and development and implementation of the system. Summary of the Invention

[0004] The purpose of this invention is to provide a hazard intelligent early warning, identification and avoidance system based on electronic traffic police for road patrols, in order to solve the problems mentioned in the background art, such as the limited coverage, slow response, high labor costs and insufficient ability to provide dynamic hazard early warnings in traditional traffic management methods that rely on manual patrols and fixed monitoring equipment for capture. These problems make it difficult to perceive and deal with road risks in real time and comprehensively, and there is an urgent need to break through the management bottleneck through intelligent means.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a hazard intelligent early warning, identification, and avoidance system based on road patrol electronic traffic police, the method comprising: S1: Synchronous acquisition of multi-source data; S2: Edge node data preprocessing; S3: Encrypted data transmission to the cloud; S4: Deep Coupling Hazard Analysis; S5: Execution of tiered response strategy; S6: Dynamic sensor calibration; S7: Optimize and review dangerous incidents; The high-definition camera unit uses a starlight-level CMOS sensor, equipped with an electric zoom lens, and supports backlight compensation and nighttime infrared imaging. It can identify vehicle inspection stickers and drivers' phone calls. Through electronic traffic police terminals, environmental sensor groups, and traffic condition monitoring equipment deployed along the road, it can simultaneously acquire vehicle dynamic parameters (instantaneous speed, steering angle, braking status), road static characteristics (number of lanes, curvature of curves, slope), environmental interference factors (wind force, haze concentration, light intensity), and traffic participant information (pedestrian location, non-motorized vehicle trajectory). The electronic traffic police terminal integrates a high-definition camera unit and millimeter-wave radar, and the environmental sensor group includes temperature sensors, humidity sensors, visibility sensors, and road icing sensors. The subsequent steps will be based on the multi-dimensional data collected in this step, followed by edge node preprocessing, encrypted cloud transmission, deep coupling analysis, and tiered response execution, forming a complete closed loop from data collection to hazard response.

[0006] Preferably, in S2, the edge computing module has a built-in acceleration chip to realize real-time processing of video streams. The initial hazard judgment logic includes determining a rear-end collision risk if the speed difference between vehicles traveling in the same direction is too large and the distance between them is too small. The raw data collected in S1 is transmitted to the edge computing module via industrial Ethernet. The data is subjected to noise reduction filtering, spatiotemporal alignment and feature extraction. Invalid frames and duplicate collection values ​​are removed. The data volume is compressed using video encoding and data compression algorithms. The initial hazard judgment is performed through preset judgment logic to identify sudden and obvious hazards.

[0007] Preferably, the 5G slicing link ensures low end-to-end latency, supports a certain number of terminals to access concurrently, and prioritizes the transmission of early warning information over ordinary data when the network is congested. A dedicated communication link is established through 5G slicing technology to encrypt and transmit the pre-processed data in S2 to the cloud analysis platform. The encryption uses an encryption algorithm and supports breakpoint resume. At the same time, the historical dangerous event database index and system upgrade package are transmitted.

[0008] Preferably, in S4, the time-series prediction model incorporates an attention weight matrix, assigning corresponding weights to features such as sudden lane change behavior, abrupt changes in road surface friction coefficient, and strong crosswinds. The model training samples contain real hazardous event data of different road types over many years. In a cloud server cluster, a deep learning model with an attention fusion mechanism is used to process the received data. The model includes an improved target detection network and a time-series prediction model, constructing a dynamic coupling model of "vehicle-road-environment" to calculate the hazard coefficient. When the hazard coefficient exceeds a threshold, an early warning is triggered.

[0009] Preferably, in S5, the V2X vehicle-road cooperative step sends precise coordinate-level avoidance paths to vehicles equipped with OBU devices via direct communication. The path planning adopts an improved algorithm that incorporates a road adhesion coefficient correction factor. Based on the hazard coefficient calculated in S4, the corresponding level of response is initiated: at a low hazard level, warning information is displayed on the roadside LED screen and voice prompts are pushed to vehicles within a certain range; at a medium hazard level, the vehicle's emergency braking assist signal is activated and the timing of traffic lights at associated intersections is adjusted; at a high hazard level, a temporary road closure procedure is initiated and nearby traffic enforcement terminals are dispatched. During the response process, the system interfaces with the vehicle's OBD interface and traffic signal control system via protocols.

[0010] Preferably, in S6, road surface parameters measured by a calibration vehicle at known speed and by humans are periodically collected as standard reference data to compensate for sensor drift and control the accuracy deviation within a reasonable range. The calibration data is uploaded to the blockchain for storage through a blockchain architecture.

[0011] Preferably, in S4, the full historical data from S1-S3 is called to construct a time series map of dangerous events, marking the parameter changes of each key node before the occurrence of danger (such as the time points of abnormal fluctuations in vehicle speed, the moment of sudden drop in road friction coefficient, and the period of sudden change in environmental visibility). The causal relationship between parameter changes and the occurrence of danger is mined through association rule algorithm to determine the direct causes (such as vehicle braking system failure) and indirect causes (such as brake overheating caused by continuous downhill road sections). A source tracing report containing time axis, spatial distribution, related factors and cause hierarchy structure is generated and exported to the traffic management department's hidden danger investigation system. At the same time, the source tracing results are fed back to the deep coupling hazard analysis stage of S4 to optimize the parameter settings of the dynamic coupling model.

[0012] Preferably, when the graded response strategy is executed in S5, it also achieves data linkage with the urban traffic command platform, synchronizing the location, type, level and response measures of dangerous events to the command platform, receiving manual intervention instructions (such as temporary traffic control plans and special vehicle dispatch instructions) issued by the command platform, adjusting the execution details of the response strategy according to the manual intervention instructions, and simultaneously providing real-time feedback on the response execution progress to the command platform, forming a dual guarantee mechanism of "automatic system response + manual collaborative command".

[0013] Compared with existing technologies, the beneficial effects of this invention are as follows: This system, through multi-source data synchronous acquisition and deep coupling analysis, can comprehensively and in real-time capture various potential hazards on the road. Combined with the collaborative mode of edge computing and cloud processing, it significantly improves the timeliness and accuracy of hazard identification, effectively shortening the time difference between the occurrence of a hazard and the system's response. With the help of a graded response strategy and V2X vehicle-road cooperative technology, precise early warning and avoidance measures can be taken for different hazard levels. This provides autonomous vehicles with automatically executed avoidance paths and also provides drivers of manually driven vehicles with clear and effective operational suggestions, significantly reducing the probability of traffic accidents. The introduction of sensor self-calibration mechanisms and hazard source analysis functions ensures the data reliability of the system's long-term operation, while providing traffic management departments with detailed hazard cause analysis reports, helping to improve road safety conditions from the root and optimize traffic management strategies. Furthermore, the linkage mechanism between the system and the urban traffic command platform achieves an efficient combination of automatic response and manual collaboration, further enhancing the ability to cope with complex and dangerous situations and laying a solid foundation for building an intelligent and refined road traffic safety management system. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the process structure of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Please see Figure 1 This invention provides a technical solution: a hazard intelligent early warning, identification, and avoidance system based on road patrol electronic traffic police, the method comprising: S1: Synchronous acquisition of multi-source data; S2: Edge node data preprocessing; S3: Encrypted data transmission to the cloud; S4: Deep Coupling Hazard Analysis; S5: Execution of tiered response strategy; S6: Dynamic sensor calibration; S7: Optimize and review dangerous incidents; The high-definition camera unit uses a starlight-level CMOS sensor, equipped with an electric zoom lens, and supports backlight compensation and nighttime infrared imaging. It can identify vehicle inspection stickers and drivers' phone calls. Through electronic traffic police terminals, environmental sensor groups, and traffic condition monitoring equipment deployed along the road, it can simultaneously acquire vehicle dynamic parameters (instantaneous speed, steering angle, braking status), road static characteristics (number of lanes, curvature of curves, slope), environmental interference factors (wind force, haze concentration, light intensity), and traffic participant information (pedestrian location, non-motorized vehicle trajectory). The electronic traffic police terminal integrates a high-definition camera unit and millimeter-wave radar, and the environmental sensor group includes temperature sensors, humidity sensors, visibility sensors, and road icing sensors. The subsequent steps will be based on the multi-dimensional data collected in this step, followed by edge node preprocessing, encrypted cloud transmission, deep coupling analysis, and tiered response execution, forming a complete closed loop from data collection to hazard response.

[0017] Furthermore, in S2, the edge computing module has a built-in acceleration chip to realize real-time processing of video streams. The initial hazard judgment logic includes determining a rear-end collision risk if the speed difference between vehicles traveling in the same direction is too large and the distance between them is too small. The raw data collected in S1 is transmitted to the edge computing module via industrial Ethernet. The data is subjected to noise reduction filtering, spatiotemporal alignment and feature extraction, invalid frames and duplicate collection values ​​are removed, and the data volume is compressed using video encoding and data compression algorithms. Finally, the initial hazard judgment is performed through preset judgment logic to identify sudden and obvious hazards.

[0018] Furthermore, in S3, the 5G slicing link ensures low end-to-end latency, supports a certain number of terminals to access concurrently, and prioritizes the transmission of early warning information over ordinary data when the network is congested. A dedicated communication link is established through 5G slicing technology to encrypt and transmit the pre-processed data in S2 to the cloud analysis platform. The encryption uses an encryption algorithm and supports breakpoint resume. At the same time, the historical dangerous event database index and system upgrade package are transmitted.

[0019] Furthermore, in S4, the time-series prediction model incorporates an attention weight matrix, assigning corresponding weights to features such as sudden lane change behavior, abrupt changes in road surface friction coefficient, and strong crosswinds. The model training samples contain real hazardous event data from different road types over many years. In a cloud server cluster, a deep learning model with an integrated attention mechanism is used to process the received data. The model includes an improved target detection network and a time-series prediction model, constructing a dynamic coupling model of "vehicle-road-environment" to calculate the hazard coefficient. When the hazard coefficient exceeds a threshold, an early warning is triggered.

[0020] Furthermore, in S5, the V2X vehicle-to-infrastructure (V2X) cooperative step sends precise coordinate-level avoidance paths to vehicles equipped with OBU devices via direct communication. The path planning uses an improved algorithm that incorporates a road adhesion coefficient correction factor. Based on the hazard coefficient calculated in S4, corresponding response levels are initiated: at low hazard levels, warning information is displayed on roadside LED screens and voice prompts are pushed to vehicles within a certain range; at medium hazard levels, the vehicle's emergency braking assist signal is activated and the timing of traffic lights at associated intersections is adjusted; at high hazard levels, a temporary road closure procedure is initiated and nearby traffic enforcement terminals are dispatched. During the response process, the system interfaces with the vehicle's OBD interface and traffic signal control system via protocols.

[0021] Furthermore, in S6, road surface parameters measured by calibrators at known speeds and by humans are periodically collected as standard reference data to compensate for sensor drift and control accuracy deviation within a reasonable range. The calibration data is uploaded to the blockchain for storage via a blockchain architecture.

[0022] Furthermore, in S4, the full historical data from S1-S3 is called to construct a time series map of hazardous events, marking the parameter changes of each key node before the occurrence of the hazard (such as the time points of abnormal fluctuations in vehicle speed, the moment of sudden drop in road friction coefficient, and the period of sudden change in environmental visibility). The causal relationship between parameter changes and the occurrence of the hazard is mined through association rule algorithm to determine the direct causes (such as vehicle braking system failure) and indirect causes (such as brake overheating caused by continuous downhill road sections). A source tracing report containing time axis, spatial distribution, related factors and cause hierarchy structure is generated and exported to the traffic management department's hidden danger investigation system. At the same time, the source tracing results are fed back to the deep coupling hazard analysis stage of S4 to optimize the parameter settings of the dynamic coupling model.

[0023] Furthermore, when the tiered response strategy is executed in S5, it also achieves data linkage with the city traffic command platform, synchronizing the location, type, level and response measures of dangerous events to the command platform, receiving manual intervention instructions (such as temporary traffic control plans and special vehicle dispatch instructions) issued by the command platform, adjusting the execution details of the response strategy according to the manual intervention instructions, and simultaneously providing real-time feedback on the response execution progress to the command platform, forming a dual guarantee mechanism of "automatic system response + manual collaborative command".

[0024] Working Principle: First, S1 multi-source data synchronous acquisition utilizes electronic traffic police terminals, environmental sensor arrays, and traffic condition monitoring equipment to comprehensively collect information on vehicle dynamics, road characteristics, environmental factors, and traffic participants, providing foundational data for subsequent processing. Next, S2 edge node data preprocessing performs noise reduction, alignment, and feature extraction on the collected raw data, eliminating invalid information and compressing the data volume. Simultaneously, pre-defined logic is used to initially determine sudden, apparent hazards. Then, S3 encrypted cloud data transmission uses 5G slicing technology to encrypt and transmit the preprocessed data to the cloud analysis platform, ensuring secure and stable data transmission. Upon reaching the cloud, S4 deep coupling hazard analysis utilizes fused attention... The system employs a deep learning model for the force mechanism, combined with a dynamic coupling model of "vehicle-road-environment" to calculate the hazard coefficient. If the threshold is exceeded, an early warning is triggered. Subsequently, the S5 graded response strategy is executed, taking different measures according to the hazard level, such as warning prompts, control signals, and dispatching law enforcement terminals. It also supports V2X vehicle-road cooperation to push avoidance paths. At the same time, the S6 sensor self-calibration periodically collects standard data to compensate for deviations, and the S7 hazard event review and optimization updates model parameters by retrospectively analyzing data. Hazard source analysis uncovers the causes and provides feedback for optimization. It also links with the urban traffic command platform to achieve automatic response and manual collaboration, forming a complete closed loop from data collection, analysis, early warning to response and continuous optimization, efficiently identifying and avoiding hazards.

[0025] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions made by those skilled in the art to the technical solution of the present invention do not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A hazard intelligent early warning, identification, and avoidance system based on electronic traffic police during road patrols, characterized in that: The method includes: S1: Synchronous acquisition of multi-source data; S2: Edge node data preprocessing; S3: Encrypted data transmission to the cloud; S4: Deep Coupling Hazard Analysis; S5: Execution of tiered response strategy; S6: Dynamic sensor calibration; S7: Optimize and review dangerous incidents; The high-definition camera unit uses a starlight-level CMOS sensor, equipped with an electric zoom lens, and supports backlight compensation and nighttime infrared imaging. It can identify vehicle inspection stickers and drivers' phone calls. Through electronic traffic police terminals, environmental sensor groups, and traffic condition monitoring equipment deployed along the road, it can simultaneously acquire vehicle dynamic parameters (instantaneous speed, steering angle, braking status), road static characteristics (number of lanes, curvature of curves, slope), environmental interference factors (wind force, haze concentration, light intensity), and traffic participant information (pedestrian location, non-motorized vehicle trajectory). The electronic traffic police terminal integrates a high-definition camera unit and millimeter-wave radar, and the environmental sensor group includes temperature sensors, humidity sensors, visibility sensors, and road icing sensors. The subsequent steps will be based on the multi-dimensional data collected in this step, followed by edge node preprocessing, encrypted cloud transmission, deep coupling analysis, and tiered response execution, forming a complete closed loop from data collection to hazard response.

2. The hazard intelligent early warning, identification, and avoidance system based on road patrol electronic traffic police according to claim 1, characterized in that: In S2, the edge computing module has a built-in acceleration chip to realize real-time processing of video streams. The initial hazard judgment logic includes determining a rear-end collision risk if the speed difference between vehicles traveling in the same direction is too large and the distance between them is too small. The raw data collected in S1 is transmitted to the edge computing module via industrial Ethernet. The data is subjected to noise reduction filtering, spatiotemporal alignment and feature extraction. Invalid frames and duplicate collection values ​​are removed. The data volume is compressed using video encoding and data compression algorithms. The initial hazard judgment is performed through preset judgment logic to identify sudden and obvious hazards.

3. The hazard intelligent early warning, identification, and avoidance system based on road patrol electronic traffic police according to claim 2, characterized in that: In S3, the 5G slicing link ensures low end-to-end latency, supports a certain number of terminals to access concurrently, and prioritizes the transmission of early warning information over ordinary data when the network is congested. A dedicated communication link is established through 5G slicing technology to encrypt and transmit the pre-processed data in S2 to the cloud analysis platform. The encryption uses an encryption algorithm and supports breakpoint resume. At the same time, the historical dangerous event database index and system upgrade package are transmitted.

4. The hazard intelligent early warning, identification, and avoidance system based on road patrol electronic traffic police according to claim 3, characterized in that: In S4, the time-series prediction model incorporates an attention weight matrix, assigning corresponding weights to features such as sudden lane change behavior, abrupt changes in road surface friction coefficient, and strong crosswinds. The model training samples contain real hazardous event data from different road types over many years. In a cloud server cluster, a deep learning model with an attention fusion mechanism is used to process the received data. The model includes an improved target detection network and a time-series prediction model, constructing a dynamic coupling model of "vehicle-road-environment" to calculate the hazard coefficient. When the hazard coefficient exceeds a threshold, an early warning is triggered.

5. The hazard intelligent early warning, identification, and avoidance system based on road patrol electronic traffic police according to claim 4, characterized in that: In S5, the V2X vehicle-road cooperative step sends precise coordinate-level avoidance paths to vehicles equipped with OBU devices via direct communication. The path planning adopts an improved algorithm that incorporates a road adhesion coefficient correction factor. Based on the hazard coefficient calculated in S4, the corresponding level of response is initiated: at a low hazard level, warning information is displayed on the roadside LED screen and voice prompts are pushed to vehicles within a certain range; at a medium hazard level, the vehicle's emergency braking assist signal is activated and the timing of traffic lights at associated intersections is adjusted; at a high hazard level, a temporary road closure procedure is initiated and nearby traffic enforcement terminals are dispatched. During the response process, the system interfaces with the vehicle's OBD interface and traffic signal control system via protocols.

6. The hazard intelligent early warning, identification, and avoidance system based on road patrol electronic traffic police according to claim 5, characterized in that: In S6, road surface parameters measured by calibrators at known speeds and by humans are periodically collected as standard reference data to compensate for sensor drift and control accuracy deviation within a reasonable range. The calibration data is uploaded to the blockchain for storage via a blockchain architecture.

7. The hazard intelligent early warning, identification, and avoidance system based on road patrol electronic traffic police according to claim 6, characterized in that: In S4, the full historical data from S1-S3 is used to construct a time series map of hazardous events, marking the parameter changes of each key node before the occurrence of the hazard (such as the time points of abnormal fluctuations in vehicle speed, the moment of sudden drop in road friction coefficient, and the period of sudden change in environmental visibility). The causal relationship between parameter changes and the occurrence of the hazard is mined through association rule algorithm to determine the direct causes (such as vehicle braking system failure) and indirect causes (such as brake overheating caused by continuous downhill road sections). A source tracing report containing time axis, spatial distribution, related factors and cause hierarchy structure is generated and exported to the traffic management department's hidden danger investigation system. At the same time, the source tracing results are fed back to the deep coupling hazard analysis stage of S4 to optimize the parameter settings of the dynamic coupling model.

8. The hazard intelligent early warning, identification, and avoidance system based on road patrol electronic traffic police according to claim 7, characterized in that: When the tiered response strategy is executed in S5, it also achieves data linkage with the city traffic command platform, synchronizing the location, type, level and response measures of dangerous events to the command platform, receiving manual intervention instructions (such as temporary traffic control plans and special vehicle dispatch instructions) issued by the command platform, adjusting the execution details of the response strategy according to the manual intervention instructions, and providing real-time feedback on the response execution progress to the command platform, forming a dual guarantee mechanism of "automatic system response + manual collaborative command".

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