Serious accident early warning system based on radar detection and real-time reporting

Abstract

The invention discloses a serious accident early warning system based on radar detection and real-time reporting, and belongs to the technical field of traffic safety. The technical problems that a traditional accident early warning system mostly depends on video monitoring or manual reports, the response speed is low, the coverage range is limited, and the system is prone to being affected by the environment are solved. The system integrates a high-precision radar detection technology, an intelligent data analysis and judgment algorithm, a real-time wireless communication module and a linkage response mechanism, and obtains dynamic information of vehicles, pedestrians and obstacles in real time through radar detection modules installed at key positions of a road. And potential collision risks and abnormal driving behaviors are identified by using an advanced data analysis algorithm. Once it is judged that a serious accident possibly occurs, the system immediately uploads early warning information to a traffic management center or a related emergency department through a wireless communication network, and triggers a linkage response mechanism, such as adjusting traffic signal lamp timing, issuing warning information, scheduling rescue resources and the like, so as to quickly deal with accident risks.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic safety technology, and in particular to a serious accident warning system based on radar detection and real-time reporting. Background Art

[0002] With the continuous increase in the number of cars and the increasing complexity of road traffic networks, traffic safety issues have become a hot topic of social concern. Traditional accident warning systems mostly rely on video surveillance or manual reporting, which have shortcomings such as slow response speed, limited coverage, and susceptibility to environmental influences.

[0003] Therefore, it is particularly important to develop a system that can provide accident warning in real time, accurately and efficiently. Summary of the invention

[0004] In view of the shortcomings and deficiencies in the prior art, the present invention provides a serious accident warning system based on radar detection and real-time reporting, which integrates high-precision radar detection technology, intelligent data analysis and judgment algorithms, real-time wireless communication modules and a linkage response mechanism to achieve real-time monitoring of road traffic conditions and timely warning of serious accidents, so as to improve the level of traffic safety management.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: The serious accident warning system based on radar detection and real-time reporting provided by the present invention includes:

[0006] Radar detection module: It is set at key locations on the road to scan the surrounding environment in real time and obtain information parameters related to the position, speed, and direction of vehicles, pedestrians, and obstacles;

[0007] Data analysis and judgment module: receives data transmitted by the radar detection module, performs real-time analysis through built-in algorithms, identifies abnormal driving behavior and potential collision risks, and improves the accuracy of judgment based on historical accident data and road conditions;

[0008] Real-time reporting module: When the data analysis and judgment module determines that a serious accident may occur, the warning information is uploaded to the traffic management center or relevant emergency departments through the wireless communication network. The warning information includes but is not limited to the accident location, expected impact range and recommended response measures;

[0009] Linkage response module: Linkage with traffic lights, variable information boards and other traffic facilities, automatically adjust the timing of traffic lights according to early warning information, issue warning information, guide vehicles and pedestrians to pass safely, and send emergency broadcasts to surrounding vehicles;

[0010] Fault self-detection and recovery mechanism: Built into the system, it is used to regularly detect the working status of key components of radar equipment and communication modules, and start backup equipment or automatic repair programs when a fault is found to ensure stable operation of the system.

[0011] Preferably, the radar detection module uses a high-precision millimeter-wave radar, which is not affected by light or weather and can achieve long-distance, high-precision environmental scanning.

[0012] Preferably, the built-in algorithm in the data analysis and judgment module combines machine learning or deep learning technology, which can continuously optimize the judgment model and improve the accuracy and timeliness of the early warning.

[0013] Preferably, the real-time reporting module supports multiple wireless communication protocols, including but not limited to 4G / 5G and NB-IoT, to ensure fast and stable transmission of warning information.

[0014] Preferably, the linkage response module also includes an intelligent dispatching function, which can automatically dispatch rescue resources, such as police cars and ambulances, according to the accident situation to quickly respond to and handle the accident.

[0015] Any of the above-mentioned serious accident warning systems also includes a user interaction interface for displaying warning information, road conditions and emergency guidelines to traffic managers, drivers and the public, so as to improve the safety awareness and response capabilities of traffic participants.

[0016] The present invention provides a serious accident warning system based on radar detection and real-time reporting. It has the following beneficial effects:

[0017] The serious accident warning system based on radar detection and real-time reporting of the present invention is a fully upgraded serious accident warning system based on radar detection and real-time reporting. The system strives to be accurate, efficient and reliable in every detail of the design to ensure that potential risks can be quickly identified in a complex and changing traffic environment and the occurrence of serious accidents can be effectively prevented.

[0018] The system integrates high-precision radar detection technology, intelligent data analysis and judgment algorithms, real-time wireless communication modules and linkage response mechanisms. The system obtains dynamic information of vehicles, pedestrians and obstacles in real time through radar detection modules installed at key locations on the road, and uses advanced data analysis algorithms to identify potential collision risks and abnormal driving behaviors. Once it is determined that a serious accident may occur, the system immediately uploads the warning information to the traffic management center or relevant emergency departments through the wireless communication network, and triggers the linkage response mechanism, such as adjusting the timing of traffic lights, issuing warning information, dispatching rescue resources, etc., to quickly respond to accident risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0021] See also Figure 1 , the present invention provides a technical solution:

[0022] The serious accident warning system based on radar detection and real-time reporting provided by the present invention is a fully upgraded serious accident warning system based on radar detection and real-time reporting. The system strives to be accurate, efficient and reliable in every detail of the design to ensure that potential risks can be quickly identified in a complex and changing traffic environment and serious accidents can be effectively prevented. The following is an in-depth explanation of the details of each module method:

[0023] 1. System architecture and component deployment

[0024] System architecture:

[0025] The overall architecture of the system includes radar detection module, data analysis and judgment module, real-time reporting module, linkage response module and fault self-detection and recovery mechanism.

[0026] Data transmission and command exchange are carried out between modules through a high-speed and stable communication network.

[0027] Component deployment:

[0028] Radar detection module: It is set at key positions on the road and is used to scan the surrounding environment in real time, obtain dynamic information parameters related to the position, speed, and direction of vehicles, pedestrians, and obstacles, and transmit them to the data analysis and judgment module for processing.

[0029] High-precision millimeter-wave radars are installed at key locations on the road, such as intersections, curves, accident-prone areas, etc. The installation location of the radar needs to be carefully planned to ensure full coverage of the target area and reduce blind spots.

[0030] Data analysis and judgment module: receives the data transmitted by the radar detection module, performs real-time analysis through built-in algorithms, identifies abnormal driving behavior and potential collision risks, and improves the accuracy of judgment based on historical accident data and road conditions, as follows:

[0031] Deployed in the traffic management center or cloud server, it performs real-time analysis by receiving data transmitted by the radar detection module.

[0032] Real-time reporting module: When the data analysis and judgment module determines that a serious accident may occur, the warning information is uploaded to the traffic management center or relevant emergency departments through the wireless communication network. The warning information includes but is not limited to the location of the accident, the expected scope of impact and the recommended response measures, as follows: It is closely integrated with the data analysis and judgment module and is responsible for uploading the warning information to the traffic management center or relevant emergency departments through the wireless communication network.

[0033] Linkage response module: connected with traffic lights, variable information boards and other traffic facilities, automatically adjusts the timing of traffic lights and issues warning information according to early warning information, guides vehicles and pedestrians to pass safely, and sends emergency broadcasts to surrounding vehicles. Details are as follows:

[0034] Fine-grained traffic signal control: The linkage response module is tightly integrated with the traffic signal system, and can fine-grainedly control traffic signals according to the content and severity of the warning information. By extending the green light time, turning the light red in advance, and other measures, the system can guide vehicles and pedestrians to safely pass through dangerous areas or take other routes, thereby effectively avoiding accidents. Dynamic information board information release: Using the variable information board system along the road, the linkage response module can release important information such as accident information, traffic control measures, detour routes, etc. in real time. This information is presented to drivers and pedestrians in an intuitive and easy-to-understand way, guiding them to make correct decisions and actions.

[0035] Linkage response module, intelligent dispatch and coordinated operations: After an accident occurs, the linkage response module can quickly start the intelligent dispatch system and automatically dispatch the nearest police car, ambulance and other rescue resources according to the type, location and severity of the accident. At the same time, the system also has a linkage mechanism with fire, medical and other departments, which can realize information sharing and coordinated operations to jointly respond to emergencies.

[0036] Fault self-check and recovery mechanism: Built into each key component, it regularly checks the working status and automatically handles faults. When a fault is found, it starts the backup device or automatic repair program to ensure stable operation of the system. The details are as follows:

[0037] Active health monitoring: To ensure the long-term stable operation of the system, the fault self-detection and recovery mechanism adopts active health monitoring technology. Through built-in sensors and self-diagnosis systems, the system can monitor the operating status of key components such as radar equipment and communication modules in real time, including key parameters such as temperature, voltage, and current. Once an abnormality or fault is found, the system will immediately issue an alarm and initiate corresponding recovery measures.

[0038] 2. Implementation of radar detection module

[0039] Radar selection and configuration:

[0040] Select a high-performance millimeter-wave radar with high-precision, multi-band, and long-distance detection capabilities. The radar detection module uses a high-precision millimeter-wave radar that is not affected by light and weather and can achieve long-distance, high-precision environmental scanning. Adjust the radar's beam shape, scanning mode, operating frequency and other parameters according to the actual road conditions, as follows:

[0041] Installation and calibration:

[0042] Radar equipment is installed at selected road locations and precisely calibrated to ensure detection accuracy.

[0043] Regularly check the installation status and working performance of the radar to ensure its normal operation.

[0044] Data collection and processing:

[0045] The radar scans the surrounding environment in real time and obtains dynamic information such as the position, speed, direction, etc. of vehicles, pedestrians and obstacles.

[0046] The radar data is processed and analyzed using deep learning algorithms to achieve accurate identification and tracking of targets. High precision and multi-band fusion: The radar detection module uses the latest generation of high-precision millimeter-wave radar, which not only has high resolution and long-distance detection capabilities, but also integrates multi-band technology to maintain stable detection performance under different weather and light conditions. The radar's beam shape and scanning mode can be dynamically adjusted according to the actual road environment to ensure all-round and no-dead-angle monitoring of vehicles, pedestrians and obstacles.

[0047] Intelligent target recognition and tracking: With the help of advanced deep learning algorithms, the radar detection module can accurately distinguish different types of traffic participants, including different types of vehicles, pedestrians and non-motor vehicles, etc. By continuously tracking the target's speed, acceleration, trajectory and other dynamic information, the system can predict its future behavior and provide a solid data foundation for subsequent data analysis and judgment.

[0048] Environmental adaptability optimization: In view of the complex and ever-changing traffic environment, the radar detection module has a built-in adaptive algorithm that can sense and adapt to changes in the surrounding environment in real time. Whether it is sudden bad weather or a sudden increase in traffic volume, the system can quickly adjust its working state to ensure the accuracy and real-time nature of the detection results.

[0049] 3. Implementation of Data Analysis and Judgment Module

[0050] The built-in algorithm in the data analysis and judgment module combines machine learning or deep learning technology to continuously optimize the judgment model and improve the accuracy and timeliness of warnings. Real-time analysis is performed through built-in intelligent algorithms. The algorithm combines historical accident data, road conditions and traffic flow characteristics, and visual features to identify abnormal driving behaviors (such as sudden stops, sudden acceleration, illegal lane changes, etc.) and potential collision risks, as follows:

[0051] Data Fusion:

[0052] Integrate data information from multiple sources such as radar detection modules, video surveillance, GPS positioning, and weather stations. Improve the accuracy and reliability of accident prediction through data fusion technology. Multi-source data fusion analysis: In addition to radar data, this module also integrates information data from multiple sources such as video surveillance, GPS positioning, and weather stations. Through advanced data fusion technology, the system can comprehensively consider various factors and conduct a more comprehensive and in-depth analysis of traffic conditions. This mutual confirmation and supplementation of multi-source data greatly improves the accuracy and reliability of accident prediction.

[0053] Intelligent algorithm application:

[0054] Build a composite algorithm model based on machine learning and deep learning to perform real-time analysis on the fused data.

[0055] The model combines historical accident data, road conditions, traffic flow characteristics and other factors to identify abnormal driving behavior and potential collision risks.

[0056] Early warning decision:

[0057] Based on the analysis results, it is determined whether a warning signal needs to be issued, and the warning level and corresponding response measures are determined. Risk grading and dynamic adjustment: The system sets multiple warning thresholds according to the risk level, and takes different response measures for different levels of risk. At the same time, the warning threshold can also be dynamically adjusted according to real-time traffic conditions to ensure the timeliness and accuracy of the warning. For example, during peak hours or in severe weather conditions, the system will automatically lower the warning threshold to increase vigilance.

[0058] The composite algorithm model based on machine learning and deep learning is the core of the data analysis and judgment module. The model continuously optimizes its prediction and judgment capabilities by continuously learning a large amount of data such as historical accident cases, road design characteristics, and traffic flow patterns. In real-time analysis, the model can quickly identify abnormal driving behavior and potential collision risks, and give corresponding warning levels and recommended measures.

[0059] 4. Implementation of real-time reporting module

[0060] The real-time reporting module supports a variety of wireless communication protocols, including but not limited to 4G / 5G and NB-IoT, to ensure fast and stable transmission of warning information. Once an accident is discovered, the warning information will be uploaded to the traffic management center or relevant emergency departments through wireless communication networks such as 4G / 5G, NB-IoT, etc. The warning information includes but is not limited to the accident location, expected impact range, recommended response measures and emergency contact information, as follows:

[0061] Multi-channel transmission:

[0062] Multi-channel parallel transmission technology is used to ensure timely uploading of early warning information.

[0063] Supports multiple wireless communication technologies such as 4G / 5G, NB-IoT, etc. to cope with different network environments.

[0064] Multi-channel parallel transmission: In order to ensure the timely upload of warning information, the real-time reporting module adopts multi-channel parallel transmission technology. By using multiple wireless communication technologies such as 4G / 5G and NB-IoT at the same time, the system can quickly switch to other channels for data transmission when a single network fails. This multi-channel redundant design greatly improves the stability and reliability of data transmission.

[0065] Encryption protection:

[0066] The warning information is encrypted to ensure the security and privacy protection during data transmission. Encryption protection and privacy security: During the data transmission process, the system uses the Advanced Encryption Standard (AES) to encrypt the warning information to ensure the security and privacy protection of the data during transmission. At the same time, the system also has a complete data access control mechanism, and only authorized users can access and use relevant data.

[0067] Confirmation and feedback:

[0068] Wait for the confirmation receipt from the recipient and automatically resend or make supplementary notifications through other channels if confirmation is not received. In order to ensure that the warning information can be received and processed in a timely manner, the system has established a confirmation and feedback mechanism. After sending the warning information, the system will wait for the confirmation receipt from the recipient and automatically resend or make supplementary notifications through other channels if confirmation is not received. This mechanism ensures the effective communication and timely processing of warning information.

[0069] 5. Implementation of the linkage response module

[0070] The linkage response module also includes an intelligent dispatching function, which can automatically dispatch rescue resources, such as police cars and ambulances, according to the accident situation, in order to quickly respond to and handle the accident. After receiving the early warning information, the linkage response module starts quickly. The module is linked with traffic facilities such as traffic lights and variable information boards to automatically adjust the timing of traffic lights, issue warning information, and guide vehicles and pedestrians to pass safely. At the same time, the module also sends emergency broadcasts to surrounding vehicles to remind drivers to avoid. In addition, the linkage response module also has an intelligent dispatching function, which can automatically dispatch police cars, ambulances and other rescue resources to the scene for processing according to the accident situation, as follows:

[0071] Traffic signal control:

[0072] Automatically adjust the timing of traffic lights based on early warning information to guide vehicles and pedestrians to pass safely.

[0073] Information board release:

[0074] Use variable message boards to publish important information such as accident information, traffic control measures, detour routes, etc. in real time.

[0075] Intelligent Scheduling:

[0076] Automatically dispatch police cars, ambulances and other rescue resources to the scene of the accident for handling.

[0077] Establish a linkage mechanism with fire, medical and other departments to achieve information sharing and coordinated operations.

[0078] 6. Implementation of fault self-detection and recovery mechanism

[0079] In order to ensure the long-term stable operation of the system, the present invention also designs a fault self-detection and recovery mechanism. This mechanism regularly detects the working status of key components such as radar equipment and communication modules, and immediately starts backup equipment or automatic repair programs once a fault is found. The details are as follows:

[0080] Active monitoring:

[0081] The working status of key components such as radar equipment and communication modules can be monitored in real time through built-in sensors and self-diagnosis systems.

[0082] Redundant design:

[0083] Redundant designs such as dual-power supply and dual-mode communication are used to improve the reliability and fault tolerance of the system. Redundant design and backup: In order to improve the reliability and fault tolerance of the system, key components are designed with redundancy. For example, the dual-power supply system can automatically switch to the other power supply when one power supply fails; the dual-mode communication module can quickly switch to another communication mode when one communication mode fails. In addition, the system is also equipped with backup equipment and automatic switching mechanisms, which can quickly start the backup equipment to maintain the normal operation of the system when a fault is detected.

[0084] Remote monitoring and diagnostics:

[0085] A remote monitoring center is established to remotely monitor and diagnose faults of the system through the cloud platform.

[0086] Maintenance personnel can view the system status in real time and remotely operate to troubleshoot or repair. Remote monitoring and diagnosis: In order to facilitate maintenance personnel to perform remote management and troubleshooting, the system has established a remote monitoring center. Through cloud platform technology, maintenance personnel can view the system's operating status in real time, receive fault alarm information, and perform fault diagnosis and repair work through remote operation. This remote monitoring and diagnosis function greatly improves the maintainability and manageability of the system.

[0087] The serious accident warning system based on radar detection and real-time reporting of the present invention also includes a user interaction interface for displaying warning information, road conditions and emergency guidelines to traffic managers, drivers and the public, thereby improving the safety awareness and response capabilities of traffic participants.

[0088] In summary, the serious accident warning system based on radar detection and real-time reporting of the present invention is a fully upgraded serious accident warning system based on radar detection and real-time reporting. The system strives to be accurate, efficient and reliable in every detail of the design to ensure that potential risks can be quickly identified in a complex and changing traffic environment and the occurrence of serious accidents can be effectively prevented.

[0089] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A serious accident warning system based on radar detection and real-time reporting, characterized in that: include: Radar detection module: It is set at key locations on the road to scan the surrounding environment in real time, obtain dynamic information parameters related to the position, speed, and direction of vehicles, pedestrians, and obstacles, and transmit them to the data analysis and judgment module for processing; Data analysis and judgment module: receives data transmitted by the radar detection module and performs real-time analysis through a built-in intelligent algorithm. The algorithm combines historical accident data, road conditions and traffic flow characteristics to visually identify abnormal driving behavior and potential collision risks, and improves the accuracy of judgment based on historical accident data and road conditions; Real-time reporting module: When the data analysis and judgment module determines that a serious accident may occur, the warning information is uploaded to the traffic management center or relevant emergency departments through the wireless communication network. The warning information includes but is not limited to the accident location, expected impact range and recommended response measures; Linkage response module: After receiving the warning information, the linkage response module is quickly activated and linked with traffic lights, variable information boards and other traffic facilities. It automatically adjusts the timing of the traffic lights according to the warning information, issues warning information, guides vehicles and pedestrians to pass safely, and sends emergency broadcasts to surrounding vehicles; Fault self-detection and recovery mechanism: The fault self-detection and recovery mechanism is built into the system to regularly detect the working status of key components of radar equipment and communication modules, and start backup equipment or automatic repair programs when a fault is found to ensure stable operation of the system.

2. The severe accident warning system according to claim 1, characterized in that: The radar detection module adopts a high-precision millimeter-wave radar, which is not affected by light and weather and can achieve long-distance and high-precision environmental scanning.

3. The severe accident warning system according to claim 1, characterized in that: The built-in algorithm in the data analysis and judgment module combines machine learning or deep learning technology, which can continuously optimize the judgment model and improve the accuracy and timeliness of the early warning.

4. The severe accident warning system according to claim 1, characterized in that: The real-time reporting module supports multiple wireless communication protocols, including but not limited to 4G / 5G and NB-IoT, to ensure fast and stable transmission of warning information.

5. The severe accident warning system according to claim 1, characterized in that: The linkage response module also includes an intelligent dispatching function, which can automatically dispatch rescue resources, such as police cars and ambulances, according to the accident situation to quickly respond to and handle the accident.

6. The severe accident warning system according to any one of claims 1 to 5, characterized in that: It also includes a user interaction interface for displaying warning information, road conditions and emergency guidelines to traffic managers, drivers and the public, thereby improving the safety awareness and response capabilities of traffic participants.

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