Road safety data all-sensing system based on internet of things

Abstract

The utility model relates to road safety data detection technical field, and disclose a kind of road safety data full perception system based on internet of things, including road information processing module, the road information processing module is by big data analysis center, road management center and AI road condition analysis center is formed, the big data analysis center, road management center and AI road condition analysis center between each other communication connection, the road information processing module and base station between communication connection.The utility model is monitored to road environment by the temperature and humidity sensor, illumination sensor and atmospheric pressure sensor of environmental perception module mounting, and in extreme weather, timely monitoring information is fed back to road test module, complementarity data fusion is formed with the multiple radar and camera of road test module, to keep stable road information monitoring under extreme weather, and provide stable road safety information support for user by vehicle-mounted communication and APP.

Description

Technical Field

[0001] This utility model relates to the field of road safety data detection technology, specifically a road safety data full-sensing system based on the Internet of Things. Background Technology

[0002] Traffic issues are closely related to people's lives. How to avoid traffic congestion and improve travel safety is a major challenge for traffic management departments. Meanwhile, for travelers, timely access to relevant route traffic information is essential. Reasonable route planning not only saves time and improves efficiency but also ensures travel safety. Traditional traffic management and control systems mostly coordinate traffic through traffic lights, cameras, and traffic controllers, which is inefficient and often results in localized traffic congestion affecting the entire area. Traditional traffic early warning systems, combined with weather forecasts, cover the entire region but lack detail on the impact of local environmental conditions on road conditions. Therefore, timely access to traffic or local road condition information for management departments and travelers is crucial. Utilizing sensors to monitor and perceive regional road conditions in real time, and combining this with big data analysis to predict road conditions, can provide valuable information for management departments and travelers.

[0003] Existing IoT-based road safety data perception systems use multiple types of sensors, smart terminals, and communication technologies to collect real-time traffic environment, vehicle behavior, and road condition data. These systems combine artificial intelligence algorithms to achieve comprehensive traffic situation analysis and risk warning. Based on deep learning models, they analyze traffic events such as wrong-way driving and accidents, generate governance suggestions such as traffic light timing optimization, and achieve real-time alarms for abnormal events and emergency resource dispatch through edge computing and cloud collaboration.

[0004] While the aforementioned existing technologies have significant beneficial effects, they still have shortcomings:

[0005] The aforementioned road perception systems integrate devices such as cameras, radar, and temperature and humidity sensors to cover data dimensions such as vehicle trajectory, road surface condition, and weather conditions. They work with the Internet of Things (IoT) to provide early warnings and dispatch of road safety conditions. However, these systems all rely on camera and radar perception. In extreme weather conditions, such as heavy rain or fog, road testing equipment cannot monitor properly, leading to a decrease in the accuracy of radar and camera perception, which affects the ability to detect accidents and the accuracy of predicted safety information. Therefore, a road safety data full perception system based on the Internet of Things is proposed. Utility Model Content

[0006] To address the shortcomings of existing technologies, this utility model provides a road safety data full-sensing system based on the Internet of Things. The system monitors the road environment through temperature and humidity sensors, light sensors, and atmospheric pressure sensors mounted on the environmental sensing module. In extreme weather conditions, the monitoring information is promptly fed back to the road test module. The system works in conjunction with multiple radars and cameras in the road test module to form complementary data fusion, thereby maintaining stable road information monitoring in extreme weather conditions. The system provides stable road safety information support to users through vehicle communication and an APP.

[0007] To achieve the above objectives, this utility model provides the following technical solution: a road safety data full-perception system based on the Internet of Things, comprising: a road information processing module, which consists of a big data analysis center, a road management center, and an AI traffic analysis center, wherein the big data analysis center, the road management center, and the AI ​​traffic analysis center are interconnected; the road information processing module is interconnected with a base station, and the road information processing module is interconnected with an MQTT IoT protocol for unified access of heterogeneous devices via the base station; the MQTT IoT protocol is wired to a road test module and an environmental perception module, and wirelessly connected to vehicle sensors; the road information processing module is interconnected with an internet cloud, and the internet cloud is interconnected with a mobile APP, a computer terminal, and vehicle sensors via network communication; the road test module includes a communication module for wireless communication and an MEC edge server for shortening data processing response time; the road test module also includes a panoramic camera for monitoring road conditions and a thermal imaging camera for auxiliary monitoring; the road test module also includes a lidar for monitoring road conditions and a millimeter-wave radar for auxiliary monitoring;

[0008] The Internet cloud provides real-time early warning and intelligent road decision support through communication connections with mobile APP, computer terminal and vehicle sensor network;

[0009] The road test module is configured with multiple sets according to the road environment and is used to provide road traffic monitoring information at fixed points in the road environment.

[0010] The environmental perception module is configured in multiple sets according to the road environment and is used to provide road environment monitoring information at fixed points in the road environment.

[0011] Preferably, the vehicle-mounted sensor includes a vehicle-mounted camera used to achieve lane line recognition, traffic sign parsing, and pedestrian detection functions through 2D image capture, as well as a vehicle-mounted lidar and BeiDou positioning module used for obstacle contour modeling and positioning.

[0012] Preferably, the big data analysis center includes a distributed cloud platform for analyzing received monitoring data and a database for storing structured data such as traffic behavior feature database and historical event database.

[0013] Preferably, the AI ​​traffic analysis center includes a deep learning model for classifying traffic incidents, and constructs a digital twin intersection based on 3D simulation technology to simulate dynamic traffic flow and optimize strategies.

[0014] Preferably, the environmental sensing module includes a communication module for wireless communication and a processor for processing environmental monitoring information. The processor is connected in sequence to a temperature and humidity sensor, a light sensor, and an atmospheric pressure sensor via wires.

[0015] Preferably, the road test module and the environmental perception module are interconnected, and the road test module, the environmental perception module, and the vehicle-mounted sensors are all connected to the base station via the MQTT Internet of Things protocol.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] In extreme environments, the sensing system of this invention can monitor in real time through sensors. Once the processor determines that it is extreme weather, it promptly transmits the environmental change information to the roadside module and base station via the communication module. Upon receiving the information, the communication module activates all its built-in panoramic camera, thermal imaging camera used for auxiliary monitoring, lidar, and millimeter-wave radar for auxiliary monitoring. The millimeter-wave radar's strong penetration and high anti-interference capabilities complement the high-precision point cloud data from the lidar. When the camera image is blurred due to raindrops or fog scattering, the millimeter-wave radar can still maintain its target detection capability. By combining the high-resolution millimeter-wave radar with the thermal imaging camera, and using infrared feature recognition to compensate for the detection blind spots when the visible light sensor fails, a multi-sensor cross-validation process is formed. Through mutual verification and processing by multiple environmental and roadside modules, road condition information monitoring can be maintained under extreme weather conditions, providing a stable sensing process for road safety information.

[0018] This comprehensive perception system, combined with the big data analysis center and the AI ​​traffic analysis center, forms an intelligent analysis hub that can process and feed back traffic monitoring information in a timely manner. At the same time, it maintains controllable communication with the road management center, facilitating on-site road handling and accident rescue at any time. During the communication transmission of monitoring information, heterogeneous devices can be uniformly accessed through the MQTT Internet of Things protocol, which can convert various monitoring sensor information into electronic data, facilitating the efficient feedback process of monitoring information.

[0019] Other features and advantages of this invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained by means of the structures pointed out in the description, claims, and drawings. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the monitoring and processing logic of the road safety data full-sensing system of this utility model;

[0021] Figure 2 This is a schematic diagram of the monitoring layout of the road test module of this utility model;

[0022] Figure 3 This is a schematic diagram of the monitoring layout of the environmental sensing module of this utility model;

[0023] Figure 4 This is a schematic diagram of the monitoring information analysis and processing flow of this utility model. Detailed Implementation

[0024] The technical solutions of the present utility model will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present utility model, and not all embodiments. Based on the embodiments of the present utility model, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present utility model.

[0025] Please see Figure 1-4 This embodiment of a road safety data full-perception system based on the Internet of Things includes a road information processing module, which consists of a big data analysis center, a road management center, and an AI traffic analysis center. The big data analysis center, the road management center, and the AI ​​traffic analysis center are interconnected. The road information processing module is connected to a base station and communicates with the MQTT IoT protocol used for unified access of heterogeneous devices through the base station. The MQTT IoT protocol is wired to a road test module and an environmental perception module, and wirelessly connected to vehicle sensors. The road information processing module is also connected to an Internet cloud, which is connected to a mobile APP, a computer terminal, and vehicle sensors via network communication.

[0026] like Figure 1-4As shown, the full-perception system in this invention is structurally similar to existing real-time road condition query systems. The main improvement lies in monitoring the road environment through temperature and humidity sensors, light sensors, and atmospheric pressure sensors mounted on the environmental perception module. In extreme weather conditions, the monitoring information is promptly fed back to the road testing module. This, combined with multiple radars and cameras on the road testing module, forms complementary data fusion, thereby maintaining stable road information monitoring even in extreme weather. Stable road safety information is provided to users through vehicle communication and an app. The vehicle sensors and base stations in this invention are existing technologies. When using this full-perception system, multiple road testing modules, multiple environmental perception modules, and vehicle sensors on the road can collect real-time monitoring information on road conditions, traffic lights, the environment, and traffic accidents. This information is then connected via the MQTT IoT protocol to form a unified signal, which is transmitted to the road information processing module through the base station. At this time, the road management center, big data analysis center, and AI road condition analysis center simultaneously receive and process the road information. The big data analysis center then connects to the distributed cloud platform. The system analyzes the collected monitoring data and incorporates it into a database containing structured data such as traffic behavior feature databases and historical event databases. Similar information is extracted and transmitted synchronously with the monitoring information to the AI ​​traffic analysis center. The AI ​​traffic analysis center uses a deep learning model to classify traffic events and constructs a digital twin intersection model based on 3D simulation technology. By simulating dynamic traffic flow and optimizing strategies, the processed traffic safety information and real-time traffic information are then transmitted to the cloud. Users can receive traffic information broadcasts and traffic safety suggestions through computer terminals, specific mobile apps, and in-vehicle communication devices. The overall process is stable. Combined with the intelligent analysis hub formed by the big data analysis center and the AI ​​traffic analysis center, it can promptly process and feedback traffic monitoring information while maintaining controllable communication with the road management center, facilitating on-site road handling and accident rescue at any time. During the communication transmission process, the monitoring information is uniformly accessed through the MQTT IoT protocol for heterogeneous devices, converting various monitoring sensor information into electronic data for efficient feedback.

[0027] like Figure 2-4As shown, the road test module includes a communication module for wireless communication and an MEC edge server for shortening data processing response time. The road test module also includes a panoramic camera for monitoring road conditions and a thermal imaging camera for auxiliary monitoring. Furthermore, it includes a lidar for monitoring road conditions and a millimeter-wave radar for auxiliary monitoring. The environmental perception module includes a communication module for wireless communication and a processor for processing environmental monitoring information. The processor is connected in sequence to a temperature and humidity sensor, a light sensor, and an atmospheric pressure sensor via wires. When extreme conditions occur on the road, the sensors can monitor the situation in real time. Once the processor determines that it is extreme weather, it promptly transmits the environmental change information to the road test module and the base station via the communication module. Upon receiving the information, the communication module activates all the built-in panoramic camera, the thermal imaging camera for auxiliary monitoring, the lidar, and the millimeter-wave radar for auxiliary monitoring. This combines the strong penetration and high anti-interference capabilities of the millimeter-wave radar with the high-precision point cloud data from the lidar, creating a complementary system. When the camera image becomes blurred due to raindrops or fog scattering, the millimeter-wave radar can still maintain its target detection capability. By combining high-resolution millimeter-wave radar with thermal imaging cameras and using infrared feature recognition to compensate for the detection blind spots when visible light sensors fail, a multi-type sensor cross-verification process is formed. Through mutual verification and processing by multiple sets of environmental and road test modules, road condition information monitoring can be maintained under extreme weather conditions, providing a stable perception process for road safety information.

[0028] Obviously, the above embodiments of this utility model are merely examples for clearly illustrating the present utility model, and are not intended to limit the implementation of the present utility model. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this utility model should be included within the protection scope of the claims of this utility model.

Claims

1. A road safety data full-sensing system based on the Internet of Things, characterized in that, include: The road information processing module comprises a big data analysis center, a road management center, and an AI traffic analysis center. These three centers are interconnected. The road information processing module communicates with a base station and, through the base station, with the MQTT IoT protocol used for unified access to heterogeneous devices. The MQTT protocol is wired to the road testing module and the environmental perception module, and wirelessly connected to the vehicle sensors. The road information processing module is also connected to an internet cloud, which in turn connects to a mobile app, a computer terminal, and the vehicle sensors via network communication. The road testing module includes a communication module for wireless communication and an MEC edge server to shorten data processing response time. It also includes a panoramic camera for monitoring road conditions and a thermal imaging camera for auxiliary monitoring. Furthermore, the road testing module includes a lidar for monitoring road conditions and a millimeter-wave radar for auxiliary monitoring. The Internet cloud provides real-time early warning and intelligent road decision support through communication connections with mobile APP, computer terminal and vehicle sensor network; The road test module is configured with multiple sets according to the road environment and is used to provide road traffic monitoring information at fixed points in the road environment. The environmental perception module is configured in multiple sets according to the road environment and is used to provide road environment monitoring information at fixed points in the road environment.

2. The Internet of Things-based road safety data full-sensing system according to claim 1, characterized in that, The vehicle-mounted sensors include vehicle-mounted cameras used for lane line recognition, traffic sign parsing, and pedestrian detection through 2D image capture, as well as vehicle-mounted lidar and BeiDou positioning modules used for obstacle contour modeling and localization.

3. The Internet of Things-based road safety data full-sensing system according to claim 1, characterized in that, The big data analysis center includes a distributed cloud platform for analyzing received monitoring data and a database for storing traffic behavior features, historical events, and structured data.

4. The Internet of Things-based road safety data full-sensing system according to claim 1, characterized in that, The AI ​​traffic analysis center includes a deep learning model for classifying traffic incidents, and uses 3D simulation technology to build digital twin intersections to simulate dynamic traffic flow and optimize strategies.

5. A road safety data full-sensing system based on the Internet of Things according to claim 1, characterized in that, The environmental sensing module includes a communication module for wireless communication and a processor for processing environmental monitoring information. The processor is connected in sequence to a temperature and humidity sensor, a light sensor, and an atmospheric pressure sensor via wires.

6. The Internet of Things-based road safety data full-sensing system according to claim 1, characterized in that, The road test module and the environmental perception module are interconnected and communicate with each other. The road test module, the environmental perception module, and the vehicle-mounted sensors are all connected to the base station via the MQTT Internet of Things protocol.

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