Multi-sensor integrated movable vehicle-road cooperation workstation and working method

Abstract

The invention discloses a multi-sensor integrated movable vehicle-road cooperation workstation and a working method. The multi-sensor integrated movable vehicle-road cooperation workstation comprises a multi-dimensional universal joint mounting seat, an auxiliary equipment mounting seat, a pneumatic lifting rod and a movable base, the upper end of the pneumatic lifting rod is fixedly connected with the multi-dimensional universal joint mounting seat, and the lower end of the pneumatic lifting rod is fixedly connected with the movable base; the auxiliary equipment mounting seat is arranged on the pneumatic lifting rod in a sleeving manner and is fixedly connected with the pneumatic lifting rod; the multi-dimensional universal joint mounting seat is located in the auxiliary equipment mounting seat, a first radar sensing device is fixedly mounted on the multi-dimensional universal joint mounting seat, and a second radar sensing device, a communication device, a roadside RSU device and an intelligent traffic camera are fixedly mounted on the auxiliary equipment mounting seat; an edge computing device and a power supply device are fixedly mounted in the movable base; the method has the advantages of improving the flexibility, deployment efficiency and data processing capability of the vehicle-road cooperation system.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent transportation data processing, and particularly to a multi-sensor integrated mobile vehicle-road collaborative workstation and a working method thereof. Background Art

[0002] With the rapid development of intelligent transportation systems, vehicle-road collaborative technology (Vehicle-to-Everything, V2X) plays an increasingly important role in enhancing traffic safety, improving road traffic flow, and supporting autonomous driving. The vehicle-road collaborative system relies on real-time data exchange between road infrastructure (such as roadside units RSU) and on-board units (OBU), and monitors traffic conditions and makes response decisions by obtaining various sensor information.

[0003] However, existing vehicle-road collaborative systems still face various technical challenges in practical applications, such as complex device deployment, insufficient data fusion efficiency, and poor flexibility and adaptability in complex and dynamic traffic environments. Traditional devices mostly adopt fixed designs, which require a large amount of time for installation and debugging, and are difficult to handle scenarios with frequent location or configuration changes. At the same time, the efficiency and accuracy of existing data fusion technologies in real-time processing of multi-source data are still insufficient, especially when dealing with large-scale and diverse traffic data, it is difficult to achieve an ideal response speed.

[0004] Therefore, the present invention provides a multi-sensor integrated mobile vehicle-road collaborative workstation and a working method thereof, which can solve the above problems. Summary of the Invention

[0005] The technical problem to be solved by the present invention is that the efficiency and accuracy of existing data fusion technologies in real-time processing of multi-source data are still insufficient, especially when dealing with large-scale and diverse traffic data, it is difficult to achieve an ideal response speed. Therefore, a multi-sensor integrated mobile vehicle-road collaborative workstation and a working method thereof are provided. The multi-sensor integrated mobile vehicle-road collaborative workstation includes: The multi-sensor integrated movable vehicle-road collaborative workstation includes a multi-dimensional universal joint mounting base, an auxiliary equipment mounting base, a pneumatic lifting rod, and a moving base; the upper end of the pneumatic lifting rod is fixedly connected to the multi-dimensional universal joint mounting base, and the lower end of the pneumatic lifting rod is fixedly connected to the moving base; the auxiliary equipment mounting base is sleeved on the pneumatic lifting rod and fixedly connected to the pneumatic lifting rod; the multi-dimensional universal joint mounting base is located within the auxiliary equipment mounting base, and a first radar sensing device is fixedly installed on the multi-dimensional universal joint mounting base, and a second radar sensing device, a communication device, a roadside RSU device, and an intelligent traffic camera are fixedly installed on the auxiliary equipment mounting base; an edge computing device and a power supply device are fixedly installed within the moving base, and the first radar sensing device, the second radar sensing device, and the intelligent traffic camera all belong to sensors; the first radar sensing device, the second radar sensing device, the communication device, the roadside RSU device, the intelligent traffic camera, and the edge computing device are all designed for modular quick disassembly; Plug-and-play components are provided on the power supply device, the first radar sensing device, the second radar sensing device, the communication device, the roadside RSU device, the intelligent traffic camera, and the edge computing device, and the plug-and-play components are a type of modular slot; The power supply device is used to supply power to the first radar sensing device, the second radar sensing device, the communication device, the roadside RSU device, the intelligent traffic camera, and the edge computing device; the first radar sensing device, the second radar sensing device, the intelligent traffic camera, and the roadside RSU device are electrically connected to the edge computing device simultaneously; the first radar sensing device, the second radar sensing device, the roadside RSU device, the intelligent traffic camera, and the edge computing device are electrically connected to the communication device simultaneously; It further includes that the roadside RSU device conducts real-time communication with in-vehicle devices through the communication device; Standardized interfaces are provided on the first radar sensing device, the second radar sensing device, the communication device, the roadside RSU device, the intelligent traffic camera, the edge computing device, and the power supply device; It further includes a working method for the multi-sensor integrated movable vehicle-road collaborative work, which is used for the multi-sensor integrated movable vehicle-road collaborative workstation as described above; Adjust the positions of the first radar sensing device, the second radar sensing device, and the intelligent traffic camera; Enter the data preprocessing process, and collect raw data through the first radar sensing device, the second radar sensing device, and the intelligent traffic camera; The raw data includes first data, second data, and third data; The first data corresponds to the data collected by the first radar sensing device and includes the distance and relative speed information of the target object; The second data corresponds to the data collected by the second radar sensing device; The third data corresponds to the data collected by the intelligent transportation camera and includes high-resolution image information for traffic condition identification and behavior analysis; The high-frequency noise in the historical data is eliminated through a low-pass filter to generate the first preprocessed data; the specific mathematical expression is: Where, is the filtered data, is the original data, is the time constant of the filter; Based on the historical data, an abnormal distribution model is constructed, and the Z-score method is used to identify abnormal data; The Z-score calculation formula is: Where, is the data point, is the data mean, is the data standard deviation; the identification of outliers is used to prevent data quality problems from affecting the overall system performance; The detected abnormal data is corrected through linear interpolation to generate the final preprocessed data; The linear interpolation formula is: For the second radar sensing device: The median filter is used to process the millimeter-wave radar data to remove pulse noise; The median filter formula is: Where, is the window size; Abnormal data detection and correction: The Z-score method is also used to identify abnormal data and linear interpolation is used for correction to ensure the integrity of the data processing process; For video data: The video data is subjected to Gaussian smoothing to generate the second preprocessed data for reducing the influence of background noise and illumination changes on the image quality; The Gaussian smoothing formula is: Where, is the standard deviation parameter, is the smoothing function; The background modeling method is adopted to detect scene changes, and the Z-score method is used to correct abnormal frames; Enter the data fusion process, and fuse the preprocessed data through the Extended Kalman Filter (EKF) algorithm to generate a unified state estimate for real-time monitoring of the traffic environment; The state estimate update formula is: Where, is the current state estimate, is the Kalman gain, is the measurement value, is the observation matrix; the state covariance update formula is: Where, is the state covariance, is the identity matrix; The above data processing process is accelerated by the embedded GPU and FPGA hardware in the edge computing device; Enter the weight allocation and mechanism adjustment process, and dynamically adjust the weights of the sensor data of different sensors according to the factual credibility, environmental conditions, and historical performance of each sensor data. Specifically, the Bayesian inference method is used to dynamically calculate the weights of each sensor through maximum likelihood estimation, and the formula includes: Where, is the sensor 's weight, is the probability of the observed data D in the state, is the state 's prior probability; Enter the fusion result output process and output the result of data fusion; It also includes: time synchronization process and fault detection and recovery process; The time synchronization process specifically includes: By adding a timestamp module to each data acquisition node, the network time protocol or GPS time synchronization protocol is used for network-wide time synchronization correction, The fault detection and recovery process specifically includes: Use the network time protocol or GPS time synchronization protocol to synchronize and correct the timestamps of each sensor node; Continuously monitor the data output status of each sensor; judge in real time whether data anomaly and sensor failure meet any one of them; if so, alarm and start the fault recovery mechanism; if not, run normally; Enter the fault recovery mechanism, enable redundant sensors or backup sensors, and the data collected by the redundant sensors or backup sensors will sequentially enter the data preprocessing process, data fusion process, weight allocation, mechanism adjustment process, and fusion result output process; Generate a fault report and send the fault report to the remote control center; Receive a fault troubleshooting instruction, re-enter the time synchronization process, and resume monitoring the data output status of each sensor continuously.

[0006] Further, the first radar sensing device is a C16 lidar, the C16 lidar is fixedly installed at the upper end of the multi-dimensional gimbal mount, and a standardized interface is provided on the C16 lidar for connecting to the communication device and the edge computing device simultaneously.

[0007] Further, the second radar sensor is a JR5000S millimeter-wave radar, the JR5000S millimeter-wave radar is fixedly installed at one end of the auxiliary equipment mount, and a standardized interface is provided on the JR5000S millimeter-wave radar for connecting to the communication device and the edge computing device simultaneously.

[0008] Further, the communication device is an SY60 5G industrial router, the SY60 5G industrial router is installed on one side of the JR5000S millimeter-wave radar, the roadside RSU device is fixedly installed at the other end of the auxiliary equipment mount, and the roadside RSU device communicates with the vehicle-mounted device in real time through the SY60 5G industrial router.

[0009] Further, two multi-dimensional gimbal mounts are also provided on the auxiliary equipment mount, the two multi-dimensional gimbal mounts are respectively arranged on both sides of the pneumatic lifting rod, and intelligent traffic cameras are fixedly installed on the two multi-dimensional gimbal mounts. A standardized interface is provided on the intelligent traffic camera for connecting to the communication device and the edge computing device simultaneously.

[0010] Further, the edge computing device is an SCX-1400 embedded workstation.

[0011] Further, the power supply device includes an intelligent power management system, a power supply battery, a charging management module, a solar panel, an AC power supply port, an AC / DC converter, and a voltage regulator; The solar panel is fixedly installed on the top of the auxiliary equipment mount, the solar panel is electrically connected to the charging management module, and the charging management module is simultaneously electrically connected to the power supply battery and the intelligent power management system; The power supply battery is electrically connected to the intelligent power management system; One end of the AC / DC converter is electrically connected to the AC power supply port, the other end of the AC / DC converter is electrically connected to the voltage regulator, and the voltage regulator is electrically connected to the intelligent power management system; The intelligent power management system is simultaneously electrically connected to the first radar sensing device, the second radar sensing device, the communication device, the roadside RSU device, the intelligent traffic camera, and the edge computing device.

[0012] Furthermore, a pneumatic pressure controller, a position sensor, an electronic braking device, and a self-locking device are installed inside the pneumatic lifting rod; the pneumatic pressure controller, the position sensor, and the electronic braking device are simultaneously electrically connected to the intelligent power management system; the pneumatic pressure controller is used to control the pneumatic system of the pneumatic lifting rod, the position sensor is used to detect the lifting position of the pneumatic lifting rod, and the electronic braking device is used to control the locking of the pneumatic lifting rod.

[0013] Furthermore, the multi-dimensional universal joint mounting base includes a first base, a first rotating seat, a second rotating seat, and a camera mounting seat; The first base is rotatably connected to the first rotating seat, and a first rotation locking mechanism is further provided at the rotating shaft where the first base and the first rotating seat are connected; The first rotating seat and the second rotating seat form a turntable mechanism; The second rotating seat is rotatably connected to the camera mounting seat, and a second rotation locking mechanism is further provided at the rotating shaft where the second rotating seat and the camera mounting seat are connected.

[0014] Implementing the present invention has the following beneficial effects: (1) Through the modular structure design and standardized interfaces, the present invention realizes the rapid replacement and upgrade of each functional module (including the RSU device), significantly improves the system deployment efficiency and maintenance convenience, and supports real-time communication with in-vehicle modules (OBUs), effectively enhancing the traffic information transmission efficiency and accuracy. This design enables the workstation to quickly adapt to different traffic environments and application requirements, and is particularly suitable for scenarios that require frequent position or configuration changes, such as temporary construction areas and major event venues.

[0015] (2) The present invention integrates a variety of advanced sensors (such as C16 lidar, JR5000S millimeter-wave radar, and intelligent traffic cameras), combines an improved extended Kalman filter algorithm and a hardware acceleration strategy, and provides efficient multi-source data fusion and real-time monitoring capabilities. This method effectively improves the accuracy of traffic environment monitoring and the system response speed, and adapts to complex and changeable traffic environments.

[0016] (3) By adopting a pneumatic lifting rod system and a multi-dimensional universal joint mounting base, and combining modular design and quick connectors, the present invention realizes the quick installation, disassembly and configuration of the workstation. This design reduces the installation time and labor costs, enabling the workstation to transform from the transportation state to the working state in a short time, and greatly enhancing the flexibility and deployment efficiency of the system.

[0017] (4) The present invention designs a multi-mode power supply device (including solar power supply, battery power supply and AC power supply), and realizes the automatic switching of multiple power supply methods through an intelligent power management system to ensure the continuous operation of the device under various environmental conditions. This diversified power supply scheme significantly reduces the downtime caused by power problems and improves the sustainability and long-term reliability of the device.

[0018] (5) By designing a time synchronization and fault recovery mechanism, the present invention ensures that the device maintains high stability and reliability under various complex environments. This design enables the system to operate efficiently in various application scenarios (such as highways, urban roads, ports and industrial parks), enhancing the applicability of the device under extreme environmental conditions.

[0019] (6) Through multi-sensor integration, efficient data fusion methods and flexible deployment designs, the present invention provides a comprehensive and highly reliable solution for intelligent transportation systems, effectively enhancing the competitiveness and application value of intelligent transportation devices in the market. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a schematic structural diagram of a multi-sensor integrated movable vehicle-road collaborative workstation provided by the present invention; Figure 2 is a schematic structural diagram of the multi-dimensional universal joint mounting base of the present invention; Figure 3 is a flowchart of the working method for the multi-sensor integrated movable vehicle-road writing collaborative work provided by the present invention; Figure 4 is a flowchart of the fault detection and recovery process provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. Embodiment

[0022] Please refer to the attached drawings of the specification Figures 1-4, this embodiment provides a multi-sensor integrated mobile vehicle-road collaborative workstation and a working method. The multi-sensor integrated mobile vehicle-road collaborative workstation includes: A multi-dimensional universal joint mounting base 12, an auxiliary equipment mounting base, a pneumatic lifting rod 10, and a mobile base 9; the upper end of the pneumatic lifting rod 10 is fixedly connected to the multi-dimensional universal joint mounting base 12, and the lower end of the pneumatic lifting rod 10 is fixedly connected to the mobile base 9; the auxiliary equipment mounting base is sleeved on the pneumatic lifting rod 10 and fixedly connected to the pneumatic lifting rod 10; the multi-dimensional universal joint mounting base 12 is located inside the auxiliary equipment mounting base, and a first radar sensing device 1 is fixedly installed on the multi-dimensional universal joint mounting base 12, and a second radar sensing device 4, a communication device 3, a roadside RSU device 11, and an intelligent traffic camera 2 are fixedly installed on the auxiliary equipment mounting base; an edge computing device 7 and a power supply device 6 are fixedly installed inside the mobile base 9, and the first radar sensing device 1, the second radar sensing device 4, and the intelligent traffic camera 2 all belong to sensors. Plug-and-play components are provided on the power supply device, the first radar sensing device, the second radar sensing device, the communication device, the roadside RSU device, the intelligent traffic camera, and the edge computing device. The plug-and-play component is a modular slot; this slot supports the hot-plug function and automatically adjusts electrical parameters through an adaptive electrical connection device to adapt to the insertion of different modules; the slot also integrates a real-time fault detection and status monitoring mechanism, continuously monitors the electrical connection and data transmission status of the module through built-in sensors and a feedback system, and automatically triggers an alarm and a recovery operation when an abnormal situation is detected to ensure the reliability and safety of the module during operation.

[0023] The power supply device 6 is used to supply power to the first radar sensing device 1, the second radar sensing device 4, the communication device 3, the roadside RSU device 11, the intelligent traffic camera 2, and the edge computing device 7; the first radar sensing device 1, the second radar sensing device 4, the intelligent traffic camera 2, and the roadside RSU device 11 are electrically connected to the edge computing device 7 at the same time; the first radar sensing device 1, the second radar sensing device 4, the roadside RSU device 11, the intelligent traffic camera 2, and the edge computing device 7 are electrically connected to the communication device at the same time. It further includes that the roadside RSU device 11 communicates with in-vehicle devices in real time through the communication device 3. The first radar sensing device 1, the second radar sensing device 4, the communication device 3, the roadside RSU device 11, the intelligent traffic camera 2, the edge computing device 7, and the power supply device 6 are all provided with standardized interfaces 8.

[0024] The first radar sensing device 1 is a C16 lidar. The C16 lidar is fixedly installed at the upper end of the multi-dimensional gimbal mount 12. A standardized interface 8 is provided on the C16 lidar for connecting to both the communication device 3 and the edge computing device 7 simultaneously. The C16 lidar provides high-precision three-dimensional point cloud data through high-frequency pulsed laser technology and a multi-layer mirror system for obstacle detection and environmental modeling, and is suitable for complex traffic environments on highways and urban roads. The C16 lidar is connected to the communication device 3 and the edge computing device 7 using an Ethernet interface to ensure fast data transmission and processing.

[0025] The second radar sensor is a JR5000S millimeter-wave radar. The JR5000S millimeter-wave radar is fixedly installed at one end of the auxiliary device mount. A standardized interface 8 is provided on the JR5000S millimeter-wave radar for connecting to both the communication device 3 and the edge computing device 7 simultaneously. The JR5000S millimeter-wave radar uses phased array antennas and Doppler frequency shift technology to achieve simultaneous detection and tracking of multiple targets, and can maintain high-resolution monitoring under adverse weather and low-light conditions. This radar is connected to the communication device through an RS485 serial port and to the edge computing device 7 through a USB interface.

[0026] The intelligent traffic camera 2 is equipped with a 4K high-definition image sensor and a deep learning processor, supports various video analysis functions (such as vehicle recognition, license plate detection, pedestrian analysis), can automatically identify abnormal traffic behaviors and generate warning information, and is suitable for real-time monitoring of various traffic scenarios. The intelligent traffic camera 2 is connected to the communication device through an Ethernet interface and conducts data transmission and processing with the edge computing module through a WiFi interface.

[0027] The working process of the sensors includes each sensor (C16 lidar, JR5000S millimeter-wave radar, and intelligent traffic camera 2) collecting traffic environment data in real time through its specific interface (such as Ethernet, USB, RS485, etc.), and quickly transmitting the data to the edge computing device 7 after preliminary processing and filtering through the communication device. In the edge computing device 7, the data undergoes further preprocessing and fusion, and the extended Kalman filter (EKF) algorithm is used for state estimation and data fusion to ensure the high precision and real-time nature of the data. The processed data communicates bidirectionally with an external control center to achieve intelligent traffic control and decision support. This integrated multi-sensor design combines multiple data interfaces and an efficient transmission method to ensure the real-time monitoring ability and data processing efficiency of the sensor module in complex traffic environments.

[0028] The communication device 3 is a SY60 5G industrial router. The SY60 5G industrial router is installed on one side of the JR5000S millimeter-wave radar. The roadside RSU device 11 is fixedly installed at the other end of the auxiliary device mounting base. The roadside RSU device 11 communicates with the vehicle-mounted device in real time through the SY60 5G industrial router. The SY60 5G industrial router supports high-speed data transmission and has the support capabilities for multiple communication protocols (such as 5G, TCP / IP, UDP).

[0029] The communication device 3 plays a key role in the communication with the sensor and the edge computing device 7. It transmits the data collected by the sensor to the edge computing device 7 in real time through a high-speed 5G network and wired interfaces (such as Ethernet, RS485), ensuring low-latency and high-bandwidth data transmission. During the data transmission process, the communication device 3 not only provides preliminary processing and error detection functions for data packets, but also ensures the security and stability of data transmission through the VPN security function and the firewall protection mechanism. At the same time, the communication device 3 supports remote configuration and management functions, and can perform remote diagnosis, debugging, and configuration modification through the TCP / IP protocol, facilitating real-time monitoring and maintenance of the system. When the system is in the remote working mode, the communication device 3 maintains continuous data communication with the external control center to achieve real-time monitoring, analysis, and decision support for the data of multiple sensors.

[0030] In terms of remote configuration and management, the SY60 5G industrial router can receive remote control instructions through the 5G network and dynamically adjust communication parameters and data transmission paths according to requirements. It also supports remote firmware upgrade and configuration backup of the device to ensure continuous and stable operation under various traffic environments and network conditions. Through these functions, the communication device effectively improves the flexibility, scalability, and reliability of the system, enabling the workstation to quickly adapt to different deployment requirements in the changing traffic environment. Two multi-dimensional gimbal mounting bases 12 are also provided on the auxiliary device mounting base. The two multi-dimensional gimbal mounting bases 12 are respectively arranged on both sides of the pneumatic lifting rod 10. Intelligent transportation cameras 2 are fixedly installed on both of the two multi-dimensional gimbal mounting bases 12. A standardized interface 8 is provided on the intelligent transportation camera 2 for connecting to both the communication device 3 and the edge computing device 7 simultaneously.

[0031] Edge computing device 7 is an SCX-1400 embedded workstation. Its hardware configuration includes a multi-core CPU (such as Intel Xeon® or Core™ i7 / i5 / i3) and a high-performance GPU (such as NVIDIA RTX 3060), which can support complex AI algorithms and multi-source data fusion processing. The device is equipped with a variety of expansion interfaces (such as PCIe, M.2, SATA), allowing flexible configuration of storage devices and providing redundant data protection (RAID 0, 1, 5, 10). To ensure stable operation under high load conditions, the device is designed with an active cooling system and overheating protection function.

[0032] In terms of real-time data processing and fusion, the edge computing device 7 realizes high-speed data exchange with the communication device 3 and the RSU device through the standardized interface 8, and receives data from the sensor. Using the high-performance computing capabilities of the embedded workstation, the edge computing device 7 first pre-processes the received data, including using an adaptive low-pass filter and Gaussian smoothing method to clean the data and eliminate high-frequency noise and interference signals. After completing the data pre-processing, the extended Kalman filter (EKF) technology is applied to fuse the multi-source data, perform state estimation and data fusion, so as to achieve accurate monitoring of the traffic environment.

[0033] To improve data processing efficiency, the edge computing module uses built-in GPU and FPGA to perform hardware acceleration on key computing tasks (such as feature extraction, filter calculation and matrix operation), thereby significantly improving processing speed and reducing power consumption. The module also designs a real-time weight distribution and adjustment mechanism to dynamically adjust the weights of different sensor data based on the real-time credibility, environmental conditions and historical performance of sensor data to ensure the accuracy and robustness of the fusion results.

[0034] In addition, in order to ensure the time synchronization and reliability of the system, the edge computing device 7 is equipped with a timestamp module on the data collection node, and the full network time synchronization correction of multi-sensor data is achieved by using the NTP (Network Time Protocol) or GPS time synchronization protocol. When the system detects data anomalies or sensor failures, the edge computing device 7 will automatically trigger the fault recovery program, and quickly switch to the redundant module through the fault detection and automatic recovery mechanism to ensure the stability of the system in various complex traffic environments.

[0035] The edge computing device 7 not only has the ability to process real-time data, but also provides powerful data storage and management functions, supports local storage of large-scale data, and performs offline analysis and modeling on historical data. The data fusion module and the data storage module communicate bidirectionally through a high-speed interface to ensure that real-time data is processed in a timely manner and historical data is effectively stored and utilized. This device plays a core role in data processing and fusion in the operation of the entire vehicle-road collaborative workstation, ensuring efficient and reliable monitoring and analysis in diverse traffic environments.

[0036] The power supply device 6 includes an intelligent power management system, a power supply battery, a charging management module, a solar panel 13, an AC power supply port 5, an AC-DC converter, and a voltage regulator. The solar panel 13 is fixedly installed on the top of the auxiliary equipment mounting seat. The solar panel 13 is electrically connected to the charging management module, and the charging management module is simultaneously electrically connected to the power supply battery and the intelligent power management system. The solar panel 13 is made of polycrystalline silicon material and has a high conversion efficiency, capable of providing sufficient power output even under low light conditions. The power supply battery is electrically connected to the intelligent power management system. The power supply battery is a high-capacity lithium battery that supports intelligent battery management functions, including charging status monitoring, voltage protection, and current management functions. One end of the AC-DC converter is electrically connected to the AC power supply port 5, the other end of the AC-DC converter is electrically connected to the voltage regulator, and the voltage regulator is electrically connected to the intelligent power management system. The intelligent power management system is simultaneously electrically connected to the first radar sensing device 1, the second radar sensing device 4, the communication device 3, the roadside RSU device 11, the intelligent traffic camera 2, and the edge computing device 7.

[0037] The entire power supply module realizes automatic switching between different power supply modes through the intelligent power management system. The intelligent power management system real-time monitors the status of each power supply module, including the power output of the solar panel 13, the battery power level and charging status, and the input of AC power supply. When the system detects that one of the power supply methods cannot provide sufficient power, the intelligent power management system will automatically switch to the standby power supply mode. For example, when the solar panel 13 cannot provide sufficient power, the system will automatically switch to battery power supply; if the battery power is insufficient, the system will switch to AC power supply, thus ensuring that the workstation can operate continuously under any circumstances.

[0038] In addition, the intelligent power management system can also include a voltage regulator and a status monitoring module, which can real-time monitor and adjust the power supply voltage to ensure the stability of power output. The design of the power supply system also integrates multiple protection functions, such as overvoltage protection, overcurrent protection, and short-circuit protection, to improve the safety and reliability of the entire system.

[0039] The multi-mode power supply design of this module significantly improves the adaptability and reliability of the system, especially in the wild or scenarios with unstable power supply. By integrating multiple power supply methods and an intelligent management system, the power supply module of the present invention can not only achieve efficient and stable power supply in different environments, but also minimize downtime and maintenance costs, ensuring the long-term stable operation and reliability of the vehicle-road collaborative workstation.

[0040] A pneumatic lift rod 10 is installed with a pneumatic controller, a position sensor, an electronic braking device and a self-locking device; the pneumatic controller, the position sensor and the electronic braking device are simultaneously electrically connected to the intelligent power management system; the pneumatic controller is used to control the pneumatic system of the pneumatic lift rod 10, the position sensor is used to detect the lifting position of the pneumatic lift rod 10, and the electronic braking device is used to control the locking of the pneumatic lift rod 10.

[0041] The multi-dimensional universal joint mounting seat 12 includes a first base 1201, a first rotating seat 1202, a second rotating seat 1203 and a camera mounting seat 1204; The first base and the first rotating seat are rotationally connected, and a first rotation locking mechanism is further provided at the rotating shaft where the first base and the first rotating seat are connected; The first rotating seat and the second rotating seat form a turntable mechanism; The second rotating seat and the camera mounting seat are rotationally connected, and a second rotation locking mechanism is further provided at the rotating shaft where the second rotating seat and the camera mounting seat are connected.

[0042] On the other hand, this embodiment also provides a working method for the movable vehicle-road writing collaboration work for multi-sensor integration, which is used for the multi-sensor integrated movable vehicle-road collaborative workstation as above, including: Adjust the positions of the first radar sensing device, the second radar sensing device and the intelligent traffic camera; Enter the data preprocessing process, and collect the original data through the first radar sensing device (lidar), the second radar sensing device (millimeter wave radar) and the intelligent traffic camera. The original data includes: First data: The data collected by the first radar sensing device (lidar), including the distance and relative speed information of the target object.

[0043] Second data: The data collected by the second radar sensing device (millimeter wave radar), which can provide stable detection results under bad weather conditions.

[0044] Third data: The data collected by the intelligent traffic camera, including high-resolution image information, which is used for traffic condition identification and behavior analysis.

[0045] Step 2: For the first radar sensing device (lidar): Low-pass filtering processing: To eliminate high-frequency noise in lidar data, a low-pass filter is used for processing to generate first preprocessed data.

[0046] The specific mathematical expression is: Where, is the filtered data, is the original data, is the time constant of the filter; Through this method, the smoothness of the signal can be effectively improved, and the influence of data fluctuations on subsequent analysis can be reduced.

[0047] Outlier detection: Based on historical data, an abnormal distribution model is constructed, and the Z-score method is used to identify abnormal data to ensure the validity and reliability of the data.

[0048] The Z-score calculation formula is: Where, is the data point, is the data mean, is the data standard deviation; The identification of outliers is used to prevent data quality problems from affecting the overall system performance.

[0049] The detected abnormal data is corrected by the linear interpolation method to generate the final preprocessed completed data; The linear interpolation method formula is: Step 3: For the second radar sensing device (millimeter-wave radar): A median filter is used to process the millimeter-wave radar data to remove pulse noise; Ensure the stability and accuracy of the data.

[0050] The median filter formula is: Where, is the window size; This method can effectively reduce the impact of instantaneous interference on the measurement results.

[0051] Outlier data detection and correction: The Z-score method is also used to identify abnormal data and the linear interpolation method is used for correction to ensure the integrity of the data processing process; Step 4: For video data: Gaussian smoothing processing is performed on the video data to generate second preprocessed data, which is used to reduce the influence of background noise and light changes on the image quality; The Gaussian smoothing formula is: Among them, is the standard deviation parameter, is the smoothing function; through this step, the clarity of the image can be significantly improved, providing a good foundation for subsequent target recognition.

[0052] Step 5: Enter the data fusion process, and fuse the preprocessed data through the Extended Kalman Filter (EKF) algorithm to generate a unified state estimate for real-time monitoring of the traffic environment.

[0053] The state estimate update formula is: Among them, is the current state estimate, is the Kalman gain, is the measured value, is the observation matrix; the state covariance update formula is: Among them, is the state covariance, is the identity matrix; Accelerate the above data processing process through the embedded GPU and FPGA hardware in the edge computing device; Enter the weight allocation and mechanism adjustment process, and dynamically adjust the weights of the sensor data of different sensors according to the factual credibility, environmental conditions, and historical performance of each sensor data. Specifically, it includes dynamically calculating the weights of each sensor through the Bayesian inference method by maximum likelihood estimation. The formula includes: Among them, is the sensor 's weight, is the probability of the observed data D in the state, is the prior probability of the state ; Enter the fusion result output process and output the result of data fusion; It also includes: time synchronization process and fault detection and recovery process; The time synchronization process specifically includes: By adding a timestamp module to each data acquisition node, use the Network Time Protocol or GPS Time Synchronization Protocol for network-wide time synchronization correction, The fault detection and recovery process specifically includes: Use the Network Time Protocol or GPS Time Synchronization Protocol to synchronize and correct the timestamps of each sensor node; Continuously monitor the data output status of each sensor; judge in real time whether data anomaly and sensor failure meet any one of them; if so, give an alarm and start the fault recovery mechanism; if not, operate normally; Enter the fault recovery mechanism, enable redundant sensors or standby sensors, and the data collected by the redundant sensors or standby sensors will successively enter the data preprocessing process, data fusion process, weight allocation, mechanism adjustment process, and fusion result output process; Generate a fault report and send the fault report to the remote control center; Receive a fault troubleshooting instruction, re-enter the time synchronization process, and resume continuously monitoring the data output status of each sensor.

[0054] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "coaxial", "bottom", "one end", "top", "middle", "the other end", "upper", "one side", "top", "inner", "front", "center", "both ends", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention.

[0055] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "set", "connected", "fixed", "swivelly connected", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements or the interaction relationship between two elements. Unless otherwise clearly limited, for those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0056] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-sensor integrated mobile vehicle-road cooperative workstation and working method, characterized in that: The multi-sensor integrated movable vehicle-road collaborative workstation includes a multi-dimensional universal joint mounting seat, an auxiliary equipment mounting seat, a pneumatic lifting rod and a mobile base; the upper end of the pneumatic lifting rod is fixedly connected to the multi-dimensional universal joint mounting seat, and the lower end of the pneumatic lifting rod is fixedly connected to the mobile base; the auxiliary equipment mounting seat is sleeved on the pneumatic lifting rod and fixedly connected to the pneumatic lifting rod; the multi-dimensional universal joint mounting seat is located in the auxiliary equipment mounting seat, and a first radar sensor device is fixedly installed on the multi-dimensional universal joint mounting seat, and a second radar sensor device, a communication device, a roadside RSU device and an intelligent traffic camera are fixedly installed on the auxiliary equipment mounting seat; an edge computing device and a power supply device are fixedly installed in the mobile base, and the first radar sensor device, the second radar sensor device and the intelligent traffic camera are all sensors; the first radar sensor device, the second radar sensor device, the communication device, the roadside RSU device, the intelligent traffic camera and the edge computing device are all modular quick-disassembly designs; The power supply device, the first radar sensor device, the second radar sensor device, the communication device, the roadside RSU device, the intelligent traffic camera and the edge computing device are all provided with a plug-and-play component, and the plug-and-play component is a modular slot; The power supply device is used to supply power to the first radar sensor device, the second radar sensor device, the communication device, the roadside RSU device, the intelligent traffic camera and the edge computing device; the first radar sensor device, the second radar sensor device, the intelligent traffic camera and the roadside RSU device are electrically connected to the edge computing device at the same time; the first radar sensor device, the second radar sensor device, the roadside RSU device, the intelligent traffic camera and the edge computing device are electrically connected to the communication device at the same time; It also includes the roadside RSU device communicating with the vehicle-mounted device in real time through the communication device; The first radar sensing device, the second radar sensing device, the communication device, the roadside RSU device, the intelligent traffic camera, the edge computing device and the power supply device are all provided with standardized interfaces; Also included is a working method for multi-sensor integrated mobile vehicle-road collaborative work, which is used for the multi-sensor integrated mobile vehicle-road collaborative workstation as described above; adjusting positions of the first radar sensor device, the second radar sensor device, and the intelligent traffic camera; Entering the data preprocessing process, collecting raw data through the first radar sensor device, the second radar sensor device and the intelligent traffic camera; The original data includes first data, second data and third data; The first data corresponds to data collected by the first radar sensor device, including distance and relative speed information of the target object; The second data corresponds to data collected by the second radar sensor device; The third data corresponds to data collected by the intelligent traffic camera, including high-resolution image information for identifying traffic conditions and behavior analysis; The high-frequency noise in the historical data is eliminated by a low-pass filter to generate the first preprocessed data; the specific mathematical expression is: in, is the filtered data, is the original data, is the time constant of the filter; Based on historical data, an abnormal distribution model is constructed and the Z-score method is used to identify abnormal data; The Z-score calculation formula is: in, is the data point, is the data mean, is the data standard deviation; the identification of outliers is used to prevent data quality issues from affecting the overall system performance; Correct the detected abnormal data through linear interpolation to generate the final preprocessed data; The linear interpolation formula is: For the second radar sensor device: The millimeter wave radar data is processed using a median filter to remove pulse noise; The median filter formula is: in, is the window size; Abnormal data detection and correction: The Z-score method is also used to identify abnormal data, and linear interpolation is used to correct it to ensure the integrity of the data processing process; For video data: Performing Gaussian smoothing on the video data to generate second preprocessed data for reducing the impact of background noise and illumination changes on image quality; The Gaussian smoothing formula is: in, is the standard deviation parameter, is a smooth function; The background modeling method is used to detect scene changes, and the Z-score method is used to correct abnormal frames; Enter the data fusion process, fuse the pre-processed data through the extended Kalman filter (EKF) algorithm to generate a unified state estimate to achieve real-time monitoring of the traffic environment; The state estimation update formula is: in, is the current state estimate, is the Kalman gain, is the measured value, is the observation matrix; the state covariance update formula is: in, is the state covariance, is the identity matrix; Accelerate the above data processing process through embedded GPU and FPGA hardware in edge computing devices; Enter the weight allocation and mechanism adjustment process, dynamically adjust the weights of sensor data of different sensors according to the factual credibility, environmental conditions and historical performance of each sensor data, which specifically includes dynamically calculating the weight of each sensor through maximum likelihood estimation using the Bayesian inference method. The formula includes: in, For sensor The weight of For The probability of observing data D in the state, Status The prior probability of Enter the fusion result output process and output the data fusion result; It also includes: time synchronization process and fault detection and recovery process; The time synchronization process specifically includes: By adding a timestamp module to each data acquisition node, the network time protocol or GPS time synchronization protocol is used to synchronize the time of the entire network. The fault detection and recovery process specifically includes: Use the network time protocol or GPS time synchronization protocol to synchronize and correct the timestamps of each sensor node; Continuously monitor the data output status of each sensor; determine in real time whether data anomalies and sensor failures are met; if so, issue an alarm and start the fault recovery mechanism; if not, operate normally; Enter the fault recovery mechanism, enable redundant sensors or backup sensors, and use the data collected by redundant sensors or backup sensors to enter the data preprocessing process, data fusion process and weight distribution process, mechanism adjustment process and fusion result output process in sequence; Generate fault reports and send them to the remote control center; Receive troubleshooting instructions, re-enter the time synchronization process, and resume continuous monitoring of the data output status of each sensor.

2. The multi-sensor integrated mobile vehicle-road cooperative workstation and working method according to claim 1, characterized in that: The first radar sensing device is a C16 laser radar, which is fixedly mounted on the upper end of the multi-dimensional universal joint mounting seat. The C16 laser radar is provided with a standardized interface for connecting to the communication device and the edge computing device at the same time.

3. The multi-sensor integrated mobile vehicle-road cooperative workstation and working method according to claim 2, characterized in that: The second radar sensor is a JR5000S millimeter-wave radar, which is fixedly mounted on one end of the auxiliary equipment mounting base. The JR5000S millimeter-wave radar is provided with a standardized interface for connecting to the communication device and the edge computing device at the same time.

4. The multi-sensor integrated mobile vehicle-road cooperative workstation and working method according to claim 3, characterized in that: The communication device is a SY60 5G industrial router, which is installed on one side of the JR5000S millimeter-wave radar. The roadside RSU device is fixedly installed on the other end of the auxiliary equipment mounting base. The roadside RSU device communicates in real time with the vehicle-mounted equipment through the SY60 5G industrial router.

5. The multi-sensor integrated mobile vehicle-road cooperative workstation and working method according to claim 4, characterized in that: Two multi-dimensional universal joint mounting seats are also provided on the auxiliary equipment mounting seat, and the two multi-dimensional universal joint mounting seats are respectively arranged on both sides of the pneumatic lifting rod. Intelligent traffic cameras are fixedly installed on the two multi-dimensional universal joint mounting seats, and the intelligent traffic cameras are provided with standardized interfaces for connecting with the communication equipment and the edge computing equipment at the same time.

6. The multi-sensor integrated mobile vehicle-road cooperative workstation and working method according to claim 5, characterized in that: The edge computing device is an SCX-1400 embedded workstation.

7. The multi-sensor integrated mobile vehicle-road cooperative workstation and working method according to claim 6, characterized in that: The power supply device includes an intelligent power management system, a power supply battery, a charging management module, a solar panel, an AC power supply port, an AC / DC converter and a voltage regulator; The solar panel is fixedly mounted on the top of the auxiliary equipment mounting base, the solar panel is electrically connected to the charging management module, and the charging management module is electrically connected to the power supply battery and the intelligent power management system at the same time; The power supply battery is electrically connected to the intelligent power management system; One end of the AC / DC converter is electrically connected to the AC power supply port, the other end of the AC / DC converter is electrically connected to the voltage regulator, and the voltage regulator is electrically connected to the intelligent power management system; The intelligent power management system is electrically connected to the first radar sensing device, the second radar sensing device, the communication device, the roadside RSU device, the intelligent traffic camera and the edge computing device at the same time.

8. The multi-sensor integrated mobile vehicle-road cooperative workstation and working method according to claim 7, characterized in that: An air pressure controller, a position sensor, an electronic brake device and a self-locking device are installed in the pneumatic lifting rod; the air pressure controller, the position sensor and the electronic brake device are electrically connected to the intelligent power management system at the same time; the air pressure controller is used to control the pneumatic system of the pneumatic lifting rod, the position sensor is used to detect the lifting position of the pneumatic lifting rod, and the electronic brake device is used to control and lock the pneumatic lifting rod.

9. The multi-sensor integrated mobile vehicle-road cooperative workstation and working method according to claim 8, characterized in that: The multi-dimensional universal joint mounting seat comprises a first base, a first rotating seat, a second rotating seat and a camera mounting seat; The first base is rotatably connected to the first rotating seat, and a first rotation locking mechanism is also provided at the rotating shaft connecting the first base and the first rotating seat; The first rotating seat and the second rotating seat constitute a rotating table mechanism; The second rotating seat is rotatably connected to the camera mounting seat, and a second rotation locking mechanism is also provided at the rotating shaft where the second rotating seat and the camera mounting seat are connected.

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