Vehicle-mounted satellite positioning device rapid detection and verification system and method

Abstract

The present invention discloses a system and method for rapid detection and verification of vehicle-mounted satellite positioning devices, wherein the system includes: a sensing device for obtaining vehicle information corresponding to the vehicle to be detected; a preprocessing module for generating standard trigger data; a scheduling module for allocating corresponding detection sub-areas for scheduling tasks and generating detection parameters; a signal simulation cluster including a signal simulator and a phased array antenna array; the scheduling module is further configured to send the detection parameters to the signal simulator for signal simulation, and broadcast the simulated signal to the detection sub-areas via the phased array antenna array; distributed detection nodes for asynchronously parsing positioning results; calculation and judgment nodes for generating detection results; and blockchain evidence storage nodes for blockchain evidence storage of the detection results. The system can effectively detect vehicle-mounted satellite positioning devices, improve detection efficiency, and at the same time, enhance the accuracy and stability of detection results.

Description

Technical Field

[0001] The present application relates to the technical field of satellite positioning device detection, and in particular to a system and method for rapid detection and verification of a vehicle-mounted satellite positioning device. Background Art

[0002] In recent years, with the continued maturity and widespread adoption of Global Navigation Satellite System (GNSS) technology, on-board satellite positioning devices have become an essential part of the road transport industry's infrastructure. Whether in urban bus dispatching, logistics and transportation monitoring, or accident responsibility determination and safety risk warnings, on-board positioning data plays a crucial role. To ensure the accuracy and reliability of positioning data, my country's Ministry of Transport has clearly stipulated that all operating vehicles must be equipped with positioning devices that meet national standards and undergo regular inspection and calibration.

[0003] In the related art, manual inspection is mostly used when inspecting vehicle-mounted satellite positioning devices; that is, on-site wiring, terminal disassembly and assembly, data collection and comparative analysis are performed manually; it can be understood that this method makes the inspection process of the vehicle-mounted satellite positioning device very time-consuming, seriously affecting the normal operation of the vehicle; moreover, the inspection process wastes a lot of manpower, and the inspection results are not accurate and stable due to human factors. Summary of the Invention

[0004] The present invention aims to at least partially address one of the technical problems in the related art. To this end, one object of the present invention is to provide a rapid detection and verification system for a vehicle-mounted satellite positioning device, which can effectively detect the vehicle-mounted satellite positioning device, improve detection efficiency, and simultaneously enhance the accuracy and stability of the detection results.

[0005] In a first aspect, an embodiment of the present invention proposes a rapid detection and calibration system for a vehicle-mounted satellite positioning device, comprising: a sensing device, the sensing device being used to obtain vehicle information corresponding to a vehicle to be detected after the vehicle to be detected enters a preset detection area; a preprocessing module, the preprocessing module being used to preprocess the vehicle information to generate corresponding standard trigger data; a scheduling module, the scheduling module being used to generate a scheduling task according to the standard trigger data, and assigning a corresponding detection sub-area to the scheduling task, and generating corresponding detection parameters according to the standard trigger data; a signal simulation cluster, the signal simulation cluster comprising a signal simulator and a phased array Antenna array, the scheduling module is also used to send the detection parameters to the signal simulator corresponding to the detection sub-area, so that the signal simulator performs corresponding signal simulation and broadcasts the simulation signal to the detection sub-area through the phased array antenna array; distributed detection node, the distributed detection node is used to obtain the positioning results fed back by the vehicle-mounted satellite positioning device to be detected, and asynchronously parse the positioning results; calculation and judgment node, the calculation and judgment node is used to perform multimodal error fusion and judgment on the analysis results to generate detection results; blockchain evidence node, the blockchain evidence node is used to perform blockchain evidence on the detection results.

[0006] According to an embodiment of the present invention, a rapid detection and verification system for a vehicle-mounted satellite positioning device is provided. A sensing device is provided to obtain vehicle information corresponding to the vehicle to be detected after the vehicle to be detected enters a preset detection area. A preprocessing module is used to preprocess the vehicle information to generate corresponding standard trigger data. A scheduling module is used to generate a scheduling task based on the standard trigger data, assign a corresponding detection sub-area to the scheduling task, and generate corresponding detection parameters based on the standard trigger data. A signal simulation cluster includes a signal simulator and a phased array antenna array. The scheduling module is further used to send the detection parameters to the signal simulator corresponding to the detection sub-area so that the signal simulator performs corresponding signal simulation and broadcasts the simulation signal to the detection sub-area through the phased array antenna array. A distributed detection node is used to obtain the positioning result fed back by the vehicle-mounted satellite positioning device to be detected and asynchronously analyze the positioning result. A calculation and judgment node is used to perform multimodal error fusion and judgment on the analysis result to generate a detection result. A blockchain evidence node is used to store the detection result on a blockchain. This achieves effective detection of the vehicle-mounted satellite positioning device, improves detection efficiency, and at the same time, improves the accuracy and stability of the detection result.

[0007] In some embodiments, the sensing device includes a ground sensing coil and a high-definition camera. The ground sensing coil is used to perform metal quality detection on a preset detection area to generate a trigger signal based on the detection result. The high-definition camera is used to acquire an image of the preset detection area based on the trigger signal to obtain image information corresponding to the vehicle to be detected.

[0008] In some embodiments, the vehicle information includes the license plate number of the vehicle to be detected, the equipment ID of the on-board satellite positioning device to be detected, the entry timestamp, the trigger coordinates and the trigger method, wherein the vehicle information is preprocessed, including: format verification of the vehicle information, and deduplication of the vehicle information after format verification; eliminating identification error information and abnormal coordinate data in the vehicle information, and encapsulating the final compliant data into a JSON package that complies with system specifications.

[0009] In some embodiments, the standard trigger data includes vehicle type, roof antenna model, real-time weather and road conditions, wherein corresponding detection parameters are generated according to the standard trigger data, including: querying a template library according to the vehicle type, the roof antenna model, the real-time weather and the road conditions to obtain a model parameter set corresponding to the vehicle type, the roof antenna model, the real-time weather and the road conditions, and using the queried model parameter set as the detection parameter.

[0010] In some embodiments, the signal simulator performs corresponding signal simulation, including: the signal simulator loads the satellite ephemeris and simulation trajectory corresponding to the detection sub-area, and obtains the real-time GPS coordinates corresponding to the vehicle to be detected; calculates the pseudorange, Doppler frequency shift and multipath delay of each satellite relative to the real-time GPS coordinates; and synthesizes the simulation signal according to the path loss, noise and antenna gain curve defined in the detection parameters.

[0011] In some embodiments, the system further includes an SNR probe, which is set corresponding to a detection sub-area, and the SNR probe is used to detect the detection sub-area to obtain a real-time sub-area signal-to-noise ratio. The scheduling module is also used to dynamically control the transmission power of each detection sub-area according to the real-time sub-area signal-to-noise ratio.

[0012] In some embodiments, the transmit power is dynamically adjusted according to the following formula:

[0013]

[0014] in, Indicates the next moment The transmission power of each detection sub-area, Indicates the current moment The transmission power of each detection sub-area, represents the gain coefficient, Indicates the set signal-to-noise ratio, Indicates the signal-to-noise ratio at the current moment.

[0015] In some embodiments, the scheduling module is further configured to obtain a real-time load rate of each of the distributed detection nodes and allocate detection tasks based on the real-time load rate, wherein the detection tasks are allocated according to the following formula:

[0016]

[0017] in, represents the number of detection tasks, Indicates the number of vehicles to be detected, Indicates the number of online distributed detection nodes, represents the adjustment coefficient, Indicates the The real-time load rate of distributed detection nodes, Indicates the maximum load factor.

[0018] In the second aspect, an embodiment of the present invention proposes a method for rapid detection and calibration of a vehicle-mounted satellite positioning device, comprising the following steps: after the vehicle to be detected enters a preset detection area, obtaining vehicle information corresponding to the vehicle to be detected; preprocessing the vehicle information to generate corresponding standard trigger data; generating a scheduling task based on the standard trigger data, and assigning a corresponding detection sub-area to the scheduling task; generating corresponding detection parameters based on the standard trigger data, and sending the detection parameters to a signal simulator corresponding to the detection sub-area, so that the signal simulator performs corresponding signal simulation, and broadcasts the simulation signal to the detection sub-area through a phased array antenna array; obtaining the positioning result fed back by the vehicle-mounted satellite positioning device to be detected, and asynchronously analyzing the positioning result; performing multimodal error fusion and judgment on the analysis result to generate a detection result, and storing the detection result on a blockchain.

[0019] In some embodiments, the vehicle information includes the license plate number of the vehicle to be detected, the equipment ID of the on-board satellite positioning device to be detected, the entry timestamp, the trigger coordinates and the trigger method, wherein the vehicle information is preprocessed, including: format verification of the vehicle information, and deduplication of the vehicle information after format verification; eliminating identification error information and abnormal coordinate data in the vehicle information, and encapsulating the final compliant data into a JSON package that complies with system specifications.

[0020] The beneficial effects of the present invention are: by effectively setting up a signal simulation environment, a simulation test is performed on the vehicle-mounted satellite positioning device to be tested, so as to achieve effective detection of the vehicle-mounted satellite positioning device; at the same time, the detection area is partitioned, and the positioning results fed back by the vehicle-mounted satellite positioning device are asynchronously analyzed through distributed detection nodes, thereby improving the detection efficiency. At the same time, through multi-modal error fusion and judgment, the accuracy and stability of the detection results are improved.

[0021] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 2 is a block diagram of a rapid detection and verification system for a vehicle-mounted satellite positioning device according to an embodiment of the present invention;

[0023] Figure 2 The figure is a flow chart of a method for rapid detection and verification of a vehicle-mounted satellite positioning device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0025] The following describes a rapid detection and verification system for a vehicle-mounted satellite positioning device according to an embodiment of the present invention with reference to the accompanying drawings.

[0026] See also Figure 1 , Figure 1 FIG. 1 is a block diagram of a rapid detection and verification system for a vehicle-mounted satellite positioning device according to an embodiment of the present invention. Figure 1 As shown, the rapid detection and verification system for the vehicle-mounted satellite positioning device includes: a sensing device 10, a preprocessing module 20, a scheduling module 30, a signal simulation cluster 40, a distributed detection node 50, a calculation and judgment node 60 and a blockchain evidence node 70.

[0027] The sensing device 10 is used to obtain vehicle information corresponding to the vehicle to be detected after the vehicle to be detected enters the preset detection area;

[0028] In some embodiments, the sensing device 10 includes a ground sensing coil and a high-definition camera. The ground sensing coil is used to perform metal quality detection on a preset detection area to generate a trigger signal based on the detection result. The high-definition camera is used to acquire an image of the preset detection area based on the trigger signal to obtain image information corresponding to the vehicle to be detected.

[0029] As an example, after a vehicle enters a preset detection area, the ground sensor coil detects changes in metal mass and immediately outputs a preliminary trigger signal; at the same time, the camera captures the preset detection area and calls the ORC algorithm for real-time license plate number recognition; in addition, when the GPS coordinates of the on-board unit enter the pre-set geographic fence, an entry notification is simultaneously issued.

[0030] The preprocessing module 20 is used to preprocess the vehicle information to generate corresponding standard trigger data.

[0031] In some embodiments, the vehicle information includes the license plate number of the vehicle to be detected, the equipment ID of the on-board satellite positioning device to be detected, the entry timestamp, the trigger coordinates and the trigger method, wherein the vehicle information is pre-processed, including: format verification of the vehicle information, and deduplication of the vehicle information after format verification; eliminating identification error information and abnormal coordinate data in the vehicle information, and encapsulating the final compliant data into a JSON package that complies with the system specifications.

[0032] As an example, after the preprocessing module 20 receives the vehicle information sent by the sensing device; first, the preprocessing module 20 synchronizes all device clocks with the server clock to the millisecond level through NTP / PTP; then, the vehicle information (for example, the license plate number of the vehicle to be detected, the device ID of the on-board satellite positioning device to be detected, the entry timestamp, the trigger coordinates and the trigger method, etc.) is format checked and deduplicated; then, OCR recognition errors and abnormal coordinate data are eliminated; then, the final compliance event data is encapsulated into a JSON package that complies with the system specifications, and temporarily stored in the local persistent cache, and cleared after the report is confirmed.

[0033] The scheduling module 30 is used to generate a scheduling task according to the standard trigger data, allocate a corresponding detection sub-area to the scheduling task, and generate corresponding detection parameters according to the vehicle information.

[0034] The signal simulation cluster 40 includes a signal simulator and a phased array antenna array. The scheduling module 30 is also used to send the detection parameters to the signal simulator corresponding to the detection sub-area so that the signal simulator performs the corresponding signal simulation and broadcasts the simulation signal to the detection sub-area through the phased array antenna array.

[0035] In some embodiments, the standard trigger data includes vehicle type, roof antenna model, real-time weather and road conditions, wherein corresponding detection parameters are generated according to the standard trigger data, including: querying the template library according to the vehicle type, roof antenna model, real-time weather and road conditions to obtain the model parameter set corresponding to the vehicle type, roof antenna model, real-time weather and road conditions, and using the queried model parameter set as the detection parameter.

[0036] As an example, upon receiving the pre-processed JSON event packet (i.e., standard trigger data), the scheduling module 30 immediately sends an ACK. Upon receiving the ACK, the triggering end clears its local cache to ensure at least one reliable delivery. Then, based on information such as vehicle type, roof antenna model, real-time weather and road conditions contained in the standard trigger data, the scheduling module 30 automatically generates and writes a "parallel processing scheduling task" into the queue, allocating execution units for subsequent parallel processes. Next, the scheduling module 30 assigns the associated detection sub-region (e.g., lane 1, lane 2, lane 3, etc.) to the "parallel processing scheduling task" from a pre-built "partition inventory," maps the detection sub-region ID to the "parallel processing scheduling task" ID, and uniformly pushes it to the parallel queues of each detection subsystem. It should be noted that the division of "detection sub-regions" does not involve hardware rearrangement, but rather the dynamic selection and activation of existing region configurations.

[0037] As an example, the detection sub-area can be partitioned according to the following formula:

[0038]

[0039] in, Indicates the number of partitions, Indicates the total area of ​​the detection area, Indicates the maximum coverage area of ​​a single detection sub-region.

[0040] Next, after each detection sub-area is configured, different vehicle types (e.g., taxis, trucks, and buses) differ in the installation method, height, and obstruction characteristics of their roof antennas, resulting in different GNSS signal reception angles, gains, and multipath effects. Furthermore, the simulation requirements for the same detection sub-area vary depending on the environment (daytime / nighttime, rainy / snowy / sunny) (e.g., dynamically increasing path loss, adding a weather attenuation model, etc.). Therefore, after receiving the standard trigger data, the scheduling module 30 queries the model parameter set based on the vehicle type, roof antenna model, real-time weather, and road conditions to select the corresponding model parameter set (preferably, the scheduling module 30 can be configured to fine-tune the parameters according to actual needs after selecting the model parameter set). The model parameter set is then sent to the signal simulator as the detection parameters. This ensures that the simulated signal generated by the signal simulator is highly consistent with the signal in the real scene, thereby improving detection accuracy.

[0041] In some embodiments, the signal simulator performs corresponding signal simulation, including: the signal simulator loads the satellite ephemeris and simulation trajectory of the corresponding detection sub-area, and obtains the real-time GPS coordinates corresponding to the vehicle to be detected; calculates the pseudorange, Doppler frequency shift and multipath delay of each satellite relative to the real-time GPS coordinates; and synthesizes the simulation signal according to the path loss, noise and antenna gain curve defined in the detection parameters.

[0042] As an example, each signal simulator instance is launched in parallel within its own detection sub-area queue. The generation of the simulated signal stream (i.e., the simulated signal) is strictly based on two core inputs: the detection parameters sent by the scheduling module 30 and the real-time GPS coordinates detected when the vehicle triggers the ground sensor coil, as reported in the vehicle information. The signal simulator operates in two phases: signal synthesis and signal distribution. During the signal synthesis phase, the simulator instance first loads the satellite ephemeris and simulated trajectory corresponding to its own detection sub-area based on the detection parameters sent by the scheduling module 30. Next, the simulator instance uses the vehicle's real-time GPS coordinates as a "test point" input and calculates parameters such as pseudorange, Doppler shift, and multipath delay for each satellite relative to this "test point." Then, based on the path loss, noise, and antenna gain curves defined in the detection parameters, it synthesizes a complete GNSS RF waveform data stream (i.e., the simulated signal). During the signal distribution phase, once the simulated signal is generated, the system transmits it to the target OBU (onboard positioning unit) via either RF broadcast or digital injection, based on the OBU device ID associated with the vehicle information. During these two steps, the vehicle's real-time GPS coordinates are used not only to accurately calculate the time delay and frequency deviation of the simulated waveform itself, but also to identify the target OBU device. This ensures that each vehicle under test can simultaneously and accurately receive a simulated signal tailored to its location and vehicle characteristics, achieving true "synchronous signal simulation."

[0043] After receiving its own simulated signal, the onboard positioning unit (OBU) first demodulates and filters it, then compares the simulated signal with the real-time received signal to calibrate its own clock and carrier phase. It then uses algorithms such as multipath mitigation and Kalman filtering to calculate and output real-time positioning results. It then generates a positioning report based on the real-time positioning results and temporarily stores the report locally for reporting purposes.

[0044] In some embodiments, the scheduling module 30 also performs high-precision GNSS signal simulation and coverage on the entire detection area based on standard trigger data. The "entire detection area" refers to the physical space integration of all detection sub-areas. In other words, a simulated signal field for the entire area is built as a whole. It can be understood that this process is not to merge the original detection sub-areas and then simulate them, but to use the signal simulators and phased array antennas on each detection sub-area that have been divided to work together to form a high-precision GNSS signal environment that is "seamlessly connected" and "balancedly covered" on a macro scale. In the aforementioned description, the detection area is divided into several detection sub-areas mainly to assign scheduling tasks to the parallel signal simulation and OBU positioning modules. This division of detection sub-areas is a task division at the logical level, which is used for scheduling and parallelization.

[0045] Parallel simulation generates a point-to-point or sub-area-level simulated signal flow for a single vehicle or a small group of vehicles under test, used for initial positioning performance testing. Distributed beamforming (DBF) generates a comprehensive scenario for all vehicles under test (whether already in the area waiting for detection or potentially entering within a short period of time). This creates a unified, controllable, and highly consistent signal background field. Its objectives are: 1. Eliminate boundary effects between sub-areas, preventing discontinuities and inconsistencies in signal amplitude, phase, or SNR between different sub-areas; 2. Support simultaneous batch testing of multiple vehicles. When dozens or hundreds of vehicles are parked or passing through the detection area simultaneously, the system ensures they are on the same signal simulation "plane" and are not interfered with by test flows from other sub-areas; 3. Continuously and dynamically maintain the simulation environment. Even if a single vehicle under test issues a "positioning anomaly alarm" during the parallel simulation phase, the system maintains signal coverage for the entire area, preventing the collapse of the entire scenario due to the termination or anomaly of a single test route.

[0046] It can be understood that this distributed signal generation and beamforming is not a repetition of the simulation of the completed detection sub-area, but rather provides a continuous GNSS simulation field for all vehicle-mounted satellite positioning devices to be tested on a larger scale, with higher frequency and in a more balanced manner. This ensures "point-to-point" accurate simulation and also ensures signal consistency and batch testing capabilities within the "entire detection area."

[0047] As an example, the scheduling module 30 first orchestrates available simulator resources, integrating pre-stored common model parameters such as ephemeris, Doppler, and pseudorange with detection parameters, and disseminating them to the M signal simulators in the signal simulation cluster. All signal simulators are connected to a central server via a high-speed Ethernet switch and synchronized using the PTP protocol, ensuring consistent output of simulated waveforms based on a millisecond-level time base. Through dynamic resource management, the scheduling module can scale or migrate resources online at any time based on task load, adapting to the parallel signal generation requirements of detection areas of varying sizes.

[0048] Then, after the signal simulation cluster is ready, based on the established detection sub-area division and the full area coverage requirements, the detection sub-area partitioning can be performed according to the following formula:

[0049]

[0050] in, Indicates the number of detection sub-regions, Indicates the total area of ​​the detection area, Indicates the maximum coverage area of ​​a single detection sub-region.

[0051] In this way, the number of detection sub-areas that need to be activated is calculated, and a signal simulator and its corresponding digital waveform shaping phased array antenna unit are allocated to each detection sub-area. The scheduling module 30 issues specific parameters such as the beam direction, transmit power baseline, and polarization mode for each array through software. Without any mechanical rotation, the antenna beam pointing can be adjusted within microseconds to achieve seamless coverage and power balance between sub-areas. This not only ensures that each detection sub-area can obtain a signal strength that matches its area and environment, but also avoids the blind spots and delays caused by the mechanical rotation of traditional single antennas, thereby providing a highly consistent and programmable simulated electromagnetic field for subsequent parallel acquisition and positioning.

[0052] After the detection sub-areas and beam configuration are complete, the scheduling module 30 uniformly distributes parameters for the corresponding detection sub-areas, including the transmit power baseline, number of satellites, and simulation trajectory, to each signal simulator. Once these parameters are loaded, the signal simulator immediately starts outputting a high-precision GNSS waveform. The omnidirectional power amplifier amplifies the signal to the target transmit power, which is then broadcast to the corresponding sub-area via the phased array antenna. The entire process, from parameter distribution to signal coverage, is completed in milliseconds, providing a stable and controllable simulated electromagnetic environment for parallel batch testing.

[0053] In some embodiments, the system also includes an SNR probe, which is set corresponding to the detection sub-area. The SNR probe is used to detect the detection sub-area to obtain the real-time sub-area signal-to-noise ratio. The scheduling module is also used to dynamically control the transmission power of each detection sub-area based on the real-time sub-area signal-to-noise ratio.

[0054] In some embodiments, the transmit power is dynamically adjusted according to the following formula:

[0055]

[0056] in, Indicates the next moment The transmission power of each detection sub-area, Indicates the current moment The transmission power of each detection sub-area, represents the gain coefficient, Indicates the set signal-to-noise ratio, Indicates the signal-to-noise ratio at the current moment.

[0057] As an example, after the signal fields of all detection sub-areas are online, the system can perform SNR monitoring and power balancing in each detection sub-area to ensure the overall consistency of the simulation environment. In response to the SNR fluctuations caused by factors such as distance and occlusion in different detection sub-areas, the transmission power of each area is adjusted in real time through the feedback control algorithm to ensure balanced signal quality in the entire area. Specifically, the system deploys several high-sensitivity SNR probes in each detection sub-area. These SNR probes receive the same simulation signal as the vehicle-mounted satellite positioning device to be detected and measure the signal-to-noise ratio in real time. The measurement results are then fed back to the central control server via ZigBee or Wi-Fi channels. The probe layout has uniform coverage and can reflect the signal quality of each zone in real time, achieving full-time monitoring of the signal environment.

[0058] Preferably, the transmit power is dynamically adjusted according to the following formula:

[0059]

[0060] in, Indicates the next moment The transmission power of each detection sub-area, Indicates the current moment The transmission power of each detection sub-area, represents the gain coefficient, Indicates the set signal-to-noise ratio, Indicates the signal-to-noise ratio at the current moment.

[0061] During this process, the control server runs the above adjustment algorithm every 200 milliseconds to continuously reduce the deviation between the actual signal-to-noise ratio and the target signal-to-noise ratio until the signal-to-noise ratio deviation of all partitions converges to the target range.

[0062] It's important to note that during dynamic balancing, all power adjustments are strictly limited to hardware safety limits to prevent power amplifier overload. After iteration, the system ensures transmit power differences within each partition are less than 0.1dB, providing a consistent, stable, and high-quality signal environment for subsequent parallel data collection, laying a solid foundation for the accuracy of batch terminal detection.

[0063] In some embodiments, the scheduling module 30 is further configured to obtain the real-time load rate of each distributed detection node and allocate detection tasks based on the real-time load rate, wherein the detection tasks are allocated according to the following formula:

[0064]

[0065] in, represents the number of detection tasks, Indicates the number of vehicles to be detected, Indicates the number of online distributed detection nodes, represents the adjustment coefficient, Indicates the The real-time load rate of distributed detection nodes, Indicates the maximum load factor.

[0066] As an example, once the SNR of each detection sub-area of ​​the signal field reaches a stable state, the satellite positioning device on the vehicle to be tested can initiate parallel data collection within the "same standardized signal environment" and proceed to the next step. Once the signal environment stabilizes, the system intelligently distributes detection tasks to each computing node based on node resource availability. The nodes then receive, parse, and persist the data stream reported by the terminal in a lock-free, parallel manner.

[0067] First, the system deploys a lightweight monitoring client in each distributed detection node (physical or containerized service), collects and reports real-time CPU utilization, memory usage, network I / O throughput, and the number of currently active detection tasks, etc., to form the load rate of the distributed detection node. Monitoring data is pushed periodically (e.g., every second) to the "Resource Manager" module of the task scheduling module 30 via heartbeat messages. Based on this data, the Resource Manager predicts the available capacity of each distributed detection node and retains historical load curves in the scheduling log for reference during subsequent dynamic capacity expansion or fault recovery.

[0068] Specifically, the detection tasks are allocated according to the following formula:

[0069]

[0070] in, represents the number of detection tasks, Indicates the number of vehicles to be detected, Indicates the number of online distributed detection nodes, represents the adjustment coefficient, Indicates the The real-time load rate of distributed detection nodes, Indicates the maximum load factor.

[0071] In addition, each distributed detection node starts a corresponding number of "data acquisition sub-processes" to receive UDP / TCP data streams from multiple vehicles. To avoid context switching and blocking caused by traditional lock mechanisms, these sub-processes use lock-free ring queues (ring buffers) for message passing: the network receiving thread pushes the original data packet into the queue, and the parsing thread dequeues it in parallel and processes the message header, checks CRC, decodes the location / speed / mileage fields, and finally writes the structured data asynchronously to the time series database through the batch write interface. In this model, as long as the serial processing ratio is Keeping it within 10%, the overall peak parallel speedup can be estimated using the following formula:

[0072]

[0073] in, Indicates the number of CPU cores.

[0074] Through the coordination of the above three links, the inspection tasks of hundreds of vehicles can be reasonably distributed to multiple nodes in high-concurrency scenarios, and multi-channel data collection such as positioning, speed and mileage can be completed efficiently and stably in a lock-free asynchronous manner, providing reliable and low-latency raw data support for subsequent GPU-accelerated error calculation and pipeline judgment.

[0075] In some embodiments, the calculation and decision node performs multimodal error fusion and decision on the analysis results, which may include: first, mapping multidimensional data such as positioning error, speed error, and mileage error to the GPU parallel computing unit, and adopting a three-stage pipeline architecture to achieve millisecond-level error fusion and automatic decision. Then, the error index is defined as: Positioning coordinate deviation, is the instantaneous speed difference, is the cumulative mileage error. Then, the GPU parallel computing model maps the above error calculation task to CUDA stream processors, execution time:

[0076]

[0077] in, (Thread synchronization overhead) is generally less than 5% of the total time, and the performance is close to linear acceleration. represents the serial time, which is the total time required to execute all error calculation tasks serially on a single processing unit.

[0078] It should be noted that the three-stage pipeline architecture is divided into three stages:

[0079] Phase 1: Data Preprocessing , including denoising, format conversion, etc.;

[0080] Phase 2: Core Error Calculation , including geometric distance calculation, speed difference calculation, and mileage accumulation;

[0081] Phase 3: Result determination , compared with the qualified threshold and generated a mark;

[0082] The overall throughput is:

[0083]

[0084] Each stage is pipelined in parallel, and each node can complete the full set of error fusion and judgment within tens of milliseconds.

[0085] Specifically, in the automatic judgment rule, the threshold is set , , ,when 、 、 If the error is correct, it is marked as "qualified", otherwise it is marked as "pending re-inspection" and an early warning is triggered. After completing error fusion and qualification determination, the system immediately enters the report generation and blockchain evidence storage stage, and the final result is output in a closed loop.

[0086] In some embodiments, the blockchain evidence node stores test results on the blockchain. First, the system writes the test results for each vehicle to be tested to Redis as a first-level cache, which is then automatically synchronized to the second and third-level persistent cache layers, forming a "hot-warm-cold" data distribution structure. By properly setting the cache expiration policy and synchronization frequency, the cache hit rate can be guaranteed to be no less than 95%, and the query response latency of the front-end or regulatory platform can be controlled within 50ms. Whether through active polling or message push, users can obtain the latest test status and pass / fail results within milliseconds, enabling real-time monitoring and rapid feedback for multiple vehicles undergoing parallel testing. Then, after the judgment process for all vehicles to be tested is completed, the system calls the template engine to uniformly populate fields such as basic vehicle information (such as license plate, OBUID), test parameters (signal environment parameters, node allocation information), error calculation data, and judgment results into a pre-designed PDF template. The template also includes reserved areas for a QR code and digital signature summary. The resulting final document not only features a unified format and clear layout, but also includes an embedded QR code for quick verification, ensuring that anyone can easily access key data and verify the report's authenticity. To ensure the authenticity and tamper-proof nature of the inspection report, the system selects multiple signing nodes to execute the BLS threshold signature algorithm in parallel. Each node locally signs a fragment of the report summary, then completes the threshold synthesis within a time window of no more than 200 milliseconds, writing the final signed summary to the consortium or public blockchain in a single transaction. This ensures that once the report is generated and uploaded to the blockchain, its content and signatures are protected by the blockchain's immutability and traceability, meeting the highest levels of security and compliance requirements. Finally, after the upload is complete, the system asynchronously sends a notification message containing the report download link and signature verification interface to each vehicle's inspection terminal and the regulatory platform, ensuring that all relevant parties have immediate access to the final document and can verify its authenticity. At the same time, the background automatically archives and stores all original monitoring data, generated PDF reports, signature summaries, and chain records, forming a complete audit log and responsibility traceability chain to meet various business needs such as subsequent quality inspection, supervision, and dispute resolution.

[0087] In summary, according to an embodiment of the present invention, a rapid detection and verification system for a vehicle-mounted satellite positioning device is provided. A sensing device is provided to obtain vehicle information corresponding to the vehicle to be detected after the vehicle to be detected enters a preset detection area. A preprocessing module is used to preprocess the vehicle information to generate corresponding standard trigger data. A scheduling module is used to generate a scheduling task based on the standard trigger data, assign a corresponding detection sub-area to the scheduling task, and generate corresponding detection parameters based on the standard trigger data. The signal simulation cluster includes a signal simulator and a phased array antenna array. The scheduling module is also used to send the detection parameters to the signal simulator corresponding to the detection sub-area so that the signal simulator performs corresponding signal simulation and broadcasts the simulation signal to the detection sub-area through the phased array antenna array. The distributed detection node is used to obtain the positioning result fed back by the vehicle-mounted satellite positioning device to be detected and asynchronously parse the positioning result. The calculation and judgment node is used to perform multimodal error fusion and judgment on the parsing result to generate a detection result. The blockchain evidence node is used to store the detection result on the blockchain. This achieves effective detection of the vehicle-mounted satellite positioning device, improves detection efficiency, and at the same time, improves the accuracy and stability of the detection result.

[0088] In the second aspect, the embodiment of the present invention proposes a method for rapid detection and verification of a vehicle-mounted satellite positioning device, such as Figure 2 As shown, the method for rapid detection and verification of the vehicle-mounted satellite positioning device includes the following steps:

[0089] S101, after a vehicle to be detected enters a preset detection area, vehicle information corresponding to the vehicle to be detected is obtained.

[0090] S102: Pre-process the vehicle information to generate corresponding standard trigger data.

[0091] S103: Generate a scheduling task according to the standard trigger data, and allocate a corresponding detection sub-area to the scheduling task.

[0092] S104: Generate corresponding detection parameters according to the standard trigger data, and send the detection parameters to the signal simulator corresponding to the detection sub-area, so that the signal simulator performs corresponding signal simulation, and broadcasts the simulation signal to the detection sub-area through the phased array antenna array.

[0093] S105: Obtain the positioning result fed back by the vehicle-mounted satellite positioning device to be detected, and perform asynchronous analysis on the positioning result.

[0094] S106: Perform multimodal error fusion and judgment on the analysis results to generate detection results, and store the detection results on the blockchain.

[0095] In some embodiments, the vehicle information includes the license plate number of the vehicle to be detected, the equipment ID of the on-board satellite positioning device to be detected, the entry timestamp, the trigger coordinates and the trigger method, wherein the vehicle information is pre-processed, including: format verification of the vehicle information, and deduplication of the vehicle information after format verification; eliminating identification error information and abnormal coordinate data in the vehicle information, and encapsulating the final compliant data into a JSON package that complies with the system specifications.

[0096] It should be noted that the above description of the rapid detection and verification system for the vehicle-mounted satellite positioning device is also applicable to the rapid detection and verification method for the vehicle-mounted satellite positioning device, and will not be described in detail here.

[0097] It should be noted that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0098] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0099] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0100] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are 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 should not be understood as limiting the present invention.

[0101] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0102] In the present invention, unless otherwise specified or limited, the terms "installed," "connected," "connect," "fixed," etc. should be understood in a broad sense. For example, they can refer to fixed connection, detachable connection, or integration; mechanical connection, electrical connection; direct connection, or indirect connection through an intermediate medium; internal communication between two components, or interaction between two components, unless otherwise specified. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0103] In the present invention, unless otherwise expressly specified or limited, when a first feature is "above" or "below" a second feature, it may mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediary. Furthermore, when a first feature is "above," "above," or "above" a second feature, it may mean that the first feature is directly above or diagonally above the second feature, or simply means that the first feature is at a higher level than the second feature. When a first feature is "below," "below," or "below" a second feature, it may mean that the first feature is directly below or diagonally below the second feature, or simply means that the first feature is at a lower level than the second feature.

[0104] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A rapid detection and verification system for a vehicle-mounted satellite positioning device, characterized in that: include: A sensing device, wherein the sensing device is used to obtain vehicle information corresponding to the vehicle to be detected after the vehicle to be detected enters a preset detection area; A preprocessing module, configured to preprocess the vehicle information to generate corresponding standard trigger data; a scheduling module, configured to generate a scheduling task according to the standard trigger data, assign a corresponding detection sub-area to the scheduling task, and generate corresponding detection parameters according to the standard trigger data; A signal simulation cluster, wherein the signal simulation cluster includes a signal simulator and a phased array antenna array, and the scheduling module is further used to send the detection parameters to the signal simulator corresponding to the detection sub-area, so that the signal simulator performs corresponding signal simulation and broadcasts the simulation signal to the detection sub-area through the phased array antenna array; A distributed detection node is used to obtain the positioning result fed back by the vehicle-mounted satellite positioning device to be detected and to perform asynchronous analysis on the positioning result; A calculation and decision node, which is used to perform multimodal error fusion and decision on the analysis results to generate a detection result; A blockchain evidence storage node, which is used to store the test results on the blockchain; The system further includes an SNR probe, which is set corresponding to the detection sub-area, and is used to detect the detection sub-area to obtain a real-time sub-area signal-to-noise ratio. The scheduling module is further used to dynamically control the transmit power of each detection sub-area according to the real-time sub-area signal-to-noise ratio; The transmit power is dynamically adjusted according to the following formula: in, Indicates the next moment The transmission power of each detection sub-area, Indicates the current moment The transmission power of each detection sub-area, represents the gain coefficient, Indicates the set signal-to-noise ratio, Indicates the signal-to-noise ratio at the current moment.

2. The vehicle-mounted satellite positioning device rapid detection and verification system according to claim 1, characterized in that: The sensing device includes a ground sensing coil and a high-definition camera. The ground sensing coil is used to perform metal quality detection on a preset detection area to generate a trigger signal based on the detection result. The high-definition camera is used to acquire an image of the preset detection area based on the trigger signal to obtain image information corresponding to the vehicle to be detected.

3. The vehicle-mounted satellite positioning device rapid detection and verification system according to claim 1, characterized in that: The vehicle information includes the license plate number of the vehicle to be detected, the device ID of the vehicle-mounted satellite positioning device to be detected, the entry time stamp, the trigger coordinates and the trigger mode, wherein the vehicle information is pre-processed, including: Performing format verification on the vehicle information and performing deduplication processing on the vehicle information after format verification; Eliminate identification error information and abnormal coordinate data in the vehicle information, and encapsulate the final compliant data into a JSON package that complies with system specifications.

4. The vehicle-mounted satellite positioning device rapid detection and verification system according to claim 1, characterized in that: The standard trigger data includes vehicle type, roof antenna model, real-time weather and road conditions, wherein corresponding detection parameters are generated according to the standard trigger data, including: A template library is queried according to the vehicle type, the roof antenna model, the real-time weather and the road status to obtain a model parameter set corresponding to the vehicle type, the roof antenna model, the real-time weather and the road status, and the queried model parameter set is used as a detection parameter.

5. The vehicle-mounted satellite positioning device rapid detection and verification system according to claim 1, characterized in that: The signal simulator performs corresponding signal simulation, including: The signal simulator loads the satellite ephemeris and simulation trajectory corresponding to the detection sub-area and obtains the real-time GPS coordinates corresponding to the vehicle to be detected; Calculating the pseudorange, Doppler shift, and multipath delay of each satellite relative to the real-time GPS coordinates; A simulation signal is synthesized according to the path loss, noise and antenna gain curves defined in the detection parameters.

6. The vehicle-mounted satellite positioning device rapid detection and verification system according to claim 1, characterized in that: The scheduling module is further configured to obtain a real-time load rate of each of the distributed detection nodes and allocate detection tasks based on the real-time load rate, wherein the detection tasks are allocated according to the following formula: in, represents the number of detection tasks, Indicates the number of vehicles to be detected, Indicates the number of online distributed detection nodes, represents the adjustment coefficient, Indicates the The real-time load rate of distributed detection nodes, Indicates the maximum load factor.

7. A method for rapid detection and verification of a vehicle-mounted satellite positioning device, characterized in that: The following steps are involved: After the vehicle to be detected enters the preset detection area, the vehicle information corresponding to the vehicle to be detected is obtained; Preprocessing the vehicle information to generate corresponding standard trigger data; Generate a scheduling task according to the standard trigger data, and assign a corresponding detection sub-area to the scheduling task; generating corresponding detection parameters according to the standard trigger data, and sending the detection parameters to a signal simulator corresponding to the detection sub-area so that the signal simulator performs corresponding signal simulation, and broadcasts the simulation signal to the detection sub-area through the phased array antenna array; Obtaining the positioning result fed back by the onboard satellite positioning device to be tested, and performing asynchronous analysis on the positioning result; Perform multimodal error fusion and judgment on the analysis results to generate detection results, and store the detection results on the blockchain; The method further includes detecting the detection sub-area to obtain a real-time sub-area signal-to-noise ratio, and dynamically controlling the transmit power of each of the detection sub-area according to the real-time sub-area signal-to-noise ratio; The transmit power is dynamically adjusted according to the following formula: in, Indicates the next moment The transmission power of each detection sub-area, Indicates the current moment The transmission power of each detection sub-area, represents the gain coefficient, Indicates the set signal-to-noise ratio, Indicates the signal-to-noise ratio at the current moment.

8. The method for rapid detection and verification of a vehicle-mounted satellite positioning device according to claim 7, wherein: The vehicle information includes the license plate number of the vehicle to be detected, the device ID of the vehicle-mounted satellite positioning device to be detected, the entry time stamp, the trigger coordinates and the trigger mode, wherein the vehicle information is pre-processed, including: Performing format verification on the vehicle information and performing deduplication processing on the vehicle information after format verification; Eliminate identification error information and abnormal coordinate data in the vehicle information, and encapsulate the final compliant data into a JSON package that complies with system specifications.

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