Rapid detection and verification system and method for vehicle-mounted satellite positioning device

Abstract

The invention discloses a rapid detection and verification system and method for a vehicle-mounted satellite positioning device. The system comprises a sensing device used for acquiring vehicle information corresponding to a to-be-detected vehicle; the preprocessing module is used for generating standard trigger data; the scheduling module is used for distributing a corresponding detection sub-region for a scheduling task and generating a detection parameter; the signal simulation cluster comprises a signal simulator and a phased-array antenna array, the scheduling module is further used for sending the detection parameters to the signal simulator so as to carry out signal simulation, and a simulation signal is broadcasted to the detection subarea through the phased-array antenna array; the distributed detection nodes are used for carrying out asynchronous analysis on the positioning result; the calculation and judgment node is used for generating a detection result; and the block chain evidence storage node is used for carrying out block chain evidence storage on the detection result. The vehicle-mounted satellite positioning device can be effectively detected, the detection efficiency is improved, and meanwhile, the accuracy and stability of a detection result are improved.

Description

Technical Field

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

[0002] In recent years, with the continuous maturity and popularization of Global Navigation Satellite System (GNSS) technology, vehicle-mounted satellite positioning devices have become one of the infrastructure in the road transportation industry. Whether it is in urban bus dispatching, logistics transportation monitoring, or in accident liability determination and safety risk warning, vehicle-mounted positioning data plays a crucial role. To ensure the accuracy and reliability of positioning data, the Ministry of Transport of China has clearly stipulated that all operating vehicles must be equipped with positioning devices that meet national standards and be tested and verified at regular intervals.

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

[0004] The present invention aims to solve at least one of the technical problems in the related technologies to some extent. For this reason, an object of the present invention is to provide a rapid detection and verification system for vehicle-mounted satellite positioning devices, which can effectively detect vehicle-mounted satellite positioning devices, improve the detection efficiency, and at the same time, improve the accuracy and stability of the detection results.

[0005] In a first aspect, an embodiment of the present invention provides a rapid detection and verification system for an in-vehicle satellite positioning device, comprising: an induction device configured 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 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, allocate a corresponding detection sub-area for the scheduling task, and generate corresponding detection parameters according to the standard trigger data; a signal simulation cluster, the 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 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 configured to obtain a positioning result fed back by the in-vehicle satellite positioning device to be detected and asynchronously analyze the positioning result; a calculation and determination node configured to perform multi-modal error fusion and determination on the analysis result to generate a detection result; and a blockchain evidence storage node configured to perform blockchain evidence storage on the detection result.

[0006] According to the rapid detection and verification system for an in-vehicle satellite positioning device of the embodiment of the present invention, by setting an induction device 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 to preprocess the vehicle information to generate corresponding standard trigger data; a scheduling module to generate a scheduling task according to the standard trigger data, allocate a corresponding detection sub-area for the scheduling task, and generate corresponding detection parameters according to the standard trigger data; 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 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 to obtain a positioning result fed back by the in-vehicle satellite positioning device to be detected and asynchronously analyze the positioning result; a calculation and determination node to perform multi-modal error fusion and determination on the analysis result to generate a detection result; and a blockchain evidence storage node to perform blockchain evidence storage on the detection result. Thus, effective detection of the in-vehicle satellite positioning device is achieved, the detection efficiency is improved, and at the same time, the accuracy and stability of the detection result are improved.

[0007] In some embodiments, the sensing device includes a ground induction coil and a high-definition camera. The ground induction coil is used to detect the metal quality of 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 device ID of the in-vehicle satellite positioning device to be detected, the entry timestamp, the trigger coordinates, and the trigger mode. Among them, preprocessing the vehicle information includes: performing format verification on the vehicle information, and performing duplicate removal processing on the vehicle information after format verification; removing the identification error information and abnormal coordinate data in the vehicle information, and encapsulating the final compliant data into a JSON package that conforms to the system specification.

[0009] In some embodiments, the standard trigger data includes vehicle type, roof antenna model, real-time weather, and road conditions. Among them, generating corresponding detection parameters according to the standard trigger data includes: 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 parameters.

[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-region, and obtains the real-time GPS coordinates corresponding to the vehicle to be detected; calculates the pseudorange, Doppler shift, and multipath delay of each satellite relative to the real-time GPS coordinates; synthesizes a 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. The SNR probe is set corresponding to the detection sub-region. The SNR probe is used to detect the detection sub-region to obtain the real-time sub-region signal-to-noise ratio. The scheduling module is further used to dynamically control the transmission power of each detection sub-region according to the real-time sub-region signal-to-noise ratio.

[0012] In some embodiments, the dynamic adjustment of the transmission power is performed according to the following formula: Wherein, represents the transmission power of the th detection sub-region at the next moment, represents the transmission power of the th detection sub-region at the current moment, represents the gain coefficient, denotes the set signal-to-noise ratio denotes the signal-to-noise ratio at the current moment.

[0013] In some embodiments, the scheduling module is further configured to obtain the 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: wherein, denotes the number of detection tasks, denotes the number of vehicles to be detected, denotes the number of online distributed detection nodes, denotes the adjustment coefficient, denotes the real-time load rate of the th distributed detection node,

[0014] In a second aspect, an embodiment of the present invention provides a method for rapid detection and verification of an in-vehicle satellite positioning device, including the following steps: after a 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 according to the standard trigger data, and allocating a corresponding detection sub-area for 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 a phased array antenna array; obtaining a positioning result fed back by the in-vehicle satellite positioning device to be detected, and asynchronously parsing the positioning result; performing multi-modal error fusion and determination on the parsing result to generate a detection result, and storing the detection result on a blockchain.

[0015] In some embodiments, the vehicle information includes the license plate number of the vehicle to be detected, the device ID of the in-vehicle satellite positioning device to be detected, the entry timestamp, the trigger coordinates, and the trigger method. Among them, preprocessing the vehicle information includes: performing format verification on the vehicle information, and performing deduplication processing on the vehicle information after format verification; removing the identification error information and abnormal coordinate data in the vehicle information, and encapsulating the final compliant data into a JSON package that conforms to the system specification.

[0016] The beneficial effects of the present invention are as follows: By effectively arranging a signal simulation environment, a simulation test is carried out on the vehicle-mounted satellite positioning device to be detected, so as to effectively detect 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, improving the detection efficiency. At the same time, through multi-modal error fusion and determination, the accuracy and stability of the detection results are improved.

[0017] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 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 2 is a flowchart of a rapid detection and verification method for a vehicle-mounted satellite positioning device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] Embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0020] The rapid detection and verification system for a vehicle-mounted satellite positioning device according to an embodiment of the present invention will be described below with reference to the drawings.

[0021] Please refer to Figure 1 , Figure 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. As Figure 1 shown, the rapid detection and verification system for a vehicle-mounted satellite positioning device includes: an induction device 10, a preprocessing module 20, a scheduling module 30, a signal simulation cluster 40, distributed detection nodes 50, a calculation and determination node 60, and a blockchain evidence storage node 70.

[0022] Among them, the induction device 10 is used to obtain vehicle information corresponding to the vehicle to be detected after the vehicle to be detected enters a preset detection area; In some embodiments, the induction device 10 includes an inductive loop and a high-definition camera. The inductive loop is used to perform metal quality detection on the preset detection area to generate a trigger signal based on the detection result, and 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.

[0023] As an example, after the vehicle enters the preset detection area, the inductive loop immediately outputs a preliminary trigger signal when it detects a change in the metal mass; at the same time, the camera captures the preset detection area and calls the ORC algorithm to perform real-time license plate number recognition; in addition, when the GPS coordinates of the on-vehicle unit enter the preset geographical fence, an in-area notice is sent synchronously.

[0024] The preprocessing module 20 is used to preprocess the vehicle information to generate corresponding standard trigger data.

[0025] In some embodiments, the vehicle information includes the license plate number of the vehicle to be detected, the device ID of the on-vehicle satellite positioning device to be detected, the in-area timestamp, the trigger coordinates, and the trigger method. Among them, preprocessing the vehicle information includes: performing format verification on the vehicle information, and performing duplicate removal processing on the vehicle information after format verification; removing the recognition error information and abnormal coordinate data in the vehicle information, and encapsulating the final compliant data into a JSON package that conforms to the system specification.

[0026] As an example, after the preprocessing module 20 receives the vehicle information sent by the induction device; first, the preprocessing module 20 synchronizes all device clocks with the server clock to the millisecond level through NTP / PTP; then, perform format verification and duplicate removal processing on the vehicle information (for example, the license plate number of the vehicle to be detected, the device ID of the on-vehicle satellite positioning device to be detected, the in-area timestamp, the trigger coordinates, and the trigger method, etc.); then, remove the OCR recognition errors and abnormal coordinate data; then, encapsulate the final compliant event data into a JSON package that conforms to the system specification, and temporarily store it in the local persistent cache, and clear it after reporting and confirmation.

[0027] The scheduling module 30 is used to generate a scheduling task according to the standard trigger data, allocate a corresponding detection sub-area for the scheduling task, and generate corresponding detection parameters according to the vehicle information.

[0028] The signal simulation cluster 40, 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 corresponding signal simulation, and broadcasts the simulation signal to the detection sub-area through the phased array antenna array.

[0029] In some embodiments, the standard trigger data includes the vehicle type, the roof antenna model, the real-time weather, and the road condition. Among them, generating corresponding detection parameters according to the standard trigger data includes: querying the template library according to the vehicle type, the roof antenna model, the real-time weather, and the road condition to obtain the model parameter set corresponding to the vehicle type, the roof antenna model, the real-time weather, and the road condition, and using the queried model parameter set as the detection parameter.

[0030] As an example, first, after the scheduling module 30 receives the preprocessed JSON event packet (i.e., the standard trigger data), it immediately sends back an ACK. After the triggering end obtains the ACK, it clears the local cache to ensure reliable delivery at least once. Then, based on information such as vehicle type, roof antenna model, real-time weather, and road conditions in the standard trigger data, the scheduling module 30 automatically generates and writes a "parallel processing scheduling task" into the queue to allocate execution units for subsequent parallel processes. Next, the scheduling module 30 allocates an associated detection sub-region (e.g., lane 1, lane 2, lane 3, etc.) for the above "parallel processing scheduling task" from the pre-constructed "partition inventory", and establishes a mapping between the detection sub-region ID and the ID of the "parallel processing scheduling task", and uniformly pushes it into the parallel queues of each detection subsystem. It should be noted that the division of the "detection sub-region" does not involve re-arranging the hardware, but dynamically selects and activates existing regional configurations.

[0031] As an example, the partitioning of the detection sub-region can be performed according to the following formula: where, represents the number of partitions, represents the total area of the detection region, represents the maximum coverage area of a single detection sub-region.

[0032] Next, after the configurations of each detection sub-region are ready, since different vehicle types (e.g., ride-hailing, trucks, buses, etc.) have differences in the installation method, height, and occlusion characteristics of the roof antenna, their receiving angles, gains, and multipath effects on GNSS signals are also different. Moreover, the simulation requirements of the same detection sub-region are different in environments such as day / night, rain / snow / clear (e.g., dynamically increasing path loss, adding weather attenuation models, etc.). Therefore, after the scheduling module 30 receives the standard trigger data, it queries the model parameter set according to the vehicle type, roof antenna model, real-time weather, and road conditions therein to select the corresponding model parameter set (preferably, it can be set that the scheduling module 30 fine-tunes the parameters according to actual needs after selecting the model parameter set); then it sends the model parameter set as detection parameters to the signal simulator. In this way, it can ensure that the simulated signals generated by the signal simulator are highly consistent with the signals in the real scenario, thereby improving the detection accuracy.

[0033] 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-region, and obtains the real-time GPS coordinates of the vehicle to be detected; calculates the pseudo-range, 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 curves defined in the detection parameters.

[0034] As an example, each signal simulator instance is started in parallel in its respective detection sub-region queue. The generation of the simulation signal stream (i.e., the simulation signal) is strictly completed based on two core inputs: one is the detection parameters sent by the scheduling module 30, and the other is the real-time GPS coordinate information detected when the vehicle triggers the ground loop in the vehicle information. The work of the signal simulator is divided into two stages, one is the signal synthesis stage, and the other is the signal distribution stage. In the signal synthesis stage, the signal simulator instance first loads the satellite ephemeris and simulation trajectory of the detection sub-region where it is located according to the detection parameters sent by the scheduling module 30. Then, taking the real-time GPS coordinates of the vehicle as the "test point" input, it calculates parameters such as the pseudo-range, Doppler frequency shift, and multipath delay of each satellite relative to this "test point". Then, according to the path loss, noise, and antenna gain curves defined in the detection parameters, it synthesizes a complete GNSS RF waveform data stream, that is, the simulation signal. In the signal distribution stage, after the simulation signal is generated, the system will send the simulation signal to the target OBU device (on-board positioning unit) in one of two ways: RF live broadcast and digital injection, according to the OBU device ID bound in the vehicle information. In these two links, the real-time GPS coordinates of the vehicle are not only used to accurately calculate the time delay and frequency offset of the simulation waveform itself, but also used to determine the target OBU device. In this way, it can be ensured that each vehicle to be detected can receive the simulation signal customized for its own position and vehicle type characteristics in parallel and accurately, realizing the "synchronous signal simulation" in the true sense.

[0035] After the on-board positioning unit (OBU) receives its corresponding simulation signal, it first performs signal demodulation and filtering processing, and then compares the simulation signal with the real-time received signal to correct the local clock and carrier phase. Subsequently, using algorithms such as multipath suppression and Kalman filtering, it calculates and outputs the real-time positioning result, generates a positioning report according to the real-time positioning result, and stores the positioning report locally for unified reporting.

[0036] In some embodiments, the scheduling module 30 also performs high-precision GNSS signal simulation and coverage for the entire detection area based on standard trigger data. Herein, the "entire detection area" refers to the comprehensive physical space of all detection sub-areas. That is to say, a simulation signal field for the whole area is built once. It can be understood that this process does not involve merging the original detection sub-areas for simulation. Instead, the signal simulators and phased array antennas on each of the already divided detection sub-areas are used to work together to form a high-precision GNSS signal environment that is "seamlessly connected" and "evenly covered" macroscopically. In the foregoing, the detection area is divided into several detection sub-areas mainly to allocate scheduling tasks to the parallel signal simulation and OBU positioning modules. This division of detection sub-areas is a task segmentation at the logical level for scheduling and parallelization.

[0037] Parallel simulation is the generation of "point-to-point" or "detection sub-area level" simulation signal flows for the detection sub-areas where a single vehicle to be detected or a small batch of vehicles to be detected are located, and is used for the first-round positioning performance test. Distributed beamforming is for the entire scene where all vehicles to be detected (whether they have entered the area and are waiting or may enter the area in a short time) are located. A unified, controllable and highly consistent signal background field is constructed. The purposes are as follows: 1. Eliminate the boundary effects between detection sub-areas and avoid problems of discontinuity and inconsistency in signal amplitude, phase or SNR between different detection sub-areas; 2. Support batch testing of multiple vehicles simultaneously. When dozens or hundreds of vehicles stay or drive through the detection area at the same time, the system can ensure that they are on the same signal simulation "plane" and are not interfered by the test flows of other detection sub-areas; 3. Continuously and dynamically maintain the simulation environment. Even if "positioning anomaly alarms" occur for individual vehicles to be detected during the parallel simulation stage, the system should continue to maintain the signal coverage of the entire area behind the scenes to avoid the collapse of the overall scene due to the end or anomaly of a certain test.

[0038] It can be understood that this distributed signal generation and beamforming is not a repetition of the simulation of the already completed detection sub-areas. Instead, on a larger scale, with higher frequency and more evenly, a continuous and consistent GNSS simulation field is provided for all vehicle-mounted satellite positioning devices to be detected, so as to ensure the accurate simulation of "point-to-point" and also ensure the signal consistency and batch testing ability within the "entire detection area" range.

[0039] As an example, first, the scheduling module 30 orchestrates the available simulator resources, fuses the general model parameters such as the pre-stored ephemeris, Doppler, and pseudorange with the detection parameters, and distributes them to M signal simulators in the signal simulation cluster. All signal simulators are interconnected with the central server through a high-speed Ethernet switch and use the PTP protocol to synchronize clocks to ensure that they output simulation waveforms consistently on a millisecond-level time basis. Through dynamic resource management, the scheduling module can expand or migrate online at any time according to the task load to adapt to the parallel signal generation requirements of detection areas of different scales.

[0040] Next, after the signal simulation cluster is ready, according to the established detection sub-region division and the full-region coverage requirements, the detection sub-regions can be partitioned according to the following formula: where, represents the number of detection sub-regions, represents the total area of the detection region, represents the maximum coverage area of a single detection sub-region.

[0041] In this way, the number of detection sub-regions to be activated is calculated, and a signal simulator and its supporting digital waveform shaping phased array antenna unit are allocated to each detection sub-region. The scheduling module 30 issues specific parameters such as the beam direction, transmit power baseline, and polarization mode of 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-regions. In this way, not only is it ensured that each detection sub-region can obtain a signal intensity matching its area and environment, but also the blind spots and delays caused by the mechanical rotation of traditional single antennas are avoided, thus providing a highly consistent and programmable simulation electromagnetic field for subsequent parallel acquisition and positioning.

[0042] Then, after the detection sub-regions and beam configurations are completed, the scheduling module 30 uniformly issues parameters such as the transmit power baseline, number of satellites, and simulation trajectory of the corresponding detection sub-regions to each signal simulator. After loading the parameters, the signal simulator immediately starts to output high-precision GNSS waveforms, amplifies the signals to the target transmit power through an omnidirectional power amplifier, and broadcasts the signals to the corresponding sub-regions by the phased array antenna. The entire process from parameter issuance to signal coverage can be completed within milliseconds, providing a stable and controllable simulation electromagnetic environment for parallel batch detection.

[0043] In some embodiments, the system further includes SNR probes. The SNR probes are set corresponding to the detection sub-regions. The SNR probes are used to detect the detection sub-regions to obtain the real-time sub-region signal-to-noise ratio, and the scheduling module is further used to dynamically control the transmit power of each detection sub-region according to the real-time sub-region signal-to-noise ratio.

[0044] In some embodiments, the dynamic adjustment of the transmission power is performed according to the following formula: where, represents the transmission power of the th detection sub-region at the next moment, represents the transmission power of the th detection sub-region at the current moment, represents the gain coefficient, represents the set signal-to-noise ratio, represents the signal-to-noise ratio at the current moment.

[0045] As an example, after the signal fields of all detection sub-regions are on line, the system can perform SNR monitoring and power balancing in each detection sub-region to ensure the overall consistency of the simulation environment. For the SNR fluctuations caused by factors such as distance and occlusion in different detection sub-regions, the transmission power of each region is adjusted in real time through a feedback control algorithm to ensure the balance of the signal quality in the whole region. Specifically, the system deploys several high-sensitivity SNR probes in each detection sub-region. These SNR probes receive the same simulation signals as the vehicle-mounted satellite positioning device to be detected and measure the signal-to-noise ratio in real time , and then feedback the measurement results to the central control server through the ZigBee or Wi-Fi channel. The probe layout is evenly covered, which can reflect the signal quality of each partition in real time and realize the full-time monitoring of the signal environment.

[0046] Preferably, the dynamic adjustment of the transmission power is performed according to the following formula: where, represents the transmission power of the th detection sub-region at the next moment, represents the transmission power of the th detection sub-region at the current moment, represents the gain coefficient, represents the set signal-to-noise ratio, represents the signal-to-noise ratio at the current moment.

[0047] In 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 deviations of all partitions converge to the target range.

[0048] It should be noted that during the dynamic equilibrium process, all power adjustments are strictly limited by the hardware safety upper and lower limits to avoid power amplifier overload. After iteration, the system can ensure that the transmitted power difference between each partition is less than 0.1 dB, thereby providing a consistent, stable, and high-quality signal environment for subsequent parallel data acquisition and laying a solid foundation for the accuracy of batch terminal detection.

[0049] 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, where the detection tasks are allocated according to the following formula: Wherein, represents the number of detection tasks, represents the number of vehicles to be detected, represents the number of online distributed detection nodes, represents the adjustment coefficient, represents the th real-time load rate of the distributed detection node, represents the maximum load rate.

[0050] As an example, when the SNR of each detection sub-region of the signal field enters a stable state, the vehicle-mounted satellite positioning device to be detected can start parallel data acquisition in the "same standardized signal environment" and enter the next step. After the signal environment is stable, the system intelligently allocates detection tasks to each computing node according to the node resource status, and the nodes internally receive, parse, and persist the data stream reported by the terminal in a lock-free parallel manner.

[0051] First, the system deploys lightweight monitoring clients inside each distributed detection node (physical or containerized service), and real-time collects and reports metrics such as CPU utilization, memory occupancy, network I / O throughput, and the current number of active detection tasks, to form the load rate of the distributed detection node (in percentage). The monitoring data is periodically (e.g., every second) pushed to the "resource manager" module of the task scheduling module 30 through heartbeat messages. The resource manager will predict the available capacity of each distributed detection node based on this data and retain the historical load curve in the scheduling log for reference during subsequent dynamic expansion or fault recovery.

[0052] Specifically, the detection tasks are allocated according to the following formula: Wherein, represents the number of detection tasks, represents the number of vehicles to be detected, represents the number of online distributed detection nodes, represents the adjustment coefficient, represents the real-time load ratio of the th distributed detection node, and represents the maximum load ratio.

[0053] Moreover, each distributed detection node starts a corresponding number of "data acquisition subprocesses" to receive UDP / TCP data streams from multiple vehicles. To avoid context switching and blocking caused by traditional lock mechanisms, a lock-free ring buffer is used inside these subprocesses for message passing: the network receiving thread pushes the original data packets into the queue, and the parsing thread dequeues and processes the packet headers, verifies CRC, and decodes the location / speed / mileage fields in parallel. Finally, the structured data is asynchronously written into the time series database through a batch writing interface. In this model, as long as the serial processing ratio remains within 10%, the overall peak parallel acceleration ratio can be estimated by the following formula: where, represents the number of CPU cores.

[0054] Through the coordination of the above three links, in a high-concurrency scenario, the detection tasks of hundreds of vehicles can be reasonably distributed among multiple nodes, and the multi-channel data acquisition of location, speed, and mileage can be efficiently and stably completed in a lock-free asynchronous manner, providing reliable and low-latency raw data support for subsequent GPU-accelerated error calculation and pipeline determination.

[0055] In some embodiments, the multi-modal error fusion and determination of the parsing results by the calculation and determination node may include: First, map multi-dimensional data such as location error, speed error, and mileage error to the GPU parallel computing unit, and adopt a three-stage pipeline architecture to achieve millisecond-level error fusion and automatic determination. Then, define the error metrics as: is the deviation of the positioning coordinate, is the instantaneous speed difference, is the cumulative mileage error. Then, the GPU parallel computing model maps the above error calculation tasks to CUDA stream processors, and the execution duration is: where, (thread synchronization overhead) is generally less than 5% of the total duration, and the performance is close to linear acceleration, represents the serial time, that is, the total time required to serially execute all error calculation tasks on a single processing unit.

[0056] It should be noted that the three-stage pipeline architecture is divided into three stages: Stage 1: Data preprocessing , including denoising, format conversion, etc.; Phase II: Core error calculation , including geometric distance calculation, speed difference calculation, and mileage accumulation; Phase III: Result determination , compare with the qualified threshold and generate a mark; The overall throughput is: Each stage is in parallel flow, and each node can complete the full set of error fusion and determination within dozens of milliseconds.

[0057] Specifically, in the automatic determination rule, set the threshold , , , when , , , then mark "qualified", otherwise "await re-inspection" and trigger an alarm. After completing the error fusion and qualified determination, the system immediately enters the report generation and blockchain evidence storage stage, and outputs the final result in a closed loop.

[0058] In some embodiments, the blockchain evidence storage node stores the detection results on the blockchain, which includes: First, the system writes the detection determination results corresponding to each vehicle to be detected into Redis as the primary cache, and then automatically synchronizes them to the secondary and tertiary persistent cache layers, forming a "hot - warm - cold" data distribution structure. By reasonably setting the cache expiration policy and synchronization frequency, the cache hit rate can be ensured to be no less than 95%, and the query response latency of the front - end or regulatory platform can be controlled within 50 ms. Whether it is an active polling or a message - push - based method, users can obtain the latest detection status and pass / fail conclusion within milliseconds, thus realizing real - time monitoring and rapid feedback for multiple vehicles in parallel detection. Next, after the determination process of all vehicles to be detected is completed, the system calls the template engine to uniformly fill fields such as vehicle basic information (such as license plate, OBUID), detection parameters (signal environment parameters, node allocation information), error calculation data, and determination results into a pre - designed PDF template. The template also reserves areas for QR codes and digital signature digests. The generated final document not only has a unified format and clear layout but also embeds QR codes for quick verification, ensuring that anyone can conveniently view the key data and verify the authenticity of the report. Then, to ensure the originality and anti - tampering property of the detection report, the system selects multiple signature nodes to execute the BLS threshold signature algorithm in parallel. Each node locally signs the report summary fragment, and then completes the threshold synthesis within a time window of no more than 200 ms, and writes the final signature digest into the consortium chain or public chain at one time. In this way, once the report is generated and uploaded to the chain, its content and signature are guaranteed by the immutability and traceability of the blockchain, meeting the highest - level security and compliance requirements. Finally, after the chain - up is completed, the system asynchronously sends notification messages containing report download links and signature verification interfaces to the detection terminals of each vehicle and the regulatory platform, ensuring that relevant parties can obtain the final document and verify its authenticity in a timely manner. At the same time, the background automatically archives and stores all the original monitoring data, the generated PDF report, the signature digest, and the chain - up records, etc., forming a complete audit log and responsibility traceability chain to meet various business requirements such as subsequent quality inspection, supervision, and dispute handling.

[0059] In summary, for the on-vehicle satellite positioning device rapid detection and verification system according to the embodiments of the present invention, an induction device is provided 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 is used to preprocess the vehicle information to generate corresponding standard trigger data; a scheduling module is used to generate a scheduling task according to the standard trigger data, allocate a corresponding detection sub-area for the scheduling task, and generate corresponding detection parameters according to the standard trigger data; 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 on-vehicle satellite positioning device to be detected and asynchronously analyze the positioning result; a calculation and determination node is used to perform multi-modal error fusion and determination on the analysis result to generate a detection result; a blockchain evidence storage node is used to perform blockchain evidence storage on the detection result. Thereby, the effective detection of the on-vehicle satellite positioning device is realized, the detection efficiency is improved, and at the same time, the accuracy and stability of the detection result are improved.

[0060] In a second aspect, an embodiment of the present invention proposes a method for rapid detection and verification of an on-vehicle satellite positioning device, as Figure 2 shown, the method for rapid detection and verification of the on-vehicle satellite positioning device includes the following steps: S101, after the vehicle to be detected enters the preset detection area, obtain the vehicle information corresponding to the vehicle to be detected.

[0061] S102, preprocess the vehicle information to generate corresponding standard trigger data.

[0062] S103, generate a scheduling task according to the standard trigger data, and allocate a corresponding detection sub-area for the scheduling task.

[0063] 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.

[0064] S105, obtain the positioning result fed back by the on-vehicle satellite positioning device to be detected, and asynchronously analyze the positioning result.

[0065] S106, perform multi-modal error fusion and determination on the analysis result to generate a detection result, and perform blockchain evidence storage on the detection result.

[0066] In some embodiments, the vehicle information includes the license plate number of the vehicle to be detected, the device ID of the on-vehicle satellite positioning device to be detected, the entry timestamp, the trigger coordinates, and the trigger mode. Among them, the preprocessing of the vehicle information includes: performing format verification on the vehicle information, and performing deduplication processing on the vehicle information after format verification; removing the recognition error information and abnormal coordinate data in the vehicle information, and encapsulating the finally compliant data into a JSON package that conforms to the system specification.

[0067] It should be noted that the above description of the on-vehicle satellite positioning device rapid detection and verification system also applies to the on-vehicle satellite positioning device rapid detection and verification method, and will not be elaborated here.

[0068] It should be noted that the logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the 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 connection with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0069] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0070] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0071] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by terms such as "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. are based on the orientation or positional relationships shown in the 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 thus should not be construed as a limitation of the present invention.

[0072] In addition, the terms "first" and "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically and clearly defined.

[0073] In the present invention, unless otherwise clearly specified and defined, terms such as "install", "connect", "couple", "fix", 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 can be the communication inside two elements or the interaction relationship between two elements, unless otherwise clearly defined. 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 circumstances.

[0074] In the present invention, unless otherwise clearly specified or limited, a first feature being "on" or "under" a second feature may mean that the first and second features are in direct contact, or the first and second features are indirectly in contact via an intermediate medium. Further, a first feature being "above", "over" and "on top of" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the horizontal height of the first feature is higher than that of the second feature. A first feature being "under", "below" and "beneath" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the horizontal height of the first feature is less than that of the second feature.

[0075] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A rapid detection and verification system for in-vehicle satellite positioning devices, characterized in that, Including: An induction device configured 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 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, allocate a corresponding detection sub-area for the scheduling task, and generate corresponding detection parameters according to the standard trigger data; A signal simulation cluster, the signal simulation cluster includes signal simulators and a phased array antenna array, and the scheduling module is further configured 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 nodes configured to obtain the positioning results fed back by the vehicle-mounted satellite positioning device to be detected and asynchronously analyze the positioning results; A calculation and determination node configured to perform multi-modal error fusion and determination on the analysis results to generate a detection result; A blockchain evidence storage node configured to perform blockchain evidence storage on the detection result.

2. The rapid detection and verification system for in-vehicle satellite positioning devices according to claim 1, characterized in that, The induction device includes a ground sense coil and a high-definition camera. The ground sense coil is configured 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 configured to perform image acquisition on the preset detection area based on the trigger signal to obtain image information corresponding to the vehicle to be detected.

3. The rapid detection and verification system for in-vehicle satellite positioning devices as described in 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 timestamp, the trigger coordinates, and the trigger method. Among them, preprocessing the vehicle information includes: Performing format verification on the vehicle information, and performing duplicate removal processing on the vehicle information after format verification; Removing the identification error information and abnormal coordinate data in the vehicle information, and encapsulating the finally compliant data into a JSON package that conforms to the system specification.

4. The rapid detection and verification system for in-vehicle satellite positioning devices as claimed in claim 1, characterized in that, The standard trigger data includes vehicle type, roof antenna model, real-time weather, and road conditions. Among them, generating corresponding detection parameters according to the standard trigger data includes: 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.

5. The rapid detection and verification system for in-vehicle satellite positioning devices 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; Synthesizing a simulation signal according to the path loss, noise, and antenna gain curve defined in the detection parameters.

6. The rapid detection and verification system for vehicle-mounted satellite positioning devices according to claim 1, characterized in that It 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 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.

7. The rapid detection and verification system for in-vehicle satellite positioning devices according to claim 6, wherein, The transmit power is dynamically adjusted according to the following formula: Among them, represents the transmission power of the th detection sub-region at the next moment, represents the transmission power of the th detection sub-region at the current moment, represents the gain coefficient, represents the set signal-to-noise ratio, represents the signal-to-noise ratio at the current moment.

8. The rapid detection and verification system for in-vehicle satellite positioning devices according to claim 1, characterized in that, The scheduling module is further used to obtain the real-time load rate of each of the distributed detection nodes, and to allocate detection tasks based on the real-time load rate, wherein the detection tasks are allocated according to the following formula: Among them, represents the number of detection tasks, represents the number of vehicles to be detected, represents the number of online distributed detection nodes, represents the adjustment coefficient, represents the real-time load rate of the th distributed detection node, represents the maximum load rate.

9. A rapid detection and verification method for an in-vehicle 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; Generate corresponding detection parameters according to the standard trigger data, and send 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 vehicle-mounted satellite positioning device to be detected, and performing asynchronous analysis on the positioning result; The analysis results are subjected to multimodal error fusion and judgment to generate detection results, which are then stored on the blockchain.

10. The rapid detection and verification method for in-vehicle satellite positioning devices according to claim 9, 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; The identification error information and abnormal coordinate data in the vehicle information are eliminated, and the final compliant data is encapsulated into a JSON package that complies with the system specification.

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