High-precision Beidou positioning terminal device and system

Abstract

The invention relates to the technical field of satellite positioning, and discloses a high-precision Beidou positioning terminal device and system, and the device comprises a Beidou multi-frequency positioning module which receives B1C, B2a and B3I frequency band signals of Beidou satellites, and generates centimeter-level original observation data; the heterogeneous data fusion access engine is used for carrying out space-time alignment on multi-source sensing data by dynamically accessing laser point cloud, visual SLAM (Simultaneous Localization and Mapping) and UWB (Ultra Wideband) positioning data; the neuromorphic space-time alignment algorithm module unifies a multi-source data coordinate system; the block chain trusted data pool module is used for anchoring the Beidou positioning data and encrypting and storing the sensing data; and a scene semantic decision module. Carrier phase dynamic characteristics are encoded into a pulse sequential sequence by a neuromorphic module in real time, a physical signal is converted into a time-space event understandable by an algorithm, a cross-modal attention mechanism is driven to automatically improve a laser point cloud weight, visual interference is suppressed, and errors of satellite denial scene positioning are reduced.

Description

Technical Field

[0001] The present invention relates to the field of satellite positioning technology, and in particular to a high-precision Beidou positioning terminal device and system. Background Art

[0002] With the in-depth application of location services in fields such as autonomous driving and smart transportation, the industry has put forward three rigid demands on positioning systems: maintaining centimeter-level positioning continuity in complex electromagnetic environments (such as densely populated urban buildings and strong industrial interference zones), achieving the unification of spatiotemporal benchmarks for multi-source heterogeneous perception data such as lidar, visual sensors, and wireless ranging, and providing a judicially verifiable positioning evidence chain for high-value scenarios (accident responsibility determination and operation audits). However, current solutions have not yet systematically covered the full-link capabilities of signal interference rejection, multi-source fusion, and trusted evidence storage, which has restricted the large-scale implementation of high-precision positioning in safety-critical scenarios.

[0003] However, in current technologies, the dynamic characteristics of the carrier phase are only used as numerical inputs and are not encoded as events, and the weights of multi-source perception data cannot be adaptively adjusted in real time, resulting in errors in satellite positioning in different scenarios. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a high-precision Beidou positioning terminal device and system to solve the problem that it is difficult to encode it into a pulse timing sequence in real time and cannot trigger the adaptive adjustment of the weights of multi-source perception data in real time, resulting in errors in satellite positioning in different scenarios.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A high-precision Beidou positioning system, comprising:

[0006] Beidou multi-frequency positioning module, which receives Beidou satellite B1C, B2a and B3I frequency band signals and generates centimeter-level raw observation data;

[0007] Heterogeneous data fusion access engine dynamically accesses laser point cloud, visual SLAM and UWB positioning data to align multi-source perception data in time and space;

[0008] The neuromorphic spatiotemporal alignment algorithm module unifies the coordinates of multi-source data through spiking neural network encoding and cross-modal attention mechanism;

[0009] The blockchain trusted data pool module uses Beidou positioning data anchoring and perception data encryption to store evidence and conduct space-time mutual verification;

[0010] The scene semantic decision module displays traffic risk warnings and decisions based on parsing trajectory topology and point cloud semantics.

[0011] Preferably, the Beidou multi-frequency positioning module includes:

[0012] The RF front-end processing unit uses a multi-band microwave integrated circuit to synchronously receive Beidou satellite B1C, B2a, and B3I frequency band signals, performs signal down-conversion and low-noise amplification, and outputs an intermediate frequency signal with a signal-to-noise ratio of ≥45dB.

[0013] The baseband signal calculation unit performs carrier stripping and pseudo-code despreading on the intermediate frequency signal through a multi-channel correlator to generate the original observation data and ,in is the carrier phase observation value, is the pseudorange observation value;

[0014] Observation quality control unit: Dynamically verifies the quality of observation data and screens centimeter-level valid data using the following formula: ;

[0015] in, is the observation quality factor, is the carrier phase measurement standard deviation, is the wavelength of the corresponding frequency band, is the pseudorange smoothing coefficient, is the absolute value of the second-order difference of pseudorange.

[0016] Preferably, the heterogeneous data fusion access engine includes:

[0017] Multi-protocol interface unit, which accesses multi-source data by dynamically adapting laser point cloud protocol, visual SLAM data stream and UWB positioning protocol;

[0018] The BeiDou space-time reference unit receives centimeter-level valid data output by the observation quality control unit of the BeiDou multi-frequency positioning module. and , establish a space-time reference coordinate system;

[0019] The dynamic weighted fusion unit calculates the weight factors of Beidou and other sensing data: ;

[0020] in: ;

[0021] is the BeiDou observation quality coefficient, is the weight factor of the i-th type of perception data, is the timestamp error of the i-th type of data, is the spatial coordinate error of the i-th type of data.

[0022] Preferably, the observation quality factor When the value is >0.05, the weight of laser point cloud and UWB data is increased to 70%, and the de-jitter filter is activated to suppress instantaneous positioning jumps.

[0023] Preferably, the neuromorphic spatiotemporal alignment algorithm module includes:

[0024] The pulse signal encoding unit generates a pulse timing characteristic sequence through the carrier phase observation value: ;

[0025] in, is the carrier phase change, is the pulse trigger threshold, is the pulse triggering moment;

[0026] The cross-modal attention projection unit associates the pulse sequence with multi-source perception data through a multi-head attention mechanism to generate a spatial projection weight matrix: ;

[0027] in, It is a pulse timing sequence, triggered by the carrier phase change. is the feature vector of multi-source perception data, is the feature dimension scaling factor;

[0028] Dynamic residual suppression unit corrects coordinate deviation by constraining projection residuals.

[0029] Preferably, if the projection residual exceeds 0.5 cm for five consecutive times, a re-fusion instruction is sent to the heterogeneous data fusion access engine, and the visual data access is frozen for 10 milliseconds.

[0030] Preferably, the blockchain trusted data pool module includes the following units:

[0031] The space-time anchoring unit uses the centimeter-level coordinates output by the Beidou multi-frequency positioning module As the blockchain genesis block;

[0032] The encrypted evidence storage unit generates the spatiotemporal fingerprint of the perception data through the SHA-256 algorithm: ;

[0033] in To align coordinates;

[0034] Mutual verification trigger unit, when positive coordinate deviation is detected When , recalculate the weight factor , and write the correction record into the blockchain.

[0035] Preferably, the scene semantic decision module includes the following units:

[0036] The trajectory topology parsing unit constructs a spatiotemporal trajectory graph G = (V, E) by aligning the coordinate sequence, where the vertex V is the position point and the edge E is the motion vector;

[0037] The point cloud semantic reconstruction unit uses laser point cloud data to identify semantic elements such as lane lines, guardrails, and obstacles, and generates a labeled raster map.

[0038] The risk decision trigger unit broadcasts warning instructions through the V2X communication interface when it detects that the trajectory conflicts with the semantic map.

[0039] A high-precision Beidou positioning terminal device includes:

[0040] Multi-frequency RF chipset, which receives BeiDou satellite frequency band signals through multi-channel microwave integrated circuits and outputs carrier phase observation values;

[0041] Sensor fusion interface, synchronous access to lidar, global shutter camera and UWB locator data through Ethernet, MIPI-CSI and SPI protocols;

[0042] Edge AI processor deploys a neuromorphic spatiotemporal alignment algorithm to output precise positioning coordinates under dynamic residual constraints;

[0043] A secure evidence storage unit generates a Beidou timestamp-encrypted positioning fingerprint;

[0044] The control unit responds to semantic decision instructions and broadcasts warning messages via DSRC / LTE-V2X.

[0045] A high-precision Beidou positioning method comprises the following steps:

[0046] S1, BeiDou signal processing, generates centimeter-level carrier phase and pseudorange raw observation data by receiving BeiDou satellite B1C, B2a and B3I frequency band signals, and performs dynamic quality control to screen valid data;

[0047] S2: Multi-source perception data fusion, dynamic access to laser point cloud, visual SLAM and UWB positioning data, calculation of dynamic weight factors based on spatiotemporal errors, establishment of a spatiotemporal reference coordinate system centered on Beidou for data alignment;

[0048] S3, neuromorphic spatiotemporal alignment, uses a spiking neural network to encode carrier phase change events to generate a pulse timing sequence, uses a cross-modal attention mechanism to solve the spatial projection weights, and outputs centimeter-level precise alignment coordinates;

[0049] S4, blockchain space-time mutual verification, uses Beidou centimeter-level coordinates as blockchain anchor points, encrypts and generates space-time fingerprints of perception data for storage, and triggers data re-integration when coordinate deviation exceeds the standard;

[0050] S5, scenario risk decision-making, builds motion trajectory topology based on precisely aligned coordinate sequences, integrates laser point cloud semantics to identify road elements, and broadcasts warning instructions in real time when risk conditions are detected.

[0051] The present invention provides a high-precision Beidou positioning terminal device and system. It has the following beneficial effects:

[0052] 1. The present invention uses the carrier phase dynamic features to be encoded into a pulse timing sequence in real time by the neuromorphic module, converting the physical signal layer characteristics into algorithmically understandable spatiotemporal events. In tunnel scenarios, satellite signal fluctuations trigger high-frequency pulses, driving the cross-modal attention mechanism to automatically increase the weight of the laser point cloud, while suppressing visual interference and reducing the positioning error in satellite denial scenarios.

[0053] 2. In the present invention, the residual exceeding standard event of the neuromorphic module triggers the mutual verification mechanism of the blockchain module and drives the recalculation of the weight factor. At the same time, the fusion engine freezes the abnormal data flow, binding the error control at the algorithm layer with the trusted evidence at the security layer, achieving the dual benefits of accuracy in resolving the residual exceeding standard event and judicial verification.

[0054] 3. The present invention uses the semantic boundaries of identified lane lines and guardrails, and real-time collision detection with the vehicle motion vector constructed by the trajectory topology analysis unit, to cross-dimensionally associate the static semantics of the environment with the dynamic behavior of the vehicle. In heavy rain scenes at night, the guardrail position identified by laser is integrated with the trajectory acceleration vector to avoid the risk of vehicle collision. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is an architecture diagram of a high-precision Beidou positioning system of the present invention;

[0056] Figure 2 This is a flow chart of a high-precision Beidou positioning method of the present invention. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solution of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0058] Please see the attached Figure 1 The embodiment of the present invention provides a high-precision Beidou positioning system, including:

[0059] Beidou multi-frequency positioning module, which receives Beidou satellite B1C, B2a and B3I frequency band signals and generates centimeter-level raw observation data;

[0060] Heterogeneous data fusion access engine dynamically accesses laser point cloud, visual SLAM and UWB positioning data to align multi-source perception data in time and space;

[0061] The neuromorphic spatiotemporal alignment algorithm module unifies the coordinates of multi-source data through spiking neural network encoding and cross-modal attention mechanism;

[0062] The blockchain trusted data pool module uses Beidou positioning data anchoring and perception data encryption to store evidence and conduct space-time mutual verification;

[0063] The scene semantic decision module displays traffic risk warnings and decisions based on parsing trajectory topology and point cloud semantics.

[0064] Specifically, the Beidou multi-frequency positioning module synchronously receives Beidou satellite three-band signals (B1C, B2a, and B3I), uses the complementary characteristics of the frequency bands to calculate high-precision carrier phase and pseudorange observations, and generates centimeter-level raw data. In complex urban canyon environments, multi-frequency differentials effectively suppress positioning drift caused by multipath effects, significantly improving positioning stability in harsh scenarios. At the same time, the three-frequency signal characteristics are used to achieve rapid ambiguity convergence, significantly shortening the positioning initialization response time. In strong electromagnetic interference scenarios, continuous positioning capabilities are maintained through intelligent frequency band switching, providing a robust spatiotemporal benchmark for multi-source fusion.

[0065] Based on the B1C, B2a, and B3I frequency band signals received by the BeiDou multi-frequency positioning module, centimeter-level raw observation data is generated, providing a spatiotemporal reference origin. The heterogeneous data fusion access engine uses a dynamic weighting algorithm to synchronously align the spatial structure information of the laser point cloud, the texture features of the visual SLAM, and the absolute position constraints of the UWB, achieving sub-centimeter-level spatiotemporal unification of multi-source perception data. In scenarios where satellite signals are blocked (such as tunnel entrances), the fusion engine automatically increases the weight of laser and UWB to 80%, and synchronizes the timestamps of multi-source data based on the BeiDou time scale, eliminating positioning jumps and maintaining continuous trajectory output.

[0066] The neuromorphic spatiotemporal alignment algorithm module encodes the phase change of the Beidou carrier into a pulse timing sequence through a pulse neural network, captures the dynamic characteristics of the satellite signal in real time, and combines the cross-modal attention mechanism to solve the spatial projection weight matrix of laser point cloud, visual SLAM and UWB data, and dynamically fuses multi-source perception data; in complex scenarios such as tunnels, pulse coding automatically suppresses visual data jitter, enhances laser point cloud-dominated positioning, and achieves sub-centimeter-level unification of multi-source coordinate systems.

[0067] The blockchain trusted data pool module uses Beidou centimeter-level positioning coordinates as the physical space-time benchmark for the blockchain genesis block. It fuses the precisely aligned coordinates using the SHA-256 algorithm to generate a space-time fingerprint, enabling encrypted and tamper-proof storage of sensor data. When the neuromorphic module detects an excess residual error, it automatically triggers multi-source data re-integration and writes the correction process into the distributed ledger. For example, in port container lifting supervision, this enables full-link trusted traceability of positioning data, building a highly reliable space-time data foundation.

[0068] The scene semantic decision module analyzes the centimeter-level positioning trajectory topology output by neuromorphic alignment, builds a motion vector model in real time, and integrates the semantic labels of lane lines, guardrails and obstacles recognized by laser point cloud. When it detects that the vehicle's lateral acceleration exceeds the standard or the distance to the obstacle is less than the safety threshold, it triggers V2X early warning broadcast and optical warning in milliseconds, achieving a leap from centimeter-level positioning to active safety.

[0069] Beidou multi-frequency positioning module includes:

[0070] The RF front-end processing unit uses a multi-band microwave integrated circuit to synchronously receive Beidou satellite B1C, B2a, and B3I frequency band signals, performs signal down-conversion and low-noise amplification, and outputs an intermediate frequency signal with a signal-to-noise ratio of ≥45dB.

[0071] The baseband signal calculation unit performs carrier stripping and pseudo-code despreading on the intermediate frequency signal through a multi-channel correlator to generate the original observation data and ,in is the carrier phase observation value, is the pseudorange observation value;

[0072] Observation quality control unit: Dynamically verifies the quality of observation data and screens centimeter-level valid data using the following formula: ;

[0073] in, is the observation quality factor, is the carrier phase measurement standard deviation, is the wavelength of the corresponding frequency band, is the pseudorange smoothing coefficient, is the absolute value of the second-order difference of pseudorange.

[0074] Specifically, the RF front-end processing unit synchronously receives the Beidou three-frequency signals (B1C / B2a / B3I) through a multi-band microwave integrated circuit, performs signal down-conversion and low-noise amplification, and ensures that the output intermediate frequency signal signal-to-noise ratio is ≥45dB. In the scenario of strong interference from 5G base stations (1.4GHz frequency band), it combines with an adaptive band-stop filter to suppress in-band noise, providing a pure signal source for centimeter-level observation.

[0075] The baseband signal solution unit uses a multi-channel correlator to perform carrier stripping and pseudo-code decoding on the intermediate frequency signal, generating carrier phase observations (millimeter-level accuracy) and pseudo-range observations (centimeter-level accuracy). By solving the ionospheric delay error through a dual-frequency combination, the ambiguity fixation efficiency of dynamic positioning is greatly improved, supporting high-frequency real-time centimeter-level raw data output.

[0076] The observation quality control unit screens valid data based on a dynamic verification formula, thereby ensuring that the output data meets centimeter-level positioning reliability standards through a multi-path error identification and filtering mechanism.

[0077] The heterogeneous data fusion access engine includes:

[0078] Multi-protocol interface unit, which accesses multi-source data by dynamically adapting laser point cloud protocol, visual SLAM data stream and UWB positioning protocol;

[0079] The BeiDou space-time reference unit receives centimeter-level valid data output by the observation quality control unit of the BeiDou multi-frequency positioning module. and , establish a space-time reference coordinate system;

[0080] The dynamic weighted fusion unit calculates the weight factors of Beidou and other sensing data: ;

[0081] in: ;

[0082] is the BeiDou observation quality coefficient, is the weight factor of the i-th type of perception data, is the timestamp error of the i-th type of data, is the spatial coordinate error of the i-th type of data.

[0083] Specifically, the multi-protocol interface unit dynamically adapts to the laser point cloud protocol (EtherCAT), visual SLAM data stream (MIPI-CSI2), and UWB positioning protocol (IEEE 802.15.4a), enabling millisecond-level access to multi-source heterogeneous data. It simultaneously analyzes Velodyne lidar point clouds, ZED vision camera feature points, and Decawave UWB ranging signals in the terminal, eliminating communication delay differences between devices (timestamp alignment error <1ms), and providing standardized data input for the fusion engine.

[0084] Beidou Space-Time Reference Unit Output centimeter-level effective data (carrier phase observation value , pseudorange observations ) as the origin, and establish a unified space-time reference through WGS-84 coordinate system conversion. In scenarios where satellite signals are interrupted (such as tunnels), BeiDou timing is used to maintain the time reference. Combined with UWB absolute position constraints, the spatial offset error of multi-source data is reduced.

[0085] The dynamic weighted fusion unit calculates the weights in real time based on the formula ( is the timestamp error, is the spatial error, BeiDou is the BeiDou quality coefficient). When heavy rain causes the visual SLAM spatial error to increase, the weight is dynamically adjusted to ensure the continuity of the fusion output trajectory.

[0086] Observation quality factor When the value is >0.05, the weight of laser point cloud and UWB data is increased to 70%, and the de-jitter filter is activated to suppress instantaneous positioning jumps.

[0087] Specifically, when the BeiDou observation quality factor is detected When the value is >0.05, the fusion weight of the laser point cloud and UWB data is automatically increased to 70% and the de-jitter filter is activated to suppress instantaneous positioning jumps caused by multipath noise in scenarios with degraded satellite signals (such as tunnels / strong electromagnetic interference) and maintain centimeter-level trajectory continuity.

[0088] The neuromorphic spatiotemporal alignment algorithm module includes:

[0089] The pulse signal encoding unit generates a pulse timing characteristic sequence through the carrier phase observation value: ;

[0090] in, is the carrier phase change, is the pulse trigger threshold, is the pulse triggering moment;

[0091] The cross-modal attention projection unit associates the pulse sequence with multi-source perception data through a multi-head attention mechanism to generate a spatial projection weight matrix:

[0092] in, It is a pulse timing sequence, triggered by the carrier phase change. is the feature vector of multi-source perception data, is the feature dimension scaling factor;

[0093] Dynamic residual suppression unit corrects coordinate deviation by constraining projection residuals.

[0094] Specifically, the pulse signal encoding unit calculates the phase change of the Beidou carrier in real time , when the change exceeds the adaptive pulse trigger threshold (θ=k·σnoise), a pulse sequence is generated , accurately capturing the dynamic characteristics of satellite signals. For example, in a vehicle's rapid acceleration scenario, a sudden change in carrier phase triggers a high-frequency pulse, significantly improving the response sensitivity to changes in motion state and providing an event-driven neuromorphic coding foundation for multi-source alignment;

[0095] Cross-modal attention projection unit

[0096] Based on the multi-head attention mechanism, the pulse timing sequence Perception feature vectors of laser point cloud, visual SLAM, and UWB Association, calculate the spatial projection weight matrix In scenarios where vision is obstructed by rain and fog, the system automatically suppresses the visual weight to 0.1 and increases the laser point cloud weight to 0.8, achieving intelligent fusion of cross-modal features and eliminating spatial deviations in multi-source data.

[0097] The dynamic residual suppression unit corrects the coordinate output of Beidou in real time by constraining the projection residual. When the continuous bends on the overpass cause the residual to exceed the standard, it immediately sends a re-fusion instruction to the fusion engine and freezes the abnormal data stream to suppress the positioning trajectory fluctuations and solve the problem of cumulative error diffusion in complex scenarios.

[0098] If the projection residual exceeds 0.5 cm for five consecutive times, a re-fusion instruction is sent to the heterogeneous data fusion access engine, and the visual data access is frozen for 10 milliseconds.

[0099] Specifically, by dynamically monitoring the projection residual and seeing it exceed the 0.5cm tolerance threshold for five consecutive times, a re-fusion instruction is sent to the heterogeneous data fusion access engine, and the visual data access is synchronously frozen for 10 milliseconds. In scenarios where strong electromagnetic interference or extreme weather causes visual data distortion, abnormal data pollution is quickly blocked, triggering real-time resetting of multi-source weights (the laser point cloud weight is increased to 85%), effectively solving the problem of cumulative error diffusion and ensuring the robustness of the centimeter-level positioning link.

[0100] The blockchain trusted data pool module includes the following units:

[0101] The space-time anchoring unit uses the centimeter-level coordinates output by the Beidou multi-frequency positioning module As the blockchain genesis block;

[0102] The encrypted evidence storage unit generates the spatiotemporal fingerprint of the perception data through the SHA-256 algorithm: ;

[0103] in To align coordinates;

[0104] Mutual verification trigger unit, when positive coordinate deviation is detected When , recalculate the weight factor , and write the correction record into the blockchain.

[0105] Specifically, the space-time anchoring unit uses the Beidou centimeter-level positioning coordinates as the hash root of the blockchain's genesis block to establish an unalterable physical space-time origin. In the port container dispatching scenario, this unit anchors the crane's positioning coordinates to the blockchain, achieving legal-level trusted evidence of the operation location.

[0106] The encrypted evidence storage unit uses the SHA-256 algorithm to fuse the precisely aligned coordinates with the Beidou original coordinates to generate a spatiotemporal fingerprint. That is, when a vehicle suddenly changes lanes on a highway, the track fingerprint is stored in real time in the distributed ledger, establishing a closed loop for judicial tracing technology.

[0107] When the mutual verification trigger unit detects that the residual of the neuromorphic alignment output exceeds the standard, it automatically triggers the recalculation of the weight factor and encrypts the coordinates of the residual abnormal point and the correction record into the blockchain.

[0108] The scene semantic decision module includes the following units:

[0109] The trajectory topology parsing unit constructs a spatiotemporal trajectory graph G = (V, E) by aligning the coordinate sequence, where the vertex V is the position point and the edge E is the motion vector;

[0110] The point cloud semantic reconstruction unit uses laser point cloud data to identify semantic elements such as lane lines, guardrails, and obstacles, and generates a labeled raster map.

[0111] The risk decision trigger unit broadcasts warning instructions through the V2X communication interface when it detects that the trajectory conflicts with the semantic map.

[0112] Specifically, the trajectory topology analysis unit constructs a spatiotemporal trajectory graph using a high-precision coordinate sequence, calculates vehicle kinematic characteristics in real time, accurately captures risk characteristics such as sudden lateral acceleration and heading deviation, and dynamically generates a trajectory topology model in complex road scenarios. This provides millisecond-level motion state analysis for risk decision-making, significantly improving active driving safety protection capabilities.

[0113] The point cloud semantic reconstruction unit analyzes the spatial distribution characteristics of laser point clouds (e.g., identifying continuous linear point clouds as lane lines, point clouds with sudden height changes as guardrails, and moving clustered point clouds as obstacles) to construct a rasterized environmental map with semantic labels. This overcomes the perception limitations of traditional visual solutions in low-visibility scenarios such as rain, fog, and at night, providing the risk decision-making module with a robust semantic representation of road structure, enabling accurate recognition of environmental factors in all weather conditions and laying the foundation for proactive safety decision-making.

[0114] The risk decision trigger unit compares the motion vector output by the trajectory topology analysis unit with the environmental semantic map generated by the point cloud semantic reconstruction unit in real time. When it detects a conflict between the vehicle trajectory and the road structure (such as deviating from the lane or approaching an obstacle), it triggers a multi-level response protocol in milliseconds, broadcasts standardized warning instructions through the V2X communication interface, and synchronously links the on-board control system to perform evasive actions, thereby achieving active safety closed-loop protection in complex traffic scenarios.

[0115] A high-precision Beidou positioning terminal device, comprising:

[0116] Multi-frequency RF chipset, which receives BeiDou satellite frequency band signals through multi-channel microwave integrated circuits and outputs carrier phase observation values;

[0117] Sensor fusion interface, synchronous access to lidar, global shutter camera and UWB locator data through Ethernet, MIPI-CSI and SPI protocols;

[0118] Edge AI processor deploys a neuromorphic spatiotemporal alignment algorithm to output precise positioning coordinates under dynamic residual constraints;

[0119] A secure evidence storage unit generates a Beidou timestamp-encrypted positioning fingerprint;

[0120] The control unit responds to semantic decision instructions and broadcasts warning messages via DSRC / LTE-V2X.

[0121] Specifically, as a hardware carrier, the terminal achieves high-sensitivity reception of Beidou tri-frequency signals through a multi-frequency RF chipset. The sensor fusion interface simultaneously accesses heterogeneous data from lidar, visual cameras, and UWB. The edge AI processor calculates centimeter-level precise positioning coordinates in real time based on the neuromorphic algorithm. The secure evidence storage unit generates Beidou timing encrypted spatiotemporal fingerprints to build a trusted data base. The control unit triggers V2X warning broadcasts at the millisecond level based on the semantic decision results, forming a hardware-level closed loop from satellite signal reception to active security decision-making.

[0122] Please see the attached Figure 2 A high-precision Beidou positioning method includes the following steps:

[0123] S1, BeiDou signal processing, generates centimeter-level carrier phase and pseudorange raw observation data by receiving BeiDou satellite B1C, B2a and B3I frequency band signals, and performs dynamic quality control to screen valid data;

[0124] S2: Multi-source perception data fusion, dynamic access to laser point cloud, visual SLAM and UWB positioning data, calculation of dynamic weight factors based on spatiotemporal errors, establishment of a spatiotemporal reference coordinate system centered on Beidou for data alignment;

[0125] S3, neuromorphic spatiotemporal alignment, uses a spiking neural network to encode carrier phase change events to generate a pulse timing sequence, uses a cross-modal attention mechanism to solve the spatial projection weights, and outputs centimeter-level precise alignment coordinates;

[0126] S4, blockchain space-time mutual verification, uses Beidou centimeter-level coordinates as blockchain anchor points, encrypts and generates space-time fingerprints of perception data for storage, and triggers data re-integration when coordinate deviation exceeds the standard;

[0127] S5, scenario risk decision-making, builds motion trajectory topology based on precisely aligned coordinate sequences, integrates laser point cloud semantics to identify road elements, and broadcasts warning instructions in real time when risk conditions are detected.

[0128] Specifically, S1 generates centimeter-level carrier phase and pseudorange raw data by receiving Beidou tri-frequency signals, performs dynamic quality control screening, and provides a highly reliable space-time reference source for multi-source fusion, solving the industry pain point that satellite signals are susceptible to interference in complex environments.

[0129] S2 dynamically accesses laser / vision / UWB multi-source data, establishes the Beidou central coordinate system based on the weight factors calculated based on the spatiotemporal errors, achieves sub-centimeter-level spatiotemporal alignment of heterogeneous perception data, and overcomes the problem of inconsistent multi-source benchmarks.

[0130] S3 generates pulse sequences by encoding carrier phase dynamic events with a pulse neural network, and solves the multi-source spatial projection weights with a cross-modal attention mechanism. It automatically suppresses abnormal data and outputs precise alignment coordinates in scenes such as tunnels / rain and fog, ensuring centimeter-level accuracy and continuity in complex environments.

[0131] S4 uses Beidou coordinates as blockchain anchor points to encrypt and generate space-time fingerprint evidence. Residual anomalies trigger re-integration with on-chain records to build a judicial-level trusted traceability base, breaking through the bottleneck of easy tampering of positioning data.

[0132] S5 builds a motion trajectory topology model based on precisely aligned coordinates, integrates laser point cloud semantics to identify road elements, detects trajectory deviation / obstacle intrusion risks in real time, and broadcasts warning instructions in a graded manner, achieving a closed-loop transition from positioning to active safety.

[0133] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A high-precision Beidou positioning system, characterized in that: include: Beidou multi-frequency positioning module, which receives Beidou satellite B1C, B2a and B3I frequency band signals and generates centimeter-level raw observation data; Heterogeneous data fusion access engine dynamically accesses laser point cloud, visual SLAM and UWB positioning data to align multi-source perception data in time and space; The neuromorphic spatiotemporal alignment algorithm module unifies the coordinates of multi-source data through spiking neural network encoding and cross-modal attention mechanism; The blockchain trusted data pool module uses Beidou positioning data anchoring and perception data encryption to store evidence and conduct space-time mutual verification; The scene semantic decision module displays traffic risk warnings and decisions based on parsing trajectory topology and point cloud semantics.

2. A high-precision Beidou positioning system according to claim 1, characterized in that: The Beidou multi-frequency positioning module includes: The RF front-end processing unit uses a multi-band microwave integrated circuit to synchronously receive Beidou satellite B1C, B2a, and B3I frequency band signals, performs signal down-conversion and low-noise amplification, and outputs an intermediate frequency signal with a signal-to-noise ratio of ≥45dB. The baseband signal calculation unit performs carrier stripping and pseudo-code despreading on the intermediate frequency signal through a multi-channel correlator to generate the original observation data and ,in is the carrier phase observation value, is the pseudorange observation value; Observation quality control unit: Dynamically verifies the quality of observation data and screens centimeter-level valid data using the following formula: in, is the observation quality factor, is the carrier phase measurement standard deviation, is the wavelength of the corresponding frequency band, is the pseudorange smoothing coefficient, is the absolute value of the second-order difference of pseudorange.

3. A high-precision Beidou positioning system according to claim 1, characterized in that: The heterogeneous data fusion access engine includes: Multi-protocol interface unit, which accesses multi-source data by dynamically adapting laser point cloud protocol, visual SLAM data stream and UWB positioning protocol; The BeiDou space-time reference unit receives centimeter-level valid data output by the observation quality control unit of the BeiDou multi-frequency positioning module. and , establish a space-time reference coordinate system; The dynamic weighted fusion unit calculates the weight factors of Beidou and other sensing data: in: is the BeiDou observation quality coefficient, is the weight factor of the i-th type of perception data, is the timestamp error of the i-th type of data, is the spatial coordinate error of the i-th type of data.

4. A high-precision Beidou positioning system according to claim 3, characterized in that: The observation quality factor When the value is >0.05, the weight of laser point cloud and UWB data is increased to 70%, and the de-jitter filter is activated to suppress instantaneous positioning jumps.

5. The high-precision Beidou positioning system according to claim 1, characterized in that: The neuromorphic spatiotemporal alignment algorithm module includes: The pulse signal encoding unit generates a pulse timing characteristic sequence through the carrier phase observation value: in, is the carrier phase change, is the pulse trigger threshold, is the pulse triggering moment; The cross-modal attention projection unit associates the pulse sequence with multi-source perception data through a multi-head attention mechanism to generate a spatial projection weight matrix: in, It is a pulse timing sequence, triggered by the carrier phase change. is the feature vector of multi-source perception data, is the feature dimension scaling factor; Dynamic residual suppression unit corrects coordinate deviation by constraining projection residuals.

6. A high-precision Beidou positioning system according to claim 5, characterized in that: If the projection residual exceeds 0.5 cm for five consecutive times, a re-fusion instruction is sent to the heterogeneous data fusion access engine, and the visual data access is frozen for 10 milliseconds.

7. The high-precision Beidou positioning system according to claim 1, characterized in that: The blockchain trusted data pool module includes the following units: The space-time anchoring unit uses the centimeter-level coordinates output by the Beidou multi-frequency positioning module As the blockchain genesis block; The encrypted evidence storage unit generates the spatiotemporal fingerprint of the perception data through the SHA-256 algorithm: in To align coordinates; Mutual verification trigger unit, when positive coordinate deviation is detected When , recalculate the weight factor , and write the correction record into the blockchain.

8. The high-precision Beidou positioning system according to claim 1, characterized in that: The scene semantic decision module includes the following units: The trajectory topology parsing unit constructs a spatiotemporal trajectory graph G = (V, E) by aligning the coordinate sequence, where the vertex V is the position point and the edge E is the motion vector; The point cloud semantic reconstruction unit uses laser point cloud data to identify semantic elements such as lane lines, guardrails, and obstacles, and generates a labeled raster map. The risk decision trigger unit broadcasts warning instructions through the V2X communication interface when it detects that the trajectory conflicts with the semantic map.

9. A high-precision Beidou positioning terminal device, characterized in that: A high-precision Beidou positioning system according to any one of claims 1 to 8, comprising: Multi-frequency RF chipset, which receives BeiDou satellite frequency band signals through multi-channel microwave integrated circuits and outputs carrier phase observation values; Sensor fusion interface, synchronous access to lidar, global shutter camera and UWB locator data through Ethernet, MIPI-CSI and SPI protocols; Edge AI processor deploys a neuromorphic spatiotemporal alignment algorithm to output precise positioning coordinates under dynamic residual constraints; A secure evidence storage unit generates a Beidou timestamp-encrypted positioning fingerprint; The control unit responds to semantic decision instructions and broadcasts warning messages via DSRC / LTE-V2X.

10. A high-precision Beidou positioning method, characterized in that: A high-precision Beidou positioning system according to any one of claims 1 to 8, comprising the following steps: S1, BeiDou signal processing, generates centimeter-level carrier phase and pseudorange raw observation data by receiving BeiDou satellite B1C, B2a and B3I frequency band signals, and performs dynamic quality control to screen valid data; S2: Multi-source perception data fusion, dynamic access to laser point cloud, visual SLAM and UWB positioning data, calculation of dynamic weight factors based on spatiotemporal errors, establishment of a spatiotemporal reference coordinate system centered on Beidou for data alignment; S3, neuromorphic spatiotemporal alignment, uses a spiking neural network to encode carrier phase change events to generate a pulse timing sequence, uses a cross-modal attention mechanism to solve the spatial projection weights, and outputs centimeter-level precise alignment coordinates; S4, blockchain space-time mutual verification, uses Beidou centimeter-level coordinates as blockchain anchor points, encrypts and generates space-time fingerprints of perception data for storage, and triggers data re-integration when coordinate deviation exceeds the standard; S5, scenario risk decision-making, builds motion trajectory topology based on precisely aligned coordinate sequences, integrates laser point cloud semantics to identify road elements, and broadcasts warning instructions in real time when risk conditions are detected.

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