Method and system for solving and low-delay returning of microsecond-level positioning data based on beidou

Abstract

The application provides a Beidou-based microsecond-level positioning data solving and low-delay back transmission method and system. The method comprises the following steps: obtaining Beidou original observation data, Beidou timing information, positioning correction data and communication link state of a target mobile object; constructing a microsecond-level double-time benchmark and generating a micro-slot Beidou observation frame; performing observation quality stratification on the micro-slot Beidou observation frame and generating a matching correction factor; performing predictive correction coupling solving based on the observation quality stratification result, the matching correction factor and the historical positioning state of the target mobile object to generate positioning state data; generating a low-delay back transmission message according to the state variation of the positioning state data, the positioning reliability and the communication link state, and sending the low-delay back transmission message to a platform side according to a back transmission priority, so that the platform side performs timing reconstruction based on a microsecond-level epoch identifier to generate low-delay positioning trajectory data. The application can improve positioning timing consistency, enhance solving reliability and reduce trajectory back transmission delay.

Description

Technical Field

[0001] This application relates to the field of satellite navigation and positioning technology, and in particular to a method and system for microsecond-level positioning data processing and low-latency backhaul based on BeiDou. Background Technology

[0002] The BeiDou Navigation Satellite System has been widely applied in scenarios such as vehicle monitoring, unmanned equipment scheduling, vehicle-road cooperation, port autonomous driving, and rail transit positioning. As the operating speed of moving objects and the real-time requirements of collaborative control continue to increase, positioning data is no longer only used for post-event trajectory recording, but also needs to serve as the data foundation for real-time scheduling, status judgment, and control decisions. Therefore, the actual observation time, solution status, and transmission delay of the positioning data have a significant impact on the system operation.

[0003] Existing BeiDou positioning terminals typically receive satellite observation data and correction data. After completing the positioning calculation at the terminal side, they upload the positioning results to the platform side at fixed intervals. The platform side generally forms the target trajectory based on the order of message reception, or performs simple sorting based on the time field carried in the positioning results. For general low-frequency positioning scenarios, the above method can meet basic location monitoring needs.

[0004] However, existing solutions still have shortcomings in low-latency, high-dynamic application scenarios. On the one hand, the positioning observation sampling, terminal processing, and communication backhaul processes lack a unified microsecond-level time stamp constraint, making it difficult for the platform to distinguish between the actual observation time and the actual reception time of the positioning results. On the other hand, the timeliness relationship between the corrected data and the current observation epoch is not precisely matched, which can easily lead to lag in the positioning solution or difficulty in judging its reliability. In addition, existing backhaul methods mostly adopt fixed message structures and fixed transmission strategies, which cannot dynamically adjust the backhaul content according to changes in positioning status, solution reliability, and link status; when messages are out of order, late, or lost, it is also difficult for the platform to recover the low-latency positioning trajectory based on the actual epoch. Therefore, it is necessary to propose a BeiDou positioning data processing scheme that can unify the positioning solution time stamp, the age of the corrected data, and the link backhaul status. Summary of the Invention

[0005] In view of this, the embodiments of this application provide a method and system for microsecond-level positioning data processing and low-latency backhaul based on BeiDou, so as to solve the problems of inconsistent positioning observation time scales, insufficient correction age matching, and difficulty in low-latency trajectory reconstruction in the prior art.

[0006] A first aspect of this application provides a method for microsecond-level positioning data processing and low-latency backhaul based on BeiDou, comprising: acquiring raw BeiDou observation data, BeiDou timing information, positioning correction data, and communication link status of a target moving object; constructing a microsecond-level dual-timescale reference based on the BeiDou timing information and the terminal's local timing information, and dividing the raw BeiDou observation data into epochs according to the microsecond-level dual-timescale reference to generate micro-timeslot BeiDou observation frames carrying sampling timescales and processing timescales; performing observation quality layering on the micro-timeslot BeiDou observation frames, and determining the positioning correction data relative to the target moving object; and performing observation quality layering on the micro-timeslot BeiDou observation frames according to the positioning correction data. The age matching results of micro-slot BeiDou observation frames generate a matching correction factor; based on the observation quality stratification results, the matching correction factor, and the historical positioning status of the target moving object, a prediction correction coupled solution is performed to generate positioning status data carrying microsecond-level epoch identifiers, solution status identifiers, and positioning reliability; based on the state change amount, positioning reliability, and communication link status of the positioning status data, a low-latency backhaul message is generated and sent to the platform side according to the backhaul priority of the low-latency backhaul message, so that the platform side can perform time sequence reconstruction based on the microsecond-level epoch identifiers to generate low-latency positioning trajectory data.

[0007] A second aspect of this application provides a BeiDou-based microsecond-level positioning data processing and low-latency backhaul system, comprising: an acquisition module for acquiring raw BeiDou observation data, BeiDou timing information, positioning correction data, and communication link status of a target moving object; a construction module for constructing a microsecond-level dual-timescale reference based on BeiDou timing information and terminal local timing information, and dividing the raw BeiDou observation data into epochs according to the microsecond-level dual-timescale reference to generate micro-timeslot BeiDou observation frames carrying sampling timescales and processing timescales; and a generation module for performing observation quality layering on the micro-timeslot BeiDou observation frames and, based on the positioning correction data... The system generates a matching correction factor based on the age matching results of the micro-slot BeiDou observation frames; the correction module is used to perform predictive correction coupled calculation based on the observation quality stratification results, the matching correction factor, and the historical positioning status of the target moving object, generating positioning status data carrying microsecond-level epoch identifiers, calculation status identifiers, and positioning reliability; the reconstruction module is used to generate low-latency backhaul messages based on the status change amount of the positioning status data, positioning reliability, and communication link status, and send them to the platform side according to the backhaul priority of the low-latency backhaul messages, so that the platform side can perform time-series reconstruction based on the microsecond-level epoch identifiers to generate low-latency positioning trajectory data.

[0008] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: The system acquires raw BeiDou observation data, BeiDou timing information, positioning correction data, and communication link status of the target moving object. Based on the BeiDou timing information and the terminal's local timing information, a microsecond-level dual-timescale reference is constructed. The raw BeiDou observation data is then divided into epochs according to this reference, generating micro-slotted BeiDou observation frames carrying sampling and processing timescales. The micro-slotted BeiDou observation frames are stratified in terms of observation quality, and a matching correction factor is generated based on the age matching results of the positioning correction data relative to the micro-slotted BeiDou observation frames. Based on the observation quality stratification results, the matching correction factor, and the historical positioning status of the target moving object, a predictive correction coupled solution is performed, generating positioning status data carrying microsecond-level epoch identifiers, solution status identifiers, and positioning reliability. Low-latency backhaul messages are generated based on the status change amount, positioning reliability, and communication link status of the positioning status data, and are sent to the platform side according to the backhaul priority of the low-latency backhaul messages. The platform side then performs time-series reconstruction based on the microsecond-level epoch identifiers to generate low-latency positioning trajectory data. This application can improve the consistency of positioning timing, enhance the reliability of solution, and reduce the latency of trajectory backhaul. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating the method for microsecond-level positioning data processing and low-latency backhaul based on BeiDou provided in the embodiments of this application. Figure 2 This is a schematic diagram of the structure of the BeiDou-based microsecond-level positioning data processing and low-latency backhaul system provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0011] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0012] In existing technologies, BeiDou positioning terminals typically acquire satellite observation data through a BeiDou receiver and combine it with differential correction data or other positioning auxiliary data to complete positioning calculations. The positioning results are then uploaded to the platform at fixed intervals. After receiving the positioning messages, the platform generally forms the trajectory of the target moving object based on the message reception order or the time field carried in the positioning results. This type of solution can meet the basic needs of scenarios such as ordinary vehicle monitoring, personnel positioning, and low-frequency trajectory recording. However, the positioning calculation process and the communication backhaul process are usually independent of each other, and the time attributes, calculation status, and link status of the positioning data are not uniformly modeled.

[0013] In highly dynamic, low-latency application scenarios, existing technologies still suffer from issues such as inconsistent positioning observation timescales, insufficient matching of correction ages, and difficulties in reconstructing low-latency trajectories. Specifically, the positioning observation sampling, terminal processing, and message transmission processes lack unified microsecond-level timescale constraints, making it difficult for the platform to accurately distinguish between the actual observation time and the actual reception time corresponding to the positioning result. Simultaneously, the timeliness relationship between positioning correction data and the current observation epoch is not finely matched, easily leading to lag in the calculation results or difficulty in determining their reliability. Furthermore, existing fixed message structures and fixed transmission strategies cannot dynamically adjust the transmitted content based on changes in positioning status, calculation reliability, and link status. When messages are out of order, delayed, or lost, the platform struggles to reconstruct the low-latency positioning trajectory according to the actual epoch.

[0014] In view of the problems existing in the prior art, this application proposes a method for microsecond-level positioning data processing and low-latency backhaul based on BeiDou. This method acquires the original BeiDou observation data, BeiDou timing information, positioning correction data, and communication link status of the target moving object. A microsecond-level dual-timescale reference is constructed based on the BeiDou timing information and the terminal's local timing information. The original BeiDou observation data is then divided into epochs according to this microsecond-level dual-timescale reference, generating micro-slotted BeiDou observation frames carrying sampling and processing timescales. Subsequently, the micro-slotted BeiDou observation frames are stratified by observation quality, and a matching correction factor is generated based on the age matching results of the positioning correction data relative to the micro-slotted BeiDou observation frames. Finally, based on the observation quality stratification results, the matching correction factor, and the historical positioning status of the target moving object, a prediction and correction coupled solution is performed to generate positioning status data carrying microsecond-level epoch identifiers, solution status identifiers, and positioning reliability.

[0015] After generating the positioning status data, this application further generates low-latency backhaul messages based on the status change amount, positioning reliability, and communication link status of the positioning status data. These messages are then sent to the platform side according to their backhaul priority, enabling the platform side to perform time sequence reconstruction based on microsecond-level epoch markers and generate low-latency positioning trajectory data. Through the above technical solution, this application integrates BeiDou timing, observation frame organization, age correction matching, positioning calculation, and link backhaul into a single technical link, which improves positioning timing consistency, enhances positioning calculation reliability, and reduces trajectory backhaul latency.

[0016] The technical solution of this application will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0017] Figure 1 This is a flowchart illustrating the microsecond-level positioning data processing and low-latency backhaul method based on BeiDou provided in this application embodiment. Figure 1 As shown, the method may specifically include: S101, acquire the BeiDou raw observation data, BeiDou timing information, positioning correction data and communication link status of the target moving object; S102, based on BeiDou timing information and terminal local timing information, constructs a microsecond-level dual time-scale reference, and divides the original BeiDou observation data into epochs according to the microsecond-level dual time-scale reference, generating micro-slot BeiDou observation frames carrying sampling time-scales and processing time-scales; S103, perform observation quality stratification on micro-timeslot BeiDou observation frames, and generate matching correction factors based on the age matching results of positioning correction data relative to micro-timeslot BeiDou observation frames; S104, based on the observation quality stratification results, matching correction factor and the historical positioning status of the target moving object, performs prediction correction coupling calculation to generate positioning status data carrying microsecond-level epoch identifier, calculation status identifier and positioning confidence; S105 generates a low-latency backhaul message based on the status change of the positioning status data, the positioning reliability, and the communication link status, and sends it to the platform side according to the backhaul priority of the low-latency backhaul message, so that the platform side can perform time sequence reconstruction based on the microsecond-level epoch identifier to generate low-latency positioning trajectory data.

[0018] In some embodiments, acquiring the target moving object's raw BeiDou observation data, BeiDou timing information, positioning correction data, and communication link status includes: The BeiDou receiving link, correction data link, and backhaul communication link are connected to the positioning terminal side of the target moving object; Collect raw BeiDou observation data and BeiDou timing information according to unified collection trigger rules, and simultaneously receive positioning correction data corresponding to the current positioning service area; Monitor the transmission status of the backhaul communication link and generate the communication link status; Add a source identifier and a receiving timestamp to the collected data and write it to the same location data cache.

[0019] Specifically, in application scenarios where the target moving object is an autonomous vehicle in a port, the positioning terminal is installed on the top of the vehicle and connects to the BeiDou receiving link, correction data link, and backhaul communication link via an internal bus. The BeiDou receiving link receives BeiDou satellite signals and outputs raw observation data and timing information. The correction data link receives differential correction data corresponding to the current port area positioning service area. The backhaul communication link sends the positioning calculation results to the edge platform or scheduling platform. When connecting to each link, the positioning terminal first reads the link identity, link working mode, and link availability status, and registers each link in the positioning data acquisition task, enabling BeiDou observation, correction reception, and backhaul monitoring to operate within the same acquisition framework.

[0020] The unified acquisition triggering rule can be jointly determined by the BeiDou time synchronization second pulse, the terminal's local high-frequency counter, and the acquisition cycle parameters. After detecting the edge of the second pulse, the positioning terminal calibrates the current count value of its local counter and generates an acquisition trigger signal within each acquisition cycle. When the acquisition trigger signal arrives, the positioning terminal reads observation content such as pseudorange, carrier phase, Doppler, satellite status, and receiver clock status within the same observation epoch from the BeiDou receiving link, and simultaneously reads timing content such as BeiDou time, week seconds, and second pulse arrival status. For the correction data link, the positioning terminal filters the regional correction stream according to the current positioning service area identifier and simultaneously receives correction content such as satellite clock error, orbital error, ionospheric delay, and tropospheric delay adjacent to the current observation epoch. Thus, the original BeiDou observation data, BeiDou timing information, and positioning correction data are acquired in association under the same acquisition triggering rule, avoiding epoch mismatch caused by independent acquisition from different links.

[0021] During the backhaul communication link monitoring process, the positioning terminal continuously records the message enqueue time, transmission start time, transmission completion time, platform confirmation time, and retransmission count. It also generates the communication link status based on the confirmation interval of adjacent messages, buffer usage, packet loss feedback, and link switching status. The communication link status is not stored separately as a network diagnostic result; instead, it establishes a correspondence with the observation data within the same acquisition period. This allows subsequent low-latency backhaul messages to identify the link load and transmission latency corresponding to the current positioning data. For example, when a vehicle enters a storage yard's obstructed area, causing increased wireless link jitter, the positioning terminal can record the increase in link latency and confirmation feedback delay during the acquisition phase, providing a basis for subsequent message priority calculations.

[0022] After completing data acquisition, the positioning terminal adds a source identifier and a reception timestamp to each type of data. The source identifier distinguishes between the BeiDou receiving link, the correction data link, and the backhaul communication link. The reception timestamp is generated from the processing timestamp in the microsecond-level dual-time-stamp reference and marks the time position when the data enters the positioning terminal's processing link. Data with added identifiers is written to the same positioning data cache according to observation epochs. The positioning data cache organizes cache units by epoch index, and each cache unit stores the observation content, timing content, correction content, and link status content for the same epoch. When subsequent positioning calculation tasks are started, complete input data can be directly read from the same cache unit, reducing cross-cache retrieval and time alignment processing.

[0023] Through the above implementation methods, the positioning terminal can form a data foundation with unified access to multiple links, unified triggering of collection, unified time stamping, and unified cache management during the data acquisition stage. This enables BeiDou observation data, timing data, correction data, and link status to complete epoch association and source differentiation before entering the calculation process, improving the data consistency of subsequent microsecond-level positioning calculations and providing traceable link status basis for the generation of low-latency backhaul strategies.

[0024] In some embodiments, a microsecond-level dual-time-scale reference is constructed based on BeiDou timing information and terminal local timing information, including: The external timing reference is determined based on the BeiDou timing information, and the continuous timing reference is determined based on the terminal's local timing information; Clock offset estimation is performed on external timing references and continuous timing references to generate time scale correction parameters; Based on the time-scale correction parameters, time-scale mapping is performed on the positioning sampling process and the terminal processing process respectively, and a microsecond-level dual time-scale reference containing sampling time-scale and processing time-scale is constructed.

[0025] Specifically, in the application scenario of autonomous vehicles in ports, after powering on, the positioning terminal reads the BeiDou time, weekday seconds, second pulse edge status, and time synchronization validity flag output by the BeiDou receiver, and uses the valid second pulse as the external time synchronization reference. The external time synchronization reference is used to determine the BeiDou system time corresponding to each whole second boundary. The positioning terminal synchronously starts the local high-frequency counter, and uses the continuous count value of the counter between adjacent second pulses as the continuous timing reference, so that the terminal forms a microsecond-level time scale between time synchronization boundaries.

[0026] When establishing a continuous timing reference, the positioning terminal performs sliding statistics on the count values ​​within multiple consecutive second pulse periods to obtain the local counting period, count increment, and count jitter status. If a second pulse experiences an abnormal arrival edge due to obstruction, short-term receiver lockout, or interface jitter, the positioning terminal marks the period as abnormal based on the preceding and following valid second pulses and the continuity of the local count, and temporarily excludes that abnormal period from clock offset estimation. For example, when a vehicle is running under a quay crane, the BeiDou signal experiences a short-term attenuation. The positioning terminal uses the previous three valid second pulses and the current counter change trend to maintain local time recursion, avoiding sudden jumps in the sampling time scale.

[0027] When estimating clock offset for external timing references and continuous timing references, the positioning terminal uses the whole second as the calibration anchor point, compares the theoretical and actual count values ​​of the local high-frequency counter at that anchor point, and generates time scale correction parameters that include zero-point offset, frequency offset, and short-term drift states. During long-term operation, the time scale correction parameters can also be smoothly updated in conjunction with the terminal temperature and power supply status, so that the correction parameters reflect the current counter deviation and suppress frequent jumps caused by single timing jitter.

[0028] Furthermore, after obtaining the time-stamp correction parameters, the positioning terminal performs time-stamp mapping on both the positioning sampling process and the terminal processing process. For the positioning sampling process, the positioning terminal maps the sampling trigger point of the raw BeiDou observation data to a sampling time-stamp, which represents the actual observation epoch corresponding to the satellite observation data. For the terminal processing process, the positioning terminal maps processing nodes such as observation data enqueueing, solution task initiation, solution result generation, message encapsulation, and message transmission to processing time-stamps. Both the sampling time-stamp and the processing time-stamp are constrained by the same time-stamp correction parameter, but they correspond to the observation occurrence time and the processing occurrence time, respectively, thus distinguishing the timeliness of the positioning data from the delay introduced by the terminal processing.

[0029] For example, when a vehicle turns on a port road, the BeiDou receiver generates a set of observation data at 10:15:30:000120 microseconds. The positioning terminal writes this set of data into its cache at 10:15:30:000450 microseconds and starts the calculation task at 10:15:30:000900 microseconds. Through a microsecond-level dual-timescale reference, the sampling timescale and processing timescale of this set of observation data are recorded separately. Even if the returned message arrives with a delay, the platform can still reconstruct the vehicle's position at the actual observation epoch based on the sampling timescale and determine the processing delay based on the processing timescale.

[0030] Through the above implementation methods, the positioning terminal can integrate the absolute time constraint of BeiDou time synchronization with the continuous time scale of local timing to form a microsecond-level dual time scale reference that serves positioning observation and terminal processing. This improves the time consistency between positioning sampling, processing and backhaul scheduling, reduces epoch mismatch caused by time synchronization jitter, local clock drift or processing queuing, and provides a unified time scale basis for subsequent micro-slot observation frame generation and low-latency trajectory reconstruction.

[0031] In some embodiments, the raw BeiDou observation data is divided into epochs according to a microsecond-level dual-time-scale reference to generate micro-slot BeiDou observation frames carrying sampling and processing time scales, including: The observation epochs corresponding to the original BeiDou observation data are determined according to the sampling time scale, and the observation information belonging to the same observation epoch is merged according to the microsecond-level time window; The time state of the positioning processing link is entered based on the merged observation information recorded by the processing time stamp; Micro-slot BeiDou observation frames are generated based on the merged observation information, sampling time scale, and processing time scale, and microsecond-level epoch identifiers are configured for each micro-slot BeiDou observation frame corresponding to the observation epoch.

[0032] Specifically, in the application scenario of continuous driving of autonomous vehicles in ports, after completing the construction of a microsecond-level dual-timescale reference, the positioning terminal determines the epoch affiliation of the raw observation data output from the BeiDou receiver link according to the sampling timescale. The sampling timescale is obtained by jointly correcting the BeiDou time synchronization information and the terminal's local timing information, and is used to represent the actual time and location of the observation information. The positioning terminal uses a preset microsecond-level time window as the merging boundary to merge multi-satellite observation information that is within the same window and corresponds to the same observation epoch, forming an observation set corresponding to that observation epoch. The observation set includes pseudorange observations, carrier phase observations, Doppler observations, satellite status information, and receiver clock status information, and can also synchronously associate motion change information output by the vehicle's inertial sensors.

[0033] During epoch division, the positioning terminal does not simply arrange the observation information according to the receiver output order. Instead, it first compares the deviation between the sampling timescale carried by each observation and the current time window boundary. When the deviation is within the allowable range, the observation information is assigned to the current observation epoch. When the deviation exceeds the allowable range but can still be corrected based on adjacent epochs, the observation information is temporarily stored in the candidate buffer and compensated and merged by combining the sampling timescales before and after. When the deviation exceeds the compensation range, a late arrival mark is added to the observation information, and its source and timescale information are retained for subsequent anomaly analysis. For example, when a vehicle passes near a yard crane, some satellite observation information arrives in an adjacent window due to interface queuing delays. The positioning terminal restores it to the true observation epoch based on the sampling timescale rather than the actual arrival order, avoiding epoch mismatches under high-dynamic driving conditions.

[0034] Furthermore, after the observation information is merged, the positioning terminal records the time status of the merged observation information entering the positioning processing link according to the processing timestamp. The processing timestamp can correspond to processing nodes such as writing the observation set to the cache, starting the quality detection task, queuing the solution task, and calling the solution task. By recording the processing timestamp, the positioning terminal can distinguish between the actual sampling time and the internal processing time of the observation information, and calculate the processing waiting time generated by the observation set within the terminal. If the processing waiting time corresponding to a certain observation epoch is higher than a set threshold, the positioning terminal can add a processing delay status to subsequent micro-slot BeiDou observation frames, so that the solution process and the backhaul process can identify the timeliness status of the observation frame.

[0035] When generating micro-slot BeiDou observation frames, the positioning terminal encapsulates the merged observation set, sampling time stamp, processing time stamp, and observation integrity status, and generates a microsecond-level epoch identifier according to the terminal identifier, BeiDou time, and microsecond-level sampling sequence. The microsecond-level epoch identifier corresponds to the observation epoch and is used throughout subsequent observation quality stratification, positioning correction data age matching, prediction correction coupling calculation, and low-latency backhaul processing. For observation frames generated by the same vehicle at the same observation epoch, even if the backhaul messages are out of order, delayed, or retransmitted, the platform can still reconstruct the timing sequence based on the microsecond-level epoch identifier.

[0036] Through the above implementation methods, the positioning terminal can complete the division of the real observation epochs with the sampling time scale, characterize the internal processing status of the terminal with the processing time scale, and associate the observation frame with the subsequent solution and backhaul process through the microsecond-level epoch identifier. This improves the time consistency of the original BeiDou observation data in low-latency scenarios, reduces the observation merging misalignment and platform trajectory reconstruction deviation, and provides a data organization basis for the subsequent positioning solution reliability evaluation and low-latency message generation.

[0037] In some embodiments, the observation quality of micro-timeslot BeiDou observation frames is stratified, and a matching correction factor is generated based on the age matching result of the positioning correction data relative to the micro-timeslot BeiDou observation frames, including: Based on the continuity, stability and geometric constraints of the observation information in the micro-slot BeiDou observation frames, an observation reliability characterization is generated. Based on the observation reliability characterization, the observation information in the micro-slot BeiDou observation frames is processed in layers to obtain the observation quality layering results. Based on the generation time stamp, reception time stamp, and applicable time period of the positioning correction data, the correction age of the positioning correction data relative to the micro-slot BeiDou observation frame is calculated; A matching correction factor is generated based on the stratification results of the corrected age and observation quality.

[0038] Specifically, after the positioning terminal of the target moving object completes the generation of the micro-timeslot BeiDou observation frame, the positioning terminal first performs a consistency check on the multi-source observation information within the observation frame. The continuity of the observation information can be judged by the carrier phase change, Doppler change trend, and pseudorange increment relationship between adjacent observation epochs; the stability of the observation information can be judged by signal strength fluctuations, residual distribution, and receiver clock state changes; the geometric constraint relationship can be judged by satellite spatial distribution, changes in observation matrix conditions, and the number of effective observations. The positioning terminal integrates the above judgment results into an observation reliability characterization, which is used to describe the degree of adaptation of each observation information in the current observation frame to the positioning solution.

[0039] After generating the observation reliability characterization, the positioning terminal performs stratified processing of the observation information based on reliability level and anomaly type. Observations with stable reliability and satisfying geometric constraints are used as the main solution observations; observations with reliability within the compensable range and continuous with adjacent epochs are used as candidate compensation observations; and observations with abnormal reliability or significantly inconsistent with the motion state are used as suppression observations. This stratified processing does not delete anomalous observations, but rather configures different participation methods for different types of observations, ensuring that the main solution process prioritizes stable observations while retaining anomalous residual information for subsequent verification.

[0040] For positioning correction data, the positioning terminal reads the generation time stamp, link reception time stamp, and applicable time period of the region carried in the correction data, and compares it with the sampling time stamp of the micro-slot BeiDou observation frame to calculate the correction age of the correction data relative to the current observation frame. If the correction age falls within the real-time matching range, the positioning terminal uses the correction data as real-time correction input; if the correction age exceeds the real-time matching range but still meets the short-term prediction conditions, the positioning terminal forms a predictive correction input based on the changing trend of adjacent correction data; if the correction age does not meet the availability conditions, the positioning terminal reduces the correction weight of the correction data and triggers a correction source availability check.

[0041] For example, when an autonomous vehicle passes through a container yard in a port, some low-elevation-angle satellites are obstructed, causing discontinuous changes in carrier phase. The positioning terminal classifies the corresponding observation information into the suppression layer; the observation information of the remaining satellites remains continuous and is used as the main solution observation information. Simultaneously, the correction data sent by the port's edge base stations lags behind the current sampling timescale by 120 milliseconds, still within the prediction correction range. The positioning terminal generates prediction correction input based on the changes in the correction data before and after, and generates a matching correction factor by combining the observation quality stratification results. This matching correction factor includes observation participation weight, correction age weight, and correction applicability state, used for subsequent prediction correction coupled solution.

[0042] Through the above implementation methods, the positioning terminal can uniformly evaluate the quality of observation information and the timeliness of correction data before the calculation, so that abnormal observations will not directly interfere with the main calculation process, and the delayed correction data can be used reasonably according to the age status, thereby improving the stability and reliability of microsecond-level positioning calculation and providing data basis for the positioning reliability identifier in the low-latency return message.

[0043] In some embodiments, prediction and correction coupling calculations are performed based on observation quality stratification results, matching correction factors, and the historical positioning status of the target moving object to generate positioning status data carrying microsecond-level epoch identifiers, calculation status identifiers, and positioning confidence levels, including: Generate the predicted positioning status corresponding to the current observation epoch based on the historical positioning status; The solution constraint weights are determined based on the observation quality stratification results and the matching correction factor, and then applied to the correction process of the predicted positioning state to obtain the current positioning solution result. Based on the residual state, correction matching state, and observation participation state of the current positioning solution, a solution state identifier and positioning confidence level are generated. The microsecond-level epoch identifier, solution state identifier, and positioning confidence level are associated with the current positioning solution and encapsulated into positioning state data.

[0044] Specifically, in application scenarios where the target moving object is an autonomous vehicle in a port, after obtaining the observation quality stratification results and matching correction factors, the positioning terminal first reads the positioning status data of the previous observation epoch and several adjacent observation epochs from the historical positioning status cache. This data includes position, speed, heading, receiver clock error, and solution status identifier. The positioning terminal determines the time interval between the current observation epoch and the historical observation epochs according to the microsecond-level epoch identifier. Combining vehicle inertial measurement data, wheel speed data, and Doppler variation trends, it recursively extrapolates the vehicle's current position, speed, and heading to generate the predicted positioning status corresponding to the current observation epoch. If the vehicle is in a straight, uniform speed state, a longer extrapolation window is used; if changes in steering, braking, or acceleration are detected, the extrapolation window is shortened, and the constraint ratio of motion information is increased.

[0045] During the calibration process, the positioning terminal determines the weights of different observation information for the solution based on the observation quality stratification results. Observation information belonging to the main solution layer is assigned a higher observation weight. Observation information belonging to the candidate compensation layer participates in calibration when geometric constraints are insufficient or the main solution residual increases. Observation information belonging to the suppression layer does not directly enter the main solution; only residual information is retained for anomaly verification. Simultaneously, the positioning terminal determines the correction weights of the positioning correction data based on the matching correction factor. Real-time matched correction data is directly used for clock error, orbital error, and propagation delay correction. Predictive matched correction data is used with reduced weight according to the correction age and trend. Mismatch correction data is not included in the current calibration process. The positioning terminal combines the observation weights and correction weights into a solution constraint weight and applies it to the calibration equation for the predicted positioning state to obtain the current positioning solution result.

[0046] For example, when an autonomous vehicle turns in a container yard at a port, the previous epoch's positioning status shows a change in the vehicle's heading, and inertial measurement data indicates an increase in yaw rate. Based on this, the positioning terminal generates a predicted positioning status with steering constraints. In the current observation frame, some satellite observations are classified into the suppression layer due to obstruction, and the remaining observation information forms the main solution layer and the candidate compensation layer. The correction data from edge base stations lags behind the sampling timescale, and the matching correction factor sets it as the predicted correction status. The positioning terminal prioritizes the observation information from the main solution layer and the predicted correction input, and calls the observation information from the candidate compensation layer when geometric constraints are insufficient, so that the current positioning solution result matches the vehicle's motion trend, the BeiDou observation status, and the timeliness of the correction data.

[0047] After the current positioning solution is generated, the positioning terminal evaluates the residual status, correction matching status, and observation participation status. The residual status reflects whether the corrected observation residuals are stable; the correction matching status reflects the age fit between the corrected data and the current observation epoch; and the observation participation status reflects the use of stratified observation information in this solution. Based on the above evaluation results, the positioning terminal generates a solution status identifier and positioning reliability, and associates and encapsulates the microsecond-level epoch identifier, solution status identifier, positioning reliability, and the current positioning solution result to form positioning status data.

[0048] Through the above implementation methods, the positioning terminal can integrate historical positioning status, current observation quality, and correction age into the prediction and correction coupling solution process, reduce the impact of high dynamic motion, satellite obstruction, and correction data lag on positioning results, improve the temporal continuity and reliability of positioning solution results, and provide a positioning data basis with status identifiers for the generation of subsequent low-latency backhaul messages.

[0049] In some embodiments, generating a low-latency return message based on the state change amount of the location status data, the location reliability, and the communication link status includes: Based on the temporal differences between the location status data and the confirmed location status data, the change in status is determined. The location feedback level is generated based on the change in status and the reliability of the location. Determine the link delay characterization based on the communication link status; Based on the positioning backhaul level and link latency characterization, the positioning status data is differentially encoded and time-stamped to generate a low-latency backhaul message carrying a microsecond-level epoch identifier and backhaul priority.

[0050] Specifically, in the application scenario of autonomous vehicles in ports, after generating the positioning status data for the current observation epoch, the positioning terminal first reads the most recently confirmed positioning status data from the platform's confirmation record and establishes a temporal correspondence based on the microsecond-level epoch identifiers carried by the two. The positioning terminal compares the position increment, velocity increment, heading change, solution state change, and positioning reliability change between the current positioning status data and the confirmed positioning status data to form a state change quantity. The state change quantity is not only used to describe the vehicle coordinate difference, but also to reflect the timeliness difference and the degree of motion change between the current positioning status and the status already known by the platform.

[0051] The positioning terminal generates a positioning feedback level based on the amount of state change and positioning reliability. When the vehicle is traveling at a low speed and smoothly along a straight road in the port area, and the positioning reliability remains stable, the positioning feedback level is configured as the normal level, and the feedback content is mainly differential status. When the vehicle enters an intersection, a yard turning area, or a loading and unloading operation area, and the amount of state change increases but the positioning reliability is still within the usable range, the positioning feedback level is configured as the enhanced level, and the feedback content adds key motion status and solution status identifiers. When the positioning reliability decreases, the solution status switches, or the amount of state change exceeds a preset threshold, the positioning feedback level is configured as the critical level, and the feedback content includes the complete positioning status and an abnormal status summary.

[0052] Meanwhile, the positioning terminal determines the link latency characterization based on the communication link status. The communication link status can include message queuing time, transmission acknowledgment time, retransmission status, buffer usage changes, and link switching status. The positioning terminal converts this information into a link latency characterization to describe whether the current backhaul link is in a smooth, jittery, or congested state. For example, when a vehicle travels to an area with densely stacked containers, the platform's acknowledgment time increases and the number of retransmissions increases. The positioning terminal determines that the link is in a jittery state and increases the transmission priority of critical positioning messages.

[0053] When generating low-latency return messages, the positioning terminal determines the message organization method based on the positioning return level and link latency characterization. For regular-level data, the positioning terminal performs differential encoding on the differences between the current position, velocity, and heading and the confirmed positioning status data, and retains microsecond-level epoch identifiers, positioning reliability, and necessary labeling information. For enhanced-level data, the positioning terminal adds a solution status identifier, status change type, and corrected matching status to the differential encoding. For critical-level data, the positioning terminal uses complete status encapsulation and adds event trigger identifiers and return priority. If the link is congested, the positioning terminal compresses regular-level messages, prioritizes sending critical-level messages, and resends differential data that can be used for trajectory reconstruction based on platform feedback after the link is restored.

[0054] The generated low-latency return message carries a microsecond-level epoch identifier and a return priority. The microsecond-level epoch identifier is used by the platform side for time-series merging according to the actual observation epochs, and the return priority is used by the terminal side for dynamic scheduling in the sending queue. Through the above implementation method, the positioning terminal can adaptively organize the return content according to changes in positioning status, solution reliability, and link latency status, reducing redundant transmission in stable states, improving the timeliness of return for critical positioning states, and reducing data loss and timing lag in the low-latency positioning trajectory generation process.

[0055] In some embodiments, the low-latency return messages are sent to the platform side according to their return priority, so that the platform side can perform time-series reconstruction based on microsecond-level epoch identifiers to generate low-latency positioning trajectory data, including: Low-latency return messages are queued according to their return priority, and the transmission timing is determined in conjunction with the communication link status. Write the sending status of the low-latency return message after it is sent into the return record; The platform performs time-series merging and state reconstruction on the received low-latency return messages based on microsecond-level epoch identifiers to generate low-latency positioning trajectory data.

[0056] Specifically, in the application scenario of autonomous vehicles in ports, after the positioning terminal generates a low-latency return message, it writes the message into a sending queue and schedules the queue according to the return priority carried in the message. The sending queue can form a hierarchical cache according to key positioning messages, enhanced positioning messages, and regular positioning messages, but each layer of cache is sorted based on a microsecond-level epoch identifier to avoid the continuous positioning status of the same vehicle losing its true time sequence due to sending scheduling.

[0057] In some examples, the positioning terminal reads the communication link status during scheduling. If the link is unobstructed, it sends messages sequentially according to the epoch order. If the link is jittery, it prioritizes sending key positioning messages carrying complete positioning status, positioning reliability degradation indicators, or motion change indicators, and reserves regular differential messages in the candidate sending area. If the link is congested, it compresses the sending frequency of low-priority messages and only sends the positioning status summary corresponding to the latest key epoch and, if necessary, label information.

[0058] When determining the transmission sequence, the positioning terminal can also adjust the queue position based on platform confirmation feedback. For messages that have not yet been confirmed by the platform but are related to the current trajectory reconstruction, the positioning terminal retains their retransmission status; for regular messages that have been covered by subsequent complete positioning status and do not affect trajectory reconstruction, the positioning terminal can perform merging processing. For example, when a vehicle is traveling in a straight line in a yard, it continuously generates multiple regular differential messages. If the link is temporarily congested, the positioning terminal will merge the multiple differential messages into a state summary covering the same epoch interval; when a vehicle enters an intersection and triggers a sharp turn event, the positioning terminal immediately increases the priority of the corresponding message, so that the platform side can obtain the positioning status of that critical epoch first.

[0059] Furthermore, after each low-latency return message is sent, the positioning terminal writes the sending status into the return record. The return record includes a microsecond-level epoch identifier, message type, sending timestamp, acknowledgment timestamp, sending channel, retransmission status, and platform feedback status. The return record is associated with the positioning status data to determine whether subsequent retransmission, merging, or discarding is necessary. If the platform reports a missing epoch, the positioning terminal locates the corresponding cached data based on the return record and generates a compensation message according to the missing epoch range; if the platform reports that the trajectory reconstruction is complete, the positioning terminal marks the corresponding message as acknowledged and releases the relevant cached resources.

[0060] After receiving low-latency return messages, the platform does not directly generate trajectories based on arrival order. Instead, it writes the messages into the timing reconstruction buffer based on microsecond-level epoch markers. For complete positioning status messages, the platform directly establishes the positioning status for the corresponding epoch. For differential messages, the platform restores the status based on confirmed preceding epochs. For compensation or late messages, the platform updates the corresponding historical epochs based on microsecond-level epoch markers and reconstructs the status of affected subsequent trajectory segments. In this way, the platform can restore the true positioning timing of the target moving object even in cases of out-of-order, late, or retransmitted messages.

[0061] Through the above implementation methods, the positioning terminal can dynamically determine the transmission order based on the backhaul priority and link status, and the platform can complete the timing merging and status reconstruction based on the microsecond-level epoch identifier, thereby improving the timeliness of key positioning data backhaul, reducing the impact of link jitter on trajectory continuity, and improving the integrity and timing accuracy of low-latency positioning trajectory data.

[0062] In some embodiments, the method further includes: The platform generates feedback information based on the receiving timestamp of the low-latency return message, the status of missing epochs, the timing reconstruction results, and the location reliability. The feedback information is sent back to the positioning terminal of the target moving object; Based on the feedback information, the positioning terminal updates the observation quality stratification parameters, age matching parameters, and low-latency backhaul message generation parameters in the subsequent positioning calculation process, and performs subsequent observation frame calculation and message backhaul according to the updated parameters.

[0063] Specifically, in the application scenario of autonomous vehicles in ports, after receiving low-latency feedback messages, the platform first records the time position of each message entering the platform's time sequence reconstruction buffer based on the received timestamp. It then compares the received timestamp with the microsecond-level epoch identifier carried in the message to determine if the message is late, out of order, or has an abnormal cross-epoch interval. Simultaneously, the platform checks the integrity of messages from the same vehicle in consecutive observation epochs. If it finds that a complete positioning status message was not received at a certain epoch, or that the differential message cannot be connected with the previous epoch, a missing epoch state is formed, and the time range, message type, and trajectory impact range corresponding to the missing epoch are marked.

[0064] During the time-series reconstruction process, the platform merges the complete positioning status message, differential message, and compensation message based on the microsecond-level epoch identifier to generate the current vehicle's positioning trajectory reconstruction result. If the trajectory reconstruction result shows position jumps, speed abrupt changes, or a continuous decrease in reliability between adjacent epochs, the platform further combines the positioning reliability, solution status identifier, and link reception delay in the positioning status data to determine whether the anomaly originates from link delay, decreased observation quality, or insufficient correction matching.

[0065] For example, if a vehicle is turning in a container yard and the platform detects that a key epoch message is 80 milliseconds late, while subsequent differential messages can be reconstructed normally, then the anomaly is classified as a short-term link delay. If the positioning reliability of multiple consecutive epochs decreases and the trajectory residual increases, then the anomaly is classified as a decrease in observation quality or an excessively old correction data age.

[0066] The platform generates feedback information based on received time-stamped statistics, missing epoch status, time-series reconstruction results, and positioning reliability. This feedback information may include a list of missing epochs, a range of late epochs, trajectory reconstruction quality levels, reliability trends, and parameter adjustment suggestions. For cases where link delays are dominant, the feedback information instructs the positioning terminal to increase the priority of critical epoch messages and reduce the density of regular differential messages. For cases where decreased observation quality is dominant, the feedback information instructs the positioning terminal to increase the intensity of observation continuity detection and adjust the observation quality stratification boundaries. For cases where the correction age is too large, the feedback information instructs the positioning terminal to tighten real-time correction matching conditions or increase the reliability labeling constraints under the predicted correction state.

[0067] After receiving the feedback information, the positioning terminal matches it with local feedback records and positioning status cache to determine the range of parameters that need to be updated. For observation quality stratification parameters, the positioning terminal adjusts the stratification thresholds corresponding to continuity, stability, and geometric constraints based on the reliability change trend reported by the platform. For age matching parameters, the positioning terminal adjusts the real-time matching range, predicted matching range, and mismatch judgment boundary based on the lateness of the correction data. For low-latency feedback message generation parameters, the positioning terminal adjusts the differential coding granularity, complete status message triggering conditions, and feedback priority rules based on the missing epoch list and link lateness status. The updated parameters are written into the subsequent positioning processing configuration and take effect when the next observation frame enters the solution link.

[0068] Through the above implementation methods, the platform can reverse the message reception status, trajectory reconstruction results, and positioning reliability to the positioning terminal, so that the positioning terminal no longer continuously performs calculations and transmissions according to fixed parameters, but updates the observation quality layering, corrects the age matching, and updates the message generation parameters in conjunction with the actual reconstruction situation of the platform. This improves the adaptability of subsequent positioning calculations, reduces the impact of duplicate, missing, and late messages on low-latency trajectories, and enhances the closed-loop adjustment capability of the entire BeiDou positioning data calculation and transmission process.

[0069] The following are system embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the system embodiments of this application, please refer to the method embodiments of this application.

[0070] Figure 2 This is a schematic diagram of the structure of a BeiDou-based microsecond-level positioning data processing and low-latency backhaul system provided in an embodiment of this application. Figure 2 As shown, the system includes: The acquisition module 201 is used to acquire the BeiDou raw observation data, BeiDou timing information, positioning correction data and communication link status of the target moving object; Module 202 is used to construct a microsecond-level dual time-stamp reference based on BeiDou timing information and terminal local timing information, and to divide the original BeiDou observation data into epochs according to the microsecond-level dual time-stamp reference to generate micro-slot BeiDou observation frames carrying sampling time stamps and processing time stamps; The generation module 203 is used to perform observation quality stratification on micro-timeslot BeiDou observation frames and generate a matching correction factor based on the age matching results of positioning correction data relative to micro-timeslot BeiDou observation frames. The correction module 204 is used to perform prediction correction coupled solution based on the observation quality stratification results, matching correction factor and historical positioning status of the target moving object, and generate positioning status data carrying microsecond-level epoch identifier, solution status identifier and positioning confidence. The reconstruction module 205 is used to generate low-latency backhaul messages based on the state change amount of the positioning status data, the positioning reliability and the communication link status, and send them to the platform side according to the backhaul priority of the low-latency backhaul messages, so that the platform side can perform time-series reconstruction based on microsecond-level epoch identifiers to generate low-latency positioning trajectory data.

[0071] In some embodiments, Figure 2 The acquisition module 201 connects to the BeiDou receiving link, correction data link and backhaul communication link on the positioning terminal side of the target moving object; it collects BeiDou raw observation data and BeiDou timing information according to the unified acquisition triggering rules, and synchronously receives positioning correction data corresponding to the current positioning service area; it monitors the transmission status of the backhaul communication link and generates the communication link status; it adds source identifier and receiving timestamp to the collected data and writes it into the same positioning data cache.

[0072] In some embodiments, Figure 2 The construction module 202 determines the external timing reference based on the BeiDou timing information and the continuous timing reference based on the terminal's local timing information; it estimates the clock offset of the external timing reference and the continuous timing reference and generates time scale correction parameters; it performs time scale mapping on the positioning sampling process and the terminal processing process based on the time scale correction parameters and constructs a microsecond-level dual time scale reference containing sampling time scale and processing time scale.

[0073] In some embodiments, Figure 2 The construction module 202 determines the observation epoch corresponding to the original BeiDou observation data according to the sampling time stamp, and merges the observation information belonging to the same observation epoch according to the microsecond-level time window; records the time status of the merged observation information entering the positioning processing link according to the processing time stamp; generates micro-timeslot BeiDou observation frames based on the merged observation information, sampling time stamp and processing time stamp, and configures the microsecond-level epoch identifier corresponding to the observation epoch for the micro-timeslot BeiDou observation frames.

[0074] In some embodiments, Figure 2The generation module 203 generates an observation reliability characterization based on the continuity, stability, and geometric constraints of the observation information in the micro-slot BeiDou observation frame; it performs hierarchical processing on the observation information in the micro-slot BeiDou observation frame according to the observation reliability characterization to obtain the observation quality hierarchical results; it calculates the correction age of the positioning correction data relative to the micro-slot BeiDou observation frame according to the generation time stamp, reception time stamp, and applicable time period of the positioning correction data; and it generates a matching correction factor based on the correction age and the observation quality hierarchical results.

[0075] In some embodiments, Figure 2 The correction module 204 generates the predicted positioning state corresponding to the current observation epoch based on the historical positioning state; determines the solution constraint weights based on the observation quality stratification results and matching correction factors, and applies the solution constraint weights to the correction process of the predicted positioning state to obtain the current positioning solution result; generates a solution state identifier and positioning confidence based on the residual state, correction matching state and observation participation state of the current positioning solution result, and associates the microsecond-level epoch identifier, solution state identifier and positioning confidence with the current positioning solution result to encapsulate it into positioning state data.

[0076] In some embodiments, Figure 2 The reconstruction module 205 determines the state change amount based on the timing difference between the positioning state data and the confirmed positioning state data; generates the positioning backhaul level based on the state change amount and positioning reliability; determines the link delay characterization based on the communication link status; and performs differential encoding and time stamp encapsulation on the positioning state data based on the positioning backhaul level and link delay characterization to generate a low-latency backhaul message carrying a microsecond-level epoch identifier and backhaul priority.

[0077] In some embodiments, Figure 2 The reconstruction module 205 performs queue scheduling of low-latency backhaul messages according to the backhaul priority and determines the transmission timing in combination with the communication link status; writes the transmission status of the low-latency backhaul messages after transmission into the backhaul record; the platform side performs timing merging and status reconstruction of the received low-latency backhaul messages according to the microsecond-level epoch identifier to generate low-latency positioning trajectory data.

[0078] In some embodiments, Figure 2 The update module 206 on the platform side generates feedback information based on the receiving timestamp of the low-latency backhaul message, the status of missing epochs, the timing reconstruction result, and the positioning reliability; it sends the feedback information to the positioning terminal side of the target moving object; based on the feedback information, the positioning terminal side updates the observation quality stratification parameters, age matching parameters, and low-latency backhaul message generation parameters in the subsequent positioning solution process, and performs subsequent observation frame solution and message backhaul according to the updated parameters.

[0079] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0080] Figure 3 This is a schematic diagram of the electronic device 3 provided in an embodiment of this application. Figure 3 As shown, the electronic device 3 of this embodiment includes: a processor 301, a memory 302, and a computer program 303 stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program 303, it implements the steps in the various method embodiments described above. Alternatively, when the processor 301 executes the computer program 303, it implements the functions of each module / unit in the various system embodiments described above.

[0081] Electronic device 3 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 3 may include, but is not limited to, processor 301 and memory 302. Those skilled in the art will understand that... Figure 3 This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or different components.

[0082] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0083] The memory 302 can be an internal storage unit of the electronic device 3, such as a hard disk or memory of the electronic device 3. The memory 302 can also be an external storage device of the electronic device 3, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 3. The memory 302 can also include both internal and external storage units of the electronic device 3. The memory 302 is used to store computer programs and other programs and data required by the electronic device.

[0084] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0085] If integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a readable storage medium (e.g., a computer-readable storage medium). Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which may be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0086] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for microsecond-level positioning data processing and low-latency backhaul based on BeiDou, characterized in that, include: Acquire raw BeiDou observation data, BeiDou timing information, positioning correction data, and communication link status of the target moving object; Based on the BeiDou timing information and the terminal local timing information, a microsecond-level dual time-scale reference is constructed, and the original BeiDou observation data is divided into epochs according to the microsecond-level dual time-scale reference to generate micro-slot BeiDou observation frames carrying sampling time-scales and processing time-scales. The observation quality of the micro-timeslot BeiDou observation frames is stratified, and a matching correction factor is generated based on the age matching result of the positioning correction data relative to the micro-timeslot BeiDou observation frames. Based on the observation quality stratification results, the matching correction factor, and the historical positioning status of the target moving object, a prediction correction coupling solution is performed to generate positioning status data carrying microsecond-level epoch identifiers, solution status identifiers, and positioning confidence. Based on the state change of the positioning status data, the positioning reliability, and the communication link status, a low-latency backhaul message is generated and sent to the platform side according to the backhaul priority of the low-latency backhaul message, so that the platform side can perform time sequence reconstruction based on the microsecond-level epoch identifier to generate low-latency positioning trajectory data.

2. The method according to claim 1, characterized in that, The acquisition of the target moving object's raw BeiDou observation data, BeiDou timing information, positioning correction data, and communication link status includes: The BeiDou receiving link, correction data link, and backhaul communication link are connected to the positioning terminal side of the target moving object; The original BeiDou observation data and BeiDou timing information are collected according to the unified collection triggering rules, and the positioning correction data corresponding to the current positioning service area is received synchronously. The transmission status of the backhaul communication link is monitored to generate the communication link status; Add a source identifier and a receiving timestamp to the collected data and write it to the same location data cache.

3. The method according to claim 1, characterized in that, The construction of a microsecond-level dual-timescale reference based on the BeiDou timing information and the terminal's local timing information includes: An external timing reference is determined based on the BeiDou timing information, and a continuous timing reference is determined based on the terminal's local timing information. Clock offset estimation is performed on the external timing reference and the continuous timing reference to generate time scale correction parameters; Based on the time-scale correction parameters, time-scale mapping is performed on the positioning sampling process and the terminal processing process respectively to construct the microsecond-level dual time-scale reference containing the sampling time-scale and the processing time-scale.

4. The method according to claim 3, characterized in that, The step of dividing the original BeiDou observation data into epochs according to the microsecond-level dual-time-scale reference to generate micro-slot BeiDou observation frames carrying sampling and processing time scales includes: The observation epoch corresponding to the original BeiDou observation data is determined according to the sampling time scale, and the observation information belonging to the same observation epoch is merged according to the microsecond-level time window; The time state of the merged observation information is entered into the positioning processing link according to the processing time stamp; The micro-slot BeiDou observation frame is generated based on the merged observation information, the sampling time stamp, and the processing time stamp, and a microsecond-level epoch identifier corresponding to the observation epoch is configured for the micro-slot BeiDou observation frame.

5. The method according to claim 1, characterized in that, The step of performing observation quality stratification on the micro-timeslot BeiDou observation frames and generating a matching correction factor based on the age matching result of the positioning correction data relative to the micro-timeslot BeiDou observation frames includes: Based on the continuity, stability and geometric constraints of the observation information in the micro-slot BeiDou observation frames, an observation reliability characterization is generated. Based on the observation reliability characterization, the observation information in the micro-slot BeiDou observation frame is processed in layers to obtain the observation quality layering result; Based on the generation time stamp, reception time stamp, and applicable time period of the positioning correction data, the correction age of the positioning correction data relative to the micro-slot BeiDou observation frame is calculated; The matching correction factor is generated based on the corrected age and the observation quality stratification results.

6. The method according to claim 1, characterized in that, The prediction and correction coupling solution based on the observation quality stratification results, the matching correction factor, and the historical positioning status of the target moving object generates positioning status data carrying microsecond-level epoch identifiers, solution status identifiers, and positioning confidence levels, including: Generate the predicted positioning status corresponding to the current observation epoch based on the historical positioning status; Based on the observation quality stratification results and the matching correction factor, the solution constraint weights are determined, and the solution constraint weights are applied to the correction process of the predicted positioning state to obtain the current positioning solution result. The solution status identifier and the positioning confidence level are generated based on the residual status, correction matching status and observation participation status of the current positioning solution result. The microsecond-level epoch identifier, the solution status identifier and the positioning confidence level are associated with the current positioning solution result and encapsulated as the positioning status data.

7. The method according to claim 1, characterized in that, The step of generating a low-latency return message based on the state change amount of the positioning status data, the positioning reliability, and the communication link status includes: The change in status is determined based on the temporal difference between the location status data and the confirmed location status data. A positioning feedback level is generated based on the state change amount and the positioning reliability. Determine the link delay characterization based on the communication link status; Based on the positioning backhaul level and the link latency characterization, the positioning status data is differentially encoded and time-stamped to generate the low-latency backhaul message carrying a microsecond-level epoch identifier and backhaul priority.

8. The method according to claim 7, characterized in that, The step of sending the low-latency return message to the platform side according to the return priority of the low-latency return message, so that the platform side can perform time sequence reconstruction based on the microsecond-level epoch identifier to generate low-latency positioning trajectory data, includes: The low-latency return messages are queued according to the return priority, and the transmission timing is determined in combination with the communication link status. Write the sending status of the low-latency return message after it has been sent into the return record; The platform performs time-series merging and state reconstruction on the received low-latency backhaul messages based on the microsecond-level epoch identifier to generate the low-latency positioning trajectory data.

9. The method according to claim 1, characterized in that, The method further includes: The platform generates feedback information based on the receiving timestamp of the low-latency return message, the missing epoch status, the timing reconstruction result, and the location reliability. The feedback information is sent to the positioning terminal of the target moving object; Based on the feedback information, the positioning terminal updates the observation quality stratification parameters, age matching parameters, and low-latency backhaul message generation parameters in the subsequent positioning calculation process, and performs subsequent observation frame calculation and message backhaul according to the updated parameters.

10. A microsecond-level positioning data processing and low-latency backhaul system based on BeiDou, characterized in that, include: The acquisition module is used to acquire the target moving object's raw BeiDou observation data, BeiDou timing information, positioning correction data, and communication link status; The module is used to construct a microsecond-level dual-time-scale reference based on the BeiDou timing information and the terminal's local timing information, and to divide the original BeiDou observation data into epochs according to the microsecond-level dual-time-scale reference to generate micro-slot BeiDou observation frames carrying sampling time scales and processing time scales; The generation module is used to perform observation quality stratification on the micro-timeslot BeiDou observation frames and generate a matching correction factor based on the age matching result of the positioning correction data relative to the micro-timeslot BeiDou observation frames. The correction module is used to perform prediction correction coupling calculation based on the observation quality stratification results, the matching correction factor and the historical positioning status of the target moving object, and generate positioning status data carrying microsecond-level epoch identifiers, calculation status identifiers and positioning confidence. The reconstruction module is used to generate a low-latency backhaul message based on the state change amount of the positioning status data, the positioning reliability and the communication link status, and send the low-latency backhaul message to the platform side according to the backhaul priority of the low-latency backhaul message, so that the platform side can perform time-series reconstruction based on the microsecond-level epoch identifier to generate low-latency positioning trajectory data.

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