Vehicle-mounted intelligent device, positioning drift correction method thereof and computer storage medium

Abstract

The application discloses a vehicle-mounted intelligent device and a positioning drift correction method and computer storage medium thereof, and relates to the field of vehicle-mounted intelligent devices.The method comprises the following steps: receiving satellite positioning data transmitted by a GNSS positioning module in real time; receiving mobile communication cellular network signal parameters of a current cell transmitted by a cellular communication module in real time; determining base station positioning data based on the mobile communication cellular network signal parameters; and performing fusion correction based on the satellite positioning data and the base station positioning data to obtain corrected positioning data.Thus, by introducing a cellular network base station assisted positioning mechanism, a backup positioning means is provided when satellite signals are lost or drift, satellite positioning drift can be discovered and effectively corrected in time, effective position information can be output by the vehicle-mounted intelligent device under various environments, service abnormities caused by drift data reporting are avoided, and the continuity and reliability of positioning services are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of in-vehicle intelligent devices, and more particularly to an in-vehicle intelligent device, a method for correcting positioning drift, and a computer storage medium thereof. Background Technology

[0002] Telematics-BOX (TBOX) is a core in-vehicle intelligent device that integrates wireless communication, satellite positioning, and sensor data acquisition. It can comprehensively collect vehicle operating status data and complete real-time data interaction between the vehicle and the cloud via mobile communication networks. Continuous and high-precision vehicle positioning is the foundation for the implementation of various in-vehicle services of the telematics-BOX, and the continuity and accuracy of vehicle positioning directly determine the overall reliability of the in-vehicle location service.

[0003] Currently, in-vehicle intelligent devices mainly rely on GPS satellite positioning modules to calculate vehicle location. However, in actual driving scenarios, satellite signals are easily interfered with by complex environments, causing problems such as GPS positioning drift, data interruption, and positioning failure. This results in the inability to output effective positioning data, leading to the interruption of vehicle location information. This directly causes the location-related services of in-vehicle intelligent devices to malfunction, which not only reduces the user's intelligent driving experience but also poses significant safety hazards.

[0004] Existing technologies mostly improve GPS positioning drift by using motion sensor-assisted correction and historical positioning data aggregation and mean correction. However, these solutions all rely on valid GPS positioning data as the basis for calculation. When the GPS signal is completely lost and positioning fails completely, positioning compensation and error correction cannot be achieved, resulting in significant limitations in scenario adaptability and making it difficult to guarantee the continuity and accuracy of vehicle positioning in complex driving environments. Summary of the Invention

[0005] The present invention aims to provide an in-vehicle intelligent device and its positioning drift correction method and computer storage medium, in order to solve the problem that current in-vehicle intelligent devices cannot achieve positioning compensation and error correction when GPS signals are completely lost and positioning is completely ineffective, and it is difficult to ensure the continuity and accuracy of vehicle positioning in complex driving environments.

[0006] To address the aforementioned technical problems, a first aspect of this invention provides a method for correcting positioning drift in a vehicle-mounted intelligent device, the method comprising: It receives satellite positioning data transmitted from the GNSS positioning module in real time; It receives mobile communication cellular network signal parameters of the current cell in real time from the cellular communication module; Based on the mobile communication cellular network signal parameters, base station positioning data is determined; The satellite positioning data and the base station positioning data are fused and corrected to obtain the corrected positioning data.

[0007] Accordingly, a second aspect of the present invention provides an in-vehicle intelligent device, including a processor and a memory, wherein the memory stores a computer program, and the processor implements the in-vehicle intelligent device positioning drift correction method provided in the first aspect of the present invention when executing the program.

[0008] Accordingly, a third aspect of the present invention provides a computer storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the vehicle-mounted intelligent device positioning drift correction method as described in the first aspect above.

[0009] Compared with existing technologies, the present invention provides an in-vehicle intelligent device, its positioning drift correction method, and a computer storage medium. This method receives satellite positioning data transmitted from a GNSS positioning module and mobile communication cellular network signal parameters of the current cell transmitted from a cellular communication module in real time. Based on these parameters, base station positioning data is determined. The satellite positioning data and base station positioning data are then fused and corrected to obtain corrected positioning data. This method eliminates the need for additional hardware costs for the in-vehicle intelligent device, fully utilizing existing resources. It can be implemented entirely using the device's existing GPS and cellular communication modules, offering advantages such as low cost and ease of implementation. By introducing a cellular network base station-assisted positioning mechanism, a backup positioning method is provided when satellite signals are lost or drifting. This allows for timely detection and effective correction of satellite positioning drift, ensuring that the in-vehicle intelligent device can output valid location information in various environments. It avoids service anomalies caused by drift data reporting, significantly improving the continuity and reliability of positioning services. This can solve the problem that current in-vehicle intelligent devices cannot achieve positioning compensation and error correction when GPS signals are completely lost and positioning fails completely, making it difficult to ensure the continuity and accuracy of vehicle positioning in complex driving environments. Attached Figure Description

[0010] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0011] Figure 1 This is a flowchart illustrating a method for correcting positioning drift in a vehicle-mounted intelligent device provided by the present invention. Figure 2This is a detailed flowchart illustrating a method for correcting positioning drift in vehicle-mounted intelligent devices provided by the present invention. Figure 3 This is a structural schematic diagram of an in-vehicle intelligent device provided by the present invention. Detailed Implementation

[0012] To facilitate understanding of the present invention, a more detailed description is provided below with reference to the accompanying drawings and specific embodiments. It should be noted that when an element is described as being "fixed to" another element, it can be directly on the other element, or one or more intermediate elements may exist between them. When an element is described as being "connected" to another element, it can be directly connected to the other element, or one or more intermediate elements may exist between them. The terms "upper," "lower," "inner," "outer," "bottom," etc., used in this specification indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0013] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0014] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0015] In one embodiment, see Figure 1 This invention provides a method for correcting positioning drift in vehicle-mounted intelligent devices, comprising: S1. Receive satellite positioning data transmitted from the GNSS positioning module in real time; S2. Receive the mobile communication cellular network signal parameters of the current cell transmitted by the cellular communication module in real time; S3. Determine base station location data based on mobile communication cellular network signal parameters; S4. Based on satellite positioning data and base station positioning data, perform fusion correction to obtain corrected positioning data.

[0016] This embodiment provides a method for correcting positioning drift in vehicle-mounted intelligent devices. It involves real-time reception of satellite positioning data transmitted from a GNSS positioning module and real-time reception of mobile communication cellular network signal parameters of the current cell transmitted from a cellular communication module. Based on these mobile communication cellular network signal parameters, base station positioning data is determined. The satellite positioning data and base station positioning data are then fused and corrected to obtain corrected positioning data. This method eliminates the need for additional hardware costs for vehicle-mounted intelligent devices, fully utilizing existing resources. It can be implemented entirely based on the existing GPS and cellular communication modules of the vehicle-mounted intelligent device, offering advantages such as low cost and ease of implementation. By introducing a cellular network base station-assisted positioning mechanism, a backup positioning method is provided when satellite signals are lost or drifting. This allows for timely detection and effective correction of satellite positioning drift, ensuring that the vehicle-mounted intelligent device can output valid location information in various environments. This avoids service anomalies caused by drift data reporting, significantly improving the continuity and reliability of positioning services. This solves the problem that current vehicle-mounted intelligent devices cannot achieve positioning compensation and error correction when GPS signals are completely lost or positioning fails completely, making it difficult to guarantee the continuity and accuracy of vehicle positioning in complex driving environments.

[0017] In one embodiment, in step S1, satellite positioning data transmitted by the GNSS positioning module is received in real time.

[0018] Specifically, please refer to Figure 3 The vehicle-mounted intelligent device 500 includes a GNSS positioning module 504 and a processor 501. The GNSS positioning module 504 is communicatively connected to the processor 501 and is used to receive satellite signals, collect the vehicle's satellite positioning data PGPS in real time, and transmit the satellite positioning data to the processor 501.

[0019] The vehicle-mounted intelligent device receives satellite signals in real time through the GNSS positioning module 504, collects the vehicle's satellite positioning data in real time, and outputs the satellite positioning data to the processor 501. The satellite positioning data includes at least one of the following information: latitude and longitude coordinates, speed, direction, and positioning accuracy.

[0020] In this invention, the satellite signals may include GPS signals and / or BeiDou signals, and the corresponding satellite positioning data collected include GPS positioning data and / or BeiDou positioning data.

[0021] In the following embodiments of the present invention, satellite signals are described using GPS signals as an example, and correspondingly, satellite positioning data are also described using GPS positioning data as an example.

[0022] In one embodiment, in step S2, the mobile communication cellular network signal parameters of the current cell transmitted by the cellular communication module are received in real time.

[0023] Specifically, please refer to Figure 3 The vehicle-mounted intelligent device 500 includes a cellular communication module 505, which is communicatively connected to a processor 501. It is used to access the mobile communication cellular network, collect the mobile communication cellular network signal parameters of the current cell in real time, and transmit the mobile communication cellular network signal parameters of the current cell to the processor 501.

[0024] The in-vehicle intelligent device accesses the mobile communication cellular network through the cellular communication module 505 to collect the mobile communication cellular network signal parameters of the current cell in real time. The mobile communication cellular network includes, but is not limited to, 4G and / or 5G cellular networks. The corresponding cellular communication module 505 includes a 4G cellular communication module 505, a 5G cellular communication module 505, and / or a 4G / 5G dual-mode cellular communication module 505.

[0025] The mobile communication cellular network signal parameters are signal quality measurement parameters of the wireless air interface, including base station information such as cell ID, signal strength, and neighbor cell information.

[0026] In this embodiment, the vehicle-mounted intelligent device accesses the mobile communication cellular network through the cellular communication module 505 and collects the mobile communication cellular network signal parameters of the current cell in real time. This provides a data foundation for the subsequent introduction of a cellular network base station-assisted positioning mechanism, ensuring that the vehicle-mounted intelligent device can output effective location information in various environments. This avoids service anomalies caused by drift data reporting and significantly improves the continuity and reliability of the positioning service.

[0027] In one embodiment, in step S3, base station positioning data is determined based on the mobile communication cellular network signal parameters.

[0028] Specifically, please refer to Figure 3 The processor 501 is equipped with a base station positioning engine module 5011, which is connected to the cellular communication module 505. It is used to calculate base station positioning data and base station three-point positioning data based on mobile cellular network signal parameters, and to match and extrapolate historical cell locations based on historical cell location data.

[0029] It should be noted that the base station positioning engine module 5011 can be implemented as a hardware module or as a software module, and the present invention does not impose any restrictions.

[0030] The mobile communication cellular network signal parameters are signal quality measurement parameters of the wireless air interface, including base station information such as cell ID, signal strength, and neighbor cell information.

[0031] The in-vehicle intelligent device accesses the mobile communication cellular network through the built-in cellular communication module 505, and then calculates the base station positioning data based on the mobile cellular network signal parameters through the base station positioning engine module 5011. This data is then used as a verification object for comparison with satellite positioning data. In the event of abnormal satellite positioning data, the device can use the base station signal for assisted positioning.

[0032] Based on the mobile communication cellular network signal parameters, the base station positioning engine module 5011 can automatically select one or a combination of the following three calculation methods to calculate base station positioning data. These three methods, ranked from lowest to highest positioning accuracy, are: Cell ID positioning, Time of Arrival (TOA) positioning, and Time Difference of Arrival (TDOA) positioning. The aforementioned Cell ID positioning, TOA, and TDOA methods are commonly used methods for calculating base station positioning data in the prior art and will not be elaborated upon further here.

[0033] In one embodiment, in step S4, the satellite positioning data and base station positioning data are fused and corrected to obtain the corrected positioning data.

[0034] Specifically, please refer to Figure 2 and Figure 3 The processor 501 is equipped with a data fusion and correction module 5012, which is communicatively connected to the base station positioning engine module 5011. It is used to perform fusion and correction based on satellite positioning data and base station positioning data to obtain corrected positioning data. Specifically, it compares, fuses, and corrects satellite positioning data and base station positioning data to obtain corrected positioning data. This includes steps S41-S44. Wherein: It should be noted that the data fusion and correction module 5012 can be implemented as a hardware module or as a software module, and the present invention does not impose any restrictions.

[0035] S41. Compare satellite positioning data with base station positioning data in real time, and conduct a quality assessment of the satellite positioning data. The quality assessment includes whether the satellite positioning data is normal or abnormal.

[0036] Specifically, the data fusion and correction module 5012 performs quality assessment on satellite positioning data. The assessment dimensions include: satellite signal quality, positioning geometric accuracy, positioning solution status, and motion consistency. Specific indicators include: number of visible satellites, number of effective positioning satellites, weighted average carrier-to-noise ratio (reflecting signal strength), horizontal accuracy factor (DOP value), positioning status indicator (valid / invalid), pseudorange residual, position jump variable, and velocity consistency deviation.

[0037] The data fusion and correction module 5012 compares satellite positioning data with base station positioning data in real time and performs quality assessment on the satellite positioning data, specifically including: When the deviation between satellite positioning data and base station positioning data is detected to be lower than the preset deviation threshold, the satellite positioning data is determined to be normal, and the satellite positioning is confirmed to be normal. When the deviation between satellite positioning data and base station positioning data exceeds a preset deviation threshold, the satellite positioning data is determined to be abnormal, indicating that the satellite positioning has drifted or failed, and a correction mechanism is triggered.

[0038] In this embodiment, by comparing and verifying satellite positioning data with base station positioning data in real time, the quality of satellite positioning data is assessed, which can promptly detect and correct satellite positioning drift and avoid service anomalies caused by drift data reporting.

[0039] S42. If the quality assessment determines that the satellite positioning data is normal, output the satellite positioning data.

[0040] When the deviation between satellite positioning data and base station positioning data is detected to be lower than the preset deviation threshold, the satellite positioning data is determined to be normal, the satellite positioning is confirmed to be normal, and the satellite positioning data is output.

[0041] Specifically, when the deviation between satellite positioning data and base station positioning data is detected to be lower than a preset deviation threshold, the satellite positioning data is determined to be normal and the satellite positioning is confirmed to be normal. At this time, the satellite positioning data is used as the primary reference and the base station positioning data is used as the secondary reference for verification. The data is then output to the data fusion and correction module 5012 for fusion and correction processing.

[0042] S43. If the quality assessment determines that the satellite positioning data is abnormal, the following correction mechanism shall be followed: First, match and extrapolate the historical cell location to obtain the historical extrapolated positioning data of the current location, and output the historical extrapolated positioning data; if the historical cell location matching and extrapolation cannot obtain the historical extrapolated positioning data of the current location, then start the base station three-point positioning based on the mobile communication cellular network signal parameters to obtain the base station three-point positioning data of the current location, and output the base station three-point positioning data.

[0043] In this invention, please refer to Figure 3 The vehicle-mounted intelligent device includes a memory 502, which is communicatively connected to a processor 501. Specifically, the memory 502 is communicatively connected to a base station positioning engine module 5011 and a data fusion and correction module 5012. The memory 502 stores a historical cell location database, which is used to store historical cell location data and historical positioning trajectory data. The historical cell location data consists of cell identifiers and corresponding timestamp information observed by the vehicle-mounted intelligent device at different historical moments. The historical cell location database supports matching and querying of the current location.

[0044] The historical cell location database uses globally unique cell identifiers (Cell ID and LAC / TAC tracking area code) as primary keys, and each record contains the following fields:

[0045] The base station positioning engine module 5011 is also used to achieve historical cell location matching and deduction based on historical cell location data.

[0046] S431. Match and extrapolate the historical cell locations to obtain the historical extrapolation positioning data of the current location.

[0047] During vehicle operation, the onboard intelligent device records and stores the cell IDs and corresponding location information of the base stations the vehicle passes through in real time, and stores them in the historical cell location database. When the vehicle enters a new cell or the GPS signal is abnormal, the system uses the historical cell sequence and the information of the currently accessed cell, combined with the vehicle's historical movement trajectory, to extrapolate and estimate the current location as a supplement and reference to the positioning data.

[0048] When the quality assessment determines that the satellite positioning data is abnormal, the vehicle-mounted intelligent device activates the historical cell location matching and extrapolation mechanism. This mechanism consists of two phases: the first phase is historical cell location matching to determine the matching reference position; the second phase is motion extrapolation to calculate the current estimated position from the matching reference position.

[0049] Phase 1: Historical cell location matching to determine the matching baseline location.

[0050] The in-vehicle intelligent device maintains a historical cell location database in its built-in memory 502 to support matching queries for the current location.

[0051] The base station positioning engine module 5011 adopts a multi-level matching strategy to try matching in the historical cell location database in turn according to different priorities until a valid matching result is obtained, and the matching reference location is determined.

[0052] Matching Strategy 1: Precise matching strategy for the current cell, which has the highest priority.

[0053] Obtain the Cell_ID and LAC / TAC of the current cell, and perform a precise query in the historical cell location database: If the record exists, the geometric median of the corresponding cell coordinates is directly taken as the matching reference position. .

[0054] At the same time, the mode of the historical heading angle of the cell is extracted as a historical direction reference.

[0055] Additionally, calculate the confidence level of the current cell's exact matching strategy:

[0056] Among them, the time decay factor , This represents the number of hours since the last update. Set the value to 0.01 to ensure that the weight of records that have been stored for too long is reduced.

[0057] Matching Strategy 2: Cell Sequence Pattern Matching Strategy, which is a secondary priority strategy.

[0058] When the exact matching strategy for the current cell fails (the current cell has no historical records), the cell sequence pattern matching strategy is used for matching, specifically: The vehicle-mounted intelligent device caches the most recently traversed (N) cell sequences (arranged in chronological order, where (N) is 5-8), and denoted as the current sequence. ; Retrieve all historical path records in the historical cell location database that have a longest common subsequence (LCS) length ≥ 3 with the current sequence; Select the historical path with the highest LCS matching degree, and extract the data from the historical path that matches the current cell. Historical coordinates of adjacent locations are used as the matching reference positions. .

[0059] Meanwhile, the formula for calculating the matching degree of the cell sequence pattern matching strategy is as follows:

[0060] Matching Strategy 3: Neighbor Intersection Location Matching Strategy, which has the second lowest priority.

[0061] When both the current cell precise matching strategy and the cell sequence pattern matching strategy fail, the neighbor cell intersection location matching strategy is used to match the neighbor cell information of the current cell, specifically: The vehicle-mounted intelligent device reads the list of neighboring cell IDs of the current cell from the system information block broadcast by the base station; Query the coordinate records of each neighboring area in the historical database to obtain a set of neighboring area coordinates; The weighted geometric center of this coordinate set (weighted by the RSSI of the signal strength of each neighboring cell) is calculated as the matching reference position. :

[0062] Matching Strategy 4: Regional Grid Matching Strategy, which has the lowest priority.

[0063] If all of the above strategies fail, a regional grid matching strategy will be adopted for matching. Specifically, the coverage area of ​​the current cell will be regarded as a rough area, the cell center coordinates (from the base station engineering parameter database) will be used as the matching position, and the confidence level will be marked as extremely low, with the weight approaching zero in subsequent fusion.

[0064] The second stage: motion deduction, which calculates the current estimated position from the matching reference position.

[0065] In obtaining the matching reference position Then, by combining the vehicle's motion status information, the current estimated position is deduced from the matching reference position. .

[0066] Specifically, the current estimated position can be calculated using the following derivation formula. :

[0067] In discrete systems, the current estimated position is obtained by accumulating displacements:

[0068] In the above formula: For the first The distance traveled within each sampling period For the first The unit vector of the driving direction within each sampling period This represents the number of sampling cycles elapsed from the last valid location to the current moment.

[0069] Since the error in the current estimated location derivation will increase infinitely over time, this invention introduces a road network constraint correction mechanism to correct the error in the derivation of the current estimated location. Make corrections to obtain the modified position. The details are as follows: The vehicle-mounted intelligent device pre-stores lightweight road network topology data locally, which includes at least the coordinates of nodes on the main road segments.

[0070] After each simulation, Projecting onto the nearest road segment yields the corrected position. :

[0071] During the projection process, if the angle between the current heading angle and the road direction exceeds 45°, the projection of that road segment is abandoned, and the next closest road with the same direction is selected.

[0072] In this invention, for certain special scenarios, such as vehicle stationary detection, unclear direction, or long-term simulation, the following processing is performed: Vehicle stationary detection: If the CAN vehicle speed is 0 and remains so for more than 10 seconds, the estimated position remains unchanged. No change, no displacement accumulation.

[0073] When the direction is unclear: If there is no effective direction data, the extrapolation degenerates into a uniform linear extrapolation and rapidly increases the error covariance, which is used to suppress the underestimation in the fusion process.

[0074] Long-duration simulation (>30 seconds): When the simulation lasts for more than 30 seconds, the system determines that the simulation result is unreliable and directly uses the three-point positioning result of the base station instead, no longer relying on the simulation.

[0075] Furthermore, to facilitate subsequent data fusion, the in-vehicle intelligent device calculates the projection confidence score for the historical community projection results. (0-100 points):

[0076] In the above formula: Historical match confidence (determined by the matching strategy); The estimated time (in seconds) from the matching reference time to the current time; To deduce the effective decay time constant, a time of 60 seconds is taken (the confidence level is zero if the time exceeds this). The sensor availability coefficient is 1.0 when CAN vehicle speed is available, 0.7 when only GPS speed is available, and 0.4 when only accelerometer integration is available.

[0077] For example, if an exact match is obtained The CAN vehicle speed has been calculated for 10 seconds and is now available. ,but:

[0078] This score is directly used as the weighting basis for historical data sources in subsequent fusion algorithms.

[0079] S432. Based on the mobile communication cellular network signal parameters, initiate base station three-point positioning to obtain base station three-point positioning data of the current location.

[0080] Specifically, the base station positioning engine module 5011 is also used to calculate base station three-point positioning data based on mobile cellular network signal parameters. Specifically, when the GPS module completely fails and historical cell location matching cannot obtain a valid location (historical matching confidence level...), When the value is less than 0.3, the vehicle-mounted intelligent device initiates the base station tri-point positioning mechanism. The base station positioning engine module 5011 simultaneously acquires cellular network signal parameters (including signal arrival time, signal strength, etc.) from at least three adjacent base stations, calculates the vehicle's position relative to the base station through the geometric intersection positioning algorithm, and obtains the base station tri-point positioning data.

[0081] In this invention, "three-point positioning" does not simply refer to the intersection of the ranging circles of three base stations. Instead, it employs a dual-mode positioning scheme that primarily uses the Chan algorithm based on TDOA (Time Difference of Arrival) and secondarily uses the least-squares circle intersection based on TOA (Time of Arrival), as detailed below: The in-vehicle intelligent device performs the following operations via the cellular communication module 505: data acquisition and preprocessing. 1. Frequency sweep measurement: Based on the serving cell, simultaneously measure the signal parameters of at least 3 (preferably 4 or more) neighboring base stations, including: Base station coordinates : Obtained from the local base station engineering parameter database or through real-time query in the cloud; Signal arrival time Arrival timestamp reported by the physical layer; Signal arrival time difference (Using the serving base station as the reference base station); Reference signal received power (RSRP) or reference signal received quality (RSRQ) is used for auxiliary weighting.

[0082] 2. Base Station Optimization Strategy: If the number of measured base stations is greater than 3, the onboard intelligent device selects the optimal base station combination based on the principle of minimizing the geometrical factor of precision (GDOP). Specifically, it enumerates all available base stations... For each of the three base station combinations, calculate the GDOP value for each combination, and select the combination with the smallest GDOP to participate in the positioning solution.

[0083] 3. NLOS (Non-Line-of-Sight) Identification: Detect the arrival time residual of each base station signal. If the measurement residual of a certain base station is significantly greater than that of other base stations (more than 3 times the standard deviation), it is determined that the signal of that base station is seriously contaminated by NLOS and it is removed from the set of base stations participating in the positioning.

[0084] In this invention, the base station positioning engine module 5011 can use one of the following positioning methods to obtain the base station three-point positioning data of the current location.

[0085] Positioning Method 1: TDOA three-point positioning method, which is used as the primary solution.

[0086] The TDOA (Trinity Time Oscillation) triangulation method determines location by measuring the time difference between the arrival of signals from in-vehicle smart devices at different base stations. Its advantage lies in the fact that it does not require strict time synchronization between the mobile terminal and the base station; only synchronization between the base stations is needed (4G / 5G base stations have achieved nanosecond-level synchronization through the IEEE 1588v2 or SyncE protocols). Its specific implementation process is as follows: 1. Positioning geometric model.

[0087] Let the number of base stations participating in the positioning be... The serving base station was selected as the reference base station (numbered 1), and the remaining base stations were numbered as follows: The coordinates of base station 1 are... The coordinates of base station i are , The coordinates of the in-vehicle intelligent device to be determined are: .

[0088] From vehicle-mounted intelligent devices to base stations The distance difference from the reference base station 1 is:

[0089] in, Speed ​​of light , The time difference of arrival of signals measured by in-vehicle intelligent devices.

[0090] Based on the definition of distance difference, we can obtain the system of equations for the hyperbola:

[0091] The system of hyperbolic equations is a nonlinear system of hyperbolic equations. Its geometric meaning is: the vehicle-mounted intelligent device is located on a hyperbola with base station 1 and base station i as its foci, and the intersection of multiple hyperbolas is the location of the vehicle-mounted intelligent device.

[0092] 2. Chan's algorithm uses two steps of weighted least squares to solve the problem.

[0093] Chan's algorithm is a classic closed-form solution method in this field. It requires no iteration, has high computational efficiency, and is particularly suitable for embedded platforms of in-vehicle intelligent devices.

[0094] Step 1: Weighted Least Squares (WLS1) Linearize the hyperbola equation.

[0095] Let the distance from reference base station 1 to the vehicle-mounted intelligent device be: ,Depend on and Expanding and subtracting, we get: Arrange in matrix form:

[0096] in: The vector to be determined (introduced) As an auxiliary variable, it converts the nonlinear equation into a linear form; A is coefficient matrix:

[0097] B is Observation vector:

[0098] Solve using the weighted least squares method:

[0099] in, The measurement error covariance matrix is ​​composed of the noise variance of each TDOA measurement (the onboard intelligent device estimates the measurement variance in real time based on the signal-to-noise ratio).

[0100] The second step is weighted least squares (WLS2): WLS1 obtained There are constraints between the components:

[0101] To improve accuracy using this constraint, a second WLS test is performed: set up The error vector is ,structure:

[0102] The final position estimate is obtained by weighted least squares again. .

[0103] The second step, WLS, significantly improves positioning accuracy, especially when the base station geometry is poor.

[0104] when Simplified processing at that time: If only 3 base stations can be acquired Then the two-step WLS degenerates into a solution by direct intersection of hyperbolas: A hyperbola is obtained using the TDOA of base station 1 and base station 2; Another hyperbola is obtained using the TDOA of base stations 1 and 3; The intersection of the two hyperbolas indicates the location of the in-vehicle intelligent device. In the specific solution, the quadratic terms are eliminated by subtracting the hyperbolic equations, transforming it into a linear equation, which can then be solved directly without iteration.

[0105] Positioning Method 2: TOA three-point positioning. This method is used as an auxiliary backup solution.

[0106] When the time synchronization accuracy between base stations is insufficient (such as 2G / 3G base stations in some areas) or the TDOA measurement value is unavailable, the vehicle-mounted intelligent device automatically switches to the TOA three-point positioning method.

[0107] The ranging principle of the TOA three-point positioning method: The base station positioning engine module 5011 measures the time it takes for the signal to travel from its own transmission to the reception of each base station. The distance to each base station was obtained. It should be noted that time synchronization between the vehicle-mounted intelligent device and the base station is achieved through round-trip time (RTT) measurement: the base station positioning engine module 5011 of the vehicle-mounted intelligent device sends a request signal, the base station responds, and the base station positioning engine module 5011 measures the total round-trip delay and divides it by 2 to eliminate time synchronization deviation.

[0108] Construct a system of circular equations: Let the number of base stations involved in the positioning be... The base station coordinates are The measured distance is :

[0109]

[0110]

[0111]

[0112] Solving for linearization and least squares: Subtract the above circle equations pairwise to eliminate the quadratic terms. This yields a system of linear equations: Convert to matrix form: ,in, The weighted least squares method is used to solve this problem. in, The covariance matrix of each distance measurement error is assigned diagonally based on the distance measurement standard deviation calculated from the signal strength RSSI.

[0113] After processing using the above positioning method, the three-point positioning data and error estimate of the current location can be obtained from the base station. After processing using the above positioning method, the three-point positioning data and error estimate of the current location can be obtained from the base station. 1. Position Output: Output the calculated coordinates As the result of the base station's three-point positioning data Output to processor 501.

[0114] 2. Positioning accuracy factor calculation: Calculate the horizontal accuracy factor (HDOP):

[0115] Where G is the positioning Jacobian matrix, which reflects the impact of base station geometry on positioning accuracy. The smaller the HDOP value (<2 is excellent, 2~5 is good, >5 is poor), the higher the positioning accuracy.

[0116] Confidence score (0-100 points):

[0117] In the above formula, Score the number of base stations participating in the positioning (M) 5 is 100 points, M=4 is 80 points, M=3 is 60 points). Geometric accuracy is scored (mapped by HDOP value, HDOP≤2 is 1.0, deduct 0.15 points for each increase of 1 in HDOP, minimum 0.3 points). To measure the residual score (1.0 for TDOA equation residual root mean square ≤ 50m, deduct 0.2 points for every additional 50m, minimum 0 points).

[0118] Although the accuracy of the three-point positioning result is lower than that of GPS (meter level), it provides a reliable backup location when GPS fails completely, and the accuracy is sufficient to support non-navigation business needs such as electronic fences, emergency rescue, and fleet management.

[0119] S44. The satellite positioning data, base station three-point positioning data and historical simulation positioning data are fused and corrected to obtain the corrected positioning data.

[0120] The goal of the data fusion and correction module 5012 in performing fusion and correction processing is to: use satellite positioning data as the primary source and verify it when the satellite positioning data is normal; and smoothly switch to the auxiliary data source of base station three-point positioning data and / or historical inference positioning data when the satellite positioning data is abnormal or fails, and correct positioning drift in real time.

[0121] Specifically, the data fusion and correction module 5012 receives positioning results and their confidence scores from three data sources: satellite positioning data, base station three-point positioning data, and historical inference positioning data, as shown in the table below.

[0122]

[0123] The specific process of data fusion and correction module 5012 performing fusion and correction processing includes: S441, the data fusion and correction module 5012 performs time registration and data synchronization on the received satellite positioning data, base station three-point positioning data and historical simulation positioning data.

[0124] Since the timestamps and update frequencies of the three data sources may differ (e.g., the update cycle for base station positioning is slightly longer), all data must be unified to the same time point before fusion. (Based on GPS sampling time).

[0125] The data fusion and correction module 5012 maintains a data cache queue, which stores the satellite positioning data, base station three-point positioning data and historical inferred positioning data and their timestamps for the most recent 3 frames.

[0126] At a certain fusion moment : Satellite positioning data: directly take the closest timestamp Valid frames; Base station tri-point positioning data: If the base station positioning time and If the deviation is less than 200ms, it is used directly; if the deviation is greater than 200ms, linear interpolation is performed using the base station positioning results from the previous and current frames to estimate. The location of the base station at any given time; Historical data analysis shows that processor 501 outputs continuously at a frequency of 1Hz, directly connecting to... Alignment is achieved without any additional processing.

[0127] S442, the data fusion and correction module 5012 assigns dynamic weights to the satellite positioning data, base station three-point positioning data and historical inference positioning data that have undergone time registration and data synchronization.

[0128] The fusion algorithm employs an adaptive weighted fusion architecture. The weights are dynamically calculated based on the confidence scores of each data source, rather than being a fixed proportion.

[0129] Let the measurement noise covariance of the three data sources be... Their relationship with confidence scores is as follows:

[0130] in, This is the baseline measurement noise variance of the data source at a full confidence level (100 points). For example, based on experimental calibration values, the GPS satellite positioning data is preset to... The base station's three-point positioning data (BS) is Historical projection positioning data is .

[0131] The corresponding weighting formula is:

[0132] The weight allocation satisfies the normalization condition: .

[0133] For example, the following are examples of weight allocation for three scenarios:

[0134] S443. Based on the weighted fusion, robust Kalman filtering is used to perform robust fusion calculation to obtain the optimal fusion position estimate.

[0135] Based on weighted fusion, robust Kalman filtering is used for optimal state estimation to suppress anomalous data contamination.

[0136] System state model: Define the system state vector (Simplified uniform velocity model, position coordinates and planar velocity).

[0137] State prediction (time update):

[0138]

[0139] in, Let Q be the uniform velocity state transition matrix, and let Q be the process noise covariance matrix.

[0140] Measurement update: Combines location measurements from three data sources into a unified measurement vector. Weighted fusion is used as the equivalent observation:

[0141] The equivalent measurement noise covariance is:

[0142] Kalman Gains and Updates:

[0143]

[0144]

[0145] Final fusion position Taken from Positional components in .

[0146] S444. While performing fusion computing, actively detect satellite positioning drift in real time and trigger a correction mechanism.

[0147] Specifically, during the fusion calculation, GPS positioning drift is detected in real time, triggering a correction mechanism. The data fusion and correction module 5012 determines that GPS positioning drift has occurred when the in-vehicle intelligent device meets the following drift detection conditions: (1) The discrepancy between satellite positioning data and base station positioning data is too large: Calculate the Euclidean distance between the GPS location and the base station positioning location, that is, calculate the discrepancy between the satellite positioning data and the base station positioning data:

[0148] like If the signal persists for more than 3 frames, it is determined that GPS drift has occurred.

[0149] The preset deviation threshold is an adaptive threshold, which samples and statistically analyzes the standard deviation of positioning jitter during periods of good GPS historical quality. Take the preset deviation threshold It can adapt to different receiver performance and avoid false triggering.

[0150] Furthermore, in addition to considering the judgment condition of the discrepancy between satellite positioning data and base station positioning data, to increase the confidence of drift judgment, the following two judgment conditions can also be considered as auxiliary criteria: (2) GPS residual anomaly: Calculate the Mahalanobis distance between GPS measurements and predictions:

[0151] like (99th percentile of the chi-square distribution with 2 degrees of freedom, approximately 9.21), indicating that the GPS measurement is an anomaly.

[0152] 3) Speed ​​inconsistency anomaly: Deviation between GPS speed and CAN vehicle speed And it lasts for more than 2 seconds. As an auxiliary criterion, this criterion is not triggered alone, but only enhances the confidence of the above two conditions.

[0153] S445. When the drift determination is valid, the data fusion and correction module 5012 performs the following correction mechanism to obtain corrected positioning data. Specifically, this includes: 1) GPS measurement covariance expansion: Multiply by the expansion coefficient This significantly reduces its weight in the next frame's fusion. The dilation coefficient increases gradually rather than abruptly, avoiding positioning jumps.

[0154] 2) Forced switchover: If Immediately and temporarily adjust the fusion weights to: Base station positioning is the primary method used temporarily.

[0155] 3) Smooth pullback: When GPS quality subsequently recovers ( When the score rises above 60, do not immediately switch back to GPS. Instead, adopt a gradual recovery strategy, which involves updating the GPS every 5 seconds. Increase by 0.1, while correspondingly decreasing and This achieves smooth regression and avoids positioning jumps caused by instantaneous switching.

[0156] S446, The fusion and correction module outputs the corrected positioning data.

[0157]

[0158] In one embodiment, see Figure 2 The vehicle-mounted intelligent device positioning drift correction method also includes: S5, the corrected positioning data is reported to the TSP (Telematics Service Provider) platform and / or call center through the communication interface module 506.

[0159] Specifically, please refer to Figure 3 The in-vehicle intelligent device also includes a communication interface module 506, which is connected to the processor 501 in-vehicle intelligent device and the TSP platform and / or the call center for data interaction between the in-vehicle intelligent device and the TSP platform and the call center.

[0160] The corrected positioning data is transmitted to the TSP platform and / or call center via MQTT or HTTP protocol by the communication interface module 506 in real time at a period of 1Hz. When the positioning status identifier switches to BS_ACTIVE or HIS_ACTIVE, an alarm flag is also carried to inform the cloud that the current GPS positioning is unavailable.

[0161] Based on the same inventive concept, please refer to Figure 3 , Figure 3 This is a schematic diagram of the structure of an in-vehicle intelligent device 500 provided in an embodiment of the present invention. In terms of hardware implementation, the in-vehicle intelligent device 500 can be a TBOX.

[0162] The in-vehicle intelligent device 500 includes: one or more processors 501, a memory 502, and one or more computer programs stored in the memory 502 and executable on the processor 501. The memory 502 and the processor 501 are coupled together via a bus system 503. The one or more computer programs are executed by the processor 501 to perform the corresponding functions in the methods described in the above method embodiments.

[0163] The processor 501 can be a Central Processing Unit (CPU), a Network Processor (NP), a hardware chip, or any combination thereof. The aforementioned hardware chip can be an Application Specific Integrated Circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The aforementioned PLD can be a Complex Programmable Logic Device (CPLD), a Field-Programmable Gate Array (FPGA), a Generic Array Logic (GAL), or any combination thereof.

[0164] Memory 502 is used to store program code, etc. Memory 502 may include volatile memory (VM), such as random access memory (RAM); memory 502 may also include non-volatile memory (NVM), such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); memory 502 may also include combinations of the above types of memory.

[0165] The memory 502 can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the methods in the embodiments of the present invention. The processor 501 executes various functional applications and data processing of the method and apparatus by running the non-volatile software programs, instructions, and modules stored in the memory 502, that is, it implements the functions of each module or unit of the method and apparatus provided in the above method embodiments.

[0166] The memory 502 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function. The data storage area may store data created based on the use of the device.

[0167] The one or more modules are stored in the memory 502. When executed by the one or more processors 501, they execute the methods in any of the above method embodiments. For example, they execute the method steps described in the above method embodiments to realize the functions of the modules described in the above device embodiments.

[0168] This invention also provides a computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a computer processor, cause the processor to perform the method described in the foregoing embodiments.

[0169] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0170] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0171] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; under the concept of the present invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the present invention as described above, which are not provided in detail for the sake of brevity; although the present invention 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; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for correcting positioning drift in vehicle-mounted intelligent devices, characterized in that, The method includes: It receives satellite positioning data transmitted from the GNSS positioning module in real time; It receives mobile communication cellular network signal parameters of the current cell in real time from the cellular communication module; Based on the mobile communication cellular network signal parameters, base station positioning data is determined; The satellite positioning data and the base station positioning data are fused and corrected to obtain the corrected positioning data.

2. The method according to claim 1, characterized in that, The process of fusing and correcting the satellite positioning data and the base station positioning data to obtain corrected positioning data includes: The satellite positioning data is compared with the base station positioning data in real time, and the quality of the satellite positioning data is assessed, including whether the satellite positioning data is normal or abnormal. If the quality assessment determines that the satellite positioning data is normal, output the satellite positioning data. If the quality assessment determines that the satellite positioning data is abnormal, the following correction mechanism shall be followed: match and extrapolate the historical cell location to obtain and output the historical extrapolated positioning data of the current location; if the historical extrapolated positioning data of the current location cannot be obtained, then start base station three-point positioning based on the mobile communication cellular network signal parameters to obtain and output the base station three-point positioning data of the current location. The satellite positioning data, the base station three-point positioning data, and the historical inference positioning data are fused and corrected to obtain the corrected positioning data.

3. The method according to claim 2, characterized in that, The step of comparing the satellite positioning data with the base station positioning data in real time and evaluating the quality of the satellite positioning data includes: When the deviation between the satellite positioning data and the base station positioning data is lower than a preset deviation threshold, the satellite positioning data is determined to be normal. When the deviation between the satellite positioning data and the base station positioning data exceeds a preset deviation threshold, the satellite positioning data is determined to be abnormal, and a correction mechanism is triggered.

4. The method according to claim 2, characterized in that, The process of matching and extrapolating historical cell locations to obtain and output historically extrapolated location data for the current location includes: A multi-level matching strategy is adopted to attempt matching in the historical cell location database according to different priorities in order to determine the matching benchmark location; After obtaining the matching reference position, the current estimated position is deduced from the matching reference position by combining the vehicle motion state information.

5. The method according to claim 4, characterized in that, The multi-level matching strategy includes: the highest priority current cell precise matching strategy, the second priority cell sequence pattern matching strategy, the second lowest priority neighbor cell intersection positioning matching strategy, and the lowest priority regional grid matching strategy.

6. The method according to claim 2, characterized in that, The process of initiating base station triangulation based on mobile communication cellular network signal parameters, obtaining and outputting base station triangulation data of the current location, includes: The cellular network signal parameters of at least three adjacent base stations are obtained. The vehicle's position relative to the base stations is calculated using the TDOA three-point positioning method as the primary scheme and TOA three-point positioning as the backup scheme, thereby obtaining the base station three-point positioning data.

7. The method according to claim 1, characterized in that, The process of fusing and correcting satellite positioning data, base station tri-point positioning data, and historical inference positioning data to obtain corrected positioning data includes: The received satellite positioning data, base station three-point positioning data, and historical inference positioning data are time-registered and synchronized. Dynamic weights are assigned to satellite positioning data that has undergone time registration and data synchronization, base station three-point positioning data, and historical inference positioning data. A robust Kalman filter is used for robust fusion calculation to obtain the optimal fusion position estimate; While performing fusion computing, the system actively detects satellite positioning drift in real time, triggers a correction mechanism, executes the correction mechanism processing, and obtains corrected positioning data.

8. The method according to claim 1, characterized in that, The method further includes: the corrected positioning data is reported to the vehicle information service provider platform and / or call center through the communication interface module.

9. A vehicle-mounted intelligent device, characterized in that, It includes a processor and a memory, the memory storing a computer program, and the processor, when executing the program, implements the vehicle-mounted intelligent device positioning drift correction method according to any one of claims 1 to 8.

10. A computer storage medium, characterized in that, The computer storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions cause the processor to perform the vehicle-mounted intelligent device positioning drift correction method according to any one of claims 1 to 8.

Images