Multi-source positioning data grid mapping method and device, electronic equipment and computer readable storage medium

Abstract

The application discloses a multi-source positioning data grid mapping method and device, electronic equipment and computer readable storage medium, including: acquiring heterogeneous positioning original messages, and encapsulating and standardizing the heterogeneous positioning original messages to generate standard multi-source heterogeneous positioning data; determining a target grid level and a target grid in a preset grid system according to a covariance matrix and an environmental degradation factor; encoding geodetic coordinate information by using the target grid level to generate a grid index corresponding to the target grid, and associating the grid index with state attributes as basic grid attribute data; performing soft boundary transition calculation based on boundary characteristics of the target grid level and the target grid to generate boundary confidence data; and generating enhanced grid data containing confidence information according to the basic grid attribute data and the boundary confidence data. Thus, the frequent jumping of grid labels caused by edge jitter is effectively eliminated, and the continuity and stability of trajectory data are ensured.

Description

Technical Field

[0001] This application relates to the technical field of satellite navigation data processing, and more specifically, to a multi-source positioning data grid mapping method, apparatus, electronic device, and computer-readable storage medium. Background Technology

[0002] In the field of location services, mapping multi-source heterogeneous positioning data to a standardized digital grid is fundamental to spatiotemporal management. Current technologies typically involve directly projecting coordinates geometrically, ignoring the accuracy differences between various augmentation sources. This results in the inability to uniformly quantify the error metrics of heterogeneous data, leading to a lack of reliability in the generated grid data.

[0003] Furthermore, traditional mapping uses static hierarchy and deterministic boundaries, without considering the impact of environmental signal degradation on positioning quality. This can easily lead to a mismatch between accuracy and grid range. Moreover, when the target is at the grid edge and positioning jitter occurs, it can easily cause frequent jumps in grid labels, which disrupts the continuity and stability of trajectory data. Summary of the Invention

[0004] In view of the above problems, this application proposes a multi-source positioning data grid mapping method, apparatus, electronic device and computer-readable storage medium that can solve the above problems.

[0005] In a first aspect, embodiments of this application provide a multi-source positioning data grid mapping method. The method includes: acquiring heterogeneous positioning raw messages, encapsulating and standardizing the heterogeneous positioning raw messages to generate standard multi-source heterogeneous positioning data; the standard multi-source heterogeneous positioning data includes at least a covariance matrix characterizing positioning accuracy and geodetic coordinate information characterizing spatial location; determining the target grid level and target grid in a preset grid system based on the covariance matrix and environmental degradation factors; the environmental degradation factors are determined based on working parameters reflecting the quality of satellite navigation signal reception; encoding the geodetic coordinate information using the target grid level to generate a grid index corresponding to the target grid, and associating the grid index with state attributes as basic grid attribute data; performing soft boundary transition calculations based on the boundary features of the target grid level and the target grid to generate boundary confidence data characterizing the reliability of location attribution; and combining the basic grid attribute data with the boundary confidence data to generate enhanced grid data containing confidence information.

[0006] Secondly, embodiments of this application also provide a multi-source positioning data grid mapping device, which includes: a conversion module for acquiring heterogeneous positioning raw messages and encapsulating and standardizing the heterogeneous positioning raw messages to generate standard multi-source heterogeneous positioning data; the standard multi-source heterogeneous positioning data includes at least a covariance matrix characterizing positioning accuracy and geodetic coordinate information characterizing spatial location; a determination module for determining the target grid level and target grid in a preset grid system based on the covariance matrix and environmental degradation factor; the environmental degradation factor is determined based on working parameters reflecting the quality of satellite navigation signal reception; a first generation module for encoding the geodetic coordinate information using the target grid level to generate a grid index corresponding to the target grid, and associating the grid index with state attributes as basic grid attribute data; a second generation module for performing soft boundary transition calculation based on the target grid level and the boundary features of the target grid to generate boundary confidence data characterizing the reliability of location attribution; and a third generation module for combining the basic grid attribute data and the boundary confidence data to generate enhanced grid data containing confidence information.

[0007] Thirdly, embodiments of this application also provide an electronic device, including a processor, a memory, and one or more application programs; the one or more application programs are stored in the memory and configured to be executed by the processor to implement the above-described multi-source location data grid mapping method.

[0008] Fourthly, embodiments of this application also provide a computer-readable storage medium storing program code, wherein the above-described multi-source positioning data grid mapping method is executed when the program code is run by a processor.

[0009] The technical solution provided in this application includes the following method: acquiring heterogeneous positioning raw messages, encapsulating and standardizing the heterogeneous positioning raw messages to generate standard multi-source heterogeneous positioning data; the standard multi-source heterogeneous positioning data includes at least a covariance matrix characterizing positioning accuracy and geodetic coordinate information characterizing spatial location; determining the target grid level and target grid in a preset grid system based on the covariance matrix and environmental degradation factor; the environmental degradation factor is determined based on working parameters reflecting the quality of satellite navigation signal reception; encoding the geodetic coordinate information using the target grid level to generate a grid index corresponding to the target grid, and associating the grid index with state attributes as basic grid attribute data; performing soft boundary transition calculation based on the boundary features of the target grid level and the target grid to generate boundary confidence data characterizing the reliability of location attribution; and combining the basic grid attribute data and the boundary confidence data to generate enhanced grid data containing confidence information. Therefore, by generating standard multi-source heterogeneous positioning data containing covariance matrices, the problem of the inability to uniformly quantify the error indicators of heterogeneous data is solved. Furthermore, by dynamically determining the target grid level in combination with environmental degradation factors, the mismatch between accuracy and grid range caused by static levels is avoided. At the same time, by using soft boundary transition calculation to generate boundary confidence data and combining it with basic attributes, the frequent jumps in grid labels caused by edge jitter of deterministic boundaries are effectively eliminated, thereby ensuring the continuity and stability of trajectory data. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments and drawings obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0011] Figure 1 A flowchart illustrating a multi-source positioning data grid mapping method provided in an embodiment of this application is shown.

[0012] Figure 2 A schematic diagram of the structure of a multi-source positioning data grid mapping device provided in an embodiment of this application is shown.

[0013] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown.

[0014] Figure 4 This illustration shows a schematic diagram of the structure of a computer-readable storage medium provided in an embodiment of this application. Detailed Implementation

[0015] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0016] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0017] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0018] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0019] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0020] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0021] This invention provides a multi-source location data grid mapping method. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, this multi-source location data grid mapping method can be executed by software or hardware installed on a terminal device or a server device. The software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0022] Please see Figure 1 , Figure 1 A flowchart illustrating a multi-source localization data grid mapping method provided in an embodiment of this application is shown. Figure 1 As shown, the method may include steps 110 to 150.

[0023] In step 110, the heterogeneous positioning raw message is obtained, and the heterogeneous positioning raw message is encapsulated and standardized to generate standard multi-source heterogeneous positioning data.

[0024] The original heterogeneous positioning messages can be low-level communication data packets or frames directly output by various enhancement source devices that have not yet undergone unified parsing and standardization. Since these data directly reflect the physical output characteristics of different hardware devices, they retain their original protocol characteristics in their internal data structures.

[0025] The sources of heterogeneous positioning raw messages are diverse, including data from space-based augmentation sources, satellite-based augmentation sources, ground-based augmentation sources, and air-based augmentation sources. The heterogeneity of heterogeneous positioning raw messages is mainly reflected in: heterogeneous communication protocols, heterogeneous field definitions, and heterogeneous information completeness.

[0026] It should be noted that the enhancement source types and message differences listed above are only one typical implementation scenario. In other possible implementations, heterogeneous positioning raw messages can also come from other types of positioning base stations, mobile terminal sensors, or other custom positioning service interfaces. Any external positioning data stream that needs to be accessed by this system for grid mapping falls within the scope of heterogeneous positioning raw messages referred to in this application.

[0027] Standard multi-source heterogeneous positioning data can be an intermediate-layer data object with a unified data structure specification, generated after parsing, cleaning, spatiotemporal alignment, and formatting and encapsulating heterogeneous positioning raw messages. Standard multi-source heterogeneous positioning data includes at least a covariance matrix characterizing positioning accuracy and geodetic coordinate information characterizing spatial location.

[0028] Specifically, in some implementations, the step of acquiring heterogeneous location raw messages and encapsulating and standardizing the heterogeneous location raw messages to generate standard multi-source heterogeneous location data may include the following steps: (1) Obtain heterogeneous positioning raw messages from different enhancement sources; (2) Parse the original heterogeneous positioning message and extract the heterogeneous data feature parameters in the original heterogeneous positioning message; (3) Based on the definition of the preset enhanced source input structure, the heterogeneous data feature parameters are mapped and recombined into the corresponding structured fields to obtain standardized multi-source heterogeneous positioning data.

[0029] In one specific implementation, heterogeneous data feature parameters are written into an enhanced source type identifier used to characterize the data source, such as space-based, satellite-based, ground-based, or air-based.

[0030] In one specific implementation, heterogeneous data feature parameters are written into spatial location information used to characterize spatial location, such as spatial rectangular coordinates or geodetic coordinates.

[0031] In one specific implementation, heterogeneous data feature parameters are written into a location covariance matrix used to characterize the distribution of positioning errors.

[0032] In one specific implementation, the time base is uniformly converted to BeiDou time and written with a BeiDou time timestamp.

[0033] In one specific implementation, it is determined whether the original heterogeneous location message contains security attributes. If it does, optional integrity parameters, such as horizontal or vertical protection level, are extracted and written. Otherwise, the field is set to empty.

[0034] After extracting and recombining the above fields, a standardized multi-source heterogeneous positioning data packet is output, that is, standardized multi-source heterogeneous positioning data, thereby shielding the format differences of the underlying heterogeneous data.

[0035] In some implementations, the step of mapping and recombining heterogeneous data feature parameters into corresponding structured fields according to the definition of a preset enhancement source input structure may also include the following steps: (1) Identify the enhancement source type of heterogeneous data feature parameters. The enhancement source types include space-based enhancement, satellite-based enhancement, ground-based enhancement, or space-based enhancement. (2) Determine the location calculation strategy and accuracy evaluation strategy that match the enhancement source type according to the preset adaptation rules; (3) The position coordinates are calculated according to the position calculation strategy; (4) The covariance matrix is ​​calculated based on the accuracy assessment strategy; (5) Encapsulate the position coordinates, covariance matrix and enhancement source type into a preset enhancement source input structure.

[0036] The received heterogeneous positioning raw message is parsed, and the feature identification field is extracted. Based on the identification field, the enhancement source type is classified as space-based enhancement, satellite-based enhancement, ground-based enhancement, or air-based enhancement.

[0037] When the original heterogeneous positioning message is space-based augmentation, it corresponds to the augmented signal calculated using pseudorange least squares; when the original heterogeneous positioning message is satellite-based augmentation, it corresponds to the satellite broadcast augmented signal containing differential corrections and integrity information; when the original heterogeneous positioning message is ground-based augmentation, it corresponds to the high-precision positioning signal using carrier phase differential technology; when the original heterogeneous positioning message is space-based augmentation, it corresponds to the autonomous positioning signal generated by receiver autonomous integrity monitoring.

[0038] Specifically, if the enhancement source type is space-based enhancement, pseudorange least squares is matched as the position calculation strategy, and a covariance calculation strategy based on weighted matrix inverse operation is matched; if the enhancement source type is satellite-based enhancement, pseudorange + SBAS differential correction strategy is matched, and a variance synthesis strategy considering user differential residuals, tropospheric and ionospheric delay errors is matched; if the enhancement source type is ground-based enhancement, carrier phase difference decomposition calculation strategy is matched, and the corresponding covariance calculation method is selected according to the fixed solution or floating-point solution state; if it is space-based enhancement, autonomous positioning solution strategy is matched, and the protection level parameters output by the RAIM / ARAIM algorithm are directly called as the accuracy evaluation basis.

[0039] For space-based augmentation, observation equations are constructed using pseudorange observations, and the three-dimensional coordinates are obtained through iterative solutions using the least squares method. During this process, dual-frequency ionospheric de-delay combined solutions can be performed to eliminate the effects of ionospheric delay. For satellite-based augmentation, based on pseudorange observations, fast-varying, slow-varying, and ionospheric grid corrections from SBAS broadcasts are introduced for correction, thereby improving positioning accuracy.

[0040] For ground-based augmentation, high-precision relative position coordinates are obtained by using carrier phase observations for double-difference processing and searching for fixed ambiguity values ​​or maintaining floating-point values. For space-based augmentation, consistency checks are performed based on the receiver's own redundant observations, and faulty satellites are removed before outputting autonomous positioning results.

[0041] To accurately characterize the reliability of positioning results, if the augmentation source type is space-based augmentation, the covariance matrix is ​​determined using the formula... , For geometric matrices, The weight matrix is ​​constructed based on satellite elevation angle or signal-to-noise ratio; if the enhancement source type is satellite-based enhancement, the focus is on calculating the superposition of variance components in the vertical or horizontal direction, tropospheric residual variance, and ionospheric grid variance.

[0042] If the enhancement source type is ground-based enhancement, the processing is differentiated according to the solution state: if it is a fixed solution, a fixed small variance model is used; if it is a floating-point solution, the covariance matrix of the floating-point solution is amplified. If the enhancement source type is empty-base enhancement, the horizontal or vertical protection level calculated by the RAIM or ARAIM algorithm is directly mapped into the covariance matrix or relevant accuracy fields to reflect the integrity risk of the system.

[0043] Therefore, the optimal algorithm is used to calculate the coordinates and covariance matrix of signals from different sources, ensuring the accuracy and reliability of the positioning results. At the same time, the differentiated raw data is uniformly encapsulated into a standard structure, providing a plug-and-play high-quality data interface for downstream applications.

[0044] In some implementations, the step of acquiring heterogeneous location raw messages and encapsulating and standardizing the heterogeneous location raw messages to generate standard multi-source heterogeneous location data may include the following steps: (1) If the original heterogeneous positioning message is geocentric coordinates, the original heterogeneous positioning message is converted by a preset iterative algorithm to obtain geodetic coordinate information; (2) Or, if the source coordinate system of the heterogeneous positioning original message is the geocentric coordinate system, then the heterogeneous positioning original message is transformed by a preset coordinate transformation model to obtain the spatial coordinate information under the target geocentric coordinate system; (3) Based on the Jacobian matrix and covariance matrix of the preset coordinate transformation model, the target covariance matrix is ​​calculated.

[0045] If the original heterogeneous positioning message's data format is a three-dimensional coordinate system in the Earth-centered Cartesian (ECEF) coordinate system... The information is converted into geodetic coordinates using a pre-defined iterative algorithm. In one specific implementation, the preset iterative algorithm can be the Bowring iteration method.

[0046] If the source coordinate system of the heterogeneous positioning original message is a geocentric coordinate system, which can be a WGS84 coordinate system, and needs to be transformed to a target geocentric coordinate system, which can be a CGCS2000 coordinate system, then a baseline transformation is performed using a preset coordinate transformation model.

[0047] In one specific implementation, the preset coordinate transformation model can be a spatial rectangular coordinate transformation of the Bursa-Wolf 14 parameter model. The transformation process can be expressed as follows: in, For rotation matrix, For scale changes, For translation parameters, For the corresponding rate parameters, For time difference.

[0048] While performing the coordinate transformation described above, in order to ensure the continuity and accuracy of the positioning accuracy information, the covariance matrix needs to be updated synchronously. Based on the Jacobian matrix of the preset coordinate transformation model and the covariance matrix of the original data, the target covariance matrix is ​​calculated using the error propagation law. This process can be expressed as: in, This is the Jacobian matrix of the preset coordinate transformation model.

[0049] In some implementations, the process of encapsulating and standardizing heterogeneous positioning raw messages also includes time reference alignment. For the most common GPS signals, a linear transformation is performed based on a preset constant offset, thereby ensuring that all observation data involved in the calculation are within the same time reference frame. This transformation process can be expressed as: in, The received GPS system time, This is a fixed number of seconds difference between GPS time and BeiDou time.

[0050] In some implementations, for distributed application scenarios involving multi-node collaboration, such as collaborative positioning between a ground reference station and a UAV mobile terminal, simply unifying the time system is insufficient; it is also necessary to ensure a high degree of consistency of physical clocks. Therefore, the IEEE 1588 precise time protocol is used for network time synchronization.

[0051] In step 120, the target grid level and target grid are determined in a preset grid system based on the covariance matrix and the environmental degradation factor; the environmental degradation factor is determined based on the working parameters that reflect the quality of satellite navigation signal reception.

[0052] The environmental degradation factor can be a correction parameter used to quantify the degree to which the current navigation signal is affected by the environment. The environmental degradation factor is not directly derived from the satellite ephemeris, but is calculated based on real-time operating parameters that reflect the quality of satellite navigation signal reception.

[0053] The preset grid system can be a multi-level spatial index structure covering the target work area, such as a quadtree, octree, or Geohash encoding system. The preset grid system can contain multiple levels of grid partitioning. Higher-level grids have wider coverage but lower resolution, while lower-level grids have smaller coverage but higher resolution.

[0054] The target grid level can be a specific resolution level within a pre-defined multi-level grid system that matches the actual reliable range of the current positioning result. The target grid can be a specific spatial unit uniquely corresponding to that level based on the current coordinate information, after the target grid level has been determined.

[0055] Specifically, in some implementations, the step of determining the target grid level and target grid in a preset grid system based on the covariance matrix and environmental degradation factor may include the following steps: (1) Extract the horizontal and vertical components from the covariance matrix and calculate the horizontal and vertical precision values ​​respectively; (2) Calculate the environmental degradation factor based on the working parameters; the working parameters include at least the current number of visible satellites, the maximum number of available satellites, the multipath effect indicator factor, the geometric accuracy factor, the nominal value of the geometric accuracy factor, and the dynamic range value of the geometric accuracy factor; (3) The effective uncertainty is calculated based on the horizontal accuracy value, the vertical accuracy value, and the environmental degradation factor; (4) In the preset grid system, select the grid level with a grid size greater than or equal to the effective uncertainty as the target grid level, and determine the target grid based on the target grid level.

[0056] In one specific implementation, the horizontal precision value can be expressed as: In one specific implementation, the vertical accuracy value can be expressed as: In one specific implementation, the environmental degradation factor calculated based on operating parameters can be expressed as: in, , and The number of weights is configurable. This represents the current number of visible satellites. To the maximum number of available satellites, As an indicator of multipath effects, Geometric precision factor, This is the nominal value of the geometric precision factor. This represents the dynamic range value of the geometric precision factor.

[0057] The currently visible satellite count can be considered the number of satellites that the receiver has actually locked onto and is using for calculations at the current moment; the maximum available satellite count can be considered the maximum number of satellites that could theoretically be observed under the current observation conditions. The ratio between the currently visible satellite count and the maximum available satellite count reflects the abundance of satellite resources. A low ratio indicates severe obstruction. This increases the degradation factor, thereby reducing the confidence in the positioning accuracy.

[0058] The multipath effect indicator is a discrete variable characterizing the degree of signal reflection interference. For example, it may take the value 0 or 1, or a normalized continuous value. It is used to directly penalize scenarios with severe multipath effects. When strong multipath interference is detected, it is used to... This item significantly increases the degree of environmental degradation.

[0059] The geometric precision factor reflects the amplification factor of the spatial distribution geometry of currently visible satellites on positioning accuracy. The smaller the value, the better the geometry. The nominal value of the geometric precision factor can be a preset ideal or benchmark geometric precision factor threshold, for example, a value of 2.0 or 3.0, representing a good geometric distribution standard. The dynamic range value of the geometric precision factor is used as the denominator parameter for normalization, representing the maximum allowable fluctuation range of the geometric precision factor. For example, this value can be 10.0 to prevent overflow of calculation results or weight imbalance due to an excessively large geometric precision factor value.

[0060] In one specific implementation, the effective uncertainty is calculated based on the horizontal accuracy value, the vertical accuracy value, and the environmental degradation factor, and can be expressed as: In some implementations, the step of selecting a grid level with a grid size greater than or equal to the effective uncertainty as the target grid level within a preset grid system may include the following steps: (1) Traverse the preset grid system and select candidate levels with grid size greater than or equal to the effective uncertainty; (2) Among the candidate levels, select the level with the smallest grid size as the target grid level.

[0061] In one specific implementation, traversing a preset grid system and filtering out candidate levels with grid sizes greater than or equal to the effective uncertainty can be expressed as: After selecting candidate levels, the level with the smallest grid size is chosen as the target grid level, i.e., the finest level is selected.

[0062] Therefore, the above scheme avoids outputting false high-precision grids when the signal is interfered with, and ensures that sufficient detail granularity is retained under high-quality conditions such as RTK fixed solution, which significantly improves the reliability and practicality of location data.

[0063] In step 130, the geodetic coordinate information is encoded using the target grid level to generate a grid index corresponding to the target grid, and the grid index is associated with the state attribute as the basic grid attribute data.

[0064] A grid index can be a unique identifier derived from geodetic coordinate information using a specific spatial encoding algorithm based on a defined target grid level. For example, a specific coordinate point can be mapped to a corresponding grid cell ID according to the grid division rules of the target level, such as Geohash encoding, S2 geometry library index, or simple row and column number encoding.

[0065] Status attribute association can be a logical binding relationship between non-spatial data reflecting the real-time status of a device or environment and a grid index. In one specific implementation, status attributes include, but are not limited to, the device's motion status, signal quality level, battery level, or task execution progress.

[0066] In some implementations, at the data structure level, this is manifested as a key-value mapping with grid index as the key and state attribute as the value, or by connecting the grid ID as a foreign key to the state table in the database table, to ensure that each spatial grid carries rich semantic information and is no longer a simple geometric container.

[0067] Basic grid attribute data can be standardized data units formed after the above encoding and association operations. It is the smallest data granular unit for subsequent visualization rendering, trajectory analysis or decision control.

[0068] Specifically, in some implementations, the steps of encoding geodetic coordinate information using the target grid hierarchy to generate a grid index corresponding to the target grid, and associating the grid index with state attributes as basic grid attribute data, may also include the following steps: (1) Convert the geodetic coordinate information into a planar grid code according to the preset coding rules; (2) The height code is obtained by performing height encoding layer by layer within the preset height range of the target grid using a binary recursive algorithm; (3) Obtain the accuracy level, enhancement type, motion vector and timestamp information corresponding to the geodetic coordinate information, and generate the corresponding accuracy level code, enhancement type code, motion vector code and timestamp code respectively; (4) The planar grid code, height code, precision level code, enhancement type code, motion vector code and timestamp code are spliced ​​together to generate basic grid attribute data.

[0069] Geodetic coordinate information is mapped to a plane according to a pre-defined aeronautical or geographic information standard. For example, using the encoding rules specified in the MH / T4063.1 standard, continuous geodetic coordinates are converted into discrete 22-bit planar grid codes, which identify the spatial position of the target point on the horizontal plane and ensure the interoperability of position data between different systems.

[0070] To accurately represent spatial information in the vertical dimension, the system operates within a preset height range. The height value is digitized internally. For example, a binary recursive algorithm is used for layer-by-layer encoding to calculate the current height. and Relationship: If < If, then it is recorded as 0. ≥ If it is 1, then it is recorded as 1, and the process is repeated until the preset target level is reached. For any remaining bits that do not reach the highest level, a specific character is used to fill them in, for example, the specific character could be *, in order to construct a complete height-encoded sequence.

[0071] Further auxiliary information reflecting positioning quality, augmentation sources, and motion status is extracted and compressed into a compact binary stream: For the precision class code (2 digits), based on the uncertainty in the vertical direction Classification by level. For example, 00 represents unknown, and 01 represents meter-level ( >1m), 10 represents decimeter level (0.1m < ≤1m), 11 represents decimeters to centimeters ( ≤0.1m).

[0072] The strong type code (2 bits) identifies the source of the differential correction signal. For example, 00 is space-based original, 01 is satellite-based augmentation, 10 is ground-based augmentation, and 11 is space-based augmentation.

[0073] For the motion vector code (8 bits), the high-bit splitting method is used. The first 4 bits represent the speed (range 0-15, resolution 10m / s), and the last 4 bits represent the heading (range 0-15, resolution 22.5°), describing the dynamic characteristics at a low bit rate.

[0074] For the timestamp code (12 bits), relative time encoding is used to save storage space, including year offset (2 bits), year-day (3 bits), hour (2 bits), minute (2 bits), second (2 bits), and sub-second (1 bit) information.

[0075] According to the predetermined bit order, the 22-bit planar grid code, height code, 2-bit precision level code, 2-bit enhancement type code, 8-bit motion vector code, and 12-bit timestamp code are sequentially concatenated into binary, and the resulting composite bit stream is the basic grid attribute data.

[0076] In step 140, soft boundary transition calculation is performed based on the target mesh level and the boundary features of the target mesh to generate boundary confidence data characterizing the reliability of location attribution.

[0077] Boundary confidence data can be a numerical metric used to quantify the reliability of a current location point belonging to a target grid.

[0078] Specifically, in some implementations, the step of performing soft boundary transition calculations based on the target mesh level and the boundary features of the target mesh to generate boundary confidence data characterizing the reliability of location attribution may include the following steps: (1) Determine the corresponding layer side length based on the target grid layer and obtain the effective uncertainty; (2) The first width value is obtained by scaling the side length of the layer using a preset scaling factor; (3) The effective uncertainty is amplified by a preset factor to obtain the second width value; (4) Take the smaller value between the first width value and the second width value as the transition band width; (5) Calculate the minimum distance from the spatial location corresponding to the geodetic coordinate information to the boundary of the target grid; (6) If the minimum distance is less than the width of the transition zone, the spatial location is determined to be in the soft boundary transition zone. Based on the standard normal cumulative distribution function, the probability value belonging to the target grid is calculated using the minimum distance and the preset horizontal accuracy parameter, and the probability value is used as the boundary confidence data.

[0079] The transition band width is not a fixed value, but is determined based on the dynamic balance between the size of the current grid level and the positioning accuracy. On the one hand, the grid side length is scaled using a preset scaling factor to obtain the first width value, which represents the safe buffer range in the grid geometry. In a specific implementation, the preset scaling factor can be 1 / 4.

[0080] On the other hand, the effective uncertainty is amplified by a preset multiple to obtain a second width value, which represents the range of positional fluctuations based on statistical principles. In one specific implementation, the preset multiple can be 2 times.

[0081] In one specific implementation, the smaller of the first width value and the second width value is taken as the transition band width, which can be expressed as: in, Determine the corresponding level edge length for the target mesh level. Therefore, when the grid is large but the accuracy is high, the transition zone should be kept too wide to avoid data ambiguity; when the grid is small but the accuracy is poor, the transition zone should be limited to the physical limits of the grid itself.

[0082] Minimum distance With the calculated transition band width Compare. If If the location is determined to be in a soft boundary transition zone, it means that although the point is geographically located within the current grid, it may belong to an adjacent grid due to its proximity to the edge or excessive positioning error.

[0083] Once the transition zone is confirmed, the black-and-white classification is no longer used; instead, the standard normal cumulative distribution function is introduced. To calculate the probability value of belonging to the target grid In one specific implementation, if the minimum distance is less than the transition zone width, the spatial location is determined to be in the soft boundary transition zone. Based on the standard normal cumulative distribution function, the probability value belonging to the target mesh is calculated using the minimum distance and a preset horizontal accuracy parameter, which can be expressed as: This ensures that at the boundary ( =0), =0.5; far from the boundary, Approaching 1.

[0084] In step 150, the basic grid attribute data and the boundary confidence data are combined to generate enhanced grid data containing confidence information.

[0085] The core payload consists of basic grid attribute data, including planar encoding, height encoding, accuracy level, enhancement type, motion vector, and timestamp. The calculated boundary confidence data, i.e. the probability value of belonging to the target grid, is attached to this payload as a key label, thereby constructing enhanced grid data containing confidence information.

[0086] Especially in the soft boundary transition zone, the enhanced grid data not only includes the attributes of the main grid and its corresponding confidence, but also associates the encoding of the adjacent sub-grids and their complementary confidence. This data structure realizes the extension from a single rigid attribution to a dual probabilistic mapping, providing a complete information unit that takes into account both spatial positioning accuracy and boundary smooth transition characteristics for subsequent data processing.

[0087] Please see Figure 2 , Figure 2This illustration shows a structural schematic diagram of a multi-source positioning data grid mapping device provided in an embodiment of this application. The multi-source positioning data grid mapping device 200 includes: a conversion module 210, a determination module 220, a first generation module 230, a second generation module 240, and a third generation module 250. Specifically: The conversion module 210 is used to acquire the heterogeneous positioning raw message, and encapsulate and standardize the heterogeneous positioning raw message to generate standard multi-source heterogeneous positioning data; the standard multi-source heterogeneous positioning data includes at least a covariance matrix representing the positioning accuracy and geodetic coordinate information representing the spatial location. The determination module 220 is used to determine the target grid level and target grid in a preset grid system based on the covariance matrix and the environmental degradation factor; the environmental degradation factor is determined based on the working parameters that reflect the quality of satellite navigation signal reception; The first generation module 230 is used to encode the geodetic coordinate information using the target grid level, generate a grid index corresponding to the target grid, and associate the grid index with the state attribute as the basic grid attribute data. The second generation module 240 is used to perform soft boundary transition calculations based on the target grid level and the boundary features of the target grid, and generate boundary confidence data that characterizes the reliability of location attribution. The third generation module 250 is used to combine the basic grid attribute data with the boundary confidence data to generate enhanced grid data containing confidence information.

[0088] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described device and module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0089] In the several embodiments provided in this application, the coupling or direct coupling or communication connection between the modules shown or discussed may be an indirect coupling or communication connection through some interface, device or module, and may be electrical, mechanical or other forms.

[0090] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0091] Please see Figure 3 , Figure 3The diagram illustrates the structure of an electronic device according to an embodiment of this application. The electronic device 300 in this application may include one or more of the following components: a processor 310, a memory 320, and one or more application programs. The one or more application programs may be stored in the memory 320 and configured to be executed by one or more processors 310. The one or more programs are configured to execute the multi-source localization data grid mapping method as described in the foregoing method embodiments.

[0092] Processor 310 may include one or more processing cores. Processor 310 connects to various parts within the electronic device 300 using various interfaces and lines, and performs various functions and processes data of the electronic device 300 by running or executing instructions, programs, code sets, or instruction sets stored in memory 320, and by calling data stored in memory 320. Optionally, processor 310 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). Processor 310 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem handles wireless communication. It is understood that the modem may also not be integrated into processor 310 and may be implemented separately using a communication chip.

[0093] The memory 320 may include random access memory (RAM) or read-only memory (ROM). The memory 320 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 320 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function, instructions for implementing the various method embodiments described below, etc. The data storage area may also store data created by the electronic device 300 during use.

[0094] Please see Figure 4 , Figure 4The diagram shows a computer-readable storage medium 400 provided in an embodiment of this application. The computer-readable storage medium 400 stores program code, which can be called by a processor to execute the multi-source positioning data grid mapping method described in the above method embodiment.

[0095] The computer-readable storage medium 400 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Optionally, the computer-readable storage medium 400 includes a non-transitory computer-readable storage medium. The computer-readable storage medium 400 has storage space for program code 410 that performs any of the method steps described above. This program code can be read from or written to one or more computer program devices. The program code 410 may be compressed, for example, in a suitable form.

[0096] Finally, it should be noted that 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.

Claims

1. A method for mapping multi-source localization data grids, characterized in that, The method includes: Obtain heterogeneous positioning raw messages, and encapsulate and standardize the heterogeneous positioning raw messages to generate standard multi-source heterogeneous positioning data; the standard multi-source heterogeneous positioning data includes at least a covariance matrix representing positioning accuracy and geodetic coordinate information representing spatial location; Based on the covariance matrix and environmental degradation factor, the target grid level and target grid are determined in a preset grid system; the environmental degradation factor is determined based on the operating parameters that reflect the quality of satellite navigation signal reception. The geodetic coordinate information is encoded using the target grid level to generate a grid index corresponding to the target grid, and the grid index is associated with the state attribute as basic grid attribute data; Based on the target mesh level and the boundary features of the target mesh, perform soft boundary transition calculation to generate boundary confidence data characterizing the reliability of location attribution; The basic grid attribute data is combined with the boundary confidence data to generate enhanced grid data containing confidence information.

2. The multi-source positioning data grid mapping method according to claim 1, characterized in that, The step involves determining the target grid level and target grid within a preset grid system based on the covariance matrix and environmental degradation factor, including: The horizontal and vertical components are extracted from the covariance matrix, and the horizontal and vertical precision values ​​are calculated respectively. The environmental degradation factor is calculated based on the operating parameters; the operating parameters include at least the current number of visible satellites, the maximum number of available satellites, the multipath effect indicator factor, the geometric accuracy factor, the nominal value of the geometric accuracy factor, and the dynamic range value of the geometric accuracy factor; The effective uncertainty is calculated based on the horizontal accuracy value, the vertical accuracy value, and the environmental degradation factor. In the preset grid system, a grid level with a grid size greater than or equal to the effective uncertainty is selected as the target grid level, and the target grid is determined based on the target grid level.

3. The multi-source positioning data grid mapping method according to claim 2, characterized in that, The step of selecting a grid level with a grid size greater than or equal to the effective uncertainty as the target grid level within the preset grid system includes: Traverse the preset grid system and filter out candidate levels with grid sizes greater than or equal to the effective uncertainty; Among the candidate levels, the level with the smallest grid size is selected as the target grid level.

4. The multi-source positioning data grid mapping method according to claim 1, characterized in that, The steps include encoding the geodetic coordinate information using the target grid hierarchy, generating a grid index corresponding to the target grid, and associating the grid index with state attributes as basic grid attribute data, including: The geodetic coordinate information is converted into a planar grid code according to a preset encoding rule; A binary recursive algorithm is used to perform height encoding layer by layer within a preset height range of the target grid to obtain the height code; Obtain the accuracy level, enhancement type, motion vector, and timestamp information corresponding to the geodetic coordinate information, and generate the corresponding accuracy level code, enhancement type code, motion vector code, and timestamp code respectively; The basic grid attribute data is generated by concatenating the planar grid code, the height code, the precision level code, the enhancement type code, the motion vector code, and the timestamp code.

5. The multi-source positioning data grid mapping method according to claim 1, characterized in that, The steps involve performing soft boundary transition calculations based on the target mesh level and the boundary features of the target mesh to generate boundary confidence data characterizing the reliability of location attribution, including: The corresponding layer side length is determined based on the target grid layer, and the effective uncertainty is obtained; The first width value is obtained by scaling the side length of the layer using a preset scaling factor; The effective uncertainty is amplified by a preset factor to obtain a second width value; The smaller value between the first width value and the second width value is taken as the transition band width; Calculate the minimum distance from the spatial location corresponding to the geodetic coordinate information to the boundary of the target grid; If the minimum distance is less than the width of the transition zone, the spatial location is determined to be in the soft boundary transition zone. Based on the standard normal cumulative distribution function, the probability value belonging to the target grid is calculated using the minimum distance and the preset horizontal accuracy parameter, and the probability value is used as the boundary confidence data.

6. The multi-source positioning data grid mapping method according to claim 1, characterized in that, The steps include obtaining heterogeneous location raw messages, encapsulating and standardizing the heterogeneous location raw messages to generate standard multi-source heterogeneous location data, including: Obtain the heterogeneous positioning raw messages from different enhancement sources; The heterogeneous location original message is parsed to extract the heterogeneous data feature parameters in the heterogeneous location original message; According to the definition of the preset enhanced source input structure, the heterogeneous data feature parameters are mapped and recombined into the corresponding structured fields to obtain the standardized multi-source heterogeneous positioning data.

7. The multi-source positioning data grid mapping method according to claim 6, characterized in that, The step, based on the definition of a preset enhanced source input structure, maps and reassembles the heterogeneous data feature parameters into corresponding structured fields, including: Identify the enhancement source type of the heterogeneous data feature parameters, wherein the enhancement source type includes space-based enhancement, satellite-based enhancement, ground-based enhancement, or space-based enhancement; Based on preset adaptation rules, determine the location calculation strategy and accuracy evaluation strategy that match the enhancement source type; The position coordinates are calculated based on the position calculation strategy described above. The covariance matrix is ​​calculated based on the accuracy evaluation strategy described above. The location coordinates, the covariance matrix, and the enhancement source type are encapsulated into the preset enhancement source input structure.

8. A multi-source positioning data grid mapping device, characterized in that, The device includes: The conversion module is used to acquire heterogeneous positioning raw messages, encapsulate and standardize the heterogeneous positioning raw messages, and generate standard multi-source heterogeneous positioning data; the standard multi-source heterogeneous positioning data includes at least a covariance matrix representing positioning accuracy and geodetic coordinate information representing spatial location. The determination module is used to determine the target grid level and target grid in a preset grid system based on the covariance matrix and the environmental degradation factor; the environmental degradation factor is determined based on the working parameters reflecting the quality of satellite navigation signal reception; The first generation module is used to encode the geodetic coordinate information using the target grid level, generate a grid index corresponding to the target grid, and associate the grid index with the state attribute as basic grid attribute data; The second generation module is used to perform soft boundary transition calculations based on the target mesh level and the boundary features of the target mesh, and generate boundary confidence data that characterizes the reliability of location attribution. The third generation module is used to combine the basic grid attribute data with the boundary confidence data to generate enhanced grid data containing confidence information.

9. An electronic device, characterized in that, include: One or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to perform the multi-source location data grid mapping method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be called by a processor to execute the multi-source positioning data grid mapping method as described in any one of claims 1-7.

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