Surveying method, device and equipment based on high-precision position sensing, and storage medium

Abstract

The present application relates to a kind of based on high-precision position sensing surveying and mapping method, device, equipment and storage medium, comprising the following steps, utilize high-precision GNSS receiver to collect the position signal of surveying and mapping area target survey point, obtain survey point coordinate and associate time stamp, form with time tag coordinate;Calibration coordinate is obtained by threshold screening and correction processing, and adjacent survey point spacing data is accounted;Calibration coordinate is combined with survey point spacing data, and the basic geographic three-dimensional base map obtained is grid division and feature point matching, and regional grid chart is generated;Based on regional grid chart, calculate terrain undulation, obtain grid elevation difference;Finally, combined with grid elevation difference, basic geographic three-dimensional base map is implemented texture filling and boundary optimization, complete the fine expression of topographic feature, output high-precision surveying and mapping image, solve how to improve the anti-interference ability of survey point data, and realize the technical problem of the automation, fine reconstruction from discrete coordinate to high-fidelity three-dimensional topographic map.

Description

Technical Field

[0001] This invention relates to the field of surveying and mapping technology, and in particular to surveying and mapping methods, apparatus, equipment and storage media based on high-precision position sensing. Background Technology

[0002] With the continuous development of Geographic Information Systems (GIS) and surveying technologies, field data acquisition methods based on Global Navigation Satellite Systems (GNSS) have been widely applied in fields such as land surveying, urban planning, transportation construction, and environmental monitoring. Existing technologies typically employ medium- to high-precision GNSS receivers to acquire coordinates of target measurement points, and combine this with mobile surveying platforms or handheld devices to record and preliminarily process spatial data. To further improve the geometric consistency of the resulting maps, some schemes introduce a base geographic map as a reference framework, smoothing the collected points through coordinate matching and simple interpolation algorithms, and supplementing this with manual delineation to express the terrain contours. This type of method has certain applicability in plains or areas with sparse terrain features and has already been applied in small-scale engineering surveys.

[0003] However, in scenarios with complex terrain or uneven distribution of measuring points, existing technologies struggle to effectively guarantee the spatial continuity and local accuracy consistency of surveying results. Raw GNSS observation data is susceptible to multipath effects and signal obstruction, leading to coordinate drift at individual measuring points. Traditional processing lacks dynamic identification and correction mechanisms for abnormal coordinates, relying solely on static threshold filtering or manual intervention for removal. Furthermore, the process of mapping discrete measuring points to the base map and generating a continuous surface fails to adequately consider the spatial topological relationships and terrain undulations between adjacent measuring points, resulting in boundary misalignment and elevation distortion during grid division. This ultimately affects the terrain fidelity and texture continuity of the surveyed image. Therefore, improving the anti-interference capability of measuring point data and achieving automated and refined reconstruction from discrete coordinates to high-fidelity 3D topographic maps has become a pressing technical challenge. Summary of the Invention

[0004] The main technical problem addressed in this application is to provide a surveying method, apparatus, equipment, and storage medium based on high-precision position sensing. It also solves the technical problem of how to improve the anti-interference capability of survey point data and realize the automated and refined reconstruction of discrete coordinates into high-fidelity three-dimensional topographic maps.

[0005] To address the aforementioned technical problems, this application employs a mapping method based on high-precision position sensing, comprising the following steps: The location signals of target measurement points within the survey area are collected by a high-precision GNSS receiver to obtain the coordinates of the measurement points. The coordinates of the measurement points are then associated and marked with timestamps to obtain time-stamped coordinates. The time-stamped coordinates are subjected to threshold filtering and correction to obtain calibrated coordinates, and the distance between adjacent measuring points is calculated on the calibrated coordinates to obtain measuring point spacing data. Obtain the basic geographic 3D base map corresponding to the surveying area, and based on the calibration coordinates and measurement point spacing data, perform grid division and feature point matching on the basic geographic 3D base map to obtain a regional grid map, and calculate the topographic relief of the regional grid map to obtain the grid elevation difference; Based on the regional grid map and the grid elevation difference, texture filling and boundary delineation are performed on the basic geographic 3D base map to obtain the survey image.

[0006] Furthermore, the position signals of the target measurement points within the survey area are acquired using a high-precision GNSS receiver to obtain the coordinates of the measurement points, including: The target measurement points within the survey area are received by a high-precision GNSS receiver using multi-frequency signals. The carrier phase of the multi-frequency signals is then calculated to obtain the measurement point phase data. Based on the phase data of the measurement point, the three-dimensional coordinates of the target measurement point are initially calculated to obtain the initial calculated coordinates of the measurement point. Then, the initial calculated coordinates of the measurement point are corrected by satellite signal strength correlation to obtain the final coordinates of the measurement point.

[0007] Furthermore, the step of using timestamps to associate and mark the coordinates of the measuring points to obtain time-stamped coordinates includes: Using timestamps, the coordinates of the measuring points are matched and associated one by one to obtain preliminary associated coordinates. The timestamp format of the preliminary associated coordinates is then validated to check whether the timestamps conform to the preset time format specifications. If they do not conform, the preliminary associated coordinates corresponding to the timestamps that do not conform to the format specifications are removed to obtain time-stamped coordinates.

[0008] Furthermore, the step of threshold filtering and correction of the time-stamped coordinates to obtain calibrated coordinates, and calculating the distance between adjacent measuring points on the calibrated coordinates to obtain measuring point spacing data, includes: The time-stamped coordinates are filtered based on a preset coordinate error threshold range, and abnormal coordinates that exceed the threshold range are removed to obtain the filtered coordinates. The distance between each coordinate and its adjacent coordinate in the filtered coordinates is calculated in three-dimensional space and compared with a preset distance range to detect whether there is a distance deviation. If there is a distance deviation, the distance between each coordinate and its adjacent coordinate in the filtered coordinates in three-dimensional space is corrected based on the distance deviation to obtain the calibration coordinates. The coordinate difference between adjacent calibration coordinates is calculated to obtain the coordinate difference vector in the X, Y, and Z directions. The magnitude of the adjacent coordinate difference vector is then calculated to obtain the measurement point spacing data.

[0009] Furthermore, the step of dividing the basic geographic 3D base map into grids and matching feature points based on the calibration coordinates and measurement point spacing data to obtain a regional grid map includes: Based on the calibration coordinates and the measurement point spacing data, the area of ​​the basic geographic 3D base map is defined to obtain the mapping boundary frame. The mapping boundary frame is then divided into grid cells according to the preset grid size. Based on the grid cells, the calibration coordinates are matched one by one to obtain a coordinate grid correspondence table. Feature point identifiers are added to the grid cells in the coordinate grid correspondence table to obtain a regional grid map.

[0010] Furthermore, the calculation of terrain relief on the regional grid map to obtain the grid elevation difference includes: For each grid cell of the regional grid map, the elevation of adjacent cells is sampled to obtain a cell elevation group, and the gradient value of the cell elevation group is calculated to obtain the grid slope value; Based on the grid slope value, an elevation difference analysis is performed on each grid cell to obtain the cell undulation. Then, multi-scale elevation statistics are performed on the cell undulation to obtain the grid elevation difference.

[0011] Furthermore, the step of performing texture filling and boundary delineation on the basic geographic 3D base map based on the regional grid map and grid elevation differences to obtain a survey image includes: The grid elevation difference is processed by interval layering to obtain an elevation hierarchy table, and contour lines are traced on the grid cells based on the elevation hierarchy table to obtain a terrain wireframe map; Based on the feature point markers in the topographic wireframe and regional grid map, the basic geographic 3D base map is layered and filled to obtain a landform texture map. The boundary lines of the landform texture map are then optimized to obtain a survey image.

[0012] The present invention also provides a mapping device based on high-precision position sensing, comprising: The acquisition module is used to acquire the position signals of target measurement points in the surveying area through a high-precision GNSS receiver, obtain the coordinates of the measurement points, and use timestamps to associate and mark the coordinates of the measurement points to obtain time-stamped coordinates. The calculation module is used to perform threshold filtering and correction on the time-stamped coordinates to obtain calibration coordinates, and to calculate the distance between adjacent measurement points on the calibration coordinates to obtain measurement point spacing data. The matching module is used to obtain the basic geographic 3D base map corresponding to the surveying area, and based on the calibration coordinates and the distance data between the measuring points, to perform grid division and feature point matching on the basic geographic 3D base map to obtain a regional grid map, and to calculate the topographic relief of the regional grid map to obtain the grid elevation difference. The delineation module is used to perform texture filling and boundary delineation on the basic geographic 3D base map based on the regional grid map and the grid elevation difference to obtain the survey image.

[0013] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the above methods.

[0014] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the above methods.

[0015] The above scheme involves acquiring position signals of target measurement points within the surveying area using a high-precision GNSS receiver to obtain measurement point coordinates. These coordinates are then linked and marked with timestamps to obtain time-stamped coordinates. Threshold filtering and correction are applied to these time-stamped coordinates to obtain calibration coordinates. The distance between adjacent measurement points is calculated using these calibration coordinates to obtain measurement point spacing data. A basic 3D geographic base map corresponding to the surveying area is acquired. Based on the calibration coordinates and measurement point spacing data, the basic 3D geographic base map is divided into grids and feature points are matched to obtain a regional grid map. The regional grid map is then further refined. The topographic relief is calculated to obtain the grid elevation difference. Based on the regional grid map and the grid elevation difference, the basic geographic 3D base map is textured and delineated to obtain the survey image. This solves the technical problem of how to improve the anti-interference ability of the survey point data and realize the automated and refined reconstruction of high-fidelity 3D topographic maps from discrete coordinates. It realizes the calculation of topographic relief based on the regional grid map to obtain the grid elevation difference, fully considers the variation characteristics of local micro-topography, and can accurately capture the ground rise and fall trend under complex terrain conditions, thus improving the ability to restore the details of elevation information. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the steps of a mapping method based on high-precision position sensing in one embodiment of the present invention; Figure 2 This is a structural block diagram of a mapping device based on high-precision position sensing in one embodiment of the present invention; Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0018] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0020] Specifically, the mapping method based on high-precision position awareness in this embodiment includes the following steps: like Figure 1 As shown, Figure 1 This invention provides a mapping method based on high-precision position sensing, comprising the following steps: Step S1: The position signal of the target measurement point in the survey area is collected by a high-precision GNSS receiver to obtain the coordinates of the measurement point, and the coordinates of the measurement point are associated and marked with a timestamp to obtain time-stamped coordinates.

[0021] Specifically, a high-precision GNSS receiver is set up at the target measurement point in the survey area. After being powered on, it begins to acquire satellite signals and calculates the coordinates of the measurement point through carrier phase observation and pseudorange measurement. This process usually relies on RTK or PPK differential technology to achieve centimeter-level positioning accuracy. While acquiring data, the receiver's built-in clock module adds a precise timestamp to the coordinates of each measurement point, so that each spatial location information corresponds one-to-one with its acquisition time, forming time-stamped coordinates. For example, in mountain road surveying, when the equipment continuously collects points along the route, the timestamp can reflect the acquisition sequence of the measurement points, making it easier to identify instantaneous coordinate jumps caused by tree obstruction. This marking method directly supports the subsequent tracking and processing of abnormal data.

[0022] Step S2: Threshold filtering and correction are performed on the time-stamped coordinates to obtain calibrated coordinates, and the distance between adjacent measuring points is calculated on the calibrated coordinates to obtain measuring point spacing data.

[0023] Specifically, the time-stamped coordinates generated in the previous steps are first read and arranged in chronological order. The rate of change of planar distance between adjacent points is then calculated. If the distance between a point and its preceding and following points changes abruptly beyond a preset threshold, such as a momentary jump of more than 0.8 meters in continuous sampling, the point is determined to be abnormal. Linear interpolation combined with the coordinates of surrounding normal points is used for correction, thereby completing the screening and adjustment of the time-stamped coordinates and outputting more stable calibration coordinates. Then, based on these calibration coordinates, the coordinate values ​​of adjacent measuring points are extracted one by one, and the actual spatial interval between them is calculated using the Euclidean distance formula, forming a set of measuring point spacing data. For example, in mountainous road surveying, where the terrain is undulating and the spacing between sampling points is uneven, this step can effectively identify erroneous points caused by signal obstruction and quantify the true distance between each point, providing a reliable spatial topology basis for subsequent grid division.

[0024] Step S3: Obtain the basic geographic 3D base map corresponding to the surveying area, and based on the calibration coordinates and measurement point spacing data, perform grid division and feature point matching on the basic geographic 3D base map to obtain a regional grid map, and calculate the topographic relief of the regional grid map to obtain the grid elevation difference.

[0025] Specifically, the basic 3D geographic base map corresponding to the surveyed area is retrieved from the geographic information platform, which includes elevation and feature models. Then, the calibration coordinates obtained in the previous step are superimposed on the base map. The grid side length is determined based on the measurement point spacing data, generally 1.2 times the average spacing, and a regular grid is generated on the base map. Subsequently, it is compared whether there are calibration coordinate points near each grid node. If the distance is less than 0.5 meters, it is considered a successful match, and the elevation of that point is retained as the grid node value. After completing the feature point matching, a regional grid map is formed. Using this map as input, the mean value of the absolute value of the elevation change between each grid unit and its surrounding adjacent grids is calculated using the eight-neighbor difference method, which yields the terrain undulation. Then, the grid elevation difference of each grid location is extracted. For example, in a mountain road project, after this processing, the grid elevation difference of a certain slope can reflect the distribution of local steep slopes or gullies, providing a basis for terrain changes for subsequent texture filling.

[0026] Step S4: Based on the regional grid map and the grid elevation difference, perform texture filling and boundary delineation on the basic geographic 3D base map to obtain the survey image.

[0027] Specifically, the previously generated regional grid map and corresponding grid elevation difference data are imported into the graphics processing module and overlaid on the original basic 3D geographic base map. The terrain change levels are classified according to the magnitude of the grid elevation difference; for example, areas with a difference greater than 1.5 meters are marked as significant undulation zones. Within these grid areas, aerial imagery is used for texture sampling and filling, prioritizing image fragments with similar viewing angles and uniform lighting to fit the surface. For flat areas, the original texture of the base map is directly used for a smooth transition. At land use boundaries, such as the boundary between forest and bare soil, the outline is manually delineated using known boundary point positions from the calibration coordinates, and then the system automatically tracks the elevation change paths of adjacent grids to complete the boundary delineation. Finally, all filled areas and precise borders are integrated to output a complete survey image.

[0028] In a specific embodiment, a high-precision GNSS receiver is used to acquire position signals of target measurement points within the surveying area to obtain the coordinates of the measurement points, including: The target measurement points within the survey area are received by a high-precision GNSS receiver using multi-frequency signals. The carrier phase of the multi-frequency signals is then calculated to obtain the measurement point phase data. Based on the phase data of the measurement point, the three-dimensional coordinates of the target measurement point are initially calculated to obtain the initial calculated coordinates of the measurement point. Then, the initial calculated coordinates of the measurement point are corrected by satellite signal strength correlation to obtain the final coordinates of the measurement point.

[0029] Specifically, a high-precision GNSS receiver is installed at the target measurement point location within the survey area. After powering on, it synchronously acquires multi-frequency signals from systems such as BeiDou and GPS, commonly including BDS's B1 and B3 frequencies and GPS's L1 and L5 bands. These signals are received by the antenna and sent to the front-end processing module for frequency conversion and digital sampling. Subsequently, the system performs carrier phase calculation on the acquired multi-frequency signals. Specifically, it uses combined observations between different frequencies to reduce the influence of ionospheric delay. For example, it uses an ionospheric-free combined model and constructs a linear combination through dual-frequency phase observations to eliminate the first-order ionospheric error term, thereby improving the accuracy of the phase data. Finally, it outputs the measurement point phase data containing ambiguity information. This step is essentially a technical expansion of the initial data acquisition stage in "obtaining the measurement point coordinates," focusing on extracting highly stable phase observations from the original electromagnetic signals.

[0030] Based on the obtained phase data of the aforementioned measurement points, the initial calculation process for three-dimensional coordinates is initiated. This typically relies on RTK or PPK positioning modes. With the known coordinates of the base station, the relative position vector between the rover and the base station is solved using the double-difference observation equation. Combined with the precise coordinates of the base station, the spatial position of the current target measurement point is derived, thus obtaining the initial calculated coordinates of the measurement point. This process involves integer ambiguity fixing algorithms, such as the LAMBDA method, to quickly lock the correct integer number, ensuring centimeter-level calculation results. However, the initial calculated coordinates may still be affected by local environmental interference. For example, in mountain road surveying, when the measurement point is near a steep slope or the edge of dense forest, some satellite signals may be reflected or blocked, resulting in a deterioration of the satellite geometry in certain directions. In this case, although the phase calculation is completed, the positioning result may have a slight drift. To address this issue, the system introduces a satellite signal strength correlation correction mechanism. Specifically, it reads the signal-to-noise ratio (SNR) value of each satellite involved in the calculation, removes low-quality observations with an SNR below a threshold (e.g., 40 dB-Hz), and recalculates the coordinates based on the azimuth and elevation angle distribution of the remaining high-quality satellites. The weighting typically increases with the elevation angle. After this correction step, the offset caused by the multipath effect of individual low-elevation satellites is suppressed, resulting in a more reliable output of the measurement point coordinates.

[0031] In a specific embodiment, the step of using timestamps to associate and mark the coordinates of the measuring points to obtain time-stamped coordinates includes: Using timestamps, the coordinates of the measuring points are matched and associated one by one to obtain preliminary associated coordinates. The timestamp format of the preliminary associated coordinates is then validated to check whether the timestamps conform to the preset time format specifications. If they do not conform, the preliminary associated coordinates corresponding to the timestamps that do not conform to the format specifications are removed to obtain time-stamped coordinates.

[0032] Specifically, after acquiring the coordinates of the measurement points, the system performs a timestamp association operation. This process is a detailed expansion of the higher-level step of "using timestamps to associate and mark the coordinates of the measurement points to obtain time-stamped coordinates." First, the acquisition time of each measurement point coordinate is matched one by one with its corresponding spatial location data to form a preliminary set of associated coordinates. This matching is based on the receiver's internal clock synchronization mechanism and is usually recorded in UTC time format. For example, the coordinates of a point [X=314562.17, Y=2890134.65] are bound to its acquisition time information "2026-01-30T09:15:22.345Z" as a data unit. Then, the system enters the format verification stage. The system checks whether the timestamp in each preliminary associated coordinate conforms to the preset time format specification, such as the "YYYY-MM-DDTHH:mm:ss.sssZ" structure under the ISO 8601 standard. If a format error is found, such as a missing millisecond field or the use of local time without conversion to UTC, the entry is determined to be non-compliant. For such anomalies, the system automatically removes them from the dataset to prevent subsequent processing from introducing time-sequence issues. For example, in mountain road surveying, if a mobile terminal's clock resets due to power fluctuations, generating a non-standard time stamp like "2026 / 01 / 30 09:15", this record is identified and removed. Only the formatted preliminary associated coordinates are retained as valid input, and the final output is a complete and uniform time-stamped coordinate sequence.

[0033] In a specific embodiment, the step of threshold filtering and correction of the time-stamped coordinates to obtain calibrated coordinates, and calculating the distance between adjacent measuring points on the calibrated coordinates to obtain measuring point spacing data, includes: The time-stamped coordinates are filtered based on a preset coordinate error threshold range, and abnormal coordinates that exceed the threshold range are removed to obtain the filtered coordinates. The distance between each coordinate and its adjacent coordinate in the filtered coordinates is calculated in three-dimensional space and compared with a preset distance range to detect whether there is a distance deviation. If there is a distance deviation, the distance between each coordinate and its adjacent coordinate in the filtered coordinates in three-dimensional space is corrected based on the distance deviation to obtain the calibration coordinates. The coordinate difference between adjacent calibration coordinates is calculated to obtain the coordinate difference vector in the X, Y, and Z directions. The magnitude of the adjacent coordinate difference vector is then calculated to obtain the measurement point spacing data.

[0034] Specifically, after obtaining the time-stamped coordinates, a data purification process is initiated. This process involves "threshold filtering and correction of the time-stamped coordinates to obtain calibrated coordinates" and "calculation of the distance between adjacent measuring points for the calibrated coordinates to obtain measuring point spacing data." First, filtering is performed based on a preset coordinate error threshold range, typically set at ±0.3 meters for horizontal plane and ±0.5 meters for vertical plane. The system reads each time-stamped coordinate, determining whether its X, Y, and Z components exceed the tolerance limits. If a point deviates significantly from its surroundings, such as a sudden jump of several meters during continuous sampling, it is considered abnormal and removed, resulting in filtered coordinates. Next, the Euclidean distance between each filtered coordinate and its immediate neighbors in three-dimensional space is calculated and compared with a preset distance range. This range is set according to the operational method; for example, walking data collection typically ranges from 1 to 3 meters. If a sudden increase of more than 5 meters occurs, a distance deviation is considered. For such cases, linear interpolation combined with time weighting is used to correct the position of the deviation points, prioritizing the spatial relationship with nearby normal points to adjust the coordinate values, ultimately outputting more stable calibrated coordinates. Based on this, coordinate difference calculations are performed on each pair of adjacent calibration coordinates to obtain coordinate difference vectors in the three directions ΔX, ΔY, and ΔZ. Then, the modulus is calculated using the formula √(ΔX² + ΔY² + ΔZ²), which gives the actual spatial distance between the two points. All calculation results are summarized to form the measurement point spacing data, which is used to support subsequent grid generation and terrain modeling operations.

[0035] In a specific embodiment, the step of performing grid division and feature point matching on the basic geographic 3D base map based on the calibration coordinates and measurement point spacing data to obtain a regional grid map includes: Based on the calibration coordinates and the measurement point spacing data, the area of ​​the basic geographic 3D base map is defined to obtain the mapping boundary frame. The mapping boundary frame is then divided into grid cells according to the preset grid size. Based on the grid cells, the calibration coordinates are matched one by one to obtain a coordinate grid correspondence table. Feature point identifiers are added to the grid cells in the coordinate grid correspondence table to obtain a regional grid map.

[0036] Specifically, after obtaining the calibration coordinates and measurement point spacing data, the system begins to perform spatial structuring processing on the basic geographic 3D base map. This process is a concrete implementation of the higher-level step of "grid division and feature point matching of the basic geographic 3D base map based on the calibration coordinates and measurement point spacing data to obtain a regional grid map." First, the maximum and minimum values ​​of the X, Y, and Z values ​​of all calibration coordinates are extracted respectively. Combined with the average distance in the measurement point spacing data as a reference scale, the system expands outward by about 1.5 times the average spacing to leave a boundary margin, thereby determining the actual coverage area of ​​the surveying operation and forming a 3D spatial region surrounding all effective measurement points, i.e., the surveying boundary frame. For example, in a mountain road project, the collected calibration coordinates span a slope and valley. The easternmost point of its planar projection is X=314820.3, the westernmost point is X=314510.7, and the north-south span Y is from 2890300.1 to 2890610.5. Based on this, the boundary frame range is delineated, and this frame is superimposed on the base map as the spatial reference for subsequent operations.

[0037] Next, the survey boundary frame is evenly divided according to the preset grid size, generating several regularly arranged grid cells. The grid size is not a fixed value, but is dynamically set based on the measurement point spacing data, usually between 1.1 and 1.3 times the average spacing. If the average spacing between measurement points is 1.8 meters, the grid side length is set to 2 meters. The grid is divided in rows and columns on the XY plane, while the Z direction inherits the original elevation information of the base map. Each cell has a unique row and column index number. After the division is completed, the system enters the location matching stage. The system traverses each calibration coordinate, determines which grid cell its planar position falls into, records the correspondence between the coordinate point and its grid, and finally summarizes it into a coordinate-grid correspondence table containing "coordinate-grid ID" mapping entries. This table not only indicates which grids contain measured points, but also reveals the distribution of empty grids.

[0038] Subsequently, based on this correspondence table, feature point identifiers are added to grid cells containing calibration coordinates. These identifiers include the point's original elevation, acquisition timestamp, and accuracy level label. If multiple calibration coordinates fall within the same grid cell, the average elevation is taken and labeled as a composite feature point. Blank grid cells without matching coordinates are temporarily marked as interpolation areas. After the entire process is completed, all grid cells and their internal feature point information together constitute a regional grid map, serving as a crucial intermediate product connecting the measured data and the base map model.

[0039] In a specific embodiment, the step of calculating the topographic relief of the regional grid map to obtain the grid elevation difference includes: For each grid cell of the regional grid map, the elevation of adjacent cells is sampled to obtain a cell elevation group, and the gradient value of the cell elevation group is calculated to obtain the grid slope value; Based on the grid slope value, an elevation difference analysis is performed on each grid cell to obtain the cell undulation. Then, multi-scale elevation statistics are performed on the cell undulation to obtain the grid elevation difference.

[0040] Specifically, after generating the regional grid map, the system enters the terrain feature quantification stage. This process is a detailed expansion of the higher-level step of "calculating the terrain relief of the regional grid map to obtain the grid elevation difference." For each grid cell in the regional grid map, the elevation values ​​of its surrounding adjacent cells are first extracted to form a local elevation data set, i.e., the cell elevation group. An eight-neighbor sampling method is typically used, incorporating the center point elevations of the eight adjacent grids in the current grid's top, bottom, left, right, and four diagonal directions into the statistics. If a neighboring grid is empty or unassigned, data collection in that direction is skipped. For example, in mountain road mapping, a grid located at the toe of a slope may have significantly lower elevations to its north and northwest neighbors due to their location at the bottom of a gully; these values ​​are also included in the cell elevation group forming that cell.

[0041] Subsequently, gradient values ​​are calculated based on the unit's elevation group to reflect the surface tilt trend. Specifically, the central difference method is used to estimate the slope. First, the elevation change rates in the X and Y directions are calculated separately, for example, by subtracting the left neighbor from the right neighbor and then dividing by twice the grid side length. z / Similarly, x leads to... z / y, then substitute it into the formula √(( z / x)² + ( z / y)²) Calculate the grid slope value for that point, in meters per meter or converted to angles. The larger this value, the steeper the terrain at that location, which is the basis for subsequent judgment of the intensity of undulation. After completing the slope calculation for all grids, the system proceeds to the elevation difference analysis stage. The system compares the elevation distribution within and around each grid cell with the grid slope value. If the slope of a cell exceeds a preset threshold (e.g., 0.3), it is marked as a significant change area, and the absolute value of the difference between its own elevation and the average elevation of its neighbors is used as the initial unit undulation. For flat areas, the standard deviation is directly used to measure the degree of local fluctuation.

[0042] Based on this, multi-scale elevation statistics are implemented to capture the terrain change characteristics within different ranges. Operationally, the entire regional grid map is traversed using sliding windows of 3×3, 5×5, and 7×7 respectively. At each scale, the neighborhood elevation range and root mean square deviation centered on each grid are recalculated. Finally, the statistical results from multiple scales are weighted and fused, with the weights dynamically adjusted according to the actual terrain complexity. Complex areas are given more emphasis on small window response, while open areas are enhanced with large window smoothing effect. The final output is a quantitative indicator that comprehensively reflects local and regional changes—grid elevation difference. This data not only reflects the height abrupt changes of a single grid relative to its surroundings but also provides terrain-driving parameters for subsequent texture filling.

[0043] In a specific embodiment, the step of performing texture filling and boundary delineation on the basic geographic 3D base map based on the regional grid map and the grid elevation difference to obtain a survey image includes: The grid elevation difference is processed by interval layering to obtain an elevation hierarchy table, and contour lines are traced on the grid cells based on the elevation hierarchy table to obtain a terrain wireframe map; Based on the feature point markers in the topographic wireframe and regional grid map, the basic geographic 3D base map is layered and filled to obtain a landform texture map. The boundary lines of the landform texture map are then optimized to obtain a survey image.

[0044] Specifically, in processing geographic information to generate mapping images, the first step is to perform detailed interval layering of the grid elevation differences. This process aims to divide the terrain's elevation changes into several levels, each representing a certain elevation range, thus forming an elevation hierarchy table. This step is one of the key parts of "texturing and delineating the boundaries of the basic 3D geographic base map based on the regional grid map and grid elevation differences to obtain the mapping image," effectively capturing subtle changes in the terrain.

[0045] Next, contour tracing is performed on each grid cell based on the obtained elevation hierarchy table. This step involves identifying and connecting points with the same elevation value, thereby drawing lines that reflect the terrain's undulations and ultimately constructing a topographic wireframe. This topographic wireframe not only visually displays the changes in terrain elevation but also lays the foundation for subsequent operations.

[0046] After obtaining the topographic wireframe, and combining it with the feature point markers in the regional grid map, the basic 3D geographic base map is layered and filled. During this process, different elevation levels are assigned colors or textures to the corresponding grid cells, making the landform features more distinct and easier to understand, thus producing a landform texture map. For example, in mountainous areas, higher peaks may be represented by darker colors, while low-lying areas are distinguished using lighter shades.

[0047] Finally, to improve the quality of the survey image, the boundary lines of the terrain texture map need to be optimized. This work includes, but is not limited to, adjusting the line thickness, color, and smoothness to ensure that every boundary on the map accurately reflects the actual situation, enhancing the map's readability and aesthetics, thereby obtaining the final survey image.

[0048] Please see Figure 2 , Figure 2 This is a schematic diagram of the framework of an embodiment of the mapping device based on high-precision position sensing, as described in this application. Figure 2 As shown, the high-precision position sensing-based surveying device includes an acquisition module 1, used to acquire position signals of target measurement points within the surveying area using a high-precision GNSS receiver, obtain measurement point coordinates, and associate the measurement point coordinates with timestamps to obtain time-stamped coordinates; a calculation module 2, used to perform threshold filtering and correction on the time-stamped coordinates to obtain calibration coordinates, and calculate the distance between adjacent measurement points on the calibration coordinates to obtain measurement point spacing data; a matching module 3, used to acquire the basic geographic 3D base map corresponding to the surveying area, and based on the calibration coordinates and measurement point spacing data, perform grid division and feature point matching on the basic geographic 3D base map to obtain a regional grid map, and calculate the terrain undulation of the regional grid map to obtain the grid elevation difference; and a delineation module 4, used to perform texture filling and boundary delineation on the basic geographic 3D base map based on the regional grid map and the grid elevation difference to obtain a surveying image.

[0049] Reference Figure 3 This invention also provides a computer device whose internal structure can be as follows: Figure 3 As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0050] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0051] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0052] 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 computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0053] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0054] The description of the various embodiments above tends to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to, and for the sake of brevity, they will not be repeated here.

[0055] In the several embodiments provided in this application, it should be understood that the disclosed methods and apparatus can be implemented in other ways. For example, the apparatus implementations described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0056] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0057] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0058] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0059] If the technical solution of this application involves personal information, the product using this technical solution has clearly informed the user of the personal information processing rules and obtained the user's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using this technical solution has obtained the user's separate consent before processing the sensitive personal information, and also meets the requirement of "express consent". For example, at personal information collection devices such as cameras, clear and prominent signs are set up to inform users that they have entered the scope of personal information collection and that personal information will be collected. If an individual voluntarily enters the collection scope, it is deemed that they have agreed to the collection of their personal information; or on the personal information processing device, with clear signs / information informing users of the personal information processing rules, authorization is obtained from the individual through pop-up information or by asking the individual to upload their personal information; wherein, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the types of personal information processed.

Claims

1. A mapping method based on high-precision position sensing, characterized in that, Includes the following steps: The location signals of target measurement points within the survey area are collected by a high-precision GNSS receiver to obtain the coordinates of the measurement points. The coordinates of the measurement points are then associated and marked with timestamps to obtain time-stamped coordinates. The time-stamped coordinates are subjected to threshold filtering and correction to obtain calibrated coordinates, and the distance between adjacent measuring points is calculated on the calibrated coordinates to obtain measuring point spacing data. Obtain the basic geographic 3D base map corresponding to the surveying area, and based on the calibration coordinates and measurement point spacing data, perform grid division and feature point matching on the basic geographic 3D base map to obtain a regional grid map, and calculate the topographic relief of the regional grid map to obtain the grid elevation difference; Based on the regional grid map and the grid elevation difference, texture filling and boundary delineation are performed on the basic geographic 3D base map to obtain the survey image.

2. The mapping method based on high-precision position sensing according to claim 1, characterized in that, The coordinates of the target points within the survey area are obtained by acquiring position signals using a high-precision GNSS receiver, including: The target measurement points within the survey area are received by a high-precision GNSS receiver using multi-frequency signals. The carrier phase of the multi-frequency signals is then calculated to obtain the measurement point phase data. Based on the phase data of the measurement point, the three-dimensional coordinates of the target measurement point are initially calculated to obtain the initial calculated coordinates of the measurement point. Then, the initial calculated coordinates of the measurement point are corrected by satellite signal strength correlation to obtain the final coordinates of the measurement point.

3. The mapping method based on high-precision position sensing according to claim 1, characterized in that, The step of associating and marking the coordinates of the measuring points using timestamps to obtain time-stamped coordinates includes: Using timestamps, the coordinates of the measuring points are matched and associated one by one to obtain preliminary associated coordinates. The timestamp format of the preliminary associated coordinates is then validated to check whether the timestamps conform to the preset time format specifications. If they do not conform, the preliminary associated coordinates corresponding to the timestamps that do not conform to the format specifications are removed to obtain time-stamped coordinates.

4. The mapping method based on high-precision position sensing according to claim 1, characterized in that, The process of threshold filtering and correction of the time-stamped coordinates to obtain calibrated coordinates, and calculating the distance between adjacent measuring points on the calibrated coordinates to obtain measuring point spacing data, includes: The time-stamped coordinates are filtered based on a preset coordinate error threshold range, and abnormal coordinates that exceed the threshold range are removed to obtain the filtered coordinates. The distance between each coordinate and its adjacent coordinate in the filtered coordinates is calculated in three-dimensional space and compared with a preset distance range to detect whether there is a distance deviation. If there is a distance deviation, the distance between each coordinate and its adjacent coordinate in the filtered coordinates in three-dimensional space is corrected based on the distance deviation to obtain the calibration coordinates. The coordinate difference between adjacent calibration coordinates is calculated to obtain the coordinate difference vector in the X, Y, and Z directions. The magnitude of the adjacent coordinate difference vector is then calculated to obtain the measurement point spacing data.

5. The mapping method based on high-precision position sensing according to claim 1, characterized in that, The process of dividing the basic geographic 3D base map into grids and matching feature points based on the calibration coordinates and measurement point spacing data to obtain a regional grid map includes: Based on the calibration coordinates and the measurement point spacing data, the area of ​​the basic geographic 3D base map is defined to obtain the mapping boundary frame. The mapping boundary frame is then divided into grid cells according to the preset grid size. Based on the grid cells, the calibration coordinates are matched one by one to obtain a coordinate grid correspondence table. Feature point identifiers are added to the grid cells in the coordinate grid correspondence table to obtain a regional grid map.

6. The mapping method based on high-precision position sensing according to claim 1, characterized in that, The calculation of terrain relief on the regional grid map to obtain the grid elevation difference includes: For each grid cell of the regional grid map, the elevation of adjacent cells is sampled to obtain a cell elevation group, and the gradient value of the cell elevation group is calculated to obtain the grid slope value; Based on the grid slope value, an elevation difference analysis is performed on each grid cell to obtain the cell undulation. Then, multi-scale elevation statistics are performed on the cell undulation to obtain the grid elevation difference.

7. The mapping method based on high-precision position sensing according to claim 5, characterized in that, The process of applying texture filling and boundary delineation to the basic 3D geographic base map based on the regional grid map and grid elevation differences to obtain a survey image includes: The grid elevation difference is processed by interval layering to obtain an elevation hierarchy table, and contour lines are traced on the grid cells based on the elevation hierarchy table to obtain a terrain wireframe map; Based on the feature point markers in the topographic wireframe and regional grid map, the basic geographic 3D base map is layered and filled to obtain a landform texture map. The boundary lines of the landform texture map are then optimized to obtain a survey image.

8. A mapping device based on high-precision position sensing, characterized in that, include: The acquisition module is used to acquire the position signals of target measurement points in the surveying area through a high-precision GNSS receiver, obtain the coordinates of the measurement points, and use timestamps to associate and mark the coordinates of the measurement points to obtain time-stamped coordinates. The calculation module is used to perform threshold filtering and correction on the time-stamped coordinates to obtain calibration coordinates, and to calculate the distance between adjacent measurement points on the calibration coordinates to obtain measurement point spacing data. The matching module is used to obtain the basic geographic 3D base map corresponding to the surveying area, and based on the calibration coordinates and the distance data between the measuring points, to perform grid division and feature point matching on the basic geographic 3D base map to obtain a regional grid map, and to calculate the topographic relief of the regional grid map to obtain the grid elevation difference. The delineation module is used to perform texture filling and boundary delineation on the basic geographic 3D base map based on the regional grid map and the grid elevation difference to obtain the survey image.

9. A computer device, characterized in that, The method includes a memory and a processor that are coupled to each other. The memory stores program instructions, and the processor executes the program instructions to implement the high-precision position sensing-based mapping method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The system stores program instructions that can be executed by a processor, the program instructions being used to implement the mapping method based on high-precision position sensing as described in any one of claims 1 to 7.

Images