Geographic information surveying and mapping data management method and system based on big data

Abstract

The invention relates to the field of geographic information data, in particular to a geographic information surveying and mapping data management method and system based on big data. The method comprises the following steps: performing grid division on acquired point cloud data, and calculating an initial gradient threshold value of a grid; respectively calculating an elevation difference and a plane distance between the target point and the first adjacent point, and taking a ratio of the elevation difference to the plane distance as a slope value of the target point; layering the grids to obtain a plurality of sub-grids, taking the difference value between the gradient value of the target point and the gradient value of the second adjacent point as the gradient protrusion degree of the target point, and calculating the gradient stability of the sub-grids according to the gradient protrusion degree; and correcting the initial gradient threshold according to the calculated gradient fluctuation degree to obtain a final gradient threshold so as to complete the management of the surveying and mapping data. Through the technical scheme of the invention, the precision of the geographic information data acquisition result can be improved, and the efficiency of surveying and mapping data management is further improved.

Description

Technical Field

[0001] The present invention relates to the field of geographic information data. More specifically, the present invention relates to a method and system for managing geographic information surveying and mapping data based on big data. Background Art

[0002] With the rapid development of information technology, especially the increasing use of big data, cloud computing, and the Internet of Things (IoT), the volume of geographic data is also rapidly increasing. As a key information processing tool, geographic information systems (GIS) are being applied in a variety of fields, including surveying and mapping, urban planning, environmental monitoring, traffic management, and disaster warning. With the acceleration of globalization and the deepening of intelligent applications, traditional methods of geographic data collection and management are no longer able to meet the increasingly complex demands of these applications.

[0003] The core of geographic information surveying and mapping data management lies in efficiently acquiring, storing, processing, and analyzing massive amounts of geographic data to provide accurate decision support. With the increasing diversity and complexity of geographic information, traditional single approaches are no longer able to meet the efficiency, real-time, and accuracy requirements of the current big data era. Therefore, how to manage the collection, processing, analysis, sharing, and application of geographic information data in this big data environment has become a critical issue facing current geographic information systems.

[0004] The paper, titled "Comparison of Handheld LiDAR Point Cloud Filtering Methods for Complex Mountain Micro-reliefs," by Xu Yajing, Yuan Xiping, Gan Shu, et al., published in the Bulletin of Surveying and Mapping, discloses that slope filtering algorithms can be used for geographic information surveying and mapping data management. However, different terrain types have different slope characteristics, and using a fixed threshold in slope filtering algorithms makes it difficult to adapt to all variations in the terrain being detected, resulting in inaccurate geographic information data collection results. Summary of the Invention

[0005] In order to solve the problem of inaccurate geographic information data collection results, the present invention proposes a geographic information surveying and mapping data management method and system based on big data.

[0006] In a first aspect, the present invention discloses a geographic information surveying and mapping data management method based on big data, comprising: acquiring point cloud data in geographic information surveying and mapping, gridding the point cloud data according to a preset size, taking any point cloud data in the grid as a target point, and calculating an initial slope threshold of any grid; respectively calculating the elevation difference and plane distance between the target point and a first adjacent point, and taking the ratio of the elevation difference to the plane distance as the slope value of the target point, where the first adjacent point is the point cloud data closest to the target point in the grid where the target point is located; layering the grid to obtain a number of sub-grids, taking the difference between the slope value of the target point and the slope value of the second adjacent point as the slope prominence of the target point, where the second adjacent point is the point cloud data closest to the target point in the sub-grid where the target point is located, and calculating the slope stability of the sub-grid according to the slope prominence; traversing to obtain the slope stability of each sub-grid, calculating the slope fluctuation degree of the grid, and correcting the initial slope threshold according to the slope fluctuation degree to obtain a final slope threshold to complete the management of surveying and mapping data.

[0007] The slope value is determined by calculating the elevation difference and planar distance ratio between the target point and adjacent points. The slope prominence is then analyzed based on the differences in slope values between adjacent points, thereby determining the slope stability of each subgrid. The initial slope threshold is dynamically adjusted by calculating the slope fluctuations of each subgrid within the grid. This not only accurately depicts the slope variation characteristics of different terrain types, but also allows for more precise differentiation between ground and non-ground points by adjusting the threshold, optimizing the management and processing of point cloud data and improving surveying and mapping accuracy.

[0008] Preferably, calculating the initial slope threshold comprises: obtaining the coordinates of the center point of the grid; the initial slope threshold satisfies the relationship: , represents the initial slope threshold, Indicates the target point With the center point The Euclidean distance between Represents the total number of point cloud data in the grid, Represents the normalization function.

[0009] The initial slope threshold can more accurately reflect the complexity of the terrain within the grid. When the point cloud is evenly distributed, the slope threshold is small, which is suitable for processing flat terrain. When the point cloud data is more dispersed or discrete, the threshold is increased, which helps to better distinguish ground points from non-ground points.

[0010] Preferably, calculating the initial slope threshold further includes: obtaining the coordinates of the center point of the grid; calculating the degree of outlier of the target point; the initial slope threshold satisfies the relationship: , represents the initial slope threshold, Indicates the target point With the center point The Euclidean distance between Represents the total number of point cloud data in the grid, Indicates the target point The degree of outliers, Represents the normalization function.

[0011] When the distribution dispersion of point cloud data within a grid is high, it means that the spatial difference between ground points and non-ground points in the area is large, which usually reflects the complexity of the terrain.

[0012] Preferably, obtaining the outlier degree includes: setting a neighborhood radius of the target point in the grid, using point cloud data other than the target point within the neighborhood radius as reference points, and using the inverse of the number of reference points as the outlier degree of the target point.

[0013] Preferably, the step of calculating the slope smoothness of a subgrid based on the slope prominence comprises: traversing and obtaining the slope prominence of all point cloud data in the subgrid, and calculating the variance of the slope prominence of all point cloud data; and calculating the slope smoothness based on the variance, wherein the variance is negatively correlated with the slope smoothness.

[0014] A larger variance indicates more dramatic slope variations within a subgrid, more complex terrain, and therefore less slope stability. Conversely, a smaller variance indicates flatter terrain and higher slope stability. This helps more accurately identify complex terrain and relatively flat areas, avoids misclassification, and improves the accuracy and reliability of geographic information data processing.

[0015] Preferably, the calculation of the slope fluctuation degree of the grid includes: for the same grid, traversing to obtain the slope stability of each subgrid; the slope fluctuation degree satisfies the relationship: , Indicates the degree of slope fluctuation, and Represents subgrids respectively and subgrids The slope stability, represents the total number of subgrids, Represents the normalization function.

[0016] It can effectively reveal the overall trend of terrain changes and reflect the slope variation between different sub-grids. A larger slope fluctuation indicates a more drastic terrain fluctuation, while a smaller fluctuation indicates a relatively flat terrain.

[0017] Preferably, obtaining the final slope threshold includes: calculating the product of the slope fluctuation degree and the initial slope threshold; in response to the slope fluctuation degree of the grid being not less than a preset threshold, taking the sum of the initial slope threshold and the product as the final slope threshold; in response to the slope fluctuation degree of the grid being less than the preset threshold, taking the difference between the initial slope threshold and the product as the final slope threshold.

[0018] Preferably, the management of surveying and mapping data includes: traversing to obtain the final slope threshold of each grid; comparing the slope value of any point cloud data in the grid with the final slope threshold, and in response to the slope value of any point cloud data being not greater than the final slope threshold, marking the point cloud data as a ground point; in response to the slope value of any point cloud data being greater than the final slope threshold, marking the point cloud data as a non-ground point; and storing and managing the ground points and non-ground points separately to complete the management of surveying and mapping data.

[0019] In a second aspect, the present invention discloses a geographic information surveying and mapping data management system based on big data, comprising: a processor; and a memory, wherein the memory stores computer instructions. When the computer instructions are executed by the processor, the system executes the above-mentioned geographic information surveying and mapping data management method based on big data.

[0020] Beneficial effects of the present invention: This method accurately reflects the smoothness and complexity of terrain undulations by precisely calculating the slope, slope prominence, and slope fluctuation of point cloud data within each grid. The calculation of the initial slope threshold incorporates the spatial distribution and outlier level of the point cloud data, helping to identify and correct anomalous data and ensuring data authenticity and reliability. Furthermore, through hierarchical analysis of grids and subgrids, slope stability assessment and fluctuation correction are further optimized, ensuring high data accuracy and consistency, and enabling more efficient surveying and mapping data management. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an illustrative and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 This is a flowchart of a geographic information surveying and mapping data management method based on big data in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0023] It should be understood that when the terms "first," "second," and the like are used in the claims, description, and drawings of the present invention, they are merely used to distinguish between different objects, rather than to describe a specific order. The terms "comprise" and "comprising" used in the description and claims of the present invention indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof.

[0024] The present invention provides a method for managing geographic information surveying and mapping data based on big data. Figure 1 As shown, a geographic information surveying and mapping data management method based on big data includes steps S1 to S4, which are described in detail below.

[0025] S1, obtain point cloud data in geographic information surveying and mapping, divide the point cloud data into grids according to a preset size, and calculate the initial slope threshold of any grid.

[0026] In one embodiment, using LiDAR to scan a geographic environment can efficiently collect a large amount of spatial information to obtain three-dimensional data of the geographic environment. LiDAR precisely measures the three-dimensional coordinates (including longitude, latitude, and altitude) of each scanned point by emitting a laser beam and receiving the signal reflected from the surface, thereby generating dense point cloud data.

[0027] Point cloud data, composed of thousands of independent 3D coordinate points, can provide a detailed representation of geographic features such as terrain, buildings, and vegetation. By processing and analyzing this point cloud data, high-precision digital elevation models, topographic maps, and building models can be created, providing crucial foundational data for urban planning, environmental monitoring, disaster assessment, and other fields.

[0028] The point cloud data is meshed according to a preset size. For example, the length, width, and height of the grid are all set to 10, that is, the preset size is 10. The preset size can be set by those skilled in the art.

[0029] Take any point cloud data in the grid as the target point and obtain the coordinates of the center point of the grid; The initial slope threshold satisfies the relationship: , represents the initial slope threshold, Indicates the target point With the center point The Euclidean distance between Represents the total number of point cloud data in the grid, Represents the normalization function.

[0030] By obtaining the coordinates of the grid center point and calculating the distance between each point and the center point, the distribution density and dispersion of the point cloud within the area can be reflected. The initial slope threshold can more accurately reflect the complexity of the terrain within the grid. When the point cloud is relatively evenly distributed, the slope threshold is small, which is suitable for processing flat terrain. When the point cloud data is more dispersed or discrete, increasing the threshold helps to better distinguish between ground points and non-ground points.

[0031] The threshold is dynamically adjusted based on the local geometric features of point cloud data, which effectively solves the problem that the fixed threshold cannot adapt to diverse terrains and can improve the accuracy and adaptability of geographic information surveying and mapping.

[0032] In one embodiment, the initial slope threshold further includes: obtaining the coordinates of the center point of the grid.

[0033] The outlier degree of the target point is calculated, including: setting a neighborhood radius of the target point in a grid, using point cloud data other than the target point within the neighborhood radius as reference points, and using the reciprocal of the number of reference points as the outlier degree of the target point.

[0034] The initial slope threshold satisfies the relationship: , represents the initial slope threshold, Indicates the target point With the center point The Euclidean distance between Represents the total number of point cloud data in the grid, Indicates the target point The degree of outliers, Represents the normalization function.

[0035] When the distribution of point cloud data within a grid is highly discrete, it means that there is a large spatial difference between ground points and non-ground points in that area, which usually reflects the complexity of the terrain. Therefore, in order to more accurately distinguish between ground points and non-ground points, it is necessary to set a larger initial slope threshold to more effectively handle complex terrain.

[0036] It should be noted that when obtaining the slope threshold within a grid, the slope distribution and landform characteristics within the grid may be comprehensively considered for calculation.

[0037] First, the degree of dispersion of point cloud data is an important indicator of terrain complexity. A wide range of point cloud data within a grid indicates a mixed distribution of ground and non-ground points in that area. This generally indicates complex terrain with numerous undulations or obstacles. Therefore, a larger slope threshold is required to more accurately distinguish ground and non-ground points.

[0038] On the other hand, topographic characteristics are a key factor in determining slope distribution patterns. Mountainous areas, with steep slopes and rugged terrain, require a larger slope threshold to effectively distinguish between ground and non-ground points. In contrast, plains or hilly areas, with shallower slopes and more gradual slopes, require a smaller slope threshold. By combining these factors, the slope threshold can be dynamically adjusted, improving the accuracy and reliability of point cloud data processing and ensuring that terrain features are properly identified and analyzed.

[0039] S2, respectively calculating the elevation difference and plane distance between the target point and the first adjacent point, and taking the ratio of the elevation difference to the plane distance as the slope value of the target point.

[0040] In one embodiment, the first adjacent point is the point cloud data closest to the target point in the grid where the target point is located. The slope value satisfies the relationship: , Indicates the target point The slope value, Indicates the target point The location coordinates of Indicates the first adjacent point The location coordinates of .

[0041] In the slope value relationship formula, the numerator represents the elevation difference between the target point and the first adjacent point. The elevation difference is the height difference between the two in the vertical direction, which reflects the degree of ground undulation; the denominator represents the plane distance between the target point and the first adjacent point, which represents the horizontal distance between them.

[0042] S3, layering the grid to obtain several subgrids, taking the difference between the slope value of the target point and the slope value of the second adjacent point as the slope prominence of the target point, and calculating the slope smoothness of the subgrid according to the slope prominence.

[0043] In one embodiment, the grid is layered with the vertical axis as the tangent plane, and the difference between the slope value of the target point and the slope value of the second adjacent point is used as the slope prominence of the target point. The second adjacent point is the point cloud data closest to the target point in the sub-grid where the target point is located.

[0044] Calculating the slope stability of the subgrid includes: traversing to obtain the slope prominence of all point cloud data in the subgrid, calculating the variance of the slope prominence of all point cloud data; and calculating the slope stability according to the variance, wherein the variance is negatively correlated with the slope stability.

[0045] It should be noted that the variance of the slope prominence of the point cloud data in the sub-grid is used to describe the slope smoothness of the sub-grid instead of directly using the variance of the slope value of the point cloud data in the sub-grid. This is because the slope prominence can more accurately reflect the significance of local terrain changes.

[0046] Specifically, slope prominence measures the degree to which the slope value of each point deviates from that of its surrounding points. This allows for the detection of anomalies or sudden changes in terrain, such as steep slopes, protruding rocks, or other topographical features. By contrast, simply calculating the variance of slope, while providing an overall overview of the slope distribution across an area, lacks sensitivity to sudden changes in local terrain and can easily overlook local variations that significantly impact the terrain structure. Therefore, the variance of slope prominence can more accurately describe the undulating characteristics of terrain, particularly in complex or irregular terrain. It helps better identify the distribution differences between ground and non-ground points, thereby improving the accuracy of terrain analysis.

[0047] S4, traverse to obtain the slope stability of each sub-grid, calculate the slope fluctuation degree of the grid, and modify the initial slope threshold according to the slope fluctuation degree to obtain the final slope threshold to complete the management of surveying and mapping data.

[0048] In one embodiment, calculating the degree of slope fluctuation of a grid includes: for the same grid, traversing and obtaining the slope stability of each subgrid.

[0049] The degree of slope fluctuation satisfies the relationship: , Indicates the degree of slope fluctuation, and Represents subgrids respectively and subgrids The slope stability, represents the total number of subgrids, Represents the normalization function.

[0050] Slope fluctuation is calculated by accumulating and normalizing the differences in slope smoothness between adjacent subgrids, reflecting the magnitude of terrain undulation within the entire grid. This calculation method can reveal areas of rapid or sudden changes in slope, particularly those with sharply undulating, steep, or irregular terrain.

[0051] Obtaining the final slope threshold includes: calculating the product of the slope fluctuation degree and the initial slope threshold; in response to the slope fluctuation degree of the grid being not less than a preset threshold, taking the sum of the initial slope threshold and the product as the final slope threshold; in response to the slope fluctuation degree of the grid being less than the preset threshold, taking the difference between the initial slope threshold and the product as the final slope threshold.

[0052] The final slope threshold satisfies the relationship: , represents the final slope threshold, represents the initial slope threshold, Indicates the degree of slope fluctuation, Indicates the preset threshold.

[0053] When the slope fluctuation of a grid exceeds a preset threshold, it indicates that the slope in that area is changing dramatically, often indicating that the terrain may be mountainous or otherwise steep. Therefore, to better distinguish ground points from non-ground points, the initial slope threshold of the grid needs to be appropriately increased. This effectively identifies ground points in complex terrain and avoids overclassification of non-ground points.

[0054] Conversely, when the slope fluctuation of a grid is below the preset threshold, it indicates that the slope variation in the area is small and the terrain is relatively flat, possibly a plain or hilly terrain. In this case, to more accurately distinguish between ground points and non-ground points, the initial slope threshold of the grid can be appropriately lowered to better adapt to these relatively flat terrain features. Therefore, by dynamically adjusting the slope threshold based on the degree of slope fluctuation, the accuracy of terrain classification can be improved, ensuring that different terrain types are properly processed.

[0055] Traverse to obtain the final slope threshold of each grid, compare the slope value of any point cloud data in the grid with the final slope threshold, and mark the point cloud data as a ground point if the slope value of any point cloud data is not greater than the final slope threshold; mark the point cloud data as a non-ground point if the slope value of any point cloud data is greater than the final slope threshold. Repeat this process to mark the point cloud data in the grid.

[0056] The ground points and non-ground points of the collected surveying and mapping data are stored and managed separately to complete the management of the surveying and mapping data.

[0057] An embodiment of the present invention also discloses a geographic information surveying and mapping data management system based on big data, including a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, a geographic information surveying and mapping data management method based on big data according to the present invention is implemented.

[0058] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0059] In the present invention, the aforementioned memory may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium may be part of, accessible to, or connectable to a device.

[0060] While this specification has shown and described several embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous modifications, variations, and alternatives will occur to those skilled in the art without departing from the concept and spirit of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in practicing the present invention.

[0061] The above are all preferred embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A geographic information surveying and mapping data management method based on big data, characterized in that: include: Obtain point cloud data from geographic information surveying and mapping, divide the point cloud data into grids according to a preset size, use any point cloud data in the grid as a target point, and calculate the initial slope threshold of any grid; Calculate the elevation difference and plane distance between the target point and the first adjacent point respectively, and use the ratio of the elevation difference to the plane distance as the slope value of the target point. The first adjacent point is the point cloud data closest to the target point in the grid where the target point is located. The grid is layered to obtain several subgrids. The difference between the slope value of the target point and the slope value of the second adjacent point is used as the slope prominence of the target point. The second adjacent point is the point cloud data closest to the target point in the subgrid where the target point is located. The slope stability of the subgrid is calculated based on the slope prominence. The slope stability of each sub-grid is obtained by traversal, the slope fluctuation degree of the grid is calculated, and the initial slope threshold is corrected according to the slope fluctuation degree to obtain the final slope threshold to complete the management of surveying and mapping data.

2. A geographic information surveying and mapping data management method based on big data according to claim 1, characterized in that: Calculating the initial slope threshold includes: Get the center point coordinates of the grid; The initial slope threshold satisfies the relationship: , represents the initial slope threshold, Indicates the target point With the center point The Euclidean distance between Represents the total number of point cloud data in the grid, Represents the normalization function.

3. A geographic information surveying and mapping data management method based on big data according to claim 1, characterized in that: Calculating the initial slope threshold further includes: Get the center point coordinates of the grid; Calculate the outlier degree of the target point; The initial slope threshold satisfies the relationship: , represents the initial slope threshold, Indicates the target point With the center point The Euclidean distance between Represents the total number of point cloud data in the grid, Indicates the target point The degree of outliers, Represents the normalization function.

4. A method for managing geographic information surveying and mapping data based on big data according to claim 3, characterized in that: Obtaining the outlier degree includes: The neighborhood radius of the target point is set within the grid, the point cloud data except the target point within the neighborhood radius is used as the reference point, and the inverse of the number of reference points is used as the outlier degree of the target point.

5. The method for managing geographic information surveying and mapping data based on big data according to claim 1, characterized in that: Calculating the slope stability of the subgrid according to the slope prominence includes: Traverse to obtain the slope prominence of all point cloud data in the subgrid, and calculate the variance of the slope prominence of all point cloud data; Slope stationarity is calculated based on variance, where variance is negatively correlated with slope stationarity.

6. A geographic information surveying and mapping data management method based on big data according to claim 1, characterized in that: The slope fluctuation degree of the calculation grid includes: For the same grid, the slope stability of each sub-grid is obtained by traversal; The degree of slope fluctuation satisfies the relationship: , Indicates the degree of slope fluctuation, and Represents subgrids respectively and subgrids The slope stability, represents the total number of subgrids, Represents the normalization function.

7. A method for managing geographic information surveying and mapping data based on big data according to claim 1, characterized in that: Obtaining the final slope threshold comprises: Calculate the product of the slope fluctuation degree and the initial slope threshold; In response to the degree of slope fluctuation of the grid being no less than a preset threshold, taking the sum of the initial slope threshold and the product as the final slope threshold; In response to the degree of slope fluctuation of the grid being less than a preset threshold, the difference between the initial slope threshold and the product is used as the final slope threshold.

8. The method for managing geographic information surveying and mapping data based on big data according to claim 1, characterized in that: The management of surveying and mapping data includes: Traverse to obtain the final slope threshold of each grid; Comparing the slope value of any point cloud data in the grid with the final slope threshold, and in response to the slope value of any point cloud data being not greater than the final slope threshold, marking the point cloud data as a ground point; In response to a slope value of any point cloud data being greater than a final slope threshold, marking the point cloud data as a non-ground point; The ground points and non-ground points are stored and managed separately to complete the management of surveying and mapping data.

9. A geographic information surveying and mapping data management system based on big data, characterized in that: include: processor; and A memory storing computer instructions, wherein when the computer instructions are executed by a processor, the system executes a geographic information surveying and mapping data management method based on big data according to any one of claims 1 to 8.

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