Geographic surveying and mapping data processing analysis method and system based on big data

Abstract

The invention relates to the technical field of data processing, in particular to a geographic surveying and mapping data processing analysis method and system based on big data. The method comprises the following steps: obtaining outlier factors of all spatial points in collected geographic surveying and mapping data through an LOF algorithm; determining an outlier index of each spatial point; constructing an adjacent range of each spatial point; determining a density distance between the spatial points; performing region division on all the spatial points to obtain each region of the geographic surveying and mapping data; determining a fitting threshold of each region for distinguishing an inner point and an outer point in the RANSAC algorithm; and fitting the target area by using an RANSAC algorithm so as to identify abnormal data points and complete processing and analysis of the geographic surveying and mapping data. According to the method, the fitting threshold value is determined by comprehensively considering the range radius value, the spatial point outlier index mean value in the target area and the data point intensity standard deviation, the fitting threshold value can be more reasonably set for different areas, and the problem of unstable fitting caused by the unified threshold value is effectively avoided.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a geographic surveying and mapping data processing and analysis method and system based on big data. Background Art

[0002] Geographic surveying and mapping data covers a variety of attribute information of the target area and has many applications in urban planning, land management, and other fields. In geographic surveying and mapping data, spatial point data is an important data type used to express the three-dimensional spatial characteristics of the target area, and can accurately present topography, building structures and other geographic elements.

[0003] However, during the collection process, affected by factors such as environmental interference and sensor errors, spatial point data may contain abnormal conditions such as noise, outliers or uneven sampling, which in turn affects the reliability of the data and the accuracy of subsequent applications; therefore, it is necessary to process the abnormal data points in the spatial point data to reduce the interference of abnormal data points, improve the quality of geographic surveying and mapping data, and ensure the effective application of geographic surveying and mapping data in urban planning, land management and other fields.

[0004] The Random Sample Consensus (RANSAC) algorithm can effectively extract the inner points of the main geometric structures of geographic surveying and mapping scenes (for example, building planes or road lines) through random sampling and model fitting, while the spatial points outside the inner points are abnormal data points, which can then identify abnormalities such as noise, outliers or uneven sampling in spatial point data, thereby improving the reliability of geographic surveying and mapping data analysis; however The algorithm uses the same fitting threshold to divide the global data into inner points and outer points, which is prone to problems such as unstable fitting and high randomness in scenes with complex surveying and mapping scenes and uneven data density, resulting in reduced accuracy in the identification of abnormal data points, which in turn affects the application of geographic surveying and mapping data in urban planning, land management and other fields. Summary of the invention

[0005] To solve The algorithm uses the same fitting threshold to divide inliers and outliers for global data, which is prone to unstable fitting and high randomness in scenes with complex surveying and mapping scenes and uneven data density, resulting in reduced accuracy in identifying abnormal data points, thereby affecting the application effect of geographic surveying and mapping data in the fields of urban planning, land management, etc. The present invention provides a geographic surveying and mapping data processing and analysis method and system based on big data.

[0006] In the first aspect, the present invention provides a geographic surveying and mapping data processing and analysis method based on big data, which adopts the following technical solutions: Geographic surveying and mapping data processing and analysis methods based on big data include: The algorithm obtains the outlier factors of all spatial points in the collected geographic surveying and mapping data; any spatial point is recorded as a target spatial point, and the outlier index of the target spatial point is determined according to the outlier factor of the target spatial point, the mean of the distance between the target spatial point and all spatial points in the neighborhood of the target spatial point, and the standard deviation of the coordinates of all spatial points in the neighborhood; the mean of the radius of the neighborhood of all spatial points is used as the range radius value, and the neighborhood range of the target spatial point is constructed according to the range radius value; the density distance between the target spatial point and any other spatial point is determined according to the distance, outlier index and the number of spatial points in the neighborhood range; all spatial points are divided into regions according to the size of the density distance to obtain several regions; the target region is determined for use in the The fitting threshold for distinguishing internal points from external points in the algorithm; according to the fitting threshold, using The algorithm fits the target area and obtains the inner and outer points of the target area to identify abnormal data points and complete the processing and analysis of geographic surveying and mapping data.

[0007] The present invention is achieved by The algorithm calculates the outlier factor of each spatial point, so that outliers or abnormal data points can be identified more accurately according to the local density changes of the spatial points, which is different from the traditional global standard-based Compared with the proposed algorithm, the present invention can process each point according to its local situation and separate the inner and outer points more accurately. By calculating the outlier index of the target spatial point, the density distance of the points in the neighborhood and other related parameters, the fitting threshold suitable for the area can be dynamically determined. This can effectively avoid the errors that may be caused by using a global fixed threshold in uneven data density or complex scenes, and improve the stability and accuracy of the fitting. In complex surveying and mapping scenes, the data distribution and density may be very uneven, and traditional It is difficult for algorithms to adapt to such changes. The present invention achieves better processing of data in complex scenarios and improves the stability of data analysis by integrating factors such as the outlier factor of spatial points, the distance within the neighborhood, and the standard deviation.

[0008] Furthermore, the neighborhood is the target space point in In the algorithm -Distance neighborhood.

[0009] Furthermore, the outlier index satisfies: ; In the formula, For spatial points in geographic surveying data The outlier index of For space point The outlier factor, For space point of -The distance between all spatial points and spatial points in the neighborhood The mean of the distances, , and The spatial points of -The distance to all spatial points in the neighborhood , and The standard deviation of the coordinates.

[0010] The present invention can more accurately identify abnormal data points in spatial points by comprehensively considering the outlier factor, the neighborhood distance mean and the coordinate standard deviation; the introduction of the coordinate standard deviation in the neighborhood of the spatial point improves the calculation accuracy of the outlier index and adapts to the complex situation of uneven distribution of spatial data.

[0011] Furthermore, constructing a proximity range of the target space point according to the range radius value includes: taking the target space point as the center and a spherical range with the range radius value as the radius as the proximity range of the target space point.

[0012] Furthermore, the density distance satisfies: ; In the formula, For spatial points in geographic surveying data With space point The density distance, For space point With space point The distance and The spatial points and spatial points The number of spatial points in the vicinity of and The spatial points and spatial points The outlier index of is a hyperparameter, is the absolute value symbol.

[0013] By comprehensively considering the number of neighboring points and the outlier index, the density distance in the present invention can more accurately reflect the local spatial characteristics, so that the algorithm can maintain good performance in different areas; the density distance calculation method combined with the outlier index can more finely distinguish normal points from abnormal points, thereby improving the accuracy of abnormal data detection; when performing subsequent regional division, the use of density distance can improve the accuracy and stability of the division.

[0014] Furthermore, it is characterized in that the distance is Euclidean distance.

[0015] Furthermore, all spatial points are divided into regions according to the size of the density distance to obtain a number of regions, including: dividing the spatial points and target pixel points whose density distance is less than a preset regional threshold into the same region until all spatial points are divided to obtain multiple regions; in response to the density distance of two spatial points that do not belong to the same region being less than a preset regional threshold, merging the regions to which the two spatial points belong until all regions are no longer merged to obtain a number of regions.

[0016] The density distance-based division method of the present invention can ensure that adjacent and similar spatial points are classified into the same area, thereby improving the rationality and accuracy of area division; through preset area thresholds and merging rules, area division can be performed efficiently, unnecessary calculation steps are reduced, and the speed of data processing is improved.

[0017] Furthermore, the fitting threshold satisfies: ; In the formula, For the The fitting threshold of the region, is the range radius value, For the The mean of the outlier index of all spatial points in the region, For the The standard deviation of the data point intensity at all spatial points in the region, is a linear normalization function.

[0018] The fitting threshold of the present invention is calculated based on the local characteristics of each region, for example, the mean of the outlier index and the standard deviation of the data point intensity, so that The algorithm can automatically adjust the threshold according to the characteristics of different areas, improving Local adaptability of the algorithm; by considering the discrete degree and intensity changes of the data in the region, the fitting threshold can more accurately reflect the true distribution of the data, thereby improving The fitting accuracy of the algorithm within this area; the introduction of the outlier index enables the fitting threshold to better identify and process outliers, thereby improving the accuracy and reliability of abnormal data point detection.

[0019] Furthermore, the use The algorithm fits the target area to obtain the inner point and the outer point of the target area to identify abnormal data points, including: randomly selecting three spatial points in the target area to form a plane according to a preset number of times, calculating the vertical distance from each spatial point in the target area except the spatial points forming the plane to the plane each time, if the vertical distance is less than the fitting threshold, then the spatial point is used as the inner point of the target area, otherwise, the spatial point is used as the outer point of the target area, and the number of times each spatial point is used as the outer point of the target area is obtained; in response to the number of times the outer point is greater than the preset abnormal threshold, the spatial point is identified as an abnormal data point in the geographic surveying and mapping data.

[0020] In a second aspect, the present invention provides a geographic surveying and mapping data processing and analysis system based on big data, which adopts the following technical solutions: The geographic surveying and mapping data processing and analysis system based on big data includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the geographic surveying and mapping data processing and analysis method based on big data is implemented.

[0021] By adopting the above technical solution, the above-mentioned geographic surveying and mapping data processing and analysis method based on big data is generated into a computer program and stored in a memory to be loaded and executed by a processor, so that a terminal device is made according to the memory and the processor for easy use.

[0022] The present invention has the following technical effects: The fitting threshold is determined by comprehensively considering the range radius value, the mean of the outlier index of the spatial points in the target area and the standard deviation of the data point intensity. The algorithm uses the same fitting threshold for global data, so that when the surveying and mapping scenes are complex and the data density is uneven, the fitting threshold can be set more reasonably for different areas, effectively avoiding the fitting instability problem caused by the unified threshold, and improving the stability and reliability of the fitting; in the process of determining the outlier index, density distance and fitting threshold of the target spatial point, multiple factors are comprehensively considered, such as the outlier factor of the spatial point, the distance from the neighboring points, the standard deviation of the coordinates of the points in the neighborhood, the number of spatial points in the neighborhood, etc., making the analysis of the spatial points more comprehensive and accurate, reducing the randomness brought by relying solely on a single standard, and making the results of data processing and analysis more reliable; through comprehensive analysis of spatial points and reasonable regional division, as well as targeted determination of fitting thresholds, it is possible to more accurately distinguish between inner and outer points in the target area, and then more accurately identify abnormal data points, compared with traditional The algorithm effectively improves the accuracy of identifying abnormal data points. Since it can identify abnormal data points more accurately, the processing and analysis of geographic surveying and mapping data is more reliable, which provides a better data foundation for the application of geographic surveying and mapping data in urban planning, land management and other fields, thereby effectively improving the application effect of geographic surveying and mapping data in these fields, enabling geographic surveying and mapping data to better serve relevant decision-making and planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of a method for processing and analyzing geographic surveying and mapping data based on big data in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0025] The embodiment of the present invention discloses a method for processing and analyzing geographic surveying and mapping data based on big data, referring to Figure 1 , comprising steps S1 to S7: S1: Pass The algorithm obtains the outlier factors of all spatial points in the collected geographic surveying and mapping data.

[0026] Implementers can set the Number of neighbors in the algorithm , for example, 20.

[0027] S2: Determine the outlier index of each spatial point.

[0028] It should be noted that geographic surveying and mapping data have the characteristics of complex and non-fixed scenes. The geographic surveying and mapping data collected in different surveying and mapping scenes also have different distribution states. In order to better eliminate the influence of noise, outliers and uneven sampling in geographic surveying and mapping data on geographic surveying and mapping data analysis, the outlier index is calculated through the coordinates of each spatial point in the geographic surveying and mapping data.

[0029] Any spatial point is recorded as a target spatial point, and the outlier index of the target spatial point is determined according to the outlier factor of the target spatial point, the mean of the distance between the target spatial point and all spatial points in the neighborhood of the target spatial point, and the standard deviation of the coordinates of all spatial points in the neighborhood, where the distance is the Euclidean distance.

[0030] Specifically, the neighborhood is the target space point in In the algorithm -Distance neighborhood.

[0031] Specifically, the outlier index satisfies: ; In the formula, For spatial points in geographic surveying data The outlier index of For space point The outlier factor, For space point of -The distance between all spatial points and spatial points in the neighborhood The mean of the Euclidean distances, , and The spatial points of -The distance from all spatial points in the neighborhood , and The standard deviation of the coordinates.

[0032] in, The larger the value, the greater the spatial point The more likely it is to be in a sparse area in geographic surveying and mapping data, the more likely the spatial point The larger the outlier index is, The smaller it is, the more space points The more likely it is to be in a dense area in geographic surveying and mapping data, the more likely the spatial point The smaller the outlier index is, the smaller the spatial point The average distance between the data points in its kth neighborhood The larger the value, the greater the spatial point The greater the distance between the spatial point and other geographic survey data points, the The more outliers, the more spatial points The larger the outlier index is, the larger the spatial point With -The average Euclidean distance between spatial points in the distance neighborhood The smaller it is, the more space points The smaller the distance between the spatial points and other geographic surveying and mapping data, the smaller the spatial points are. The denser the surrounding space points are, the The smaller the outlier index is; The larger the value, the greater the spatial point The more discrete the distribution of the surrounding space points is, the The more likely it is that in sparse areas of geographic survey data, the more likely it is that the spatial points The more likely it is an outlier data point, the more likely it is a spatial point. The larger the outlier index is, The smaller the value, the smaller the space point. The denser the distribution of the surrounding space points, the more likely the space points are The more likely it is that in dense areas of geographic surveying data, spatial points The smaller the outlier index is.

[0033] S3: Construct the neighborhood of each spatial point.

[0034] The average value of the radius of the neighborhood of all spatial points is taken as the range radius value, and the neighborhood range of the target spatial point is constructed according to the range radius value.

[0035] Specifically, constructing a proximity range of the target space point according to the range radius value includes: The spherical range with the target space point as the center and the range radius value as the radius is the proximity range of the target space point.

[0036] S4: Determine the density distance between points in space.

[0037] It should be noted that geographic surveying and mapping data may have different densities, noise levels, and outlier ratios in different scenarios. Geographic surveying and mapping data in the same scenario may also have different densities in different areas of the scene due to different objects in the scene. The robustness of the algorithm enables it to be better applied in different geographic surveying and mapping scenarios. The present invention calculates the density distance through the distance between spatial points in geographic surveying and mapping data, the outlier index, and the number of spatial points in the vicinity, so as to subsequently divide the geographic surveying and mapping data into regions.

[0038] The density distance between the target spatial point and any other spatial point is determined according to the distance between the target spatial point and any other spatial point, the outlier index, and the number of spatial points in the vicinity.

[0039] Specifically, the density distance satisfies: ; In the formula, For spatial points in geographic surveying data With space point The density distance, For space point With space point The Euclidean distance of and The spatial points and spatial points The number of spatial points in the vicinity of and The spatial points and spatial points The outlier index of is a hyperparameter, is the absolute value symbol.

[0040] Implementers can set the hyperparameter according to their specific implementation, for example, 0.5.

[0041] in, The smaller it is, the more likely it is that the two spatial points are on the same object or the same plane in the geographic surveying and mapping scene, and the more likely it is that the two points should be divided into the same area. The smaller the density distance between the two points, The larger it is, the more likely it is that the two spatial points are not on the same object or the same plane in the geographic surveying and mapping scene, and the more inappropriate it is to classify the two points into the same area, and the greater the density distance between the two points; For space point With space point The difference between the number of spatial points in the vicinity of the same object is due to the similarity of the surface morphology of the same object, so the number of spatial points in the vicinity of the spatial point on the same object is similar. The larger the space point With space point The more likely it is that they are not on the same object or the same plane in the geographic mapping scene, the less likely they should be classified into the same area, and the greater the density distance between the two points, The smaller the space point With space point The more likely it is that two points are on the same object or the same plane in the geographic surveying scene, the more they should be divided into the same area, and the smaller the density distance between the two points is. In order to avoid the situation where the number of data points in the vicinity of two spatial points on different objects is equal due to the accidental nature of the data, resulting in the density distance between the data points on different objects being 0, which leads to the two points being divided into the same area, we use Avoid this from happening; For space point With space point The difference in outlier index is due to the fact that the outlier index of the spatial points on the same object is similar. The larger the space point With space point The more likely it is that they are not on the same object or the same plane in the geographic mapping scene, the less likely they should be classified into the same area, and the greater the density distance between the two points, The smaller the space point With space point The more likely it is that two points are on the same object or the same plane in the geographic mapping scene, the more they should be divided into the same area, and the smaller the density distance between the two points, the same way, using avoid The impact of the situation where the value is 0 on the regional division.

[0042] S5: Divide all spatial points into regions to obtain geographic surveying and mapping data for each region.

[0043] According to the size of the density distance, all space points are divided into regions to obtain a number of regions.

[0044] Specifically, according to the size of the density distance, all spatial points are divided into regions to obtain several regions, including: Divide the spatial points whose density distance is less than a preset regional threshold and the target pixel points into the same region until all spatial points are divided to obtain multiple regions; In response to the density distance between two spatial points that do not belong to the same region being less than a preset region threshold, the regions to which the two spatial points belong are merged until all regions are no longer merged, thereby obtaining a plurality of regions.

[0045] Implementers can set the regional threshold, for example, 10, based on their specific implementation.

[0046] S6: Determine the areas to be used in The fitting threshold for distinguishing inliers from outliers in the algorithm.

[0047] It should be noted that due to The algorithm uses the same fitting threshold to divide the internal and external points for global data, while geographic surveying and mapping data may have uneven distribution of data points due to different scenes. The algorithm is sensitive to the fitting threshold of internal point judgment, and is prone to unstable fitting and high randomness problems when the data density is uneven or the anomaly is serious. Therefore, this step calculates the fitting threshold of each area through the spatial points in each area of ​​the geographic surveying and mapping data. In this step, the range radius value is used as the basic value of the fitting threshold of each area. The implementer can set the basic value according to the specific implementation situation.

[0048] The target area is determined based on the range radius value, the mean of the outlier index of all spatial points in the target area of ​​the plurality of areas, and the standard deviation of the data point intensity of all spatial points in the target area. The fitting threshold for distinguishing inliers from outliers in the algorithm.

[0049] Specifically, the fitting threshold satisfies: ; In the formula, For the The fitting threshold of the region, is the range radius value, For the The mean of the outlier index of all spatial points in the region, For the The standard deviation of the data point intensity at all spatial points in the region, is a linear normalization function.

[0050] in, The larger the value is, the more outliers all the spatial points in the region are. In order not to exclude valid data by mistake, the fitting threshold should be set more leniently. The smaller it is, the more concentrated all the spatial points in the area are. In order to effectively fit the surface of objects in geographic surveying and mapping scenes, the fitting threshold should be set more strictly to make the fitting threshold smaller; The larger the value is, the greater the volatility of the data point intensity of the spatial points in the area, and the worse the consistency of the data in the area. A larger fitting threshold should be used to avoid the spatial points on the objects in the geographic surveying and mapping scene being identified as abnormal points. The smaller it is, the smaller the volatility of the data point intensity in the area is, the stronger the consistency of the data in the area is, and the easier it is to identify the abnormal points in the area. Therefore, a smaller fitting threshold can be used to distinguish the abnormal data. The smaller it is, the smaller the fitting threshold should be used.

[0051] S7: Exploitation The algorithm fits the target area to identify abnormal data points and complete the processing and analysis of geographic surveying and mapping data.

[0052] According to the fitting threshold, using The algorithm fits the target area to obtain the inner and outer points of the target area to identify abnormal data points. When applied to geographic surveying and mapping data, it can avoid the interference of abnormal data points, reduce the impact of abnormal conditions such as noise, outliers or uneven sampling in geographic surveying and mapping data, and complete the processing and analysis of geographic surveying and mapping data based on big data.

[0053] Specifically, the use The algorithm fits the target area to obtain the inner and outer points of the target area to identify abnormal data points, including: According to the preset number of times, three spatial points are randomly selected in the target area to form a plane, and the vertical distance from each spatial point in the target area except the spatial points forming the plane to the plane is calculated each time. If the vertical distance is less than the fitting threshold, the spatial point is regarded as an inner point of the target area, otherwise, the spatial point is regarded as an outer point of the target area, and the number of times each spatial point is regarded as an outer point of the target area is obtained; In response to the number of outliers being greater than a preset anomaly threshold, the spatial point is identified as an abnormal data point in the geographic surveying and mapping data.

[0054] Implementers can set preset times and abnormal thresholds according to specific implementation conditions. For example, if there is space points, then the preset number is ,in, is the round-up symbol; the abnormal threshold is .

[0055] Among them, the vertical distance from the spatial point to the plane refers to the length of the line segment between the point and the foot of the perpendicular from the spatial point to the plane. The vertical distance can be obtained by the vector method, which will not be repeated here.

[0056] An embodiment of the present invention also discloses a geographic surveying and mapping data processing and analysis system based on big data, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a geographic surveying and mapping data processing and analysis method based on big data according to the present invention is implemented.

[0057] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0058] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope 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 protection scope of the present invention.

Claims

1. A geographic surveying and mapping data processing and analysis method based on big data, characterized in that: include: pass The algorithm obtains the outlier factors of all spatial points in the collected geographic surveying and mapping data; Any spatial point is recorded as a target spatial point, and the outlier index of the target spatial point is determined according to the outlier factor of the target spatial point, the mean of the distances between the target spatial point and all spatial points in the neighborhood of the target spatial point, and the standard deviation of the coordinates of all spatial points in the neighborhood; The average value of the radius of the neighborhood of all spatial points is used as the range radius value, and the neighborhood range of the target spatial point is constructed according to the range radius value; Determine the density distance between the target spatial point and any other spatial point according to the distance between the target spatial point and any other spatial point, the outlier index, and the number of spatial points in the vicinity; According to the size of the density distance, all spatial points are divided into regions to obtain a number of regions; The target area is determined based on the range radius value, the mean of the outlier index of all spatial points in the target area of ​​the plurality of areas, and the standard deviation of the data point intensity of all spatial points in the target area. The fitting threshold that distinguishes inliers from outliers in the algorithm; According to the fitting threshold, using The algorithm fits the target area and obtains the inner and outer points of the target area to identify abnormal data points and complete the processing and analysis of geographic surveying and mapping data.

2. The method for processing and analyzing geographic surveying and mapping data based on big data according to claim 1, characterized in that: The neighborhood is the target space point in In the algorithm -Distance neighborhood.

3. The method for processing and analyzing geographic surveying and mapping data based on big data according to claim 2 is characterized in that: The outlier index satisfies: ； In the formula, For spatial points in geographic surveying data The outlier index of For space point The outlier factor, For space point of -The distance between all spatial points and spatial points in the neighborhood The mean of the distances, , and The spatial points of -The distance to all spatial points in the neighborhood , and The standard deviation of the coordinates.

4. The method for processing and analyzing geographic surveying and mapping data based on big data according to claim 1, characterized in that: The step of constructing a proximity range of the target space point according to the range radius value includes: The spherical range with the target space point as the center and the range radius value as the radius is the proximity range of the target space point.

5. The method for processing and analyzing geographic surveying and mapping data based on big data according to claim 1, characterized in that: The density distance satisfies: ； In the formula, For spatial points in geographic surveying data With space point The density distance, For space point With space point The distance and The spatial points and spatial points The number of spatial points in the vicinity of and The spatial points and spatial points The outlier index of is a hyperparameter, is the absolute value symbol.

6. The geographic surveying and mapping data processing and analysis method based on big data according to claim 1, 3 or 5, characterized in that: The distance is the Euclidean distance.

7. The method for processing and analyzing geographic surveying and mapping data based on big data according to claim 1, characterized in that: According to the size of the density distance, all spatial points are divided into regions to obtain several regions, including: Divide the spatial points whose density distance is less than a preset regional threshold and the target pixel points into the same region until all spatial points are divided to obtain multiple regions; In response to the density distance between two spatial points that do not belong to the same region being less than a preset region threshold, the regions to which the two spatial points belong are merged until all regions are no longer merged, thereby obtaining a plurality of regions.

8. The method for processing and analyzing geographic surveying and mapping data based on big data according to claim 1, characterized in that: The fitting threshold satisfies: ； In the formula, For the The fitting threshold of the region, is the range radius value, For the The mean of the outlier index of all spatial points in the region, For the The standard deviation of the data point intensity at all spatial points in the region, is a linear normalization function.

9. The method for processing and analyzing geographic surveying and mapping data based on big data according to claim 1, characterized in that: The utilization The algorithm fits the target area to obtain the inner and outer points of the target area to identify abnormal data points, including: According to the preset number of times, three spatial points are randomly selected in the target area to form a plane, and the vertical distance from each spatial point in the target area except the spatial points forming the plane to the plane is calculated each time. If the vertical distance is less than the fitting threshold, the spatial point is regarded as an inner point of the target area, otherwise, the spatial point is regarded as an outer point of the target area, and the number of times each spatial point is regarded as an outer point of the target area is obtained; In response to the number of outliers being greater than a preset anomaly threshold, the spatial point is identified as an abnormal data point in the geographic surveying and mapping data.

10. A geographic surveying and mapping data processing and analysis system based on big data, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the geographic surveying and mapping data processing and analysis method based on big data according to any one of claims 1 to 9 is implemented.

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