A new basic surveying and mapping multimodal geographic entity data surveying method and system

Abstract

The present invention provides a novel basic surveying and mapping multimodal geographic entity data surveying and mapping method and system, which relates to the field of data processing technology. The method comprises: selecting three reference points reflecting the geographic features and data distribution characteristics in the surveying and mapping area; analyzing the three reference points respectively according to their specific data distribution characteristics to obtain data analysis results; calculating the crossover values ​​between the three reference points according to the data analysis results of the three reference points; evaluating the geographic entity data using the crossover values ​​to obtain evaluation results; and correcting the geographic entity data according to the evaluation results to obtain corrected geographic entity data. The present invention can realize accurate surveying and mapping of geographic entities.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a novel basic surveying and mapping multimodal geographic entity data surveying and mapping method and system. Background Art

[0002] When dealing with multimodal data, traditional geographic surveying and mapping methods will overlay or fuse data from different sources, but some of these methods fail to deeply explore the inherent connections and differences between the data. The limitation of this approach is that it may not be able to fully explore and utilize the rich information provided by multimodal data. Due to the lack of detailed analysis of the complex relationships between data, this method may lead to insufficient accuracy of surveying and mapping results, or even contradictions or inconsistencies between data.

[0003] In addition, when faced with complex geographical features, traditional surveying and mapping methods may not be flexible enough and difficult to adjust accordingly according to specific circumstances. This lack of adaptability makes it challenging for traditional methods to meet diverse and high-precision surveying and mapping needs. Therefore, although traditional methods can use multimodal data for surveying and mapping, they still need to be improved in terms of data depth analysis and surveying and mapping accuracy. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a new basic surveying and mapping multimodal geographic entity data surveying method and system, which can realize accurate surveying and mapping of geographic entities.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] In a first aspect, a novel basic surveying and mapping multimodal geographic entity data surveying and mapping method is provided, the method comprising:

[0007] Collect multimodal data of the survey area, including remote sensing image data, topographic map data, 3D model data and point cloud data;

[0008] Preprocess multimodal data to form standardized geographic entity data;

[0009] Select three reference points in the survey area that reflect the geographical features and data distribution characteristics of the survey area;

[0010] Analyze the three reference points respectively according to their specific data distribution characteristics to obtain data analysis results;

[0011] According to the data analysis results of the three reference points, the intersection values ​​between the three reference points are calculated;

[0012] Using the cross value, the geographic entity data is evaluated to obtain an evaluation result;

[0013] According to the evaluation results, the geographic entity data is corrected to obtain corrected geographic entity data;

[0014] Output the corrected geographic entity data as standardized surveying and mapping results.

[0015] Furthermore, three reference points reflecting the geographical features and data distribution characteristics of the surveying and mapping area are selected within the surveying and mapping area, including:

[0016] According to the geographical characteristics of the survey area and the overall distribution of the data, a K value is set through the silhouette coefficient, and the K value represents the number of clusters expected to be formed;

[0017] Randomly select K points from the standardized geographic entity data as the initial center points;

[0018] For each point in the data set, calculate its distance from the K center points and assign each point to the cluster represented by the corresponding center point;

[0019] For each cluster, calculate the average coordinates of all data points within it, obtain the new center point coordinates, and update the center point of the cluster. Repeat the steps of assigning data points to clusters and updating the center point until the preset number of iterations is reached. Stop the iteration and mark each data point with the cluster number to which it belongs.

[0020] Output the coordinates of the center points of K clusters and the number of data points contained in each cluster; for each formed cluster, traverse all the data points in it;

[0021] According to the coordinates of all data points within the cluster, calculate the geometric center of all data points;

[0022] The geometric center of all data points is used as the candidate reference point of the cluster;

[0023] According to the spatial distribution and data density characteristics of the candidate reference points, an evaluation value is assigned to each candidate point;

[0024] The candidate reference points are sorted according to the evaluation values ​​to select the corresponding three points as the final reference points, and the coordinates and related information of the selected three final reference points are output.

[0025] Furthermore, the calculation formula of the evaluation value is:

[0026] ;

[0027] in, , , and is the weight coefficient; Represents the total number of data points in the cluster; and All data points in the cluster are Maximum and minimum values ​​on the coordinates; and They are Maximum and minimum values ​​on the coordinates; and are the coordinates of the candidate reference points; and It is the first The coordinates of the data points; is the minimum rectangular area of ​​the cluster; It is the first The distance from a data point to its nearest neighbor; is the index value of the data point in the cluster.

[0028] Furthermore, the three reference points are analyzed according to their specific data distribution characteristics to obtain data analysis results, including:

[0029] For each reference point, determine the area centered on the reference point, calculate the number of data points within the central area, and divide the area by the number of data points to get the data density;

[0030] For each data point, calculate the Euclidean distance between the data point and the reference point, find the average of all Euclidean distances, and take its reciprocal as the centrality indicator;

[0031] According to the data point y The coordinates are sorted if y If the coordinates are the same, press x Sort the coordinates; put the first three points after sorting into the stack; starting from the fourth point, scan each point in turn and make the following judgments:

[0032] If the angle formed by the current point and the two points at the top of the stack is "right turn", then pop the point on the top of the stack until the angle formed by the current point and the two points at the top of the stack is "left turn" or "straight", and push the current point into the stack; when all points have been scanned, the points left in the stack are the vertices of the convex hull; determine the vertex order of the convex polygon based on the points in the stack; divide the convex polygon into multiple triangles and calculate the sum of the areas of multiple triangles to obtain the total area of ​​the convex polygon; traverse all data points and find x Coordinates and y The maximum and minimum values ​​of the coordinates, these four values ​​will define the four corners of the bounding box; use the four corners to calculate the area of ​​the bounding box; divide the convex hull area by the bounding box area to get the compactness;

[0033] Find the nearest neighbor of each data point and calculate the distance between each data point and its nearest neighbor; take the average of all the nearest neighbor distances as the uniformity indicator;

[0034] The data density, centrality index, compactness and uniformity index of each reference point are analyzed to obtain the data distribution characteristics of different reference points. The data distribution characteristics of different reference points are the data analysis results.

[0035] Furthermore, the nearest neighbor of each data point is found, and the distance between each data point and its nearest neighbor is calculated; the average value of the distances of all nearest neighbors is used as a uniformity indicator, including:

[0036] According to the dimension of the data points, a kd tree is constructed using all the data points. The kd tree is a tree data structure that stores instance points in a k-dimensional space for retrieval.

[0037] For each point in the data set, use it as a query point and search for its nearest neighbor in the kd tree;

[0038] Starting from the root node, compare the coordinate values ​​of the query point with the current node. According to the partitioning rules of the kd tree, determine whether to recursively search the left subtree or the right subtree. Recursively search downward until a leaf node is reached. The data point represented by the leaf node is temporarily considered to be the nearest neighbor of the query point.

[0039] Perform a backtracking operation to check whether there is a corresponding point on the path from the current node to the root node by comparing the distance between the query point and the node on the path. If a corresponding point is found during the backtracking process, the nearest neighbor point is updated.

[0040] For each query point, its nearest neighbor is found, and the Euclidean distance between the query point and the nearest neighbor is calculated. After traversing all data points and finding the corresponding nearest neighbor, all calculated nearest neighbor distances are added together to get the total distance; the total distance is divided by the number of data points to get the average nearest neighbor distance, which is a uniformity indicator.

[0041] Further, according to the data analysis results of the three reference points, the crossover values ​​between the three reference points are calculated, including:

[0042] According to the data analysis results of the three reference points, Calculate the intersection value between three reference points;

[0043] in, Indicates area and The intersection area of ​​; Indicates area and The intersection area of ​​; Indicates area and The intersection area of ​​; Indicates area area; Indicates area area; Indicates area area; Represents the average overlap between the three regions.

[0044] Furthermore, the geographic entity data is evaluated using the cross value to obtain evaluation results, including:

[0045] Read geographic entity data from the database, including entity identification and location information;

[0046] For each pair of geographic entities, the intersection value thereof is compared with a set threshold value to obtain a comparison result; based on the comparison result, the association level of each pair of geographic entities is determined;

[0047] Create a new data table in the database to record association level information; insert the identifier, intersection value and association level information of each pair of geographic entities into the data table;

[0048] The geographic entity pairs with the association level are queried from the data table, and the geographic entity pairs with the association level are identified by analyzing the query results. The geographic entity pairs with the association level are the evaluation results.

[0049] Furthermore, according to the evaluation results, the geographic entity data is corrected to obtain corrected geographic entity data, including:

[0050] According to the geographic entity pairs with association levels, the reasons for the low cross-values ​​are analyzed to obtain the analysis results;

[0051] According to the analysis results, the corresponding correction strategy is determined, and according to the correction strategy, the problematic geographic entity data is cleaned and corrected to obtain the corrected geographic entity data.

[0052] Second, a new basic surveying and mapping multimodal geographic entity data surveying and mapping system, including:

[0053] The collection module is used to collect multimodal data of the surveying area, and the multimodal data includes remote sensing image data, topographic map data, three-dimensional model data and point cloud data;

[0054] A preprocessing module, used for preprocessing multimodal data to form standardized geographic entity data;

[0055] A selection module is used to select three reference points in the surveying and mapping area that reflect the geographical features and data distribution characteristics in the surveying and mapping area;

[0056] An analysis module is used to analyze the three reference points respectively according to their specific data distribution characteristics to obtain data analysis results;

[0057] A calculation module, used for calculating the intersection value between the three reference points according to the data analysis results of the three reference points;

[0058] An evaluation module, used for evaluating geographic entity data using the cross value to obtain an evaluation result;

[0059] The correction module is used to correct the geographic entity data according to the evaluation results to obtain the corrected geographic entity data; and output the corrected geographic entity data as a standardized surveying and mapping result.

[0060] According to a third aspect, a computing device includes:

[0061] one or more processors;

[0062] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0063] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0064] The above scheme of the present invention includes at least the following beneficial effects.

[0065] The present invention can more comprehensively and accurately reflect the geographical features of the surveying and mapping area by collecting and comprehensively analyzing multimodal data (including remote sensing image data, topographic map data, three-dimensional model data and point cloud data). Through in-depth analysis and cross-validation of these data, the accuracy and reliability of surveying and mapping can be significantly improved. The present invention selects reference points that reflect the geographical features and data distribution characteristics in the surveying and mapping area, and conducts targeted data analysis, so that the method can better adapt to different geographical environments and data distribution conditions, thereby improving the flexibility and applicability of the surveying and mapping method.

[0066] The present invention uses crossover values ​​to evaluate geographic entity data and corrects the data according to the evaluation results. This correction method can more accurately identify and correct errors and inconsistencies in the data, thereby obtaining more accurate geographic entity data. The present invention outputs the corrected geographic entity data as standardized surveying and mapping results, which not only facilitates the subsequent application and processing of the data, but also improves the efficiency of data sharing and exchange. Through the automated data preprocessing, analysis and correction process, the present invention can reduce manual intervention and improve the efficiency and automation level of surveying and mapping work. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a flow chart of a novel basic surveying and mapping multimodal geographic entity data surveying and mapping method provided by an embodiment of the present invention.

[0068] Figure 2 It is a schematic diagram of a new basic surveying and mapping multimodal geographic entity data surveying and mapping system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0070] like Figure 1 As shown, an embodiment of the present invention proposes a novel basic surveying and mapping multimodal geographic entity data surveying and mapping method, the method comprising the following steps:

[0071] Step 11, collecting multimodal data of the surveying area, the multimodal data including remote sensing image data, topographic map data, three-dimensional model data and point cloud data;

[0072] Step 12, preprocessing the multimodal data to form standardized geographic entity data;

[0073] Step 13, selecting three reference points in the surveying and mapping area that reflect the geographical features and data distribution characteristics in the surveying and mapping area;

[0074] Step 14, analyzing the three reference points respectively according to their specific data distribution characteristics to obtain data analysis results;

[0075] Step 15, calculating the intersection value between the three reference points according to the data analysis results of the three reference points;

[0076] Step 16, using the cross value, evaluating the geographic entity data to obtain an evaluation result;

[0077] Step 17, according to the evaluation result, the geographic entity data is corrected to obtain corrected geographic entity data;

[0078] Step 18, outputting the corrected geographic entity data as standardized surveying and mapping results.

[0079] In the embodiment of the present invention, step 11 can comprehensively obtain multiple data sources in the surveying and mapping area, including remote sensing images, topographic maps, three-dimensional models and point clouds, etc., to ensure the comprehensiveness and diversity of information. Step 12, by pre-processing the collected multimodal data, noise, outliers and format inconsistencies in the data can be eliminated, standardized geographic entity data can be formed, and data quality and processing efficiency can be improved. Step 13, select reference points that reflect the geographical features and data distribution characteristics in the surveying and mapping area, which can representatively reflect the situation of the entire area. Step 14, specific data distribution characteristics analysis is performed on the selected reference points, which can deeply explore the correlation and difference between each modal data. Step 15, by calculating the cross value between the three reference points, the consistency and complementarity between different modal data can be quantitatively evaluated. Step 16, using the cross value to evaluate the geographic entity data, the accuracy and reliability of the data can be comprehensively and objectively evaluated. Step 17, according to the evaluation results, the geographic entity data is corrected, which can improve the deficiencies and errors in the data in a targeted manner and improve the accuracy and credibility of the data. Step 18, output the corrected geographic entity data as standardized surveying and mapping results to facilitate data sharing, exchange and further application, and improve the versatility and interoperability of the data.

[0080] In another preferred embodiment of the present invention, the above step 11 collects multimodal data of the surveying area, and the multimodal data includes remote sensing image data, topographic map data, three-dimensional model data and point cloud data, including:

[0081] Use satellite remote sensing technology to obtain high-resolution remote sensing images of the surveying and mapping area. These images include images of different bands such as multispectral, panchromatic, and infrared, which can reflect different characteristics of the surface and ensure the timeliness of remote sensing images. Select the latest or specific time period image data as needed, conduct preliminary quality checks on remote sensing images, and eliminate data with severe cloud and fog obstruction or poor imaging quality; obtain topographic map data of the surveying and mapping area from public map resources. These data include contour lines, elevation points, water systems, roads and other geographical elements; select topographic maps of appropriate scales according to surveying and mapping needs to ensure accurate expression of topographic details, and conduct quality checks on topographic map data. Digital processing is performed and converted into an electronic format that can be used for subsequent analysis. The three-dimensional model data of the surveying and mapping area is obtained by using technical means such as oblique photogrammetry and laser scanning. These data can truly reflect the three-dimensional shape and spatial relationship of the objects. The accuracy and integrity of the three-dimensional model data are verified to ensure that the data quality meets the surveying and mapping requirements. The point cloud data of the surveying and mapping area is obtained through laser radar scanning or high-precision cameras carried by drones and other equipment. The point cloud data consists of a large number of three-dimensional coordinate points and can accurately describe the shape of the surface and objects. The point cloud data is processed by denoising and filtering to improve the purity and availability of the data.

[0082] In another preferred embodiment of the present invention, the above step 12, preprocessing the multimodal data to form standardized geographic entity data, includes:

[0083] Convert multimodal data from different sources into a unified format or standard to facilitate subsequent data processing and analysis. For example, convert remote sensing images, topographic maps, 3D models, and point cloud data into a common geographic information system (GIS) format. Ensure that all multimodal data use the same coordinate system and elevation benchmark to eliminate spatial position deviations caused by inconsistent coordinate systems, and perform projection transformation or coordinate transformation operations on data that require coordinate transformation.

[0084] The quality of multimodal data is checked, including the integrity, accuracy, and consistency of the data. When errors or outliers are found in the data, necessary corrections or elimination operations are performed. For example, bad points in remote sensing images are repaired, and wrong elevation points in topographic maps are corrected. According to the scope of the surveying and mapping area, multimodal data are cropped to remove the data outside the study area. Multimodal data are standardized, such as scaling data values ​​to a unified range or unit to eliminate dimensional differences between different data sources.

[0085] In a preferred embodiment of the present invention, the above step 13, selecting three reference points in the surveying and mapping area that reflect the geographical features and data distribution characteristics in the surveying and mapping area, may include:

[0086] Step 131, according to the geographical features of the surveying and mapping area and the overall distribution of the data, a K value is set by the silhouette coefficient, and the K value represents the number of clusters expected to be formed, which specifically includes: collecting geographical data of the surveying and mapping area, including topographic maps, remote sensing images, etc., and performing preliminary visual analysis of the data to understand the terrain undulations, water system distribution, and the general situation of urban and rural areas; using geographic information system (GIS) tools to extract key geographical features, such as elevation variation range, major rivers and their basins, urban-rural boundaries, etc.; quantitatively describing these features, such as calculating terrain slope, river length and basin area, urban-rural area ratio, etc.; performing spatial distribution analysis on standardized geographic entity data to identify dense and sparse areas of data points; using statistical methods (such as kernel density estimation) to evaluate the density of data points, analyze the distribution of data points in geographic space, and identify whether there are obvious clusters or outliers; determining the parameter range for calculating the silhouette coefficient, that is, the possible value range of the K value, which is based on a preliminary judgment of the data scale and distribution characteristics; for each possible K value, performing the following operations:

[0087] Randomly select K initial center points (or use a specific initialization method, such as K-means); assign data points to the nearest cluster based on the K center points; calculate the silhouette coefficient of each cluster. The silhouette coefficient is a measure based on cluster cohesion and inter-cluster separation, and its value ranges from -1 to 1.

[0088] The specific calculation method of the silhouette coefficient is as follows: for each data point, calculate its average distance to other points in the same cluster (cohesion) and its average distance to the nearest point in a different cluster (separation). Then, subtract the cohesion from the separation and divide it by the larger value of the two to get the silhouette coefficient of the point. Finally, calculate the average of the silhouette coefficients of all data points as the overall silhouette coefficient under this K value.

[0089] Compare the silhouette coefficients under different K values, select the K value with the largest silhouette coefficient as the number of clusters expected to be formed, and record the selected K value and its corresponding silhouette coefficient.

[0090] Step 132, randomly selecting K points from the standardized geographic entity data as initial center points, specifically includes: obtaining a standardized geographic entity data set, in which each data point has corresponding coordinate information; performing a preliminary spatial distribution analysis on the data set to understand the dense and sparse areas of the data points; in order to ensure that the K points extracted are relatively uniform in spatial distribution, a constrained random sampling method can be used to divide the surveying and mapping area into several sub-areas, ensuring that each sub-area contains at least a certain number of data points, and determining the number of center points extracted from each sub-area based on the number of sub-areas and data distribution; for each sub-area, a sampling probability is set based on the number of data points within it to ensure that more center points are extracted from areas with dense data points. , while ensuring that there are representative points in sparse areas; using a random number generator, randomly select one or more data points as initial center point candidates in each sub-area according to the extraction probability, check the distance between the candidate center points, and ensure that the minimum distance between them is greater than a preset threshold to avoid excessive concentration of center points. If the distance between the candidate center points is less than the threshold, re-extract until the distance constraint is met; after the above steps, each sub-area will obtain a set of candidate center points that meet the distance constraint. From the candidate center points of each sub-area, randomly select or select a point according to some criteria (such as the closest to the sub-area center) as the representative center point of the sub-area, and merge the representative center points selected from all sub-areas to form the final set of K initial center points.

[0091] Step 133, for each point in the data set, calculate its distance from the K center points, and assign each point to the cluster represented by the corresponding center point, specifically including:

[0092] Create an empty list of length K to store the information of K clusters. Each cluster information can be represented as a dictionary or object, including attributes such as the coordinates of the center point and the list of data points included; traverse each point in the data set, recorded as point P, and for each point P, calculate the Euclidean distance between it and the K center points; store the calculated K Euclidean distances in a temporary list and record the corresponding center point index; find the minimum distance value and the corresponding center point index from the temporary list. This nearest center point is the representative of the cluster to which point P will be assigned; according to the index of the nearest center point, add point P to the data point list of the corresponding cluster. If the cluster list is empty or has not been initialized, create a data point list for the cluster first; after assigning point P, update the information of the corresponding cluster, including the number of data points, possible cluster boundaries, etc.; repeat the above steps until all points in the data set are assigned to the corresponding cluster.

[0093] Step 134, for each cluster, calculate the average coordinates of all data points within it, obtain the new center point coordinates, and update the center point of the cluster, repeat the steps of assigning data points to clusters and updating the center point until a preset number of iterations is reached, stop the iteration, and mark each data point with the cluster number to which it belongs, specifically including:

[0094] Set the preset number of iterations, denoted as max_iterations, which is a positive integer indicating the maximum number of iterations executed by the algorithm; set the threshold for the change of the center point, denoted as tolerance, which is a positive decimal used to determine whether the center point is stable (i.e. whether the change is small enough), and initialize the iteration counter, denoted as iteration, with an initial value of 0. When iteration is less than max_iterations, perform the following steps:

[0095] For each cluster, traverse all the data points inside it and calculate the average value of the coordinates of these data points, including the average value of the x-coordinate and the average value of the y-coordinate (if it is three-dimensional data, it also includes the average value of the z-coordinate), and use the calculated coordinate average as the new center point coordinate of the cluster.

[0096] Replace the current center point coordinates of each cluster with the calculated new center point coordinates. Repeat step 133 based on the new center point coordinates, that is, recalculate the distance between each point in the data set and each center point, and assign it to the cluster represented by the nearest center point; after redistributing the data points, compare the changes between the new and old center point coordinates. The magnitude of the change can be determined by calculating the Euclidean distance between the new and old center points. If the changes of all center points are less than tolerance, the algorithm is considered to have converged and the iteration can be stopped. If the center point change is greater than or equal to tolerance, increase the iteration counter iteration value and return to continue the iteration; if max_iterations iterations are reached, the iteration is stopped regardless of whether the center point is stable. After the iteration, mark the cluster number to which each data point belongs. This can be achieved by adding a field in the data point structure, which stores the index or number of the cluster to which the point belongs. Output the center point coordinates of the K clusters after the iteration ends, and output the cluster number to which each data point belongs.

[0097] Step 135, output the coordinates of the center points of the K clusters and the number of data points contained in each cluster; for each formed cluster, traverse all the data points therein, specifically including:

[0098] For the K clusters that have been formed, traverse each cluster, obtain the coordinates of the center point of the currently traversed cluster, and record the coordinates of the center point of the cluster. This information can be saved to a file or output in other forms for subsequent viewing and analysis. Calculate the number of data points contained in the current cluster and record the number; for each cluster, initialize an empty list to store the detailed information of all data points in the cluster (such as coordinates, attributes, etc.), traverse all data points in the current cluster, and add the relevant information of each data point to the corresponding list.

[0099] Step 136, based on the coordinates of all data points in the cluster, calculate the geometric center of all data points, specifically including: for each cluster, prepare two variables, respectively used to accumulate the sum of x-coordinates and y-coordinates (if it is three-dimensional data, a variable is also required to accumulate the sum of z-coordinates); for the cluster currently being processed, traverse all data points contained in it; during the traversal process, add the x-coordinate and y-coordinate (and possible z-coordinate) of each data point to the corresponding sum variable; in the process of traversing the data points, each time a data point is processed, the counter is increased by 1; after the traversal is completed, divide the sum of the x-coordinates by the number of data points to obtain the average value of the x-coordinates; similarly, divide the sum of the y-coordinates (and z-coordinates, if it is three-dimensional data) by the number of data points to obtain the average value of the y-coordinates (and z-coordinates); combine the calculated x-coordinate average value and y-coordinate average value (and z-coordinate average value, if it is three-dimensional data) to form the geometric center coordinates of the cluster.

[0100] Step 137, taking the geometric centers of all data points as candidate reference points of the cluster, specifically includes:

[0101] The geometric center is the average value of the coordinates of all data points in the cluster. It represents the center position of the cluster data points in space. As a candidate reference point, the geometric center can reflect the main characteristics of the cluster in spatial distribution, including the center position and density of the cluster. For each cluster, the calculated geometric center coordinates are obtained from the previous step. The geometric center of each cluster is directly set as the candidate reference point of the cluster. The coordinates of the candidate reference point are the coordinates of the geometric center, including x-coordinate and y-coordinate (and z-coordinate for three-dimensional data).

[0102] Step 138, assigning an evaluation value to each candidate point according to the spatial distribution and data density characteristics of the candidate reference points, specifically including: analyzing the spatial distribution of the candidate reference points, considering the distance and relative position between the candidate reference points and other points, evaluating the data density of the area where the candidate reference points are located, and the candidate points in the area with high data density have higher evaluation values, and comprehensively considering the spatial distribution and data density characteristics to assign an evaluation value to each candidate point;

[0103] Step 139, sorting the candidate reference points according to the evaluation values ​​to select the corresponding three points as the final reference points, and outputting the coordinates and related information of the selected three final reference points, specifically including:

[0104] For each candidate reference point of each cluster, calculate its evaluation value. The higher the evaluation value, the more the candidate reference point can represent the characteristics of the cluster to which it belongs. According to the calculated evaluation value, sort all candidate reference points from high to low. This step can be implemented using a sorting algorithm (such as quick sort, merge sort, etc.). From the sorted list of candidate reference points, select the first three points with the highest evaluation values ​​as the final reference points. If there are less than three candidate reference points, select all candidate reference points as the final reference points. Output the coordinate information of these three final reference points, including x-coordinate and y-coordinate (also including z-coordinate for three-dimensional data).

[0105] In the embodiment of the present invention, step 131, by setting the K value through the silhouette coefficient, can reasonably determine the number of clusters expected to be formed, ensuring that the selected reference points can fully and not overly complexly reflect the geographical characteristics of the surveying and mapping area. Step 132, randomly select the initial center point as the starting point of the cluster analysis, ensure the objectivity and unbiasedness of the analysis, and avoid subjective errors caused by human selection. Steps 133 to 134, by iteratively calculating the distance between each point and the center point and assigning clusters, and updating the cluster center point, can gradually optimize the division of clusters, so that the final clusters are more compact and reasonable, thereby more accurately reflecting the actual distribution of the data. Steps 135 to 137, traverse the data points of each cluster and calculate the geometric center, which is used as a candidate reference point, ensuring that the reference point can represent the overall position and distribution characteristics of the data points in the cluster, and improving the representativeness and reliability of the reference point. Step 138, by evaluating the spatial distribution and data density characteristics of the candidate reference points, assigning an evaluation value to each candidate point, the quality of the candidate points can be quantitatively evaluated. Step 139, sorting the candidate reference points according to the evaluation values ​​and selecting the final three reference points ensures that the selected reference points are the most representative and valuable points, so as to more effectively support the subsequent data evaluation and correction work. At the same time, the coordinates and related information of the selected reference points are output, which facilitates the standardization and sharing of surveying and mapping results.

[0106] In a preferred embodiment of the present invention, the calculation formula of the evaluation value is:

[0107] ;

[0108] in, , , and is the weight coefficient; Represents the total number of data points in the cluster; and All data points in the cluster are Maximum and minimum values ​​on the coordinates; and They are Maximum and minimum values ​​on the coordinates; and are the coordinates of the candidate reference points; and It is the first The coordinates of the data points; is the minimum rectangular area of ​​the cluster; It is the first The distance from a data point to its nearest neighbor; is the index value of the data point in the cluster.

[0109] In the embodiment of the present invention, the formula comprehensively considers multiple aspects of the cluster, including the distribution range of the data points (reflected by the difference between the maximum and minimum values ​​of the x and y coordinates), the concentration of the data points relative to the candidate reference points (reflected by the inverse of the sum of the distances), the shape characteristics of the cluster (characterized by the minimum rectangular area ratio of the cluster), and the closeness between the data points (described by the average distance to the nearest neighbor). By adjusting the weight coefficient , , and , some evaluation factors can be emphasized or weakened according to specific application scenarios and needs. For example, in some cases, more attention may be paid to the concentration of data points, while in other cases, more attention may be paid to the shape characteristics of the cluster. By calculating specific evaluation values, the advantages and disadvantages of candidate reference points can be quantitatively compared. This quantitative method not only facilitates sorting and selecting final reference points, but also provides an objective measurement standard, which helps to reduce the bias caused by subjective judgment. Since the evaluation value takes into account multiple factors, the final reference point selected is more likely to reflect the main characteristics of the cluster in space, and also helps to improve the accuracy and effectiveness of the entire analysis process.

[0110] In a preferred embodiment of the present invention, the above step 14 analyzes the three reference points respectively according to their specific data distribution characteristics to obtain data analysis results, including:

[0111] Step 141, for each reference point, determine the area range centered on the reference point, calculate the number of data points within the area range at the center, and obtain the data density by dividing the area of ​​the area by the number of data points, specifically including: for each reference point, define an area range (for example, a circular area or a square area centered on the reference point); count the number of data points within the area range, which can be done by traversing all data points and checking whether they are located in the defined area; calculate the area of ​​the area (for example, if it is a square area, use the square of the side length to calculate); divide the area of ​​the area by the number of data points to obtain the data density corresponding to the reference point.

[0112] Step 142, for each data point, calculate the Euclidean distance between the data point and the reference point, find the average of all Euclidean distances, and take the reciprocal as the centrality indicator, specifically including: for each data point, use the Euclidean distance formula to calculate the distance between it and the current reference point, find the average of all calculated Euclidean distances, and take the reciprocal of the average as the centrality indicator, which reflects the degree to which the data points are clustered around the reference point.

[0113] Step 143, based on the data point y The coordinates are sorted if y If the coordinates are the same, press x Sort the coordinates; put the first three points after sorting into the stack; starting from the fourth point, scan each point in turn and make the following judgments: if the angle formed by the current point and the two points at the top of the stack is "turn right", then pop the point on the top of the stack until the angle formed by the current point and the two points at the top of the stack is "turn left" or "go straight", and push the current point into the stack; when all points have been scanned, the points left in the stack are the vertices of the convex hull; determine the vertex order of the convex polygon based on the points in the stack; divide the convex polygon into multiple triangles and calculate the sum of the areas of multiple triangles to obtain the total area of ​​the convex polygon; traverse all data points and find x Coordinates and y The maximum and minimum values ​​of the coordinates, which define the four corners of the bounding box; use the four corners to calculate the area of ​​the bounding box; divide the convex hull area by the bounding box area to get the compactness, which includes:

[0114] Create a list of data points, each containing x and y coordinates; sort the data points using a stable sorting algorithm (such as the merge sort algorithm). Sort by y coordinate first, and if the y coordinates are the same, sort by x coordinate. Create an empty stack to store the vertices of the convex hull, and push the first three sorted data points into the stack in turn. These three points will be the starting points for constructing the convex hull; starting from the fourth sorted point, traverse each data point in turn, and for the currently traversed data point, perform the following operations:

[0115] Determine the direction of the corner and update the stack, get the two points at the top of the stack, and form an angle together with the current point; determine whether the corner is "turn right", "turn left" or "go straight" by calculating the cross product of the vectors. If the corner is "turn right", pop the top point of the stack and repeat this step until the corner becomes "turn left" or "go straight", and push the current point into the stack.

[0116] When all data points have been traversed, the points left in the stack are the vertices of the convex hull. These vertices are connected in the order they are in the stack to form a convex polygon. The vertex order of the convex polygon is determined according to the points in the stack, and the convex polygon is divided into multiple triangles. Each triangle consists of three adjacent vertices. The area of ​​each triangle is calculated using Heron's formula or the vector cross product method, and the areas of all triangles are added together to obtain the total area of ​​the convex polygon.

[0117] Traverse all data points and find the maximum x coordinate max and the minimum value x min , and the maximum value of the y coordinate y max and the minimum value y min , these four values ​​define the four corners of the bounding box: (x min ,y min ), (x max ,y min ), (x max ,y max ), (x min ,y max ); Calculate the area of ​​the bounding box using the formula: (x max -x min ) ×(y max -y min ). Divide the convex hull area by the bounding box area to get the compactness, which is a value between 0 and 1, reflecting the compactness of the data point distribution. The closer the value is to 1, the more compact the data point distribution is; the closer the value is to 0, the looser the data point distribution is.

[0118] Step 144, find the nearest neighbor point of each data point, calculate the distance between each data point and its nearest neighbor point; take the average value of all the nearest neighbor point distances as the uniformity index, specifically including: for each data point, find its nearest neighbor point, which can be completed by calculating the distance between the current point and all other data points and selecting the point with the smallest distance, calculate the distance between each data point and its nearest neighbor point, and average all the calculated nearest neighbor point distances as the uniformity index, which reflects the uniformity of the data point distribution.

[0119] Step 145, analyzing the data density, centrality index, compactness and uniformity index of each reference point to obtain the data distribution characteristics of different reference points, the data distribution characteristics of different reference points are the data analysis results, specifically including: summarizing the data density, centrality index, compactness and uniformity index of each reference point, and performing a comprehensive analysis on these indexes to understand the data distribution characteristics of different reference points. For example, the data density of different reference points can be compared to identify the area where the data points are concentrated; or the centrality index can be compared to find out which reference points the data points are more concentrated.

[0120] In an embodiment of the present invention, by calculating data density, centrality index, compactness and uniformity index, the distribution of data around each reference point can be fully understood. Each calculation in step 14 generates a specific numerical value, which serves as an objective criterion for evaluating data distribution characteristics. Quantitative evaluation helps to reduce subjective judgment and make the analysis results more reliable and comparable. The calculation of convex hull and bounding box reveals the spatial range and shape characteristics of data points, and in particular, the calculation of compactness helps to identify the tightness of data distribution. For example, in cluster analysis, a reference point with the best data distribution characteristics can be selected as the center of the cluster. Understanding data distribution can help optimize resource allocation. For example, in geospatial analysis, knowing which areas have denser data points can guide more efficient deployment of resources (such as service facilities). Through the automated calculation process, data analysis results can be quickly obtained, avoiding the tedious and time-consuming manual analysis and calculation, thereby improving analysis efficiency.

[0121] In a preferred embodiment of the present invention, the above step 144, finding the nearest neighbor point of each data point, calculating the distance between each data point and its nearest neighbor point; taking the average value of all the nearest neighbor point distances as the uniformity index, includes:

[0122] Step 1441, based on the dimensions of the data points, use all the data points to build a kd tree. The kd tree is a tree data structure that stores instance points in a k-dimensional space for retrieval, specifically including: determining the dimension k of the data points, selecting a dimension as the starting partitioning dimension, usually starting from the first dimension, sorting all data points according to the value of the current partitioning dimension, selecting the midpoint of the sorted array as the root node, dividing the array into left and right parts, recursively repeating the above steps for the left and right parts of the data, selecting the next dimension as the partitioning dimension each time recursively (if the last dimension is reached, loop back to the first dimension), until all data points are used as leaf nodes, and the constructed kd tree is saved.

[0123] Step 1442, for each point in the data set, use it as a query point, and search for its nearest neighbor point in the kd tree, specifically including: for each point in the data set, use it as a query point, initialize a variable to save the currently found nearest neighbor point and the corresponding distance, and start searching from the root node of the kd tree;

[0124] Step 1443, starting from the root node, compare the coordinate values ​​of the query point with the current node, and determine whether to recursively search the left subtree or the right subtree according to the partitioning rule of the kd tree, and recursively search downward until a leaf node is reached. The data point represented by the leaf node is temporarily considered to be the nearest neighbor of the query point, specifically including: comparing the coordinate value of the query point with the current node, determining the position of the query point on the current partitioning dimension, if the current dimension value of the query point is less than the value of the current node, recursively search the left subtree; otherwise, recursively search the right subtree, and recursively search downward until a leaf node is reached. The data point represented by the leaf node is temporarily regarded as the nearest neighbor of the query point, and the distance is recorded.

[0125] Step 1444, perform a backtracking operation, by comparing the distance between the query point and the node on the path, to check whether there is a corresponding point on the path from the current node to the root node. If a corresponding point is found during the backtracking process, the nearest neighbor point is updated. Specifically, it includes: after the recursive search is completed, start the backtracking operation, for each node on the search path, check the distance between its splitting hyperplane and the query point, if the distance is less than the currently recorded nearest neighbor distance, it means that there may be closer neighbors, and it is necessary to further check the other side subtree of the node (the side that has not been visited during the search process). If a closer point is found during the backtracking process, the nearest neighbor point and the corresponding distance are updated, and backtracking continues until the root node is reached.

[0126] Step 1445, for each query point, find its nearest neighbor point, and calculate the Euclidean distance between the query point and the nearest neighbor point. After traversing all data points and finding the corresponding nearest neighbor point, add all the calculated nearest neighbor point distances to get the total distance; divide the total distance by the number of data points to get the average nearest neighbor point distance, which is the uniformity index, specifically including: for each query point, record the nearest neighbor point and the corresponding Euclidean distance finally found; after traversing all data points and finding the corresponding nearest neighbor point, add all the calculated nearest neighbor point distances to get the total distance; divide the total distance by the number of data points to get the average nearest neighbor point distance; this average nearest neighbor point distance is the uniformity index, which reflects the uniformity of the data point distribution. If the uniformity index is small, it means that the data points are distributed densely and evenly; if the index is large, it means that the data points are distributed sparsely or unevenly.

[0127] In an embodiment of the present invention, a kd tree is an efficient data structure, particularly suitable for nearest neighbor search in high-dimensional space. By constructing a kd tree, the time complexity required for searching the nearest neighbor point can be significantly reduced, thereby improving the overall computational efficiency. Using a kd tree for nearest neighbor search can ensure that the exact nearest neighbor point of each data point is found, rather than an approximate value, which ensures the accuracy of the calculated uniformity index. As a general data structure, the kd tree can easily process data sets of different dimensions and sizes. Therefore, the method has good scalability and can adapt to data analysis tasks of various sizes. The uniformity index, as an intuitive indicator to measure the uniformity of data point distribution, helps to better understand the spatial distribution characteristics of data. By visualizing the index, dense and sparse areas in the data set can be intuitively identified.

[0128] In a preferred embodiment of the present invention, the above step 15, calculating the intersection value between the three reference points according to the data analysis results of the three reference points, includes:

[0129] Step 151, based on the data analysis results of the three reference points, Calculate the intersection value between three reference points;

[0130] in, Indicates area and The intersection area of ​​; Indicates area and The intersection area of ​​; Indicates area and The intersection area of ​​; Indicates area area; Indicates area area; Indicates area area; Represents the average overlap between the three regions.

[0131] In an embodiment of the present invention, this step provides a specific numerical value (crossover value X) for quantifying the degree of overlap between the three regions. By calculating the crossover value, decision makers can evaluate the mutual impact of different regions based on the degree of overlap. For example, in urban planning, the spatial layout can be optimized based on the overlap of different functional areas; in ecological research, the overlap of different ecological regions can be analyzed to evaluate biodiversity. Compared with visual judgment or manual calculation of overlapping areas, using a formula to automatically calculate the crossover value can greatly improve processing speed and accuracy.

[0132] In a preferred embodiment of the present invention, step 16, using the crossover value to evaluate the geographic entity data to obtain an evaluation result, includes:

[0133] Step 161, reading geographic entity data from the database, including entity identification and location information, specifically includes: using appropriate database connection tools (such as JDBC, ODBC, etc.) and credentials to connect to the database storing geographic entity data; running an SQL query to select the geographic entity data table and retrieve the identification (such as ID) and location information (such as latitude and longitude coordinates or geographic boundaries) of all geographic entities therefrom; loading the query results into memory, usually storing them in the form of a data structure (such as a list, a collection, or a data frame) for subsequent processing.

[0134] Step 162, for each pair of geographic entities, compare their intersection values ​​with the set threshold value to obtain a comparison result; determine the association level of each pair of geographic entities based on the comparison result, specifically including: pre-setting one or more intersection value thresholds based on actual needs, and these thresholds will be used to determine the association level between geographic entities. For each geographic entity read from the database, pair it with all other entities to form a geographic entity pair; compare the intersection value of each pair of geographic entities with the set threshold value to obtain a comparison result; assign an association level (such as "high association", "medium association", "low association" or "no association") to each pair of geographic entities based on the comparison result of the intersection value and the threshold value. For example, the following four threshold points can be set:

[0135] T_high: lower threshold of high association;

[0136] T_medium: lower threshold for medium association (also upper threshold for high association);

[0137] T_low: lower threshold for low association (also upper threshold for medium association);

[0138] T_none: Upper threshold for no association (set to a small positive number to exclude cases with almost no overlap).

[0139] For each pair of geographic entities, the calculated intersection value is compared with the threshold set above, and the corresponding association level is assigned to each pair of geographic entities according to the comparison result:

[0140] If the crossover value ≥ T_high, then "high association" is assigned.

[0141] If T_medium ≤ crossover value < T_high, "medium association" is assigned.

[0142] If T_low≤crossover value<T_medium, assign "low association".

[0143] If the crossing value < T_low, "no association" is assigned.

[0144] Step 163, creating a new data table in the database for recording association level information; inserting the identifier, intersection value and association level information of each pair of geographic entities into the data table, specifically includes: designing a new data table in the database for storing association level information. The table should contain fields such as the identifier, intersection value and association level of the geographic entity pair; using SQL statements to create a new data table in the database; for each pair of geographic entities, inserting its identifier, calculated intersection value and determined association level as a record into the newly created data table.

[0145] Step 164, querying geographic entity pairs with association levels from the data table, and identifying geographic entity pairs with association levels by analyzing the query results. The geographic entity pairs with association levels are evaluation results, which specifically include:

[0146] Determine the level of association you want to retrieve, such as "high association". Construct an SQL query statement to select the records in the newly created data table that match the required level of association. The query should contain the identifier of the geographic entity pair and possible other related information. Use the database management tool or the database connection function in the programming environment to execute the constructed SQL query statement and obtain the query result set, which contains the geographic entity pairs that meet the specific level of association.

[0147] Start traversing each record in the query result set. For each record, check its association level field to ensure that it matches the required association level. For each geographic entity pair that meets the conditions, record its ID and other important information. This information can be temporarily stored in an in-memory data structure (such as a list or dictionary) for further processing.

[0148] In an embodiment of the present invention, by performing cross-value evaluation on geographic entity data, the degree of association between entities can be identified and quantified, thereby discovering potential data errors or inconsistencies. This helps to improve the accuracy and completeness of geographic data. The determination of the association level provides decision makers with richer information, enabling them to make more informed decisions based on the degree of association between entities. For example, in urban planning, resource allocation and spatial layout can be optimized based on the association level between different geographic entities. By recording the association level information in a database and creating a special data table for query, the efficiency of geographic entity data analysis can be greatly improved. The information on the association level can be used as an important attribute in a geographic information system (GIS) to enhance data visualization. By representing different association levels through different colors, sizes or shapes, the relationship between geographic entities can be made more intuitive and easy to understand. In the field of geographic information, it is often necessary to integrate information from different data sources. By using cross-values ​​to evaluate the association level of geographic entity data, a unified metric can be provided for the fusion of multi-source data, thereby improving the accuracy and efficiency of data fusion.

[0149] In a preferred embodiment of the present invention, step 17, based on the evaluation result, correcting the geographic entity data to obtain corrected geographic entity data, includes:

[0150] Step 171, according to the geographic entity pairs with association levels, analyze the reasons for the low cross-values ​​to obtain the analysis results, specifically including: screening out the geographic entity pairs with association levels but low cross-values ​​from the evaluation results, which represent problems or inconsistencies in the data; conducting a detailed data review for the screened low cross-value entity pairs, including checking the attribute information, spatial location, boundary definition, etc. of the entities, to determine the specific reasons that may lead to the low cross-values; classifying the reasons found in the review. Common reasons include data entry errors, coordinate system mismatch, outdated data, inaccurate entity boundary definition, etc., and record and describe the reasons for each category; record the analysis results in the form of documents or reports, including the cause analysis, impact range and possible data correction suggestions for each low cross-value entity pair.

[0151] Step 172, according to the analysis results, determine the corresponding correction strategy, and according to the correction strategy, clean and correct the problematic geographic entity data to obtain the corrected geographic entity data, which specifically includes: based on the analysis results of step 171, formulate a specific correction strategy for each cause category. For example, for data entry errors, it is necessary to recheck and correct the original data; for coordinate system mismatch, data conversion is required. Prioritize the correction strategies according to the severity of the problem and the urgency of the correction. This helps to ensure that the most critical and most influential problems in data quality are solved first. According to the correction strategy and priority order, the problematic geographic entity data is cleaned, which includes deleting duplicate or invalid data, correcting erroneous data, updating outdated data, etc. For entities that need to adjust the spatial location or boundary definition, use professional geographic information system (GIS) tools to perform precise correction operations to ensure that the corrected data meets the actual situation and accuracy requirements of the geographic entity. After completing the data cleaning and correction, the data is verified and reviewed, which includes recalculating the intersection value to ensure that the correction measures effectively improve the accuracy and consistency of the data.

[0152] In the embodiment of the present invention, by analyzing and correcting low-crossing value geographic entity pairs, the accuracy of geographic entity data can be significantly improved. The data cleaning and correction process helps to remove errors and redundant data, thereby improving the overall data quality. More accurate data sets can reduce errors and anomalies in the processing process, thereby improving the operating efficiency of geographic information systems (GIS) and other related systems.

[0153] Step 18, outputting the corrected geographic entity data as standardized surveying and mapping results, specifically including:

[0154] First, the corrected geographic entity data is sorted to ensure the integrity and consistency of the data. According to the requirements of standardized surveying and mapping results, the data is formatted, including the naming of data fields, unification of data types, standardization of coordinate systems, etc.

[0155] According to actual needs and usage scenarios, select the appropriate output format of surveying and mapping results, such as Shapefile (.shp), GeoJSON, KML, etc., to ensure that the selected format is compatible with most geographic information system (GIS) software or platforms. Before data output, configure relevant output parameters, such as the selection of coordinate system, data projection method, and retention of field attributes.

[0156] Use GIS software or data conversion tools to export the organized geographic entity data to the selected output format. During the export process, pay close attention to the integrity and accuracy of the data to ensure that no data is lost or deformed during the conversion process. Perform a detailed inspection of the exported surveying and mapping results, including data integrity, accuracy, and correctness of the format. Use GIS software to visualize the results so that the quality and effect of the data can be more intuitively checked. If necessary, correct and adjust the data until the expected output requirements are met.

[0157] like Figure 2 As shown, an embodiment of the present invention further provides a novel basic surveying and mapping multimodal geographic entity data surveying and mapping system, comprising:

[0158] The collection module is used to collect multimodal data of the surveying area, including remote sensing image data, topographic map data, three-dimensional model data and point cloud data;

[0159] A preprocessing module, used for preprocessing multimodal data to form standardized geographic entity data;

[0160] A selection module is used to select three reference points in the surveying and mapping area that reflect the geographical features and data distribution characteristics in the surveying and mapping area;

[0161] An analysis module is used to analyze the three reference points respectively according to their specific data distribution characteristics to obtain data analysis results;

[0162] A calculation module, used for calculating the intersection value between the three reference points according to the data analysis results of the three reference points;

[0163] An evaluation module, used for evaluating geographic entity data using the cross value to obtain an evaluation result;

[0164] The correction module is used to correct the geographic entity data according to the evaluation results to obtain the corrected geographic entity data; and output the corrected geographic entity data as a standardized surveying and mapping result.

[0165] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0166] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0167] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0168] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A new basic surveying and mapping multimodal geographic entity data surveying and mapping method, characterized in that: The method comprises: Collect multimodal data of the survey area, including remote sensing image data, topographic map data, 3D model data and point cloud data; Preprocess multimodal data to form standardized geographic entity data; Select three reference points in the survey area that reflect the geographical features and data distribution characteristics of the survey area; Analyze the three reference points respectively according to their specific data distribution characteristics to obtain data analysis results; According to the data analysis results of the three reference points, the crossover values ​​between the three reference points are calculated; Using the cross value, the geographic entity data is evaluated to obtain an evaluation result; According to the evaluation results, the geographic entity data is corrected to obtain corrected geographic entity data; Output the corrected geographic entity data as standardized surveying and mapping results; Three reference points reflecting the geographical features and data distribution characteristics of the survey area are selected within the survey area, including: According to the geographical characteristics of the survey area and the overall distribution of the data, a K value is set through the silhouette coefficient, and the K value represents the number of clusters expected to be formed; Randomly select K points from the standardized geographic entity data as the initial center points; For each point in the data set, calculate its distance from the K center points and assign each point to the cluster represented by the corresponding center point; For each cluster, calculate the average coordinates of all data points within it, obtain the new center point coordinates, and update the center point of the cluster. Repeat the steps of assigning data points to clusters and updating the center point until the preset number of iterations is reached. Stop the iteration and mark each data point with the cluster number to which it belongs. Output the coordinates of the center points of K clusters and the number of data points contained in each cluster; for each formed cluster, traverse all the data points in it; According to the coordinates of all data points within the cluster, calculate the geometric center of all data points; The geometric center of all data points is used as the candidate reference point of the cluster; According to the spatial distribution and data density characteristics of the candidate reference points, an evaluation value is assigned to each candidate point; Sort the candidate reference points according to the evaluation values ​​to select the corresponding three points as the final reference points, and output the coordinates and related information of the selected three final reference points; The calculation formula for the evaluation value is: ； in, is the weight coefficient; Represents the total number of data points in the cluster; and All data points in the cluster are Maximum and minimum values ​​on the coordinates; and They are Maximum and minimum values ​​on the coordinates; are the coordinates of the candidate reference points; It is the first The coordinates of the data points; is the minimum rectangular area of ​​the cluster; It is the first The distance from a data point to its nearest neighbor; is the index value of the data point in the cluster.

2. According to the novel basic surveying and mapping multimodal geographic entity data surveying and mapping method of claim 1, it is characterized in that: The three reference points are analyzed according to their specific data distribution characteristics to obtain data analysis results, including: For each reference point, determine the area centered on the reference point, calculate the number of data points within the central area, and divide the area by the number of data points to get the data density; For each data point, calculate the Euclidean distance between the data point and the reference point, find the average of all Euclidean distances, and take its reciprocal as the centrality indicator; According to the data point y The coordinates are sorted if y If the coordinates are the same, press x Sort the coordinates; put the first three points after sorting into the stack; starting from the fourth point, scan each point in turn and make the following judgments: If the angle formed by the current point and the two points at the top of the stack is "right turn", then pop the point on the top of the stack until the angle formed by the current point and the two points at the top of the stack is "left turn" or "straight", and push the current point into the stack; when all points have been scanned, the points left in the stack are the vertices of the convex hull; determine the vertex order of the convex polygon based on the points in the stack; divide the convex polygon into multiple triangles and calculate the sum of the areas of multiple triangles to obtain the total area of ​​the convex polygon; traverse all data points and find x Coordinates and y The maximum and minimum values ​​of the coordinates, these four values ​​will define the four corners of the bounding box; use the four corners to calculate the area of ​​the bounding box; divide the convex hull area by the bounding box area to get the compactness; Find the nearest neighbor of each data point and calculate the distance between each data point and its nearest neighbor; take the average of all the nearest neighbor distances as the uniformity indicator; The data density, centrality index, compactness and uniformity index of each reference point are analyzed to obtain the data distribution characteristics of different reference points. The data distribution characteristics of different reference points are the data analysis results.

3. A new basic surveying and mapping multimodal geographic entity data surveying and mapping method according to claim 2, characterized in that: Find the nearest neighbor of each data point and calculate the distance between each data point and its nearest neighbor; The average of all nearest neighbor distances is used as a uniformity indicator, including: According to the dimension of the data points, a kd tree is constructed using all the data points. The kd tree is a tree data structure that stores instance points in a k-dimensional space for retrieval. For each point in the data set, use it as a query point and search for its nearest neighbor in the kd tree; Starting from the root node, compare the coordinate values ​​of the query point with the current node. According to the partitioning rules of the kd tree, determine whether to recursively search the left subtree or the right subtree. Recursively search downward until a leaf node is reached. The data point represented by the leaf node is temporarily considered to be the nearest neighbor of the query point. Perform a backtracking operation to check whether there is a corresponding point on the path from the current node to the root node by comparing the distance between the query point and the node on the path. If a corresponding point is found during the backtracking process, the nearest neighbor point is updated. For each query point, its nearest neighbor is found, and the Euclidean distance between the query point and the nearest neighbor is calculated. After traversing all data points and finding the corresponding nearest neighbor, all calculated nearest neighbor distances are added together to get the total distance; the total distance is divided by the number of data points to get the average nearest neighbor distance, which is a uniformity indicator.

4. A novel basic surveying and mapping multimodal geographic entity data surveying and mapping method according to claim 3, characterized in that: According to the data analysis results of the three reference points, the crossover values ​​between the three reference points are calculated, including: According to the data analysis results of the three reference points, Calculate the intersection value between three reference points; in, Indicates area and The intersection area of ​​; Indicates area and The intersection area of ​​; Indicates area and The intersection area of ​​; Indicates area area; Indicates area area; Indicates area area; Represents the average overlap between the three regions.

5. A novel basic surveying and mapping multimodal geographic entity data surveying and mapping method according to claim 4, characterized in that: The geographic entity data is evaluated using the cross value to obtain evaluation results, including: Read geographic entity data from the database, including entity identification and location information; For each pair of geographic entities, the intersection value thereof is compared with a set threshold value to obtain a comparison result; based on the comparison result, the association level of each pair of geographic entities is determined; Create a new data table in the database to record association level information; insert the identifier, intersection value and association level information of each pair of geographic entities into the data table; The geographic entity pairs with the association level are queried from the data table, and the geographic entity pairs with the association level are identified by analyzing the query results. The geographic entity pairs with the association level are the evaluation results.

6. A new basic surveying and mapping multimodal geographic entity data surveying and mapping method according to claim 5, characterized in that: According to the evaluation results, the geographic entity data is corrected to obtain the corrected geographic entity data, including: According to the geographic entity pairs with association levels, the reasons for the low cross-values ​​are analyzed to obtain the analysis results; According to the analysis results, the corresponding correction strategy is determined, and according to the correction strategy, the problematic geographic entity data is cleaned and corrected to obtain the corrected geographic entity data.

7. A new basic surveying and mapping multimodal geographic entity data surveying and mapping system, characterized in that: Applied to the method according to any one of claims 1 to 6, comprising: The collection module is used to collect multimodal data of the surveying area, and the multimodal data includes remote sensing image data, topographic map data, three-dimensional model data and point cloud data; A preprocessing module, used for preprocessing multimodal data to form standardized geographic entity data; A selection module is used to select three reference points in the surveying and mapping area that reflect the geographical features and data distribution characteristics in the surveying and mapping area; An analysis module is used to analyze the three reference points respectively according to their specific data distribution characteristics to obtain data analysis results; A calculation module, used for calculating the intersection value between the three reference points according to the data analysis results of the three reference points; An evaluation module, used for evaluating geographic entity data using the cross value to obtain an evaluation result; The correction module is used to correct the geographic entity data according to the evaluation results to obtain the corrected geographic entity data; and output the corrected geographic entity data as a standardized surveying and mapping result.

8. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 6.

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