City-level surveying and mapping geographic information standard system fusion construction method and system

Abstract

The invention relates to the technical field of geographic information surveying and mapping, in particular to a city-level surveying and mapping geographic information standard system fusion construction method and system, and the method comprises the following steps: obtaining surveying and mapping data space coordinates, extracting terrain boundaries, ground feature distribution and road intersection features, calculating boundary point spacing, and calling a database to divide a topological structure. And detecting precision, spatial offset and overlapping conditions, and generating a topological correlation result. According to the method, by deeply analyzing the space coordinates, the time sequence, the geographic boundary and the classification information of the surveying and mapping data, the organizability and logicality of the data are improved, the tracking and updating capabilities of the data are enhanced through the refinement of the topological structure, the dynamic monitoring of the ground feature change is realized, the regional conflict is reduced through the optimization of the boundary adjustment mechanism, and the accuracy and the reliability of the surveying and mapping data are improved. The accuracy and adaptability of boundary data are improved, the consistency and application value of surveying and mapping data are enhanced through accurate matching of classification information and adaptive weight adjustment of data fusion, and accurate fusion of the data in a multi-source heterogeneous environment is ensured.

Description

Technical Field

[0001] The present invention relates to the field of geographic information surveying and mapping technology, and in particular to a method and system for integrating and constructing a city-level surveying and mapping geographic information standard system. Background Art

[0002] The field of geographic information surveying and mapping technology includes the collection, processing, analysis and management of geographic spatial data, involving surveying and mapping technology, remote sensing technology, geographic information system and positioning and navigation technology. The core content of this technical field is to obtain geographic data through various surveying and mapping methods such as satellite remote sensing, drone surveying and mapping, lidar scanning, etc., and use data processing and geographic information system technology for data storage, analysis and application. Geographic information surveying and mapping technology is widely used in land and resources management, urban planning, environmental monitoring, traffic management and other fields, aiming to provide high-precision and high-efficiency geographic information support. At present, with the development of technologies such as big data and artificial intelligence, geographic information surveying and mapping technology is evolving towards automation and intelligence, promoting the improvement of data processing efficiency and information service capabilities.

[0003] Among them, the city-level surveying and mapping geographic information standard system integration construction method and system refers to the use of standardized construction and integration strategies to achieve the unification of the data standard system within the city level, targeting the multi-source heterogeneity and non-uniform standards of surveying and mapping geographic information data. This method and system mainly covers technical matters such as data classification standard formulation, format standardization processing, data semantic consistency verification, standardized data conversion and mapping, and completes the data integration construction through geographic information model construction, multi-source data fusion processing, data consistency verification rule definition, etc., so that various types of surveying and mapping geographic information data can be stored, managed and applied according to a unified standard system.

[0004] Existing technologies have limitations in constructing the topological structure of surveying and mapping data, resulting in a relatively scattered data organization method, which affects the traceability and correlation analysis capabilities of the data. The use of time series information is relatively static, lacking in-depth mining of the rate of change of land objects, resulting in low accuracy in identifying spatial expansion trends, affecting the monitoring capabilities of urban construction changes. Boundary matching relies on fixed rules and lacks a flexible adjustment mechanism, resulting in data distortion problems in the process of regional boundary adjustment, affecting the scientific nature of spatial division. The matching method of geographic classification information is relatively extensive, and semantic consistency is not fully considered, which can easily cause confusion in the functional classification of land objects and affect the application value of surveying and mapping data. The matching weight adjustment method in the data fusion process is relatively fixed, lacking the ability to adaptively optimize based on data distribution characteristics, affecting the comprehensive adaptability of multi-source surveying and mapping data. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for integrating and constructing a city-level surveying and mapping geographic information standard system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a method for integrating and constructing a city-level surveying and mapping geographic information standard system, comprising the following steps:

[0007] S1: Obtain the spatial coordinates of surveying and mapping data, extract the terrain boundary, object distribution and road intersection features, calculate the boundary point spacing, call the database to divide the topological structure, detect accuracy, spatial offset and overlap, and generate topological association results;

[0008] S2: Based on the topological association results, obtain time series information, extract construction changes, changes in terrain height and road network adjustment trends, calculate change rates, analyze regional expansion trends and spatial change continuity, screen abnormal change areas, and determine the matching degree between expansion trends and terrain constraints in combination with terrain boundary data to obtain geographic object change rate values;

[0009] S3: calling the change rate value, obtaining administrative divisions, river channels and road connection point data, analyzing the boundary expansion trend, comparing adjacent boundary data, calculating the cross-region matching value, adjusting the expansion direction, correcting abnormal boundaries, and generating a geographic boundary matching coefficient;

[0010] S4: calling the geographic boundary matching coefficient, obtaining geographic classification information, extracting building functions, transportation facilities and green space types, calculating functional consistency, comparing matching degrees, screening conflicting areas, adjusting feature annotations, and obtaining classification matching values.

[0011] As a further solution of the present invention, the topological association results include the topological structure of the geographic unit, boundary point spacing data, spatial marker deviation information, and overlapping area identification; the geographic object change rate value includes the ground feature change rate, regional expansion rate, boundary change rate, and expansion trend matching degree; the geographic boundary matching coefficient includes cross-regional matching value, boundary adjustment parameter, anomaly correction parameter, and expansion direction optimization value; the classification matching value includes ground feature function consistency, classification matching degree, functional conflict area, and semantic category difference.

[0012] As a further solution of the present invention, the specific steps of obtaining the spatial coordinates of surveying and mapping data, extracting the terrain boundary, distribution of objects and road intersection features, calculating the boundary point spacing, calling the database to divide the topological structure, detecting the accuracy, spatial offset and overlap, and generating the topological association result are as follows:

[0013] S101: Acquire spatial coordinate information of surveying and mapping data, extract spatial features of terrain boundary points, feature distribution areas and road intersections, calculate distribution indexes of terrain boundary points, filter distance values ​​between adjacent points, and associate spatial feature points of feature distribution areas and road intersections with terrain boundary points to generate spatial feature distribution parameters;

[0014] S102: Based on the spatial feature distribution parameters, identify the spatial distance index between the boundary points, extract the coordinate values ​​of the adjacent boundary points, analyze the Euclidean distances of all boundary points, count the numerical value sets between the boundary points, and detect the distance anomaly according to the coordinate deviation of the surveying and mapping data, filter out the data beyond the preset range, calculate the offset between the boundary points, and obtain the distance value set between the boundary points;

[0015] S103: Based on the distance value set between boundary points, call the city-level surveying and mapping geographic information database, divide the topological structure of geographic units, extract the topological relationship between adjacent geographic units, calculate the spatial offset between units, integrate spatial correlation data, and generate topological correlation results.

[0016] As a further solution of the present invention, based on the topological association results, the time series information is obtained, the construction changes, the height changes of the ground objects and the road network adjustment trends are extracted, the change rate is calculated, the regional expansion trend and the continuity of the spatial change are analyzed, the abnormal change area is screened, and the matching degree between the expansion trend and the terrain constraint is judged in combination with the terrain boundary data. The specific steps of obtaining the change rate value of the geographic object are as follows:

[0017] S201: Call the topological association result, obtain the time series information of the surveying and mapping data, extract the urban construction change records, the height change value of the ground objects and the road network adjustment trend, calculate the ground object change rate at adjacent time points, filter the key change data in the time series, and generate the time series ground object change parameters;

[0018] S202: based on the time series feature change parameters, extract the feature boundary coordinates, identify the expansion area change values ​​at adjacent time points, count the expansion rate values, filter out areas with mismatched expansion rates, calculate the expansion boundary change rate, and compare the regional expansion rates based on the data increments of the time period, determine the continuity of the spatial change, and obtain the expansion boundary change rate value;

[0019] S203: calling the extension boundary change rate value and terrain boundary point data, calculating the offset angle between the extension direction and the terrain boundary, screening the area where the offset angle exceeds the threshold, counting the change rate, and obtaining the geographic object change rate value.

[0020] As a further solution of the present invention, the specific steps of calling the change rate value, obtaining administrative division, river and road connection point data, analyzing the boundary expansion trend, comparing adjacent boundary data, calculating the cross-region matching value, adjusting the expansion direction, correcting the abnormal boundary, and generating the geographic boundary matching coefficient are as follows:

[0021] S301: Calling the geographic object change rate value, obtaining administrative division boundaries, river water boundary and road connection point data, extracting the spatial coordinates of the boundary area, analyzing the boundary expansion trend, comparing the data distribution differences of adjacent boundaries, screening the areas with abnormal boundary expansion direction, and generating boundary expansion trend parameters;

[0022] S302: Based on the boundary extension trend parameter, identify the cross-region extension matching value, extract the boundary coordinates of the adjacent regions, calculate the boundary connection rate between the differentiated regions, count the expansion rate ratio between the boundary adjacent regions, compare the expansion rate difference to filter the data points with deviation in the matching degree, analyze the boundary fusion degree between the regions, correct the boundary data with the matching value exceeding the threshold, and obtain the cross-region extension matching value;

[0023] S303: Call the cross-region extension matching value, adjust the boundary extension direction, correct the boundary parameters of the abnormal area, calculate the direction change angle index before and after the boundary adjustment, filter the data with the adjustment angle deviation out of the range, calculate the matching stability of the corrected area, and obtain the geographic boundary matching coefficient.

[0024] As a further solution of the present invention, the calculation formula of the direction change angle before and after the boundary adjustment is specifically:

[0025] ;

[0026] in, Represents the angle of direction change before and after the boundary adjustment. Represents the coordinate value of the first boundary point before boundary adjustment. Represents the coordinate value of the second boundary point before boundary adjustment. Represents the coordinate value of the first boundary point after boundary adjustment. Represents the coordinate value of the second boundary point after boundary adjustment. Represents the inverse tangent operation, which is used to calculate the direction angle of the boundary line before and after adjustment. Represents the absolute value operation.

[0027] As a further solution of the present invention, the specific steps of calling the geographic boundary matching coefficient, obtaining geographic classification information, extracting building functions, transportation facilities and green space types, calculating functional consistency, comparing matching degrees, screening conflicting areas, adjusting feature annotations, and obtaining classification matching values ​​are as follows:

[0028] S401: calling the geographic boundary matching coefficient, obtaining the geographic classification information of the surveying and mapping data, extracting the urban building function classification, transportation facility usage and green space distribution type, screening the feature areas with inconsistent functional categories, comparing the spatial coverage of the categories, extracting the classification conflict areas, and generating the feature function category distribution parameters;

[0029] S402: Based on the distribution parameters of the functional categories of the features, analyzing the consistency of the features, extracting the functional attributes of adjacent features, comparing the classification matching, screening the functional conflicting areas, analyzing the semantic category differences, extracting the classification attribute values ​​of the conflicting areas, calculating the matching values ​​of the categories according to the functional category weights, adjusting the category labels with large matching deviations, screening the adjusted classification weight values, and obtaining the feature classification matching values;

[0030] S403: Calling the feature classification matching value, adjusting the feature annotation information of the classification conflict, correcting the feature labels of the functional category conflict, calculating the change rate of the category labels before and after the adjustment, screening the areas where the category adjustment range exceeds the set threshold, and calculating the overall matching degree of the corrected classification data to obtain the classification matching value.

[0031] As a further solution of the present invention, the formula for calculating the change rate of the representative category label before and after adjustment is specifically:

[0032] ;

[0033] in, Represents the rate of change before and after the category label is adjusted, Represents the number of land feature categories before adjustment, Represents the number of ground feature categories after adjustment, Represents the absolute value operation to ensure that the rate of change is positive. Indicates the percentage conversion of the calculated rate of change.

[0034] As a further solution of the present invention, the method includes, S5: calling the classification matching value, integrating the spatial structure, time change and boundary characteristics, calculating the matching degree, adjusting the fusion weight, screening the area where the matching deviation exceeds the standard, calculating the data consistency, and generating the surveying and mapping data fusion result;

[0035] The surveying and mapping data fusion results include spatial matching degree, time change trend, boundary constraint characteristics, fusion weight, matching deviation screening value, and data structure consistency;

[0036] S501: calling the classification matching value, integrating the spatial structure information, time change trend and boundary constraint characteristics of the surveying and mapping data, extracting the spatial distribution characteristics of the region, calculating the data matching degree, screening the area where the matching deviation exceeds the threshold, extracting the matching error value, and generating the data matching degree parameter;

[0037] S502: Based on the data matching degree parameter, the data structure consistency is analyzed, the spatial structure characteristics of the surveying and mapping data are extracted, the fusion weight is adjusted according to the spatial distribution characteristics of the surveying and mapping data, the data distribution law of the differentiated areas is compared, the areas with abnormal matching degree are screened, the structural deviation values ​​before and after data fusion are calculated, the change rate of the data structure adjustment is calculated, the areas where the data structure deviation exceeds the threshold are identified, and the data structure consistency coefficient is obtained;

[0038] S503: Call the data structure consistency coefficient, adjust the data fusion strategy, correct the spatial data with matching deviations exceeding the range, calculate the matching error values ​​before and after the fusion data adjustment, filter the areas where the adjustment range exceeds the set threshold, count the corrected data, and obtain the surveying and mapping data fusion results.

[0039] The city-level surveying and mapping geographic information standard system integration construction system includes:

[0040] The spatial topology construction module obtains the spatial coordinates of the surveying and mapping data, extracts the terrain boundary points, the distribution area of ​​the objects and the road intersections, calculates the spatial distance between the boundary points, calls the database to divide the geographical unit topology structure, detects the marking accuracy, determines the location of the objects based on the spatial offset, adjusts the unit division according to the overlapping area information, corrects the topological connection in combination with the distribution form of the objects, and generates the geographical unit topology structure data;

[0041] The time series analysis module calls the geographic unit topological structure data, obtains time series information, calculates the regional change rate, compares the expansion paths of adjacent time points, calculates the expansion boundary change rate, determines the adaptability of the expansion trend and terrain constraints, and obtains the geographic object change rate value;

[0042] The boundary matching optimization module calls the change rate value of the geographic object, obtains the data of administrative divisions, river waters and road connection points, calculates the change trend of regional boundaries, compares the data distribution of adjacent boundaries, determines the continuity of boundaries based on the road connection point information, adjusts the boundary expansion direction, corrects abnormal boundaries, and obtains the geographic boundary matching coefficient;

[0043] The geographic classification consistency module calls the geographic boundary matching coefficient, obtains geographic classification information, extracts building functions, transportation facilities and green space types, calculates functional consistency, compares matching degrees, screens functional conflict areas, adjusts feature annotations, and obtains geographic object classification matching values;

[0044] The surveying and mapping data fusion module calls the classification matching value of the geographic object, integrates the spatial structure information, time change trend and boundary constraint characteristics, calculates the data matching degree, adjusts the fusion weight, screens the matching deviation area, calculates the data structure consistency, and obtains the surveying and mapping data fusion result.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are:

[0046] In the present invention, by deeply analyzing the spatial coordinates, time series, geographic boundaries and classification information of surveying and mapping data, the organization and logic of the data are improved, the refinement of the topological structure enhances the tracking and updating capabilities of the data, and dynamic monitoring of changes in land objects is achieved. The optimization of the boundary adjustment mechanism reduces regional conflicts and improves the accuracy and adaptability of boundary data. The precise matching of classification information and the adaptive weight adjustment of data fusion enhance the consistency and application value of surveying and mapping data, and ensure the accurate fusion of data in a multi-source heterogeneous environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 It is a schematic diagram of the steps of the present invention;

[0049] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0051] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0052] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0053] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0054] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0055] See also Figure 1 ,The fusion construction method of the city-level surveying and mapping geographic information standard system includes the following steps:

[0056] S1: Obtain the spatial coordinate information of surveying and mapping data, extract the spatial features of terrain boundary points, land object distribution areas and road intersections, calculate the spatial distance index between boundary points, call the city-level surveying and mapping geographic information database, divide the topological structure of geographic units, detect marking accuracy, spatial offset and overlap, and generate topological association results;

[0057] S2: Call the topological association results, obtain the time series information of the surveying and mapping data, extract the urban construction change records, the height change values ​​of the objects and the road network adjustment trends, calculate the change rate of the objects at adjacent time points, analyze the change trend based on the data increment of the time period, compare the regional expansion rate to determine the continuity of the spatial change, filter the object areas with mismatched change trends, calculate the expansion boundary change rate, call the terrain boundary point data, determine whether the regional expansion trend meets the terrain constraints, and obtain the change rate value of the geographic object;

[0058] S3: Call the geographic object change rate value, obtain the administrative division boundary, river water boundary and road connection point data, analyze the expansion trend of the boundary area, compare the data distribution differences of adjacent boundaries, calculate the cross-region expansion matching value, adjust the boundary expansion direction, correct the abnormal area boundary parameters, and generate the geographic boundary matching coefficient;

[0059] S4: Call the geographic boundary matching coefficient, obtain the geographic classification information of the surveying and mapping data, extract the urban building function classification, transportation facility use and green space distribution type, calculate the consistency of the function of the objects, compare the classification matching degree, filter the functional conflict area, calculate the semantic category difference, adjust the annotation information of the objects with classification conflict, and obtain the classification matching value;

[0060] S5: Call the classification matching value, integrate the spatial structure information, time change trend and boundary constraint characteristics of the surveying and mapping data, calculate the data matching degree, adjust the fusion weight according to the distribution characteristics of the surveying and mapping data, filter the areas where the matching deviation exceeds the threshold, calculate the data structure consistency, and generate the surveying and mapping data fusion result.

[0061] The topological association results include the topological structure of the geographic unit, boundary point spacing data, spatial marker deviation information, and overlapping area identification. The geographic object change rate values ​​include the feature change rate, regional expansion rate, boundary change rate, and expansion trend matching degree. The geographic boundary matching coefficient includes the cross-region matching value, boundary adjustment parameter, anomaly correction parameter, and expansion direction optimization value. The classification matching value includes the feature function consistency, classification matching degree, functional conflict area, and semantic category difference. The surveying and mapping data fusion results include spatial matching degree, temporal change trend, boundary constraint characteristics, fusion weight, matching deviation screening value, and data structure consistency.

[0062] The specific steps of S1 are:

[0063] S101: Acquire spatial coordinate information of surveying and mapping data, extract spatial features of terrain boundary points, feature distribution areas and road intersections, calculate distribution indexes of terrain boundary points, filter distance values ​​between adjacent points, and associate spatial feature points of feature distribution areas and road intersections with terrain boundary points to generate spatial feature distribution parameters;

[0064] First, you need to parse the coordinate point information from the surveying and mapping data file, which usually includes X, Y, and Z three-dimensional coordinates. At the same time, check the data format to ensure that the data is complete and not missing. After parsing, screen the coordinate points and remove data anomalies. For example, if the Z value of a coordinate point obviously deviates from the average elevation of the surrounding data points (such as a deviation of more than 5 meters), the point is determined to be an anomaly and is removed. Then, the terrain boundary points are extracted. The elevation gradient calculation method of the adjacent points is used to calculate the elevation change amplitude of the adjacent points. When the elevation change of a point exceeds the set threshold (such as the elevation change of the adjacent points exceeds 2 meters), the point is identified as a terrain boundary point and its coordinate information is stored. At the same time, the extraction of the distribution area of ​​the object needs to be based on point density analysis. The surveying and mapping area is divided into regular grids (such as 10 meters × 10 meters), and the density of the surveying and mapping points in each grid is calculated. For example, if the number of surveying and mapping points in a grid exceeds the set threshold (such as 10 points), the grid is marked as a distribution area of ​​the object. The extraction of road intersections requires analysis of the road surveying and mapping data. First, screen The point data that meets the road characteristics are obtained, and then the connection relationship of the road points is analyzed to determine the possible intersection points. If the angle formed by a certain point and the extension direction of multiple roads is within the set range (such as 60°~120°), the point is marked as a road intersection point. Subsequently, the distribution of terrain boundary points is calculated, including the total number of boundary points, point density, adjacent point spacing and other indicators. For example, the boundary point density per square kilometer is calculated, and its average spacing and standard deviation are counted. In order to screen the reasonable distance value between adjacent points, all adjacent boundary points are traversed, their spacing is calculated, and the screening criteria are set. For example, if the adjacent point spacing is less than 1 meter or greater than 50 meters, the point pair is eliminated to ensure the rationality of the data. Finally, combined with the spatial characteristics of the land feature distribution area and the road intersection, it is associated with the terrain boundary points, the distance from these feature points to the nearest boundary point is calculated, and the attribution condition (such as less than 5 meters) is set. When the nearest boundary point distance of a feature point meets the set conditions, it is attributed to the corresponding boundary point category, and finally the spatial feature distribution parameters are generated.

[0065] S102: Based on the spatial feature distribution parameters, identify the spatial distance index between the boundary points, extract the coordinate values ​​of the adjacent boundary points, analyze the Euclidean distances of all boundary points, count the numerical value sets between the boundary points, and detect the distance anomaly according to the coordinate deviation of the surveying and mapping data, filter out the data beyond the preset range, calculate the offset between the boundary points, and obtain the distance value set between the boundary points;

[0066] First, it is necessary to analyze the spatial distance indicators between boundary points, extract the coordinate values ​​of all boundary points, and calculate the distances between adjacent boundary points. In this process, all boundary point pairs are arranged according to the adjacent relationship, their spatial distances are calculated, and a distance data set between boundary points is generated. Subsequently, the distance data set of boundary points is analyzed, and the mean and standard deviation of the distances of all boundary points are calculated. This is used as a benchmark to screen abnormal data. For example, if a distance value exceeds three times the standard deviation of the mean, it is considered that the distance value may be abnormal and needs to be further verified or eliminated. Subsequently, combined with the coordinate deviation of the surveying and mapping data, the distance changes between boundary points are further analyzed to detect possible offset anomalies and screen out data that exceeds the set offset range. For example, the maximum offset of the boundary point is set to 10 meters. If the offset of a pair of adjacent points exceeds this range, the distance data of the pair is judged to be abnormal and eliminated. Finally, the filtered data is used to calculate the offset between boundary points and form a distance value set between boundary points, which will be used for subsequent spatial topological analysis.

[0067] S103: Based on the distance value set between the boundary points, call the city-level surveying and mapping geographic information database, divide the topological structure of the geographic units, extract the topological relationship between adjacent geographic units, calculate the spatial offset between the units, integrate the spatial association data, and generate the topological association result;

[0068] It is necessary to call the city-level surveying and mapping geographic information database, retrieve the boundary information of the geographic units in the target area from the database, and divide the topological structure of the geographic units according to the spatial distribution of the boundary points. First, determine the boundary range of each geographic unit and divide the surveying and mapping area into multiple interconnected units, each unit consisting of multiple boundary points. Then, extract the topological relationship between adjacent geographic units, determine the spatial connection between adjacent units, and calculate the offset between units. The specific operation is to select the boundary point set of adjacent units, calculate their center positions, and analyze their offset degree according to the boundary changes of the two units, such as counting the coordinates of the center points of adjacent units and calculating their displacement difference. If the offset of the center point position of an adjacent unit is large (such as greater than 10 meters), its topological relationship needs to be further adjusted. Subsequently, the spatial association data of all adjacent units are integrated, the unit pairs that meet the offset criteria are connected, and their topological relationships are updated in the surveying and mapping database. Finally, a topological association result is generated, which contains the spatial connection, offset data and topological connection information of adjacent units, and this information is stored in the surveying and mapping database for subsequent geographic analysis and application.

[0069] The specific steps of S2 are:

[0070] S201: Call the topological association result, obtain the time series information of the surveying and mapping data, extract the urban construction change records, the height change value of the ground objects and the road network adjustment trend, calculate the ground object change rate at adjacent time points, filter the key change data in the time series, and generate the time series ground object change parameters;

[0071] First, search the surveying and mapping database to obtain the time series information of the surveying and mapping data, which contains urban geographic data at different time points, including building distribution, terrain height, road structure, etc., parse the time series data, extract the urban construction change records at each time point, and screen the plot information of added, removed and height changed objects. For example, by comparing the building boundary coordinates at two consecutive time points, identify the newly added or demolished buildings and record their coordinate changes. At the same time, calculate the height change value of the object, extract the Z coordinate change of the same plot at different time points, and set the benchmark height change threshold (such as 0.5 meters). When the height change of a certain object exceeds the threshold, record the change data. Then, analyze the adjustment trend of the road network. For the road topology at adjacent time points, determine the newly added, expanded or reduced roads, and record the displacement of road intersections. Then, calculate the rate of change of features at adjacent time points, count the area change of each plot, and calculate the area change rate per unit time based on the time interval. For example, if a building expands by 200 square meters in a year, its change rate is 200 square meters / year. Subsequently, filter the key change data in the time series and set the screening threshold. For example, if the area change rate of a feature is less than 5 square meters / year, it is considered that the change is not significant and is not included in the key change data. Finally, a time series feature change parameter is formed, which includes the feature change rate at each time point, road adjustment trend and building height change value.

[0072] S202: based on the time series feature change parameters, extract the feature boundary coordinates, identify the expansion area change values ​​at adjacent time points, count the expansion rate values, filter out areas with mismatched expansion rates, calculate the expansion boundary change rate, and compare the regional expansion rates based on the data increments of the time period, determine the continuity of the spatial change, and obtain the expansion boundary change rate value;

[0073] First, extract the coordinates of the feature boundaries, filter out all the changed feature boundary points from the time series data, and mark the expansion areas of adjacent time points. Then, identify the area change value of the feature expansion, calculate the newly added building area in each time period, count the expansion rate, set the statistical period (such as one year) and calculate the ratio of the expansion area to the period. For example, if an area expands by 500 square meters in one year, the expansion rate is 500 square meters / year. Then, filter out areas with mismatched expansion rates, analyze the rate distribution of all expansion plots, calculate the mean and standard deviation, and set the screening threshold. For example, if the expansion rate of a plot exceeds twice the standard deviation of the mean, it is judged as an abnormal area and is removed or marked. Then, calculate the expansion boundary change rate. By comparing the boundary positions of adjacent time points, calculate the ratio of boundary expansion. For example, if the boundary of a building expands outward by 10 meters within a year, and the initial width of the building is 50 meters, the boundary change rate is 20%. Subsequently, compare the regional expansion rate based on the data increment of the time period to judge the continuity of spatial change. Count the expansion rates of multiple time points and calculate the fluctuation range. For example, if the expansion rate of a region is 100 square meters / year, 105 square meters / year and 110 square meters / year respectively in the past three years, the fluctuation range is small, and it is judged that the continuity of change is high. Finally, obtain the expansion boundary change rate value, which is used for subsequent spatial analysis.

[0074] S203: calling the value of the rate of change of the extension boundary and the data of the terrain boundary points, calculating the offset angle between the extension direction and the terrain boundary, screening the area where the offset angle exceeds the threshold, counting the rate of change, and obtaining the value of the rate of change of the geographic object;

[0075] First, the coordinates of the boundary points of the extension direction of the feature are extracted, and the vector information of the extension direction is calculated. The main direction of the extension is determined by comparing the boundary coordinates before and after the extension. Then, the offset angle between the extension direction and the terrain boundary is calculated, the coordinates of the terrain boundary points are extracted, the angle between the extension direction and the boundary direction is analyzed, and the angle calculation range (such as 0°~180°) is set. Subsequently, the areas where the offset angle exceeds the threshold are screened, and the angle threshold (such as 45°) is set. When the angle between the extension direction of a certain area and the terrain boundary is greater than the threshold, it is considered that the extension direction of the area deviates from the terrain boundary and is marked. Then, the change rate is statistically calculated, the expansion rate in different areas is calculated, and its trend is analyzed based on the time series. For example, if the expansion rate of a certain area has continued to rise in the past three years, its growth trend is recorded. Finally, the change rate value of the geographic object is obtained, which is used for subsequent geospatial evaluation.

[0076] The specific steps of S3 are:

[0077] S301: Calling the geographic object change rate value, obtaining administrative division boundaries, river water boundary and road connection point data, extracting the spatial coordinates of the boundary area, analyzing the boundary expansion trend, comparing the data distribution differences of adjacent boundaries, screening the areas with abnormal boundary expansion direction, and generating boundary expansion trend parameters;

[0078] First, obtain the administrative boundaries, river boundaries and road connection point data from the surveying and mapping database, parse these boundary data, extract the spatial coordinates of each boundary area, and screen the boundary points with significant changes. Then, analyze the boundary expansion trend, compare the boundary coordinate data of different periods, calculate the boundary movement direction and distance, and screen out the areas where the boundary points have obvious movement in the time series. For example, if the boundary point of an administrative district moves outward by 100 meters within a year, calculate its expansion trend and store the data. Then, compare the data distribution differences of adjacent boundaries, extract the changes in the boundaries of adjacent administrative districts or rivers, and calculate the boundary. The change in distance between boundaries. For example, if the water boundary of a river expands outward by 50 meters within a year, while the boundary of the adjacent administrative district does not change, the boundary expansion difference of the area is recorded. Then, the areas with abnormal boundary expansion direction are screened, the expansion direction of each boundary point is calculated, and the direction deviation threshold is set. For example, if the expansion direction of a boundary point deviates from the original boundary direction by more than 45 degrees, the point is marked as abnormal. Finally, the boundary expansion trend parameter is generated, which includes the spatial expansion information of administrative division boundaries, river boundaries and road connection points, as well as the expansion direction, expansion rate and spatial distribution difference of the boundaries of each area.

[0079] S302: Based on the boundary extension trend parameter, identify the cross-region extension matching value, extract the boundary coordinates of the adjacent regions, calculate the boundary connection rate between the differentiated regions, count the expansion rate ratio between the boundary adjacent regions, compare the expansion rate difference to filter the data points with deviation in the matching degree, analyze the boundary fusion degree between the regions, correct the boundary data with the matching value exceeding the threshold, and obtain the cross-region extension matching value;

[0080] First, identify the cross-regional expansion matching values, extract the boundary coordinates of adjacent regions, screen the geographical regions with adjacent relationships, and calculate their boundary change trends. Then, calculate the boundary connection rate between differentiated regions, count the matching of adjacent boundary points, and determine whether the boundaries of the two regions are continuous. For example, if the boundaries of two administrative regions have expanded outward in the past year, but the expansion directions are consistent and the expansion distances are similar, then their connection rates are high. On the contrary, if the boundary expansion direction of a certain region deviates significantly from the adjacent region, then the connection rate is low. Then, count the expansion rate ratios between the adjacent boundary regions, and compare the expansion rates of different regions. For example, if the expansion rate of a certain region is calculated to be 200 meters / year, and the expansion rate of the adjacent region is 50 meters / year, then the rate ratio is 4. Then, compare The expansion rate difference filters the data points with deviations in the matching degree and sets the rate deviation threshold (such as 2 times). If the expansion rate of a certain area exceeds twice the rate of the adjacent area, the expansion rate of the area is determined to be abnormal and marked. Subsequently, the degree of boundary fusion between areas is analyzed, the adjacent relationship of boundary points is calculated, and the spatial distribution consistency of boundary point pairs is statistically analyzed. If there is a large spatial misalignment of boundary points in adjacent areas (such as a displacement difference of more than 10 meters), the degree of fusion is determined to be low. Next, the boundary data with matching values ​​exceeding the threshold is corrected, and the areas with low matching degrees are adjusted, such as correcting the positions of boundary points in adjacent areas to make them more matched. Finally, the cross-regional expansion matching value is obtained, which is used to adjust the matching of cross-regional boundaries and ensure the spatial consistency of boundary data.

[0081] S303: calling the cross-region extension matching value, adjusting the boundary extension direction, correcting the boundary parameters of the abnormal region, calculating the direction change angle index before and after the boundary adjustment, filtering the data whose adjustment angle deviation exceeds the range, and calculating the matching stability of the corrected region to obtain the geographic boundary matching coefficient;

[0082] The specific calculation formula for the direction change angle before and after boundary adjustment is:

[0083] ;

[0084] in, Represents the angle of direction change before and after the boundary adjustment. Represents the coordinate value of the first boundary point before boundary adjustment. Represents the coordinate value of the second boundary point before boundary adjustment. Represents the coordinate value of the first boundary point after boundary adjustment. Represents the coordinate value of the second boundary point after boundary adjustment. Represents the inverse tangent operation, which is used to calculate the direction angle of the boundary line before and after adjustment. Represents absolute value operation;

[0085] This formula is used to calculate the angle of change in direction before and after the boundary is adjusted. and represents adjusting the coordinates of the first two specific boundary points, and and To ensure the practical application and accuracy of the data, it is assumed that the following data are obtained from the geographic information system:

[0086] Adjust the coordinates of the front boundary point A ;

[0087] Adjust the coordinates of the front boundary point B ;

[0088] Coordinates of the adjusted boundary point A' ;

[0089] Coordinates of the adjusted boundary point B' ;

[0090] First, calculate the direction of the boundary line before and after adjustment:

[0091] The direction angle before adjustment is:

[0092] ;

[0093] The adjusted direction angle is:

[0094] ;

[0095] These angles are then used to calculate the angle of change in direction :

[0096] ;

[0097] This shows that despite the slight adjustment of the coordinates, the direction of the border has not changed. This result shows that during the border adjustment process, although the border points have a slight displacement, the overall border direction remains unchanged, and the impact on the calculation of the geographic border matching coefficient is minimal. The results of this step provide direct evidence in confirming the effectiveness of the border adjustment measures and verify that the matching stability of the region remains the same after the border adjustment.

[0098] The specific steps of S4 are:

[0099] S401: calling the geographic boundary matching coefficient, obtaining the geographic classification information of the surveying and mapping data, extracting the urban building function classification, transportation facility usage and green space distribution type, screening the feature areas with inconsistent functional categories, comparing the spatial coverage of the categories, extracting the classification conflict areas, and generating the feature function category distribution parameters;

[0100] First, the surveying and mapping database is retrieved to obtain geographic classification information, and the data on urban building function classification, transportation facility use and green space distribution type are analyzed. The spatial coordinates and area information of each category are extracted and stored in the classification index table. Then, the land feature areas with inconsistent functional categories are screened, and the classification attributes of adjacent land features are analyzed to determine whether there is an unreasonable category boundary. For example, if an industrial plot appears next to an office area and there is no obvious transition area between the two, the area is marked as an inconsistent functional category area. Subsequently, the spatial coverage of the categories is compared, the spatial proportion of each category is calculated, and the functional categories with a low area proportion are screened. For example, if the green space proportion of a certain area is lower than the set standard (such as 10%), the abnormal proportion of the functional category of the area is recorded. Then, the classification conflict area is extracted to analyze whether there is a high-conflict area in the urban functional layout. For example, if a high-density residential area appears next to a highway, its classification conflict is more obvious and should be further marked. Finally, the land feature function category distribution parameter is generated, which includes the spatial coordinates of different functional categories, the matching degree of adjacent categories, the area proportion and classification conflict information.

[0101] S402: Based on the distribution parameters of the functional categories of the features, analyzing the consistency of the features, extracting the functional attributes of adjacent features, comparing the classification matching, screening the functional conflicting areas, analyzing the semantic category differences, extracting the classification attribute values ​​of the conflicting areas, calculating the matching values ​​of the categories according to the functional category weights, adjusting the category labels with large matching deviations, screening the adjusted classification weight values, and obtaining the feature classification matching values;

[0102] First, analyze the consistency of land features, extract the functional attributes of all adjacent land features, compare their classification matching, and calculate the consistency of functional categories of adjacent plots. For example, if the building function of a certain area is for commercial use, and its adjacent plots are for industrial use, then its matching degree is low and further analysis is required. Then, filter the functional conflict areas, count all the plots with low matching degrees, and set the matching degree threshold. For example, if the classification matching degree of an area is less than 50%, mark it as a conflict area. Then, analyze the semantic category difference, extract the classification attribute values ​​of the conflicting areas, and calculate the similarity of the functional categories. For example, if an area contains parks and ecological wetlands, its functional categories are relatively close, and if an area has both warehousing and high-density residential buildings, its category difference is large and further adjustment is required. Then, based on Functional category weight, calculate the matching value of the category, set the weight values ​​of different categories, for example, the weight of commercial land is set to 0.8, the weight of industrial land is set to 0.6, and the weight of green land is set to 0.9, and calculate the matching scores of adjacent categories. If the matching score of adjacent categories is lower than the set benchmark value (such as 0.5), the category relationship needs to be adjusted. Subsequently, adjust the category labels with large matching deviations, filter all categories with matching deviations beyond the set range, and make corrections. For example, if the matching degree of a certain area is lower than 30% of the standard value, the category label needs to be modified. Then, filter the adjusted classification weight values, count all adjusted category weights, and ensure that the corrected category distribution is more in line with reality. Finally, obtain the matching value of the land feature classification, which is used to further optimize the urban functional layout.

[0103] S403: calling the feature classification matching value, adjusting the feature annotation information of the classification conflict, correcting the feature labels of the functional category conflict, calculating the change rate of the category labels before and after the adjustment, screening the areas where the category adjustment range exceeds the set threshold, and calculating the overall matching degree of the corrected classification data to obtain the classification matching value;

[0104] The formula for calculating the rate of change before and after the representative category label adjustment is:

[0105] ;

[0106] in, Represents the rate of change before and after the category label is adjusted, Represents the number of land feature categories before adjustment, Represents the number of land feature categories after adjustment, Represents the absolute value operation to ensure that the rate of change is positive. Indicates the percentage conversion of the calculated rate of change;

[0107] This formula is used to calculate the rate of change before and after the feature classification is adjusted. and Represent the number of feature categories before and after the adjustment. These data are usually obtained through the feature classification system of the Geographic Information System (GIS) to ensure the accuracy and traceability of the data. For the actual calculation, consider a simple example. Suppose that the change in the number of feature categories before and after the adjustment in a specific area is monitored through the feature classification system.

[0108] Assumptions: -Number of landform categories before adjustment - Adjusted number of terrain types

[0109] First, calculate the difference in category changes:

[0110] ;

[0111] Then calculate the rate of change :

[0112] ;

[0113] This calculation shows that the rate of change in the category labels is 20%. This value is used to determine whether further adjustments are needed or whether it exceeds the threshold set by the management department. It is usually used to monitor the effectiveness and accuracy of the land classification system.

[0114] The results show that the number of adjusted feature categories has increased, reflecting the impact of possible development activities or updated feature data. This rate of change helps decision makers understand the development speed of a particular area and conduct further planning or management accordingly.

[0115] Parameter Description: represents the percentage change rate after classification adjustment, Represents the number of land feature categories before adjustment, Represents the number of ground feature categories after adjustment, represents the difference in the number of categories, Indicates taking the absolute value to ensure the rate of change is positive. Indicates converting the rate of change into percentage format.

[0116] The specific steps of S5 are:

[0117] S501: calling the classification matching value, integrating the spatial structure information, time change trend and boundary constraint characteristics of the surveying and mapping data, extracting the spatial distribution characteristics of the region, calculating the data matching degree, screening the area where the matching deviation exceeds the threshold, extracting the matching error value, and generating the data matching degree parameter;

[0118] First, the spatial structure information of the surveying and mapping data is parsed, the coordinate information of different geographic objects is extracted, and the spatial correlation relationship of each feature is analyzed, such as the distance, direction and spatial coverage between adjacent features. Then, the time change trend is parsed, the time series of historical surveying and mapping data is analyzed, the expansion rate of each feature is calculated, and the feature areas with abnormal change rates are screened out. For example, if the area of ​​a building has increased by more than 50% in the past year, the change rate of this area is abnormal and needs further analysis. Subsequently, the boundary constraint characteristics are parsed, the spatial coordinates of administrative division boundaries, river boundaries and road boundaries are extracted, and the stability of the boundaries is calculated. For example, the average annual change of a road boundary is counted. If the change exceeds the set threshold (such as 10 meters), the boundary constraint characteristics are abnormal. Then, the extracted area Spatial distribution characteristics, calculate the proportion of each type of land features in the entire area, and count their spatial distribution patterns, such as calculating the distribution ratio of green space, commercial area, and industrial area, and comparing historical data to analyze their distribution trends. Subsequently, calculate the data matching degree, filter out areas where the matching deviation exceeds the threshold, and set a matching degree threshold (such as 80%). If the data matching degree of a certain area is lower than this value, then its data error is large and needs further correction. Next, extract the matching error value, calculate the mean and standard deviation of the matching error of each area, and filter out areas where the error deviates greatly from the mean. For example, if the matching error of a certain plot is higher than twice the standard deviation of the mean, then its data has a large deviation. Finally, generate a data matching degree parameter, which includes the spatial matching situation of each feature, temporal change trend, and boundary stability analysis results.

[0119] S502: Based on the data matching degree parameter, the data structure consistency is analyzed, the spatial structure characteristics of the surveying and mapping data are extracted, the fusion weight is adjusted according to the spatial distribution characteristics of the surveying and mapping data, the data distribution law of the differentiated areas is compared, the areas with abnormal matching degree are screened, the structural deviation values ​​before and after data fusion are calculated, the change rate of the data structure adjustment is calculated, the areas where the data structure deviation exceeds the threshold are identified, and the data structure consistency coefficient is obtained;

[0120] First, analyze the consistency of the data structure, extract the spatial structural characteristics of the surveying and mapping data, and calculate the spatial connectivity of each feature. For example, count the degree of connection between adjacent plots and judge the continuity of the road network. Then, adjust the fusion weight according to the spatial distribution characteristics of the surveying and mapping data, set the fusion weight values ​​of different categories of data, such as the weight of building data is set to 0.7, the weight of green space data is set to 0.9, and adjust the calculation ratio when merging data. Then, compare the data distribution rules of differentiated areas, and count the spatial distribution patterns of features in different areas. For example, calculate the comparison value of the proportion of industrial land in a certain area with the surrounding areas. If the proportion of industrial land in a certain area is more than 30% higher than that of the adjacent areas, it is determined that its data distribution is abnormal. Then, filter out areas with abnormal matching degree and set the abnormal matching degree threshold (such as the matching degree is lower than 70%). If the matching degree of a certain area is If the value is lower than this value, there is a large deviation in its spatial data structure. Subsequently, the structural deviation value before and after data fusion is calculated, the matching situation before and after data adjustment is analyzed, and the spatial error change value is calculated. For example, the matching error of a certain area before data adjustment is 15%, and the error is reduced to 8% after adjustment. Then, the error reduction rate is calculated. Next, the change rate of the data structure adjustment is calculated, the degree of structural change in each area is calculated, and a change rate threshold (such as 10%) is set. If the change rate of a certain area is higher than this value, its data structure is further adjusted. Subsequently, the areas where the data structure deviation exceeds the threshold are identified, all data structure deviation values ​​are calculated, and areas beyond the benchmark range are screened. For example, if the deviation of a certain area exceeds three times the standard deviation of the mean, it is marked. Finally, the data structure consistency coefficient is obtained. This coefficient is used to measure the rationality of the data structure adjustment and provide a basis for subsequent adjustments.

[0121] S503: calling the data structure consistency coefficient, adjusting the data fusion strategy, correcting the spatial data whose matching deviation exceeds the range, calculating the matching error value before and after the fusion data adjustment, screening the area whose adjustment range exceeds the set threshold, and counting the corrected data to obtain the surveying and mapping data fusion result;

[0122] First, adjust the data fusion strategy, analyze the areas with low matching degree, and recalculate their spatial matching relationship, such as comparing the distribution of objects in adjacent areas and resetting the priority of data merging. Then, correct the spatial data with matching deviations beyond the range, filter out the areas with large matching errors, and adjust their object categories. For example, if an area was originally classified as green space, but according to the latest surveying and mapping data, it has been transformed into a commercial area, then update its object label. Then, calculate the matching error value before and after the fusion data adjustment, and count the matching error changes of all adjusted areas. For example, if the matching error of a certain area is 12% before adjustment and drops to 6% after adjustment, then record its error change value. Then, filter out the areas whose adjustment range exceeds the set threshold, set the adjustment amplitude threshold (such as 20%). If the data adjustment amplitude of a certain area exceeds this value, further evaluate the rationality of its adjustment. Then, count the corrected data, calculate the data matching degree of all corrected areas, and compare the data consistency before and after the adjustment. For example, the matching degree of a certain area before correction is 75%, and the matching degree after correction is increased to 90%. Finally, obtain the surveying and mapping data fusion result, which is used to evaluate the accuracy of data fusion and provide reference information for subsequent surveying and mapping data updates.

[0123] See also Figure 2 , the city-level surveying and mapping geographic information standard system integration construction system, including:

[0124] The spatial topology construction module obtains the spatial coordinates of the surveying and mapping data, extracts the terrain boundary points, the distribution area of ​​the objects and the road intersections, calculates the spatial distance between the boundary points, calls the database to divide the geographical unit topology structure, detects the marking accuracy, determines the location of the objects based on the spatial offset, adjusts the unit division according to the overlapping area information, corrects the topological connection in combination with the distribution form of the objects, and generates the geographical unit topology structure data;

[0125] The time series analysis module calls the topological structure data of the geographic unit, obtains the time series information, calculates the regional change rate, compares the expansion paths of adjacent time points, calculates the expansion boundary change rate, determines the adaptability of the expansion trend and terrain constraints, and obtains the change rate value of the geographic object;

[0126] The boundary matching optimization module calls the change rate value of the geographic object, obtains the data of administrative divisions, river waters and road connection points, calculates the change trend of regional boundaries, compares the data distribution of adjacent boundaries, determines the continuity of boundaries based on the information of road connection points, adjusts the boundary expansion direction, corrects abnormal boundaries, and obtains the geographic boundary matching coefficient;

[0127] The geographic classification consistency module calls the geographic boundary matching coefficient, obtains geographic classification information, extracts building functions, transportation facilities and green space types, calculates functional consistency, compares matching degrees, screens functional conflict areas, adjusts feature annotations, and obtains geographic object classification matching values;

[0128] The surveying and mapping data fusion module calls the classification matching value of geographic objects, integrates spatial structure information, time change trends and boundary constraint characteristics, calculates data matching degree, adjusts fusion weights, screens matching deviation areas, calculates data structure consistency, and obtains surveying and mapping data fusion results.

[0129] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for integrating and constructing a city-level surveying and mapping geographic information standard system, characterized in that: The following steps are involved: S1: Obtain the spatial coordinates of surveying and mapping data, extract the terrain boundary, object distribution and road intersection features, calculate the boundary point spacing, call the database to divide the topological structure, detect accuracy, spatial offset and overlap, and generate topological association results; S2: Based on the topological association results, obtain time series information, extract construction changes, changes in terrain height and road network adjustment trends, calculate change rates, analyze regional expansion trends and spatial change continuity, screen abnormal change areas, and determine the matching degree between expansion trends and terrain constraints in combination with terrain boundary data to obtain geographic object change rate values; S3: calling the change rate value, obtaining administrative divisions, river channels and road connection point data, analyzing the boundary expansion trend, comparing adjacent boundary data, calculating the cross-region matching value, adjusting the expansion direction, correcting abnormal boundaries, and generating a geographic boundary matching coefficient; S4: calling the geographic boundary matching coefficient, obtaining geographic classification information, extracting building functions, transportation facilities and green space types, calculating functional consistency, comparing matching degrees, screening conflicting areas, adjusting feature annotations, and obtaining classification matching values.

2. The method for integrating and constructing a city-level surveying and mapping geographic information standard system according to claim 1 is characterized in that: The topological association result includes the geographic unit topological structure, boundary point spacing data, spatial marker deviation information, and overlapping area identification. The geographic object change rate value includes the feature change rate, regional expansion rate, boundary change rate, and expansion trend matching degree. The geographic boundary matching coefficient includes the cross-region matching value, boundary adjustment parameter, anomaly correction parameter, and expansion direction optimization value. The classification matching value includes the feature function consistency, classification matching degree, functional conflict area, and semantic category difference.

3. The method for integrating and constructing a city-level surveying and mapping geographic information standard system according to claim 1 is characterized in that: The specific steps of obtaining the spatial coordinates of surveying and mapping data, extracting the terrain boundaries, distribution of objects and road intersection features, calculating the distance between boundary points, calling the database to divide the topological structure, detecting accuracy, spatial offset and overlap, and generating topological association results are as follows: S101: Acquire spatial coordinate information of surveying and mapping data, extract spatial features of terrain boundary points, feature distribution areas and road intersections, calculate distribution indexes of terrain boundary points, filter distance values ​​between adjacent points, and associate spatial feature points of feature distribution areas and road intersections with terrain boundary points to generate spatial feature distribution parameters; S102: Based on the spatial feature distribution parameters, identify the spatial distance index between the boundary points, extract the coordinate values ​​of the adjacent boundary points, analyze the Euclidean distances of all boundary points, count the numerical value sets between the boundary points, and detect the distance anomaly according to the coordinate deviation of the surveying and mapping data, filter out the data beyond the preset range, calculate the offset between the boundary points, and obtain the distance value set between the boundary points; S103: Based on the distance value set between boundary points, call the city-level surveying and mapping geographic information database, divide the topological structure of geographic units, extract the topological relationship between adjacent geographic units, calculate the spatial offset between units, integrate spatial correlation data, and generate topological correlation results.

4. The method for integrating and constructing a city-level surveying and mapping geographic information standard system according to claim 1 is characterized in that: Based on the topological association results, the time series information is obtained, the construction changes, the height changes of the objects and the road network adjustment trends are extracted, the change rate is calculated, the regional expansion trend and the continuity of the spatial changes are analyzed, the abnormal change areas are screened, and the matching degree between the expansion trend and the terrain constraints is judged in combination with the terrain boundary data. The specific steps for obtaining the change rate value of the geographic object are as follows: S201: Call the topological association result, obtain the time series information of the surveying and mapping data, extract the urban construction change records, the height change value of the ground objects and the road network adjustment trend, calculate the ground object change rate at adjacent time points, filter the key change data in the time series, and generate the time series ground object change parameters; S202: based on the time series feature change parameters, extract the feature boundary coordinates, identify the expansion area change values ​​at adjacent time points, count the expansion rate values, filter out areas with mismatched expansion rates, calculate the expansion boundary change rate, and compare the regional expansion rates based on the data increments of the time period, determine the continuity of the spatial change, and obtain the expansion boundary change rate value; S203: calling the extension boundary change rate value and terrain boundary point data, calculating the offset angle between the extension direction and the terrain boundary, screening the area where the offset angle exceeds the threshold, counting the change rate, and obtaining the geographic object change rate value.

5. The method for integrating and constructing a city-level surveying and mapping geographic information standard system according to claim 1, characterized in that: The specific steps of calling the change rate value, obtaining administrative divisions, river and road connection point data, analyzing boundary expansion trends, comparing adjacent boundary data, calculating cross-region matching values, adjusting expansion directions, correcting abnormal boundaries, and generating geographic boundary matching coefficients are as follows: S301: Calling the geographic object change rate value, obtaining administrative division boundaries, river water boundary and road connection point data, extracting the spatial coordinates of the boundary area, analyzing the boundary expansion trend, comparing the data distribution differences of adjacent boundaries, screening the areas with abnormal boundary expansion direction, and generating boundary expansion trend parameters; S302: Based on the boundary extension trend parameter, identify the cross-region extension matching value, extract the boundary coordinates of the adjacent regions, calculate the boundary connection rate between the differentiated regions, count the expansion rate ratio between the boundary adjacent regions, compare the expansion rate difference to filter the data points with deviation in the matching degree, analyze the boundary fusion degree between the regions, correct the boundary data with the matching value exceeding the threshold, and obtain the cross-region extension matching value; S303: Call the cross-region extension matching value, adjust the boundary extension direction, correct the boundary parameters of the abnormal area, calculate the direction change angle index before and after the boundary adjustment, filter the data with the adjustment angle deviation out of the range, calculate the matching stability of the corrected area, and obtain the geographic boundary matching coefficient.

6. The method for integrating and constructing a city-level surveying and mapping geographic information standard system according to claim 5 is characterized in that: The specific calculation formula of the direction change angle before and after the boundary adjustment is: ； in, Represents the angle of direction change before and after the boundary adjustment. Represents the coordinate value of the first boundary point before boundary adjustment. Represents the coordinate value of the second boundary point before boundary adjustment. Represents the coordinate value of the first boundary point after boundary adjustment. Represents the coordinate value of the second boundary point after boundary adjustment. Represents the inverse tangent operation, which is used to calculate the direction angle of the boundary line before and after adjustment. Represents the absolute value operation.

7. The method for integrating and constructing a city-level surveying and mapping geographic information standard system according to claim 1 is characterized in that: The specific steps of calling the geographic boundary matching coefficient, obtaining geographic classification information, extracting building functions, transportation facilities and green space types, calculating functional consistency, comparing matching degrees, screening conflicting areas, adjusting feature annotations, and obtaining classification matching values ​​are as follows: S401: calling the geographic boundary matching coefficient, obtaining the geographic classification information of the surveying and mapping data, extracting the urban building function classification, transportation facility use and green space distribution type, screening the feature areas with inconsistent functional categories, comparing the spatial coverage of the categories, extracting the classification conflict areas, and generating the feature function category distribution parameters; S402: Based on the distribution parameters of the functional categories of the features, analyzing the consistency of the features, extracting the functional attributes of adjacent features, comparing the classification matching, screening the functional conflicting areas, analyzing the semantic category differences, extracting the classification attribute values ​​of the conflicting areas, calculating the matching values ​​of the categories according to the functional category weights, adjusting the category labels with large matching deviations, screening the adjusted classification weight values, and obtaining the feature classification matching values; S403: Calling the feature classification matching value, adjusting the feature annotation information of the classification conflict, correcting the feature labels of the functional category conflict, calculating the change rate of the category labels before and after the adjustment, screening the areas where the category adjustment range exceeds the set threshold, and calculating the overall matching degree of the corrected classification data to obtain the classification matching value.

8. The method for integrating and constructing a city-level surveying and mapping geographic information standard system according to claim 7 is characterized in that: The formula for calculating the change rate of the representative category label before and after adjustment is specifically: ； in, Represents the rate of change before and after the category label is adjusted, Represents the number of land feature categories before adjustment, Represents the number of ground feature categories after adjustment, Represents the absolute value operation to ensure that the rate of change is positive. Indicates the percentage conversion of the calculated rate of change.

9. The method for integrating and constructing a city-level surveying and mapping geographic information standard system according to claim 1, characterized in that: The method comprises, S5: calling the classification matching value, integrating the spatial structure, time change and boundary characteristics, calculating the matching degree, adjusting the fusion weight, screening the area where the matching deviation exceeds the standard, calculating the data consistency, and generating the surveying and mapping data fusion result; The surveying and mapping data fusion results include spatial matching degree, time change trend, boundary constraint characteristics, fusion weight, matching deviation screening value, and data structure consistency; S501: calling the classification matching value, integrating the spatial structure information, time change trend and boundary constraint characteristics of the surveying and mapping data, extracting the spatial distribution characteristics of the region, calculating the data matching degree, screening the area where the matching deviation exceeds the threshold, extracting the matching error value, and generating the data matching degree parameter; S502: Based on the data matching degree parameter, the data structure consistency is analyzed, the spatial structure characteristics of the surveying and mapping data are extracted, the fusion weight is adjusted according to the spatial distribution characteristics of the surveying and mapping data, the data distribution law of the differentiated areas is compared, the areas with abnormal matching degree are screened, the structural deviation values ​​before and after data fusion are calculated, the change rate of the data structure adjustment is calculated, the areas where the data structure deviation exceeds the threshold are identified, and the data structure consistency coefficient is obtained; S503: Call the data structure consistency coefficient, adjust the data fusion strategy, correct the spatial data with matching deviations exceeding the range, calculate the matching error values ​​before and after the fusion data adjustment, filter the areas where the adjustment range exceeds the set threshold, count the corrected data, and obtain the surveying and mapping data fusion results.

10. The city-level surveying and mapping geographic information standard system integration construction system is characterized by: According to the method for integrating and constructing a city-level surveying and mapping geographic information standard system according to any one of claims 1 to 9, the system comprises: The spatial topology construction module obtains the spatial coordinates of the surveying and mapping data, extracts the terrain boundary points, the distribution area of ​​the objects and the road intersections, calculates the spatial distance between the boundary points, calls the database to divide the geographical unit topology structure, detects the marking accuracy, determines the location of the objects based on the spatial offset, adjusts the unit division according to the overlapping area information, corrects the topological connection in combination with the distribution form of the objects, and generates the geographical unit topology structure data; The time series analysis module calls the geographic unit topological structure data, obtains time series information, calculates the regional change rate, compares the expansion paths of adjacent time points, calculates the expansion boundary change rate, determines the adaptability of the expansion trend and terrain constraints, and obtains the geographic object change rate value; The boundary matching optimization module calls the change rate value of the geographic object, obtains the data of administrative divisions, river waters and road connection points, calculates the change trend of regional boundaries, compares the data distribution of adjacent boundaries, determines the continuity of boundaries based on the road connection point information, adjusts the boundary expansion direction, corrects abnormal boundaries, and obtains the geographic boundary matching coefficient; The geographic classification consistency module calls the geographic boundary matching coefficient, obtains geographic classification information, extracts building functions, transportation facilities and green space types, calculates functional consistency, compares matching degrees, screens functional conflict areas, adjusts feature annotations, and obtains geographic object classification matching values; The surveying and mapping data fusion module calls the classification matching value of the geographic object, integrates the spatial structure information, time change trend and boundary constraint characteristics, calculates the data matching degree, adjusts the fusion weight, screens the matching deviation area, calculates the data structure consistency, and obtains the surveying and mapping data fusion result.

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