Method for constructing soil pollution source map by automatic multi-source heterogeneous data fusion

Abstract

The application discloses a soil pollution source one-map construction method for automatic multi-source heterogeneous data fusion, relates to the technical field of soil pollution prevention and control and geographic information data fusion, and collects pollution source census, ecological environment statistics, pollution discharge permission, mine tailing pond and third national land surveying plot data and administrative division data; data cleaning is completed through field alignment, coordinate conversion, unique identification code generation, vector conversion and partition county splitting; point positions are automatically corrected according to hierarchical reference data, abnormal points are removed and road point positions are corrected; a geographic information corresponding table is generated through one-to-many spatial connection, spatial fusion is completed after transposition and back connection of the plot, entity information is connected in batches through the unique identification code, and a soil pollution source one-map is formed; the application realizes full-process automatic processing, significantly improves efficiency and precision, and provides support for soil pollution census, risk prevention and control and land planning.

Description

Technical Field

[0001] This invention relates to the field of soil pollution prevention and control and geographic information data fusion technology, and in particular to a method for constructing a single map of soil pollution sources by fusion of automated multi-source heterogeneous data. Background Technology

[0002] In soil ecological environment management, it is necessary to identify potential pollution sources based on land use status maps, clarify the pollution history and risk level of the plots, and provide support for land use planning and source control.

[0003] The existing data fusion of soil pollution sources has the following main technical shortcomings:

[0004] Insufficient accuracy of location coordinates: Some pollution source locations are the coordinates of the enterprise's main entrance, manually entered without verification, and are easily included in road or non-construction land use map patches, which cannot be matched with the correct land use type patches, resulting in fusion errors.

[0005] Manual processing is inefficient and prone to errors: the number of map patches in prefecture-level cities reaches millions and the number of pollution source locations reaches tens of thousands. Manual verification and integration is time-consuming and costly, making it difficult to meet the timeliness requirements.

[0006] Difficulties in integrating heterogeneous multi-source data: Data fields, units, and formats from the first and second pollution source surveys, ecological and environmental statistics, sewage discharge permits, and mine tailings ponds are inconsistent, making it impossible to fully display and associate them directly in ArcGIS, resulting in insufficient information sharing.

[0007] Point-area fusion algorithms are inefficient: Traditional traversal matching requires calling all map features point by point, which is computationally intensive, prone to rework, and results in low fusion coverage and accuracy, with many points unable to be effectively connected.

[0008] Therefore, there is an urgent need for an automated, high-precision, and high-efficiency multi-source data fusion method to achieve accurate matching and full information integration between pollution source data and land use status maps, and to construct a standardized and reusable "single map" of soil pollution sources. Summary of the Invention

[0009] This invention provides a method for constructing a unified map of soil pollution sources by fusion of automated multi-source heterogeneous data. It achieves high-precision, high-efficiency, and full-information fusion of pollution source data and land use patches to form a unified spatial base map that can be directly used for soil pollution source control and screening of survey targets.

[0010] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0011] This invention provides a method for constructing a single map of soil pollution sources by fusion of multi-source heterogeneous data, including:

[0012] S1. Data Collection: Acquire multi-source pollution source data and land use spatial data. The multi-source pollution source data includes data from the first pollution source census, data from the second pollution source census, ecological and environmental statistics data from 2011 to 2025, pollutant discharge permit data, mine data, and tailings dam data. The land use spatial data includes map data from the third national land survey, administrative division data, and regional control detailed planning.

[0013] S2. Data Alignment and Cleaning: Perform field mapping, coordinate decimal conversion, unit of measurement unification, unique identifier generation, table to vector conversion, and splitting of vector data according to administrative divisions for multi-source pollution data;

[0014] S3. Data Spatial Information Verification: An automated matching program is used to conduct preliminary verification of pollution source locations by district and county. Points outside the administrative region, points on the road and normal points are separated by cropping and erasing tools. Points on the road are subjected to nearby adsorption correction and merged to obtain the corrected point data.

[0015] S4. Spatial Fusion of Point and Area Data: Using the map patches from the Third National Land Survey as target elements, one-to-many spatial connections are made to generate a point-area geographic information correspondence table and complete the table transposition. Based on the map patch identification code, back-connection is used to generate point-area geographic information fusion map patches.

[0016] S5. Point-area entity information fusion: Using the unique identifier as the association key, the entity information of pollution sources is batch linked to the point-area geographic information correspondence table to generate a point-area entity information fusion table, resulting in a "single map" of soil pollution sources.

[0017] Furthermore, in step S2, the basic information table of industrial enterprises, raw and auxiliary material information table, wastewater discharge table, and exhaust gas emission table from the second pollution source census data are transposed and merged using the UNIQUE, TOROW, and FILTER functions; the discharge permit data are integrated using the XLOOKUP function, and the discharge permit information is determined by dual matching of enterprise name and unified social credit code.

[0018] Furthermore, in step S3, the shpNameMatch.exe program is used to conduct point name matching and correction by district and county, and the points on the road are subjected to proximity adsorption correction.

[0019] Furthermore, in step S4, the spatial connection is set as a one-to-many connection, the UNIQUE function is used to extract the unique patch identifier code, and the TRANSPOSE function is used to integrate the location information of multiple pollution sources corresponding to the same patch into the same row.

[0020] Furthermore, in step S5, the XLOOKUP function is used to batch link the enterprise's basic information, main raw and auxiliary material information, pollutant production and discharge information, and pollution discharge permit information to the point-to-area geographic information correspondence table based on the unique identifier.

[0021] Furthermore, the unique identifier is generated by concatenating the data year, data source, organization code, and the detailed name of the reporting unit.

[0022] Furthermore, in step S2, the coordinate decimal conversion uses the formula: Decimal degree = degree + minute / 60 + second / 3600.

[0023] Furthermore, in step S3, the points outside the administrative region, the points on the road, and the normal points are separated by the cropping and erasing tools, and the normal points and the corrected points on the road are merged for subsequent point-to-surface fusion.

[0024] Furthermore, before performing point-area data spatial fusion, the land use status map patches are screened according to construction land type, and road land is removed. Only the screened construction land map patches are used for subsequent spatial connection.

[0025] Compared with the prior art, the technical solution disclosed in this invention has the following beneficial effects:

[0026] By adopting a unified collection of multi-source pollution source data and land use spatial data, the problem of existing data being scattered and from disorganized sources can be solved, providing a complete data foundation for subsequent integration.

[0027] By standardizing the cleaning process through field mapping, coordinate transformation, unit unification, unique identifier generation, table to vector conversion, and splitting by district and county, heterogeneous differences in multi-source data are eliminated, data format and attribute unification is achieved, and data standardization and integrability are improved.

[0028] Through automated verification by district and county, point separation and road point correction, point coordinate deviations are corrected, abnormal and invalid points are eliminated, the spatial positioning accuracy of pollution sources is improved, and point-to-surface matching errors are avoided;

[0029] By using one-to-many spatial connections, table transposition, and back-connection processing based on patch identifier codes, point-area geographic information can be efficiently matched and integrated, significantly improving the speed of large-scale data processing and reducing manual operation costs and repetitive workload.

[0030] By using unique identifiers as the association keys to link entity information in batches, all attributes of pollution sources are fully preserved, forming a standardized "single map" of soil pollution sources. This directly supports the screening of soil pollution survey targets and land use planning, making it more practical. This achieves high-precision, high-efficiency, and comprehensive information fusion of pollution source data and land use patches, forming a unified spatial base map that can be directly used for soil pollution source control and survey target screening. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a schematic diagram of the process for constructing a single map of soil pollution sources by fusion of automated multi-source heterogeneous data provided in an embodiment of the present invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] like Figure 1 As shown, this invention provides a method for constructing a single map of soil pollution sources by fusion of multi-source heterogeneous data, including:

[0036] S1. Data Collection: Acquire multi-source pollution source data and land use spatial data. The multi-source pollution source data includes data from the first pollution source census, data from the second pollution source census, ecological and environmental statistics data from 2011 to 2025, pollutant discharge permit data, mine data, and tailings dam data. The land use spatial data includes map data from the third national land survey, administrative division data, and regional control detailed planning.

[0037] By uniformly collecting multi-source pollution data and land use spatial data, a comprehensive and complete basic dataset is constructed, providing a unified, complete, and processable data foundation for subsequent data alignment and cleaning, spatial verification, point-to-area fusion, and entity information linkage, thus avoiding incomplete fusion and unreliable results due to missing data or scattered sources.

[0038] S2. Data Alignment and Cleaning: Perform field mapping, coordinate decimal conversion, unit of measurement unification, unique identifier generation, table to vector processing, and split vector data according to administrative divisions for multi-source pollution data.

[0039] The unique identifier is generated by concatenating the data year, data source, organization code, and the detailed name of the reporting unit.

[0040] In step S2, the basic information table of industrial enterprises, raw and auxiliary material information table, wastewater discharge table, and exhaust gas emission table from the second pollution source census are used to transpose the raw and auxiliary material information and merge multiple tables using the UNIQUE, TOROW, and FILTER functions; the discharge permit data are integrated using the XLOOKUP function, and the discharge permit information is determined by dual matching of enterprise name and unified social credit code.

[0041] In step S2, the coordinate decimal conversion uses the formula: Decimal degree = degree + minute / 60 + second / 3600.

[0042] By performing field mapping, coordinate format unification, unit conversion, unique identifier generation, table to vector conversion, and splitting by administrative division on multi-source heterogeneous pollution source data, and by specifically integrating the industrial enterprise basic information table, raw and auxiliary material information table, wastewater discharge table, exhaust gas emission table, and pollution discharge permit data from the second pollution source census, heterogeneous differences in format, fields, coordinates, and units of data from different sources and years are eliminated. This achieves standardization and normalization of pollution source data, providing standardized data with unified format, unique identifier, spatial identifiability, and batch processing capability for subsequent spatial information verification, point-to-area fusion, and entity information association, thus avoiding matching failures, fusion errors, and information loss caused by data heterogeneity.

[0043] S3. Data Spatial Information Verification: An automated matching program is used to conduct preliminary verification of pollution source locations by district and county. Points outside the administrative region, points on the road and normal points are separated by cropping and erasing tools. Points on the road are subjected to nearby adsorption correction and merged to obtain the corrected point data.

[0044] In step S3, the shpNameMatch.exe program is used to conduct point name matching and correction by district and county. The point name and coordinate matching database are matched to output the corrected coordinates, and the points on the road are subjected to nearby adsorption correction.

[0045] The coordinate matching and correction process steps and principles are as follows: The correction process includes four steps: name preprocessing and cleaning, accurate text matching, coordinate orientation and replacement, and road point correction. There are no complex geometric reconstruction calculations, the running logic is clear in steps, the matching accuracy is controllable, and the correction traceability is clear.

[0046] (1) Standardization of original site names, cleaning and noise reduction treatment

[0047] The program has a built-in fixed prefix dictionary and a suffix removal dictionary. These dictionaries can also be customized by adding or removing entries. For each original location name to be corrected, it automatically removes common prefixes that may interfere with matching (such as custom redundant prefixes like "location," "area," "region," "project," etc.) and industry-standard fixed suffixes at the end of the name (such as "Limited Company," "Industrial Company," "Development Company," "Business Department," "Project Department," "Plot," "Site," etc.). The removal process only deletes specified fixed character combinations, retaining the core, unique identification fields of the name, generating a cleaned, non-redundant, interference-free, and accurately comparable standard name for the location. The standardized extraction formula for cleaned location names is as follows:

[0048] (Formula 1)

[0049] In the formula, N clean : The standardized and simplified name of the core location after batch removal and cleaning of prefixes and suffixes; N raw : The initial complete name of the original point to be calibrated, including various redundant prefixes and industry suffixes, which is the original input data of the program; DelPrefix: Prefix removal function, which automatically deletes all preset fixed prefix characters at the beginning of the original point name; DelSuffix: Suffix removal function, which automatically deletes all preset fixed suffix characters at the end of the name after the prefix has been removed.

[0050] (2) After cleaning, the name and coordinates are matched to the point name in the database to determine the exact and identical match.

[0051] First, a coordinate matching database was established. The coordinate accuracy of points in the known discharge permit data, detailed soil pollution survey data, contaminated site management system data, and POI data is relatively high. The data is prioritized from highest to lowest as follows: detailed soil pollution survey data, contaminated site management system data, commercial map API interface data, and historical POI data. The coordinate matching database underwent name standardization, cleaning, and noise reduction processing, using the same method as the original point name standardization process.

[0052] The cleaned standard names of points, after prefix and suffix removal, are compared and verified one by one with the standard names inherent in each data entry in the coordinate matching database, according to priority. A strict text-to-text matching rule is enforced, disregarding fuzzy matching, approximate matching, homophonic matching, and partial keyword matching. A match is considered valid only when the text characters, character order, and number of characters are completely identical, and the uniquely matched POI target data is selected. The most reliable spatial location is determined by comparing the point source to be corrected with authoritative data from different sources at each level.

[0053] (Formula 2)

[0054] In the formula, MatchFlag is a name matching status flag variable used to determine whether the match is successful. 1 represents a successful name match and 0 represents a failed name match. N1, N2, N3, and N4 are the names corresponding to the detailed soil pollution status survey data, polluted land management system data, commercial map API interface data, and historical POI data in the coordinate matching database, respectively, and serve as the benchmark reference text for matching comparison.

[0055] (3) After a successful match, the coordinates are directly assigned to replace the original point coordinates.

[0056] After the program completes the name matching verification and confirms that the match is successful, it does not perform coordinate weighted averaging, coordinate offset correction, or coordinate interpolation calculation. Instead, it directly replaces the initial offset coordinates of the original point with the high-precision longitude and latitude coordinates of the matching database data. The point graphic elements remain unchanged, and all point attribute information except coordinates is retained. Finally, the program completes the precise correction and matching of point coordinates.

[0057] (Formula 3)

[0058] In the formula, X raw Y raw These are the initial x and y coordinates of the original point, and the original offset coordinates to be corrected (to be retained when matching fails); X match Y match These are the x and y coordinates of the successfully matched data; X new Y new These are the corrected coordinates.

[0059] (4) Topological determination of road patches at locations and correction of nearby construction patches

[0060] After completing the coordinate replacement and correction, the program automatically performs spatial topology overlay detection on the points with updated coordinates to determine whether the corrected points fall within the road vector map area. If the point is located inside a road map patch or on the road boundary line, it is determined that the point's location is unreasonable. The system automatically starts the nearest spatial distance retrieval algorithm to calculate the straight-line spatial distance between the point within the road and the boundaries and interiors of all construction land map patches in the entire area. It then selects the only construction land map patch with the closest spatial distance and automatically snaps and translates the point's coordinates to the compliant location of the nearest construction land map patch. If the point itself does not fall within a road map patch and the initial location is compliant, no snapping and translation operation is performed, and the coordinates remain unchanged. This ensures that all business points are ultimately located compliantly in the construction land area, avoiding the problems of points covering roads and non-compliant locations.

[0061] The logic for determining the location of road patches is as follows:

[0062] (Formula 4)

[0063] In the formula, InRoadFlag is a variable indicating the road location status of the point, used to determine whether the corrected point overlaps with a road patch; 1 indicates that the point is located within a road patch and needs to be corrected, while 0 indicates that the point's location is compliant and does not require adjustment. road It is a collection of all road surface patches in the road vector data, serving as the benchmark layer for topological judgment of point location compliance.

[0064] The correction formula for aligning a point to the coordinates of the construction land is as follows:

[0065] (Formula 5)

[0066] In the formula, X final Y final These are the point coordinates after correction. SnapToNearBuilt is a nearby construction land snapping correction function that automatically calculates the straight-line distance between the point and all construction land patches, filters the nearest construction land, and completes the nearby snapping and shifting of the point coordinates. The specific calculation logic is as follows:

[0067] 1. Input the coordinates P(X) of the current violation location within the road. new Y new )

[0068] 2. Load the global construction map patch layer.

[0069] 3. Calculate the shortest straight-line distance from point p to each construction map patch.

[0070] 4. Find the minimum value among all distances and locate the nearest construction map patch.

[0071] 5. Calculate the nearest projection point (the nearest point on the boundary of the patch) from the given location.

[0072] 6. Move / snap the point coordinates to the nearest point.

[0073] 7. Output the final compliant coordinates Pfinal(X) final ,Y final )

[0074] The formula for the shortest distance from a point to a construction map patch is as follows:

[0075] D i =MinDistance(P(X new Y new Built i )

[0076] Recently selected construction land screening formula:

[0077] D min =min(D1,D2,...,D n )

[0078] Nearest point projection calculation (coordinates of the adsorption point):

[0079] P snap =NearestPoint(P,Built min )

[0080] The final output formula of the function is:

[0081] (X final ,Y final )=P snap

[0082] In the formula, P(X) new Y new ) is the original point that falls within the road and needs to be corrected; Built i It is the i-th construction map patch, D i It is the shortest distance from the point to the i-th construction map patch; D min It is the shortest distance from the location to all construction land; Built min It is the construction map patch closest to the location; P snap These are the coordinates of the snap-in point on the nearest construction patch in the map; (X final ,Y final ) is the final compliant location coordinate of the function output.

[0083] In step S3, the points outside the administrative region, the points on the road, and the normal points are separated by the cropping and erasing tools. The normal points and the corrected points on the road are then merged for subsequent point-to-surface fusion.

[0084] Before performing point-area data spatial fusion, the land use status map patches are screened according to the construction land type, and road land is removed. Only the screened construction land map patches are used for subsequent spatial connection.

[0085] By performing automated point verification, point type separation, and road point correction in districts and counties, and in conjunction with the screening of construction land and the removal of road land in the current land use map, spatial deviations of pollution source points are corrected, invalid and abnormal points are removed, and the legal fusion spatial range is locked. This ensures that pollution source points are accurately located in the construction land map, avoiding problems such as point-to-area matching errors and fusion into roads or non-construction land at the spatial level. It provides a foundation of accurate, spatially compliant, and reliably matched point data and map patches for subsequent point-to-area data spatial fusion, significantly improving fusion accuracy and result usability.

[0086] S4. Spatial Fusion of Point and Area Data: Using the map patches from the Third National Land Survey as target elements, one-to-many spatial connections are made to generate a point-area geographic information correspondence table and complete the table transposition. Based on the map patch identification code, back-connection is used to generate point-area geographic information fusion map patches.

[0087] In step S4, the spatial connection is set as a one-to-many connection. The UNIQUE function is used to extract the unique patch identifier code, and the TRANSPOSE function is used to integrate the location information of multiple pollution sources corresponding to the same patch into the same row.

[0088] Through one-to-many spatial connections, table transposition, and backlinking based on map patch identifiers, the corrected pollution source location information is spatially matched and integrated with the land use status map patches. This enables batch association of one map patch with multiple pollution source locations, structurally mounting scattered point source information onto unified spatial maps, forming standardized point-area geographic information correspondences and fused maps. This provides a stable, standardized, and directly usable spatial data base for subsequent entity information attachment and "one map" construction.

[0089] The core formula for generating one-to-many output records is:

[0090]

[0091]

[0092] In the formula, T i S is the i-th target feature in the target layer; S is the connecting layer; S j It is the j-th connection feature in the connection layer; R(T) i S j () represents the target element Ti and the connecting element S j Whether a specified spatial relationship is satisfied, such as intersection, containment, being located inside, or within a distance, etc., in this invention, intersection refers to the relationship between the two entities; M i Is it related to the target element T? i The set of all connecting elements that satisfy spatial relationships; n i The target element T i Number of matched connection elements; S i,k It is the k-th connect element in the matching set Mi; OutputRow i,k Indicates that the target element T i A spatial connection output record generated by the k-th matching connection element; Geom(T) i ) represents the target element T i The original spatial geometry; Attr(T) i ) represents the target element T i All built-in attribute field information; Attr(S) i,k) represents all attribute field information corresponding to the k-th connected feature in the matching set of the i-th target feature.

[0093] S5. Point-area entity information fusion: Using the unique identifier as the association key, the entity information of pollution sources is batch linked to the point-area geographic information correspondence table to generate a point-area entity information fusion table, resulting in a "single map" of soil pollution sources.

[0094] In step S5, the XLOOKUP function is used to batch link the enterprise's basic information, main raw and auxiliary material information, pollutant production and discharge information, and pollution discharge permit information to the point-area geographic information correspondence table based on the unique identifier.

[0095] Using a unique identifier as the association key, and through batch data linking, complete entity information such as basic enterprise information, main raw and auxiliary materials, pollutant production and discharge, and pollution discharge permits are accurately linked with a point-area geographic information correspondence table that has been spatially matched. This forms a point-area entity information fusion table that integrates spatial information and business attributes, and ultimately constructs a complete "single map" of soil pollution sources that can be directly used for soil pollution supervision and general survey screening.

[0096] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0097] The block diagrams of devices, apparatuses, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0098] It should also be noted that in the apparatus, device, and method of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of the present invention.

[0099] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0100] It should be understood that the qualifying terms "first", "second", "third", "fourth", "fifth" and "sixth" used in the description of the embodiments of the present invention are only used to more clearly illustrate the technical solutions and are not intended to limit the scope of protection of the present invention.

[0101] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

[0102] Example:

[0103] 1. Data Collection: A city was selected as the target area, which contains 20 districts (counties and cities). Land use status map data A (a total of 1.6 million map patches in the city, of which 540,000 are construction land patches), first pollution source census data D1 (containing approximately 3,000 entities), second pollution source census data D2 (containing approximately 7,000 entities), ecological and environmental statistics data D3 from 2011 to 2025 (containing approximately 20,000 entities), pollutant discharge permit data D4 (containing approximately 9,500 entities), and mine tailings dam data D5 (containing 70 entities) were collected to construct the original dataset D (containing approximately 40,000 entities).

[0104] 2. Convert the coordinates of all entities in dataset D to decimal using the formula: Decimal degree = degrees + minutes / 60 + seconds / 3600.

[0105] 3. Data Spatial Information Verification. The shpNameMatch.exe program was used to perform point name matching and correction by district and county. Of the approximately 40,000 entities in dataset D, 6,300 data points required verification. Specifically, 3,600 point coordinates were inaccurate, and 2,700 points were located on roads. This improved the accuracy of the verified entity coordinates.

[0106] 4. Spatial fusion of point and area data. This involves merging the data set D after information verification. newThe entities were merged with the land use status map patch A. After merging, 39,500 entities were incorporated into 13,000 land use status map patches, with an average of 3 entities per patch, mostly data from different years for the same pollutant. The remaining entities that could not be merged were output to form a list for manual verification.

[0107] Beneficial effects:

[0108] (1) Compared with the traditional manual method, it takes about 3 days to complete the spatial information verification of district (county, city) data and 2 days to complete the point-area spatial fusion of 20 districts (counties, cities). Using this method, the spatial information verification of 20 districts (counties, cities) can be completed in only 1 hour, and the point-area spatial fusion of 20 districts (counties, cities) can be completed in only half an hour, thus improving the processing efficiency.

[0109] (2) Improved the accuracy of data geographic coordinates.

[0110] (3) It can quickly identify the list of entities that need to be manually checked, making it easier for staff to locate the work objects and improve the efficiency of manual verification.

Claims

1. A method for constructing a single map of soil pollution sources by fusion of automated multi-source heterogeneous data, characterized in that, include: S1. Data Collection: Acquire multi-source pollution source data and land use spatial data. The multi-source pollution source data includes data from the first pollution source census, data from the second pollution source census, ecological and environmental statistics data from 2011 to 2025, pollutant discharge permit data, mine data, and tailings dam data. The land use spatial data includes map data from the third national land survey, administrative division data, and regional control detailed planning. S2. Data Alignment and Cleaning: Perform field mapping, coordinate decimal conversion, unit of measurement unification, unique identifier generation, table to vector conversion, and splitting of vector data according to administrative divisions for multi-source pollution data; S3. Data Spatial Information Verification: An automated matching program is used to conduct preliminary verification of pollution source locations by district and county. Points outside the administrative region, points on the road and normal points are separated by cropping and erasing tools. Points on the road are subjected to nearby adsorption correction and merged to obtain the corrected point data. S4. Spatial Fusion of Point and Area Data: Using the map patches from the Third National Land Survey as target elements, one-to-many spatial connections are made to generate a point-area geographic information correspondence table and complete the table transposition. Based on the map patch identification code, back-connection is used to generate point-area geographic information fusion map patches. S5. Point-area entity information fusion: Using the unique identifier as the association key, the entity information of pollution sources is batch linked to the point-area geographic information correspondence table to generate a point-area entity information fusion table, resulting in a "single map" of soil pollution sources. In step S3, the points outside the administrative region, the points on the road, and the normal points are separated by the cropping and erasing tools, and the normal points and the corrected points on the road are merged for subsequent point-area fusion. Before performing point-area data spatial fusion, the land use status map patches are screened according to the construction land type, and road land is removed. Only the screened construction land map patches are used for subsequent spatial connection.

2. The method for constructing a single map of soil pollution sources by fusion of automated multi-source heterogeneous data according to claim 1, characterized in that, In step S2, the basic information table of industrial enterprises, raw and auxiliary material information table, wastewater discharge table, and exhaust gas emission table from the second pollution source census are used to transpose the raw and auxiliary material information and merge multiple tables using the UNIQUE, TOROW, and FILTER functions; the discharge permit data are integrated using the XLOOKUP function, and the discharge permit information is determined by dual matching of enterprise name and unified social credit code.

3. The method for constructing a single map of soil pollution sources by fusion of automated multi-source heterogeneous data according to claim 1, characterized in that, In step S3, the shpNameMatch.exe program is used to conduct point name matching and correction by district and county, and the points on the road are subjected to nearby adsorption correction.

4. The method for constructing a single map of soil pollution sources by fusion of automated multi-source heterogeneous data according to claim 1, characterized in that, In step S4, the spatial connection is set as a one-to-many connection. The UNIQUE function is used to extract the unique patch identifier code, and the TRANSPOSE function is used to integrate the location information of multiple pollution sources corresponding to the same patch into the same row.

5. The method for constructing a single map of soil pollution sources by fusion of automated multi-source heterogeneous data according to claim 1, characterized in that, In step S5, the XLOOKUP function is used to batch link the enterprise's basic information, main raw and auxiliary material information, pollutant production and discharge information, and pollution discharge permit information to the point-area geographic information correspondence table based on the unique identifier.

6. The method for constructing a single map of soil pollution sources by fusion of automated multi-source heterogeneous data according to claim 1, characterized in that, The unique identifier is generated by concatenating the data year, data source, organization code, and the detailed name of the reporting unit.

7. The method for constructing a single map of soil pollution sources by fusion of automated multi-source heterogeneous data according to claim 1, characterized in that, In step S2, the coordinate decimal conversion uses the formula: Decimal degree = degree + minute / 60 + second / 3600.

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