Multi-source spatial data automatic processing method and system for water resource investigation

Abstract

The invention discloses a multi-source spatial data automatic processing method and system for water resource investigation. The method comprises the following steps: accessing multi-source heterogeneous water resource survey data; performing automatic quality inspection based on a preset rule base; multi-source data fusion is realized based on the reservoir unique identification code through an attribute intelligent association engine; executing attribute normalization and geometric transformation on the fused data; and finally, performing productization packaging and output based on a preset product catalog and a standard framing rule, automatically generating a mapsheet reservoir DEM product conforming to a framing standard and associated metadata thereof, and automatically deriving and outputting a thematic spatial data product to form a multi-level and multi-type spatial data product set conforming to a water resource investigation standard. According to the invention, full-process automation from data access to standardized product output is realized, and the consistency of data processing efficiency and result quality is remarkably improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of geographic information and water resources investigation, and particularly relates to a multi-source spatial data automatic processing method and system for water resources investigation. BACKGROUND

[0002] Water resources investigation is an important basic work of national natural resource monitoring and management, involving the collection and processing of information such as reservoir distribution, water body range, water level characteristics, etc. At present, the processing of water resources investigation spatial data generally faces the following technical bottlenecks: 1. Difficult to fuse multi-source heterogeneous data, manual intervention, low efficiency. Data comes from different departments, different periods, different measurement methods, resulting in various data formats (such as IMG, TIFF, Shapefile, Excel, etc.), non-uniform coordinate system and projection, different attribute field definition and storage method. Data processing personnel need to frequently switch between multiple software platforms such as ArcGIS and FME, and perform manual data format conversion, coordinate projection, attribute field matching and connection. For example, the spatial attributes of the reservoir DEM image range, row and column number, and the attributes such as the name and water level in the Excel table are often associated by relying on manual comparison and repetitive operations, which not only slows down the processing speed, but also is prone to errors when processing large-scale reservoir data.

[0003] 2. Single data quality checking means, inconsistent standard implementation, uneven result quality. Existing technologies lack the ability to automatically check the internal logic consistency of multi-source data. For example, it is impossible to automatically check whether the "reservoir code" of the same reservoir in different source tables is consistent, or to quickly detect abnormal holes in DEM data and check whether the vector surface and DEM image are spatially matched. Quality inspection relies heavily on manual visual inspection and experience, making it difficult to systematically implement water resources investigation data standards, resulting in potential problems in attribute completeness, logical consistency and spatial topological relationship of the final result data set.

[0004] 3. Standardized product production process is fragmented, and the degree of automation and intelligence is insufficient. Generating final data products that meet the requirements of the standard (such as reservoir DEM map products under standard division and thematic normal water storage level inundation range products) usually requires multiple independent and sequential manual steps. For example, to produce 1:10,000 standard division reservoir DEM products, manual judgment is required to determine which maps the reservoir is involved, the directory is created manually, projection conversion and cutting are performed for each map, and the corresponding metadata table is filled in manually. This process is tedious and repetitive, and it is difficult to ensure that all products follow uniform division rules and metadata standards. Similarly, when deriving thematic products such as inundation range, water level attributes need to be extracted manually, raster calculation, vector conversion and area calculation are required, and batch automatic derivation based on rules cannot be achieved.

[0005] 4. Weak metadata management, leading to difficulties in data traceability and product reuse. Throughout the processing chain, metadata such as data production information, processing history, and attribute sources are often scattered or missing, failing to be effectively and automatically linked to and structured by the spatial data ontology. This results in difficulties in data traceability, a lack of basis for product credibility assessment, and is also detrimental to the long-term management and reuse of data.

[0006] In summary, existing spatial data processing technologies for water resources surveys suffer from prominent problems such as low automation, difficulty in implementing standardization, limited production efficiency, and difficulty in ensuring quality consistency. Therefore, there is an urgent need for an automated and intelligent processing solution that can connect the entire process from data access, quality inspection, fusion, and normalization to product packaging, in order to improve the overall efficiency of water resources survey data production, ensure data quality consistency, and support the rapid generation of various types of standardized spatial data products. Summary of the Invention

[0007] An automated method and system for multi-source spatial data processing in water resource surveys The purpose of this invention is to overcome the shortcomings of existing technologies and methods for processing multi-source spatial data in water resource surveys, and to provide an automated, integrated, highly reliable, and efficient data processing method and system to solve the efficiency and quality bottlenecks in processing multi-source heterogeneous spatial data in water resource surveys.

[0008] This invention is achieved through the following technical solution: An automated processing method for multi-source spatial data used in water resource surveys, characterized by the following steps: S1: Access water resources survey data; the data must contain at least two different sources or formats, and must include initial reservoir DEM data and null data tables that have been preprocessed according to water resources survey standards. S2: Based on a pre-set water resources survey data standard rule base, perform automated quality checks on the accessed data; S3: Through the attribute intelligent association engine, based on the preset unique identifier of the reservoir, the data that has passed the quality inspection is mapped and linked to generate a fused dataset. S4: Based on the built-in transformation rules of the water resources survey data standard rule base, perform attribute normalization and geometric transformation on the fused dataset to generate standard-compliant individual reservoir DEM data and its metadata; S5: Based on the preset water resources survey product catalog and map division rules, perform product packaging and output operations on the DEM data of the individual reservoirs, automatically generate and organize a multi-level and multi-type spatial data product set that conforms to the water resources survey standards.

[0009] Preferably, step S1 further includes a data preprocessing sub-step before data access: generating initial reservoir DEM data based on contour lines and elevation point data using the Create TIN and TIN to Raster tools, or using the Topo to Raster tool, on a GIS platform; and generating a reservoir DEM metadata database template and corresponding empty metadata tables based on water resources survey standards.

[0010] Preferably, the automated quality check in S2 includes: spatial topology consistency check, attribute integrity check, and logical consistency check; wherein, the logical consistency check includes checking whether the values ​​of the unique identifier and sequence number key fields of the same reservoir are consistent in structured tabular data from different sources.

[0011] Preferably, in step S3, the operations performed by the attribute intelligent association engine include: coordinate system unification processing, data range calculation and formatting, and attribute matching based on keywords and spatial relationships; wherein, it also includes a cross-zone judgment sub-step: calculating the projection zone number involved based on the longitude range of the reservoir DEM, and marking cross-zone attributes if the zone numbers are inconsistent.

[0012] Preferably, the attribute normalization in S4 is achieved through conditional expressions, specifically by converting the clauses about attribute value ranges in the water resources survey data standard rule base into assignment rules that include conditional judgments, and then normalizing the attributes of the fused dataset through the conditional execution module of the data processing platform.

[0013] Preferably, the "product packaging and output operation" in S5 includes at least the first type of output operation: automatically generating map sheet reservoir DEM products that conform to the map sheet standard and their associated map sheet metadata based on standard map sheet division rules.

[0014] Furthermore, the generation of the reservoir DEM product and its map sheet metadata includes the following sub-steps: S51: Based on geographic coordinates, perform spatial overlay analysis on the DEM data of the individual reservoir and the standard map sheet vector data to determine the relevant map sheets; S52: Create a corresponding standard catalog structure for each determined map sheet; S53: Project the map sheet vector data to the same plane coordinate system as the individual reservoir DEM data, and perform outward buffering on the map sheet vector surface; S54: Use the buffered map sheet to perform batch cropping of multiple individual reservoir DEM data falling within its range to obtain one or more map sheet reservoir DEM data files under that map sheet; S55: For each of the map sheet reservoir DEM data files, map sheet attribute records are automatically created in the index file of the corresponding map sheet, and the attribute information of the cropped map sheet reservoir DEM is automatically attached to the map sheet attribute records through the attribute intelligent association engine to generate the map sheet metadata.

[0015] Preferably, the attribute information automatically attached in S55 includes: the coordinates of the upper left corner of the cropped reservoir DEM, the number of rows, the number of columns, and the production method attribute from the structured table data.

[0016] Preferably, the "product packaging and output operation" in S5 also includes a second type of output operation: automatically deriving and outputting thematic spatial data products based on the DEM data of the individual reservoir and its associated attribute information.

[0017] Furthermore, the second type of output operation includes generating a product for the normal water level flood range, the sub-steps of which include: S61: Load the normal water level attribute of the reservoir from the structured table data; S62: Using a grid calculator, the area in the DEM data of the individual reservoir whose elevation value is less than or equal to the normal water level attribute is calculated as the inundation range grid surface; S63: Convert the flooded area grid surface into a vector surface and calculate its area; S64: Use the vector surface to cut the DEM data of the individual reservoir to obtain the DEM data of the inundated reservoir; S65: Output the DEM data of the reservoir within the inundation range, the vector surface, and the reservoir information table with updated inundation area attributes.

[0018] An automated processing system for multi-source spatial data used in water resource surveys, characterized in that it comprises: The module is used to access water resources survey data, which contains at least two different sources or formats, and must include initial reservoir DEM data and null metadata tables that have been preprocessed according to water resources survey standards. A module for automatically performing quality checks on the accessed data based on a pre-built standard rule base for water resources survey data; This module is used to perform attribute mapping and linking on the quality-checked data through an attribute intelligent association engine based on a preset unique identifier code of the reservoir to generate a fused dataset. This module is used to perform attribute normalization and geometric transformation on the fused dataset according to the transformation rules built into the standard rule base of the water resources survey data, so as to generate standard-compliant individual reservoir DEM data and its metadata. This module is used to perform product packaging and output operations on the DEM data of individual reservoirs based on a preset water resources survey product catalog and map rules, so as to automatically generate and organize a multi-level, multi-type spatial data product set that conforms to water resources survey standards. The beneficial effects of this invention are:

[0019] Full-process automation: By integrating data access, quality inspection, attribute association, normalization and transformation and product packaging into a fully automated process, manual intervention is significantly reduced and data processing efficiency and consistency are improved.

[0020] Strong multi-source data fusion capability: Through the attribute intelligent association engine, based on the unique identifier of the reservoir, it realizes automatic matching and attribute attachment of multi-source heterogeneous data, improving the accuracy and completeness of data fusion.

[0021] Standardized and flexible output: It supports the automatic generation of map sheet products based on standard sheet division and the binding of metadata, while also supporting the automated production of derivative thematic products (such as flooded area products), meeting the output needs of multi-level and multi-type products.

[0022] A robust quality assurance mechanism is in place: automated quality checks are achieved through a pre-built rule base, covering multiple dimensions such as space, attributes, and logic, to ensure the quality of input data and provide a reliable foundation for subsequent processing.

[0023] High scalability and applicability: The method is based on a rule base and engine-driven approach, and can be adapted to different standards or survey tasks by updating rules. It is suitable for various multi-source spatial data processing scenarios related to water resource surveys. Attached Figure Description

[0024] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart of the method of the present invention.

[0025] Figure 2 This is the operating interface of the system for generating a 10,000-scale reservoir DEM in this embodiment of the invention.

[0026] Figure 3 This is the 10,000-part multi-level directory in this embodiment of the invention.

[0027] Figure 4 These are multiple individual reservoir DEMs cut out from a certain 10,000-page map in this embodiment of the invention (the middle part of the file name has been hidden for confidentiality).

[0028] Figure 5These are multiple shapefile records within 10,000 map sheets in this embodiment of the invention, each corresponding to a single reservoir DEM (the middle part of the file name has been omitted for confidentiality).

[0029] Figure 6 This is the operating interface of the DEM system for generating individual reservoirs in this embodiment of the invention.

[0030] Figure 7 These are multiple individual reservoir DEM folders and their next-level files in this embodiment of the invention (the middle part of the filenames has been hidden for confidentiality). Detailed Implementation

[0031] To enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0032] This invention provides an automated processing method for multi-source spatial data in water resource surveys, see [link to relevant documentation]. Figure 1 (As shown in the flowchart of this method), the method includes the following steps: S1: Access water resource survey data from at least two different sources or in at least two different formats.

[0033] First, optionally, perform a data preprocessing sub-step to generate initial data and templates: S11: Based on contour lines and elevation point data, generate initial reservoir DEM data using the Create TIN and TIN to Raster tools, or the Topo to Raster tool, on the GIS platform. To ensure DEM accuracy, when the contour lines on the original topographic map are relatively dense, the TIN method is used to construct the DEM; when the contour lines are relatively sparse, the TopoToRaster method is used to construct the DEM.

[0034] S12: Generate a reservoir DEM metadata database template and corresponding empty metadata tables based on water resources survey standards.

[0035] Subsequently, data access is performed: S13: In a data processing platform (such as FME), use the appropriate reader to read in one or more files according to different data formats, including but not limited to img image data files, GIS vector data files, Excel or CSV structured table files, etc. For example, after the initial batch import of reservoir DEMs, the extent polygons of each reservoir DEM can be automatically generated using a raster to polygon conversion tool (such as RasterToPolygonCoercer), and the fragmented polygons of the same reservoir can be merged into a single geometric feature based on the reservoir's unique identifier. If extent polygons already exist, the automatic generation step is skipped.

[0036] S2: Based on a pre-built standard rule base for water resources survey data, automatically check the quality of the accessed data.

[0037] Step S2 includes the following sub-steps, corresponding to the spatial topology, attribute integrity, and logical consistency checks in the claims: S21: Perform a spatial topology consistency check. Check whether the vector metadata surface and the corresponding reservoir DEM spatially overlap.

[0038] S22: Perform a logical consistency check. Check whether the values ​​of key fields such as the unique identifier and serial number of the same reservoir are consistent in structured table data from different sources.

[0039] S23: Perform an attribute integrity check. Check if any necessary attribute fields are missing.

[0040] S24: Check data integrity, for example, use tools such as DonutHoleExtractor to check whether there are voids or other anomalies in the reservoir DEM.

[0041] If any of the above checks fails, the process can be paused and a prompt for correction will be displayed. The process can be restarted after the data is corrected.

[0042] S3: Through the attribute intelligent association engine, based on the preset unique identifier of the reservoir, the data is mapped and linked to generate a fused dataset.

[0043] In step S3, the attribute intelligent association engine performs the following operations to support the feature described in claim 9: S31: Coordinate system unification. Convert the original reservoir DEM from planar coordinates of different projection zones to geographic coordinates (latitude and longitude).

[0044] S32: Data Range Calculation and Formatting. Obtain the bounding rectangle of the projected reservoir DEM, read the latitude and longitude coordinates of its lower left and upper right corners, and convert them to standard degree minute and second format.

[0045] S33: Zone Crossing Determination. Calculate the projection zone number of the reservoir's DEM based on the minimum and maximum longitude values ​​of the rectangle surrounding it. If the zone numbers are inconsistent, mark it as a cross-zone reservoir DEM.

[0046] S34: Attribute matching and linking based on keywords and spatial relationships. Using the reservoir's unique identifier as the key field and combining spatial range relationships, image attributes (such as number of rows, number of columns, and top-left corner coordinates), production method attributes, and survey line spacing attributes are linked to the corresponding metadata fields.

[0047] S4: Based on the built-in transformation rules of the water resources survey data standard rule base, perform attribute normalization and geometric transformation on the fused dataset to generate standard-compliant individual reservoir DEM data and its metadata.

[0048] Step S4 includes the following sub-steps: S41: Attribute normalization is achieved through conditional expressions. Specifically, the clauses regarding attribute value ranges in the water resources survey standards are transformed into assignment rules that include conditional judgments, and the attributes are normalized and assigned values ​​through conditional expressions in a data processing platform (such as FME's AttributeManager).

[0049] S42: Date and Format Standardization. The format of fields such as production date, data source timeliness, and product version date is converted to a unified YYYY / MM / DD format.

[0050] S43: Geometric Transformation and Coordinate Calculation. Calculate the central meridian and projection zone number based on the latitude and longitude range, and perform coordinate system unification if necessary.

[0051] S44: Elevation Anomaly Handling. Set the nodata values ​​in the image to a standard value (e.g., -9999), and remove data that still have elevation anomalies after processing.

[0052] S5: Based on the preset water resources survey product catalog and map division rules, perform productized packaging and output operations on the DEM data of individual reservoirs.

[0053] Step S5 includes the following two types of output operations: (a) First type of output operation: Based on standard map sheet division rules, automatically generate map sheet reservoir DEM products and metadata.

[0054] As an example, the process may include the following steps to illustrate S51-S55 of claims 3-4: S51: Based on geographic coordinates, spatial overlay analysis is performed on the DEM data of individual reservoirs and standard map sheet vector data (such as 1:10,000 map sheets) to determine the relevant map sheets.

[0055] S52: For each defined map sheet, create a corresponding standard hierarchical folder structure in the output directory. (See...) Figure 2 Generate the operating interface for the 10,000-segment reservoir DEM system; Figure 3 (10,000-page multi-level directory) S53: Project the map sheet vector data to the same plane coordinate system as the individual reservoir DEM data, and extend the map sheet vector surface outward (e.g., extend it outward by 100 meters).

[0056] S54: Using the buffered map sheet, batch cropping of DEM data for multiple individual reservoirs falling within its range yields one or more DEM data files for reservoirs within that map sheet. Files resulting in blank images after cropping are not retained. (See...) Figure 4 (DEM of multiple individual reservoirs cropped from a certain map sheet) S55: For each reservoir DEM data file, automatically create a map sheet attribute record in the corresponding map sheet index file (e.g., Shapefile). Then, through an intelligent attribute association engine, automatically attach the attribute information of the cropped reservoir DEM (e.g., top-left corner coordinates, number of rows, number of columns, and production method attributes) to the map sheet attribute record, generating map sheet metadata (see...). Figure 5 Multiple shapefile records (local fields) within a 10,000 map sheet, each corresponding one-to-one with the DEM of a single reservoir.

[0057] (ii) Second type of output operation: Based on the DEM data of individual reservoirs and their associated attribute information, automatically derive and output thematic spatial data products.

[0058] As an example, the process of generating a product within the normal water level flood range may include the following steps to illustrate S61-S65 of claim 6: S61: Load the normal water level attribute of the reservoir from structured tabular data.

[0059] S62: Using a raster calculator (such as RasterExpressionEvaluator), areas in the DEM data of individual reservoirs whose elevation values ​​are less than or equal to the normal water level attribute are calculated as inundation range raster surfaces.

[0060] S63: Convert the flooded area grid surface into a vector surface and calculate its area.

[0061] S64: Use this vector surface to cut the DEM data of a single reservoir to obtain the DEM data of the reservoir within the inundation area.

[0062] S65: Outputs DEM data of the inundated reservoir area, vector surfaces, and a reservoir information table with updated inundation area attributes.

[0063] In addition, the system can automatically output final data products in various formats according to standards, and automatically organize them into a multi-level directory that conforms to water resources survey standards, realizing "one-click" product packaging and output (see...). Figure 6 Generate the DEM system interface for a single reservoir; Figure 7 (Multiple individual reservoir DEM folders and their sub-folders).

[0064] Regarding the implementation method of the system: The automated processing system for multi-source spatial data used in water resource surveys can be implemented using servers, workstations, or dedicated computing devices configured with corresponding software modules. The system includes: a module for accessing water resource survey data; a module for automated quality checks based on a rule base; a module for data fusion via an attribute-based intelligent association engine; a module for attribute normalization and geometric transformation of the fused data; and a module for product packaging and output. These modules can be built and integrated by writing and deploying computer programs (e.g., FME workspaces, Python scripts, etc.) that execute steps S1 to S5 of the above methods.

[0065] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. For those skilled in the art, various improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An automated processing method for multi-source spatial data used in water resource surveys, characterized in that, Includes the following steps: S1: Access water resources survey data; the data must contain at least two different sources or formats, and must include initial reservoir DEM data and null data tables that have been preprocessed according to water resources survey standards. S2: Based on a pre-set water resources survey data standard rule base, perform automated quality checks on the accessed data; S3: Through the attribute intelligent association engine, based on the preset unique identifier of the reservoir, the data that has passed the quality inspection is mapped and linked to generate a fused dataset. S4: Based on the built-in transformation rules of the water resources survey data standard rule base, perform attribute normalization and geometric transformation on the fused dataset to generate standard-compliant individual reservoir DEM data and its metadata; S5: Based on the preset water resources survey product catalog and map division rules, perform product packaging and output operations on the DEM data of the individual reservoirs, automatically generate and organize a multi-level and multi-type spatial data product set that conforms to the water resources survey standards.

2. The method according to claim 1, characterized in that, The "product packaging and output operation" in S5 includes at least the first type of output operation: Based on standard map sheet division rules, the system automatically generates map sheet DEM products for reservoirs that conform to the map sheet division standards, along with their associated map sheet metadata.

3. The method according to claim 2, characterized in that, The generation of reservoir DEM products and their map sheet metadata includes the following sub-steps: S51: Based on geographic coordinates, perform spatial overlay analysis on the DEM data of the individual reservoir and the standard map sheet vector data to determine the relevant map sheets; S52: Create a corresponding standard catalog structure for each determined map sheet; S53: Project the map sheet vector data to the same plane coordinate system as the individual reservoir DEM data, and perform outward buffering on the map sheet vector surface; S54: Use the buffered map sheet to perform batch cropping of multiple individual reservoir DEM data falling within its range to obtain one or more map sheet reservoir DEM data files under that map sheet; S55: For each of the map sheet reservoir DEM data files, map sheet attribute records are automatically created in the index file of the corresponding map sheet, and the attribute information of the cropped map sheet reservoir DEM is automatically attached to the map sheet attribute records through the attribute intelligent association engine to generate the map sheet metadata.

4. The method according to claim 3, characterized in that, The attribute information automatically attached in S55 includes: the coordinates of the upper left corner, the number of rows, the number of columns of the cropped map sheet reservoir DEM, and the production method attribute from the structured table data.

5. The method according to claim 1, characterized in that, The "product packaging and output operation" in S5 also includes a second type of output operation: Based on the DEM data of the individual reservoirs and their associated attribute information, thematic spatial data products are automatically derived and output.

6. The method according to claim 5, characterized in that, The second type of output operation includes generating a product for the normal water level flood range, and its sub-steps include: S61: Load the normal water level attribute of the reservoir from the structured table data; S62: Using a grid calculator, the area in the DEM data of the individual reservoir whose elevation value is less than or equal to the normal water level attribute is calculated as the inundation range grid surface; S63: Convert the flooded area grid surface into a vector surface and calculate its area; S64: Use the vector surface to cut the DEM data of the individual reservoir to obtain the DEM data of the inundated reservoir; S65: Output the DEM data of the reservoir within the inundation range, the vector surface, and the reservoir information table with updated inundation area attributes.

7. The method according to claim 1, characterized in that, In step S1, a data preprocessing sub-step is included before the data is accessed: S11: Based on contour line data and elevation point data, generate initial reservoir DEM data using the Create TIN and TIN to Raster tools, or the Topo to Raster tool, on the GIS platform. S12: Generate a reservoir DEM metadata database template and corresponding empty metadata tables based on water resources survey standards.

8. The method according to claim 1, characterized in that, The automated quality checks in S2 include: spatial topology consistency checks, attribute integrity checks, and logical consistency checks; wherein, the logical consistency checks include checking whether the values ​​of the unique identifier and sequence number key fields of the same reservoir are consistent in structured tabular data from different sources.

9. The method according to claim 1, characterized in that, In S3, the operations performed by the attribute intelligent association engine include: coordinate system unification processing, data range calculation and formatting, and attribute matching based on keywords and spatial relationships; it also includes a cross-zone judgment sub-step: calculating the projection zone number involved based on the longitude range of the reservoir DEM, and marking cross-zone attributes if the zone numbers are inconsistent.

10. The method according to claim 1, characterized in that, The attribute normalization in S4 is achieved through conditional expressions. Specifically, the clauses regarding attribute value ranges in the water resources survey data standard rule base are transformed into assignment rules that include conditional judgments. The attributes of the fused dataset are then normalized and assigned values ​​through the conditional execution module of the data processing platform.

11. An automated processing system for multi-source spatial data used in water resource surveys, characterized in that, include: The module is used to access water resources survey data, which contains at least two different sources or formats, and must include initial reservoir DEM data and null metadata tables that have been preprocessed according to water resources survey standards. A module for automatically performing quality checks on the accessed data based on a pre-built standard rule base for water resources survey data; This module is used to perform attribute mapping and linking on the quality-checked data through an attribute intelligent association engine based on a preset unique identifier code of the reservoir to generate a fused dataset. This module is used to perform attribute normalization and geometric transformation on the fused dataset according to the transformation rules built into the standard rule base of the water resources survey data, so as to generate standard-compliant individual reservoir DEM data and its metadata. This module is used to perform product packaging and output operations on the DEM data of individual reservoirs based on a preset water resources survey product catalog and map rules, so as to automatically generate and organize a multi-level, multi-type spatial data product set that conforms to water resources survey standards.

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