Territorial space domain data analysis method and system based on DSL (Digital Subscriber Line) technology

Abstract

The invention provides a territorial space domain data analysis method and system based on a DSL (Digital Subscriber Language) technology, and the method comprises the steps: defining a data namespace of a territorial space domain by adopting a domain-specific language, the types of the data namespace at least comprising a source data configuration class, a basic entity configuration class, a professional entity configuration class and a comprehensive entity configuration class; collecting a source pasting data set; constructing a basic entity data set; calculating the basic entity to obtain a professional entity; performing statistics and clustering analysis on all professional entities to complete data analysis; according to the method, the domain-specific language technology is applied to the territorial space field for the first time, natural resource business knowledge and related GIS spatial data can be subjected to standard extraction, the processes of data analysis and the like are standardized, a rapid and automatic means is provided for natural resource data collection, processing, business analysis and the like, and the method is suitable for popularization and application. The digital transformation of the territorial space field is facilitated, and the scientificity and accuracy of comprehensive decision and implementation management of natural resources are improved.

Description

Technical Field

[0001] The present invention relates to the field of Domain-Specific Languages ​​(DSL) technology, and more specifically, to a land space domain data analysis method and system based on DSL technology. Background Art

[0002] The data structure of the Geographic Information System (GIS) commonly used in the field of land and space has unique spatial heterogeneity and there is a professional usage threshold for the surveying and mapping field. At the same time, due to the complexity of natural resources business rules across time and space, across businesses, and across departments and bureaus, the entire process of data collection, governance, analysis, and display applications often requires excessive reliance on expert experience and complex manual processing of specific data by professional and technical personnel.

[0003] The current data governance and analysis in the field of land and space mainly have the following deficiencies: 1) Natural resource business is complex and has poor readability: Natural resource business mainly includes comprehensive management sectors such as investigation and monitoring, spatial planning, use control, development and utilization, and ecological restoration, as well as single elements of national land space resources such as mountains, rivers, forests, fields, lakes, and grasslands. Technical personnel often need to master a lot of professional knowledge to achieve relevant business analysis goals.

[0004] 2) The maintenance cost of land and space data is high, and the management model needs to be optimized: In the past, land and space data was mainly indexed and manually updated through the traditional management model of "layer" data resource directory. In recent years, land and space data has exploded, and the traditional management model has high update and maintenance costs when facing massive and complex data. At the same time, managers in different business links lack a unified understanding of the configuration content of the data resource directory, and cannot achieve standardized connection between each other, which easily leads to data redundancy and confusion, and the management model needs to be optimized.

[0005] 3) The efficiency of GIS data spatial relationship processing and analysis is too low: Due to the spatial heterogeneity of GIS data, the hierarchical organization model based on traditional GIS relational databases requires a large amount of spatial overlay calculations when performing spatial analysis. Especially as the amount of data increases exponentially, the GIS spatial time consumption will also increase rapidly, making it difficult to meet the needs of spatial relationship calculations. Summary of the invention

[0006] In order to overcome the defects of the above-mentioned prior art in lacking standardized and normalized land and space data analysis, use, management and maintenance processes, the present invention provides a land and space data analysis method and system based on DSL technology, which highly integrates domain-specific language technology and GIS geographic information technology, can standardize natural resource business knowledge and GIS spatial data features, effectively standardize data analysis processes, and provide rapid and automated means for natural resource data collection, processing and business analysis, which is helpful to promote the digital transformation of the land and space field and improve the scientificity and accuracy of comprehensive decision-making and implementation management of natural resources.

[0007] In order to solve the above technical problems, the technical solution of the present invention is as follows: A land space data analysis method based on DSL technology includes the following steps: S1: Use domain-specific language to define the data namespace of the national land space domain, and the types of the data namespace include at least: source data configuration class, basic entity configuration class, professional entity configuration class and comprehensive entity configuration class; S2: Collect multi-source heterogeneous data in the field of land and space from different data sources and different file formats in a specific area and put them into the folder of the source data configuration class to obtain the source data set; S3: By configuring the filtering conditions, different data in the source data set are referenced, the referenced data is gridded, a data set containing multiple attributes and attribute values ​​based on the geohash grid is constructed and merged into a folder of the basic entity configuration class, and the basic entity data set is obtained; S4: Determine the algorithm rules corresponding to different business tags by configuring the business tags of the professional entity, use the different algorithm rules to operate the data in the basic entity data set, put the tag grid data obtained after the operation into the folder of the professional entity configuration class, and obtain the professional entity data set; S5: Use a preset statistical algorithm to reference and combine the label grid data in the professional entity data set; use a preset clustering algorithm to perform cluster analysis on each group of combined label grid data, and place all cluster analysis results in a folder of the comprehensive entity configuration class to complete the data analysis in the field of national land space.

[0008] Preferably, in step S1, when defining the data namespace of the national land space domain using a domain-specific language, at least the name of the data namespace myNameSpace and the type of the data namespace spaceType are defined; The data namespace type spaceType at least includes: a source data configuration class dataSet, a basic entity configuration class basicEntity, a professional entity configuration class specificEntity and a comprehensive entity configuration class integratedEntity.

[0009] Preferably, in step S1, the syntax definition of the source data configuration class includes at least: the unique identification name dataSetName of the source data configuration class data, the absolute path path of the shape space file, the renamed file name fileName of the shape space file, the coordinate system code espgSource to which the collected data needs to be converted, and the referenced data dictionary configuration dataSetDictionary.

[0010] Preferably, the syntax definition of the data dictionary configuration dataSetDictionary includes at least: the unique identification name of the data dictionary configuration dataSetDictionaryName, the stored table name dictionaryName, the stored table alias aliasName, the data source business classification businessType, the data sequence dataSequential, the data coverage range dataRange, the data source source, the data type dataType, the field name fieldName, the field alias fieldliasName, the field data type fieldDataType, and the field mandatory condition validation.

[0011] Preferably, in the step S1, the syntax definition of the basic entity configuration class at least includes: the name of the basic entity entityName, the unique identification code of the basic entity entityNameKey, the basic entity type entityType, the grid level size gridLevel, the administrative region code region of the specific region, the attribute name attributeName, the attribute data type attributeType, the year of the attribute data attributeTime, the gridding method of the source data griddingMethod, the referenced source data identifier sourceName, the referenced source data field sourceAttribute, and the referenced source data filtering condition criteria; The basic entity type entityType includes at least: any one of the natural geography type nature, the resource element type resource, the business management type management or the social economy type socioEconomic.

[0012] Preferably, in step S1, the syntax definition of the professional entity configuration class at least includes: the name of the professional entity entityName2, the unique identification code of the professional entity entityNameKey2, the professional entity type entityType2, the evaluation scale entityLevel, the administrative region code region of the specific region, the label name labelName, the label set name labelSetName, the label type labelType, the label time sequence labelYear, and the algorithm rule aggMethod; The professional entity type entityType2 includes at least one or more of location conditions qwtj, social economy shjj, landscape features jgfm, land use tdly, infrastructure jcss, historical culture lswh, environmental protection hjbh or disaster risk zhfx; The label set name labelSetName at least includes: any one of: location condition qwtj, population situation rkqk, economic industry jjcy, topography dxdm, hydrological analysis swfx, landscape pattern jggj, land use structure ydjg, land quality dlzl, use intensity syqd, land use change ydbh, management control glkz or land area dlmj; The label type labelType includes at least: any one of quantity sl, quality zl, structure jg, space kj or time series sx; The algorithm rule aggMethod at least includes: any one of summing sum, subtracting sub, multiplying mul, dividing div, solving the nearest distance gridNearestDistance, solving the buffer quantity gridBuffer, and judging whether the buffer exists gridBufferBoole.

[0013] Preferably, in step S1, the syntax definition of the comprehensive entity configuration class at least includes: the name of the comprehensive entity entityName3, the type of the comprehensive entity entityType3, the administrative region code region of the specific region, the unique identification code entityNameKey3 of the comprehensive entity, the unique identification name featureName of the element, the element data type dataType2, the alias name featureAlias ​​of the element, the element label type featureType, the label specificEntity.labelName of the referenced professional entity, and the statistical algorithm staMethod of the administrative region label value; The type of the comprehensive entity entityType3 includes at least: any one of comprehensive evaluation, deduction or optimization; The featureType of the element label includes at least one of land, population, wealth or material; The statistical algorithm staMethod at least includes: summing up and averaging up to avg.

[0014] Preferably, in step S2, the multi-source heterogeneous data in the field of land and space are specifically data acquired by a GIS geographic information system, and the file formats include at least any one of tiff, shp, gdb, mdb and img.

[0015] It is worth mentioning that in the field of national land space, the most typical and commonly used natural resource data is the data of the GIS geographic information system, which includes vector data such as shp, mdb, gdb, and raster data such as tiff and img. The present invention can integrate domain-specific language technology and GIS geographic information technology to facilitate data management and analysis.

[0016] Preferably, in step S5, the preset clustering algorithm is specifically a KMeans clustering algorithm.

[0017] The present invention also provides a land space data analysis system based on DSL technology, which uses the above-mentioned land space data analysis method based on DSL technology, including: Predefined unit: used to define the data namespace of the national land space domain using a domain-specific language, and the types of the data namespace include at least: source data configuration class, basic entity configuration class, professional entity configuration class and comprehensive entity configuration class; Paste source data collection unit: used to collect multi-source heterogeneous data in the land space field of different data sources and different file formats in a specific area and put them into the folder of the paste source data configuration class to obtain the paste source data set; Basic entity generation unit: used to reference different data in the source data set by configuring filtering conditions, grid the referenced data, construct a data set containing multiple attributes and attribute values ​​based on the geohash grid, and merge and put it into the folder of the basic entity configuration class to obtain the basic entity data set; Professional entity construction unit: used to determine the algorithm rules corresponding to different business tags by configuring the business tags of the professional entity, use different algorithm rules to operate the data in the basic entity data set, put the label grid data obtained after the operation into the folder of the professional entity configuration class, and obtain the professional entity data set; Comprehensive entity analysis unit: used to use a preset statistical algorithm to reference and combine the label grid data in the professional entity data set; use a preset clustering algorithm to perform cluster analysis on each group of combined label grid data, and place all cluster analysis results in a folder of the comprehensive entity configuration class to complete data analysis in the field of national land space.

[0018] Compared with the prior art, the technical solution of the present invention has the following beneficial effects: The present invention provides a land and space data analysis method and system based on DSL technology, which applies domain-specific language technology to the land and space field for the first time, highly integrates domain-specific language technology and GIS geographic information technology, can standardize natural resource business knowledge and data, effectively standardize data analysis, use and management and maintenance processes, and provide rapid and automated means for natural resource data collection, data processing, model building and business analysis, which is helpful to promote the digital transformation of the land and space field and improve the scientificity and accuracy of comprehensive decision-making and implementation management of natural resources; Specifically, the beneficial effects of the present invention include the following: 1) Easy connection between business and technology, improving business data analysis efficiency: Based on the abstract programming language configuration file, the business and data processing rules are analyzed in code, which reduces the difficulty for developers to understand professional rules, and realizes one-stop generation of data collection, data processing, model building, and visualization, effectively reducing the time cost of data analysis applications, and the efficiency is about 5-6 times higher than that of previous manual processing; 2) Facilitate the management and maintenance of natural resource data: Entity details are classified and referenced in the form of container loading in the namespace, forming a hierarchical entity data configuration detailed dictionary structure. Only a few configuration files in a single namespace need to be managed to quickly realize the automatic linkage update of multiple entity data results, avoid redundant and chaotic data management, and reduce the maintenance cost of data storage, query, use and update; 3) Provide the ability to strengthen the standardized integration of natural data resources: Based on the syntax declaration rules for parsing the natural resources business knowledge generation standard, the multi-source heterogeneous spatial data distributed in different business links can be gridded and fused into a unified grid unit data model, overcoming the difficulty of data fusion caused by the spatial heterogeneity of different business data within natural resources or the connection of "two-dimensional tables" across departments and bureaus; 4) Improve the standardization and intelligent application service capabilities of natural resource data analysis: Relying on the natural resource intelligent analysis component technology encapsulated in the land and space domain language, it can more flexibly adapt to the embedding of various comprehensive algorithms and business models, which further expands the breadth and depth of natural resource data analysis services, such as serving the land and space element spatial performance evaluation for multi-business and multi-field collaborative analysis, regional coordinated development, urban expansion boundaries, farmland occupation risk simulation and other land and space comprehensive management scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of a land space data analysis method based on DSL technology provided in Example 1.

[0020] Figure 2 This is an architecture diagram of a land space data analysis method based on DSL technology provided in Example 2.

[0021] Figure 3 This is a flow chart of source data collection provided in Example 2.

[0022] Figure 4 Generate a flow chart for the basic entities provided in Example 2.

[0023] Figure 5 Construct a flow chart for the professional entities provided in Example 2.

[0024] Figure 6 This is a flowchart of the comprehensive entity analysis provided in Example 2. DETAILED DESCRIPTION

[0025] The drawings are for illustrative purposes only and should not be construed as limiting the present patent; In order to better illustrate the present embodiment, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0026] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0027] Example 1 like Figure 1 As shown, this embodiment provides a land space data analysis method based on DSL technology, including the following steps: S1: Use domain-specific language to define the data namespace of the national land space domain, and the types of the data namespace include at least: source data configuration class, basic entity configuration class, professional entity configuration class and comprehensive entity configuration class; S2: Collect multi-source heterogeneous data in the field of land and space from different data sources and different file formats in a specific area and put them into the folder of the source data configuration class to obtain the source data set; S3: By configuring the filtering conditions, different data in the source data set are referenced, the referenced data is gridded, a data set containing multiple attributes and attribute values ​​based on the geohash grid is constructed and merged into a folder of the basic entity configuration class, and the basic entity data set is obtained; S4: Determine the algorithm rules corresponding to different business tags by configuring the business tags of the professional entity, use the different algorithm rules to operate the data in the basic entity data set, put the tag grid data obtained after the operation into the folder of the professional entity configuration class, and obtain the professional entity data set; S5: Use a preset statistical algorithm to reference and combine the label grid data in the professional entity data set; use a preset clustering algorithm to perform cluster analysis on each group of combined label grid data, and place all cluster analysis results in a folder of the comprehensive entity configuration class to complete the data analysis in the field of national land space.

[0028] In the specific implementation process, the data namespace of the land space domain is first defined using a domain-specific language. By defining and creating a namespace, code elements can be organized and isolated, and the configuration information of various types of TDL projects can be managed. In this embodiment, the types of data namespaces are divided into four categories: source data configuration class, basic entity configuration class, professional entity configuration class, and comprehensive entity configuration class; In this embodiment, it is necessary to configure the parameters of various namespaces. For example, Table 1 shows the configuration declaration rules and parameter descriptions of the "basic entity configuration class". Table 1 Configuration declaration rules and parameter descriptions of "Basic entity configuration class"

[0029] Then, multi-source heterogeneous data in the field of land and space from different data sources and different file formats in a specific area are collected from the outside and put into the folder of the source data configuration class to obtain the source data set; Then, different data in the source data set are referenced by configuring filtering conditions, the referenced data is gridded, and a data set containing multiple attributes and attribute values ​​based on the geohash grid is constructed and merged into the folder of the basic entity configuration class to obtain the basic entity data set; by referencing source data from different data sources, different data filtering conditions are defined according to business needs, and TDL files of different basic entities and business attributes are defined. In this way, multiple types of basic entities and attribute content can be produced; In this embodiment, geohash refers to a geographic coding system formed under a specific spatiotemporal framework, which is one of the universal coding systems of spatiotemporal grids. Its principle is to recursively grade the two-dimensional plane of the earth according to the principle of dichotomy and divide it into smaller grids. It converts the longitude and latitude coordinates of the earth's surface into a short string, which can represent a unique geographical location range in a grid dimension and is mainly used for spatial indexing of spatiotemporal data. The length n of the string is the size of the n-level grid. The longer the string length is, the larger the grid level is. The geohash grid can generally be divided into 12 levels, and its spatial accuracy is gradually improved. For example, the accuracy of the level 1 grid is about 5000 km×5000 km, the accuracy of the level 7 grid is about 151 m×151 m, and the accuracy of the level 12 grid is about 37.2 mm×18.6 mm. The gridding process involves defining a unified spatiotemporal unit, namely the geohash grid mentioned above, and establishing an organizational model for spatial data relationships based on this; using techniques such as overlap spatial overlay analysis, multiple source data are spatially linked to the geohash grid, and through statistical aggregation methods, the geohash grid is used as a grouping unit to count spatial attributes and attribute value information, such as area, perimeter, number, etc.; based on the entity classification defined by business rules, the corresponding attributes and attribute values ​​are mapped into the grid entity, and finally the basic entity is generated; Then, the algorithm rules corresponding to different business tags are determined by configuring the business tags of the professional entity, and the data in the basic entity data set are operated using different algorithm rules. The label grid data obtained after the operation is placed in the folder of the professional entity configuration class to obtain the professional entity data set; Finally, according to the preset statistical algorithm, the label grid data in the professional entity data set is referenced and combined to gather the identification values ​​within each specific area (administrative district); the preset clustering algorithm is used to perform cluster analysis on each group of combined label grid data to generate the evaluation level value of the combined elements within each administrative district, and all cluster analysis results are placed in the folder of the comprehensive entity configuration class to complete the data analysis in the field of national land space; This method aims to strengthen the standardization and automation of spatiotemporal data, which is conducive to the rapid application of data management and analysis and meets the needs of data application; This method facilitates the connection between natural resource business and GIS geographic information technology: through domain-specific language technology, natural resource business knowledge is summarized and refined, and complex natural resource business processes are simplified, thereby forming a standardized land and space domain language technology classification system, so that technical personnel no longer need to manage business details, effectively reducing business learning costs and communication costs, and improving the readability of natural resource business; This method is convenient for the maintenance and management of land and space data: relying on the land and space domain language, a standardized data classification management space and grammar declaration rules are established, and the natural resource data is converted into a "pseudo-code" form in the form of a data configuration file to realize the instance code management of land and space data, get rid of the potential confusion and redundancy caused by the traditional table directory management mode, and realize the automatic update of land and space data analysis results, thereby improving the maintainability of natural resource data; This method improves the efficiency of GIS spatial data processing and analysis: relying on the national land space domain language to generate a data model standard configuration file with grid space unit characteristics, the unified grid unit can get rid of the problem of low analysis efficiency caused by spatial data heterogeneity, and realize the rapid generation of data analysis results. At the same time, technical personnel can flexibly parse business key information through the model configuration file, realize the automation of the entire process of data processing and calculation, effectively reduce development time and cost, and meet the needs of rapid response to natural resource business analysis needs.

[0030] Example 2 like Figure 2 As shown, this embodiment provides a land space data analysis method based on DSL technology, including the following steps: S1: Use domain-specific language to define the data namespace of the national land space domain, and the types of the data namespace include at least: source data configuration class, basic entity configuration class, professional entity configuration class and comprehensive entity configuration class; S2: Collect multi-source heterogeneous data in the field of land and space from different data sources and different file formats in a specific area and put them into a folder of the paste source data configuration class to obtain a paste source data set; in this embodiment, the multi-source heterogeneous data in the field of land and space is data obtained by the GIS geographic information system, and the file format includes at least any one of tiff, shp, gdb, mdb and img; S3: By configuring the filtering conditions, different data in the source data set are referenced, the referenced data is gridded, a data set containing multiple attributes and attribute values ​​based on the geohash grid is constructed and merged into a folder of the basic entity configuration class, and the basic entity data set is obtained; S4: Determine the algorithm rules corresponding to different business tags by configuring the business tags of the professional entity, use the different algorithm rules to operate the data in the basic entity data set, put the tag grid data obtained after the operation into the folder of the professional entity configuration class, and obtain the professional entity data set; S5: Use a preset statistical algorithm to reference and combine the label grid data in the professional entity data set; use the KMeans clustering algorithm to perform cluster analysis on each group of combined label grid data, and put all cluster analysis results into a folder of the comprehensive entity configuration class to complete the data analysis in the field of land space; In the step S1, when the data namespace of the national land space domain is defined using a domain-specific language, at least the name of the data namespace myNameSpace and the type of the data namespace spaceType are defined; The data namespace type spaceType at least includes: source data configuration class dataSet, basic entity configuration class basicEntity, professional entity configuration class specificEntity and integrated entity configuration class integratedEntity; In the step S1, the syntax definition of the source data configuration class at least includes: the unique identification name dataSetName of the source data configuration class data, the absolute path path of the shape space file, the renamed file name fileName of the shape space file, the coordinate system code espgSource to which the collected data needs to be converted, and the referenced data dictionary configuration dataSetDictionary; The syntax definition of the data dictionary configuration dataSetDictionary at least includes: the unique identification name dataSetDictionaryName of the data dictionary configuration, the stored table name dictionaryName, the stored table alias aliasName, the data source business classification businessType, the data sequence dataSequential, the data coverage dataRange, the data source source, the data type dataType, the field name fieldName, the field alias fieldliasName, the field data type fieldDataType, and the field mandatory condition validation; In the step S1, the syntax definition of the basic entity configuration class at least includes: the name of the basic entity entityName, the unique identification code of the basic entity entityNameKey, the basic entity type entityType, the grid level size gridLevel, the administrative region code region of the specific region, the attribute name attributeName, the attribute data type attributeType, the year of the attribute data attributeTime, the gridding method of the source data griddingMethod, the referenced source data identifier sourceName, the referenced source data field sourceAttribute, and the referenced source data filtering condition criteria; The basic entity type entityType includes at least: any one of: natural geography type nature, resource element type resource, business management type management or social economy type socioEconomic; In the step S1, the syntax definition of the professional entity configuration class at least includes: the name of the professional entity entityName2, the unique identification code of the professional entity entityNameKey2, the professional entity type entityType2, the evaluation scale entityLevel, the administrative region code region of the specific region, the label name labelName, the label set name labelSetName, the label type labelType, the label time sequence labelYear, and the algorithm rule aggMethod; The professional entity type entityType2 includes at least one or more of location conditions qwtj, social economy shjj, landscape features jgfm, land use tdly, infrastructure jcss, historical culture lswh, environmental protection hjbh or disaster risk zhfx; The label set name labelSetName at least includes: any one of: location condition qwtj, population situation rkqk, economic industry jjcy, topography dxdm, hydrological analysis swfx, landscape pattern jggj, land use structure ydjg, land quality dlzl, use intensity syqd, land use change ydbh, management control glkz or land area dlmj; The label type labelType includes at least: any one of quantity sl, quality zl, structure jg, space kj or time series sx; The algorithm rule aggMethod at least includes: any one of summing sum, subtracting sub, multiplying mul, dividing div, solving the nearest distance gridNearestDistance, solving the buffer quantity gridBuffer, and judging whether the buffer exists gridBufferBoole; In the step S1, the syntax definition of the comprehensive entity configuration class at least includes: the name of the comprehensive entity entityName3, the type of the comprehensive entity entityType3, the administrative region code region of the specific region, the unique identification code entityNameKey3 of the comprehensive entity, the unique identification name featureName of the element, the element data type dataType2, the alias name featureAlias ​​of the element, the element label type featureType, the label specificEntity.labelName of the referenced professional entity, and the statistical algorithm staMethod of the administrative region label value; The type of the comprehensive entity entityType3 includes at least: any one of comprehensive evaluation, deduction or optimization; The featureType of the element label includes at least one of land, population, wealth or material; The statistical algorithm staMethod at least includes: summing up and averaging up to avg.

[0031] In the specific implementation process, the specific steps of this method are as follows: Step 1: Use a domain-specific language to define the data namespace in the land and space domain. By defining and creating a namespace, code elements can be organized and isolated, and the configuration information of various types of TDL projects can be managed. In this embodiment, the types of data namespaces are divided into four categories: source data configuration class, basic entity configuration class, professional entity configuration class, and comprehensive entity configuration class; 1) In this embodiment, the syntax of the namespace is defined as follows: namespace <mynamespace> { <spacetype> <spacedesc> } in, <mynamespace>Required, defines the name of the namespace; <spacetype>Specifies the type option of the namespace type. The optional values ​​include dataSet (source data configuration class), basicEntity (basic entity configuration class), specificEntity (professional entity configuration class), and integratedEntity (integrated entity configuration class). <spacesec>Optional, description of the namespace; eg: optional. Future expansion industries will also be isolated through namespaces. 2) Configuration example: namespacejcst{ basicEntity "7-level geohash grid basic entity of natural resources panoramic elements in the province" } Logical process: By defining the jcst namespace and configuring it as the basicEntity basic entity configuration class, a jcst-exclusive folder will be created to store configuration files and data content related to the basic entities of the natural resources panoramic elements in the province; Step 2: Collect multi-source heterogeneous data in the land space field from different data sources and different file formats (such as tiff, shp, gdb, mdb, img, etc.) in a specific area from the outside, and synchronize the externally collected source original data to the folder of the dataSet source data namespace to obtain the source data set; 1) In this embodiment, the syntax definition of the source data is as follows: dataSet <datasetname> { <path> <filename> <espgsource> @ <datasetdictionary> } in, <datasetname>Required, defines the unique identifier name of the dataSet; <path>Required, the absolute path of the shape space file; <filename>Required, the renamed file name of the shape space file; <espgsource>Required. Collect the coordinate system code that needs to be converted. 4490 (CGCS2000 coordinate system) is commonly used. <datasetdictionary>Required, references data dictionary configuration; 2) The syntax definition of dataSetDictionary data dictionary configuration is as follows: dataSetDictionary <datasetdictionaryname> { <dictionaryname> <aliasname> <businesstype> <datasequential> <datarange> <source> <datatype> fieldList{ <fieldname> <fieldliasname> <fielddatatype> <validation> ...... } } in, <datasetdictionaryname>Required, defines the unique identifier name of dataSetDictionary; <dictionaryname>Required, the name of the storage table; <aliasname>Required, the stored table alias; <businesstype>Required, data source business classification, basic geography | survey planning | business management | public government affairs; <datasequential>Required, data timing; <datarange>Required, data coverage; <source> Required, data source, middle platform / external data; <datatype>Required, data type; <fieldname>Required, field name; <fieldliasname>Required, field alias; <fielddatatype>Required, field data type; <validation>Required, field required condition; 3) Configuration example: Configuration file name: Land Change Survey Land Class Map Collection.tdl, the content is as follows: / *Note: 2024 provincial land change survey land classification map data configuration* / shpDataSetdltb2024ShpDataSet{ " / upload / 2024 / 08 / 10 / Land Change Survey.shp" dltb2024 "4490" @dltb2024DataSetDictionary } / *Note: 2024 Provincial Land Change Survey Land Classification Map Data Dictionary* / dataSetDictionarydltb2024DataSetDictionary{ dltb2024 "Land Type Map" "Survey Planning" "2024" "440000" "External Data" fieldList{ dlbm "land type code" varcharrequire dlyt "land use" varcharrequire dlmj "land type area" doublerequire } } like Figure 3 As shown in the figure, the logical process of collecting source data is: read the configuration content of shpDataSet and dataSetDictionary line by line, and parse it into the corresponding configuration item parameters and parameter values ​​in the definition; as in the above example configuration, transfer the shape space file with the path / upload / 2024 / 08 / 10 / Land Change Survey.shp to the dataSet namespace folder and rename it to dltb2024.shp file, and use the open source spatial component Geotools to read the dltb2024.shp file (the component Geotools supports reading a variety of common spatial format files). tiff, shp, gdb, mdb, img, etc.), and parse the feature fields dlbm, dlyt, dlmj of the dltb2024.shp file line by line, and the default geometry spatial information field. Use the jdbc database link to store the parsed results of the shape file in the dltb2024 data table under the storage space named dataSet of the PostgreSQL database; table fields: objectID, dlbm, dlyt, dlmj, geometry; field annotations are: unique ID, land class code, land class use, land class area, spatial information; After the data content is collected and stored successfully, a source table is generated with the name of dltb2024, the alias of the source table is land type map, the classification is survey planning, the data year is 2024, the administrative area code of the data range is 440000, and the source is the data collection record of external data; Step 3: By configuring the filtering conditions, reference different data in the source data set, grid the referenced data, build a data set containing multiple attributes and attribute values ​​based on the geohash grid, merge and put it into the folder of the basic entity configuration class, and obtain the basic entity data set; by referencing the source data of different data sources, define different data filtering conditions according to business needs, and define TDL files of different basic entities and business attributes. In this way, multiple types of basic entities and attribute content can be produced; 1) In this embodiment, the syntax definition of the basic entity is as follows: Profile Name: <entityname>.tdl <entitytype> <gridlevel> <region> basicEntity <entitynamekey> { <attributename> <attributetype> { <attributename> <attributetime> @ <griddingmethod> <sourcename> { [ <sourceattribute> ][ <criteria>*] } } } in, <entityname>Required, the name of the basic entity is defined by the configuration file name; <entitynamekey>Required, unique identifier of the basic entity; <entitytype>Required, entity type, nature (physical geography) | resource (resource element class) | management (business management class) | socioEconomic (socioeconomic class); <gridlevel>Required, specify the grid level size, L7 / L6 / L5; <region>Required, the administrative district code of the coverage area; <entityname>Required, defines the unique identifier of the entity; <attributename>Required, attribute name; <attributetype>Required, attribute data type; <attributetime>Required, the year of the attribute data; <griddingmethod>Required, source data gridding method, such as polygonGridding (vector pattern gridding); <sourcename>Required, the referenced source data identifier; <sourceattribute>Required, the referenced source data field; <criteria>Required, referenced source data filtering conditions, used to define the filter; 2) Configuration example: The configuration file name is: Farmland Basic Entity.tdl, and the content is as follows: resource L7 "440000" gridEntitycultivatedlandGridEntity{ cultivatedland2023_areadouble{ "Area of ​​cultivated land" "2024" griddingMethod(polygonGridding) dltb2024{ dlbmcriteria("dlbmlike'01%'") } } ...... } like Figure 4 As shown in the figure, the logical process of using the source data to generate the basic entity data is as follows: read the basic entity class configuration file farmland basic entity.tdl line by line, and parse the corresponding parameter name and parameter value defined in the basic entity class; read the source database table dltb2024 of the PostgreSQL database through the jdbc database link, and fuzzy match the query field dlbm (land class code) field according to the data row whose field value prefix is ​​01. The sql execution statement is: select * from dataSet.dltb2024 where dlbmlike'01%', and return the data result row; Grid the result rows, extract the spatial information of the geometry field of the result rows, and use the spatial intersection method of the overlay function method of the open source spatial processing library geopandas in Python to perform spatial intersection operations with the 7-level geohash spatial grid; the parameter call is as follows: geopandas.overlay (source table geometry spatial information dataset, geohash spatial grid dataset, how='intersection'); The spatial operation result returns a new spatial graph of the intersection of the two spaces, and then calculates the intersection area (xjmj) of the new spatial graph; import the result into the PostgreSQL database table, generate fields objectID (original graph ID of the source table), geohash (grid code), geometry (new spatial graph after intersection), xjmj (intersection area), and the table name is dltb2024_geohash_mx (detailed table of land type map patches and grid), and a geohash spatial grid may intersect with multiple geometry spatial information graphs of the source table. Based on the geohash spatial grid, group and summarize the attribute values within the grid; summarize the cultivated land area within each geohash spatial grid, and the SQL statement is: select geohash, sum(xjmj) as 'cultivated land area in 2024' from jcst.dltb2024_geohash_mx, and store the result in the PostgreSQL database table with fields: geohash, cultivated land area in 2024, and the table name is jcst. Cultivated land basic entity; Similarly, by referring to the source data of different data sources and defining different data filtering conditions according to business requirements, define tdl files for different basic entities and business attributes. Through the above method, multiple types of basic entities and attribute contents can be generated; Step 4: Determine the algorithm rules corresponding to different business labels by configuring the business labels of professional entities, use different algorithm rules to operate on the data in the basic entity data set, and put the labeled grid data obtained after the operation into the folder of the professional entity configuration class to obtain the professional entity data set; this step is based on referring to the grid attribute data of the basic entity, and constructs the business labels of the professional entity through entity configuration, label configuration, filter condition configuration, algorithm configuration, etc.; 1) In this embodiment, the syntax definition of the professional entity is as follows: Configuration file name: <entityname>.tdl specificEntity <entitynamekey> { <entitytype> <region> <entitylevel> [ <labelname> { <labelsetname> <labeltype> <labelname> <labelyear> relationField(geohash) aggMethod([baseEntity.fieldName]*){ <aggmethodname> } }]* } in, <entityname>Required, the name of the professional entity is defined by the profile name; <entitynamekey>Required, unique identification code of the professional entity; <entitytype>Required, entity type, qwtj (location conditions) | shjj (social economy) | jgfm (landscape features) | tdly (land use) | jcss (infrastructure) | lswh (history and culture) | hjbh (environmental protection) | zhfx (disaster risk); <entitylevel>Required, evaluation scale, the default value is 1; <region>Required, coverage range, default value is 440000; <labelname>Required, tag name, unique identification code of the tag; <labelsetname>Required, tag group name, qwtj (location conditions) | rkqk (population situation) | jjcy (economic industry) | dxdm (topography) | swfx (hydrological analysis) | jggj (landscape pattern), ydjg (land use structure) | dlzl (land quality) | syqd (use intensity) | ydbh (land use change) | glkz (management control) | dlmj (land area); <labeltype>Required, label type, sl (quantity) | zl (quality) | jg (structure) | kj (space) | sx (time series); <labelyear>Required, label timing; <aggmethod>Required, algorithm configuration, sum (sum) | sub (difference) | mul (multiplication) | div (division) | gridNearestDistance (nearest distance) | gridBuffer (number of buffers) | gridBufferBoole (whether the buffer exists); 2) Algorithm configuration description: aggMethod: indicates the aggregation calculation of the configured basic entity attributes or constants; baseEntity.fieldName: identifies the TDL_KEY corresponding to the base entity and the TDL_KEY corresponding to the selected attribute; sum(): performs sum operation on the selected attributes or constants; formula: attribute value a + attribute value b; sub(): Performs a difference operation on the selected attribute or constant; formula: attribute value a - attribute value b; mul(): multiplies the selected attribute or constant; formula: attribute value a*attribute value b; div(): Perform division operation on the selected attribute or constant; formula: attribute value a ÷ attribute value b; gridNearestDistance(): The nearest distance algorithm calculates the nearest distance of the grid between two attributes; Logical process: Based on the grid a set where the basic entity attributes are located, traverse each source basic grid, use the Java open source spatial processing geotools tool library and geohash tool library, and use the source basic grid as the center to find and return the geohash set of 1.2.3...n circles. If a geohash in the n circles is in the target basic entity attribute grid set, then the shortest distance from the source basic grid to the target grid is n; gridBuffer(): Buffer quantity algorithm, calculates how many buffer grids exist within a certain range; Logical process: Based on the grid a set where the basic entity attributes are located, traverse each source basic grid, use the Java open source spatial processing geotools tool library and geohash tool library, and use the source basic grid as the center to find and return the geohash set of the 1.2.3.4.5.6 circles. If there are n geohash target basic entity attribute grid sets in the 6-circle geohash set, then the number of buffers of the source basic grid is n; gridBufferBoole(): Buffer existence algorithm, calculates whether there is a buffer grid within a certain range; Logical process: Based on the grid a set where the basic entity attributes are located, traverse each source basic grid, use the Java open source spatial processing geotools tool library and geohash tool library, and use the source basic grid as the center to find and return the geohash set of the 1.2.3.4.5.6 circles. If there is at least one geohash target basic entity attribute grid in the geohash set of the 6 circles, the buffer of the source basic grid is marked as 1 (true), otherwise it is marked as 0 (false); 3) Configuration example: The configuration file name is: Land Use Group.tdl, and the content is as follows: specificEntitylandusedEntity{ / *Land Use* / "tdly" "440000" "1" / *Calculate the newly added cultivated land area in 2024* / xzgdmj2024{ "ydbh" "sx" "xzgdmj2024" "Newly added cultivated land area" "2024" relationField(geohash) aggMethod(cultivatedlandGridEntity.cultivatedland2024_area,cultivatedlandGridEntity.cultivatedland2023_area){ sub() } } … } like Figure 5 As shown in the figure, the logical process of professional entity construction is as follows: read the land use group .tdl file, a configuration file of the professional entity class, line by line, and parse it into the corresponding parameter name and parameter value defined by the professional entity class; read the PostgreSQL database through the jdbc database link, and check whether the basic entity table with the unique identification code cultivatedlandGridEntity exists, and at the same time check whether the basic entity attributes with the unique identification codes cultivatedland2024_area and cultivatedland2023_area exist, and throw an exception if they do not exist; If the basic entity and attribute referenced by the professional entity tag exist, the grid data of the attribute is first collected into a temporary table created by the program, and then the algorithm configured for the tag is determined; if the sub difference algorithm is configured as shown in the example, the program will perform difference operations on the two configured attributes, calculate the difference between the two attributes of the grids with the same geohash, and then generate the operation results; if other algorithms are configured in the algorithm configuration, the program will automatically select the corresponding algorithm for operation, such as summation, multiplication, division, etc. When the attributes of the basic entity are successfully calculated and the calculation results are generated, the results need to be inserted into the library table of the corresponding professional entity; before inserting the data, it will be determined whether the corresponding PostgreSQL database table and field exist. If they exist, the calculated data will be deleted and then re-inserted; for example, in the example, the corresponding configured professional entity is land use group, the label name is newly added cultivated land area, and the label time sequence is 2024, then the program will insert the result data into the PostgreSQL database table with the database name "zyst.Land Use Group" and the field name "Newly Added Cultivated Land Area in 2024"; It can be seen that by configuring different tags, different aggregation operations can be performed on different attributes of different basic entities, so as to generate operation results and insert them into different fields of different professional entity library tables; Step 4: According to the preset statistical algorithm (such as summation and averaging), reference and combine the label grid data in the professional entity data set, so as to gather the identification values ​​within each specific area (administrative district); use the preset clustering algorithm to perform cluster analysis on each group of combined label grid data, generate the evaluation grade value of the combined elements within each administrative district, and put all cluster analysis results into the folder of the comprehensive entity configuration class to complete the data analysis in the field of land space; 1) In this embodiment, the syntax definition of the comprehensive entity is as follows: Profile Name: <entityname>.tdl <entitytype> <region> integratedEntity <entitynamekey>{ featurelabel{ <featurename> <datatype> { <featurealias> <featuretype> aggMethod{ <specificEntity.labelName>[ <stamethod>] ...... } } ...... } } in, <entityname>Required, the name of the composite entity is defined by the configuration file name; <entitytype>Required, entity type, evaluation (comprehensive evaluation) | deduction (simulation deduction) | optimization (comprehensive optimization); <region>Required, the administrative district code of the coverage area; <entitynamekey>Required, defines the unique identifier of the entity; <featurename>Required, unique identifier name of the element; <datatype>Required, element data type; <featurealias>Required, the alias name of the feature; <featuretype>Required, element label type, land, population, wealth, material; <specificEntity.labelName> Required, references the unique identifier of the professional entity and the unique identifier of the label; <stamethod>Required, statistical algorithm for administrative district label values, sum (total) | avg (average); 2) Configuration example: The configuration file name is: Food Security Comprehensive Evaluation.tdl, and the content is as follows: evaluation 440000 integratedEntitygrainProduction{ featurelabel{ slwdfloat{ "Stable quantity" land aggMethod{ tdly.gdmj2024sum() tdly.xzgdmj2024sum() tdly.gbzntzb2024avg() } } } } 3) This embodiment also includes comprehensive entity visualization configuration, and the syntax definition is as follows: @ <integratedentity> visualIntegratedEntity <visualname> { <labelname> { <labelunit> <maptypelist> <mapclassification> <chartname> <charttype> <chartvaluetype> } ...... } in,@ <integratedentity>Required, references the comprehensive entity identifier; <visualname>Required, visual configuration identification code; <labelname>Required, reference the tag through @IntegratedEntity; <labelunit>Required, conversion unit; 1 square meter | 2 square kilometers | 3 acres | 4 hectares; <maptypelist>Required, map type, xzqMap administrative map | gridMap grid map; <mapclassification>Required, map classification method; 1 equal spacing | 2 K-means clustering grouping; <chartname>Required, chart name; <charttype>Required, chart type, column chart|pie chart|block chart; <chartvaluetype>Required. Chart value type, num for quantity | per for percentage; 4) Example of comprehensive entity visualization configuration: @grainProduction visualIntegratedEntityvisualGrainProduction{ slwd{ 3 xzqMap2 "Stable situation of cultivated land quantity" column num } } As Figure 6 shown, the logical process of comprehensive entity analysis is as follows: Read the configuration file "Comprehensive evaluation of food security.tdl" line by line and parse it into the corresponding parameter names and parameter values defined in the comprehensive entity class; According to the data references of the professional entities and labels of tdly.gdmj2024, tdly.xzgdmj2024, and tdly.czzyfx, read the data content of the database tables (land use), field "Cultivated land area in 2024", field "Newly added cultivated land area in 2024", and field "Proportion of high-standard farmland in 2024" respectively, and calculate the original values of the cultivated land area in 2024, newly added cultivated land area in 2024, and proportion of high-standard farmland in 2024 within each municipal scope according to the municipal code (field sjdm) where geohash is located and the administrative region statistical algorithm of the label, (sum: sum of cultivated land area in 2024), (sum: sum of newly added cultivated land area in 2024), (avg: proportion of high-standard farmland in 2024). SQL statements: select sjdm, sum("Cultivated land area in 2024") as "Cultivated land area in 2024" from zyst.land use group by sjdm; select sjdm, sum("Newly added cultivated land area in 2024") as "Newly added cultivated land area in 2024" from zyst.land use group by sjdm; select sjdm, avg("Proportion of high-standard farmland in 2024") as "Proportion of high-standard farmland in 2024" from zyst.land use group by sjdm; The name of the database instance zhst (comprehensive entity namespace) under which the administrative district statistical results are stored is: the database table of comprehensive food security evaluation; the fields include region (administrative district code), regionType (administrative district type, city level | district and county level), labelName (label name), value (value), valueType (data type, original value | evaluation grade value), among which the field regionType is city level, and the field valueType is the original value; Grouping and clustering calculation; according to the city-level administrative district code, read the three label values ​​of the comprehensive evaluation table of food security respectively. Each city-level administrative district code contains three label values, forming a three-dimensional data list. Use the KMeans function of the cluster submodule in the open source machine learning module Sklearn in Python to cluster and group the multidimensional data list into 5 groups; method call: kmeans result = sklearn.cluster.KMeans(n_clusters=5,random_state=0,algorithm="lloyd").fit(data list) The clustering results, a set of 3D data corresponding to a grade value ranging from 1 to 5 for each city code, where the field regionType is the city level, the field valueType is the evaluation grade value, and the field labelName is the quantity stability, are stored in the "Comprehensive Evaluation of Food Security" result table; Similarly, for the aggregate evaluation level of the administrative district type at the district or county level, it is only necessary to replace the administrative district aggregation identifier sjdm (city-level code) in the processing process with xjdm (county-level code); Through the above logical processing, the results show that the comprehensive comparative evaluation index values ​​of quantity stability (in terms of cultivated land quantity stability) at the municipal, district and county levels in the comprehensive evaluation of food security are based on business comprehensive comparison; In addition, this embodiment can also configure the comprehensive entity visualization parameters to set the display unit of stable quantity to mu (unit conversion formula: 1 square meter = 0.000001 square kilometer, 1 square meter = 0.0015 mu, 1 square meter = 0.0001 hectare), and use the K-means clustering grouping algorithm to display data in the form of (province, city, district, county, street, town) administrative division map; define the statistical information of each sub-administrative district that displays the stability of cultivated land quantity in the form of grouped quantity sum and bar chart; The key innovation of this method is to apply domain-specific language technology to the field of land and space for the first time, and to combine domain-specific language technology with GIS geographic information technology innovation. By analyzing the concept rules of natural resource business, a land and space domain language classification management container is established, and a data analysis model of a unified grid space unit is constructed through grammatical declaration rules. Multi-source heterogeneous data are merged into a unified grid unit. After algorithm configuration components such as spatial processing algorithms and spatial analysis algorithms, a data analysis model suitable for different natural resource business topics such as land, ocean, minerals, and forests is formed. Finally, the visualization configuration component can be further applied to generate a data configuration file that meets the needs of natural resource business analysis. This embodiment uses data classification container management technology to carry out technical transformation of the natural resources business knowledge system, forming a data entity classification container management technology system in the field of land and space, including namespace, data source (vector, raster, library table), entity (basic entity, professional entity, comprehensive entity), filter (algorithm configuration, visualization configuration), etc. This embodiment also declares grammatical rules for each main module of the above-mentioned classification container, forming a parsing technology that configures the entire process from data collection, data processing, and model analysis, where the configuration parameters involved in the source data set include description configuration and dictionary configuration, which can automatically filter and collect natural resource source data; the entity data set configuration includes description configuration, attribute configuration, filter condition configuration, algorithm configuration, visualization configuration, etc., which can automatically grid the filtered source data and quickly output the model analysis visualization results; In addition, this embodiment also combines filter embedding technology to embed filters into the data configuration file in the form of component encapsulation reference; among them, for the algorithm configuration module, the data grid processing algorithm components are encapsulated according to different spatial data organization modes such as vectors, rasters, and tables, and the model calculation components are encapsulated according to the business analysis type; for the visualization configuration module, the visualization presentation components are encapsulated according to different entity classification types such as basic elements, business tags, and comprehensive evaluations, including maps, chart controls, data unit conversion rules and other contents, which can be flexibly referenced for the main modules of the classification container according to actual needs.

[0032] The same or similar reference numerals correspond to the same or similar components; The terms used in the drawings to describe positional relationships are only used for illustrative purposes and should not be construed as limiting this patent; Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.< / chartvaluetype> < / charttype> < / chartname> < / mapclassification> < / maptypelist> < / labelunit> < / labelname> < / visualname> < / integratedentity> < / chartvaluetype> < / charttype> < / chartname> < / mapclassification> < / maptypelist> < / labelunit> < / labelname> < / visualname> < / integratedentity> < / stamethod> < / featuretype> < / featurealias> < / datatype> < / featurename> < / entitynamekey> < / region> < / entitytype> < / entityname> < / stamethod> < / featuretype> < / featurealias> < / datatype> < / featurename> < / entitynamekey> < / region> < / entitytype> < / entityname> < / aggmethod> < / labelyear> < / labeltype> < / labelsetname> < / labelname> < / region> < / entitylevel> < / entitytype> < / entitynamekey> < / entityname> < / aggmethodname> < / labelyear> < / labelname> < / labeltype> < / labelsetname> < / labelname> < / entitylevel> < / region> < / entitytype> < / entitynamekey> < / entityname> < / criteria> < / sourceattribute> < / sourcename> < / griddingmethod> < / attributetime> < / attributetype> < / attributename> < / entityname> < / region> < / gridlevel> < / entitytype> < / entitynamekey> < / entityname> < / criteria> < / sourceattribute> < / sourcename> < / griddingmethod> < / attributetime> < / attributename> < / attributetype> < / attributename> < / entitynamekey> < / region> < / gridlevel> < / entitytype> < / entityname> < / validation> < / fielddatatype> < / fieldliasname> < / fieldname> < / datatype> < / datarange> < / datasequential> < / businesstype> < / aliasname> < / dictionaryname> < / datasetdictionaryname> < / validation> < / fielddatatype> < / fieldliasname> < / fieldname> < / datatype> < / datarange> < / datasequential> < / businesstype> < / aliasname> < / dictionaryname> < / datasetdictionaryname> < / datasetdictionary> < / espgsource> < / filename> < / path> < / datasetname> < / datasetdictionary> < / espgsource> < / filename> < / path> < / datasetname> < / spacesec> < / spacetype> < / mynamespace> < / spacedesc> < / spacetype> < / mynamespace>

Claims

1. A land space data analysis method based on DSL technology, characterized in that: The following steps are involved: S1: Use domain-specific language to define the data namespace of the national land space domain, and the types of the data namespace include at least: source data configuration class, basic entity configuration class, professional entity configuration class and comprehensive entity configuration class; S2: Collect multi-source heterogeneous data in the field of land and space from different data sources and different file formats in a specific area and put them into the folder of the source data configuration class to obtain the source data set; S3: By configuring the filtering conditions, different data in the source data set are referenced, the referenced data is gridded, a data set containing multiple attributes and attribute values ​​based on the geohash grid is constructed and merged into a folder of the basic entity configuration class, and the basic entity data set is obtained; S4: Determine the algorithm rules corresponding to different business tags by configuring the business tags of the professional entity, use the different algorithm rules to operate the data in the basic entity data set, put the tag grid data obtained after the operation into the folder of the professional entity configuration class, and obtain the professional entity data set; S5: Use a preset statistical algorithm to reference and combine the label grid data in the professional entity data set; use a preset clustering algorithm to perform cluster analysis on each group of combined label grid data, and place all cluster analysis results in a folder of the comprehensive entity configuration class to complete the data analysis in the field of national land space.

2. According to claim 1, a land space data analysis method based on DSL technology is characterized in that: In the step S1, when the data namespace of the national land space domain is defined using a domain-specific language, at least the name of the data namespace myNameSpace and the type of the data namespace spaceType are defined; The data namespace type spaceType at least includes: a source data configuration class dataSet, a basic entity configuration class basicEntity, a professional entity configuration class specificEntity and a comprehensive entity configuration class integratedEntity.

3. According to claim 1, a land space data analysis method based on DSL technology is characterized in that: In step S1, the syntax definition of the source data configuration class includes at least: the unique identification name dataSetName of the source data configuration class data, the absolute path path of the shape space file, the renamed file name fileName of the shape space file, the coordinate system code espgSource to which the collected data needs to be converted, and the referenced data dictionary configuration dataSetDictionary.

4. According to claim 3, a land space data analysis method based on DSL technology is characterized in that: The syntax definition of the data dictionary configuration dataSetDictionary includes at least: the unique identification name of the data dictionary configuration dataSetDictionaryName, the stored table name dictionaryName, the stored table alias aliasName, the data source business classification businessType, the data sequence dataSequential, the data coverage range dataRange, the data source source, the data type dataType, the field name fieldName, the field alias fieldliasName, the field data type fieldDataType, and the field mandatory condition validation.

5. According to claim 1, a land space data analysis method based on DSL technology is characterized in that: In the step S1, the syntax definition of the basic entity configuration class at least includes: the name of the basic entity entityName, the unique identification code of the basic entity entityNameKey, the basic entity type entityType, the grid level size gridLevel, the administrative region code region of the specific region, the attribute name attributeName, the attribute data type attributeType, the year of the attribute data attributeTime, the gridding method of the source data griddingMethod, the referenced source data identifier sourceName, the referenced source data field sourceAttribute, and the referenced source data filtering condition criteria; The basic entity type entityType includes at least: any one of the natural geography type nature, the resource element type resource, the business management type management or the social economy type socioEconomic.

6. The method for analyzing land space data based on DSL technology according to claim 1 is characterized in that: In the step S1, the syntax definition of the professional entity configuration class at least includes: the name of the professional entity entityName2, the unique identification code of the professional entity entityNameKey2, the professional entity type entityType2, the evaluation scale entityLevel, the administrative region code region of the specific region, the label name labelName, the label set name labelSetName, the label type labelType, the label time sequence labelYear, and the algorithm rule aggMethod; The professional entity type entityType2 includes at least one or more of location conditions qwtj, social economy shjj, landscape features jgfm, land use tdly, infrastructure jcss, historical culture lswh, environmental protection hjbh or disaster risk zhfx; The label set name labelSetName at least includes: any one of: location condition qwtj, population situation rkqk, economic industry jjcy, topography dxdm, hydrological analysis swfx, landscape pattern jggj, land use structure ydjg, land quality dlzl, use intensity syqd, land use change ydbh, management control glkz or land area dlmj; The label type labelType includes at least: any one of quantity sl, quality zl, structure jg, space kj or time series sx; The algorithm rule aggMethod at least includes: any one of summing sum, subtracting sub, multiplying mul, dividing div, solving the nearest distance gridNearestDistance, solving the buffer quantity gridBuffer, and judging whether the buffer exists gridBufferBoole.

7. The method for analyzing land space data based on DSL technology according to claim 1 is characterized in that: In the step S1, the syntax definition of the comprehensive entity configuration class at least includes: the name of the comprehensive entity entityName3, the type of the comprehensive entity entityType3, the administrative region code region of the specific region, the unique identification code entityNameKey3 of the comprehensive entity, the unique identification name featureName of the element, the element data type dataType2, the alias name featureAlias ​​of the element, the element label type featureType, the label specificEntity.labelName of the referenced professional entity, and the statistical algorithm staMethod of the administrative region label value; The type of the comprehensive entity entityType3 includes at least: any one of comprehensive evaluation, deduction or optimization; The featureType of the element label includes at least one of land, population, wealth or material; The statistical algorithm staMethod at least includes: summing up and averaging up to avg.

8. The method for analyzing land space data based on DSL technology according to claim 1 is characterized in that: In step S2, the multi-source heterogeneous data in the field of land and space are specifically data obtained by a GIS geographic information system, and the file formats include at least any one of tiff, shp, gdb, mdb and img.

9. The method for analyzing land space data based on DSL technology according to claim 1 is characterized in that: In the step S5, the preset clustering algorithm is specifically the KMeans clustering algorithm.

10. A land space data analysis system based on DSL technology, applying a land space data analysis method based on DSL technology as described in any one of claims 1 to 9, characterized in that: include: Predefined unit: used to define the data namespace of the national land space domain using a domain-specific language, and the types of the data namespace include at least: source data configuration class, basic entity configuration class, professional entity configuration class and comprehensive entity configuration class; Paste source data collection unit: used to collect multi-source heterogeneous data in the land space field of different data sources and different file formats in a specific area and put them into the folder of the paste source data configuration class to obtain the paste source data set; Basic entity generation unit: used to reference different data in the source data set by configuring filtering conditions, grid the referenced data, construct a data set containing multiple attributes and attribute values ​​based on the geohash grid, and merge and put it into the folder of the basic entity configuration class to obtain the basic entity data set; Professional entity construction unit: used to determine the algorithm rules corresponding to different business tags by configuring the business tags of the professional entity, use different algorithm rules to operate the data in the basic entity data set, put the label grid data obtained after the operation into the folder of the professional entity configuration class, and obtain the professional entity data set; Comprehensive entity analysis unit: used to use a preset statistical algorithm to reference and combine the label grid data in the professional entity data set; use a preset clustering algorithm to perform cluster analysis on each group of combined label grid data, and place all cluster analysis results in a folder of the comprehensive entity configuration class to complete data analysis in the field of national land space.

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