Mass address data-oriented space boundary region matching optimization method and device

Abstract

The invention relates to the technical field of software systems and data processing, in particular to a mass address data-oriented space boundary region matching optimization method and device, and the method comprises the following steps: constructing an address set, modeling a space boundary, carrying out set-level matching, associating an application layer, and establishing an incremental updating mechanism. Address matching efficiency is greatly improved, one-by-one comparison is changed into set comparison, unnecessary repeated operation is greatly reduced, computing resource consumption is remarkably reduced, interaction frequency with a space library is reduced, CPU and memory resources are saved, system response capability is enhanced, low delay can still be kept under the condition of high concurrency, and the method is easy to expand and maintain. The method is flexible in set division, convenient to adapt to different regions and different data formats, and supports incremental updating, and in conclusion, the method is suitable for various application scenes and can be applied to multiple fields such as population monitoring, smart cities, social governance and public safety.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of software systems and data processing technology, in particular to a massive address data space boundary region matching optimization method and device. BACKGROUND

[0002] In current various types of government affairs, city governance and other information systems, there are usually multiple core data basic libraries, including natural person, legal person, public facility and other multi-source information libraries, spatial geographic information libraries (such as address space coordinates, administrative division, street grid, building layer, etc.). In order to realize the specific region analysis of non-standard administrative division in the business system, it is necessary to match the address information of legal persons or natural persons with multiple circle layers in the spatial geographic information library, that is, to judge whether it "falls into" some specific designated spatial regions.

[0003] The existing technology generally adopts a one-to-one accurate comparison method: that is, for each original address record, it is matched and converted with the address template in the spatial library one by one, and then it is judged one by one which spatial region it belongs to. This method is acceptable when the data volume is small, but when facing massive address data, the following problems will be brought about:

[0004] High consumption of computing resources: each matching needs to scan the entire spatial library or a large number of candidate regions;

[0005] Slow response speed: since it is single processing, it is difficult to parallelize and is low in efficiency;

[0006] Frequent system calls: each request triggers a query to the spatial library, resulting in high interface pressure;

[0007] Poor scalability: as the data continues to grow, the performance decreases significantly, and the user experience is poor.

[0008] Therefore, a new address space mapping method is urgently needed, which can greatly reduce resource consumption, improve processing efficiency and meet real-time needs in large-scale data scenarios while ensuring accuracy. SUMMARY

[0009] The purpose of the present application is to provide a massive address data space boundary region matching optimization method and device to solve the problems raised in the background technology.

[0010] To achieve the above purpose, the present application provides the following technical solutions:

[0011] The present application provides a massive address data space boundary region matching optimization method, comprising the following steps:

[0012] S1. Construct an address set, input the original address field of the multi-source database for standardization cleaning, cluster to generate an address set, output a standard address and original address mapping table;

[0013] S2. Spatial boundary modeling, input the regional boundary in the spatial geographic library, assign a unique ID to each spatial range, and store the boundary coordinate set;

[0014] S3. Set-level matching, use GIS to calculate the inclusion relationship between the address set and the spatial region, and generate a wide table mapping structure;

[0015] S4. Correlation application layer, associate the wide table with the original data table through the original address field, and output a view that supports spatial region filtering;

[0016] S5. Establish an incremental update mechanism, when an address is added, sequentially execute only the standardized incremental address, supplement to the address set, and add new wide table rows, when the boundary changes, sequentially execute the addition of new spatial regions, and add new wide table columns, and after the boundary adjustment, distinguish the version by the year suffix.

[0017] Preferably, the implementation steps of step S1 are:

[0018] S1.1. Extract the original address field from the natural person library, legal person library, and public facility library data sources through the JDBC interface or API, generate a temporary storage table containing the data source type, original record ID, and original address text, and this table is used as the input source for address standardization cleaning;

[0019] S1.2. Receive the original address text output by S1.1, and perform three-level processing:

[0020] The first level resolves the province, city, district, street, and house number components according to the administrative division hierarchy;

[0021] The second level applies rule library cleaning to fuzzy words (such as deleting the "nearby" modifier) and alias mapping (such as "Shanghai" to "Shanghai");

[0022] The third level outputs structured standard address text, and generates a cleaning mapping table that records the correspondence between the original ID and the standard address;

[0023] S1.3. Based on the standard address text generated by S1.2, use the administrative tree hierarchical clustering algorithm to extract the city-level + district-level + street-level keywords in the address as clustering features (such as "Beijing City / Haidian District / Zhongguancun Street"), merge addresses with the same three-level features into the same set, assign a unique set ID to each set and record the list of included original IDs, and output the address set registration table to the subsequent matching;

[0024] S1.4. Integrate the cleaning mapping table of S1.2 and the address set registry table of S1.3 to generate a global address mapping table, which records the binding relationship among set ID, original ID and standard address, as the address reference data source for subsequent space matching.

[0025] Preferably, the implementation steps of step S2 are as follows:

[0026] S2.1. Read the administrative division polygon layer data in GeoJSON format from the spatial geographic information library, parse the polygon boundary coordinate set, and each polygon represents an independent space region (such as a business district or a new city). The extracted coordinate set is transmitted to the region registration process;

[0027] S2.2. Assign a globally unique region ID (naming rule, region type_name) to each space boundary extracted in S2.1, and create a record in the spatial region metadata table, which contains region ID, official name, boundary coordinate set, and effective timestamp. This table serves as the boundary reference data source for space matching;

[0028] S2.3. When detecting changes in the boundary coordinates of an existing space region, add a new version record to the metadata table in S2.2, copy the original region ID and append a year suffix (such as R_ZZ Demonstration Zone_2024), retain the historical version coordinate set and mark the invalid time, and ensure that the new and old versions coexist in the wide table.

[0029] Preferably, the implementation steps of step S3 are as follows:

[0030] S3.1. Poll the address set registry table output by S1.3 and the spatial region metadata table in S2.2, generate an independent matching task for each combination of address set and spatial region, the task ID format is "set ID_region ID", and the task queue is submitted to the GIS computing engine in batches;

[0031] S3.2. When executing the task queue, obtain the geographic coordinates of all standard addresses in the address set (converted through a geographic coding service), calculate the convex hull polygon of the set coordinates, call the ST_Within function of PostGIS to judge the inclusion relationship between the convex hull polygon and the target space region, and output the Boolean matching result;

[0032] S3.3. Construct a sparse matrix according to the matching result of S3.2, the row key of the matrix is the address set ID, the column key is the spatial region ID, and the cell stores ternary values (1 represents full inclusion / 0 represents no inclusion / NULL represents calculation exception), and generate an initial version of the address region mapping wide table;

[0033] S3.4. The wide table generated in S3.3 is persisted to a columnar database, the table structure is dynamically expanded, a column is automatically added each time a spatial region is added, a row is automatically added each time an address set is added, and only the changed amount is stored in the cell value to reduce IO consumption;

[0034] S3.5. When a NULL value is detected in the wide table, an artificial review process is triggered, the original address sample of the abnormal set and the associated spatial region boundary are extracted, the conflict point is marked in the GIS visualization interface, and the review result is written back to update the wide table value.

[0035] Preferably, the implementation steps of step S4 are:

[0036] S4.1. A virtual view "spatial positioning view" is created by associating the original data temporary table output in S1.1, the global address mapping table output in S1.4, and the address region mapping wide table output in S3.4 through SQL JOIN operation, and the view fields include all columns of the original data and the region marker column of the wide table;

[0037] S4.2. When the view is called by the application layer, the target region ID parameter is passed in, the filter condition is automatically generated, and all associated original records of legal persons, natural persons, and public facilities in the spatial region are returned, realizing multi-source data aggregation query;

[0038] S4.3. On the query result of S4.2, the data source type is classified and counted, and the distribution density of natural persons in the region, the number of industrial classifications of legal institutions, and the service coverage rate of public facilities are respectively summarized to generate a comprehensive spatial analysis report.

[0039] Preferably, the implementation steps of step S5 are:

[0040] S5.1. The original database change log is monitored, and when the number of newly added address records reaches a set threshold (such as 1000) or a timing task is triggered, the update timestamp of the original address temporary table is compared, the original ID and address text of the newly added records are extracted, and an incremental address temporary table is generated;

[0041] S5.2. The incremental address text in S5.1 is sent to the standardization process in S1.2, but only the incremental record processing is performed, and the newly added standard address set is output to avoid reprocessing of full data;

[0042] S5.3. The incremental standard address output in S5.2 is matched with the existing address set mode, if the address meets the characteristics of a certain set (such as "Zhongguancun Avenue * number"), the original ID is appended to the set; if there is no match, a new set is created, a new set ID is assigned, and the set registration update in S1.3 is triggered;

[0043] S5.4. Perform spatial matching of S3 full process on the new address set (including newly created set and updated inventory set) generated in S5.3, and write the calculation result to the new row of the address area mapping wide table, and only update the matching value of the change column of the existing set;

[0044] S5.5. When a new version of the spatial geographic library is released, register a new division (such as R_new Free Trade Zone) in the spatial area metadata table of S2.2, and start the GIS batch calculation of S3.2 for all address sets (including the set updated in S5.3), to generate a new column of the wide table and fill in the matching result;

[0045] S5.6. Detect the spatial area invalidation event (such as administrative division adjustment), mark the end time of the version of the area in S2.3, and simultaneously set the corresponding column in the wide table to a read-only state, and the application layer query automatically filters the invalid version data.

[0046] The application further provides a mass address data spatial boundary area matching optimization device, which comprises:

[0047] An address collection module is configured to extract address information from an original database;

[0048] An address standardization module is configured to normalize, denoise and structure parse the address;

[0049] An address set construction module is configured to divide the address into a plurality of sets based on address semantics and rules;

[0050] A spatial boundary set module is configured to set the spatial boundary ID, standard naming and range boundary address to be determined;

[0051] A spatial matching module is configured to match the address set with the spatial area;

[0052] A mapping relationship management module is configured to maintain the mapping relationship wide table between the address set and the spatial area;

[0053] An address and spatial matching view module is configured to construct a view through the original data table and the mapping relationship wide table, and provide different application scenario queries;

[0054] A monitoring and updating module is configured to support automatic updating and abnormality detection of the mapping relationship.

[0055] Compared with the prior art, the application has the following beneficial effects:

[0056] The application greatly improves address matching efficiency, changes from "comparison by piece" to "set comparison", greatly reduces unnecessary repeated operation, significantly reduces the consumption of computing resources, reduces the interaction frequency with the space library, saves CPU and memory resources, enhances the system response capability, can still maintain low delay under high concurrency, is easy to expand and maintain, the set division is flexible, is convenient for adapting to different regions and different data formats, supports incremental update, and the application is suitable for multiple application scenarios and can be used in multiple fields such as population monitoring, smart city, social governance and public safety. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 The application is a kind of massive address data space boundary area matching optimization device module topology. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0059] Embodiment 1, please refer to Figure 1 The application provides a kind of massive address data space boundary area matching optimization device, comprising:

[0060] Address acquisition module, address acquisition module is used to extract address information from original database;

[0061] Address standardization module, address standardization module is used to normalize address, denoising, structured analysis;

[0062] Address set construction module, address set construction module is based on address semantics and rules, for address is divided into multiple sets;

[0063] Space boundary set module, space boundary set module is used to set the space boundary ID to be determined, standard name and range boundary address;

[0064] Space matching module, space matching module is used to match address set with space area;

[0065] Mapping relationship management module, mapping relationship management module is used to maintain the mapping relationship wide table between address set and space area;

[0066] Address and space matching view module, address and space matching view module is used to build view through original data table and mapping relationship wide table, provide different application scenario query;

[0067] A monitoring and updating module for supporting automatic updating and abnormality detection of the mapping relationship;

[0068] Embodiment 2, based on the above device, the application further proposes a massive address data space boundary region matching optimization method, specifically, comprising the following steps:

[0069] S1. Constructing an address set, inputting the original address field of a multi-source database for standardization cleaning, clustering to generate an address set, and outputting a standard address and original address mapping table;

[0070] S1.1. Extracting the original address field from the natural person library, the legal person library and the public facility library data source through the JDBC interface or API, generating a temporary storage table containing the data source type, the original record ID and the original address text, and taking the table as the input source for address standardization cleaning;

[0071] S1.2. Receiving the original address text output by S1.1, and performing three-level processing:

[0072] The first level resolves the province, city, district, street and door number components according to the administrative division level;

[0073] The second level applies the rule library to clean the ambiguous words (such as deleting the "nearby" modifier) and alias mapping (such as "Shanghai" to "Shanghai");

[0074] The third level outputs the structured standard address text, and generates a cleaning mapping table of the record original ID and the standard address corresponding relationship;

[0075] S1.3. Based on the standard address text generated by S1.2, the administrative tree level clustering algorithm is adopted to extract the city level + district level + street level keywords in the address as the clustering features (such as "Beijing City / Haidian District / Zhongguancun Street"), and the addresses with the same three-level features are merged into the same set, each set is assigned a unique set ID and records the original ID list contained, and the address set registration table is output to the subsequent matching;

[0076] S1.4. Integrating the cleaning mapping table of S1.2 and the address set registration table of S1.3 to generate a global address mapping table, which records the binding relationship among the set ID, the original ID and the standard address, and serves as the address reference data source for subsequent space matching;

[0077] S2. Space boundary modeling, inputting the region boundary in the space geographic library, assigning a unique ID to each space range, and storing the boundary coordinate set;

[0078] S2.1. Read the administrative district polygon layer data in GeoJSON format from the spatial geographic information library, parse the polygon boundary coordinate set, each polygon represents an independent spatial area (such as a business district, a new city), and the extracted coordinate set is transmitted to the area registration process;

[0079] S2.2. Assign a globally unique area ID (naming rule, area type_name) to each spatial boundary extracted in S2.1, create a record in the spatial area metadata table, which contains the area ID, official name, boundary coordinate set, and effective timestamp, and use this table as the boundary reference data source for spatial matching;

[0080] S2.3. When detecting changes in the boundary coordinates of an existing spatial area, add a new version record to the metadata table in S2.2, copy the original area ID and append the year suffix (such as R_ZZ Demonstration Area_2024), retain the historical version coordinate set and mark the invalid time, and ensure that the new and old versions coexist in the wide table;

[0081] S3. Set-level matching, use GIS calculation to batch determine the inclusion relationship between address set and spatial area, and generate a wide table mapping structure;

[0082] S3.1. Poll the address set registration table output by S1.3 and the spatial area metadata table in S2.2, generate an independent matching task for each combination of address set and spatial area, the task ID format is "set ID_area ID", and the task queue is batch submitted to the GIS calculation engine;

[0083] S3.2. When executing the task queue, get the geographic coordinates of all standard addresses in the address set (converted through a geographic coding service), calculate the convex hull polygon of the set coordinates, call the ST_Within function of PostGIS to judge the inclusion relationship between the convex hull polygon and the target spatial area, and output the Boolean matching result;

[0084] S3.3. Construct a sparse matrix according to the matching result of S3.2, the matrix row key is the address set ID, the column key is the spatial area ID, and the cell stores a ternary value (1 indicates full inclusion / 0 indicates no inclusion / NULL indicates calculation exception), generate an initial version of the address area mapping wide table;

[0085] S3.4. Persist the wide table generated by S3.3 to a columnar database, dynamically expand the table structure, automatically add a column every time a new spatial area is added, and automatically add a row every time a new address set is added, and only store the change amount in the cell value to reduce IO consumption;

[0086] S3.5. When monitoring that there is a NULL value in the wide table, trigger the manual review process, extract the original address sample of the abnormal set and the associated space area boundary, mark the conflict point on the GIS visualization interface, and write the review result back to update the wide table value;

[0087] In a specific application, see Table 1:

[0088] Table 1 Address ID and Spatial Area ID Mapping Table

[0089]

[0090] S4. Associated application layer, associate the wide table and the original data table through the original address field, output the view supporting filtering by spatial area;

[0091] S4.1. Associate the original data temporary table output by S1.1, the global address mapping table output by S1.4, and the address area mapping wide table output by S3.4 through SQL JOIN operation, create a virtual view "space positioning view", and the view field includes all columns of the original data and the area mark column of the wide table;

[0092] S4.2. When the view is called by the application layer, the target area ID parameter is passed in, the filtering condition is automatically generated, and all associated legal persons, natural persons, and public facility original records in the space area are returned, realizing multi-source data aggregation query;

[0093] S4.3. On the query result of S4.2, classify and count by data source type, respectively, summarize the distribution density of natural persons in the area, the number of industrial classifications of legal institutions, and the service coverage rate of public facilities, and generate a comprehensive spatial analysis report;

[0094] S5. Establish an incremental update mechanism, when an address is added, sequentially execute only the standardized incremental address, supplement to the address set, and add and update the wide table row, when the boundary is changed, sequentially execute the addition of the spatial area and the addition of the wide table column, and after the boundary adjustment, distinguish the version by the year suffix;

[0095] S5.1. Monitor the original database change log, when the number of added address records reaches a set threshold (such as 1000) or a timing task is triggered, compare the update timestamp of the original address temporary table, extract the original ID and address text of the added record, and generate an incremental address temporary table;

[0096] S5.2. Send the incremental address text of S5.1 to the standardization process of S1.2, but only execute the processing of incremental records, output the added standard address set, and avoid reprocessing of full data;

[0097] S5.3. Match the incremental standard address output by S5.2 with the existing address set mode, if the address meets certain set feature mode (such as "Zhongguancun Avenue * number"), the original ID is added to the set; if there is no match, a new set is created, a new set ID is assigned and the set registration update of S1.3 is triggered;

[0098] S5.4. For the new address set generated by S5.3 (including newly created set and updated inventory set), perform spatial matching of S3 full process, and write the calculation result to the new row of address area mapping wide table, and only update the matching value of the change column of the existing set;

[0099] S5.5. When the spatial geographic library releases a new version, register a new region (such as R_new Free Trade Zone) in the spatial region metadata table of S2.2, start the GIS batch calculation of S3.2 for all address sets (including the updated set of S5.3), generate a new column of wide table and fill in the matching result;

[0100] S5.6. Detect the spatial region invalidation event (such as administrative division adjustment), mark the end time of the region version in S2.3, and set the corresponding column to read-only state in the wide table, and the application layer query automatically filters the invalid version data.

[0101] Through the above steps, the application extracts and de-duplicates multiple original address information to generate a to-be-matched set address library, and generates multiple circle boundary libraries that need to be matched, and matches the set in the spatial library and the circle boundary library, and feeds back the matching result in the form of a wide table. In subsequent matching, the incremental address library and the incremental circle boundary library need to be calculated, and the difference is matched, and then the matched wide table is used as a result table to associate with each original table, which can meet the needs of multi-boundary screening of different data sources, thereby significantly reducing the number of matches, improving the response efficiency and resource utilization of the overall system;

[0102] Therefore, the application greatly improves the address matching efficiency, changes from "comparison one by one" to "set comparison", greatly reduces unnecessary repeated operation, significantly reduces the consumption of computing resources, reduces the interaction frequency with the spatial library, saves CPU and memory resources, enhances the response ability of the system, and still maintains low delay in high concurrency, is easy to expand and maintain, the set division is flexible, is convenient for adapting to different regions and different data formats, supports incremental update, and the application is suitable for multiple application scenarios, and can be used in multiple fields such as population monitoring, smart city, social governance and public safety.

[0103] In the description of the specification, reference to "one embodiment", "an example", "a specific example" or the like means that a particular feature, structure, material or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. The appearances of the phrases "in one embodiment", "an example", "a specific example" or the like in various places in the specification are not necessarily referring to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0104] The preferred embodiments of the application disclosed above are only to help explain the application. The preferred embodiments do not describe all the details of the application and limit the application to the specific embodiments described. Obviously, many modifications and variations can be made in light of the contents of the specification. The specification selects and specifically describes these embodiments in order to better explain the principles and practical application of the application, so that those skilled in the art can well understand and utilize the application. The application is limited only by the claims and their full scope and equivalents.

Claims

1. A method for optimizing the boundary region matching of massive address data space, characterized in that: The following steps are involved: S1. Build an address set, input the original address fields of the multi-source database for standardization and cleaning, cluster to generate an address set, and output a mapping table between standard addresses and original addresses; S2. Spatial boundary modeling: input the region boundaries in the spatial geographic library, assign a unique ID to each spatial range, and store the boundary coordinate set; S3. Set-level matching: Using GIS calculations to batch determine the inclusion relationship between address sets and spatial regions, generating a wide table mapping structure. S4. The association application layer associates the wide table with the original data table through the original address field, and outputs a view that supports filtering by spatial region. S5. Establish an incremental update mechanism. When a new address is added, standardize the incremental address, add it to the address set, and then add and update the wide table row. When the boundary changes, add a new spatial area and then add and update the wide table column. After the boundary is adjusted, use the year suffix to distinguish the versions.

2. The method for optimizing the boundary region matching of massive address data space according to claim 1, characterized in that: Implementation steps of step S1: S1.

1. Extract the original address fields from the natural person database, legal person database, and public facility database data sources through the JDBC interface or API. Generate a temporary storage table containing the data source type, original record ID, and original address text. This table serves as the input source for address standardization and cleaning. S1.

2. Receive the original address text output by S1.1 and perform three-level processing: The first level analyzes the province, city, district, street, and house number components according to the administrative division level; The second level applies the rule base to clean fuzzy words and alias mapping; The third level outputs structured standard address text and generates a cleansing mapping table that records the correspondence between the original ID and the standard address; S1.

3. Based on the standard address text generated in S1.2, use the administrative tree hierarchical clustering algorithm to extract city-level, district-level, and street-level keywords from the address as clustering features. Addresses with the same three-level features are grouped together into the same set. Each set is assigned a unique set ID and a list of the original IDs included is recorded. The address set registry is then output for subsequent matching. S1.

4. Integrate the cleaning mapping table of S1.2 and the address set registry of S1.3 to generate a global address mapping table. This table records the binding relationship between the set ID, the original ID, and the standard address, and serves as the address reference data source for subsequent spatial matching.

3. The method for optimizing boundary region matching for massive address data space according to claim 1, characterized in that: Implementation steps of step S2: S2.

1. Read administrative division surface layer data in GeoJSON format from the spatial geographic information database, parse the polygon boundary coordinates, and pass the extracted coordinates to the region registration process. S2.

2. Assign a globally unique region ID to each spatial boundary extracted in S2.1 and create a record in the spatial region metadata table. The fields include the region ID, official name, boundary coordinate set, and effective timestamp. This table serves as the boundary reference data source for spatial matching. S2.

3. When a change in the boundary coordinates of an existing spatial region is detected, a new version record is added to the metadata table of S2.2, copying the original region ID and appending the year suffix. The historical version coordinate set is retained and the expiration time is marked to ensure that the old and new versions coexist in the wide table.

4. The method for optimizing boundary region matching for massive address data space according to claim 1, characterized in that: Implementation steps of step S3: S3.

1. Poll the address set registry output by S1.3 and the spatial region metadata table of S2.2, and generate an independent matching task for each combination of address set and spatial region. The task ID format is "set ID_region ID". The task queue is submitted in batches to the GIS calculation engine. S3.

2. When executing the task queue, obtain the geographic coordinates of all standard addresses in the address set, calculate the convex hull polygon of the set coordinates, call the PostGIS ST_Within function to determine the inclusion relationship between the convex hull polygon and the target spatial area, and output the Boolean value matching result; S3.

3. Build a sparse matrix based on the matching results of S3.

2. The matrix row keys are the address set IDs, the column keys are the spatial region IDs, and the cells store ternary values. This generates the initial version of the address region mapping wide table. S3.

4. Persist the wide table generated in S3.3 in a columnar database. The table structure is dynamically expanded, with columns automatically added for each new spatial region and rows automatically added for each new address set. Cell values ​​only store the amount of changes to reduce I / O consumption. S3.

5. When NULL values ​​are detected in the wide table, the manual review process is triggered to extract the original address samples and associated spatial area boundaries of the abnormal set, mark the conflict points on the GIS visualization interface, and rewrite the review results to update the wide table values.

5. The method for optimizing boundary region matching for massive address data space according to claim 1, characterized in that: Implementation steps of step S4: S4.

1. Use an SQL JOIN operation to join the original data temporary table output by S1.1, the global address mapping table output by S1.4, and the address region mapping wide table output by S3.

4. Create a virtual view called "Spatial Location View." The view fields contain all columns of the original data and the wide table region marker column. S4.

2. When the application layer calls a view, it passes in the target region ID parameter, automatically generates filtering conditions, and returns the original records of all associated legal entities, natural persons, and public facilities within that spatial region, enabling multi-source data aggregation queries. S4.

3. Based on the query results of S4.2, statistics are classified by data source type, summarizing the distribution density of natural persons in the region, the number of industrial classifications of legal entities, and the service coverage rate of public facilities to generate a comprehensive spatial analysis report.

6. The method for optimizing boundary region matching for massive address data space according to claim 1, characterized in that: Implementation steps of step S5: S5.

1. Monitor the original database change log. When the number of newly added address records reaches the set threshold or a scheduled task is triggered, compare the update timestamp of the original address temporary table, extract the original ID and address text of the newly added record, and generate an incremental address temporary table. S5.

2. Feed the incremental address text from S5.1 into the standardization process of S1.2, but only process the incremental records and output the newly added standard address set to avoid reprocessing the entire data. S5.

3. Match the incremental standard address output from S5.2 with the existing address set pattern. If the address matches a set feature pattern, append its original ID to the set. If there is no match, create a new set, assign a new set ID, and trigger the set registration update in S1.

3. S5.

4. For the new address set generated in S5.3, perform the full S3 spatial matching process. Write the calculation results to the new row of the wide address region mapping table. For the existing set, only update the matching values ​​of the changed columns. S5.

5. When a new version of the spatial geospatial library is released, register the new regions in the spatial region metadata table of S2.

2. Start GIS batch calculations in S3.2 for all address sets, generate new columns in the wide table, and populate them with matching results. S5.

6. When a spatial region failure event is detected, the expiration time of the region version is marked in S2.3, and the corresponding column in the wide table is simultaneously set to read-only. Application layer queries automatically filter out the failed version data.

7. A device for optimizing the matching of boundary regions of massive address and data spaces, applied to a method for optimizing the matching of boundary regions of massive address and data spaces as claimed in any one of claims 1 to 6, characterized in that: include: An address collection module, the address collection module is used to extract address information from the original database; An address standardization module, which is used to normalize, remove noise, and perform structural analysis on addresses; An address set construction module, wherein the address set construction module is used to divide addresses into multiple sets based on address semantics and rules; A space boundary collection module, which is used to set the space boundary ID, standard naming, and range boundary address that need to be determined; A spatial matching module, configured to match an address set with a spatial area; A mapping relationship management module, the mapping relationship management module is used to maintain a wide table of mapping relationships between address sets and spatial regions; An address and space matching view module, which is used to construct a view through the original data table and the mapping relationship wide table to provide queries in different application scenarios; A monitoring and updating module is used to support automatic updating and anomaly detection of mapping relationships.

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