A spatial reasoning address back-calculation positioning method

Abstract

The present invention relates to a spatial reasoning address reverse calculation positioning method, including: adding POI data and geographic entity data to a standard address library to construct a composite address dictionary; constructing a tree of addresses in the composite address dictionary in the order of administrative division levels to obtain an address tree corresponding to each address; when recalculating the address, first obtain the address to be matched, and perform preprocessing and word segmentation on the address to be matched in turn, obtain the address elements of each level after the word segmentation of the address to be matched, and construct an address tree of the address to be matched; and search the address tree of each level of the address elements after the word segmentation of the address to be matched in the database to obtain the positioning result of the address to be matched. The advantages are: Therefore, this method can effectively improve the matching rate and positioning accuracy, is suitable for place name address data of different regions, categories, sources, and qualities, and meets the spatialization requirements of various place name address text information in smart city construction.

Description

Technical Field

[0001] The present invention relates to the technical field of address processing, and in particular to an address reverse calculation and positioning method based on spatial reasoning. Background Art

[0002] A place name refers to a proper name of a geographical entity, and an address refers to a structured description of the location of a natural or human geographical entity at a specific spatial location with a place name. Place name and address information, as the most basic information for smart city construction, is widely used in all walks of life. However, due to the problems of non-standard, inaccurate, and inconsistent data sources in the existing place name and address data applications, the reusability of place name and address data is greatly reduced, and it is difficult to spatialize government information from different departments and to converge it to a unified geographical location, making it impossible to conduct data mining, analysis, and comprehensive utilization of multiple data sources. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a spatial reasoning address back-calculation positioning method with higher address positioning accuracy in view of the above-mentioned prior art.

[0004] The technical solution adopted by the present invention to solve the above technical problem is: a spatial reasoning address reverse calculation positioning method, characterized in that it includes the following steps:

[0005] Step 1: Add POI data and geographic entity data to the standard address library to construct a composite address dictionary;

[0006] Step 2: Establish an address hierarchy tree in a tree structure according to the order of administrative division levels in the address dictionary in the composite address dictionary, and add a limited area index to the administrative level with at least two indexes after the administrative division in the address hierarchy tree;

[0007] Step 3: construct each address in the composite address dictionary according to the address hierarchy tree in step 2 above, obtain the address tree corresponding to each address, and construct all address trees into a database;

[0008] Step 4: Obtain the address to be matched, and perform preprocessing and word segmentation on the address to be matched in sequence to obtain the address elements of each level after word segmentation of the address to be matched;

[0009] Step 5: construct the address elements of each level after the word segmentation of the address to be matched according to the address hierarchy tree in step 2 to construct an address tree of the address to be matched;

[0010] Step 6: Search the address elements of each level after the word segmentation of the address to be matched with the address tree in the database, and determine whether the address elements of each level of the address to be matched can find corresponding nodes in the address tree of the database. If so, form a set of node names with the address tree nodes corresponding to the address elements of each level, and then verify the hierarchical relationship between the nodes to obtain an address path that matches the address to be matched, and end; if not, proceed to step 7;

[0011] Step 7, searching for the address closest to the Nth level address element of the address to be matched from the Nth level addresses corresponding to all address trees in step 3, where N is the total level of administrative divisions;

[0012] And determine whether the distance LC between the nearest address in the retrieved address tree and the Nth level address element of the address to be matched is less than or equal to the preset value. If so, the coordinate position of the nearest address in the retrieved address tree is used as the positioning result of the address to be matched; if not, the location of the address element of the address to be matched is retrieved in the preset administrative division unit corresponding to all address trees, and the coordinate position of the retrieved address tree is used as the positioning result of the address to be matched.

[0013] In this solution, the standard database in step 1 is composed of standard addresses compiled by the civil affairs department.

[0014] To further improve the accuracy of address positioning, the construction of the composite address dictionary in step 1 also includes the following operations: adding a general name dictionary consisting of localized idioms, abbreviations or rare words of place names to the standard address library.

[0015] Preferably, the standard addresses in step 2 are divided into six levels according to the administrative division level, which are in the following order:

[0016] First level: Province;

[0017] Second level: city;

[0018] The third level: district or county;

[0019] The fourth level: town, township or street;

[0020] The fifth level: village, community or road;

[0021] Sixth level: house number, building number or unit number.

[0022] In order to maximize the accuracy of position positioning based on the relationship between addresses, it is preferred to add village communities and grassroots grids to the fourth level of the address hierarchy tree as limited area indexes.

[0023] There are many ways to pre-process the address to be matched, and preferably, the pre-processing operation performed on the address to be matched in step 4 at least includes: removing letters, removing special characters, and supplementing administrative levels.

[0024] To prevent address positioning errors, the following steps are included between step 4 and step 5:

[0025] According to the address hierarchy tree, the errors in the relationship between the address elements at each level after the address segmentation to be matched and the errors in the upper and lower hierarchical relationships between the nodes with the same name are corrected.

[0026] To improve the accuracy of address positioning, in step 7, after the location of the address element of the address to be matched is retrieved in the preset administrative division units corresponding to all address trees, step 8 is also included:

[0027] Step 8. Calculate the place name similarity between the coordinate position retrieved from all address trees and the address to be matched, and determine whether the place name similarity is greater than or equal to S, where S is a preset place name similarity threshold. If so, the coordinate position retrieved from all address trees is used as the location result of the address to be matched; if not, the location result of the address to be matched is not found.

[0028] Preferably, the preset administrative division units in step 7 are: grassroots grids, village communities or streets.

[0029] Compared with the prior art, the advantages of the present invention are: by adding POI data and geographic entity data to the standard address library to construct a composite address dictionary, the accuracy of address positioning is improved; in addition, by segmenting the address and building a tree to form an address tree, the matching efficiency can be improved. Therefore, the present method can effectively improve the matching rate and positioning accuracy, and is applicable to place name and address data of different regions, categories, sources, and qualities, meeting the spatialization requirements of various place name and address text information in smart city construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 1 is a flowchart of an address reverse calculation and positioning method according to an embodiment of the present invention;

[0031] Figure 2 4 is a block diagram of an address hierarchy tree in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The present invention is further described in detail below with reference to the accompanying drawings.

[0033] like Figure 1 As shown, the address reverse calculation positioning method of spatial reasoning in this embodiment includes the following steps:

[0034] Step 1: Add POI data and geographic entity data to the standard address database to construct a composite address dictionary;

[0035] Among them, the standard database in this step is composed of standard addresses compiled by the civil affairs department, and the standard address is the house number address compiled by the place name office of the municipal and district civil affairs bureaus in accordance with the "Place Name Management Regulations", that is, the legal and standard house number address data; the word segmentation library obtained after the house number address data compiled by the civil affairs department is used as the standard dictionary, which is mainly an administrative division dictionary library, including city, district, county, township street, village community, road (street, road, lane), community and other word segmentation; in order to further improve the accuracy of address reverse calculation positioning, the construction of the composite address dictionary in this step also includes the following operations: adding a general name dictionary composed of localized idioms, abbreviations or rare words of place names to the standard address library; in addition, by adding POI data (i.e.: points of interest) and geographic entity data to the standard address library, the standard address library is supplemented, the dictionary word segmentation volume is further enriched, and the accuracy and matching rate of subsequent address word segmentation are improved;

[0036] Among them, POI data are divided into categories: shopping, catering, life services, transportation facilities, tourist attractions, leisure and entertainment, sports and fitness, education and training, culture and media, medical care, finance, real estate, companies and enterprises, government agencies, etc. Among them, shopping is divided into shopping centers, department stores, supermarkets, convenience stores, markets, etc.; among them, shopping is further divided into shopping centers, supermarkets, shops, etc. Some of the features in this POI data are not well-known enough. Therefore, when using POI points with insufficient popularity, users do not know the specific location of the POI point, and the reference significance of the POI point is not great. Therefore, it is necessary to screen the POI points in advance to improve the indicativeness; geographic entity data refers to natural water bodies such as reservoirs and lakes, scenic spots and scenic areas, as well as representative entity data such as hydraulic facilities and buildings;

[0037] Step 2: Establish an address hierarchy tree in a tree structure according to the order of administrative division levels in the address dictionary in the composite address dictionary, and add a limited area index to the administrative level with at least two indexes after the administrative division in the address hierarchy tree;

[0038] Among them, the standard address is divided into six levels according to the administrative division level, in order of priority:

[0039] First level: Province;

[0040] Second level: city;

[0041] The third level: district or county;

[0042] The fourth level: town, township or street;

[0043] The fifth level: village, community or road;

[0044] The sixth level: house number, building number or unit number;

[0045] The administrative division elements include provinces, cities, districts, counties, towns, streets, and villages. They appear in the spatial structure in the form of planes and have a layer-by-layer inclusion relationship. However, there are diversity problems in the administrative division relationship. For example, roads within the city cross districts and streets. Roads and administrative divisions do not constitute a strict subordinate relationship. Therefore, this method will not strictly limit the district, county, and township levels, and add a basic area restriction layer. In addition to township streets, village communities and grassroots grid scales are added as the fourth-level restricted area index; the specific relationship is as follows: Figure 2 As shown;

[0046] Step 3: construct each address in the composite address dictionary according to the address hierarchy tree in step 2 above, obtain the address tree corresponding to each address, and construct all address trees into a database;

[0047] Step 4: Obtain the address to be matched, and perform preprocessing and word segmentation on the address to be matched in sequence to obtain the address elements of each level after word segmentation of the address to be matched;

[0048] The pre-processing operation for the address to be matched includes at least one of: removing letters, removing special characters, and supplementing administrative levels. Of course, in order to improve the positioning accuracy of the address to be matched, after obtaining the address elements of each level after the segmentation of the address to be matched, the following steps are also included: correcting the relationship errors of the address elements of each level after the segmentation of the address to be matched according to the address hierarchy tree and processing the wrong upper and lower hierarchical relationships between nodes with the same name;

[0049] Step 5: construct the address elements of each level after the word segmentation of the address to be matched according to the address hierarchy tree in step 2 to construct an address tree of the address to be matched;

[0050] In this embodiment, the address to be matched is constructed in the form of an address tree, so that the administrative division level of the address can be clearly seen, which is convenient for comparing each administrative division level and improves the search efficiency of each administrative division level in the address to be matched;

[0051] Step 6: Search the address elements of each level after the word segmentation of the address to be matched with the address tree in the database, and determine whether the address elements of each level of the address to be matched can find corresponding nodes in the address tree of the database. If so, form a set of node names with the address tree nodes corresponding to the address elements of each level, and then verify the hierarchical relationship between the nodes to obtain an address path that matches the address to be matched, and end; if not, proceed to step 7;

[0052] Step 7, searching for the address closest to the Nth level address element of the address to be matched from the Nth level addresses corresponding to all address trees in step 3, where N is the total level of administrative divisions;

[0053] And determine whether the distance LC between the nearest address in the retrieved address tree and the Nth level address element of the address to be matched is less than or equal to the preset value. If so, the coordinate position of the nearest address in the retrieved address tree is used as the positioning result of the address to be matched; if not, the location of the address element of the address to be matched is retrieved in the preset administrative division unit corresponding to all address trees, and the coordinate position of the retrieved address tree is used as the positioning result of the address to be matched.

[0054] In this embodiment, the preset administrative division units are township streets, village communities or grassroots grids;

[0055] Wherein, after the location of the address element of the address to be matched is retrieved in the preset administrative division units corresponding to all address trees, step 8 is also included:

[0056] Step 8. Calculate the place name similarity between the coordinate position retrieved from all address trees and the address to be matched, and determine whether the place name similarity is greater than or equal to S, where S is a preset place name similarity threshold. If so, the coordinate position retrieved from all address trees is used as the location result of the address to be matched; if not, the location result of the address to be matched is not found.

[0057] To facilitate understanding of the reverse calculation positioning method in the present invention, a specific address to be matched is used as an example for explanation in this embodiment. For example, the address to be matched is No. 110, E Road, D Street, C District, B City, A Province, where A, B, C, D, and E are all aliases for real addresses. The positioning process of the address to be matched is as follows:

[0058] If the record of "No. 110, E Road, D Street, C District, B City, A Province" is retrieved from the addresses in all address trees, the location result of the address to be matched is "No. 110, E Road, D Street, C District, B City, A Province";

[0059] If the record of "No. 110, E Road, D Street, C District, B City, A Province" is not found in the addresses of all address trees, then the addresses adjacent to "No. 110, E Road, D Street, C District, B City, A Province" are retrieved. For example, when "No. 105, E Road, D Street, C District, B City, A Province" and "No. 120, E Road, D Street, C District, B City, A Province" are retrieved, the distance between "No. 110" and "No. 105" is calculated to be 5 numbers, and the distance between "No. 110" and "No. 120" is 10 numbers. Assuming that the distance between two adjacent house numbers is the same, "No. 105, E Road, D Street, C District, B City, A Province" is the closest location to "No. 110, E Road, D Street, C District, B City, A Province";

[0060] 1. When the distance between "No. 105, E Road, D Street, C District, B City, A Province" and "No. 110, E Road, D Street, C District, B City, A Province" is less than or equal to the preset value, then "No. 105, E Road, D Street, C District, B City, A Province" is the location result of the address to be matched;

[0061] 2. When the distance between "No. 105, E Road, D Street, District C, City B, Province A" and "No. 110, E Road, D Street, District C, City B, Province A" is greater than the preset value, the grassroots grid to which "No. 110, E Road, D Street, District C, City B, Province A" belongs is located first, and it is determined whether the grassroots grid to which the address to be matched belongs is located. If so, the final positioning result of the address to be matched "No. 105, E Road, D Street, District C, City B, Province A" is determined to be: "F grassroots grid, District C, City B, Province A"; if not, the village community or street to which "No. 110, E Road, D Street, District C, City B, Province A" belongs is located; therefore, the final positioning result of the address to be matched "No. 105, E Road, D Street, District C, City B, Province A" is determined to be: "F grassroots network / village community / township street, District C, City B, Province A".

Claims

1. A spatial reasoning address back-calculation positioning method, characterized in that: The steps include: Step 1: Add POI data and geographic entity data to the standard address library to construct a composite address dictionary; Step 2: Establish an address hierarchy tree in a tree structure according to the order of administrative division levels in the address dictionary in the composite address dictionary, and add a limited area index to the administrative level with at least two indexes after the administrative division in the address hierarchy tree; Step 3: construct each address in the composite address dictionary according to the address hierarchy tree in step 2 above, obtain the address tree corresponding to each address, and construct all address trees into a database; Step 4: Obtain the address to be matched, and perform preprocessing and word segmentation on the address to be matched in sequence to obtain the address elements of each level after word segmentation of the address to be matched; Step 5: construct the address elements of each level after the word segmentation of the address to be matched according to the address hierarchy tree in step 2 to construct an address tree of the address to be matched; Step 6: Search the address tree in the database for each level of address elements after word segmentation of the address to be matched, and determine whether the corresponding nodes can be found in the address tree of the database. If so, form a set of node names for the address tree nodes corresponding to each level of address elements, and then verify the hierarchical relationship between the nodes to obtain an address path that matches the address to be matched, and end; if not, proceed to step 7; Step 7, searching for the address closest to the Nth level address element of the address to be matched from the Nth level addresses corresponding to all address trees in step 3, where N is the total level of administrative divisions; And determine whether the distance LC between the nearest address in the retrieved address tree and the Nth level address element of the address to be matched is less than or equal to the preset value. If so, the coordinate position of the nearest address in the retrieved address tree is used as the positioning result of the address to be matched; if not, the location of the address element of the address to be matched is retrieved in the preset administrative division unit corresponding to all address trees, and the coordinate position of the retrieved address tree is used as the positioning result of the address to be matched.

2. The address reverse calculation positioning method of spatial reasoning according to claim 1 is characterized in that: The standard database in step 1 is composed of standard addresses compiled by the civil affairs department.

3. The address reverse calculation positioning method of spatial reasoning according to claim 2 is characterized in that: The construction of the composite address dictionary in step 1 also includes the following operations: Add a general name dictionary consisting of localized idioms, abbreviations or rare words in place names to the standard address database.

4. The address reverse calculation positioning method of spatial reasoning according to claim 3 is characterized in that: The standard addresses in step 2 are divided into six levels according to the administrative division level, in the following order: First level: Province; Second level: city; The third level: district or county; The fourth level: town, township or street; The fifth level: village, community or road; Sixth level: house number, building number or unit number.

5. The address reverse calculation positioning method of spatial reasoning according to claim 4 is characterized in that: The fourth level of the address hierarchy tree adds village communities and grassroots grids as limited area indexes.

6. The method for address reverse calculation and positioning by spatial reasoning according to any one of claims 1 to 5, characterized in that: The pre-processing operation performed on the address to be matched in step 4 at least includes: removing letters, removing special characters, and supplementing administrative levels.

7. The address reverse calculation positioning method of spatial reasoning according to claim 6 is characterized by: The following steps are also included between step 4 and step 5: According to the address hierarchy tree, the errors in the relationship between the address elements at each level after the address segmentation to be matched and the errors in the upper and lower hierarchical relationships between the nodes with the same name are corrected.

8. The method for address reverse calculation and positioning based on spatial reasoning according to claim 7, characterized in that: In the step 7, after the location of the address element of the address to be matched is retrieved in the preset administrative division units corresponding to all address trees, the step 8 is also included: Step 8. Calculate the place name similarity between the coordinate position retrieved from all address trees and the address to be matched, and determine whether the place name similarity is greater than or equal to S, where S is a preset place name similarity threshold. If so, the coordinate position retrieved from all address trees is used as the location result of the address to be matched; if not, the location result of the address to be matched is not found.

9. The method for address reverse calculation and positioning by spatial reasoning according to claim 8, characterized in that: The preset administrative division units in step 7 are: grassroots grids, village communities or streets.

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