Multi-source place name data processing method and device

Abstract

The invention provides a multi-source geographical name data processing method and device. The method comprises the steps that an initial geographical name data set, an incremental geographical name data set and an administrative division data set within a task area range are acquired; preprocessing the initial place name data set and the incremental place name data set to obtain a first place name data set and a second place name data set which meet the general format requirement; by taking the lowest administrative unit level as a basic matching fusion unit, carrying out fusion processing on the place name data belonging to the same basic matching fusion unit in the first place name data set and the second place name data set to obtain a third place name data set; translating and writing place name data in the third place name data set to obtain a candidate place name data set of the target language type; and sequentially carrying out space compliance processing and edge matching integrity processing on the candidate place name data set to obtain a target place name data set. According to the technical scheme, the processing efficiency of the multi-source place name data, the accuracy of the processing result and the like can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of geographic information technology, and in particular to a method and device for processing multi-source place name data. Background Art

[0002] Place names are the proper names people give to natural or human geographical entities at a specific spatial location. They are important basic geographic information and social public information resources, and play an important role in national defense construction, economic development, etc. With the increasingly close global data cooperation and the rapid development of Internet technology, the demand for place name data from all walks of life is increasing. Place name data is also an important surveying and mapping geographic information data. The construction and development of global geographic information resources requires the continuous and efficient acquisition and updating of place name data at home and abroad. There are a large number of open source place name data and other data containing administrative place names that can be obtained for free on the Internet, such as GeoNames, OSM (Open Street Map), GADM (Database of Global Administrative Areas), Geocode Earth, etc.

[0003] However, since the sources of Internet place name data are different, there are overlaps and redundancies among multi-source place names. The existing technology is only able to simply acquire, clean, classify, map and fuse multi-source place names, and there are still objective problems such as poor fusion results. Summary of the invention

[0004] In view of this, the purpose of this application is to provide a method and device for processing multi-source place name data. The technical solution of this application can effectively improve the processing efficiency of multi-source place name data and the accuracy of processing results.

[0005] The present application provides a method for processing multi-source place name data, the processing method comprising:

[0006] Obtain the initial place name dataset, incremental place name dataset, and administrative division dataset within the mission area;

[0007] Preprocessing the initial place name dataset and the incremental place name dataset to obtain a first place name dataset and a second place name dataset that meet the general format requirements;

[0008] Taking the lowest administrative unit level as the basic matching fusion unit, fusing the place name data belonging to the same basic matching fusion unit in the first place name data set and the second place name data set to obtain a fused third place name data set; wherein the lowest administrative unit level is determined according to the administrative division data set;

[0009] Performing translation processing on the place name data in the third place name data set based on preset translation rules to obtain a candidate place name data set of the target language type;

[0010] The candidate place name dataset is processed in turn for spatial compliance and edge integrity to obtain a target place name dataset.

[0011] Optionally, the preprocessing includes at least one of the following processing methods: data format conversion, spatial reference system unification, structure normalization processing, data cleaning, place name classification mapping, field mapping, romanization transcription and attribute encoding.

[0012] Optionally, taking the lowest administrative unit level as a basic matching and fusion unit, and fusing the place name data belonging to the same basic matching and fusion unit in the first place name data set and the second place name data set, comprises:

[0013] For the place name data in the first place name data set and the place name data in the second place name data set under the same basic matching fusion unit, determining whether the category codes of the two place name data are consistent, and determining whether the foreign name attribute values ​​of the two place name data are consistent;

[0014] If the two are consistent, the two place name data are fused to form the place name data in the third place name data set.

[0015] Optionally, if the category codes of the two place name data are consistent but the foreign name attribute values ​​of the two place name data are inconsistent, the fusion processing of the place name data belonging to the same basic matching fusion unit in the first place name data set and the second place name data set includes:

[0016] According to the place name common name synonym comparison rule base, identify whether two place name data are synonymous variants;

[0017] If yes, the two place name data are merged;

[0018] If not, the edit distance algorithm is used to determine the place name similarity between the two place name data;

[0019] If the place name similarity between two place name data is greater than the similarity threshold, the two place name data will be fused.

[0020] Optionally, the calculation formula for the place name similarity is:

[0021] S=1-E / L

[0022] Among them, S is the similarity of place names, E is the Levenshtein edit distance between two place name data, and L is the maximum length of the foreign name string of the two place name data.

[0023] Optionally, the step of performing translation processing on the place name data in the third place name data set based on a preset translation rule to obtain a candidate place name data set of the target language type includes:

[0024] For each place name data in the third place name data set, identifying the language type to which the place name data belongs;

[0025] Determine the target translation rule according to the language type of the place name data and the target language type;

[0026] According to the target transliteration rule, firstly perform place name syllable segmentation and transliteration translation on the proper name part of the place name data, and then perform common name translation translation on the common name part of the place name data;

[0027] The word order of the translated proper nouns and common nouns is adjusted to obtain candidate place name data of the target language type, and a candidate place name data set is formed based on all the candidate place name data.

[0028] Optionally, the step of sequentially performing spatial compliance processing and edge integrity processing on the candidate place name dataset to obtain a target place name dataset includes:

[0029] For each candidate place name data in the candidate place name data set, the spatial topological relationship of the candidate place name data is verified according to the corresponding core vector element data, and the abnormality correction and abnormality record are performed on the verification result;

[0030] Obtaining the edge reference data corresponding to the candidate place name dataset;

[0031] According to the edge reference data and a preset edge processing strategy, the edge place name data in the candidate place name dataset after spatial compliance processing is processed to obtain a target place name dataset.

[0032] The present application also provides a device for processing multi-source place name data, the device comprising:

[0033] An acquisition module is used to acquire the initial place name dataset, incremental place name dataset and administrative division dataset within the mission area;

[0034] A preprocessing module, used for preprocessing the initial place name dataset and the incremental place name dataset to obtain a first place name dataset and a second place name dataset that meet the general format requirements;

[0035] A fusion module, used to take the lowest administrative unit level as a basic matching fusion unit, and to fuse the place name data belonging to the same basic matching fusion unit in the first place name data set and the second place name data set to obtain a fused third place name data set; wherein the lowest administrative unit level is determined according to the administrative division data set;

[0036] A translation module, used for performing translation processing on the place name data in the third place name data set based on preset translation rules to obtain a candidate place name data set of a target language type;

[0037] The collaborative processing module is used to perform spatial compliance processing and edge integrity processing on the candidate place name dataset in sequence to obtain a target place name dataset.

[0038] Optionally, the preprocessing includes at least one of the following processing methods: data format conversion, spatial reference system unification, structure normalization processing, data cleaning, place name classification mapping, field mapping, romanization transcription and attribute encoding.

[0039] Optionally, when the fusion module is used to take the lowest administrative unit level as the basic matching fusion unit and to perform fusion processing on the place name data belonging to the same basic matching fusion unit in the first place name data set and the second place name data set, the fusion module is used to:

[0040] For the place name data in the first place name data set and the place name data in the second place name data set under the same basic matching fusion unit, determining whether the category codes of the two place name data are consistent, and determining whether the foreign name attribute values ​​of the two place name data are consistent;

[0041] If the two are consistent, the two place name data are fused to form the place name data in the third place name data set.

[0042] Optionally, if the category codes of the two place name data are consistent but the foreign name attribute values ​​of the two place name data are inconsistent, when the fusion module is used to fuse the place name data belonging to the same basic matching fusion unit in the first place name data set and the second place name data set, the fusion module is used to:

[0043] According to the place name common name synonym comparison rule base, identify whether two place name data are synonymous variants;

[0044] If yes, the two place name data are merged;

[0045] If not, the edit distance algorithm is used to determine the place name similarity between the two place name data;

[0046] If the place name similarity between two place name data is greater than the similarity threshold, the two place name data will be fused.

[0047] Optionally, the calculation formula for the place name similarity is:

[0048] S=1-E / L

[0049] Among them, S is the similarity of place names, E is the Levenshtein edit distance between two place name data, and L is the maximum length of the foreign name string of the two place name data.

[0050] Optionally, when the translation module is used to perform translation processing on the place name data in the third place name data set based on a preset translation rule to obtain a candidate place name data set of the target language type, the translation module is used to:

[0051] For each place name data in the third place name data set, identifying the language type to which the place name data belongs;

[0052] Determine the target translation rule according to the language type of the place name data and the target language type;

[0053] According to the target transliteration rule, firstly perform place name syllable segmentation and transliteration translation on the proper name part of the place name data, and then perform common name translation translation on the common name part of the place name data;

[0054] The word order of the translated proper nouns and common nouns is adjusted to obtain candidate place name data of the target language type, and a candidate place name data set is formed based on all the candidate place name data.

[0055] Optionally, when the collaborative processing module is used to sequentially perform spatial compliance processing and edge integrity processing on the candidate place name dataset to obtain the target place name dataset, the collaborative processing module is used to:

[0056] For each candidate place name data in the candidate place name data set, the spatial topological relationship of the candidate place name data is verified according to the corresponding core vector element data, and the abnormality correction and abnormality record are performed on the verification result;

[0057] Obtaining the edge reference data corresponding to the candidate place name dataset;

[0058] According to the edge reference data and a preset edge processing strategy, the edge place name data in the candidate place name dataset after spatial compliance processing is processed to obtain a target place name dataset.

[0059] An embodiment of the present application also provides an electronic device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the steps of the processing method as described above are performed.

[0060] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the processing method described above are executed.

[0061] An embodiment of the present application provides a method and device for processing multi-source place name data, the processing method comprising: obtaining an initial place name data set, an incremental place name data set and an administrative division data set within the mission area; preprocessing the initial place name data set and the incremental place name data set to obtain a first place name data set and a second place name data set that meet the general format requirements; taking the lowest administrative unit level as the basic matching fusion unit, fusing the place name data belonging to the same basic matching fusion unit in the first place name data set and the second place name data set to obtain a fused third place name data set; wherein the lowest administrative unit level is determined according to the administrative division data set; translating the place name data in the third place name data set based on preset translation rules to obtain a candidate place name data set of the target language type; and sequentially performing spatial compliance processing and edge integrity processing on the candidate place name data set to obtain a target place name data set.

[0062] In this way, the technical solution provided by this application solves the problems of low matching efficiency, non-standard translation, and low quality and accuracy of place name fusion in the existing technology by improving matching algorithms, standardizing place name translation methods, and adding collaborative processing and verification. At the same time, it provides a reasonable and feasible solution for global multi-source place name fusion updates and collaborative verification.

[0063] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0065] Figure 1 A flowchart of a method for processing multi-source place name data provided by an embodiment of the present application;

[0066] Figure 2 Examples of transliterations for Russian;

[0067] Figure 3 A schematic diagram of the process of translating and writing place name data provided by this application;

[0068] Figure 4 A schematic diagram of the structure of a device for processing multi-source place name data provided by an embodiment of the present application;

[0069] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0070] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application usually described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, each other embodiment obtained by those skilled in the art without making creative work belongs to the scope of protection of the present application.

[0071] Place names are the proper names people give to natural or human geographical entities at a specific spatial location. They are important basic geographic information and social public information resources, and play an important role in national defense construction, economic development, etc. With the increasingly close global data cooperation and the rapid development of Internet technology, the demand for place name data from all walks of life is increasing. Place name data is also an important surveying and mapping geographic information data. The construction and development of global geographic information resources requires the continuous and efficient acquisition and updating of place name data at home and abroad. There are a large number of open source place name data and other data containing administrative place names that can be obtained for free on the Internet, such as GeoNames, OSM (Open Street Map), GADM (Database of Global Administrative Areas), Geocode Earth, etc.

[0072] However, due to the different sources of Internet place name data, there are overlaps and redundancies among multi-source place names. The existing technology is only to simply acquire, clean, classify, map and fuse multi-source place names. There are still objective problems such as low matching and fusion efficiency, non-standard translation, low accuracy of results, and lack of multi-source collaborative verification.

[0073] Based on this, the embodiments of the present application provide a method and device for processing multi-source place name data to solve objective problems such as low matching and fusion efficiency, non-standard translation, low accuracy of results, and lack of multi-source collaborative verification.

[0074] See also Figure 1 , Figure 1 The following is a flow chart of a method for processing multi-source place name data provided by an embodiment of the present application. Figure 1 As shown in , the processing method provided in the embodiment of the present application includes:

[0075] S101, obtaining an initial place name dataset, an incremental place name dataset, and an administrative division dataset within the mission area.

[0076] S102: Preprocess the initial place name dataset and the incremental place name dataset to obtain a first place name dataset and a second place name dataset that meet general format requirements.

[0077] S103, taking the lowest administrative unit level as the basic matching and fusion unit, fusing the place name data belonging to the same basic matching and fusion unit in the first place name data set and the second place name data set to obtain a fused third place name data set.

[0078] S104: Perform translation processing on the place name data in the third place name data set based on preset translation rules to obtain a candidate place name data set of the target language type.

[0079] S105, performing spatial compliance processing and edge integrity processing on the candidate place name dataset in sequence to obtain a target place name dataset.

[0080] Regarding step S101, in this step, the initial place name dataset is generally a dataset containing all or most of the place name data within the scope of the task; the incremental place name dataset generally includes new or updated place name data, which is used to supplement the deficiencies of the initial dataset; the administrative division dataset is used to describe the boundary information of administrative regions at all levels to ensure that the data can be classified and processed according to administrative divisions.

[0081] For example, in one implementation provided in the present application, the GeoNames initial place name data set Name1 is obtained according to the scope of the mission area, and Name1 contains multiple types of initial place name elements N1i; the multi-source place name data on other Internet place name websites, map websites, real estate websites, cultural and tourism websites, diplomatic websites, international organizations and other websites are collected and obtained using algorithms such as breadth-themed priority web crawlers and depth-themed priority web crawlers to form an incremental place name data set Name2, and Name2 contains multiple types of incremental place name elements N2j. Among them, N1i and N2j record the latitude and longitude coordinates, place name classification, place name name, collection / update date and other field attributes of each place name.

[0082] Obtain the corresponding GADM administrative division dataset Bound1 according to the Name1 range. In this way, extract the multi-level administrative division surface layer in Bound1 as the basic unit of place name matching and fusion.

[0083] Among them, the elements in Bound1 record the name, administrative unit level, geographic spatial location range, official language and other attribute fields of each country in the world. The present invention uses the lowest administrative unit level of the country that can be obtained as the basic matching and fusion unit. In this way, the Name1 and Name2 place name elements are matched, merged, updated, translated and other operations are performed one by one in the basic matching and fusion unit.

[0084] With respect to step S102, in this step, the initial place name data set is preprocessed to obtain a first place name data set, and the incremental place name data set is preprocessed to obtain a second place name data set.

[0085] In one embodiment provided in the present application, the preprocessing includes at least one of the following processing methods: data format conversion, spatial reference system unification, structure normalization processing, data cleaning, place name classification mapping, field mapping, romanization transcription and attribute encoding.

[0086] The data format conversion process can uniformly convert data formats (such as CSV, shapefile, JSON, etc.) from different sources (the sources of the initial place name dataset and the incremental place name dataset are generally different) into a common geospatial database format (such as Geodatabase, *.gdb), and ensure that all place name data use the same geometry type (such as point features).

[0087] The spatial reference system unified processing can unify the spatial reference systems of all data into a universal geographic coordinate system "GCS_WGS_1984".

[0088] The structure normalization process can unify the data structure and ensure that the names, types, and lengths of all data source fields remain consistent.

[0089] The data cleaning process can be used to clean up redundancy and errors in the data to ensure the accuracy and completeness of the data.

[0090] The place name classification mapping process can be used to classify according to place name types (such as cities, rivers, mountains, etc.), and map classification fields of different data sources to a unified category code. Specifically, the place name type in each data source can be mapped to a standard category field through a lookup table or preset rules.

[0091] The field mapping is used to uniformly map attribute fields in different data sources to the merged data structure to ensure field consistency. For example, uniformly map place name attribute fields (such as foreign names, Chinese names, etc.) in different data sources to the target field.

[0092] The romanization transcription is used to perform standardized romanization transcription on place names in non-Latin letters to ensure the international applicability of the place names.

[0093] The attribute code is used to encode foreign language names and Chinese names to ensure that data does not appear garbled in a multi-language environment.

[0094] In addition, it can also include incremental data source processing (such as optionally adding additional fields based on the characteristics of the incremental data source), etc.

[0095] For example, an implementation method for preprocessing the initial place name dataset and the incremental place name dataset provided in the present application includes: converting the place name data format in the initial place name dataset Name1 and the incremental place name dataset Name2 into a common geographic spatial database Geodatabase (*.gdb) format for storage, uniformly converting the geometry type into the shapefile vector point feature data format, and uniformly converting the spatial reference into the common "GCS_WGS_1984" geographic coordinate system, and performing structural normalization, data cleaning, place name classification mapping, field mapping, romanization transcription, and attribute encoding operations in turn to ensure structural consistency between data sources.

[0096] The basic fields of the data attribute structure of the partial place name classification mapping comparison table and the processed data set are shown in Table 1 and Table 2 (the third place name data set after fusion is also shown in Table 2), where Table 1 is the partial place name classification mapping comparison table and Table 2 is the place name data attribute field structure table. As shown in Tables 1 and 2, the unique data source identification code SRCID, category code, foreign name, Chinese name, administrative region code, collection / update date and other fields of each place name are included. Fields can be added at will according to the incremental data source. Other attribute data not in this table in the initial data source should be retained, and they are combined into one attribute content and placed in the "SRCATTR" field. The "ENAME1" field is filled with the standard Roman alphabet spelling of the place name, and the attribute values ​​of the foreign name field are uniformly set to the UTF-8 (Unicode Transformation format-8bit) data encoding format to eliminate the software display garbled code problem caused by special characters in different foreign languages ​​(such as Autovíade Andalucía in Spanish). The attribute values ​​of the Chinese name field are uniformly converted to the Unicode data encoding format to facilitate subsequent name query matching and collaborative processing of map annotation display.

[0097] Table 1:

[0098]

[0099] Table 2:

[0100]

[0101]

[0102] Among them, Romanization transcription: For the place names of some countries or regions, their place names are marked and displayed in the local language of the country. In this case, it is necessary to perform Romanization transcription operations on the language according to the local language transcription rules formulated by the country. For an example, please refer to Figure 2 , Figure 2 It is an example of a Russian transcription comparison table. It should be noted that the full name of the Russian language is Russian, which is referred to as Russian in this application.

[0103] Then, after performing operations such as unifying the upper and lower case letters of each place name point element in the Name1 and Name2 datasets that have undergone the above processing, special characters are processed, and then the place name matching and fusion processing can be performed.

[0104] With respect to step S103, the lowest administrative unit level is determined according to the administrative division data set.

[0105] The administrative division dataset includes administrative unit names and administrative unit levels, which define the level of each administrative region, such as national, provincial, county, township, etc. Administrative unit levels can help identify the hierarchical relationship between regions. The lowest administrative unit usually refers to the smallest administrative region. Using this lowest unit as the matching basis can ensure the spatial consistency and accuracy of place name data.

[0106] Here, the lowest administrative unit level is taken as the basic matching fusion unit, and the place name data belonging to the same basic matching fusion unit in the first place name data set and the second place name data set are fused, including: dividing the first place name data set and the second place name data set according to the lowest administrative unit, and fusing the place name data belonging to the same administrative unit in the two place name data sets.

[0107] For example, if a district is the lowest administrative unit, all place name data belonging to a district in the first place name dataset and the second place name dataset are extracted and fused separately.

[0108] With respect to step S103, in one implementation mode provided in the present application, taking the lowest administrative unit level as the basic matching and fusion unit, and fusing the place name data belonging to the same basic matching and fusion unit in the first place name dataset and the second place name dataset, comprises:

[0109] S1031. For the place name data in the first place name data set and the place name data in the second place name data set under the same basic matching fusion unit, determine whether the category codes of the two place name data are consistent, and determine whether the foreign name attribute values ​​of the two place name data are consistent.

[0110] S1032. If both are consistent, the two place name data are merged to form the place name data in the third place name data set.

[0111] With respect to step S1031, the category codes and foreign name attribute values ​​of the two place name data can be determined by looking up a table.

[0112] With respect to step S1032, when the two place name data are fused, the fusion may be performed based on the place name data belonging to the first place name data set, or based on the place name data having the most attribute fields.

[0113] In another implementation manner provided by the present application, if the category codes of the two place name data are consistent but the foreign name attribute values ​​of the two place name data are inconsistent, the fusion processing of the place name data belonging to the same basic matching fusion unit in the first place name data set and the second place name data set includes:

[0114] S1033, identifying whether two place name data are synonymous variants according to a place name common name synonym comparison rule base;

[0115] S1034: If yes, merge the two place name data.

[0116] S1035: If not, use the edit distance algorithm to determine the place name similarity between the two place name data.

[0117] S1046: If the place name similarity between the two place name data is greater than the similarity threshold, the two place name data are fused.

[0118] For step S1033, the place name common name synonym replacement rule base is pre-established, for example, a GSRI that statistically summarizes common common name synonym comparison rule bases with specific semantics and high frequency of occurrence, such as Raod, Rd, Highway, hyway, Hwy, Square, Sq, Drive, etc., all represent roads and are considered to represent the same place name during algorithm matching judgment.

[0119] The identification of whether the two place name data are synonymous variants is to determine whether the two place name data have word abbreviations, abbreviations, synonyms, etc.

[0120] Regarding step S1034, the fusion rule is the same as step S1032 and will not be described again here.

[0121] With respect to step S1035, in one implementation provided in the present application, the calculation formula for the place name similarity is:

[0122] S=1-E / L

[0123] Among them, S is the similarity of place names, E is the Levenshtein edit distance between two place name data, and L is the maximum length of the foreign name string of two place name data. The larger S is, the higher the similarity of the two place names is.

[0124] Regarding step S1036, the similarity threshold is pre-set or dynamically determined, which is not limited here.

[0125] As an example, the fusion processing of place name data is explained through the following content. This solution innovatively proposes to use an improved edit distance algorithm (I_Levenshtein Distance) based on the lowest administrative unit to associate and match each place name data in the first place name dataset N1i (originally belonging to the initial place name dataset) and the second place name dataset N2j (originally belonging to the incremental place name dataset) within the lowest administrative unit in the mission area. The specific algorithm is as follows:

[0126] 1) First, a place name synonym replacement rule base is constructed, and statistics are summarized on the common common name synonym comparison rule base GSRI with specific semantics and high frequency of occurrence. For example, Raod, Rd, Highway, hyway, Hwy, Square, Sq, Drive, etc. all mean roads and are considered to represent the same place name in the algorithm matching judgment.

[0127] 2) When the CLSID category codes of the two place name data in the N1i and N2j datasets are consistent and the foreign name ENAME1 attribute values ​​are exactly the same, they are directly merged.

[0128] 3) When the CLSID category codes of the two place name data in the N1i and N2j datasets are consistent, but the foreign name ENAME1 attribute values ​​are not completely consistent, the common name synonym comparison rule library GSRI matching search of the two is added to determine whether they belong to word abbreviations, abbreviations, synonyms, etc. If the judgment result is True, they are directly merged. If the judgment result is false, the edit distance algorithm (Levenshtein Distance) is used to calculate the character similarity of the foreign name ENAME1 of the two place name elements, and the elements with a similarity of more than 90% are merged.

[0129] For example, please refer to Table 3, which is a comparison table of synonyms of common place names provided in this application. According to this table, it can be determined whether two place name data are synonymous variants.

[0130] Table 3:

[0131]

[0132] Regarding step S104, in this step, the fused place name dataset is transliterated, that is, the place names are converted into the spelling of the target language, which may specifically involve Romanization, phonetic conversion of Chinese place names, or standardized translation of foreign place names.

[0133] In one implementation provided by the present application, the step of performing translation processing on the place name data in the third place name data set based on a preset translation rule to obtain a candidate place name data set of the target language type includes:

[0134] S1041. For each place name data in the third place name data set, identify the language type to which the place name data belongs.

[0135] S1042: Determine a target translation rule according to the language type of the place name data and the target language type.

[0136] S1043, firstly performing place name syllable segmentation and transliteration translation on the proper name part of the place name data according to the target transliteration rule, and then performing common name translation translation on the common name part of the place name data.

[0137] S1044. Adjust the word order of the translated proper nouns and common nouns to obtain candidate place name data of the target language type, and form a candidate place name data set based on all the candidate place name data.

[0138] With respect to step S1041 , in this step, the language type of each place name is identified, wherein different transliteration and translation rules may be set for different language types.

[0139] Regarding step S1042, in this step, a corresponding translation rule is selected according to the language type (such as English) and the target language type (such as Chinese) of the identified place name.

[0140] Among them, the target translation rules can be determined based on the established translation rule library. The translation rule library is used to store multilingual transcription rule tables of various countries and transliteration rule tables of various languages ​​required in the translation process of overseas place names.

[0141] Regarding step S1043, in this step, the place name data is split into two parts: a proper name and a common name, and then processed separately.

[0142] Proper noun part: refers to the unique name part of a place name, usually referring to a specific geographical location. This part is usually translated phonetically, that is, the pronunciation of the original language is converted into the written form of the target language according to the pronunciation rules of the target language.

[0143] Common name part: refers to the common part of the place name, which usually describes geographical features, such as "river", "mountain", "city", etc. This part is generally translated directly, that is, its meaning is translated directly.

[0144] Regarding step S1044, in this step, after the translation is completed, the word order of the place name is adjusted to conform to the grammatical structure of the target language. For example, some languages ​​may say proper nouns first and then common nouns, while other languages ​​do the opposite.

[0145] After adjusting the word order, all processed place name data are combined into a candidate place name dataset.

[0146] For example, please refer to Figure 3 , Figure 3 A schematic diagram of the process of translating and writing place name data provided by this application, such as Figure 3 As shown, continuing with the above example, each element place name to be translated in the place name dataset Name3 (the third place name dataset) is extracted, and the language type (such as Russian, German, English, etc.) of the place name is determined based on the features such as text expression, etymology and spelling rules; secondly, the element place names are analyzed for components with reference to the translation rule library and other materials, and translated with reference to the corresponding translation guidelines, wherein the "proper name" part of the place name is first segmented into place name syllables and then transliterated, and the "common name" part of the place name is translated into common name; then, the word order of the translated "proper name" and "common name" is adjusted, for example, some foreign common names are placed in front, and the Chinese translation should adjust the "common name" to the back; finally, the translated place name translation result Name3 candidate place name dataset is output.

[0147] Regarding step S105, in this step, the spatial compliance processing includes checking the geometric position of the place name to ensure that it complies with the spatial specifications, such as whether it is located in the correct administrative area and whether there is a logical conflict with other spatial elements.

[0148] The edge integrity processing includes ensuring that the place name data at the boundaries of different regions are seamlessly connected to avoid data overlap or breakage problems.

[0149] In an implementation manner provided in the present application, the step of sequentially performing spatial compliance processing and edge integrity processing on the candidate place name dataset to obtain a target place name dataset includes:

[0150] S1051. For each candidate place name data in the candidate place name data set, the spatial topological relationship of the candidate place name data is verified according to the corresponding core vector element data, and anomalies are corrected and recorded for the verification results.

[0151] S1052: Obtain the edge reference data corresponding to the candidate place name dataset.

[0152] S1053. Based on the edge connection reference data and a preset edge connection processing strategy, the edge connection place name data in the candidate place name dataset that has been processed for spatial compliance are processed to obtain a target place name dataset.

[0153] For step S1051, each candidate place name data in the candidate place name data set is checked for spatial compliance, that is, the spatial topological relationship between these place names and their related core vector feature data is verified. This process ensures that the geographical location and other spatial features of the candidate place names are correct.

[0154] Among them, if the topological relationship is found to be abnormal (for example, the location of a place name is not within the legal administrative area, or conflicts with the surrounding geographical boundaries), it is necessary to correct it automatically or manually, and record these abnormal situations for subsequent processing and analysis.

[0155] With respect to step S1052, corresponding edge reference data is obtained according to the geographic boundaries in the candidate place name dataset.

[0156] Border reference data is generally used to describe how place name data is connected or matched with adjacent areas.

[0157] For step S1053, the principles followed by the preset edge connection processing strategy include: no duplication or omission of edge place name data, and consistency of the same elements.

[0158] In this way, after the edge connection process, what is finally obtained is a target place name dataset with no boundary conflicts and complete boundaries.

[0159] As an example, the process of place name collaborative processing (spatial compliance processing and edge integrity processing) is explained through the following content.

[0160] The translated Name3 place name data results are used as auxiliary reference data sets with basic geographic information 4D products, high-precision remote sensing images, map publications of various countries, etc. in the corresponding range to conduct spatial topology checks (spatial compliance processing) and edge verification (edge ​​integrity processing) to ensure the rationality of the location relationship of the data results and the consistency of the names.

[0161] The spatial topology check includes verifying the spatial topology relationship between the translated Name3 place name data and the core vector element DLG results. The DLG data anomalies found are recorded in the "DLGBJ" field, and the Name3 place name data anomalies found are recorded in the "DMBJ" field. The specific requirements are as follows:

[0162] 1) Within 1km, river place names with the same Chinese or foreign names and rivers in the DLG are considered to be the same river. Railways and major roads are treated the same as rivers.

[0163] 2) If the river place name point is no more than 200m away from the corresponding linear river, or is located within a surface river, the Chinese and foreign names should be unified.

[0164] 3) The place names of bays and straits should fall outside the coastline or on the surface water system.

[0165] 4) Lake place names should be located within the corresponding surface water system, and the Chinese names should be unified.

[0166] 5) The distance between the railway station place name and the railway is no more than 200m.

[0167] 6) The distance between road place names and corresponding roads should not exceed 100m, and the Chinese names should be unified.

[0168] 7) The distance between railway place names and the corresponding railways should not exceed 100m, and the Chinese names should be unified.

[0169] 8) The distance between railway branch lines, railway turnouts, railway sidings and other place names and the main railway should be no more than 200m, and the Chinese names should be unified.

[0170] 9) The port terminal location is no more than 200m away from the river, or no more than 1km away from the coastline on land.

[0171] 10) The waterway place name point is no more than 100m away from the linear river, or is located within the surface river, or is located outside the coastline.

[0172] 11) Reservoir place names should be located within the surface water system.

[0173] 12) Correct unreasonable water drop point locations.

[0174] 13) First-level administrative center place names and second-level administrative center place names should be located in the city’s prosperous areas or residential areas.

[0175] 14) Airport place names should be located within 100 meters of the airport, and those collected incorrectly should be deleted.

[0176] 15) The dam place names should be located within 50 meters of the dam, and those collected incorrectly should be deleted.

[0177] 16) Mining place names should be located within 200 meters of the mining area.

[0178] 17) Administrative place names at the third level and below should be located within 200 meters of the building complex.

[0179] Border verification is to check and process the data of the same geographic entity between the place name data sets of different production units. The principles followed are as follows: there is no duplication or omission in the border place name data, the same elements maintain consistency, and the uniqueness of cross-border national and political place names is guaranteed.

[0180] In this way, the present application proposes a method for matching multi-source place names by obtaining the national lowest-level administrative division boundaries as basic units, and associating and matching the multi-source place name elements within the lowest administrative unit within the task area one by one, thereby improving the matching efficiency of multi-source place names and reducing the error rate of multi-source place name matching.

[0181] It is proposed to adopt an improved edit distance algorithm (I_LevenshteinDistance) based on the lowest administrative unit. By constructing the place name common name synonym replacement rule base GSRI, the abbreviations, abbreviations, synonyms and other influencing factors of the place name are excluded in the algorithm matching judgment, and the place name similarity threshold is set to improve the accuracy of place name matching fusion.

[0182] A multilingual translation method based on the transliteration table is proposed, which mainly includes the construction of a translation rule library, language and component analysis, syllable segmentation, word order adjustment, etc., realizing independent multi-source and multi-lingual Chinese-foreign translation.

[0183] The collaborative processing and verification of place name data and other reference materials has been added, which has improved the rationality and consistency of the spatial position matching relationship between place name data results and other basic geographic information data products.

[0184] Based on the same inventive concept, a processing device corresponding to the processing method is also provided in the embodiment of the present application. Since the principle of solving the problem by the device in the embodiment of the present application is similar to the above-mentioned processing method in the embodiment of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0185] See also Figure 4 , Figure 4 The schematic diagram of the structure of a device for processing multi-source place name data provided by the embodiment of the present application is as follows. Figure 4 As shown in , the processing device 400 includes:

[0186] An acquisition module 410 is used to acquire an initial place name dataset, an incremental place name dataset, and an administrative division dataset within the mission area;

[0187] A preprocessing module 420, used for preprocessing the initial place name dataset and the incremental place name dataset to obtain a first place name dataset and a second place name dataset that meet the general format requirements;

[0188] A fusion module 430 is used to fuse the place name data belonging to the same basic matching fusion unit in the first place name data set and the second place name data set, taking the lowest administrative unit level as the basic matching fusion unit, to obtain a fused third place name data set; wherein the lowest administrative unit level is determined according to the administrative division data set;

[0189] A translation module 440, configured to perform translation processing on the place name data in the third place name data set based on a preset translation rule to obtain a candidate place name data set of a target language type;

[0190] The collaborative processing module 450 is used to perform spatial compliance processing and edge integrity processing on the candidate place name dataset in sequence to obtain a target place name dataset.

[0191] Optionally, the preprocessing includes at least one of the following processing methods: data format conversion, spatial reference system unification, structure normalization processing, data cleaning, place name classification mapping, field mapping, romanization transcription and attribute encoding.

[0192] Optionally, when the fusion module 430 is used to take the lowest administrative unit level as the basic matching fusion unit and to perform fusion processing on the place name data belonging to the same basic matching fusion unit in the first place name data set and the second place name data set, the fusion module 430 is used to:

[0193] For the place name data in the first place name data set and the place name data in the second place name data set under the same basic matching fusion unit, determining whether the category codes of the two place name data are consistent, and determining whether the foreign name attribute values ​​of the two place name data are consistent;

[0194] If the two are consistent, the two place name data are fused to form the place name data in the third place name data set.

[0195] Optionally, if the category codes of the two place name data are consistent but the foreign name attribute values ​​of the two place name data are inconsistent, when the fusion module 430 is used to fuse the place name data belonging to the same basic matching fusion unit in the first place name data set and the second place name data set, the fusion module 430 is used to:

[0196] According to the place name common name synonym comparison rule base, identify whether two place name data are synonymous variants;

[0197] If yes, the two place name data are merged;

[0198] If not, the edit distance algorithm is used to determine the place name similarity between the two place name data;

[0199] If the place name similarity between two place name data is greater than the similarity threshold, the two place name data will be fused.

[0200] Optionally, the calculation formula for the place name similarity is:

[0201] S=1-E / L

[0202] Among them, S is the similarity of place names, E is the Levenshtein edit distance between two place name data, and L is the maximum length of the foreign name string of the two place name data.

[0203] Optionally, when the translation module 440 is used to perform translation processing on the place name data in the third place name data set based on a preset translation rule to obtain a candidate place name data set of the target language type, the translation module 440 is used to:

[0204] For each place name data in the third place name data set, identifying the language type to which the place name data belongs;

[0205] Determine the target translation rule according to the language type of the place name data and the target language type;

[0206] According to the target transliteration rule, firstly perform place name syllable segmentation and transliteration translation on the proper name part of the place name data, and then perform common name translation translation on the common name part of the place name data;

[0207] The word order of the translated proper nouns and common nouns is adjusted to obtain candidate place name data of the target language type, and a candidate place name data set is formed based on all the candidate place name data.

[0208] Optionally, when the collaborative processing module 450 is used to sequentially perform spatial compliance processing and edge integrity processing on the candidate place name dataset to obtain the target place name dataset, the collaborative processing module 450 is used to:

[0209] For each candidate place name data in the candidate place name data set, the spatial topological relationship of the candidate place name data is verified according to the corresponding core vector element data, and the abnormality correction and abnormality record are performed on the verification result;

[0210] Obtaining the edge reference data corresponding to the candidate place name dataset;

[0211] According to the edge reference data and a preset edge processing strategy, the edge place name data in the candidate place name dataset after spatial compliance processing is processed to obtain a target place name dataset.

[0212] See also Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 5 As shown in , the electronic device 500 includes a processor 510 , a memory 520 and a bus 530 .

[0213] The memory 520 stores machine-readable instructions executable by the processor 510. When the electronic device 500 is running, the processor 510 communicates with the memory 520 via the bus 530. When the machine-readable instructions are executed by the processor 510, the above-mentioned Figure 1 as well as Figure 3 The specific implementation of the steps in the method embodiment shown can be found in the method embodiment, and will not be repeated here.

[0214] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the computer program can execute the above-mentioned Figure 1 as well as Figure 3 The specific implementation of the steps in the method embodiment shown can be found in the method embodiment, and will not be repeated here.

[0215] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0216] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0217] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0218] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0219] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application can essentially be embodied in the form of a software product, or in other words, the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0220] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for processing multi-source place name data, characterized in that: The processing method comprises: Obtain the initial place name dataset, incremental place name dataset, and administrative division dataset within the mission area; Preprocessing the initial place name dataset and the incremental place name dataset to obtain a first place name dataset and a second place name dataset that meet the general format requirements; Taking the lowest administrative unit level as the basic matching fusion unit, fusing the place name data belonging to the same basic matching fusion unit in the first place name data set and the second place name data set to obtain a fused third place name data set; wherein the lowest administrative unit level is determined according to the administrative division data set; Performing translation processing on the place name data in the third place name data set based on preset translation rules to obtain a candidate place name data set of the target language type; The candidate place name dataset is processed in turn for spatial compliance and edge integrity to obtain a target place name dataset.

2. The processing method according to claim 1, characterized in that: The preprocessing includes at least one of the following processing methods: data format conversion, spatial reference system unification, structure normalization processing, data cleaning, place name classification mapping, field mapping, romanization transcription and attribute encoding.

3. The processing method according to claim 1, characterized in that: The method of taking the lowest administrative unit level as the basic matching and fusion unit and fusing the place name data belonging to the same basic matching and fusion unit in the first place name data set and the second place name data set comprises: For the place name data in the first place name data set and the place name data in the second place name data set under the same basic matching fusion unit, determining whether the category codes of the two place name data are consistent, and determining whether the foreign name attribute values ​​of the two place name data are consistent; If the two are consistent, the two place name data are fused to form the place name data in the third place name data set.

4. The processing method according to claim 3, characterized in that: If the category codes of the two place name data are consistent, but the foreign name attribute values ​​of the two place name data are inconsistent, the fusion processing of the place name data belonging to the same basic matching fusion unit in the first place name data set and the second place name data set includes: According to the place name common name synonym comparison rule base, identify whether two place name data are synonymous variants; If yes, the two place name data are merged; If not, the edit distance algorithm is used to determine the place name similarity between the two place name data; If the place name similarity between two place name data is greater than the similarity threshold, the two place name data will be fused.

5. The processing method according to claim 4, characterized in that: The calculation formula of the place name similarity is: S=1-E / L Among them, S is the similarity of place names, E is the Levenshtein edit distance between two place name data, and L is the maximum length of the foreign name string of the two place name data.

6. The processing method according to claim 1, characterized in that: The step of performing translation processing on the place name data in the third place name data set based on the preset translation rule to obtain a candidate place name data set of the target language type includes: For each place name data in the third place name data set, identifying the language type to which the place name data belongs; Determine the target translation rule according to the language type of the place name data and the target language type; According to the target transliteration rule, firstly perform place name syllable segmentation and transliteration translation on the proper name part of the place name data, and then perform common name translation translation on the common name part of the place name data; The word order of the translated proper nouns and common nouns is adjusted to obtain candidate place name data of the target language type, and a candidate place name data set is formed based on all the candidate place name data.

7. The processing method according to claim 1, characterized in that: The step of sequentially performing spatial compliance processing and edge integrity processing on the candidate place name dataset to obtain a target place name dataset includes: For each candidate place name data in the candidate place name data set, the spatial topological relationship of the candidate place name data is verified according to the corresponding core vector element data, and the abnormality correction and abnormality record are performed on the verification result; Obtaining the edge reference data corresponding to the candidate place name dataset; According to the edge reference data and a preset edge processing strategy, the edge place name data in the candidate place name dataset after spatial compliance processing is processed to obtain a target place name dataset.

8. A device for processing multi-source place name data, characterized in that: The processing device comprises: An acquisition module is used to acquire the initial place name dataset, incremental place name dataset and administrative division dataset within the mission area; A preprocessing module, used for preprocessing the initial place name dataset and the incremental place name dataset to obtain a first place name dataset and a second place name dataset that meet the general format requirements; A fusion module, used to take the lowest administrative unit level as a basic matching fusion unit, and to fuse the place name data belonging to the same basic matching fusion unit in the first place name data set and the second place name data set to obtain a fused third place name data set; wherein the lowest administrative unit level is determined according to the administrative division data set; A translation module, used for performing translation processing on the place name data in the third place name data set based on preset translation rules to obtain a candidate place name data set of a target language type; The collaborative processing module is used to perform spatial compliance processing and edge integrity processing on the candidate place name dataset in sequence to obtain a target place name dataset.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to execute the steps of the processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the processing method according to any one of claims 1 to 7 are executed.

Images