Address data processing method, electronic device and computer program product

Abstract

The invention discloses an address data processing method, electronic equipment and a computer program product. The method comprises the steps that first address data and second address data are obtained, the first address data are determined based on address storage level information, and the second address data are used for representing graph quotient address data; performing data fusion on the first address data and the second address data to obtain a fusion result; comparing the fusion result with the first address data to obtain a comparison result; and in response to determining that the fusion result and the first address data correspond to the same address according to the comparison result, updating the first address data by using the fusion result. According to the invention, the technical problem that the standard address coverage is not comprehensive when an operator processes the address data in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of address data processing, and in particular to an address data processing method, electronic equipment and computer program product. Background Art

[0002] Operators need to select a precise site before launching a service to determine whether the resources at the launch location are sufficient. If resources are insufficient, resource expansion is required in advance. Currently, when selecting a site, since operators need to coordinate multiple systems to manage addresses, there are problems such as incomplete standard address data, missing levels, and non-standard data, which results in the inability to select an accurate standard address to view resources.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present invention provide an address data processing method, an electronic device and a computer program product to at least solve the technical problem in the related art that the standard address coverage is not comprehensive when the operator performs address data processing.

[0005] According to one embodiment of the present invention, there is provided an address data processing method, comprising: acquiring first address data and second address data, wherein the first address data is determined based on address storage level information, and the second address data is used to represent address data; performing data fusion on the first address data and the second address data to obtain a fusion result; comparing the fusion result with the first address data to obtain a comparison result; in response to determining that the fusion result and the first address data correspond to the same address based on the comparison result, updating the first address data using the fusion result.

[0006] Optionally, data fusion is performed on the first address data and the second address data to obtain a fusion result, including: performing word segmentation processing on the first address data to obtain a first word segmentation result; performing word segmentation processing on the second address data to obtain a second word segmentation result; determining a similarity data set based on the first word segmentation result and the second word segmentation result; and performing data fusion on the first address data and the second address data based on the similarity data set to obtain a fusion result.

[0007] Optionally, determining a similarity data set based on the first word segmentation result and the second word segmentation result includes: determining first character information based on the first word segmentation result; determining second character information based on the second word segmentation result; arranging the first character information according to a preset arrangement rule to obtain a first character set, and arranging the second character information to obtain a second character set; determining a similarity data set based on the first character set and the second character set.

[0008] Optionally, the first address data and the second address data are data-fused based on the similarity data set to obtain a fusion result, including: in response to the similarity data set being a non-empty set, filtering the similarity values ​​in the similarity data set to obtain a filtered similarity data set; sorting the similarity values ​​in the filtered similarity data set according to a preset sorting rule to obtain a sorted similarity data set; and adjusting the first address data based on the similarity values ​​in the sorted similarity data set that are higher than a preset similarity threshold to obtain a fusion result.

[0009] Optionally, the first address data and the second address data are fused based on the similarity data set to obtain a fusion result including: in response to the similarity data set being an empty set, obtaining first longitude and latitude information and second longitude and latitude information, wherein the first longitude and latitude information is used to represent the longitude and latitude information corresponding to the first address data, and the second longitude and latitude information is used to represent the longitude and latitude information corresponding to the second address data; filtering the second longitude and latitude information based on a preset longitude and latitude threshold to obtain a filtering result; and adjusting the first address data based on the filtering result to obtain a fusion result.

[0010] Optionally, the fusion result is compared with the first address data to obtain a comparison result including: obtaining a first building quantity, wherein the first building quantity is used to indicate the building quantity corresponding to the first address data; performing character splitting processing on the fusion result based on a preset splitting rule to obtain a splitting result; obtaining a second building quantity based on the splitting result, wherein the second building quantity is used to indicate the building quantity corresponding to the fusion result; and comparing the first building quantity with the second building quantity to obtain a comparison result.

[0011] Optionally, comparing the first building quantity with the second building quantity to obtain a comparison result includes: in response to the first building quantity being consistent with the second building quantity, determining that the comparison result is that the fusion result corresponds to the same address as the first address data.

[0012] According to one embodiment of the present invention, there is also provided an address data processing device, including: an acquisition module, used to acquire first address data and second address data, wherein the first address data is determined based on address storage level information, and the second address data is used to represent image business address data; a processing module, used to perform data fusion on the first address data and the second address data to obtain a fusion result; a comparison module, used to compare the fusion result with the first address data to obtain a comparison result; and a determination module, used to update the first address data using the fusion result in response to determining that the fusion result and the first address data correspond to the same address based on the comparison result.

[0013] Optionally, the processing module is also used to: perform word segmentation on the first address data to obtain a first word segmentation result; perform word segmentation on the second address data to obtain a second word segmentation result; the determination module is also used to determine a similarity data set based on the first word segmentation result and the second word segmentation result; the processing module is also used to perform data fusion on the first address data and the second address data based on the similarity data set to obtain a fusion result.

[0014] Optionally, the determination module is also used to: determine the first character information based on the first word segmentation result; determine the second character information based on the second word segmentation result; arrange the first character information according to a preset arrangement rule to obtain a first character set, and arrange the second character information to obtain a second character set; determine a similarity data set based on the first character set and the second character set.

[0015] Optionally, the processing module is also used to: in response to the similarity data set being a non-empty set, filter the similarity values ​​in the similarity data set to obtain a filtered similarity data set; sort the similarity values ​​in the filtered similarity data set according to a preset sorting rule to obtain a sorted similarity data set; and adjust the first address data based on the similarity values ​​in the sorted similarity data set that are higher than a preset similarity threshold to obtain a fusion result.

[0016] Optionally, the acquisition module is also used to obtain first longitude and latitude information and second longitude and latitude information in response to the similarity data set being an empty set, wherein the first longitude and latitude information is used to represent the longitude and latitude information corresponding to the first address data, and the second longitude and latitude information is used to represent the longitude and latitude information corresponding to the second address data; the processing module is also used to: filter the second longitude and latitude information based on a preset longitude and latitude threshold to obtain a filtering result; and adjust the first address data based on the filtering result to obtain a fusion result.

[0017] Optionally, the acquisition module is also used to acquire the first building quantity, wherein the first building quantity is used to indicate the building quantity corresponding to the first address data; the processing module is also used to perform character splitting processing on the fusion result based on a preset splitting rule to obtain a splitting result; the acquisition module is also used to acquire the second building quantity based on the splitting result, wherein the second building quantity is used to indicate the building quantity corresponding to the fusion result; the comparison module is also used to compare the first building quantity with the second building quantity to obtain a comparison result.

[0018] Optionally, the determination module is further used to determine, in response to the first building quantity being consistent with the second building quantity, that the comparison result is that the fusion result corresponds to the same address as the first address data.

[0019] According to one embodiment of the present invention, there is further provided an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the above-mentioned address data processing method is executed when the program is running.

[0020] According to one embodiment of the present invention, a computer-readable storage medium is provided, including: the computer-readable storage medium includes a stored executable program, wherein when the executable program runs, the device where the storage medium is located is controlled to execute the above-mentioned address data processing method.

[0021] According to one embodiment of the present invention, a computer program product is further provided, comprising: a computer program, and when the computer program is executed by a processor, the above-mentioned address data processing method is implemented.

[0022] In an embodiment of the present invention, a method of obtaining first address data and second address data is adopted to fuse the first address data and the second address data to obtain a fusion result, and the fusion result is compared with the first address data to obtain a comparison result. Finally, in response to determining that the fusion result and the first address data correspond to the same address based on the comparison result, the first address data is updated using the fusion result. By fusing the standard address with the third-party map data and using the fusion result to verify the accuracy of the standard address, the purpose of improving the quality and integrity of the address data is achieved, thereby achieving the technical effect of precise business site selection, and further solving the technical problem of incomplete standard address coverage when operators perform address data processing in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0024] Figure 1 is a hardware structure block diagram of an address data processing method according to one embodiment of the present invention;

[0025] Figure 2 is a flow chart of an address data processing method according to one embodiment of the present invention;

[0026] Figure 3 is a schematic diagram of another address data processing method according to one embodiment of the present invention;

[0027] Figure 4 is a schematic diagram of another address data processing method according to one embodiment of the present invention;

[0028] Figure 5is a schematic diagram of another address data processing method according to one embodiment of the present invention;

[0029] Figure 6 It is a structural block diagram of an address data processing device according to one embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] In related technologies, map provider address: user address obtained through the Internet or big data.

[0033] AOI (Area Of Internet): It indicates the name of institutions, enterprises, shops and other units, and is marked on the corresponding buildings. AOI is a type of map business address.

[0034] POI (Point Of Internet): Mainly used to express regional geographical entities in maps. POI is a type of map address, such as schools, markets, etc.

[0035] Standard address: the address maintained and used by the operator's internal system. Address storage levels: city (level 1), district and county (level 2), township / street (level 3), urban village / administrative village / road (level 4), street / lane / natural village (level 5), house number (level 6), building (level 7), unit (level 8), floor (level 9), household (level 10).

[0036] Site selection: The operator selects the address for the purpose of opening a service. The final address selected is based on the standard address.

[0037] Resource coverage: Deploy resources at addresses where services need to be opened. The resources deployed include equipment, optical cables, etc.

[0038] Address code: A map business address type that complies with the standard address level 10 specification, mainly used to associate standard addresses with AOI and POI data.

[0039] Address block: The address is divided into blocks by segmentation elements, and the segmented character string is called an address block.

[0040] Word segmentation: Split the text into character dimensions.

[0041] According to an embodiment of the present invention, a method embodiment of an address data processing method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0042] The address data processing method embodiment provided in the embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG. 1 is a hardware structure block diagram of an address data processing method according to one embodiment of the present invention. Figure 1 As shown, the computer terminal 10 (or electronic device 10) may include one or more (102a, 102b, ..., 102n are used to illustrate) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0043] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10 (or electronic device). As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0044] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the address data processing method in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, the above-mentioned address data processing method is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0045] The transmission module 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission module 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission module 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0046] The display may be, for example, a touch screen type liquid crystal display (LCD), which may enable a user to interact with a user interface of the computer terminal 10 (or electronic device).

[0047] It should be noted that, in some optional embodiments, the above Figure 1 The computer device (or electronic device) shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. It should be noted that Figure 1This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the above-described computer device (or electronic device).

[0048] Figure 2 is a flow chart of an address data processing method according to one embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0049] Step S20, obtaining first address data and second address data, wherein the first address data is determined based on the address storage level information, and the second address data is used to represent the address data of the business;

[0050] In step S20, the first address data is used to represent a standard address constructed based on the address storage level.

[0051] The second address data is used to represent the map provider address data, that is, the user address obtained through the Internet or big data.

[0052] Step S22, performing data fusion on the first address data and the second address data to obtain a fusion result;

[0053] In step S22, the above fusion result is used to represent the address data obtained by adjusting the first address data by using the second address data.

[0054] Step S24, comparing the fusion result with the first address data to obtain a comparison result;

[0055] Specifically, the number of buildings corresponding to the fusion result is compared with the number of buildings corresponding to the first address data to obtain a comparison result.

[0056] Step S26, in response to determining that the fused result and the first address data correspond to the same address according to the comparison result, the first address data is updated using the fused result.

[0057] Specifically, when it is determined based on the comparison result that the fusion result corresponds to the same address as the first address data, the address code, AOI, and POI of the second address data are stored in the alias library of the first address data. When the business order is placed and the site is selected, the first address data, that is, the standard address, can be selected through a multi-dimensional text description.

[0058] Furthermore, based on the addresses of the unintegrated maps, the addresses of the unintegrated communities, factories, etc. are identified, and these addresses are classified and organized into reports and sent to the business department for business opportunity expansion.

[0059] Based on the above steps S20 to S26, the first address data and the second address data are obtained in a manner of fusion, and the fusion result is obtained, and the fusion result is compared with the first address data to obtain a comparison result. Finally, in response to determining that the fusion result and the first address data correspond to the same address based on the comparison result, the first address data is updated using the fusion result. By fusing the standard address with the third-party map vendor data and using the fusion result to verify the accuracy of the standard address, the purpose of improving the quality and integrity of the address data is achieved, thereby achieving the technical effect of precise business site selection, and further solving the technical problem of incomplete standard address coverage when operators perform address data processing in related technologies.

[0060] Optionally, in step S22, data fusion is performed on the first address data and the second address data to obtain a fusion result including:

[0061] Step S221, performing word segmentation processing on the first address data to obtain a first word segmentation result;

[0062] Step S222, performing word segmentation processing on the second address data to obtain a second word segmentation result;

[0063] Specifically, the second address data is split by elements, and the split characters are used as the base for searching the vocabulary, that is, the address block. All address blocks are established at each level of the address tree.

[0064] Furthermore, it can be seen from the data specifications of the standard address and the map business address that the granularity of the address names between the two is different. Therefore, the standard address and the map business address are described in separate address tree structures.

[0065] For example, Figure 3 is a schematic diagram of another address data processing method according to one embodiment of the present invention, such as Figure 3As shown, it is the hierarchical structure of the address tree, which mainly includes cities, districts and counties, streets, streets, lanes, communities, buildings, units, floors, and households. Taking Guangzhou as an example, the root node of the address tree is Guangzhou. The first-level leaf nodes represent districts. For example, Guangzhou includes Yuexiu District, Tianhe District, etc., and other streets included in Guangzhou are no longer described one by one. The second-level leaf nodes represent streets. For example, Yuexiu District includes Liuhua Street, Hongqiao Street, Liurong Street, etc., and the streets included in other districts are no longer described one by one. The third level is the street and lane nodes. For example, Hongqiao Street includes Dongfeng Road, Huanshi Road, Xiaobei Road, etc., and the streets and lanes included in other streets are no longer described one by one. The fourth level is the leaf nodes are community nodes. For example, Huanshi Road includes Community 1, Community 2, Community 3, etc., and the communities included in other streets and lanes are no longer described one by one. The fifth level is the leaf nodes are building nodes. For example, Community 3 includes Building 1, Building 2, Building 3, etc., and the buildings included in other communities are no longer described one by one. The sixth level is the leaf nodes are unit nodes. For example, building 3 includes unit 1 and unit 2, and the units included in other buildings are not described one by one. The seventh layer is a leaf node, which is a floor node. For example, unit 1 includes floor 1, floor 2, floor 3, etc., and the floors included in other units are not described one by one. The eighth layer is a leaf node, which is a household node. For example, floor 3 includes household 1 and household 2, etc., and the households included in other floors are not described one by one.

[0066] Exemplarily, the map business address is split by elements as the basis for the vocabulary to search. All address blocks will be established at each level of the address block. First, the parent level (city) of the address tree is established into N address blocks and stored as hash (key), and then the first-level sub-address (district and county) under each city is established into N1 address blocks and stored as hash (content), so there are N1 hashes (key, content). For example, in the map business address, it is "No. 1-1, 1st Floor, Unit 1, Yiside Apartment, No. 18, Huanshi Road, Yuexiu District, Guangzhou", and the vocabulary is dict[] = {"Guangzhou City", "Yuexiu District", "Huanshi Road", "No. 18", "Yside Apartment"}. The second word segmentation is stored in the form of a hash table as shown in Table 1.

[0067] Table 1 Hash table form of the second word segmentation result

[0068] Key Content Guangzhou Yuexiu District Yuexiu District Hongqiao Subdistrict Hongqiao Subdistrict Huanshi Road Huanshi Road Huanshi Road Eside Apartments No.18

[0069] Specifically, in Table 1, the Key is "Guangzhou City", and its corresponding Content is "Yuexiu District"; the Key is "Yuexiu District", and its corresponding Content is "Hongqiao Street"; the Key is "Hongqiao Street", and its corresponding Content is "Huanshi Road"; the Key is "Huanshi Road", and its corresponding Content is "Huanshi Road"; the Key is "Iside Apartment", and its corresponding Content is "No. 18".

[0070] Furthermore, first, the parent level (city) of the address number is established into N address blocks and stored as hash (key), and then the first-level sub-address (county) under each city is established into N1 address blocks and stored as hash (content), so there are N1 hashes (key, content). The word segmentation characters are searched in the address blocks of the hash table. For example, "Guang" is included in hash (key) 1, so the word segmentation search for "zhou" is then performed downward, and it can be continuously searched in hash [1] (key), and so on, until the hash [1] (key) search is completed, and then all the content under the "Guangzhou" key is searched for word segmentation, and the search method is the same as above. Take the retrieved hash[1](content) as hash[2](key), and all first-level child nodes, second-level child nodes, and third-level child nodes under hash2(key) as hash2(content) to create hash(2), and so on, creating hash[n](key, content) until the retrieval is completed.

[0071] Step S223, determining a similarity data set based on the first word segmentation result and the second word segmentation result;

[0072] Specifically, the full name corresponding to the first address is obtained according to the first word segmentation result, and the abbreviation corresponding to the second address is obtained according to the second word segmentation result. And the similarity data set of the full name and the abbreviation is determined according to the first word segmentation result and the second word segmentation result.

[0073] Step S224: performing data fusion on the first address data and the second address data based on the similarity data set to obtain a fusion result.

[0074] Specifically, when the similarity dataset is a non-empty set, the full name and abbreviation of the address with a similarity greater than a preset similarity threshold are taken to fuse the address data of the commercial address and the standard address to obtain a fusion result. When the similarity dataset is an empty set, the address data is fused based on the longitude and latitude positions to obtain a fusion result.

[0075] Based on the above steps S221 to S224, the first address data is segmented to obtain a first segmentation result; the second address data is segmented to obtain a second segmentation result; a similarity data set is determined based on the first segmentation result and the second segmentation result; the first address data and the second address data are fused based on the similarity data set to obtain a fusion result, and hash table technology is used to store and retrieve address blocks. Compared with traditional depth traversal or breadth traversal, matching time can be reduced and the efficiency of the overall process can be improved. Moreover, by calculating the similarity, even if the map business address uses an abbreviation, it can be accurately matched with the full name of the standard address, thereby improving the coverage of data fusion.

[0076] Optionally, in step S223, determining the similarity dataset based on the first word segmentation result and the second word segmentation result includes:

[0077] Step S2231, determining first character information according to the first word segmentation result;

[0078] Step S2232, determining second character information according to the second word segmentation result;

[0079] Specifically, the full name corresponding to the first address is obtained according to the first word segmentation result, and the abbreviation corresponding to the second address is obtained according to the second word segmentation result.

[0080] Step S2233, arranging the first character information according to a preset arrangement rule to obtain a first character set, and arranging the second character information to obtain a second character set;

[0081] Step S2234: determine a similarity data set based on the first character set and the second character set.

[0082] Specifically, the characters of the full name in the first word segmentation result and the abbreviation in the second word segmentation result are arranged as discretized characters. The full name corresponding to the first address and the abbreviation corresponding to the second address are arranged and combined to generate A (full name) = {a1, a2, a3, a4, ..., ai} (i = m! / (m-0)! + m! / (m-1)! + ... + m! / (mk)! (accumulated until k = m-1), m = the length of charA), B (abbreviation) = {b1, b2, b3, b4, ..., bj} (j = n! / (n-0)! + n! / (n-1)! + ... + n! / (nk)! (accumulated until k = n-1, n = the length of charB). Taking the abbreviation string as the target set, the similarity between A and B is S (A, b) = p / j*100% (p is the number of matching concatenated strings of A and B).

[0083] Exemplarily, the standard address is "Guangzhou University, No. 230, Outer Ring West Road, University Town, Panyu District, Guangzhou City, Guangdong Province". The address hierarchy is shown in Table 2. Then, according to the community address hierarchy, the address name of "Guangzhou University" is extracted and put into the full address library. For the map merchant address "Guangda, Panyu District, Guangzhou City, Guangdong Province", the abbreviated address of "Guangda" is extracted according to the hierarchy of the map merchant address and put into the abbreviated address library.

[0084] Table 2 Address Hierarchy Table

[0085] Tiers Address Name City Guangzhou Districts and counties Panyu District street university town streets and alleys Outer Ring Road West Community Guangzhou University House number No. 230

[0086] The full address is "Guangzhou University", and 4! / 0! + 4! / 1! + 4! / 2! + 4! / 3! = 24 / 1 + 24 / 1 + 24 / 2 + 24 / 6 = 24 + 24 + 12 + 4 = 64 kinds of strings can be split out, as shown in Table 3. For the map merchant address "Guangda", 2! / 0! + 2! / 1! = 2 + 2 = 4 kinds of strings can be split out, as shown in Table 4. According to the table, the number of identical characters p = 4, so the similarity S(A, B) = 4 / 4 * 100% = 100%.

[0087] Based on the above steps S2231 to S2234, determine the first character information according to the first word segmentation result; determine the second character information according to the second word segmentation result; arrange the first character information according to the preset arrangement rule to obtain the first character set, and arrange the second character information to obtain the second character set; determine the similarity data set based on the first character set and the second character set. By using the permutation and combination of characters and similarity calculation, it is possible to identify and correct spelling mistakes or different expressions of addresses, thereby improving the accuracy of address data fusion.

[0088] Table 3 First Character Set Table of "Guangzhou University"

[0089] Serial number Word Splitting Serial number Word Splitting Serial number Word Splitting 1 Guangzhou University 23 Xueda Guangzhou 45 Xuezhou Guang 2 Guangzhou University 24 Xuedazhouguang 46 University of California 3 Guangda State University 25 Guangzhou University 47 Xue Daguang 4 Guangdazhou 26 Guangzhou Studies 48 Learn from Dazhou 5 Guangxue State University 27 Guangda State 49 Guangzhou 6 Learn from the Great Continent 28 Guangda University 50 Vast 7 Zhouguang University 29 Guangxuezhou 51 Broad Learning 8 Guangzhou University 30 Guangxue University 52 Zhou Guang 9 State University Guangxue 31 State of Guangdong 53 State University 10 State University 32 Zhou Guangxue 54 State school 11 State University 33 State University 55 Da Guang 12 State University 34 state university 56 Big State 13 Guangzhou University 35 State University 57 University 14 Daguang Xuezhou 36 State University 58 Xue Guang 15 Dazhou Guangxue 37 Greater Guangzhou 59 Xuezhou 16 Dazhou Xueguang 38 Da Guang Xue 60 Xueda 17 University Guangzhou 39 Dazhouguang 61 wide 18 University State Broadcasting 40 Great State Studies 62 state 19 Guangzhou University 41 University Square 63 big 20 Learn from Guangzhou 42 university state 64 study 21 Xuezhou Guangda 43 Study in Guangzhou 22 Xuezhou Daguang 44 Learn from the vast

[0090] Table 4 Second Character Set Table of "Guangda"

[0091] Serial number Word Splitting 1 Vast 2 Da Guang 3 wide 4 big

[0092] Optionally, in step S224, data fusion is performed on the first address data and the second address data based on the similarity data set, and the obtained fusion result includes:

[0093] Step S2241, in response to the similarity data set being a non-empty set, screen the similarity values in the similarity data set to obtain the screened similarity data set;

[0094] Step S2242, sorting the similarity values ​​in the filtered similarity data set according to a preset sorting rule to obtain a sorted similarity data set;

[0095] Step S2243: Based on the similarity values ​​in the sorted similarity data set that are higher than a preset similarity threshold, the first address data is adjusted to obtain a fusion result.

[0096] Specifically, when the similarity data set is a non-empty set, first, the similarity values ​​in the similarity data set are screened, and the data with a similarity value of 0 is screened out to obtain a screened similarity data set. Then, the similarity values ​​in the screened similarity data set are sorted from large to small to obtain a sorted similarity data set. Finally, based on the sorted similarity data set, similarity values ​​higher than a preset similarity threshold are screened out, and the first address data is supplemented or corrected using the second address data corresponding to the similarity value to obtain a fusion result.

[0097] Furthermore, the standard address data is split and verified according to the address storage level. If the middle level or the last level is missing, the first address data is completed and corrected by splitting the second address data corresponding to the similarity value higher than the preset similarity threshold.

[0098] Based on the above steps S2241 to S2243, in response to the similarity data set being a non-empty set, the similarity values ​​in the similarity data set are filtered to obtain a filtered similarity data set; the similarity values ​​in the filtered similarity data set are sorted according to a preset sorting rule to obtain a sorted similarity data set; based on the similarity values ​​in the sorted similarity data set that are higher than a preset similarity threshold, the first address data is adjusted to obtain a fusion result, and by using data higher than the preset similarity threshold to adjust the standard address data, the missing hierarchical information in the standard address can be automatically identified and completed, and the existing erroneous or non-standard address descriptions can be corrected, thereby enhancing the integrity and standardization of the standard address database and improving the overall data quality.

[0099] Optionally, in step S224, data fusion is performed on the first address data and the second address data based on the similarity data set, and the fusion result obtained includes:

[0100] Step S2244, in response to the similarity data set being an empty set, obtaining first longitude and latitude information and second longitude and latitude information, wherein the first longitude and latitude information is used to represent the longitude and latitude information corresponding to the first address data, and the second longitude and latitude information is used to represent the longitude and latitude information corresponding to the second address data;

[0101] Step S2245, filtering the second longitude and latitude information based on the preset longitude and latitude thresholds to obtain a filtering result; adjusting the first address data based on the filtering result to obtain a fusion result.

[0102] Specifically, when the similarity data set is an empty set, first, obtain the longitude and latitude information corresponding to the first address data and the longitude and latitude information corresponding to the second address data; then, filter the second longitude and latitude information based on the preset longitude and latitude threshold to obtain the filter result; finally, adjust the first address data based on the filter result to obtain the fusion result. For example, at the level above the community, the matching distance is 200 meters, and at the building level, the matching distance is 30 meters.

[0103] Based on the above steps S2244 to S2245, in response to the similarity data set being an empty set, first longitude and latitude information and second longitude and latitude information are obtained, wherein the first longitude and latitude information is used to represent the longitude and latitude information corresponding to the first address data, and the second longitude and latitude information is used to represent the longitude and latitude information corresponding to the second address data; the second longitude and latitude information is filtered based on a preset longitude and latitude threshold to obtain a filtering result; based on the filtering result, the first address data is adjusted to obtain a fusion result. When the correspondence between the address data cannot be determined by text matching, geographic coordinates based on longitude and latitude are used for fusion, which provides a supplementary matching method. The location-based matching mechanism can handle addresses that have differences in text but are geographically close, ensuring that even in the case of inconsistent text descriptions, the most matching address can be found through geographic coordinates, thereby improving the comprehensiveness and reliability of data fusion.

[0104] Optionally, in step S24, the fusion result is compared with the first address data, and the comparison result obtained includes:

[0105] Step S241, obtaining a first building quantity, wherein the first building quantity is used to indicate the building quantity corresponding to the first address data;

[0106] Step S242, performing character splitting processing on the fusion result based on a preset splitting rule to obtain a splitting result;

[0107] Step S243, obtaining the second building quantity based on the split result, wherein the second building quantity is used to indicate the building quantity corresponding to the fusion result;

[0108] Step S244, comparing the first building quantity with the second building quantity to obtain a comparison result.

[0109] Specifically, the number of buildings corresponding to the first address is obtained, and the fusion result is hierarchically decomposed to obtain the number of buildings corresponding to the fusion result. The number of buildings corresponding to the first address is compared with the number of buildings corresponding to the fusion result to obtain a comparison result, wherein the comparison result is used to indicate whether the first address data and the fusion result are the same address.

[0110] Based on the above steps S241 to S244, the number of the first building is obtained; the fusion result is subjected to character splitting processing based on the preset splitting rules to obtain the splitting result; the number of the second building is obtained based on the splitting result; the number of the first building is compared with the number of the second building to obtain a comparison result. By comparing the number of buildings, it can be verified whether the fusion result truly reflects the actual situation of the standard address, thereby enhancing the reliability of the fusion data.

[0111] Optionally, in step S244, the first building quantity is compared with the second building quantity, and the comparison result obtained includes:

[0112] Step S2441, in response to the first building quantity being consistent with the second building quantity, determining that the comparison result is that the fusion result and the first address data correspond to the same address.

[0113] Specifically, when the number of buildings corresponding to the first address and the number of buildings corresponding to the fusion result are the same, it is determined that the comparison result is that the first address data and the fusion result are the same address.

[0114] Based on the above step S244, in response to the fact that the number of the first building is consistent with the number of the second building, it is determined that the comparison result is that the fusion result corresponds to the same address as the first address data, and the similarity data is sorted, with the data with the highest similarity placed in front, so that the most matching records can be given priority when merging the data, thereby improving the efficiency of the fusion process. In addition, by using data above the preset similarity threshold to adjust the standard address data, it is possible to automatically identify and complete the missing hierarchical information in the standard address, and at the same time correct any errors or non-standard address descriptions that may exist, thereby enhancing the integrity and standardization of the standard address data.

[0115] Figure 4 is a schematic diagram of another address data processing method according to one embodiment of the present invention, such as Figure 4 As shown, the application scenarios of the address data processing method include assisted site selection and business opportunity mining, wherein assisted site selection includes but is not limited to: assisted site selection in the operator's customer relationship management system (Customer Relationship Management), assisted site selection in the business opportunity system, etc., and business opportunity mining includes but is not limited to: business opportunity mining of operators.

[0116] In the resource center, data fusion is used to match standard addresses and map vendor addresses, where the matching results include: exact match, semi-exact match, and rough match. Exact matches include: consistent name and same location; consistent name and different location; fuzzy name match and same location. Semi-exact matches include: fuzzy name match and different location. Rough matches include: matching the unmatched address data with the nearest location under the previous level address. Manually mark and confirm the standard address and map vendor address, instantiate and manage the confirmed consistent address data, mark and process the data with mapping errors, mark and process the questionable data, and export the questionable address data.

[0117] The data sharing platform is responsible for data reception and data transmission, and the Geographic Information System (GIS) platform is responsible for data sharing and rectification among map vendors.

[0118] Furthermore, the following rules are used to verify the standard address.

[0119] 1) Audit and verify the levels of communities, natural villages, courtyards, and buildings:

[0120] A) For communities, natural villages, and courtyards that are not associated with standard addresses, if it is confirmed that this data is not managed in the standard address, the standard address will be supplemented according to the corresponding level.

[0121] B) For communities, natural villages and courtyards that have been associated with standard addresses, the system automatically checks whether the standard addresses have any missing address levels. If there are any missing levels, the system automatically splits the address and then automatically updates the split data to the standard address to standardize the standard address.

[0122] C) The system automatically checks the number of subordinate addresses in the standard address and map provider address area. If the number is inconsistent, an inconsistent report will be output.

[0123] The following rules are used to audit and verify the unit, floor, and household levels:

[0124] A) For units, floors, and households that are not associated with standard addresses, if it is confirmed that this data is not managed in the standard address, the standard address shall be supplemented according to the level corresponding to the map provider address.

[0125] B) For units, floors and households that have been associated with standard addresses, the system automatically checks whether there are any missing levels in the standard addresses. If a level is missing, the system automatically splits the address and then automatically updates the split data to the standard address to standardize the standard address.

[0126] Illustratively, the matching rules are shown in Table 5.

[0127] Table 5 Matching rules for address levels above the community

[0128]

[0129] Figure 5 is a schematic diagram of another address data processing method according to one embodiment of the present invention, such as Figure 5 As shown, based on the addresses of the unintegrated maps, the addresses of the unintegrated communities, factories, etc. are automatically identified, and such addresses are classified and organized to form a report that is sent to the business department for business opportunity expansion. Figure 5 This is the process of standard address normative audit. For the GIS platform, address codes, AOIs, and POIs are obtained according to the initial data of the map provider, so as to conduct normative audit on the standard address. The address codes of both data are preprocessed in layers and formatted, and the map provider address is compared with the standardized address. If the comparison result indicates that the standard address meets the norm, the address codes of both data are preprocessed in the first 5 levels, the 8th level, and the 9th level. The map provider address is compared with the standardized address according to the preprocessing level. If the comparison result shows that the standard address does not meet the norm, the questionable data in the map provider data is classified as image error correction data. If the comparison result is correct, the standard data is sent to a higher-level processing platform. In the higher-level processing platform, first, the standard address code is layered, and then the data is confirmed twice to confirm the terminal address above level 9. Then, the data management, incomplete address, upper and lower address management, etc. in the processing platform are associated with the address code, POI, and AOI. Finally, the final data confirmation is performed manually.

[0130] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present invention.

[0131] In the embodiment of the present invention, an address data processing device is also provided, which is used to implement the above-mentioned embodiment and preferred implementation mode, and the descriptions have been made no further. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware is also possible and conceivable.

[0132] Figure 6 is a structural block diagram of an address data processing device according to one embodiment of the present invention. Figure 6 As shown, the device comprises:

[0133] An acquisition module 601 is used to acquire first address data and second address data, wherein the first address data is determined based on address storage level information, and the second address data is used to represent the address data of the image business;

[0134] The processing module 602 is used to perform data fusion on the first address data and the second address data to obtain a fusion result;

[0135] A comparison module 603 is used to compare the fusion result with the first address data to obtain a comparison result;

[0136] The determination module 604 is configured to update the first address data using the fusion result in response to determining that the fusion result and the first address data correspond to the same address according to the comparison result.

[0137] Optionally, the processing module 602 is also used to: perform word segmentation processing on the first address data to obtain a first word segmentation result; perform word segmentation processing on the second address data to obtain a second word segmentation result; the determination module 604 is also used to determine a similarity data set based on the first word segmentation result and the second word segmentation result; the processing module 602 is also used to perform data fusion on the first address data and the second address data based on the similarity data set to obtain a fusion result.

[0138] Optionally, the determination module 604 is also used to: determine the first character information based on the first word segmentation result; determine the second character information based on the second word segmentation result; arrange the first character information according to a preset arrangement rule to obtain a first character set, and arrange the second character information to obtain a second character set; determine a similarity data set based on the first character set and the second character set.

[0139] Optionally, the processing module 602 is also used to: in response to the similarity data set being a non-empty set, filter the similarity values ​​in the similarity data set to obtain a filtered similarity data set; sort the similarity values ​​in the filtered similarity data set according to a preset sorting rule to obtain a sorted similarity data set; and adjust the first address data based on the similarity values ​​in the sorted similarity data set that are higher than a preset similarity threshold to obtain a fusion result.

[0140] Optionally, the acquisition module 601 is also used to obtain first longitude and latitude information and second longitude and latitude information in response to the similarity data set being an empty set, wherein the first longitude and latitude information is used to represent the longitude and latitude information corresponding to the first address data, and the second longitude and longitude information is used to represent the longitude and latitude information corresponding to the second address data; the processing module 602 is also used to: filter the second longitude and longitude information based on a preset longitude and latitude threshold to obtain a filtering result; and adjust the first address data based on the filtering result to obtain a fusion result.

[0141] Optionally, the acquisition module 601 is also used to obtain the first building quantity, wherein the first building quantity is used to indicate the building quantity corresponding to the first address data; the processing module 602 is also used to perform character splitting processing on the fusion result based on a preset splitting rule to obtain a splitting result; the acquisition module 601 is also used to obtain the second building quantity based on the splitting result, wherein the second building quantity is used to indicate the building quantity corresponding to the fusion result; the comparison module 603 is also used to compare the first building quantity with the second building quantity to obtain a comparison result.

[0142] Optionally, the determination module 604 is further configured to determine, in response to the first building quantity being consistent with the second building quantity, that the comparison result is that the fusion result corresponds to the same address as the first address data.

[0143] According to one embodiment of the present invention, there is further provided an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the above-mentioned address data processing method is executed when the program is running.

[0144] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0145] Step S1, obtaining first address data and second address data, wherein the first address data is determined based on address storage level information, and the second address data is used to represent address data;

[0146] Step S2, performing data fusion on the first address data and the second address data to obtain a fusion result;

[0147] Step S3, comparing the fusion result with the first address data to obtain a comparison result;

[0148] Step S4, in response to determining that the fused result and the first address data correspond to the same address according to the comparison result, the first address data is updated using the fused result.

[0149] According to one embodiment of the present invention, a computer-readable storage medium is provided, including: the computer-readable storage medium includes a stored executable program, wherein when the executable program runs, the device where the storage medium is located is controlled to execute the above-mentioned address data processing method.

[0150] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0151] Step S1, obtaining first address data and second address data, wherein the first address data is determined based on address storage level information, and the second address data is used to represent address data;

[0152] Step S2, performing data fusion on the first address data and the second address data to obtain a fusion result;

[0153] Step S3, comparing the fusion result with the first address data to obtain a comparison result;

[0154] Step S4, in response to determining that the fused result and the first address data correspond to the same address according to the comparison result, the first address data is updated using the fused result.

[0155] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0156] According to one embodiment of the present invention, a computer program product is further provided, comprising: a computer program, and when the computer program is executed by a processor, the above-mentioned address data processing method is implemented.

[0157] Optionally, in this embodiment, the computer program product may be configured to store computer program instructions for performing the following steps:

[0158] Step S1, obtaining first address data and second address data, wherein the first address data is determined based on address storage level information, and the second address data is used to represent address data;

[0159] Step S2, performing data fusion on the first address data and the second address data to obtain a fusion result;

[0160] Step S3, comparing the fusion result with the first address data to obtain a comparison result;

[0161] Step S4, in response to determining that the fused result and the first address data correspond to the same address according to the comparison result, the first address data is updated using the fused result.

[0162] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0163] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0164] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0165] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be 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 interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0166] 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 units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0167] In addition, each functional unit in each embodiment of the present invention 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. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0168] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of 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 invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0169] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for processing address data, characterized in that: include: Acquire first address data and second address data, wherein the first address data is determined based on address storage level information, and the second address data is used to represent the address data of the business; Performing data fusion on the first address data and the second address data to obtain a fusion result; Comparing the fusion result with the first address data to obtain a comparison result; In response to determining, based on the comparison result, that the fused result and the first address data correspond to the same address, the first address data is updated using the fused result.

2. The address data processing method according to claim 1, characterized in that: The first address data and the second address data are subjected to data fusion to obtain a fusion result, including: Perform word segmentation processing on the first address data to obtain a first word segmentation result; Perform word segmentation processing on the second address data to obtain a second word segmentation result; Determine a similarity dataset based on the first word segmentation result and the second word segmentation result; The first address data and the second address data are fused based on the similarity data set to obtain the fusion result.

3. The address data processing method according to claim 2, characterized in that: Determining a similarity dataset based on the first word segmentation result and the second word segmentation result includes: Determine first character information according to the first word segmentation result; Determine second character information according to the second word segmentation result; Arranging the first character information according to a preset arrangement rule to obtain a first character set, and arranging the second character information to obtain a second character set; The similarity dataset is determined based on the first set of characters and the second set of characters.

4. The address data processing method according to claim 2, characterized in that: Performing data fusion on the first address data and the second address data based on the similarity data set to obtain the fusion result includes: In response to the similarity data set being a non-empty set, filtering similarity values ​​in the similarity data set to obtain a filtered similarity data set; Sorting the similarity values ​​in the filtered similarity data set according to a preset sorting rule to obtain a sorted similarity data set; Based on similarity values ​​in the sorted similarity data sets that are higher than a preset similarity threshold, the first address data is adjusted to obtain the fusion result.

5. The address data processing method according to claim 2, characterized in that: Performing data fusion on the first address data and the second address data based on the similarity data set to obtain the fusion result includes: In response to the similarity data set being an empty set, acquiring first longitude and latitude information and second longitude and latitude information, wherein the first longitude and latitude information is used to represent the longitude and latitude information corresponding to the first address data, and the second longitude and latitude information is used to represent the longitude and latitude information corresponding to the second address data; Filter the second longitude and latitude information based on a preset longitude and latitude threshold to obtain a filtering result; The first address data is adjusted based on the screening result to obtain the fusion result.

6. The address data processing method according to claim 1, characterized in that: The fusion result is compared with the first address data to obtain a comparison result including: Obtaining a first building quantity, wherein the first building quantity is used to indicate the building quantity corresponding to the first address data; Performing character splitting processing on the fusion result based on a preset splitting rule to obtain a splitting result; Acquire a second building quantity based on the split result, wherein the second building quantity is used to represent the building quantity corresponding to the fusion result; The first building quantity is compared with the second building quantity to obtain the comparison result.

7. The address data processing method according to claim 6, characterized in that: Comparing the first building quantity with the second building quantity to obtain the comparison result includes: In response to the first building quantity being consistent with the second building quantity, the comparison result is determined to be that the fusion result and the first address data correspond to the same address.

8. An electronic device, characterized in that: include: A memory storing an executable program; A processor, configured to run the program, wherein the program, when running, executes the address data processing method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is run, the device where the storage medium is located is controlled to execute the address data processing method according to any one of claims 1 to 7.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the address data processing method according to any one of claims 1 to 7 when being executed by a processor.

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