CN110795472A - Address standardization method, system, equipment and medium based on fuzzy matching - Google Patents
Address standardization method, system, equipment and medium based on fuzzy matching Download PDFInfo
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Abstract
The invention provides an address standardization method, system, equipment and medium based on fuzzy matching, wherein the method comprises the following steps: receiving a user positioning request and acquiring address parameters from the user positioning request; calling a Goods API and a local standardized ES library to obtain a preset number of address nodes with higher similarity to the address parameters; calculating the distance deviation value of the longitude and latitude corresponding to each address node and the address parameter; and selecting the address node corresponding to the minimum value in the distance deviation values as a real address and returning the real address to the user. By using the implementation of the invention, address standardization is realized in delivery internet services such as take-out, city-sharing service, express delivery and the like, the order processing efficiency of enterprises is improved, the operation cost of the enterprises is reduced, and the core competitiveness of the enterprises is enhanced.
Description
Technical Field
The invention relates to the technical field of intention label screening, in particular to an address standardization method, system, equipment and medium based on fuzzy matching.
Background
With the rise of platforms such as e-commerce, take-out and home-visit life service, the lack of addresses, address errors and the like, the problem that how to correct and standardize addresses is one of the problems that enterprises need to solve urgently is endless. In general, there are several problems with enterprise address standardization:
1) the address data is dispersed, and the manual correlation analysis efficiency is low and the effect is poor.
2) The data island problem exists inside and outside the enterprise.
3) The problems of address missing, address abnormity and the like are difficult to identify and analyze.
Disclosure of Invention
In order to solve the technical problem, the invention provides an address standardization method, system, equipment and medium based on fuzzy matching.
The invention provides an address standardization method based on fuzzy matching, which comprises the following steps:
receiving a user positioning request and acquiring address parameters from the user positioning request;
calling a Goods API and a local standardized ES library to obtain a preset number of address nodes with higher similarity to the address parameters;
calculating the distance deviation value of the longitude and latitude corresponding to each address node and the address parameter;
and selecting the address node corresponding to the minimum value in the distance deviation values as a real address and returning the real address to the user.
In one embodiment, the calling the high API and the local standardized ES library to obtain a preset number of address nodes with higher similarity to the address parameter includes:
calling a Goods open platform address code API to acquire an administrative division code, longitude and latitude and an address grade to which the address parameter belongs;
judging whether the corresponding field of the address grade is province, city, district or unknown;
and if the address level corresponding field is not any one of province, city, county and unknown, searching a preset number of address nodes which are matched with the address parameters with high degree in the ES address library by using the administrative division code.
In one embodiment, the calling the high API and the local standardized ES library to obtain the preset number of address nodes with higher similarity to the address parameter further includes obtaining the preset number of address nodes with higher similarity to the address parameter by using a chinese part-of-speech method, and specifically includes:
analyzing the address parameters to obtain an administrative division corresponding to the address parameters, wherein the administrative division comprises at least one of province, direct administration city, district and detailed address parts;
searching corresponding address nodes in the provincial and municipal districts according to the administrative districts, matching detailed addresses with data in an ES address library through Chinese word segmentation, and calculating text similarity;
and selecting a preset number of address nodes with higher text similarity.
In one embodiment, if the address level corresponding field is any one of province, city, county and unknown, the chinese word segmentation method is used to obtain a preset number of address nodes with higher similarity to the address parameters.
In another aspect, the present invention further provides an address normalization system based on fuzzy matching, where the system includes:
the data receiving unit is used for receiving the user positioning request and acquiring the address parameter from the user positioning request;
the matching unit is used for calling a Goodpastel API and a local standardized ES library to acquire a preset number of address nodes with higher similarity to the address parameters;
the offset calculation unit is used for calculating the distance offset value of the longitude and latitude corresponding to the address parameters of each address node;
and the data loopback unit is used for selecting the address node corresponding to the minimum value in the distance deviation value as a real address and returning the address node to the user.
In one embodiment, the matching unit is specifically configured to:
calling a Goods open platform address code API to acquire an administrative division code, longitude and latitude and an address grade to which the address parameter belongs;
judging whether the corresponding field of the address grade is province, city, district or unknown;
and if the address level corresponding field is not any one of province, city, county and unknown, searching a preset number of address nodes which are matched with the address parameters with high degree in the ES address library by using the administrative division code.
In one embodiment, the matching unit is further configured to obtain a preset number of address nodes with higher similarity to the address parameter by using a chinese word segmentation method, and specifically includes:
analyzing the address parameters to obtain an administrative division corresponding to the address parameters, wherein the administrative division comprises at least one of province, direct administration city, district and detailed address parts;
searching corresponding address nodes in the provincial and municipal districts according to the administrative districts, matching detailed addresses with data in an ES address library through Chinese word segmentation, and calculating text similarity;
and selecting a preset number of address nodes with higher text similarity.
In one embodiment, if the address level correspondence field is any one of province, city, prefecture and unknown, the matching unit is further configured to: and acquiring a preset number of address nodes with higher similarity to the address parameters by using the Chinese word segmentation method.
On one hand, the embodiment of the invention also provides electronic equipment, wherein the electronic equipment is used for running a program, and the fuzzy matching-based address standardization method is executed when the program runs.
In one aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a program is stored, where the program, when executed by an electronic device, implements the address normalization method based on fuzzy matching.
The embodiment of the invention obtains the address parameters by receiving the user request, obtains the data of a plurality of address nodes with the highest similarity through the Goods API and the local standardized ES library, calculates the distance deviation value, selects the address with the minimum distance deviation value and returns the address to the user, realizes address standardization in delivery internet services such as takeaway, city-sharing service, express delivery and the like, improves the order processing efficiency of enterprises, reduces the operation cost of the enterprises and enhances the core competitiveness of the enterprises.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of an address normalization method based on fuzzy matching according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a process of address matching using a Goodpastel API and an ES library according to an embodiment of the present invention;
FIG. 3 is a second flowchart illustrating address matching using a Goodpastel API and an ES library according to an embodiment of the present invention;
FIG. 4 is a third schematic flowchart illustrating address matching by using a Goodpastel API and an ES library according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an address normalization system based on fuzzy matching according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a user screening method based on an intention label, which roughly comprises the following steps as shown in figure 1:
and step S11, receiving the user positioning request and acquiring the address parameter from the user positioning request.
And step S12, calling the Goodpastel API and the local standardized ES library to acquire a preset number of address nodes with higher similarity to the address parameters.
And step S13, calculating the distance deviation value of the longitude and latitude corresponding to each address node and the address parameter.
And step S14, selecting the address node corresponding to the minimum value in the distance deviation values as a real address and returning the real address to the user.
Fig. 2 is a schematic flow chart illustrating address matching by using a gold API and an ES library according to an embodiment of the present invention. As shown in fig. 2, when fuzzy matching is performed in step S12, the following steps may be performed:
and step S21, calling a Gaode open platform address code API, and acquiring the administrative division code, the longitude and latitude and the address grade to which the address parameter belongs.
In specific real time, a high-resolution open platform address coding API is usually called first to convert the address parameters into mars coordinates.
After receiving a plaintext address input by a user, calling a high open platform address coding API, converting the address into a Mars coordinate, such as a following 'location' field, and determining longitude and latitude information of the Mars coordinate.
“adcode”:“110108”
“street”：[]
“number”：[]
“location”：“116.308264，39.995304”
"level": "Point of interest"
Then, the mars coordinates are analyzed to obtain the administrative division code (as shown in the "opcode" field) and the address level (as shown in the "level" field) to which the address parameters belong.
And step S22, judging whether the address level corresponding field is province, city, district or unknown.
Step S23, if the address level corresponding field is not any of province, city, county or unknown, searching a preset number of address nodes with high matching degree with the address parameters in the ES address library by using the administrative division code.
For example, if the address level "field obtained by API call is not" province "," city "," district "or" unknown ", then we consider that the high resolution is successful, obtain longitude and latitude and administrative district codes, and then search for several address nodes with the highest similarity in the ES address library by using the longitude and latitude parameters in the administrative district where the node is located. When the address grade is 'province', 'city', 'district' and 'unknown', the longitude and latitude conversion matching is considered to be failed, and the result of searching the ES address base by using the address parameters is taken as the standard.
Fig. 3 is a schematic flow chart illustrating address matching by using chinese word segmentation according to another embodiment of the present invention. When fuzzy matching is performed in step S12, chinese participle matching may be used, which specifically includes the following steps:
step S31, analyzing the address parameter to obtain an administrative division corresponding to the address parameter, where the administrative division includes at least one of province, direct prefecture city, district and detail address parts.
And step S32, searching corresponding address nodes in the province and city area according to the administrative division, matching the detailed addresses with data in an ES address library through Chinese word segmentation, and calculating text similarity.
And step S33, selecting address nodes with higher text similarity and preset number.
For example, after receiving a plaintext address input by a user, firstly extracting provinces, cities, counties and the like in the input address parameters, dividing the address into province/direct administration cities, districts/counties, detailed addresses and the like, searching corresponding address node data in province/city according to administrative division names, matching the detailed addresses with data in an ES address library through Chinese word segmentation, calculating text similarity, and matching the text similarity to a plurality of address node data with the highest similarity.
The preset number may be selected to be 5, or may be set to be other values, and the embodiment of the present invention is not limited.
In one embodiment, when the determination result of step S22 in fig. 2 is yes, that is, when the address level corresponding field is any one of province, city, county and unknown, the step shown in fig. 3 may be utilized to search the ES address library for a preset number of address nodes.
After the preset number of address nodes are obtained, comparing the longitude and latitude corresponding to the address nodes with the longitude and latitude obtained by calling the Goods API, calculating a distance deviation value, and selecting the address node with the minimum distance deviation value to return to the user.
The process of the present invention is further illustrated by the following specific example:
for example, the user enters the address parameter "Yanshan Tianchi travel center, Beijing City" converted to coordinates "116.184712, 40.654428" using the God Address code API. The following information is obtained by analyzing Chinese word segmentation: beijing City and detailed Address: the Yanshan Tianchi tourist center is matched with 5 addresses with the highest similarity in the sample table, deviation values are calculated according to the obtained longitude and latitude, the address corresponding to the minimum deviation value is 'Yanshan Tianchi tourist company in Beijing city', the corresponding text similarity is 0.9, and then a standardized address is output: the similarity score of the southern 50 m Yanshan Tianchi conference center of the intersection of Changchong road and Luan Chi road in Yanqing district of Beijing: 90.
in an embodiment, the method for obtaining an address node shown in fig. 2 and the method for obtaining an address node shown in fig. 3 can be applied in combination, and the specific flow is shown in fig. 4. The detailed process refers to the processes shown in fig. 2 and fig. 3, and will not be described herein again.
In summary, due to the adoption of the above scheme, the embodiment of the invention has the following beneficial effects: address standardization is realized in delivery internet services such as take-out, city-sharing service and express delivery, order processing efficiency of enterprises is improved, operation cost of the enterprises is reduced, and core competitiveness of the enterprises is enhanced.
Based on the same inventive concept as the address normalization method based on fuzzy matching shown in fig. 1, the embodiment of the present application further provides an address normalization system based on fuzzy matching, as described in the following embodiments. Because the principle of the address standardization system based on fuzzy matching for solving the problem is similar to the address standardization method based on fuzzy matching, the implementation of the address standardization system based on fuzzy matching can refer to the implementation of the address standardization method based on fuzzy matching, and repeated details are not repeated.
Fig. 5 is a schematic structural diagram of an address normalization system based on fuzzy matching according to an embodiment of the present invention. As shown in fig. 5, the address normalization system based on fuzzy matching mainly includes: data receiving section 51, matching section 52, offset calculating section 53, and data loopback section 54.
The data receiving unit 51 is configured to receive a user positioning request and obtain an address parameter from the user positioning request; the matching unit 52 is configured to call a high-resolution API and a local standardized ES library to obtain a preset number of address nodes with higher similarity to the address parameters; the offset calculation unit 53 is configured to calculate a distance offset value between each address node and the corresponding longitude and latitude of the address parameter; the data loopback unit 54 is configured to select an address node corresponding to the minimum value in the distance offset values as a real address and return the address node to the user.
In one embodiment, when the fuzzy matching is performed by the matching unit 52, the following operations are specifically performed: calling a Goods open platform address code API to acquire an administrative division code, longitude and latitude and an address grade to which the address parameter belongs; judging whether the corresponding field of the address grade is province, city, district or unknown; and if the address level corresponding field is not any one of province, city, county and unknown, searching a preset number of address nodes which are matched with the address parameters with high degree in the ES address library by using the administrative division code.
In another embodiment, when the matching unit 52 is used to perform fuzzy matching, the unit is further configured to obtain a preset number of address nodes with higher similarity to the address parameter by using a chinese word segmentation method, and specifically perform the following operations: analyzing the address parameters to obtain an administrative division corresponding to the address parameters, wherein the administrative division comprises at least one of province, direct administration city, district and detailed address parts; searching corresponding address nodes in the provincial and municipal districts according to the administrative districts, matching detailed addresses with data in an ES address library through Chinese word segmentation, and calculating text similarity; and selecting a preset number of address nodes with higher text similarity.
In one embodiment, if the address level corresponding field is any one of province, city, county and unknown, the matching unit 52 is further configured to: and acquiring a preset number of address nodes with higher similarity to the address parameters by using the Chinese word segmentation method.
An embodiment of the present invention further provides a computer-readable storage medium, on which a program is stored, where the program, when executed by an electronic device, implements the address normalization method based on fuzzy matching.
Accordingly, embodiments of the present invention also provide a computer program product, which, when executed on a data processing device, is adapted to perform a procedure for initializing the following method steps:
receiving a user positioning request and acquiring address parameters from the user positioning request;
calling a Goods API and a local standardized ES library to obtain a preset number of address nodes with higher similarity to the address parameters;
calculating the distance deviation value of the longitude and latitude corresponding to each address node and the address parameter;
and selecting the address node corresponding to the minimum value in the distance deviation values as a real address and returning the real address to the user.
Further, an embodiment of the present invention further provides an electronic device, where the electronic device includes a processor, a memory, and a program stored in the memory and capable of running on the processor, and the following steps are implemented when the program is executed: .
Receiving a user positioning request and acquiring address parameters from the user positioning request;
calling a Goods API and a local standardized ES library to obtain a preset number of address nodes with higher similarity to the address parameters;
calculating the distance deviation value of the longitude and latitude corresponding to each address node and the address parameter;
and selecting the address node corresponding to the minimum value in the distance deviation values as a real address and returning the real address to the user.
The electronic equipment in the embodiment of the invention can be a server, a PC, a PDA, a mobile phone and the like.
It will be appreciated that the program may be a computer program product as described above.
In summary, due to the adoption of the above scheme, the embodiment of the invention has the following beneficial effects: address standardization is realized in delivery internet services such as take-out, city-sharing service and express delivery, order processing efficiency of enterprises is improved, operation cost of the enterprises is reduced, and core competitiveness of the enterprises is enhanced.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. An address normalization method based on fuzzy matching, the method comprising:
receiving a user positioning request and acquiring address parameters from the user positioning request;
calling a Goods API and a local standardized ES library to obtain a preset number of address nodes with higher similarity to the address parameters;
calculating the distance deviation value of the longitude and latitude corresponding to each address node and the address parameter;
and selecting the address node corresponding to the minimum value in the distance deviation values as a real address and returning the real address to the user.
2. The method of claim 1, wherein the calling the high-resolution API and the local standardized ES library to obtain a predetermined number of address nodes with a high similarity to the address parameter comprises:
calling a Goods open platform address code API to acquire an administrative division code, longitude and latitude and an address grade to which the address parameter belongs;
judging whether the corresponding field of the address grade is province, city, district or unknown;
and if the address level corresponding field is not any one of province, city, county and unknown, searching a preset number of address nodes which are matched with the address parameters with high degree in the ES address library by using the administrative division code.
3. The method according to claim 1 or 2, wherein the calling a high-resolution API and a local standardized ES library to obtain a preset number of address nodes with a higher similarity to the address parameter further comprises obtaining a preset number of address nodes with a higher similarity to the address parameter by using a chinese lexical method, and specifically comprises:
analyzing the address parameters to obtain an administrative division corresponding to the address parameters, wherein the administrative division comprises at least one of province, direct administration city, district and detailed address parts;
searching corresponding address nodes in the provincial and municipal districts according to the administrative districts, matching detailed addresses with data in an ES address library through Chinese word segmentation, and calculating text similarity;
and selecting a preset number of address nodes with higher text similarity.
4. The method according to claim 3, wherein if the address level corresponding field is any one of province, city, county and unknown, the Chinese word segmentation method is used to obtain a preset number of address nodes with high similarity to the address parameters.
5. An address normalization system based on fuzzy matching, the system comprising:
the data receiving unit is used for receiving the user positioning request and acquiring the address parameter from the user positioning request;
the matching unit is used for calling a Goodpastel API and a local standardized ES library to acquire a preset number of address nodes with higher similarity to the address parameters;
the offset calculation unit is used for calculating the distance offset value of the longitude and latitude corresponding to the address parameters of each address node;
and the data loopback unit is used for selecting the address node corresponding to the minimum value in the distance deviation value as a real address and returning the address node to the user.
6. The system according to claim 5, wherein the matching unit is specifically configured to:
calling a Goods open platform address code API to acquire an administrative division code, longitude and latitude and an address grade to which the address parameter belongs;
judging whether the corresponding field of the address grade is province, city, district or unknown;
and if the address level corresponding field is not any one of province, city, county and unknown, searching a preset number of address nodes which are matched with the address parameters with high degree in the ES address library by using the administrative division code.
7. The system according to claim 5 or 6, wherein the matching unit is further configured to obtain a preset number of address nodes with higher similarity to the address parameter by using a chinese word segmentation method, and specifically includes:
analyzing the address parameters to obtain an administrative division corresponding to the address parameters, wherein the administrative division comprises at least one of province, direct administration city, district and detailed address parts;
searching corresponding address nodes in the provincial and municipal districts according to the administrative districts, matching detailed addresses with data in an ES address library through Chinese word segmentation, and calculating text similarity;
and selecting a preset number of address nodes with higher text similarity.
8. The system according to claim 7, wherein if the address level correspondence field is any one of province, city, county, and unknown, the matching unit is further configured to: and acquiring a preset number of address nodes with higher similarity to the address parameters by using the Chinese word segmentation method.
9. An electronic device, wherein the electronic device is configured to run a program, and wherein the program is configured to execute the fuzzy matching based address normalization method according to any one of claims 1 to 4 when the program is run.
10. A computer-readable storage medium, having stored thereon a program which, when executed by an electronic device, implements the fuzzy matching based address normalization method of any one of claims 1-4.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111339233A (en) * | 2020-02-26 | 2020-06-26 | 集奥聚合(北京)人工智能科技有限公司 | Address safety identification control method |
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| CN111967043A (en) * | 2020-07-29 | 2020-11-20 | 深圳开源互联网安全技术有限公司 | Method and device for determining data similarity, electronic equipment and storage medium |
| CN112269804A (en) * | 2020-11-06 | 2021-01-26 | 厦门美亚亿安信息科技有限公司 | Fuzzy retrieval method and system for memory data |
| CN112732779A (en) * | 2020-12-29 | 2021-04-30 | 合肥市智享亿云信息科技有限公司 | Method for analyzing address text by big data based on site POI |
| CN112862604A (en) * | 2021-04-25 | 2021-05-28 | 腾讯科技（深圳）有限公司 | Card issuing organization information processing method, device, equipment and storage medium |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101882163A (en) * | 2010-06-30 | 2010-11-10 | 中国科学院地理科学与资源研究所 | Fuzzy Chinese address geographic evaluation method based on matching rule |
| CN106269528A (en) * | 2015-05-20 | 2017-01-04 | 阿里巴巴集团控股有限公司 | The method and device of address sorting information is provided |
| CN108038090A (en) * | 2017-12-26 | 2018-05-15 | 北京明朝万达科技股份有限公司 | A kind for the treatment of method and apparatus of Text Address |
| CN108804398A (en) * | 2017-05-03 | 2018-11-13 | 阿里巴巴集团控股有限公司 | The similarity calculating method and device of address text |
| CN109978430A (en) * | 2017-12-28 | 2019-07-05 | 青岛日日顺电器服务有限公司 | A kind of method, apparatus, server and storage medium parsing station address |
-
2019
- 2019-11-11 CN CN201911094604.6A patent/CN110795472A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101882163A (en) * | 2010-06-30 | 2010-11-10 | 中国科学院地理科学与资源研究所 | Fuzzy Chinese address geographic evaluation method based on matching rule |
| CN106269528A (en) * | 2015-05-20 | 2017-01-04 | 阿里巴巴集团控股有限公司 | The method and device of address sorting information is provided |
| CN108804398A (en) * | 2017-05-03 | 2018-11-13 | 阿里巴巴集团控股有限公司 | The similarity calculating method and device of address text |
| CN108038090A (en) * | 2017-12-26 | 2018-05-15 | 北京明朝万达科技股份有限公司 | A kind for the treatment of method and apparatus of Text Address |
| CN109978430A (en) * | 2017-12-28 | 2019-07-05 | 青岛日日顺电器服务有限公司 | A kind of method, apparatus, server and storage medium parsing station address |
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