CN113535822A - Bank connection number matching method and matching device, storage medium and equipment - Google Patents
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Abstract
The invention discloses a matching method of bank numbers, a matching device, a storage medium and equipment. The matching method comprises the following steps: acquiring a constructed data statistical model and remittance information filled by a client, wherein the data statistical model comprises a first mapping relation between a bank number and a bank card number and a second mapping relation between the bank number and a bank name, and the remittance data comprises a payee account number and a payee bank fuzzy name; matching according to the account number of the payee and the first mapping relation to obtain a plurality of candidate bank connection numbers; matching according to the candidate bank contact numbers and the second mapping relation to obtain candidate bank names; calculating the fuzzy matching score value of each candidate bank name according to the fuzzy name of the collection bank and the plurality of candidate bank names; and taking the candidate bank name with the highest fuzzy matching score value as the accurate name of the collection bank, thereby determining the accurate bank connection number. The efficiency of entering of bank name and bank allies oneself with line number can be improved, work load has been reduced.
Description
Technical Field
The invention belongs to the technical field of machine learning, and particularly relates to a bank connection number matching method and device, a computer readable storage medium and computer equipment.
Background
In the cross-bank transfer business, a client usually fills three elements (a receiver account number, a receiver account name and a receiver bank name), a receiver bank number needs to be filled in a transfer channel of a people bank, each business network point of each bank of the people bank has a unique bank connection number, and a specific branch line of the bank can be quickly positioned according to the number so as to transfer money and remit money.
At present, a manual counter mode is adopted to supplement and record bank affiliate numbers, and the following obvious disadvantages exist in the traditional working mode of manually inquiring and inputting the affiliate numbers by the counter: 1) the workload is large, the work content is repeated and time is consumed. 2) Some specific remittance scenarios cannot be automated, increasing labor costs. 3) The system has no verification mechanism, the manual entry is easy to generate errors, the probability of refunding and refunding is increased, the working efficiency is reduced, and the remittance period is increased, so that the customer satisfaction is reduced. 4) In the existing operation flow, the manual inquiry and additional recording mode of the joint line number not only makes the whole operation flow more complicated, but also brings inconvenience and waste of a large amount of time to customers and bank workers due to a large amount of non-intelligent operations, and the difficulty of manual searching is increased by inaccurate line name data provided by the customers.
Disclosure of Invention
(I) technical problems to be solved by the invention
How to improve the matching efficiency and the automatic input degree of bank numbers.
(II) the technical scheme adopted by the invention
A matching method of bank connection numbers comprises the following steps:
acquiring a constructed data statistical model and remittance information filled by a client, wherein the data statistical model comprises a first mapping relation between a bank number and a bank card number and a second mapping relation between the bank number and a bank name, and the remittance data comprises a payee account number and a payee bank fuzzy name;
matching according to the payee account and the first mapping relation to obtain a plurality of candidate bank connection numbers;
matching according to the candidate bank contact numbers and the second mapping relation to obtain candidate bank names;
calculating the fuzzy matching score value of each candidate bank name according to the fuzzy name of the collection bank and the candidate bank names;
and taking the candidate bank name with the highest fuzzy matching score value as the accurate name of the collection bank, and determining the accurate bank connection number according to the accurate name of the collection bank.
Preferably, the first mapping relationship is a mapping relationship between nine prefix bits of the bank serial number and six prefix bits of the bank card number; the method for obtaining a plurality of candidate bank union numbers according to the matching of the payee account and the first mapping relation comprises the following steps: and obtaining nine prefixes of the candidate bank serial numbers according to the six prefixes of the payee account and the first mapping relation.
Preferably, the second mapping relationship is a mapping relationship between nine prefix bits of the bank contact number and the bank name; the method for obtaining a plurality of candidate bank names according to the plurality of candidate bank contact numbers and the second mapping relation matching comprises the following steps: and obtaining a plurality of candidate bank names according to the nine prefix and the second mapping relation of the candidate bank serial numbers.
Preferably, the data statistical model further includes a third mapping relationship between three digits of a bank contact number prefix and a bank name, and after the candidate bank names are obtained by matching according to the candidate bank contact numbers and the second mapping relationship, the matching method further includes:
determining bank characteristic information according to the third mapping relation and the fuzzy name of the collection bank;
and screening partial candidate bank names from the plurality of candidate bank names according to the bank characteristic information to serve as the names of banks to be selected.
Preferably, the method for calculating the fuzzy matching score value of each candidate bank name according to the fuzzy name of the collection bank and the plurality of candidate bank names comprises the following steps:
removing the fuzzy name of the collection bank and the area information and the bank characteristic value in the name of the bank to be selected, and respectively obtaining a collection bank short name corresponding to the fuzzy name of the collection bank and a bank short name corresponding to the name of the bank to be selected;
and carrying out fuzzy matching on the short name of the collection bank and the short names of the banks to be selected one by one according to a fuzzy matching algorithm to obtain a fuzzy matching score value of the short name of each bank to be selected.
Preferably, the matching method further comprises:
judging whether the highest fuzzy matching score value is a preset score value or not;
and if not, generating a manual operation instruction, wherein the manual operation instruction is used for prompting an operator to input the name of the money receiving bank.
Preferably, the matching method further comprises:
and generating a new first mapping relation according to nine prefixes of bank contact numbers corresponding to the names of the payee banks input by an operator and six prefixes of the account numbers of the payee banks, and adding the new first mapping relation to the data statistical model.
The invention also discloses a matching device of the bank number, which comprises:
the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring a constructed data statistical model and remittance information filled by a customer, the data statistical model comprises a first mapping relation between a bank united line number and a bank card number and a second mapping relation between the bank united line number and a bank name, and the remittance data comprises a payee account number and a payee bank fuzzy name;
the first matching unit is used for obtaining a plurality of candidate bank connection numbers according to the payee account and the first mapping relation in a matching mode;
the second matching unit is used for matching according to the candidate bank serial numbers and the second mapping relation to obtain a plurality of candidate bank names;
the fuzzy score calculating unit is used for calculating the fuzzy matching score value of each candidate bank name according to the fuzzy name of the collection bank and the candidate bank names;
and the determining unit is used for taking the candidate bank name with the highest fuzzy matching score value as the accurate name of the collection bank and determining the accurate bank connection number according to the accurate name of the collection bank.
The invention also discloses a computer readable storage medium, which stores the matching program of the bank connection number, and the matching program of the bank connection number is executed by a processor to realize the matching method of the bank connection number.
The invention also discloses a computer device, which comprises a computer readable storage medium, a processor and a matching program of the bank connection number stored in the computer readable storage medium, wherein the matching program of the bank connection number is executed by the processor to realize the matching method of the bank connection number.
(III) advantageous effects
The invention discloses a matching method of bank numbers, which has the following technical effects compared with the traditional method:
according to the scheme, accurate payee account numbers and pre-constructed data statistical models are firstly adopted to screen out a plurality of candidate bank names, a fuzzy matching algorithm is further adopted to match the fuzzy names of the payee banks with the candidate bank names one by one to obtain accurate bank names, so that the collection bank united line number is determined.
Drawings
Fig. 1 is a flowchart of a method for matching bank contact numbers according to a first embodiment of the present invention;
fig. 2 is a detailed step diagram of a bank contact number matching method according to a first embodiment of the present invention;
fig. 3 is a flowchart of a bank contact number matching method according to a second embodiment of the present invention;
fig. 4 is a functional block diagram of a bank contact number matching apparatus according to a third embodiment of the present invention;
fig. 5 is a schematic block diagram of a computer device according to a fifth embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Before describing in detail the various embodiments of the present application, the inventive concepts of the present application are first briefly described: when the cross-bank transfer is carried out through the people bank channel, the number of a gathering line needs to be filled, the number of a bank is added and recorded in a manual mode in the prior art, line name data provided by a client are generally inaccurate, and therefore the difficulty of manual searching and the workload of manual addition are increased. According to the scheme, accurate payee account numbers and pre-constructed data statistical models are firstly adopted to screen out a plurality of candidate bank names, a fuzzy matching algorithm is further adopted to match the fuzzy names of the payee banks with the candidate bank names one by one to obtain accurate bank names, so that the collection bank united line number is determined.
Specifically, as shown in fig. 1 and fig. 2, the method for matching bank join numbers in the first embodiment includes the following steps:
step S10: acquiring a constructed data statistical model and remittance information filled by a client, wherein the data statistical model comprises a first mapping relation between a bank number and a bank card number and a second mapping relation between the bank number and a bank name, and the remittance data comprises a payee account number and a payee bank fuzzy name;
step S20: matching according to the payee account and the first mapping relation to obtain a plurality of candidate bank connection numbers;
step S30: matching according to the candidate bank contact numbers and the second mapping relation to obtain candidate bank names;
step S40: calculating the fuzzy matching score value of each candidate bank name according to the fuzzy name of the collection bank and the candidate bank names;
step S50: and taking the candidate bank name with the highest fuzzy matching score value as the accurate name of the collection bank, and determining the accurate bank connection number according to the accurate name of the collection bank. .
Specifically, in step S10, the remittance information written by the client mainly includes the payee account, fuzzy name of the payee bank, and the payee account and payee account as precise information, and there is no error, and the payee bank name may have inaccuracy problems such as irregular writing format, shorthand, missed writing, and wrong writing, and thus is defined as fuzzy name of the payee bank. In the business handling of banking, after the teller adopts a manual input mode to carry out additional recording, the data statistical model is dynamically updated so as to continuously increase the statistical data of the data statistical model. Illustratively, the first mapping relationship is a mapping relationship between nine prefix bits of the bank serial number and six prefix bits of the bank card number, and the data example of the first mapping relationship is as follows: 427020 &: 102581099, respectively; 427020 &: 102584000, respectively; 427020 &: 102581000, respectively; 427020 &: 102581001....... The second mapping relationship is a mapping relationship between nine prefix bits of the bank contact number and the bank name, and a data example of the second mapping relationship is as follows: 102581000 &: 102581000546 &: the Guangzhou five-mountain branch of China Industrial and commercial Bank, Inc. &: 102100099996 &: 5810 &: guangzhou &: guangzhou &: the Guangdong (a Chinese character of Guangdong). 402829507 &: 402829507042 &: gansu Lin \27950, rural cooperative bank eight mile spread branch &: 402821000015 &: 8295 &: hydro-thermal power supply, (i) 27950: and f, stigmass &: gansu. 402372700 &: 402372700171 &: tang Ji branch of the rural commercial Bank of Mongolian, Anhui, Inc.: 402361018886 &: 3727 &: mongolian &: and Bozhou &: an emblem.
In another embodiment, the data statistical model further includes a third mapping relationship between three digits of the bank contact number prefix and the bank name, and the data example of the third mapping relationship is as follows: 102 &: china industrial and commercial bank stocks ltd; 102 &: china industrial and commercial bank stocks, ltd; 102 &: china industrial and commercial shares, ltd; 102 &: china Industrial and commercial Bank; 102 &: china industry and commerce; 102 &: a business bank; 102 &: go.
In step S20, the method for obtaining a plurality of candidate bank join numbers according to the payee account and the first mapping relationship matching includes: and obtaining nine prefixes of the candidate bank serial numbers according to the six prefixes of the payee account and the first mapping relation. The rough bank contact number candidate range is matched in advance according to the account number of the payee filled by the client.
In step S30, the method for obtaining a plurality of candidate bank names according to the plurality of candidate bank contact numbers and the second mapping relationship matching includes: and obtaining a plurality of candidate bank names according to the nine prefix and the second mapping relation of the candidate bank serial numbers. That is, the rough bank name candidate range is further determined according to the bank serial number candidate range determined in step S20.
In step S40, the specific step of calculating the fuzzy matching score value of each candidate bank name according to the fuzzy name of the collection bank and the plurality of candidate bank names includes: removing the area information and the bank characteristic value in the fuzzy name of the collection bank and the name of the candidate bank, and respectively obtaining a collection bank short name corresponding to the fuzzy name of the collection bank and a candidate bank short name corresponding to the name of the candidate bank; and carrying out fuzzy matching on the collection bank abbreviation and each candidate bank abbreviation one by one according to a fuzzy matching algorithm to obtain a fuzzy matching score value of each candidate bank abbreviation.
Illustratively, if the fuzzy name of the collection bank filled by the customer is the five-mountain branch bank of the Guangzhou city of the bank, the regional information Guangzhou city is removed, the bank characteristic value bank is removed, and the five-mountain branch bank for short is obtained. If the candidate bank name is Guangzhou five-mountain branch of China Industrial and commercial Bank GmbH, removing the regional information Guangzhou city, and removing the bank characteristic value of China Industrial and commercial Bank GmbH to obtain the collection bank, named as the five-mountain branch for short. Therefore, the complexity of the matching field can be reduced, the calculation amount is reduced, and the matching speed is improved.
Further, fuzzy matching is carried out on the collection bank abbreviation and each candidate bank abbreviation one by one, and a fuzzy matching score value of each candidate bank abbreviation is obtained. For example, "Wushan tributary" and "Wushan tributary" score 100.00, and "Beijing Zhongyou tributary" and "Wushan tributary" score 28.57.
Further, in step S50, the candidate bank name with the highest fuzzy matching score is used as the accurate name of the receiving bank, and the accurate bank connection number is determined according to the accurate name of the receiving bank, so as to complete the intelligent matching between the bank name and the bank connection number.
Specifically, the matching method further includes: judging whether the highest fuzzy matching score value is a preset score value or not; and if not, generating a manual operation instruction, wherein the manual operation instruction is used for prompting an operator to input the name of the money receiving bank. If yes, matching is finished. Illustratively, the preset score is 100, and when the highest fuzzy matching score is 100, it indicates that the fuzzy name of the collection bank is identical to the accurate name of the collection bank obtained by automatic matching. When the highest fuzzy match score is not 100, e.g., 98 points, the teller is required to manually enter or manually select the correct bank name and bank line number from the existing list.
Further, the matching method further comprises: and generating a new first mapping relation according to nine prefixes of bank contact numbers corresponding to the names of the payee banks input by an operator and six prefixes of the account numbers of the payee banks, and adding the new first mapping relation to the data statistical model so as to dynamically update the data statistical model.
In another embodiment, as shown in fig. 3, the method for matching bank connection numbers includes the following steps:
step S10: acquiring a constructed data statistical model and remittance information filled by a client, wherein the data statistical model comprises a first mapping relation between a bank number and a bank card number and a second mapping relation between the bank number and a bank name, and the remittance data comprises a payee account number and a payee bank fuzzy name;
step S20: matching according to the payee account and the first mapping relation to obtain a plurality of candidate bank connection numbers;
step S30: matching according to the candidate bank contact numbers and the second mapping relation to obtain candidate bank names;
step S41: determining bank characteristic information according to the third mapping relation and the fuzzy name of the collection bank;
step S42: and screening partial candidate bank names from the plurality of candidate bank names according to the bank characteristic information to serve as the names of banks to be selected.
Step S51: and calculating the fuzzy matching score value of each bank name to be selected according to the fuzzy name of the collection bank and the names of the banks to be selected.
Step S52: and taking the name of the bank to be selected with the highest fuzzy matching score value as the accurate name of the collection bank, and determining the accurate bank connection number according to the accurate name of the collection bank.
Specifically, the specific processes of step S10, step S20, and step S30 in the second embodiment are the same as those of step S10, step S20, and step S30 in the first embodiment, and are not repeated herein.
Further, in step S41, the data statistics model pre-constructs a third mapping relationship between the three digits of the bank affiliate number prefix and the bank name, the fuzzy name of the collection bank filled by the customer has a plurality of possible forms, and the fuzzy name may be, by way of example, a chinese industrial and commercial bank company, a chinese industrial and commercial bank, or a work bank, and the corresponding bank characteristic information is determined to be 102. In fact, three digits of the prefix of the bank affiliate number of each bank are unique, and three digits of the prefix of the bank affiliate number of each bank are different, for example, China industrial and commercial banks, China construction banks, China agricultural banks and the like.
After the bank characteristic information is determined, comparing the bank characteristic information with the candidate bank contact numbers one by one, and reserving the contact numbers with the same three digits as the bank characteristic information in the prefix of the candidate bank contact numbers, thereby further screening out part of the candidate bank names from the plurality of candidate bank names as the names of banks to be selected, wherein the names of the banks to be selected correspond to the reserved candidate bank contact numbers.
Illustratively, in step S30, the obtained candidate bank affiliate numbers are 102100009, 102121000, 102581000, 105584001215, 105584000021 and 105584000005, and in step S41, the bank characteristic information is 102, then in step S42, the candidate bank affiliate numbers of 102100009, 102121000 and 102581000 are reserved, so as to screen out the corresponding candidate bank name. The number of candidate bank names is less than the number of candidate bank names. In this way, in subsequent step S51 and step S52, the amount of calculation can be further reduced, and the matching efficiency can be improved.
As shown in fig. 4, in the third embodiment, the matching device for bank join number includes an acquisition unit 100, a first matching unit 200, a second matching unit 300, a blur score calculation unit 400, and a determination unit 500. The obtaining unit 100 is configured to obtain a constructed data statistics model and remittance information filled by a customer, where the data statistics model includes a first mapping relationship between a bank connection number and a bank card number and a second mapping relationship between the bank connection number and a bank name, and the remittance data includes a payee account number and a payee bank fuzzy name. The first matching unit 200 is configured to obtain a plurality of candidate bank connection numbers according to the payee account and the first mapping relationship through matching. The second matching unit 300 is configured to obtain a plurality of candidate bank names according to the plurality of candidate bank connection numbers and the second mapping relationship. The fuzzy score calculating unit 400 is configured to calculate a fuzzy matching score value of each candidate bank name according to the fuzzy name of the collection bank and the plurality of candidate bank names. The determining unit 500 is configured to use the candidate bank name with the highest fuzzy matching score as the accurate name of the receiving bank, and determine an accurate bank connection number according to the accurate name of the receiving bank. The specific processing procedures of the obtaining unit 100, the first matching unit 200, the second matching unit 300, the blur score calculating unit 400, and the determining unit 500 refer to the descriptions of the first embodiment and the second embodiment, which are not repeated herein.
Further, in another embodiment, the matching device further includes a third matching unit 301, where the third matching unit 301 is configured to determine the bank feature information according to the third mapping relationship and the fuzzy name of the collection bank; and screening partial candidate bank names from the plurality of candidate bank names according to the bank characteristic information to serve as the names of banks to be selected. For a specific processing procedure of the third matching unit 301, reference may be made to the related description of the second embodiment, which is not described herein again.
Further, the fuzzy score calculating unit 400 is further configured to calculate a fuzzy matching score value of each candidate bank name according to the fuzzy name of the collection bank and the names of the plurality of candidate banks. The determining unit 500 is further configured to use the name of the bank to be selected with the highest fuzzy matching score value as the accurate name of the receiving bank, and determine an accurate bank connection number according to the accurate name of the receiving bank.
The fourth embodiment also discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a banking number matching program, and the banking number matching program is executed by a processor to implement the banking number matching method.
Further, this embodiment also discloses a computer device, which includes, on a hardware level, as shown in fig. 5, a processor 12, an internal bus 13, a network interface 14, and a computer-readable storage medium 11. The processor 12 reads a corresponding computer program from the computer-readable storage medium and then runs, forming a request processing apparatus on a logical level. Of course, besides software implementation, the one or more embodiments in this specification do not exclude other implementations, such as logic devices or combinations of software and hardware, and so on, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or logic devices. The computer-readable storage medium 11 stores a banking serial number matching program, and the banking serial number matching program is executed by a processor to implement the banking serial number matching method.
Computer-readable storage media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer-readable storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
Although a few embodiments of the present invention have been shown and described, it would be appreciated by those skilled in the art that changes may be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the claims and their equivalents, and that such changes and modifications are intended to be within the scope of the invention.
Claims (10)
1. A matching method of bank numbers is characterized by comprising the following steps:
acquiring a constructed data statistical model and remittance information filled by a client, wherein the data statistical model comprises a first mapping relation between a bank number and a bank card number and a second mapping relation between the bank number and a bank name, and the remittance data comprises a payee account number and a payee bank fuzzy name;
matching according to the payee account and the first mapping relation to obtain a plurality of candidate bank connection numbers;
matching according to the candidate bank contact numbers and the second mapping relation to obtain candidate bank names;
calculating the fuzzy matching score value of each candidate bank name according to the fuzzy name of the collection bank and the candidate bank names;
and taking the candidate bank name with the highest fuzzy matching score value as the accurate name of the collection bank, and determining the accurate bank connection number according to the accurate name of the collection bank.
2. The matching method of bank connection number according to claim 1, wherein the first mapping relationship is a mapping relationship between nine prefix bits of bank connection number and six prefix bits of bank card number; the method for obtaining a plurality of candidate bank union numbers according to the matching of the payee account and the first mapping relation comprises the following steps: and obtaining nine prefixes of the candidate bank serial numbers according to the six prefixes of the payee account and the first mapping relation.
3. The method for matching bank affiliation numbers according to claim 2, wherein the second mapping relationship is a mapping relationship between nine prefixes of bank affiliation numbers and bank names; the method for obtaining a plurality of candidate bank names according to the plurality of candidate bank contact numbers and the second mapping relation matching comprises the following steps: and obtaining a plurality of candidate bank names according to the nine prefix and the second mapping relation of the candidate bank serial numbers.
4. The method as claimed in claim 3, wherein the data statistics model further comprises a third mapping relationship between three digits of the bank connection number prefix and the bank name, and after obtaining a plurality of candidate bank names according to the plurality of candidate bank connection numbers and the second mapping relationship, the method further comprises:
determining bank characteristic information according to the third mapping relation and the fuzzy name of the collection bank;
and screening partial candidate bank names from the plurality of candidate bank names according to the bank characteristic information to serve as the names of banks to be selected.
5. The method for matching bank join numbers according to claim 4, wherein the method for calculating the fuzzy matching score value of each candidate bank name according to the fuzzy name of the collection bank and the plurality of candidate bank names comprises:
removing the fuzzy name of the collection bank and the area information and the bank characteristic value in the name of the bank to be selected, and respectively obtaining a collection bank short name corresponding to the fuzzy name of the collection bank and a bank short name corresponding to the name of the bank to be selected;
and carrying out fuzzy matching on the short name of the collection bank and the short names of the banks to be selected one by one according to a fuzzy matching algorithm to obtain a fuzzy matching score value of the short name of each bank to be selected.
6. The method of matching bank line numbers according to claim 2, further comprising:
judging whether the highest fuzzy matching score value is a preset score value or not;
and if not, generating a manual operation instruction, wherein the manual operation instruction is used for prompting an operator to input the name of the money receiving bank.
7. The method of matching bank line numbers according to claim 6, further comprising:
and generating a new first mapping relation according to nine prefixes of bank contact numbers corresponding to the names of the payee banks input by an operator and six prefixes of the account numbers of the payee banks, and adding the new first mapping relation to the data statistical model.
8. A matching device for bank connection numbers, the matching device comprising:
the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring a constructed data statistical model and remittance information filled by a customer, the data statistical model comprises a first mapping relation between a bank united line number and a bank card number and a second mapping relation between the bank united line number and a bank name, and the remittance data comprises a payee account number and a payee bank fuzzy name;
the first matching unit is used for obtaining a plurality of candidate bank connection numbers according to the payee account and the first mapping relation in a matching mode;
the second matching unit is used for matching according to the candidate bank serial numbers and the second mapping relation to obtain a plurality of candidate bank names;
the fuzzy score calculating unit is used for calculating the fuzzy matching score value of each candidate bank name according to the fuzzy name of the collection bank and the candidate bank names;
and the determining unit is used for taking the candidate bank name with the highest fuzzy matching score value as the accurate name of the collection bank and determining the accurate bank connection number according to the accurate name of the collection bank.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a banking number matching program, which when executed by a processor implements the banking number matching method of any one of claims 1 to 7.
10. A computer device comprising a computer-readable storage medium, a processor, and a banking number matching program stored in the computer-readable storage medium, wherein the banking number matching program, when executed by the processor, implements the banking number matching method of any one of claims 1 to 7.
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