WO2019227576A1 - Procédé et appareil de vérification de facture, dispositif informatique et support de stockage - Google Patents

Procédé et appareil de vérification de facture, dispositif informatique et support de stockage Download PDF

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Publication number
WO2019227576A1
WO2019227576A1 PCT/CN2018/094362 CN2018094362W WO2019227576A1 WO 2019227576 A1 WO2019227576 A1 WO 2019227576A1 CN 2018094362 W CN2018094362 W CN 2018094362W WO 2019227576 A1 WO2019227576 A1 WO 2019227576A1
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WIPO (PCT)
Prior art keywords
supplier information
version field
black
black name
name supplier
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PCT/CN2018/094362
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English (en)
Chinese (zh)
Inventor
潘庚生
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平安科技（深圳）有限公司
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Publication of WO2019227576A1 publication Critical patent/WO2019227576A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/40—Document-oriented image-based pattern recognition
    • G06V30/41—Analysis of document content
    • G06V30/412—Layout analysis of documents structured with printed lines or input boxes, e.g. business forms or tables
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/22—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition

Definitions

  • the present application relates to the field of communication technologies, and in particular, to an invoice verification method, device, computer equipment, and storage medium.
  • the embodiments of the present application provide an invoice verification method, device, computer equipment, and storage medium to solve the problem that the supplier cannot be verified when performing a payment operation with the supplier at present.
  • An invoice verification method includes:
  • the supplier information on the invoice to be verified is obtained from the verification request, and the supplier information includes an identification version field and an entry version field of the supplier;
  • the matching process is performed using the input version field and the identification version field as fixed phrases, and a verification result is generated based on the black name supplier information obtained by the matching process. ;
  • performing the fuzzy search in the blacklist database through the identification version field to obtain the black name supplier information associated with the supplier information includes:
  • the black name supplier information including the keywords is acquired from the black list database based on the SQL language to obtain the black name supplier information associated with the supplier information.
  • performing the fuzzy search in the blacklist database through the identification version field to obtain the black name supplier information associated with the supplier information includes:
  • the black name supplier information associated with the supplier information is used to perform matching processing using the input version field and the identification version field as fixed phrases, respectively, and the black name supplier obtained according to the matching processing.
  • Information generation verification results include:
  • the black name supplier information, the input version field, and the identification version field obtained by the two matching processes are all the same, generate a verification result according to the black name supplier information obtained by the first matching process or the second matching process;
  • the constructing a blacklist library includes:
  • the suppliers whose transaction bad debts reach the preset amount threshold and the aging reaches the preset aging threshold are selected as black name supplier information and added to the blacklist database.
  • the constructing the blacklist library further includes:
  • the holding company corresponding to each black name supplier is obtained from the controlling relationship, and the holding company is added to the black list database as the black name supplier information.
  • An invoice checking device includes:
  • a building module for building a blacklist library that includes blackname vendor information
  • An obtaining module configured to obtain the supplier information on the invoice to be verified from the verification request when the verification request sent by the client is obtained, where the supplier information includes an identification version field of the supplier and an entry Version field
  • a fuzzy search module configured to perform a fuzzy search in a blacklist database through an identification version field to obtain black name supplier information associated with the supplier information;
  • a matching module configured to perform matching processing using the input version field and the identification version field as fixed phrases based on the black name supplier information associated with the supplier information, and according to the black name supplier obtained by the matching processing Information generation verification results;
  • a sending module is configured to send the verification result to a client, so that the client receives and outputs the verification result.
  • the matching module includes:
  • a matching unit configured to perform a first matching process on the black name supplier information associated with the entry version field and the supplier information, and provide a black name supply associated with the identification version field and the supplier information Quotient information performs a second matching process;
  • the first generating unit is configured to generate a calibration according to the black name supplier information obtained by the first matching process or the second matching process if the black name supplier information obtained by the two matching processes, the input version field, and the identification version field are the same Test results
  • the second generating unit is configured to generate a verification result according to the black name supplier information obtained by the second matching process if the black name supplier information obtained by the two matching processes is different.
  • a computer device includes a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor.
  • the processor executes the computer-readable instructions, the following steps are implemented:
  • the supplier information on the invoice to be verified is obtained from the verification request, and the supplier information includes an identification version field and an entry version field of the supplier;
  • the matching process is performed using the input version field and the identification version field as fixed phrases, and a verification result is generated based on the black name supplier information obtained by the matching process. ;
  • One or more non-volatile readable storage media storing computer-readable instructions, which when executed by one or more processors, cause the one or more processors to perform the following steps:
  • the supplier information on the invoice to be verified is obtained from the verification request, and the supplier information includes an identification version field and an entry version field of the supplier;
  • the matching process is performed using the input version field and the identification version field as fixed phrases, and a verification result is generated based on the black name supplier information obtained by the matching process. ;
  • FIG. 1 is a schematic diagram of an application environment of an invoice verification method according to an embodiment of the present application
  • FIG. 2 is a flowchart of an invoice verification method according to an embodiment of the present application.
  • step S203 is a flowchart of step S203 in the invoice verification method according to an embodiment of the present application.
  • step S203 is a flowchart of step S203 in the invoice verification method according to an embodiment of the present application.
  • step S204 in the invoice verification method according to an embodiment of the present application.
  • FIG. 6 is a principle block diagram of an invoice verification device in an embodiment of the present application.
  • FIG. 7 is a schematic diagram of a computer device in an embodiment of the present application.
  • the invoice verification method provided in the embodiment of the present application can be applied in the application environment as shown in FIG. 1, including a client and a server, where the client communicates with the server through a network.
  • the client may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices.
  • the server can be implemented by an independent server or a server cluster composed of multiple servers.
  • the client and server jointly complete the verification of the information on the invoice.
  • the server is used to construct and manage a blacklist database, perform operations of adding, deleting, and modifying the black name information on the record in the blacklist database, and perform a blacklist verification analysis in response to a verification request from the client.
  • the client is used to initiate a verification request, and output and display the verification result obtained from the server.
  • the verification is a verification of the supplier information on the invoice
  • the blacklist database includes a plurality of black name supplier information.
  • an invoice verification method is provided.
  • the method is applied to the server in FIG. 1 as an example, and includes the following steps:
  • step S201 a blacklist library is constructed, and the blacklist library includes black name supplier information.
  • the embodiment of the present application constructs a blacklist library on the server side in advance, and improves the data source of the blackname supplier information in the blacklist library to improve the data reliability of the blacklist library and enrich the data in the blacklist library. Types, providing a more comprehensive verification service.
  • the step S201 may include:
  • the black name user information is obtained from a preset credit system, and the black name user information is added to the black list database as black name supplier information.
  • the preset credit system includes, but is not limited to, a national credit system, a banking system, and a credit reporting agency.
  • the embodiment of the present application directly obtains black-named user information from the national credit system, banking system, and credit reporting agencies.
  • Each black-named user information includes, but is not limited to, black-name supplier names and records of black-related behaviors. This includes, but is not limited to, the reasons for black sex and the time when black sex acts occurred.
  • the data sources of the constructed blacklist database include data from the national credit system, banking system, and credit reporting agencies.
  • the data of the blacklist database is highly reliable and the data types are complete, which is conducive to providing more comprehensive information. Calibration services.
  • the step S201 may further include:
  • the suppliers whose transaction bad debts reach the preset amount threshold and the aging reaches the preset aging threshold are selected as black name supplier information and added to the blacklist database.
  • the embodiment of the present application may also filter black-named supplier information based on the bad debts of the transaction from the big data accumulated by the enterprise itself.
  • the associated supplier is used as black name supplier information and added to the black list database.
  • the preset amount threshold and the preset aging threshold are both parameters for judging whether a supplier corresponding to a transaction bad debt is a blacklist.
  • the preset amount threshold is a blacklisted transaction for a supplier corresponding to a transaction bad debt.
  • the lower limit of the amount, and the preset aging threshold is the lower limit of the aging that the supplier corresponding to the transaction bad debt is a blacklist.
  • the step S201 may further include:
  • the holding company corresponding to each black name supplier is obtained from the controlling relationship, and the holding company is added to the black list database as the black name supplier information.
  • the controlling relationship refers to a relationship between enterprises that controls other enterprises by holding a certain number of shares.
  • the holding relationship may be stored in a mapping table.
  • the company M in the holding relationship is the holding company of the company N. If the company M exists in the blacklist supplier information of the blacklist database, then by querying the holding relationship, it can be obtained that the company M is the holding of the company N Company, then the company N is also used as the black name supplier information and added to the black list database, thereby further expanding the data amount of the black name information in the black list database, which is beneficial to achieve a more comprehensive verification To improve the ability of financial risk management and control.
  • the blacklist library may be deployed on a cloud platform, so as to provide data services to internal enterprises and external enterprises, and implement a SAAS service model.
  • step S202 when the verification request sent by the client is obtained, the supplier information on the invoice to be verified is obtained from the verification request, and the supplier information includes the identification version field of the supplier and the entry. Edition field.
  • the supplier identification field refers to the supplier information obtained by image recognition of the invoice image information
  • the entry version field refers to the supplier information obtained by manual entry.
  • the user can enter the supplier information on the invoice face of the invoice to be verified on the client, and upload the invoice image information by scanning with a scanner.
  • the client or server on the client side receives the supplier information and obtains the entry version field of the supplier; and performs OCR identification on the invoice image information to obtain the supplier information on the invoice face, that is, the supplier's identification version Field.
  • a verification request is then generated based on the supplier's identification version field and the entry version field, and the verification request is sent to the server to initiate a verification process for the invoice to be verified.
  • the server parses the verification request, and obtains the supplier information on the invoice to be verified, including the identification version field and the entry version field of the supplier.
  • step S203 a fuzzy search is performed in the blacklist database through the identification version field to obtain black name supplier information associated with the supplier information.
  • the embodiment of the present application first uses the identification version field as a verification object, and performs a fuzzy search on the identification version field in a blacklist database.
  • the fuzzy search may be split according to the identification version field, and the keywords in the search term are used as search terms to perform a logical AND operation to obtain the black name supplier information associated with the identification version field from a blacklist database.
  • the black-name supplier information with low reference meaning is initially filtered out.
  • step S204 based on the black name supplier information associated with the supplier information, a matching process is performed using the input version field and the identification version field as fixed phrases, respectively, and the black name supplier obtained according to the matching process The information generates a verification result.
  • the input version field and the identification version field are used as verification objects, respectively, to perform accurate matching processing.
  • the precise matching process refers to using the input version field and the identification version field as fixed search phrases, performing a logical AND operation, and obtaining the input version field or the input version field from the associated black name supplier information. Black name supplier information with exactly the same identification field.
  • the input version field and the identification version field are respectively used as a fixed phrase to perform a matching process, and then generate a verification result based on the two matching processes, which is beneficial to improving the accuracy of the matching process and further improving supply Verification accuracy of business information.
  • step S205 the verification result is sent to the client, so that the client receives and outputs the verification result.
  • the verification result includes a conclusion of whether it is a blacklist and a name of the blacklist.
  • the conclusion of whether it is a blacklist includes: the supplier information on the invoice to be verified is a blacklist, the supplier information on the invoice to be verified is not a blacklist, and the supply on the invoice to be verified
  • the business information is suspected blacklist.
  • the name of the blacklist is the blacklisted vendor information in the blacklist library that matches the check.
  • the embodiment of the present application further sends the verification result to the client, so that the client receives and outputs the verification result, and at the same time as completing the blacklist verification, the SAAS (Software- as-a-Service (software as a service) service, which can provide black name verification services to internal and external enterprises.
  • SAAS Software- as-a-Service (software as a service) service
  • the fuzzy search may be performed based on a structured query language SQL.
  • step S203 that is, performing a fuzzy search in the blacklist database through the identification version field to obtain the black name supplier information associated with the supplier information includes the following steps:
  • step S301 word segmentation processing is performed on the identification version field, and keywords in the supplier information are acquired according to a result of the word segmentation processing.
  • the supplier information is usually the company name, such as XX (city) XX Technology Co., Ltd.
  • the embodiment of the present application constructs a word segmentation meta database in advance, and uses the word segmentation meta database to store the word segmentation rules and standard information on the region, company name, and company form on which the word segmentation process is based.
  • the word segmentation rule refers to a rule defined according to whether a company name includes specified characteristic characters, and the specified characteristic characters include, but are not limited to, for example, "province", "city”, “technology”, “clothing”, “ Information Technology “,” Communication “,” Limited Liability Company “,” Company Limited “. Each segmentation rule corresponds to a different segmentation procedure.
  • the position where the characteristic character appears is obtained according to the word segmentation rule, and information before the characteristic character or between two characteristic characters is intercepted to obtain several word segmentation.
  • information between "province” and "city” is intercepted, and the information between "city” and “limited liability company” is intercepted.
  • the intercepted information is compared with the standard information in the segmentation meta database to identify the attributes of each segmentation, that is, whether the segmentation is a regional noun, a company name, or a company form, which further ensures the accuracy of the segmentation.
  • the intercepted information can be identified as a regional noun, and can be specifically identified as a municipal-level region; After the information between the intercepted "city” and “limited liability company” is compared with the standard information, the intercepted information can be identified as the company name.
  • a company name is used as a keyword, that is, a participle whose attribute is a company name is used as a keyword.
  • step S302 the black name supplier information including the keywords is acquired from the black list database based on the SQL language to obtain black name supplier information associated with the supplier information.
  • the embodiment of the present application sets a specified SQL statement based on the keywords.
  • the specified SQL statement may be:
  • the embodiment of the present application sets a LIKE condition according to the keywords.
  • two percent signs (%%) can be used. such as:
  • the SQL statement can be set to:
  • the flow_supplier indicates the black name supplier information list in the blacklist library
  • the suppliername indicates the black name supplier name.
  • the flow_supplier table name in the suppliername contains "safety” "Technology” records are all queried out to complete a fuzzy search to obtain a name similar to the identification version field, that is, all black name supplier information that includes the word "Ping An Technology" in the identification version field.
  • the black name supplier information associated with the identification version field is obtained from the black list database through a fuzzy search, and the black name supplier information with a lower reference meaning is preliminarily filtered, which is helpful to improve the verification.
  • Efficiency and accuracy; and SQL language is highly non-procedural, which effectively reduces development costs.
  • a fuzzy search may be performed based on a cosine similarity algorithm. As shown in FIG. 4, in step S203, that is, performing a fuzzy search in the blacklist database through the identification version field to obtain the black name supplier information associated with the supplier information includes the following steps:
  • step S401 word segmentation processing is performed on the identification version field, and word segmentation processing is performed on each black name supplier information in the black list database.
  • the supplier information is usually a company name, such as XX (City) YY Technology Co., Ltd.
  • the word segmentation here refers to segmenting the Chinese character sequence into meaningful words. For example, the above-mentioned XX (city) YY Technology Co., Ltd. is divided into "XX, YY, Technology, Co., Ltd.”.
  • the manner of word segmentation includes, but is not limited to, a word segmentation method based on string matching, a word segmentation method based on understanding, and a word segmentation method based on statistics.
  • step S402 for each black name supplier information and identification version field to be compared, a word segmentation sequence corresponding to the black name supplier information and identification version field to be compared is generated according to the result of the word segmentation processing.
  • the embodiment of the present application extracts one piece of black name supplier information from the black list database without replacement as the black name supplier information to be compared. Generate a corresponding word segmentation sequence according to the black name supplier information to be compared and the identification version field.
  • the word segmentation sequence refers to a sequence composed of the black name supplier information to be compared and the word segmentation included in the identification field.
  • the black name supplier information A to be compared is XX City YY Technology Co., Ltd.
  • the word segmentation obtained after the word segmentation processing includes XX, City, YY, Technology, Ltd .
  • the identification version field B is XXYY Technology Limited Companies
  • the word segmentation obtained after the word segmentation processing includes XX, YY, technology, limited company.
  • the word segmentation corresponding to the black name supplier information to be compared and the identification version field is combined to obtain the word segmentation sequence corresponding to the black name supplier information A and the identification version field B to be compared: XX, city, YY, Technology Co., Ltd.
  • step S403 a word frequency vector corresponding to the black name supplier information to be compared and a word frequency vector corresponding to the recognition version field are generated according to the word segmentation sequence.
  • the word frequency refers to the number of times each participle appears in the identification version field or the black name supplier information to be compared.
  • the participle "XX” appears in the black name supplier information A or the identification version field B that are to be compared, and the number of occurrences is 1.
  • the participle "city" is in the black name to be compared.
  • the number of occurrences in the supplier information A is 1, the number of occurrences in the identification version field B is 0, and so on.
  • the word frequency vector represents a feature vector of the identified version field / to be compared black name supplier information, and each word frequency in the vector represents a corresponding segmentation contribution to the identified version field / to be compared black name supplier information. degree.
  • the word segmentation sequences corresponding to the black name supplier information A and the identification version field B to be compared are: XX, city, YY, technology, and company limited.
  • the word frequency vector corresponding to the black name supplier information A to be compared is (1,1,1,1,1)
  • the word frequency vector corresponding to the recognition version field B is (1,0,1,1,1) .
  • step S404 the cosine similarity between the word frequency vector corresponding to the black name supplier information to be compared and the word frequency vector corresponding to the recognition version field is calculated.
  • a cosine similarity algorithm (cosine similarity) is used to calculate an angle cosine value between two word frequency vectors to obtain a similarity between the identification version field and the black name supplier information to be compared, thereby The comparison between the identification version field and each black name supplier information in the black list database is converted into calculating the similarity between the two word frequency vectors. It is assumed that the black name supplier information to be compared corresponds to a word frequency vector P, the identification version field corresponds to a word frequency vector Q, and the cosine similarity calculation formula is:
  • P i represents the word frequency corresponding to the i-th participle in the word frequency vector P
  • Q i represents the word frequency corresponding to the i-th participle in the word frequency vector Q
  • n represents the total number of participles in the word segmentation sequence.
  • step S405 the black name supplier information whose cosine similarity is greater than or equal to a preset similarity threshold is acquired.
  • the value of the included angle ⁇ is 0, indicating that the two word frequency vectors are the same, that is, the identification field and the black name supplier information to be compared.
  • the smaller the cosine of the included angle the greater the value of the included angle ⁇ , the more unrelated the two word frequency vectors; if the cosine of the included angle is 0, it means that the two word frequency vectors are orthogonal, and the included angle ⁇
  • the value is 90 degrees, and the identification field is not related to the black name supplier information to be compared.
  • a preset similarity threshold is preset, an error space is reserved, and then the similarity calculated in step S304 is compared with the preset similarity threshold to filter out greater than or equal to the preset similarity.
  • Threshold similarity the black name supplier information to be compared corresponding to the similarity is recorded as the associated black name supplier information, thereby completing a fuzzy search of the black name supplier information, filtering out the low reference meaning
  • the black name supplier information is beneficial to improve the efficiency and accuracy of the check; and the cosine similarity algorithm effectively avoids that the fields with the same meaning are considered to be dissimilar when the length or order is inconsistent, simple and fast. Speed and accuracy are high.
  • the black name supplier information associated with the supplier information based on the supplier information described in step S204 performs matching processing using the input version field and the identification version field as fixed phrases, respectively. And generating a verification result based on the black name supplier information obtained by the matching process includes:
  • step S501 a first matching process is performed on black name supplier information associated with the input version field and the supplier information, and black name supply associated with the identification version field and the supplier information is performed.
  • the quotient information performs a second matching process.
  • the black name supplier information associated with the supplier information is the black name supplier information related to the supplier information on the invoice to be verified, which is obtained through the fuzzy search performed in step S203.
  • the embodiment of the present application uses multiple matching, and performs a matching process using the input version field and the identification version field as fixed phrases, that is, the input version field is related to the supplier information.
  • the first matching of the black name supplier information is performed to obtain the black name supplier information that is the same as the entry version field; and the black name supplier information that associates the identification version field with the supplier information is performed.
  • the second matching is to obtain the black name supplier information that is the same as the identification version field.
  • a verification result is generated based on the two matchings to improve the accuracy of the matching process and further improve the verification accuracy of the supplier information.
  • step S502 if the black name supplier information obtained by the two matching processes, the input version field and the identification version field are all the same, a verification result is generated according to the black name supplier information obtained by the first matching process or the second matching process.
  • the embodiment of the present application compares the black name supplier information obtained by the two matching processes; if the black name supplier information obtained by the first matching process is the same as the black name supplier information obtained by the second matching process At the same time, the black name supplier information, the input version field, and the identification version field obtained by the two matching processes are compared. If the black name supplier information, the input version field, and the identification version field obtained by the two matching processing are all compared, The same, it is confirmed that the supplier information on the invoice to be verified belongs to the blacklist, and a verification result is generated.
  • the verification result includes the conclusion of whether it is a blacklist and the name of the blacklist.
  • the verification result may be: the supplier information on the invoice to be verified is a black list, and the name of the black list is XXX (based on the black name supplier information obtained by matching).
  • step S503 if the black name supplier information obtained by the two matching processes is not the same, a verification result is generated based on the black name supplier information obtained by the second matching process.
  • the black name supplier information obtained in the first matching process is different from the black name supplier information obtained in the second matching process, it is confirmed that the supplier information on the invoice to be verified is suspected black List and generate verification results.
  • the verification result may be: the supplier information on the invoice to be verified is a suspected blacklist, and the name of the blacklist is XXX (the black name supplier information obtained by the second match); to inform the user There is a black name supplier similar to the supplier information on the invoice to be verified, and it is confirmed.
  • the black name supplier information associated with the supplier includes China XX Technology Co., Ltd., China XX Insurance Co., Ltd., China (XX) Technology Co., Ltd., and the like.
  • the input version field obtained by manually entering the supplier information is China XX Insurance Co., Ltd.
  • the identification version field obtained by identifying the supplier information through OCR technology is China XX Insurance Co., Ltd.
  • the black name supplier information obtained from the second matching process is China XX Insurance Co., Ltd., and it is consistent with the entry version field and the identification version field. Then confirm that the supplier information on the invoice to be verified belongs to the black list.
  • the supplier information on the invoice to be verified is a blacklist, and the name of the blacklist is China XX Insurance Co., Ltd.
  • the input version field obtained by manually entering the supplier information is China XX Technology Co., Ltd.
  • the identification version field obtained by identifying the supplier information by OCR technology is China (XX) Technology Co., Ltd.
  • the black name supplier information is China XX Technology Co., Ltd.
  • the second matching black name supplier information is China Technology Co., Ltd.
  • the supplier information on the invoice to be verified is a suspected blacklist
  • the name of the blacklist is China Science and Technology Co., Ltd. Please confirm.
  • the embodiment of the present application improves the accuracy of the matching process by setting a double matching process, thereby improving the accuracy of checking the supplier information.
  • an invoice verification device is provided, and the invoice verification device corresponds to the invoice verification method in the embodiment described above.
  • the invoice verification device includes a construction module, an acquisition module, a fuzzy search module, a matching module, and a sending module.
  • the detailed description of each function module is as follows:
  • a building module 61 configured to build a blacklist library, where the blacklist library includes black name supplier information;
  • the obtaining module 62 is configured to obtain the supplier information on the invoice to be verified from the verification request when the verification request sent by the client is obtained, where the supplier information includes an identification version field of the supplier and Entry field
  • a fuzzy search module 63 configured to perform a fuzzy search in a blacklist database through an identification version field to obtain black name supplier information associated with the supplier information;
  • a matching module 64 is configured to perform matching processing based on the black name supplier information associated with the supplier information, respectively, using the input version field and the identification version field as fixed phrases, and supply the black name based on the matching processing.
  • the sending module 65 is configured to send the verification result to the client, so that the client receives and outputs the verification result.
  • the fuzzy search module 63 includes:
  • a keyword obtaining unit 631 configured to perform word segmentation processing on the identification version field, and obtain keywords in the supplier information according to a result of the word segmentation processing;
  • the SQL search unit 632 is configured to obtain black name supplier information including the keywords from the black list database based on the SQL language, so as to obtain black name supplier information associated with the supplier information.
  • the fuzzy search module 63 includes:
  • the word segmentation processing unit 633 is configured to perform word segmentation processing on the identification version field and word segmentation processing on each black name supplier information in the blacklist database;
  • Word segmentation sequence generating unit 634 for each black name supplier information and identification version field to be compared, according to the result of word segmentation processing, generate a word segmentation sequence corresponding to the black name supplier information and identification version field ;
  • a word frequency vector generating unit 635 configured to generate a word frequency vector corresponding to the black name supplier information to be compared and a word frequency vector corresponding to a recognition version field according to the word segmentation sequence;
  • a similarity calculation unit 636 configured to calculate a cosine similarity between the word frequency vector corresponding to the black name supplier information to be compared and the word frequency vector corresponding to the recognition version field;
  • the obtaining unit 637 is configured to obtain black name supplier information whose cosine similarity is greater than or equal to a preset similarity threshold.
  • the matching module 64 includes:
  • a matching unit 641 configured to perform a first matching process on the black name supplier information associated with the input version field and the supplier information, and the black name associated with the identification version field and the supplier information Supplier information performs a second matching process;
  • a first generating unit 642 is configured to generate the black name supplier information, the input version field, and the identification version field obtained by the two matching processes according to the black name supplier information obtained by the first matching process or the second matching process. Verification result
  • the second generating unit 643 is configured to generate a verification result according to the black name supplier information obtained by the second matching process if the black name supplier information obtained by the two matching processes is different.
  • the building module 61 includes:
  • a first construction unit 611 configured to obtain black name user information from a preset credit system, and add the black name user information to the black list database as black name supplier information; and / or
  • the second constructing unit 612 is configured to filter suppliers whose transaction bad debts reach a preset amount threshold and the aging reaches a preset aging threshold according to the transaction bad debt records between related suppliers, and add the information as black name supplier information. To the blacklist library.
  • the building module 61 further includes:
  • the third constructing unit 613 is used for importing the holding relationship between enterprises; for each black name supplier information in the black list database, obtaining the holding company corresponding to each black name supplier from the holding relationship, The holding company is added to the blacklist database as the black name supplier information.
  • Each module in the above-mentioned invoice verification device may be implemented in whole or in part by software, hardware, and a combination thereof.
  • the above-mentioned modules may be embedded in the hardware in or independent of the processor in the computer device, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.
  • a computer device is provided.
  • the computer device may be a server, and its internal structure diagram may be as shown in FIG. 7.
  • the computer device includes a processor, a memory, a network interface, and a database connected through a system bus.
  • the processor of the computer device is used to provide computing and control capabilities.
  • the memory of the computer device includes a non-volatile storage medium and an internal memory.
  • the non-volatile storage medium stores an operating system, computer-readable instructions, and a database.
  • the internal memory provides an environment for the operation of the operating system and computer-readable instructions in a non-volatile storage medium.
  • the database of the computer equipment is used to store black name supplier information and perform operations of adding, deleting, and modifying the black name supplier information on file.
  • the network interface of the computer device is used to communicate with an external terminal through a network connection.
  • the computer-readable instructions are executed by a processor to implement an invoice verification method.
  • a computer device including a memory, a processor, and computer-readable instructions stored on the memory and executable on the processor.
  • the processor executes the computer-readable instructions
  • the implementation is as shown in FIG. 2 to FIG. 5 the steps described in any of the embodiments.
  • a computer-readable storage medium is provided, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by a processor, the steps shown in any one of the embodiments of FIG. 2 to FIG. 5 are implemented.
  • the computer-readable instructions can be stored in a non-volatile computer.
  • Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory can include random access memory (RAM) or external cache memory.
  • RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
  • SRAM static RAM
  • DRAM dynamic RAM
  • SDRAM synchronous DRAM
  • DDRSDRAM dual data rate SDRAM
  • ESDRAM enhanced SDRAM
  • SLDRAM synchronous chain (Synchlink) DRAM
  • SLDRAM synchronous chain (Synchlink) DRAM
  • Rambus direct RAM
  • DRAM direct memory bus dynamic RAM
  • RDRAM memory bus dynamic RAM

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Abstract

L'invention concerne un procédé de vérification de facture. Le procédé de vérification consiste à : construire une bibliothèque de liste noire, la bibliothèque de liste noire comprenant des informations de fournisseur de nom noir; lorsqu'une requête de vérification envoyée par un client est obtenue, obtenir des informations de fournisseur sur une facture à vérifier à partir de la requête de vérification, les informations de fournisseur comprenant un champ version d'identification et un champ version d'entrée du fournisseur; effectuer une recherche floue dans la bibliothèque de liste noire au moyen du champ version d'identification pour obtenir des informations de fournisseur de nom noir associées aux informations de fournisseur; sur la base des informations de fournisseur de nom de noir associées aux informations de fournisseur, effectuer un traitement de mise en correspondance en utilisant respectivement le champ version d'entrée et le champ version d'identification en tant que groupe de mots fixe, et générer un résultat de vérification selon le traitement de mise en correspondance; et envoyer le résultat de vérification au client, de telle sorte que le client reçoit et délivre en sortie le résultat de vérification. La présente invention résout le problème existant d'incapacité de vérifier un fournisseur lorsque le paiement est effectué sur le fournisseur, et améliore la précision de vérification.
PCT/CN2018/094362 2018-05-31 2018-07-03 Procédé et appareil de vérification de facture, dispositif informatique et support de stockage WO2019227576A1 (fr)

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