Account intelligent cancel-after-verification method, device and equipment and storage medium

Abstract

The invention discloses an intelligent account verification method and device, equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining the to-be-processed data of a target customer when the account payment of the target customer is monitored; loading target rule information of the target client in an enabled state from a target database through a rule generator of a rule matching engine; based on the target rule information, performing data filtering on the to-be-processed data to obtain target to-be-processed data; and based on an intelligent matching model of the rule matching engine, performing service screening on the target to-be-processed data to obtain target screened data, and performing cancel-after-verification processing on the target screened data. According to the invention, intelligent verification of a large amount of accounts can be realized. The account verification efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, equipment and storage medium for intelligent write-off of accounts receivable. Background Art

[0002] Traditional accounts receivable write-off business is mostly done manually. Business personnel check and write off accounts one by one through the collection list provided by the customer. At the same time, a manual ledger needs to be maintained to manage and record the collection and write-off of all buyer accounts payable. This is time-consuming and labor-intensive and involves certain business and operational risks.

[0003] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of this application is to provide a method, device, equipment and storage medium for intelligent receivables settlement, aiming to solve the technical problems that the traditional receivables settlement method is time-consuming and labor-intensive and has business risks and operational risks.

[0005] To achieve the above purpose, the present application proposes an intelligent remittance verification method, which includes:

[0006] When monitoring the target customer's account repayment, obtain the target customer's pending data;

[0007] Loading target rule information of the target customer in an enabled state from a target database through a rule generator of a rule matching engine;

[0008] Based on the target rule information, filtering the data to be processed to obtain target data to be processed;

[0009] Based on the intelligent matching model of the rule matching engine, the target data to be processed is subjected to business screening to obtain target screening data, and the target screening data is then processed for verification.

[0010] In one embodiment, the step of performing business screening on the target data to be processed based on the intelligent matching model of the rule matching engine to obtain the target screening data includes:

[0011] In the transaction amount scenario, the smart matching model of the rule matching engine is set to the amount priority strategy; or, in the no transaction amount scenario, the smart matching model of the rule matching engine is set to the date priority strategy;

[0012] Based on the intelligent matching model, the target data to be processed is subjected to business screening to obtain initial screening data;

[0013] After receiving the manual calibration information for the initial screening data, target screening data is determined according to the manual calibration information, and the smart matching model is fine-tuned based on the target screening data.

[0014] In one embodiment, the step of performing business screening on the target data to be processed based on the intelligent matching model to obtain initial screening data includes:

[0015] When the smart matching model is set to the amount priority strategy, the target data to be processed is traversed through the smart matching model, and it is determined whether there is account data identical to the account repayment in the target data to be processed;

[0016] If the target data to be processed contains the same account data as the account repayment, the account data is allocated as the account repayment;

[0017] If there is no account data identical to the account repayment in the target data to be processed, the account data with the smallest amount is selected from the target data to be processed as the account repayment allocation, and the allocated account repayment is re-allocated based on the amount priority strategy until all account repayment allocations are completed;

[0018] Use all account repayment allocations as initial screening data.

[0019] In one embodiment, before the step of loading the target rule information of the target customer in an enabled state from the target database through the rule generator of the rule matching engine, the step further includes:

[0020] The rule information is stored through the target database, wherein the rule information includes a rule table, a rule object table, a rule detail table and an automatic task configuration table, wherein the rule table is used to record the configuration rules of different customers, the rule object table is used to define the object scope in which different configuration rules are effective, the rule detail table is used to record the actual comparison conditions, and the automatic task configuration table is used to configure the automatic task processing time and time interval for different customers.

[0021] In one embodiment, the step of loading the target rule information of the target customer in an enabled state from the target database through the rule generator of the rule matching engine includes:

[0022] Loading the target rule in the enabled state for the target customer from the rule table of the target database through the rule generator of the rule matching engine;

[0023] Acquire a target rule object from the rule object table according to the rule identifier of the target rule;

[0024] Acquire the target rule details in the enabled state from the rule details table through the rule identifier of the target rule;

[0025] Target rule information is determined according to the target rule, the target rule object, and the target rule details.

[0026] In one embodiment, the step of filtering the data to be processed based on the target rule information to obtain the target data to be processed includes:

[0027] When the target rule is an amount matching rule, the first data to be processed that is less than or equal to the account repayment is filtered out from the data to be processed; or, when the target rule is a date matching rule, the first data to be processed that is within the account repayment period is filtered out from the data to be processed;

[0028] When the target rule object is a buyer of the remittance or a payment receiving account, filtering out the second data to be processed from the first data to be processed according to the buyer of the remittance or the payment receiving account;

[0029] An actual comparison condition is determined according to the target rule details, and target data to be processed is screened out from the second data to be processed according to the actual comparison condition, wherein the actual comparison condition includes a comparison field, a comparison method, and a comparison element.

[0030] In one embodiment, when monitoring the target customer's account repayment, the step of obtaining the target customer's pending data includes:

[0031] Determining a loading mode of the target customer;

[0032] When the loading mode is the settlement engine mode, obtaining the target customer's to-be-processed data from the push transaction of the account system;

[0033] When the loading mode is a database model, all the to-be-processed data of the target customer are loaded from the target database.

[0034] In addition, to achieve the above purpose, the present application also proposes an intelligent account remittance verification device, the intelligent account remittance verification device comprising:

[0035] An acquisition module is used to acquire the target customer's pending data when monitoring the target customer's account repayment;

[0036] A loading module, used for loading target rule information of the target customer in an enabled state from a target database through a rule generator of a rule matching engine;

[0037] A filtering module, used for filtering the data to be processed based on the target rule information to obtain target data to be processed;

[0038] The screening module is used to perform business screening on the target data to be processed based on the intelligent matching model of the rule matching engine, obtain target screening data, and perform verification processing on the target screening data.

[0039] In addition, to achieve the above-mentioned purpose, the present application also proposes an intelligent account settlement device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the intelligent account settlement method as described above.

[0040] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the intelligent account write-off method as described above are implemented.

[0041] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the intelligent account write-off method as described above.

[0042] One or more technical solutions proposed in this application have at least the following technical effects:

[0043] The intelligent receivables write-off method, device, equipment and storage medium proposed in the present application obtain the target customer's pending data when monitoring the target customer's account repayment; load the target rule information of the target customer in an enabled state from the target database through the rule generator of the rule matching engine; filter the pending data based on the target rule information to obtain the target pending data; perform business screening on the target pending data based on the intelligent matching model of the rule matching engine to obtain target screened data, and write off the target screened data, which solves the technical problems that the traditional receivables write-off method is time-consuming and labor-intensive and has business risks and operational risks. Compared with the prior art, the present application can automatically load the pending data after monitoring the customer's account repayment, and automatically write off the accounts that meet the automatic write-off rules after gradually comparing the pending data through the rule matching engine, which can not only improve the efficiency of receivables write-off, but also effectively reduce the business risks and operational risks generated by manual matching. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0046] Figure 1 A flowchart of the first embodiment of the intelligent receivables write-off method for this application;

[0047] Figure 2 A schematic diagram of the automatic processing flow provided in Example 1 of the intelligent write-off method for accounts receivable of this application;

[0048] Figure 3 An entity-relationship diagram provided for the first embodiment of the method for intelligent write-off of accounts receivable of this application;

[0049] Figure 4 A schematic diagram of the overall internal logic flow provided for the first embodiment of the intelligent receivables write-off method for this application;

[0050] Figure 5 A flow chart of the second embodiment of the intelligent receivables write-off method for this application;

[0051] Figure 6 This is a schematic diagram of the module structure of the intelligent account write-off device of the embodiment of the present application;

[0052] Figure 7 This is a schematic diagram of the device structure of the hardware operating environment involved in the intelligent account write-off method in the embodiment of the present application.

[0053] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0054] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0055] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0056] The main solution of the embodiment of the present application is: when monitoring the target customer's account repayment, obtain the target customer's pending data; load the target customer's enabled target rule information from the target database through the rule generator of the rule matching engine; based on the target rule information, filter the pending data to obtain the target pending data; based on the intelligent matching model of the rule matching engine, perform business screening on the target pending data to obtain the target screening data, and write off the target screening data. The present application can realize the intelligent write-off of large batches of accounts receivable.

[0057] It can be seen from the above embodiments that by acquiring the target customer's pending data when monitoring the target customer's account repayment; loading the target rule information of the target customer in an enabled state from the target database through the rule generator of the rule matching engine; filtering the pending data based on the target rule information to obtain the target pending data; performing business screening on the target pending data based on the intelligent matching model of the rule matching engine to obtain target screened data, and performing write-off processing on the target screened data, the technical problems that the traditional account write-off method is time-consuming and labor-intensive and has business risks and operational risks are solved. Compared with the prior art, the present application can automatically load the pending data after monitoring the customer's account repayment, and automatically write off the accounts that meet the automatic write-off rules after gradually comparing the pending data through the rule matching engine, which can not only improve the efficiency of account write-off, but also effectively reduce the business risks and operational risks generated by manual matching.

[0058] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, an intelligent receivables verification device, etc. The following takes the intelligent receivables verification device as an example to illustrate this embodiment and the following embodiments.

[0059] Based on this, the present application embodiment provides a method for intelligently writing off accounts receivable, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the intelligent receivables write-off method for this application.

[0060] In this embodiment, the intelligent receivables write-off method includes steps S10 to S40:

[0061] Step S10, when monitoring the target customer's account repayment, obtaining the target customer's pending data;

[0062] It should be noted that when the target customer's account repayment is monitored, the Quartz job scheduling framework is used through Java to implement the scheduled polling execution of account write-offs. The Quartz job scheduling framework can also be replaced with the Xxl-job distributed task scheduling framework.

[0063] In a specific implementation, the target customer's to-be-processed data may be started and loaded according to the automatic processing time and time interval configured for the target customer in the automatic task configuration table in the target database.

[0064] In a feasible implementation manner, the step of obtaining the target customer's pending data when monitoring the target customer's account repayment includes: determining the target customer's loading mode when monitoring the target customer's account repayment; when the loading mode is the settlement engine mode, obtaining the target customer's pending data from the push transaction of the account system; when the loading mode is the database model, loading all the target customer's pending data from the target database.

[0065] It should be noted that if Figure 2 As shown, there are two modes for obtaining the pending data of the target customers, namely the settlement engine mode and the database mode. In the settlement engine mode, the accounting system actively pushes transactions through Kafka message notifications. In the settlement engine mode, the account is matched to obtain the effective rules, and then the accounts receivable data that meet all the conditions of the effective rules are matched. According to the current transaction amount, the accounts receivable data is searched for whether there is completely equal account receivable data. If there is completely equal, it is directly processed. If not, it is allocated from a small amount first. After each allocation, it is compared whether there is an equal amount. After all the amounts are allocated, all the accounts receivable with allocated amounts are uniformly written off and the data is updated. The database model loads all pending account receivable data from the database, and automatically writes off the accounts receivable that meet the automatic write-off rules after filtering them out through the rule engine.

[0066] It should be noted that a single customer can generally only configure one mode, such as only the settlement engine mode, or only the database mode.

[0067] In a feasible implementation, before the step of loading the target rule information of the target customer in an enabled state from the target database through the rule generator of the rule matching engine, it also includes: storing the rule information through the target database, wherein the rule information includes a rule table, a rule object table, a rule detail table and an automatic task configuration table, the rule table is used to record the configuration rules of different customers, the rule object table is used to define the object scope of different configuration rules, the rule detail table is used to record the actual comparison conditions, and the automatic task configuration table is used to configure the automatic task processing time and time interval for different customers.

[0068] In the specific implementation, Figure 3 As shown, the customer's rule information can be stored through the MySQL database (i.e., the target database), which mainly stores four tables, namely the rule table, rule object table, rule detail table, and automatic task configuration table, which are used to store configurations for different business scenarios and different customers. Among them, the rule table is the main table, which mainly records different rules configured for different customers. The rules have an enabled state and an disabled state; the rule object table is mainly used for the scope of objects for which the rule is effective, such as the buyer of the account, or the collection account number and other information; the rule detail table is mainly used to record the actual comparison conditions, including comparison fields, comparison methods, comparison values ​​and other elements, and can also be enabled or disabled separately for the details; the automatic task configuration table mainly configures the automatic processing time and time interval for different customers.

[0069] Step S20, loading target rule information of the target customer in an enabled state from a target database through a rule generator of a rule matching engine;

[0070] It should be noted that the rule matching engine includes a rule generator and an intelligent matching model, wherein the rule generator is mainly responsible for generating target rule information for target customers, and the intelligent matching model is mainly responsible for data matching and selection.

[0071] In a feasible implementation manner, the step of loading the target rule information of the target customer in an enabled state from the target database through the rule generator of the rule matching engine includes: loading the target rule of the target customer in an enabled state from the rule table of the target database through the rule generator of the rule matching engine; obtaining the target rule object from the rule object table through the rule identifier of the target rule; obtaining the target rule details in an enabled state from the rule details table through the rule identifier of the target rule; and determining the target rule information according to the target rule, the target rule object and the target rule details.

[0072] It should be noted that the rule table mainly records different rules configured for different customers. The rules have an activation state and an inactivation state, and can be configured by the account manager according to the actual situation. The rules can be amount matching rules, date matching rules, etc. The target rule object can be the buyer of the account, the receiving account, etc. The target rule details can be the actual comparison conditions, including comparison fields, comparison methods, comparison values ​​and other elements.

[0073] In the specific implementation, the rule generator can load all the enabled rules maintained by the target customer, and then obtain the rule object and rule details from the rule object table and the rule detail table respectively through the rule ID, and assemble them into rule entities for subsequent data filtering.

[0074] Step S30, filtering the data to be processed based on the target rule information to obtain target data to be processed;

[0075] In a feasible implementation manner, the step of filtering the data to be processed based on the target rule information to obtain the target data to be processed includes: when the target rule is an amount matching rule, filtering out the first data to be processed that is less than or equal to the account repayment from the data to be processed; or, when the target rule is a date matching rule, filtering out the first data to be processed that is within the account repayment period from the data to be processed; when the target rule object is an account buyer or a collection account number, filtering out the second data to be processed from the first data to be processed according to the account buyer or the collection account number; determining the actual comparison conditions according to the target rule details, and filtering out the target data to be processed from the second data to be processed according to the actual comparison conditions, wherein the actual comparison conditions include comparison fields, comparison methods, and comparison elements.

[0076] It should be noted that when the target rule is the amount matching rule, it means that the data needs to be initially screened according to the account amount. For example, all account data that is less than or equal to the account repayment needs to be screened out. This ensures that each time the account is repaid, the account data that is equal to the account repayment can be written off. Or, all account data within the account repayment period needs to be screened out. This ensures that all account data that is about to expire or has expired can be written off as soon as possible.

[0077] It should be noted that the target rule object can be set in advance for each customer by the account manager, for example, set to the account buyer or the payment account, so that the relevant account data can be further filtered out based on the account buyer or the payment account.

[0078] Step S40, based on the intelligent matching model of the rule matching engine, business screening is performed on the target data to be processed to obtain target screening data, and verification processing is performed on the target screening data.

[0079] In the specific implementation, Figure 4 As shown in the figure, the rule matching engine has two core components: rule generator and intelligent matching model; rule generator: mainly responsible for generating rules, by loading all the rules maintained by the current customer that are enabled, and then obtaining the rule object and rule details through the rule ID, assembling them into rule entities for subsequent data filtering; intelligent matching model: mainly responsible for data matching and selection, based on the GLM large language model, through the training of large quantities of accounts receivable write-off data, continuously optimizing the matching and selection logic.

[0080] This embodiment obtains the target customer's pending data when monitoring the target customer's account repayment; loads the target rule information of the target customer in an enabled state from the target database through the rule generator of the rule matching engine; filters the pending data based on the target rule information to obtain target pending data; performs business screening on the target pending data based on the intelligent matching model of the rule matching engine to obtain target screened data, and writes off the target screened data, thereby solving the technical problems that the traditional account write-off method is time-consuming and labor-intensive and has business risks and operational risks. Compared with the prior art, the present application can automatically load the pending data after monitoring the customer's account repayment, and automatically write off the accounts that meet the automatic write-off rules after gradually comparing the pending data through the rule matching engine, which can not only improve the efficiency of account write-off, but also effectively reduce the business risks and operational risks generated by manual matching.

[0081] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 5 , step S40 also includes steps S401 to S403:

[0082] Step S401, in a transaction amount scenario, setting the smart matching model of the rule matching engine to an amount priority strategy; or, in a scenario without a transaction amount, setting the smart matching model of the rule matching engine to a date priority strategy;

[0083] It should be noted that if there is a transaction amount, the amount priority strategy will be used for matching; if there is no transaction amount, the date priority strategy will be used for matching.

[0084] It can be understood that the transaction amount scenario refers to the scenario where some accounts are paid before they are due; the customer has no transaction amount scenario refers to the scenario where all accounts are due but the customer has not paid.

[0085] It should be noted that the date priority strategy is generally used in scenarios with no transaction amount. The accounts receivable data that are due soon will be screened out for write-off first, and then they will also be subject to manual calibration to continuously optimize the matching accuracy of the matching model.

[0086] Step S402, based on the intelligent matching model, performing business screening on the target data to be processed to obtain initial screening data;

[0087] It should be noted that the smart matching model refers to a trained large language model; the smart matching model can be based on the GLM large language model or other commonly used large language models.

[0088] In the specific implementation, data with exactly equal amounts can be found from the target data to be processed based on the current transaction amount (i.e., the account repayment amount). If there is no exactly equal data, the allocation starts with the small-amount account data. After each allocation, compare whether the account data is equal to the remaining amount. After all current transaction amounts have been allocated, all account data with allocated amounts are used as initial screening data.

[0089] Step S403, after receiving the manual calibration information for the initial screening data, determining the target screening data according to the manual calibration information, and fine-tuning the intelligent matching model based on the target screening data.

[0090] It should be noted that after obtaining the initial screening data, the account manager needs to perform manual calibration (that is, receive the manual calibration information from the account manager), and then feed the adjusted target screening data back to the large language model for training, in order to continuously optimize the matching effect and selection logic of the intelligent matching model.

[0091] In a feasible implementation manner, the step of performing business screening on the target data to be processed based on the intelligent matching model to obtain initial screening data includes: when the intelligent matching model is set to an amount priority strategy, traversing the target data to be processed through the intelligent matching model, and determining whether there is account data identical to the account repayment in the target data to be processed; if there is account data identical to the account repayment in the target data to be processed, the account data is used as account repayment allocation; if there is no account data identical to the account repayment in the target data to be processed, the minimum amount of account data is screened out from the target data to be processed as account repayment allocation, and the allocated account repayments are re-allocated based on the amount priority strategy until all account repayment allocations are completed; all account repayment allocations are used as initial screening data.

[0092] It should be noted that, based on the current transaction amount (i.e. the account repayment amount), check whether there is account repayment data with exactly equal amount in the account repayment data. If there is, it will be processed directly. If there is no exactly equal, it will be allocated starting from a small amount first. After each allocation, check again whether there is account repayment data in the account repayment data that is equal to the allocated account repayment amount. After all the amounts have been allocated, all the account repayment data with allocated amounts will be used as the initial screening data for unified write-off.

[0093] In this embodiment, in the transaction amount scenario, the intelligent matching model of the rule matching engine is set to the amount priority strategy; or, in the scenario without transaction amount, the intelligent matching model of the rule matching engine is set to the date priority strategy; based on the intelligent matching model, the target data to be processed is subjected to business screening to obtain initial screening data; after receiving manual calibration information for the initial screening data, the target screening data is determined according to the manual calibration information, and the intelligent matching model is fine-tuned based on the target screening data. In the above manner, the account data for write-off can be quickly screened out based on the intelligent matching model with a pre-configured priority strategy, thereby improving the efficiency of write-off.

[0094] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the intelligent receivables write-off method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0095] This application also provides an intelligent account remittance verification device, please refer to Figure 6 , the intelligent account write-off device comprises:

[0096] An acquisition module 10 is used to acquire the target customer's pending data when monitoring the target customer's account repayment;

[0097] A loading module 20, used for loading target rule information of the target customer in an enabled state from a target database through a rule generator of a rule matching engine;

[0098] A filtering module 30, configured to filter the data to be processed based on the target rule information to obtain target data to be processed;

[0099] The screening module 40 is used to perform business screening on the target data to be processed based on the intelligent matching model of the rule matching engine, obtain target screening data, and perform verification processing on the target screening data.

[0100] The intelligent receivables verification device provided by the present application adopts the intelligent receivables verification method in the above embodiment, which can solve the technical problems that the traditional receivables verification method is time-consuming and labor-intensive and has business risks and operational risks. Compared with the prior art, the beneficial effects of the intelligent receivables verification device provided by the present application are the same as the beneficial effects of the intelligent receivables verification method provided by the above embodiment, and the other technical features of the intelligent receivables verification device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0101] The present application provides an intelligent receivables settlement device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the intelligent receivables settlement method in the above-mentioned embodiment 1.

[0102] Reference below Figure 7 , which shows a schematic diagram of the structure of an intelligent receivables verification device suitable for implementing the embodiment of the present application. The intelligent receivables verification device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The intelligent receivables settlement device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0103] like Figure 7As shown, the intelligent remittance verification device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM) 1004. Various programs and data required for the operation of the intelligent remittance verification device are also stored in RAM 1004. The processing device 1001, ROM 1002, and RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the intelligent remittance device for accounts receivable to communicate wirelessly or wired with other devices to exchange data. Although the figure shows an intelligent remittance device for accounts receivable with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0104] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0105] The intelligent receivables verification device provided by the present application adopts the intelligent receivables verification method in the above embodiment, which can solve the technical problems that the traditional receivables verification method is time-consuming and labor-intensive and has business risks and operational risks. Compared with the prior art, the beneficial effects of the intelligent receivables verification device provided by the present application are the same as the beneficial effects of the intelligent receivables verification method provided by the above embodiment, and the other technical features in the intelligent receivables verification device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0106] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0107] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0108] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the intelligent account write-off method in the above-mentioned embodiment.

[0109] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0110] The computer-readable storage medium may be included in the intelligent receivables verification device; or it may exist independently without being assembled into the intelligent receivables verification device.

[0111] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the intelligent receivables verification device, the intelligent receivables verification device: when monitoring the target customer's account repayment, obtain the target customer's pending data; load the target customer's enabled target rule information from the target database through the rule generator of the rule matching engine; based on the target rule information, filter the pending data to obtain the target pending data; based on the intelligent matching model of the rule matching engine, perform business screening on the target pending data to obtain the target screening data, and perform cancellation processing on the target screening data. This application can realize the intelligent cancellation of large quantities of accounts receivable.

[0112] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0113] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0114] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0115] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned intelligent receivables verification method, and can solve the technical problems that the traditional receivables verification method is time-consuming and labor-intensive and has business risks and operational risks. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the intelligent receivables verification method provided in the above-mentioned embodiment, and will not be repeated here.

[0116] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned intelligent account write-off method when executed by a processor.

[0117] The computer program product provided by this application can solve the technical problems that the traditional account reimbursement method is time-consuming and labor-intensive and has business risks and operational risks. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the account reimbursement intelligent reimbursement method provided by the above embodiment, which will not be repeated here.

[0118] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for intelligently writing off accounts receivable, characterized in that: The method comprises: When monitoring the target customer's account repayment, obtain the target customer's pending data; Loading target rule information of the target customer in an enabled state from a target database through a rule generator of a rule matching engine; Based on the target rule information, filtering the data to be processed to obtain target data to be processed; Based on the intelligent matching model of the rule matching engine, the target data to be processed is subjected to business screening to obtain target screening data, and the target screening data is then processed for verification.

2. The method according to claim 1, characterized in that The step of performing business screening on the target data to be processed based on the intelligent matching model of the rule matching engine to obtain the target screening data includes: In the transaction amount scenario, the smart matching model of the rule matching engine is set to the amount priority strategy; or, in the no transaction amount scenario, the smart matching model of the rule matching engine is set to the date priority strategy; Based on the intelligent matching model, the target data to be processed is subjected to business screening to obtain initial screening data; After receiving the manual calibration information for the initial screening data, target screening data is determined according to the manual calibration information, and the smart matching model is fine-tuned based on the target screening data.

3. The method according to claim 2, characterized in that The step of performing business screening on the target data to be processed based on the intelligent matching model to obtain initial screening data includes: When the smart matching model is set to the amount priority strategy, the target data to be processed is traversed through the smart matching model, and it is determined whether there is account data identical to the account repayment in the target data to be processed; If the target data to be processed contains the same account data as the account repayment, the account data is allocated as the account repayment; If there is no account data identical to the account repayment in the target data to be processed, the account data with the smallest amount is selected from the target data to be processed as the account repayment allocation, and the allocated account repayment is re-allocated based on the amount priority strategy until all account repayment allocations are completed; Use all account repayment allocations as initial screening data.

4. The method according to claim 1, characterized in that Before the step of loading the target rule information of the target customer in an enabled state from the target database through the rule generator of the rule matching engine, the step further includes: The rule information is stored through the target database, wherein the rule information includes a rule table, a rule object table, a rule detail table and an automatic task configuration table, wherein the rule table is used to record the configuration rules of different customers, the rule object table is used to define the object scope in which different configuration rules are effective, the rule detail table is used to record the actual comparison conditions, and the automatic task configuration table is used to configure the automatic task processing time and time interval for different customers.

5. The method according to claim 4, characterized in that The step of loading the target rule information of the target customer in an enabled state from the target database through the rule generator of the rule matching engine comprises: Loading the target rule in the enabled state for the target customer from the rule table of the target database through the rule generator of the rule matching engine; Acquire a target rule object from the rule object table according to the rule identifier of the target rule; Acquire the target rule details in the enabled state from the rule details table through the rule identifier of the target rule; Target rule information is determined according to the target rule, the target rule object, and the target rule details.

6. The method according to claim 5, characterized in that The step of filtering the data to be processed based on the target rule information to obtain the target data to be processed includes: When the target rule is an amount matching rule, the first data to be processed that is less than or equal to the account repayment is filtered out from the data to be processed; or, when the target rule is a date matching rule, the first data to be processed that is within the account repayment period is filtered out from the data to be processed; When the target rule object is a buyer of the remittance or a payment receiving account, filtering out the second data to be processed from the first data to be processed according to the buyer of the remittance or the payment receiving account; An actual comparison condition is determined according to the target rule details, and target data to be processed is screened out from the second data to be processed according to the actual comparison condition, wherein the actual comparison condition includes a comparison field, a comparison method, and a comparison element.

7. The method according to claim 1, characterized in that When monitoring the target customer's account repayment, the step of obtaining the target customer's pending data includes: Determining a loading mode of the target customer; When the loading mode is the settlement engine mode, obtaining the target customer's to-be-processed data from the push transaction of the account system; When the loading mode is a database model, all the to-be-processed data of the target customer are loaded from the target database.

8. An intelligent account write-off device, characterized in that: The device comprises: An acquisition module is used to acquire the target customer's pending data when monitoring the target customer's account repayment; A loading module, used for loading target rule information of the target customer in an enabled state from a target database through a rule generator of a rule matching engine; A filtering module, used for filtering the data to be processed based on the target rule information to obtain target data to be processed; The screening module is used to perform business screening on the target data to be processed based on the intelligent matching model of the rule matching engine, obtain target screening data, and perform verification processing on the target screening data.

9. An intelligent account receivables verification device, characterized in that: The device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the intelligent receivables write-off method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the intelligent receivables write-off method according to any one of claims 1 to 7 are implemented.

Images