Intelligent account checking method, system and equipment based on dynamic link and storage medium

Abstract

The invention relates to an intelligent reconciliation method, system and device based on a dynamic link and a storage medium. Link parameters are dynamically loaded, a data link connected with an external system is created, and the dynamic link is generated; obtaining order data from an enterprise internal system through the dynamic link; acquiring bill data from a third-party system through the dynamic link; matching the order data with the bill data according to a preset multi-level matching rule, and performing account checking and account sales processing on the successfully matched data; according to the account checking method, multiple internal and external systems are flexibly accessed, the manual account checking efficiency is improved, and the account checking error rate is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of financial automatic reconciliation, and in particular to an intelligent reconciliation method, system, device and storage medium based on a dynamic link. BACKGROUND

[0002] In today's enterprise financial operations, reconciliation is a crucial and tedious task. Traditional inter-enterprise reconciliation, especially with external institutions such as banks and payment platforms, generally relies on manual or semi-automated processes. In traditional reconciliation schemes, financial personnel need to manually export internal accounting data (order flow) from enterprise internal systems (such as ERP systems) and then check it against electronic statements provided by external institutions (such as banks). In existing reconciliation schemes, the core reconciliation process is highly dependent on manual work, requiring financial personnel to verify key information such as transaction date, amount, and voucher number for each transaction record to identify matching items and investigate discrepancies.

[0003] As can be seen from the above, the current reconciliation process is not only highly repetitive, but also consumes a lot of manpower and time. Therefore, the entire process is easily disturbed by human errors, information transmission lags, and internal "system silos" (such as business systems and financial systems not fully integrated), resulting in low reconciliation efficiency and high error rates, posing a risk of funds, especially during peak periods such as the end of the month or quarter, when reconciliation workload explodes, putting a lot of pressure on financial personnel and severely restricting the operational efficiency and financial automation level of the enterprise. In addition, existing systems often require customized development when interfacing with different external systems, resulting in long interfacing periods, high development costs, and strong system coupling, making it difficult to adapt flexibly and quickly to the development and changes of enterprise business. SUMMARY

[0004] The present application provides an intelligent reconciliation method, system, device and storage medium based on a dynamic link to at least solve the problems of low reconciliation efficiency and high error rate in traditional reconciliation schemes in related technologies.

[0005] In a first aspect, the present application provides an intelligent reconciliation method based on a dynamic link, comprising: loading link parameters dynamically, creating a data link connected to an external system, and generating a dynamic link; obtaining order data from an enterprise internal system through the dynamic link; obtaining bill data from a third-party system through the dynamic link; matching the order data and the bill data according to a preset multi-level matching rule, and performing reconciliation and cancellation processing on the matching data; generating an exception work order for matching exceptions and sending a reminder message to the user.

[0006] Preferably, the dynamic loading link parameter creates a data link connected with an external system, generates a dynamic link, and comprises: obtaining link configuration information from a database or a configuration file, the link configuration information comprising a link protocol type, a connection parameter, and a data conversion parameter; judging whether the link to be generated is a link type supported by the enterprise internal system according to the link configuration information; if the judgment result is yes, creating a corresponding standard client according to the connection parameter and the data conversion parameter to obtain a dynamic link instance; if the judgment result is no, loading a preset link script through a dynamic link engine and executing the link script to create a dynamic link instance; storing the created dynamic link instance in a server of the enterprise internal system as a data channel for obtaining the order data and the bill data.

[0007] Preferably, the connection parameter comprises at least one of an IP address, a port, a timeout time, and an interface address; and the conversion parameter comprises at least one of an interface input parameter, an interface output parameter, and a mapping relationship between an output parameter and a system field.

[0008] Preferably, the obtaining of the bill data from the third-party system through the dynamic link comprises: calling a third-party system API through the dynamic link and automatically pulling a bill file; judging whether the bill file is in a preset format, if the judgment result is yes, directly extracting bill data from the bill file; if the judgment result is no, parsing the bill file through a preset intelligent data recognition algorithm to obtain bill data in a required format; saving the bill data into a structured database.

[0009] Preferably, the preset multi-level matching rule comprises: an exact matching mode for judging whether there is unique corresponding order data of the bill data on its occurrence day and before the occurrence day; a summary matching mode for judging whether there is unique corresponding multiple order data of the bill data on its occurrence day and before the occurrence day; a fuzzy matching mode for judging whether there is order data that can be matched with the bill data on its occurrence day and before the occurrence day.

[0010] Preferably, the matching of the order data and the bill data according to the preset multi-level matching rule and the account reconciliation processing of the matched data comprise: Loop through the unreconciled bill and order data, perform precise matching according to the exact matching mode, and mark the bill and order data that are successfully matched. The system iterates through billing and order data that fail to match precisely, then matches them according to the summary matching mode, and marks the billing and order data that successfully match in the summary. Obtain fuzzy matching elements, loop through and summarize the bill data and order data that failed to match, match according to the fuzzy matching elements and the fuzzy matching mode, mark the bill data and order data that successfully matched, and generate a verification work order for manual verification. Perform write-off processing on the marked billing and order data.

[0011] Preferably, generating an exception work order for matching abnormal bills includes: Obtain matching criteria. During the matching process, mark bill data and order data that exceed the matching criteria as unmatched records and generate an exception work order.

[0012] Secondly, this application provides an intelligent reconciliation system based on dynamic links, used to implement any of the above-mentioned intelligent reconciliation methods based on dynamic links, the system comprising: The dynamic link engine is used to dynamically load link parameters, create data links for connecting to external systems, and generate dynamic links. The order module is used to obtain order data from the enterprise's internal system through the dynamic link; The billing module is used to obtain billing data from a third-party system via the dynamic link; The reconciliation module is used to match the order data with the bill data according to a preset multi-level matching rule, and to process the successfully matched data for reconciliation and cancellation. The work order module is used to generate abnormal work orders for bills that match abnormalities and send reminder messages to users.

[0013] Thirdly, this application provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the intelligent reconciliation method based on dynamic links as described in the first aspect above.

[0014] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the intelligent reconciliation method based on dynamic links as described in the first aspect above.

[0015] The intelligent reconciliation method, system, device, and storage medium based on dynamic links provided in this application have at least the following technical effects: By dynamically loading link parameters, a data link connecting to external systems is created, generating a dynamic link. Order data is obtained from the enterprise's internal system through the dynamic link; billing data is obtained from a third-party system through the dynamic link; the order data and billing data are matched according to a preset multi-level matching rule, and the successfully matched data is reconciled and cleared; abnormal bills are generated into an exception work order and a reminder message is sent to the user. This application, through the above reconciliation method, flexibly accesses multiple internal and external systems, improves the efficiency of manual reconciliation, and reduces the reconciliation error rate.

[0016] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart of an intelligent reconciliation method based on dynamic links in one embodiment of this application; Figure 2 This is a flowchart of generating a dynamic link in one embodiment of this application; Figure 3 This is a flowchart of obtaining billing data in one embodiment of this application; Figure 4 This is a flowchart of reconciling and clearing order invoices in one embodiment of this application; Figure 5 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0019] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0020] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0021] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0022] Existing reconciliation methods are susceptible to human error, information delays, and system silos (such as the lack of integration between business and financial systems), resulting in low efficiency and financial risks. This is especially true at the end of the month / quarter when reconciliation pressure is concentrated, hindering enterprise operational efficiency. To address the problems of slow integration with existing enterprise systems, low efficiency and high error rates in manual reconciliation, and untimely reconciliation in traditional reconciliation systems, this application provides an intelligent reconciliation method, system, device, and storage medium based on dynamic links.

[0023] Example 1 This application provides an intelligent reconciliation method based on dynamic links, such as... Figure 1 As shown, this intelligent reconciliation method is implemented through the following steps.

[0024] Step S01: Dynamically load link parameters, create a data link connecting to the external system, and generate a dynamic link.

[0025] Specifically, link configuration information is obtained from a database or configuration file, including link protocol type, connection parameters, and data conversion parameters. Based on the link configuration information, it is determined whether the link to be generated is a link type supported by the enterprise's internal system. If the determination result is yes, a corresponding standard client is created based on the connection parameters and the data conversion parameters to obtain a dynamic link instance. If the determination result is no, a preset link script is loaded and executed through the dynamic link engine to create a dynamic link instance. The successfully created dynamic link instance is stored in the server of the enterprise's internal system as a data channel for obtaining the order data and the billing data.

[0026] In this embodiment, the connection parameters include at least one of IP address, port, timeout time, and interface address; the conversion parameters include at least one of interface input parameters, interface output parameters, and the mapping relationship between output parameters and fields within the system.

[0027] Step S02: Obtain order data from the enterprise's internal system through the dynamic link.

[0028] Step S03: Obtain billing data from a third-party system via the dynamic link. Specifically, the third-party system API is called via the dynamic link to automatically retrieve billing files; it is determined whether the billing file is in a preset format. If the determination result is yes, billing data is directly extracted from the billing file; if the determination result is no, the billing file is parsed using a preset intelligent data recognition method (e.g., for unstructured text files, natural language processing and keyword matching can be used for parsing; for fixed-format PDF files, data can be extracted using OCR recognition) to obtain billing data that meets the format requirements; the billing data is then saved to a structured database.

[0029] Step S04: Match the order data with the bill data according to the preset multi-level matching rules, and process the successfully matched data for reconciliation and cancellation.

[0030] Specifically, the multi-level matching rules in this embodiment include an exact matching mode, a summary matching mode, and a fuzzy matching mode. The exact matching mode determines whether the bill data has a unique corresponding order data on or before its occurrence date; the summary matching mode determines whether the bill data has multiple uniquely corresponding order data on or before its occurrence date; and the fuzzy matching mode determines whether the bill data has order data that can be fuzzily matched on or before its occurrence date.

[0031] In the specific matching process, the system iterates through unreconciled billing and order data, performs precise matching according to the precise matching mode, and marks the billing and order data that are precisely matched. It then iterates through billing and order data that fail to be precisely matched, performs matching according to the summary matching mode, and marks the billing and order data that are summarizing and matching successfully. Next, it obtains fuzzy matching elements, iterates through summarizing the billing and order data that fail to be matched, performs matching according to the fuzzy matching elements and the fuzzy matching mode, marks the billing and order data that are fuzzily matched, and generates a verification work order for manual review. Finally, it processes the marked billing and order data for cancellation.

[0032] Step S05: Generate an exception work order for the bills with mismatch and send a reminder message to the user. Specifically, obtain the matching limit conditions. During the matching process, mark the bill data and order data that exceed the matching limit conditions as unmatched records and generate exception work orders.

[0033] In summary, the intelligent reconciliation method based on dynamic links provided in this application, through the core design of a dynamic link engine, changes the dependence on external systems from "hard-coded" to "configurable," enabling flexible integration. When integrating with a new system, only configuration or script writing is required to quickly establish the link, without modifying the core business code. In the data processing stage, the system, through full-process automation (from data collection, reconciliation to write-off) and a multi-level intelligent matching mechanism (precise matching, summary matching, fuzzy matching), liberates financial personnel from tedious manual verification, thereby greatly improving efficiency. At the same time, the accuracy of intelligent matching is far higher than that of manual visual inspection, and it can work stably 24 / 7, effectively avoiding errors caused by human fatigue and negligence, and systematically reducing the reconciliation error rate. For the few abnormal cases that the machine cannot decide, the system automatically generates work orders to guide precise human intervention, forming an efficient human-machine collaboration closed loop.

[0034] Example 2 This embodiment is in Figure 1 Based on this, the process of dynamically loading and generating dynamic links through the link engine in step S01 is as follows: Figure 2 As shown: Step S011: First, obtain the dynamic link parameter information from the database or configuration file; Step S012, next, determine whether it is a supported link through parameter information. For example, if the parameter configuration link type is HTTP, then create an HTTP related client and set the client's related parameters (loaded link configuration, such as IP, port, timeout, interface address, etc.) and data conversion parameters (loaded link configuration, such as interface input parameters, interface output parameters, and the mapping relationship between output parameters and fields in the system, etc.). Step S013, then, if it is an unsupported link, the link script is loaded through the dynamic link engine; Step S014: Finally, create the link and store it in the service for use by the order and billing modules.

[0035] Figure 1 In step S03, users can retrieve the required invoices via imported invoices or configured dynamic links through third-party system APIs. Platforms like Alipay, WeChat, and various banks typically offer open APIs; as long as an account is registered on the corresponding open platform, the user can obtain the relevant invoice data through the API. For example, Alipay retrieves invoice data for the corresponding date via `alipay.data.dataservice.bill.downloadurl.query`. If the user has not registered a developer account, they can also manually download invoices provided by the third party. By parsing the invoice file, real-time and next-day reconciliation can be achieved.

[0036] For specific references Figure 3 : Step S031: The user actively uploads the bill file, or the system retrieves the bill file via the link the following day; Step S032: Determine whether it is an EXCEL file. If not, parse the bill using bill intelligent recognition in step S033. Step S034: Parse the obtained EXCEL bill data file and save the bill data to a structured database for later reconciliation and settlement.

[0037] Figure 1 In step S04, the obtained bill data can be used to perform reconciliation and cancellation operations. The specific process of the reconciliation and cancellation operation is as follows: Figure 4 As shown: Step S041: Obtain unreconciled billing data and order data. The order data comes from the internal system. Step S042: Determine that the bill and order data obtained this time are not empty. If they are empty, it means that there is no data to reconcile, and the process ends directly. Steps S043 and S044: Loop through each unreconciled bill data and determine whether there is a unique corresponding order data on or before the date the bill was issued (unique condition: whether the account, amount, and income / expense flag are consistent). If there is corresponding data, mark the relationship between the bill and the order, and then proceed with the reconciliation and cancellation process. Steps S045 and S046: Loop through each unreconciled bill data and determine whether there are multiple unique order data on or before the date the bill was issued (unique conditions: account number, total amount of multiple orders, and whether the income / expense flags are consistent). If there is corresponding data, mark the relationship between the bill and the order, and then proceed with the reconciliation and cancellation process. Step S047: Loop through each unreconciled bill data and determine whether the bill date and previous dates can be fuzzily matched with the corresponding order data (fuzzy matching elements: account, order amount, income / expense flag, balance, remarks, etc. are used for precision matching). If there is corresponding data, mark the relationship between the bill and the order, and then perform reconciliation and cancellation processing, and generate a work order for manual verification. Step S048: Mark the bills that have not been reconciled this time. If the limit conditions (number of times, number of days, etc., are configured through configuration parameters) are exceeded, an abnormal work order will be generated for manual processing. Step S049: Update the reconciled and cancelled bills and orders in the structured database.

[0038] Specifically, this embodiment provides a more detailed explanation of the multi-level matching process. First, the first level of exact matching is performed. Specifically, all bill data is traversed. During the traversal, one bill data is first obtained, and then an order with the same account, amount, and time as the bill is searched from all order data. When all information is completely consistent, the match is confirmed, indicating that the bill data and the order data match and can be marked as completed.

[0039] Then, a second-level summary matching is performed on the bill and order data that did not match. For example, if the system finds a bank statement (bill) with a large amount and no unique corresponding order is found, it will search for several invoices (orders) with the same account number and issued around the same time from the unreconciled order data, whose sum of amounts is exactly equal to the transaction amount. This indicates that the transaction (bill) corresponds to these invoices (orders), and the match is confirmed and marked. For example, consider the following data: Bill A: Account 1001001, Bill 10000, Date 2025-05-01; Order B: Account 1001001, Purchase of computers 5000, Date 2025-05-01; Order C: Account 1001001, Purchase of office supplies 5000, Date 2025-05-01; Order D: Account 1001002, Purchase of server room equipment 5000, Date 2025-05-01. After summarizing and matching, it can be confirmed that bill A uniquely corresponds to orders B and C.

[0040] Finally, a third level of fuzzy matching is performed on the remaining bills and order data. Specifically, fuzzy matching is performed through intelligent reasoning after the first two matching methods fail. During the matching process, various elements are combined for matching. However, because the matching degree is not 100% certain, a successful match needs to be reported to the staff (generating a work order) for confirmation.

[0041] Fuzzy matching analyzes the similarity of various elements, such as character similarity, tag similarity, voice similarity, and number similarity. If the similarity meets the user's preset requirements, such as similar account numbers (e.g., one character in the name is an asterisk *), similar amounts (there may be slight differences due to transaction fees), related remarks (the transaction notes contain your invoice number), and similar dates (the transaction date is one day later than your invoice date, possibly due to nighttime settlement), then a successful match can be confirmed. For example, suppose the information of a bill and order is as follows: Bill: Wang*wu, 2023-10-28, income 198 yuan, remarks "goods refund"; Order: Wang Wu, 2023-10-27, owed -200 yuan (refund), remarks "Order No.: 12345".

[0042] During the fuzzy matching process, since "Wang*wu" and "Wangwu", "198 yuan" and "200 yuan" are highly similar, the time is close, and both are refunds, the system will consider them to be the same transaction, that is, the fuzzy match is successful, and a work order to be confirmed will be generated.

[0043] Example 3 Secondly, embodiments of this application provide an intelligent reconciliation system based on dynamic links, used to implement the intelligent reconciliation method based on dynamic links as described in any of the preceding embodiments. The system of this embodiment includes: The dynamic link engine is used to dynamically load link parameters, create data links for connecting to external systems, and generate dynamic links. The order module is used to obtain order data from the enterprise's internal system through the dynamic link; The billing module is used to obtain billing data from a third-party system via the dynamic link; The reconciliation module is used to match the order data with the bill data according to a preset multi-level matching rule, and to process the successfully matched data for reconciliation and cancellation. The work order module is used to generate abnormal work orders for bills that match abnormalities and send reminder messages to users.

[0044] The system operation process in this embodiment refers to the implementation process of the above-described intelligent reconciliation method embodiment, and will not be repeated here.

[0045] This embodiment integrates five major functional modules—order module, billing module, reconciliation module, work order module, and dynamic link engine—through high cohesion. That is, each module focuses on its own business. For example, the order module only handles order-related business, and the operation of obtaining orders is handled through dynamic links; the billing module only handles bill-related business, and the operation of obtaining bills is also handled through dynamic links. This reduces coupling with external systems and achieves fully automated, unmanned data collection, automatic bill reconciliation and cancellation, automatic anomaly location, and data closed-loop repair.

[0046] Thirdly, embodiments of this application provide an electronic device, Figure 5 This is a block diagram illustrating an electronic device according to an exemplary embodiment. (e.g.) Figure 5 As shown, the electronic device may include a processor 11 and a memory 12 storing computer program instructions.

[0047] Specifically, the processor 11 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0048] The memory 12 may include a large-capacity storage device for data or instructions. For example, and not limitingly, the memory 12 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 12 may include removable or non-removable (or fixed) media. Where appropriate, the memory 12 may be internal or external to a data processing device. In a particular embodiment, the memory 12 is non-volatile memory. In a particular embodiment, the memory 12 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), an electrically alterable read-only memory (EAROM), or flash memory, or a combination of two or more of these. Where appropriate, the RAM can be Static Random-Access Memory (SRAM) or Dynamic Random-Access Memory (DRAM). DRAM can be Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), Extended Data Out Dynamic Random-Access Memory (EDODRAM), Synchronous Dynamic Random-Access Memory (SDRAM), etc.

[0049] The memory 12 can be used to store or cache various data files that need to be processed and / or communicated, as well as possible computer program instructions executed by the processor 11.

[0050] The processor 11 reads and executes computer program instructions stored in the memory 12 to implement any of the intelligent reconciliation methods based on dynamic links in the above embodiments.

[0051] In one embodiment, the electronic device may further include a communication interface 13 and a bus 10. Wherein, as... Figure 5 As shown, the processor 11, memory 12, and communication interface 13 are connected through bus 10 and communicate with each other.

[0052] The communication interface 13 is used to enable communication between the various modules, devices, units, and / or equipment in the embodiments of this application. The communication interface 13 can also enable data communication with other components such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations.

[0053] Bus 10 includes hardware, software, or both, that couples components of an electronic device together. Bus 10 includes, but is not limited to, at least one of the following: data bus, address bus, control bus, expansion bus, and local bus. For example, and not as a limitation, bus 10 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 10 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application considers any suitable bus or interconnection.

[0054] Fourthly, embodiments of this application provide a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the intelligent reconciliation method based on dynamic links provided in the first aspect.

[0055] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0056] In a possible implementation, the present invention can also be implemented as a program product comprising program code, which, when the program product is run on a terminal device, is used to cause the terminal device to perform steps implementing the intelligent reconciliation method based on dynamic links provided in the first aspect.

[0057] The program code for executing the present invention can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0058] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0059] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A smart reconciliation method based on dynamic links, characterized in that, include: Dynamically load link parameters, create data links connecting to external systems, and generate dynamic links; Order data is obtained from the enterprise's internal system through the aforementioned dynamic link; Billing data is obtained from a third-party system via the dynamic link; The order data and the bill data are matched according to a preset multi-level matching rule, and the successfully matched data are reconciled and cleared. For bills with discrepancies, generate an exception ticket and send a notification to the user.

2. The method according to claim 1, characterized in that, The dynamic loading of link parameters, the creation of a data link connecting to an external system, and the generation of a dynamic link include: Obtain link configuration information from a database or configuration file, the link configuration information including link protocol type, connection parameters and data conversion parameters; Based on the link configuration information, determine whether the link to be generated is a link type supported by the enterprise's internal system; If the judgment result is yes, then a corresponding standard client is created according to the connection parameters and the data conversion parameters to obtain a dynamic link instance; if the judgment result is no, then a preset link script is loaded and executed through the dynamic link engine to create a dynamic link instance. The successfully created dynamic link instance is stored in the server of the enterprise's internal system to serve as a data channel for obtaining the order data and the billing data.

3. The method according to claim 2, characterized in that, The connection parameters include at least one of IP address, port, timeout time, and interface address; the conversion parameters include at least one of interface input parameters, interface output parameters, and the mapping relationship between output parameters and fields within the system.

4. The method according to claim 1, characterized in that, The process of obtaining billing data from a third-party system via the dynamic link includes: The dynamic link is used to call the API of a third-party system and automatically retrieve billing files; Determine whether the bill file is in a preset format. If the determination result is yes, extract the bill data directly from the bill file. If the determination result is no, parse the bill file using a preset intelligent data recognition algorithm to obtain bill data that meets the format requirements. The billing data is saved to a structured database.

5. The method according to claim 1, characterized in that, The preset multi-level matching rules include: The exact match mode is used to determine whether there is a unique corresponding order data for the bill data on or before its occurrence date; The summary matching mode is used to determine whether the billing data has multiple uniquely corresponding order data on or before its occurrence date; The fuzzy matching mode is used to determine whether there are any order data that can be fuzzily matched to the billing data on or before its occurrence date.

6. The method according to claim 5, characterized in that, The step of matching the order data with the billing data according to a preset multi-level matching rule, and then performing reconciliation and cancellation processing on the successfully matched data, includes: Loop through the unreconciled bill and order data, perform precise matching according to the exact matching mode, and mark the bill and order data that are successfully matched. The system iterates through billing and order data that fail to match precisely, then matches them according to the summary matching mode, and marks the billing and order data that successfully match in the summary. Obtain fuzzy matching elements, loop through and summarize the bill data and order data that failed to match, match according to the fuzzy matching elements and the fuzzy matching mode, mark the bill data and order data that successfully matched, and generate a verification work order for manual verification. Perform write-off processing on the marked billing and order data.

7. The method according to claim 1, characterized in that, The process of generating an exception work order from the matched abnormal bills includes: Obtain matching criteria. During the matching process, mark bill data and order data that exceed the matching criteria as unmatched records and generate an exception work order.

8. An intelligent reconciliation system based on dynamic links, characterized in that, The system for implementing the intelligent reconciliation method based on dynamic links as described in any one of claims 1-7, the system comprising: The dynamic link engine is used to dynamically load link parameters, create data links for connecting to external systems, and generate dynamic links. The order module is used to obtain order data from the enterprise's internal system through the dynamic link; The billing module is used to obtain billing data from a third-party system via the dynamic link; The reconciliation module is used to match the order data with the bill data according to a preset multi-level matching rule, and to process the successfully matched data for reconciliation and cancellation. The work order module is used to generate abnormal work orders for bills that match abnormalities and send reminder messages to users.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements the intelligent reconciliation method based on dynamic links as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the intelligent reconciliation method based on dynamic links as described in any one of claims 1 to 7.

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