Method, system and device for processing long-term payment, and medium

Abstract

The invention discloses a method, system and device for processing long-term payment and a medium, and relates to the field of intelligent management, and the method comprises the steps: automatically downloading a receivable and payable account data file in a target time period; monitoring a local downloading directory, renaming and classifying and storing the files according to a preset rule after it is detected that downloading of the data files is completed, and recording a downloading log; performing structured processing on the data file by using a data processing tool, and performing risk identification and grade marking on the processed data according to a multi-dimensional business rule; and generating an accounting risk statistical report according to the formatting template, calling a cryptographic algorithm to generate an abstract hash value for the report content, and supporting an abstract verification receipt mechanism for a receiver to confirm the integrity of the file content. According to the method and the system, the downloading behavior of the billing data is converted from manual operation to automatic triggering, and the risk judgment result is embedded into the multi-dimensional rule system to be dynamically evaluated, so that the risk perception capability and the response efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent management, and in particular to a method, system, device, and medium for processing long-term outstanding accounts. Background Technology

[0002] As a fundamental sector of the national economy, the power industry's financial management involves numerous cross-departmental and cross-project financial transactions, particularly in accounts receivable and payable management, which is characterized by high complexity, large data volumes, and stringent timeliness requirements. With the advancement of business-finance integration and digital transformation in power grid companies, traditional manual processing methods are no longer sufficient to meet the demands for real-time monitoring, risk warnings, and the clearing of long-standing outstanding accounts. For example, in scenarios such as clearing outstanding payments to enterprises and business-finance data governance, it is necessary to trace each business transaction back to the contract, project, and voucher levels, which places higher demands on the automation and intelligence of data processing.

[0003] Currently, the industry widely uses information systems (such as power grid management platforms) for financial data management. However, the data silos between systems are serious, and there is a lack of dynamic risk early warning mechanisms, resulting in delayed risk response (see "Financial Management Practices of Power Enterprises", China Electric Power Press, 2022). In addition, the clearing of long-term outstanding accounts relies on manual statistics and reporting, which is inefficient, error-prone, and makes it difficult to achieve pre-emptive warnings and in-process control. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention provides a method, system, device and medium for processing long-term outstanding accounts.

[0005] Therefore, the problem that this invention aims to solve is that the clearing of long-term outstanding accounts relies on manual statistics and reporting, which is inefficient, error-prone, and makes it difficult to achieve early warning and in-process control.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a method for handling long-term outstanding accounts receivable, comprising: automatically downloading accounts receivable and payable data files for a target time period using an automated browser control tool; monitoring the local download directory, and upon detecting the completion of the data file download, renaming and classifying the files according to preset rules and recording download logs; using data processing tools to perform structured processing on the data files, and performing risk identification and level marking on the processed data according to multi-dimensional business rules; generating an outstanding account risk statistical report based on a formatted template, calling a cryptographic algorithm to generate a digest hash value for the report content, and supporting a digest verification receipt mechanism for the recipient to confirm the integrity of the file content; and pushing the statistical report and digest hash value to the account of the person responsible for the outstanding accounts through an internal communication platform interface.

[0007] As a preferred embodiment of the method for processing long-term outstanding receivables described in this invention, the automatic download of accounts receivable and payable data files within the target time period includes: an automated browser control tool loading target browser parameters, initializing the automated browser driver, simulating user behavior to perform a system login operation, and automatically redirecting to the accounts receivable data export page; the automated browser control tool simulating a call to the provided data download interface to trigger the download operation of accounts receivable data files within the target time period.

[0008] The beneficial effects of this preferred technical solution are: it automatically identifies the target time period, login parameters and report download path, realizes a fully automated download control process for accounts receivable and payable data files, avoids traditional manual login and click operations, and improves work efficiency.

[0009] As a preferred embodiment of the method for handling long-term outstanding receivables described in this invention, the renaming and categorizing of files according to preset rules includes: after data download is triggered, automatically monitoring the local target directory through a monitoring mechanism to determine whether the data file has been completely downloaded; after confirming that the file download is complete, calculating the file's integrity summary value, renaming the file according to a preset naming template, and automatically dividing the storage path according to the file's source and purpose, moving the file to the corresponding category directory; and generating a download log record after the file is categorized and stored.

[0010] As a preferred embodiment of the method for processing long-term outstanding receivables described in this invention, the step of using a data processing tool to perform structured processing on the data file includes: initializing a data processing task, calling the data processing tool, loading the downloaded receivables data file, and performing preliminary cleaning operations; performing field standardization and structure reorganization processing on the pre-cleaned receivables data; filling in missing fields or null values ​​for records based on historical project ledger templates; and finally exporting the processed data in a standard format.

[0011] As a preferred embodiment of the method for handling long-term outstanding receivables as described in this invention, the step of risk identification and level labeling of the processed data based on multi-dimensional business rules includes: loading a preset risk identification rule set and establishing a multi-dimensional assessment framework; performing multi-dimensional feature extraction and rule matching on the processed receivables data; performing judgment analysis on the receivables data according to the assessment framework to generate feature labels reflecting the risk status of the receivables data; generating risk level labels for the receivables data based on the feature labels according to a preset level classification logic, and writing the results into the data structure.

[0012] The beneficial effects of this preferred technical solution are as follows: it introduces a risk level classification mechanism, constructs a risk identification system based on multi-dimensional business rules, and can comprehensively assess the risk of outstanding accounts from multiple business perspectives, thereby improving the comprehensiveness and objectivity of risk assessment. As a preferred embodiment of the method for processing long-term outstanding accounts receivable according to the present invention, the step of generating an outstanding account risk statistical report based on a formatted template includes: calling a preset formatted template; performing hierarchical organization and layout rendering on the account data with completed risk level marking; generating a list of outstanding accounts receivable items with different risk levels in a set order; generating a standardized report file; calling a cryptographic algorithm tool library to perform digest calculation on the report file content; generating a fixed-length digest hash value; and constructing a unique identifier code using the digest hash value for integrity verification; embedding the generated digest hash value as an independent field into the report metadata; and supporting the recipient to perform acknowledgment verification at the report receiving end through digest verification to confirm whether the received report is consistent with the sent version; if the verification is successful, generating a confirmation acknowledgment identifier.

[0013] As a preferred embodiment of the method for handling long-term outstanding payments described in this invention, the step of pushing the payment to the account of the person responsible for the outstanding payment includes: automatically establishing a mapping table of the responsible person's account based on the responsible person's field information recorded in the report, and calling the communication platform interface parameters in the configuration file to initialize the message push service connection; constructing push content according to a preset message template; sending the constructed message content and the statistical report file as attachments to the corresponding responsible person's account through the communication platform interface; and generating a log record of the message ID, recipient identifier, push time, and attachment hash value after successful transmission.

[0014] To address the aforementioned technical problems, this invention provides the following technical solution: a system for processing long-term outstanding accounts receivable, comprising: a download module, a risk marking module, a report generation module, and a push module; the download module automatically downloads accounts receivable and payable data files within a target time period using an automated browser control tool, monitors the local download directory, and renames and categorizes the files according to preset rules after the data file download is complete, while recording a download log; the risk marking module uses a data processing tool to perform structured processing on the data files, and identifies and marks the processed data for risk based on multi-dimensional business rules; the report generation module generates an outstanding account risk statistical report according to a formatted template, calls a cryptographic algorithm to generate a digest hash value for the report content, and supports a digest verification receipt mechanism for the recipient to confirm the integrity of the file content; the push module pushes the statistical report and digest hash value to the account of the person responsible for the outstanding accounts through an internal communication platform interface.

[0015] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of a method for processing long-term outstanding receivables as described above.

[0016] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for processing long-term outstanding accounts as described above.

[0017] The beneficial effects of this invention are as follows: By introducing technologies such as automated browser control, file monitoring and processing, data structured modeling, multi-dimensional risk identification, and enterprise communication push, this invention achieves intelligent management of long-term outstanding accounts throughout the entire process. The downloading of outstanding data is transformed from manual operation to automatic triggering; the data cleaning and structuring process is standardized by standardized templates; risk assessment results are dynamically evaluated by embedding them into a multi-dimensional rule system; the integrity of statistical reports is ensured through encryption algorithms; and finally, the data is promptly pushed to the responsible person's account via the enterprise communication interface, achieving closed-loop tracking. This transforms the inefficient and sluggish accounts receivable clearing process of the traditional model into a secure, efficient, and traceable intelligent control mechanism, improving the enterprise's risk perception and response efficiency in complex financial scenarios. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a method for handling long-term outstanding accounts receivable in Example 1. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a method for handling long-term outstanding accounts receivable, including: S1: Automatically download accounts receivable and payable data files for the target time period using an automated browser control tool.

[0023] S2: Monitors the local download directory. After detecting that a data file has been downloaded, it renames and categorizes the file according to preset rules and records the download log.

[0024] S3: Use data processing tools to structure data files and identify and classify risks in the processed data according to multi-dimensional business rules.

[0025] S4: Generate an account risk statistics report based on the format template, call the cryptographic algorithm to generate a digest hash value for the report content, and support a digest verification receipt mechanism for the recipient to confirm the integrity of the file content.

[0026] S5: Push statistical reports and summary hash values ​​to the account of the person responsible for the outstanding accounts via the internal communication platform interface.

[0027] It should be noted that current data acquisition relies on manual operation, requiring users to log into the financial system and download account files. This process is cumbersome, inefficient, and prone to errors leading to data omissions or formatting errors. Secondly, data integration capabilities are limited, with data silos existing between different systems. Field formats are inconsistent, and structures are non-standard, severely impacting the quality of subsequent analysis. Thirdly, the risk identification mechanism is lagging, relying heavily on manual aggregation and monthly reports, lacking real-time early warning capabilities. This makes it difficult to promptly identify high-risk outstanding accounts, easily leading to the potential for long-term backlog of accounts.

[0028] Therefore, in response to the above problems, such as Figure 1 As shown, through steps S1-S5, the system first automates the login to the financial system and the collection of accounts receivable data files. Then, the downloaded results are monitored and their naming is standardized before being imported into the data processing module for unified cleaning, field mapping, and structural integration. Based on this, the system identifies risk items and assigns them level labels according to preset multi-dimensional business rules. A visual statistical report is then generated using a standard template format, and its content is encrypted to ensure data integrity. Finally, the report, along with hash verification information, is sent to the responsible person through the internal communication platform interface, forming a closed-loop process of risk identification, encrypted reporting, and push notifications. This achieves automatic control of outstanding accounts information and precise risk targeting.

[0029] Example 2, the second embodiment of the present invention, differs from the first embodiment in that: a method for processing long-term outstanding accounts further includes, in step S1, automatically downloading the accounts receivable and payable data file for the target time period, comprising the following steps A1-A2: A1: The automated browser control tool loads the target browser parameters, initializes the automated browser driver, simulates user behavior to perform system login operations, and automatically redirects to the account data export page; on the page, the program automatically recognizes controls such as date input boxes, query buttons, and export buttons according to preset positioning rules, automatically fills in the target time period parameters, and completes the account data filtering.

[0030] A2: The automated browser control tool simulates the call to the provided data download interface to trigger the download operation of the account data file for the target time period; during the download process, it monitors the page feedback and browser status, supports file duplication detection and timeout retry mechanism, and ensures that the download task can be completed stably even under network jitter or response delay.

[0031] Specifically, in step A1, the target browser parameters include a configuration file containing information such as the login address, user credentials, target report page, and time period parameters.

[0032] In this embodiment of the application, step A1, simulating user behavior to perform system login operations, adopts automated login and page operation based on the browser automation control library (Selenium), including the following steps A111-A113: A111: Reads preset parameters from the local configuration file, including the target system login address, username, password, report page, start and end dates, export format, etc.

[0033] A112: The browser is launched by calling the Selenium driver, simulating user operations by entering the username and password in sequence, completing the verification code recognition, and redirecting to the accounts receivable and payable download page.

[0034] A113: Locate the start date input box, end date input box, and export button on the target page using XPath (Extensible Path Language) or CSS Selector, automatically fill in the target time period, and simulate clicking the export button to start the file download.

[0035] In an optional implementation, simulating user behavior to perform system login operations can also employ asynchronous automated browser flow control based on Playwright (Microsoft's open-source browser automation library), including the following steps A121-A123: A121: Use Playwright to load login parameters and target page links, initialize the kernel browser, and enable headless mode.

[0036] A122: Log in to the target system via the asynchronous API (Asynchronous Application Programming Interface). Playwright automatically waits for the page to load completely and then redirects to the account download page, ensuring that the page elements are interactive before proceeding to the next step.

[0037] A123: Using the interface provided by Playwright, accurately identify the date selection control and fill in the target time period, while handling possible pop-ups or control loading delays, and finally calling the simulated click function to automatically trigger the data export operation.

[0038] In another alternative implementation, simulating user behavior to perform system login operations can also be done via a non-browser method based on an open data interface API, including the following steps A131-A133: A131: The target system pre-opens its data service interface, providing Token authentication (token authentication mechanism) and the ability to export account data; the program sends a POST request via HTTPS (Hypertext Transfer Security Protocol) to obtain an access token, which serves as the identity credential for subsequent data requests; A132: Construct an HTTP request URL (Uniform Resource Locator) or a JSON request body (JavaScript Object Notation) to directly call the data download interface. The parameters include start and end times, report type, export format, and other information.

[0039] A133: Directly writes the contents of the account data file returned by the server to the local disk without opening a browser page. It also supports the HTTP response status code and digest hash check field for receiving files, so as to perform file integrity verification and download log recording later.

[0040] It should be noted that this step implements a fully automated download control process for accounts receivable and payable data files, avoiding traditional manual login and clicking operations, significantly improving work efficiency and reducing the workload of personnel; by automatically identifying target time periods, login parameters, and report download paths, it can adapt to different system versions and interface changes, possessing strong system adaptability and maintainability; through mechanisms such as timeout retries, exception capture, and browser behavior simulation, it improves download stability and success rate under network jitter or page loading abnormalities.

[0041] Furthermore, in step S2, renaming and classifying files according to preset rules includes the following steps B1-B3: B1: After the data download is triggered, the local target directory is automatically monitored through a monitoring mechanism to determine whether the data file has been completely downloaded; the determination can be made based on filename matching rules, file extensions, or browser download task identifiers.

[0042] B2: After confirming that the file has been downloaded, calculate the file's integrity summary value, rename the file according to the preset naming template, and automatically allocate storage paths based on the file's source and purpose, moving the file to the corresponding category directory, such as accounts receivable, accounts payable, other categories, etc.

[0043] B3: After completing the file classification and storage, a download log record is generated.

[0044] Specifically, the renaming rules in step B2 include elements such as download date, data type, and system source identifier; the log records in step B3 include the original file name, the renamed path, the digest hash value, the download time, the monitoring timestamp, and the monitoring program session number; in the event of a file download failure or interruption, the monitoring module automatically triggers a retry or abnormal alarm mechanism and marks the error type and the number of retries in the log for subsequent tracing and auditing.

[0045] In this embodiment of the application, in step B1, the monitoring mechanism adopts file system event monitoring based on Watchdog (a watchdog file system event monitoring library), including the following steps B111-B113: B111: Initialize the file listening service, call the object in the Watchdog library to bind the target download directory, and register a custom event handler.

[0046] B112: A callback event is triggered when the system detects that a new file has been created or an existing file has been modified. In the callback, the file extension, creation time and naming pattern are used to determine whether it is an account data file being downloaded.

[0047] B113: Check whether the file writing status is stable by polling (e.g., the file size does not change for N seconds). Once the writing is confirmed to be complete, send a download completion signal to the subsequent processing module.

[0048] In an optional implementation, the monitoring mechanism may also employ periodic directory scanning based on a timed polling mechanism, including the following steps B121-B123: B121: Configure a scheduled task (e.g., execute every 5 seconds) to scan the specified download directory and record the name, size, and last modified time of all files in the current directory.

[0049] B122: In each poll, compare the results of the previous record. If a new file is found and its size remains unchanged between two consecutive polls, then the file is considered to have been written.

[0050] B123: For files that meet the stability judgment criteria, trigger the download completion event, enter the file renaming and classification process, and record the number of polling and detection timestamps for tracking.

[0051] In another alternative implementation, the monitoring mechanism may also employ an asynchronous detection mechanism based on browser or operating system download status feedback, including the following steps B131-B133: B131: When initiating a download action, the browser's developer tools protocol or internal download manager interface is called through the integrated browser automation tool to obtain the unique identifier and initial state of the current download task.

[0052] B132: Continuously and asynchronously listen to the status feedback fields of the download task (such as the status changing to "completed", i.e. "completed"), and confirm whether the file has been successfully written to the disk through the status code or event field.

[0053] B133: Once the download completion status is confirmed, record the download completion time, file path and task ID, and automatically trigger the next processing stage, while avoiding additional listening or polling of the file system.

[0054] It should be noted that this step utilizes a real-time monitoring mechanism of the local download directory to promptly identify the file writing completion status, preventing misprocessing due to incomplete file downloads. The automated execution of file naming and classification rules ensures standardized naming conventions, directory structures, and metadata storage for accounting data files, facilitating subsequent archiving and auditing. The introduction of summary calculation and download log recording mechanisms enables traceable and retrospective management of the download process, supporting historical comparison, error location, and accountability.

[0055] Furthermore, in step S3, the data file is structured using data processing tools, including the following steps C1-C4: C1: Initializes the data processing task, calls the data processing tool, loads the downloaded accounts data file, and performs preliminary cleaning operations. The preliminary cleaning operations include deleting blank rows, removing redundant items from the table header and footer, standardizing field naming formats, and standardizing date and number formats to ensure that each field conforms to the unified parsing specifications.

[0056] C2: Perform field standardization and structure reorganization on the pre-cleaned accounts receivable data.

[0057] C3: For records with missing fields or null values, fill them in according to the historical project ledger template.

[0058] C4: Finally, export the processed data in a standard format.

[0059] In this embodiment of the application, step C2, the field standardization and structural reorganization process adopts a semantic normalization method based on the field mapping dictionary, including the following steps C211-C213: C211: Calls the local preset field mapping dictionary, which maps common field names (such as project number, engineering ID, contract number) from the source system to standard field names.

[0060] C212: The program iterates through the field names of the structured data table, performs field replacement and standardized naming according to the key-value pairs defined in the mapping dictionary, and marks unknown fields as pending review and logs them for manual supplementation.

[0061] C213: The data structure after field normalization meets the unified three-level nested relationship format of project-contract-voucher, and can be used for unified parsing and risk identification processing in the future.

[0062] In an optional implementation, field standardization and restructuring can also employ an automatic merging method based on regular expressions and fuzzy matching, including the following steps C221-C223: C221: Reads the original set of field names and calls regular expressions to detect whether the fields contain specific keyword patterns, such as matching field names with semantic features like project, contract, and invoice.

[0063] C222: For fields that do not conform to the standard fields but have spelling differences or abbreviations, use a string similarity algorithm to perform fuzzy matching, classify fields with a matching degree exceeding the threshold into known standard fields, and record the matching level and replacement results.

[0064] C223: Outputs a set of normalized field names and generates a mapping table for matching pairs between the original field names and the standard fields, for manual review and future reuse.

[0065] In another alternative implementation, field standardization and structure reorganization can also employ a field completion and structure expansion method driven by historical ledger templates, including the following steps C231-C233: C231: Load the standard ledger templates used in the past three years of accounts receivable processing history, and extract the field names, data organization levels and typical value distributions to build a field knowledge base.

[0066] C232: Performs field name comparison, data type verification, and typical value comparison analysis on the currently downloaded original account data. Fields that exist in the template but are missing in the current data are set to "missing fields can be filled", and the field names and default value structures of empty fields are automatically filled in.

[0067] C233: For data tables with hierarchical nesting structures, expand and merge them according to the association rules within the template, and finally output a standard structured data table, marked with "template comparison passed" or "fields inconsistent" status.

[0068] It should be noted that this step transforms unstructured or inconsistently formatted accounting data into structured tables with a unified format, improving data readability, computability, and comparability. The introduction of a field semantic mapping mechanism effectively resolves issues such as inconsistent field naming and the misuse of synonyms in the source systems, enhancing the semantic consistency of data fusion. Through operations such as missing field imputation, redundant item cleanup, and hierarchical data expansion, the quality control of the raw data is strengthened, ensuring the accuracy and completeness of subsequent analysis results.

[0069] Furthermore, in step S3, risk identification and level labeling of the processed data based on multi-dimensional business rules includes the following steps C5-C7: C5: Load the preset risk identification rule set and establish a multi-dimensional assessment framework.

[0070] C6: Perform multi-dimensional feature extraction and rule matching on the processed accounts receivable data, analyze and judge the accounts receivable data according to the evaluation framework, and generate feature labels that reflect the risk status of the accounts receivable data.

[0071] C7: Based on the feature labels and according to the preset level classification logic, generate risk level labels for the accounts receivable data and write the results into the data structure.

[0072] Specifically, in step C5, the rule set includes multiple indicators such as aging threshold, amount range, contract type, and historical processing records of responsible units, and establishes a unified risk assessment parameter structure for subsequent risk assessment of each account.

[0073] In this embodiment of the application, step C7, the risk level labeling adopts a risk level generation method based on condition rule matching, including the following steps C711-C713: C711: Preset several judgment rules to pass the priority of high-risk labels down. For example, if a certain account record contains any high-risk feature (such as overdue, abnormal amount, or historically delayed responsible unit), it will be directly judged as "high risk".

[0074] C712: If the high-risk rule is not hit, the system further determines whether the number or weight accumulation of medium-risk features reaches the medium-risk standard. If it does, it is judged as "medium-risk"; otherwise, it is considered as "low-risk".

[0075] C713: Write the final judgment result into the "Risk Level" field, and record the rule number and timestamp used for matching, for subsequent tracing and review.

[0076] In an optional implementation, the risk level labeling can also employ a risk level generation method based on a weighted scoring model, including the following steps C721-C723: C721: Define a score for each risk feature and set the weight of each risk dimension in the configuration file. For example, set the weight of the aging dimension to 0.4, the amount dimension to 0.3, and the historical response record to 0.3.

[0077] C722: For each account record, the system calculates its scores in each dimension and obtains a comprehensive risk score by weighted summation; the score range is usually from 0 to 100.

[0078] C723: Determine the level threshold based on the score range, such as ≥70 as "high risk", 40-69 as "medium risk", and below 40 as "low risk", and write the judgment result into the accounts data structure, while outputting the score details of each dimension for manual review.

[0079] In another alternative implementation, the risk level labeling can also employ a risk level generation method based on rule matrix mapping, including the following steps C731-C733: C731: Construct a two-dimensional risk level mapping matrix, where rows and columns represent the levels of two main risk dimensions (such as aging level × amount level), and the cells in the matrix are the corresponding comprehensive risk levels.

[0080] C732: For each account record, the system locates its row and column position in the matrix based on its key dimension identifiers and reads the risk level label of the corresponding cell.

[0081] C733: The level result returned by the matrix is ​​used as the "risk level" field of the record, and the combination of this dimension and the matching path are recorded in the log to support the transparency of multi-dimensional cross-judgment.

[0082] It should be noted that this step establishes a risk identification system based on multi-dimensional business rules, which can comprehensively assess the risk of outstanding accounts from multiple business perspectives, thereby improving the comprehensiveness and objectivity of risk assessment. A risk level classification mechanism is introduced, enabling managers to quickly identify high-risk projects and implement a refined management strategy of focused attention, categorized handling, and differentiated control.

[0083] Furthermore, in step S4, generating the outstanding account risk statistical report based on the formatted template includes the following steps D1-D3: D1: Call the preset format template to perform hierarchical sorting and layout rendering on the accounts receivable data that have been marked with risk level, generate a list of accounts receivable items with different risk levels in the set order, and generate a standardized report file.

[0084] D2: Call the cryptographic algorithm tool library to perform digest calculation on the report file content, generate a fixed-length digest hash value, and construct a unique identifier code using the digest hash value for integrity verification.

[0085] D3: The generated digest hash value is embedded as an independent field into the report metadata. It also supports the recipient to perform a receipt verification on the report receiving end through digest verification to confirm whether the received report is consistent with the sent version. If the verification is successful, a confirmation receipt identifier is generated.

[0086] Specifically, in step D2, the cryptographic algorithm uses GMSSL (a domestic cryptographic algorithm library) to perform SM3 hash encryption on the report digest, generating a digest value (a 64-bit hexadecimal string).

[0087] Furthermore, in step S5, the process of pushing the information to the account of the person responsible for the outstanding balance includes the following steps E1-E3: E1: Based on the responsible person field information recorded in the report, automatically create a responsible person account mapping table and call the communication platform interface parameters in the configuration file to initialize the message push service connection.

[0088] E2: Construct push content according to the preset message template.

[0089] E3: The completed message content and statistical report file are sent as attachments to the corresponding responsible person's account through the communication platform interface. After successful sending, a log is generated to record the message ID, recipient identifier, push time, and attachment hash value. If the responsible person does not confirm receipt within the set time, the system can activate the reminder mechanism to send a second reminder, and write the number of reminders and response time into the tracking record for subsequent statistical analysis.

[0090] Specifically, in step E2, the pushed content includes fields such as the risk level of the outstanding account, project identifier, number of overdue days, name of the responsible unit, report download path and summary hash value, and inserts a message prompt in a uniform format; for high-risk account records, the system automatically adds a "priority processing" label to the message and generates a confirmation link for the responsible person to click to confirm receipt.

[0091] Example 3, the third embodiment of the present invention, differs from the previous two embodiments in that it provides a system for processing long-term outstanding accounts receivable, including a download module, a risk marking module, a report generation module, and a push module. The download module automatically downloads accounts receivable and payable data files within a target time period using an automated browser control tool, monitors the local download directory, and renames and categorizes the files according to preset rules after download completion, while also recording download logs. The risk marking module uses data processing tools to perform structured processing on the data files and identifies and marks the processed data for risk levels based on multi-dimensional business rules. The report generation module generates an outstanding account risk statistical report based on a formatted template, calls a cryptographic algorithm to generate a digest hash value for the report content, and supports a digest verification receipt mechanism for the recipient to confirm the integrity of the file content. The push module pushes the statistical report and digest hash value to the account of the person responsible for the outstanding accounts through an internal communication platform interface.

[0092] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0093] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0094] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0095] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented in combination with any of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0096] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for handling long-term outstanding accounts receivable, characterized in that: include, Automatically download accounts receivable and payable data files for the target time period using automated browser control tools; Monitor the local download directory, and after detecting that a data file has been downloaded, rename and categorize the file according to preset rules, and record the download log. Data files are structured using data processing tools, and the processed data is risk-identified and graded according to multi-dimensional business rules. Generate a risk statistics report on outstanding accounts based on a formatted template, call a cryptographic algorithm to generate a digest hash value for the report content, and support a digest verification receipt mechanism for the recipient to confirm the integrity of the file content; The statistical report and summary hash value are pushed to the account of the person responsible for the outstanding accounts through the internal communication platform interface.

2. The method for handling long-term outstanding accounts as described in claim 1, characterized in that: The automatically downloaded accounts receivable and payable data files for the target time period include... The automated browser control tool loads the target browser parameters, initializes the automated browser driver, simulates user behavior to perform system login operations, and automatically redirects to the account data export page; The automated browser control tool simulates the invocation of the provided data download interface, triggering the download operation of the account data file for the target time period.

3. The method for handling long-term outstanding accounts as described in claim 2, characterized in that: The process of renaming and classifying files according to preset rules includes... After the data download is triggered, the local target directory is automatically monitored through a monitoring mechanism to determine whether the data files have been completely downloaded. After confirming that the file download is complete, calculate the file's integrity digest value, rename the file according to the preset naming template, and automatically allocate storage paths based on the file's source and purpose, moving the file to the corresponding category directory; Download logs are generated after the files are categorized and stored.

4. The method for handling long-term outstanding accounts as described in claim 3, characterized in that: The method of using data processing tools to perform structured processing on data files includes, Initialize the data processing task, call the data processing tool, load the downloaded accounts data file, and perform preliminary cleaning operations; Field standardization and structure reorganization were performed on the pre-cleaned accounts receivable data; For records with missing fields or null values, fill them in using the historical project ledger template; Finally, the processed data is exported in a standard format.

5. The method for handling long-term outstanding accounts as described in claim 4, characterized in that: The process of risk identification and level labeling of the processed data based on multi-dimensional business rules includes, Load the preset risk identification rule set and establish a multi-dimensional assessment framework; Multi-dimensional feature extraction and rule matching are performed on the processed accounts receivable data. The accounts receivable data are judged and analyzed according to the evaluation framework, and feature labels reflecting the risk status of the accounts receivable data are generated. Based on the feature tags and according to the preset level classification logic, risk level labels for accounts receivable data are generated, and the results are written into the data structure.

6. The method for handling long-term outstanding accounts as described in claim 5, characterized in that: The process of generating a post-receipt risk statistical report based on a formatted template includes... The system calls a preset format template to perform hierarchical organization and layout rendering on the accounts receivable data that have been marked with risk levels, generates a list of accounts receivable items with different risk levels in a set order, and generates a standardized report file. The cryptographic algorithm tool library is invoked to perform digest calculation on the report file content, generate a fixed-length digest hash value, and construct a unique identifier code using the digest hash value for integrity verification; The generated digest hash value is embedded as an independent field in the report metadata. At the same time, the recipient can perform a receipt verification on the report receiving end through digest verification to confirm whether the received report is consistent with the sent version. If the verification is successful, a confirmation receipt identifier is generated.

7. The method for handling long-term outstanding accounts as described in claim 6, characterized in that: The push to the account of the person responsible for the outstanding account includes, Based on the responsible person field information recorded in the report, an account mapping table for the responsible person is automatically created, and the communication platform interface parameters in the configuration file are called to initialize the message push service connection; Construct push content according to a preset message template; The completed message content and statistical report file are sent as attachments to the corresponding responsible person's account through the communication platform interface. After successful sending, a log is generated to record the message ID, recipient identifier, push time, and attachment hash value.

8. A system for processing long-term outstanding accounts, using a method for processing long-term outstanding accounts as described in any one of claims 1 to 7, characterized in that: It includes a download module, a risk marking module, a report generation module, and a push module; The download module uses an automated browser control tool to automatically download accounts receivable and payable data files within a target time period, monitors the local download directory, and renames and categorizes the files according to preset rules after the data files are downloaded, and records the download log. The risk labeling module uses data processing tools to perform structured processing on data files, and performs risk identification and level labeling on the processed data according to multi-dimensional business rules; The report generation module generates a statistical report on outstanding account risks based on a formatted template, calls a cryptographic algorithm to generate a digest hash value for the report content, and supports a digest verification receipt mechanism for the recipient to confirm the integrity of the file content. The push module pushes the statistical report and summary hash value to the account of the person responsible for the outstanding accounts through the internal communication platform interface.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for processing long-term outstanding accounts 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 computer program is executed by the processor, it implements the steps of the method for processing long-term outstanding accounts as described in any one of claims 1 to 7.

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