Non-intrusive invoice data acquisition and consecutive number violation intelligent identification and early warning system and method

Abstract

The invention provides an audit scene-oriented non-intrusive invoice data acquisition and consecutive number violation intelligent identification and early warning system and method, and belongs to the field of invoice data processing. The system comprises a data acquisition module for acquiring invoice original data on an enterprise financial system based on an RPA robot; the data storage and verification module is used for cleaning and storing the collected data; the continuous number comparison module is used for judging illegal invoices, reading verified current-year invoice data from a database at regular time through an RPA robot, and traversing invoice numbers one by one; the RPA robot can call the basic rule base to calculate adjacent difference values of the traversed invoice numbers pair by pair, mark violation data in real time and generate a unique temporary identifier for each piece of violation data; the electronic image acquisition module is linked with the consecutive number comparison module to realize accurate acquisition and standard storage of illegal invoice images; according to the method, the RPA robot full-process automation replaces manual operation, the efficiency is high, and the accuracy rate is high.

Description

Technical Field

[0001] This application relates to the field of automated invoice data processing technology, and in particular to a non-intrusive invoice data collection and intelligent identification and early warning system and method for consecutive number violations in audit scenarios. Background Technology

[0002] In the daily financial auditing work of enterprises, invoice compliance review is a key link in ensuring the authenticity and legality of financial data. Among them, the review of consecutive invoice numbers is an important part of preventing false and irregular reimbursement. Traditional invoice compliance review relies on manual operation. Financial personnel need to manually log into the company's financial system, export the invoice data for the year one by one, and then manually filter the invoice numbers and compare consecutive numbers using tools such as Excel.

[0003] This traditional operating model has revealed its limitations in practical applications:

[0004] 1. Inefficiency: When a company has a large number of invoices, manual data collection and comparison analysis take a lot of time, especially during peak financial settlement periods such as the end of the month and the end of the year, which can easily lead to delays in the review process.

[0005] 2. Accuracy is difficult to guarantee. During manual operation, problems such as data omission and incorrect number comparison are prone to occur, making it impossible to effectively identify compliance risks brought about by consecutive invoices.

[0006] 3. Untimely response: After manual review, it is necessary to manually organize the violation information and notify the responsible personnel, which may cause the best opportunity for risk control to be missed.

[0007] Furthermore, in existing technologies, some enterprises attempt to achieve simple data filtering through built-in functions of their financial systems. This often requires developing inter-system communication interfaces or building dedicated review systems, which is not only costly and time-consuming but may also affect the stability of existing financial systems. Moreover, it fails to form a closed loop of "data collection - compliance review - risk alerts," still requiring manual intervention to connect each step, thus failing to fundamentally solve the pain points of manual review. Therefore, there is an urgent need for a method that can achieve non-intrusive invoice data collection, consecutive number review, and risk alerts to improve the level of enterprise invoice compliance management. Summary of the Invention

[0008] The purpose of this application is to provide a non-intrusive invoice data collection and intelligent identification and early warning system and method for consecutive number violations in audit scenarios. This system addresses the pain points of traditional manual invoice compliance verification, such as low efficiency, accuracy susceptible to human error, and lengthy review cycles. Existing technical solutions require the development of inter-system communication interfaces or the construction of dedicated review systems, which are costly and time-consuming, and may also affect the stability of existing corporate financial systems. Furthermore, they fail to achieve full automation of the "data collection-compliance review-risk alert" process, still requiring manual intervention to connect each step, and do not fundamentally solve the pain points of manual review.

[0009] The technical solution adopted in this application is: a non-intrusive invoice data collection and consecutive number violation intelligent identification and early warning system, including:

[0010] Data acquisition module: Collects raw invoice data from the enterprise's financial system using an RPA robot;

[0011] Data storage and verification module: This module stores the raw invoice data collected by the data acquisition module into the database and cleans the data using RPA's built-in verification rules during the storage process.

[0012] The consecutive number comparison module includes a real-time traversal unit, a rule configuration unit, and a result marking unit, wherein:

[0013] Timed traversal unit: The RPA robot periodically reads the verified invoice data for the current year from the database and traverses the invoice numbers one by one;

[0014] Rule configuration unit: includes a basic rule library, which contains general audit rules. The RPA robot can call the basic rule library to calculate the adjacent differences of each pair of invoice numbers after traversal and mark the non-compliant data in real time.

[0015] Result Marking Unit: Synchronizes the violation data marked by the real-time traversal unit to the result database, and generates a unique temporary identifier for each violation data. This identifier is associated with the core information of the invoice and serves as the location index for subsequent electronic image acquisition.

[0016] Electronic image acquisition module: works in conjunction with the consecutive number comparison module to achieve accurate acquisition and standardized storage of images of non-compliant invoices.

[0017] Furthermore, it also includes a results push module, which is used to push result documents and image files to the audit specialist on a regular basis. The result documents store the violation data, and the impact files store images of the violation invoices associated with the violation data.

[0018] Furthermore, the electronic image acquisition module includes:

[0019] Image positioning unit: The RPA robot reads the unique temporary identifier of the violation data in the result database, obtains the combined query conditions of document number and invoice number, and automatically returns to the corresponding enterprise financial system;

[0020] Automatic download and sorting unit: The RPA robot simulates the operation of manually clicking the "Download Electronic Image" button, obtains electronic image files in PDF or JPG format, automatically names them according to the standardized naming rule of "Year-Month-Invoice Number-Violation Type", and stores them in a specified path on the intranet. At the same time, it establishes an association index between the image file storage path and the violation data in the results database, realizing one-click association retrieval of image files and violation information.

[0021] Anomaly Handling Unit: Set up a real-time download status monitoring mechanism. If anomalies such as missing image files, download timeout, or file corruption are detected, the system will automatically mark the invoice as "image missing" in the results database and send an anomaly alert containing invoice information to the auditors through preset channels.

[0022] Furthermore, the rule configuration unit also includes a visual configuration interface, which allows auditors to operate without coding and directly adjust the consecutive number judgment criteria and expand the comparison dimensions.

[0023] Furthermore, the basic rule base includes a consecutive number comparison algorithm, which automatically identifies batches of consecutive numbered invoices that violate regulations, as detailed below:

[0024] For the cleaned structured invoice data belonging to the target review year, sort the invoice numbers in ascending order to generate a globally ordered invoice number sequence:

[0025] ;

[0026] in Let i be the i-th invoice number, and The date of issuance is not distinguished.

[0027] Obtain the currently effective consecutive number determination threshold t and number difference threshold d from the rule configuration unit;

[0028] For a globally ordered sequence S, calculate the difference between adjacent numbers. ;

[0029] Traversing the difference sequence Identify a subsequence where the difference between t-1 consecutive invoices is equal to d, i.e., there are t consecutive invoices whose numbers differ by d in turn, and record the corresponding range of invoice numbers.

[0030] All invoices in the subsequence that meet the conditions are marked as "batch consecutive number violation";

[0031] Record violations in the annual violation results table and establish a temporary identifier linked to the "document number and invoice number" for subsequent image location.

[0032] The default value of t is 2, meaning that if the difference between two consecutive invoices is d, it can be determined as a violation of consecutive numbering rules.

[0033] A non-intrusive invoice data collection and intelligent identification and early warning method for consecutive number violations, employing the aforementioned non-intrusive invoice data collection and intelligent identification and early warning system for consecutive number violations, includes the following steps:

[0034] I. System initialization configuration;

[0035] II. Automatic collection and verification of invoice data;

[0036] III. Automatic comparison of invoice data;

[0037] IV. Automatic acquisition of electronic images of irregular invoices.

[0038] Furthermore, it also includes:

[0039] V. Results of violations will be automatically pushed out.

[0040] Furthermore, the automatic comparison of invoice data in step three specifically includes:

[0041] S7: The data acquisition module transmits the original invoice data list to the data storage and verification module in real time for data cleaning and storage;

[0042] S8: After the data collection for the day is completed, the built-in consecutive number comparison algorithm is called periodically to automatically determine the consecutive number relationship of all extracted invoice numbers;

[0043] S9: After traversing and reading the invoice data, call the basic rule library in the rule configuration unit, sort the invoice numbers in ascending order by the issuance date, and calculate the difference between adjacent invoice numbers; if data that meets the consecutive number determination rules is detected, mark the group of invoices as "batch consecutive number violation";

[0044] S10: The result marking unit completes the information of the identified violation data, adds fields such as violation type, review time, and rule number, and generates a unique temporary identifier for each violation data; it synchronously writes the violation data into the violation result database and establishes a relationship between the temporary identifier and "document number and invoice number" as a positioning index for subsequent image acquisition, and at the same time generates a review log and uploads it to the RPA control center.

[0045] A computer device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method.

[0046] A computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps of the method.

[0047] The advantages of this application over the prior art are as follows:

[0048] 1. Efficiency Improvement: By replacing manual operations with full-process automation using RPA robots, it supports 24 / 7 uninterrupted operation, improves review efficiency, shortens the review cycle, and effectively improves the problem of review delays.

[0049] 2. Accuracy Guarantee: Based on standardized review rules, RPA robots can perform indiscriminate execution, avoiding human interference, and the review accuracy rate is consistently above 99.9%, effectively reducing the risk of misjudgment and omission.

[0050] 3. Cost reduction and digital adaptation: Reduce the investment of dedicated personnel and lower costs; at the same time, it can be seamlessly integrated with the company's existing financial system, breaking down data silos.

[0051] 4. Enhanced risk control: Automatically generate review result reports to ensure that the review process is traceable and the data is verifiable.

[0052] 5. Reduce system modification and compatibility costs: This invention uses RPA to simulate manual operation without relying on the internal communication interface of the enterprise's financial system, and requires no modification to any system configuration or code. Attached Figure Description

[0053] The following description, in conjunction with the accompanying drawings, further illustrates this application:

[0054] Figure 1 A login authorization flowchart provided for an embodiment of this application;

[0055] Figure 2 A data acquisition flowchart provided for embodiments of this application;

[0056] Figure 3 An automatic comparison flowchart provided for embodiments of this application;

[0057] Figure 4 A flowchart for automatically acquiring electronic images of non-compliant invoices provided in this application embodiment;

[0058] Figure 5 This is a schematic diagram of the computer device structure provided in an embodiment of this application. Detailed Implementation

[0059] like Figures 1 to 5 As shown, this application proposes a non-intrusive invoice data collection and intelligent identification and early warning system for consecutive number violations in audit scenarios, including:

[0060] Data Acquisition Module: This module uses RPA technology for interface-free simulated operation data acquisition. It does not require changes to the internal interface of the enterprise's financial system. Through the RPA robot, it simulates the complete operation process of manual login, data query, export and sorting, and directly collects the original invoice data from the interface of various types of enterprise financial systems, including key information such as document number, invoice code, invoice number, reimbursement person, and issuance date.

[0061] Data storage and verification module: This module includes a database storage unit and an RPA built-in verification rule unit, which works in conjunction with the data acquisition module to achieve real-time data storage. The raw invoice data output by the data acquisition module is directly transmitted to a preset database for storage. During storage, the RPA built-in verification rule unit automatically performs data filtering, using preset format and validity verification rules to remove invalid data such as empty numbers, incorrect formats, and duplicate entries, ensuring the integrity and validity of the invoice data stored in the database, providing a reliable data foundation for subsequent comparison.

[0062] Consecutive Number Comparison Module: This module is the core comparison execution unit, including a real-time traversal unit, a rule configuration unit, and a result marking unit.

[0063] Timed traversal unit: Once the data collected within the period is summarized, the comparison is started immediately. The RPA robot reads the verified invoice data of the current year from the database at regular intervals and traverses the invoice numbers one by one, which greatly shortens the waiting period for manual comparison.

[0064] The rule configuration unit includes a basic rule base and a visual configuration interface. It incorporates general audit rules such as classifying consecutive numbers as consecutive numbers when the difference between two consecutive numbers is 1. The RPA robot can call the rule base to calculate the adjacent differences for each pair of invoice numbers after iteration, marking violations in real time. The visual configuration interface supports code-free operation by auditors. For example, they can directly adjust the consecutive number judgment criteria (e.g., changing "difference of 1" to "difference of 2" for consecutive numbers with an interval) and expand the comparison dimensions to adapt to diverse audit scenarios.

[0065] Result Marking Unit: Synchronizes the violation data marked by the real-time traversal unit (including violation type, invoice code, invoice number, reimbursement person, related invoice list, etc.) to the result database, and generates a unique temporary identifier for each violation data. This identifier is associated with the core information of the invoice and serves as a location index for subsequent electronic image acquisition, eliminating the need for manual recording and transmission of violation information.

[0066] Electronic image acquisition module: This module includes an image positioning unit, an automatic download and sorting unit, and an anomaly handling unit. It works in conjunction with the consecutive number comparison module to achieve accurate acquisition and standardized storage of images of non-compliant invoices.

[0067] Image positioning unit: The RPA robot reads the unique temporary identifier of the non-compliant data in the result database, obtains the combined query conditions of document number and invoice number, and automatically returns to the corresponding enterprise financial system. It simulates the operation process of a human in the system's invoice details query interface, accurately locates the electronic image display page of the corresponding invoice, and can access electronic image resources without developing an image interface, avoiding system modification.

[0068] Automatic download and organization unit: The RPA robot simulates the operation of manually clicking the "Download Electronic Image" button, obtains electronic image files, automatically names them according to the standardized naming rule of "Year-Month-Invoice Number-Violation Type", and stores them in a specified path on the intranet. At the same time, it establishes an association index between the image file storage path and the violation data in the results database, realizing one-click association retrieval of image files and violation information, avoiding confusion caused by manual naming and storage loss.

[0069] Anomaly Handling Unit: A real-time download status monitoring mechanism is set up. If anomalies such as missing image files, download timeouts, or file corruption are detected, the invoice is automatically marked as "image missing" in the results database. An anomaly alert containing invoice information is sent to auditors through preset channels (such as WeChat or email). This eliminates the need for manual verification of download status, improving the efficiency of anomaly handling.

[0070] Results Push Module: This module is an independent push unit used to periodically push results documents and image files to the audit specialist via email or WeChat.

[0071] This application also provides a non-intrusive invoice data collection and intelligent identification and early warning method for consecutive number violations in audit scenarios. Based on the above system, it mainly includes the following steps:

[0072] I. System initialization configuration, specifically including:

[0073] S1: Complete the basic parameter configuration in the RPA designer. The configuration should be executed sequentially by module. First, configure access to the enterprise financial system. Enter the access address of the enterprise financial system in the RPA robot client. Use the built-in element recognition function of RPA to locate key operation nodes such as the login interface, invoice query interface, and data export button of the enterprise financial system. At the same time, configure the enterprise WeChat communication parameters to ensure that the RPA robot can send the login QR code and login success notification to the audit specialist.

[0074] S2: After completing the access configuration, execute the verification rule configuration. Preset the verification validity rules in the RPA built-in verification rule unit, that is, the invoice code is a 10-digit or 12-digit number, the invoice number is an 8-digit number, and there are no duplicate invoice code and invoice number combinations.

[0075] S3: Complete the consecutive number review rule configuration. Set the basic rules through the visual configuration interface, which is to determine the adjacent difference of invoice numbers that are considered to be consecutive number violations. At the same time, set the rule effective period to the natural month, which means that consecutive number review will be automatically performed on the invoice data accumulated in the current month and the current year every month. Save the rules to the basic rule library and associate them with the consecutive number comparison module of the RPA robot.

[0076] S4: Complete the push and storage configuration, set the list of push recipients, create a year-month hierarchical storage directory structure in the file server, and enter the directory path into the RPA's automatic download and sorting unit.

[0077] II. Automatic Invoice Data Collection and Verification: The specific implementation process includes the following steps:

[0078] S5: Login authorization, the process is as follows Figure 1 As shown, the process includes: the RPA robot initiates the company's financial system verification interface according to a preset time cycle, and verifies the current login status through a loop detection mechanism. If no login status is detected, the robot automatically sends a login QR code to the audit specialist via WeChat and waits continuously within the QR code's validity period; if the QR code expires, it is resent, and the above operation is executed cyclically, with a maximum number of retries set; when the page is detected as logged in, the robot immediately sends a login success notification to the audit specialist. After the audit specialist completes identity authorization by scanning the QR code, the robot will automatically access the company's financial system on the same day based on this authorization information. The entire process simulates a manual operation trajectory and involves no system interface calls.

[0079] S6: After the robot successfully logs in, it will collect data. The process is as follows: Figure 2 As shown, specifically: based on the preset interface element positioning coordinates, the robot automatically clicks the "Invoice Management → Reimbursement Invoice Inquiry" menu in the enterprise financial system, enters the filter condition "issuance date from January 1st of the current year to the current date" in the query condition input box, and clicks the "Query" button; after the query results are loaded, the robot automatically collects the document numbers in the list, and then automatically clicks the document number to enter the details page, collects the invoice information in the invoice details, including but not limited to invoice number, voucher number, department name, handler, document type and document number, and stores the collected information in the invoice business data table of the database.

[0080] III. Automatic invoice data comparison, the process is as follows: Figure 3 As shown, its specific implementation process includes the following steps.

[0081] S7: The data acquisition module transmits the original invoice data list to the data storage and verification module in real time. This module first calls the verification rule unit to verify each piece of data, remove invalid data and duplicate data, and achieve data cleaning. Valid data that passes verification is automatically written to the database and a collection log is generated and uploaded to the RPA control center.

[0082] S8: After the data collection for the day is completed, the built-in consecutive number comparison algorithm will be called periodically to automatically determine the consecutive relationship of all extracted invoice numbers. The algorithm realizes the process of "data extraction → relationship recognition → result update" through modular design.

[0083] S9: After traversing and reading the invoice data, call the basic rule library in the rule configuration unit, sort the invoice numbers in ascending order by the issuance date, and calculate the difference between adjacent invoice numbers; for example, if it is detected that "the difference between two consecutive numbers is 1" (such as invoice numbers 12345678 and 12345679), then mark the group of invoices as "batch consecutive number violation".

[0084] S10: The result marking unit completes the information of the identified violation data, adds fields such as violation type, review time, and rule number, and generates a unique temporary identifier for each violation data (format: "WG + year + 6-digit random number", such as WG2024123456); the violation data is synchronously written to the violation result database, and a relationship is established between the temporary identifier and "document number, invoice number" as a location index for subsequent image acquisition. At the same time, a review log is generated and uploaded to the RPA control center.

[0085] Based on the above steps, the specific principle of the consecutive number comparison algorithm can be summarized as follows:

[0086] 1) Data Extraction

[0087] For the cleaned structured invoice data belonging to the target review year, sort the invoice numbers in ascending order to generate a globally ordered invoice number sequence:

[0088] ;

[0089] in Let i be the i-th invoice number, and The date of issue is not distinguished.

[0090] 2) Relationship identification, including:

[0091] (1) Rule definition:

[0092] Set the consecutive number determination threshold to t (the minimum number of consecutive violations, default t=2), and the number difference threshold to d (default d=1, user-configurable to other integers). For a globally ordered sequence S, if a subsequence exists... satisfy: Then mark the subsequence as a violation of the rule of consecutive numbers.

[0093] (2) Processing logic:

[0094] Rule reading: Retrieve the currently effective t and d from the configuration unit;

[0095] Difference calculation: For a globally ordered sequence S, calculate the difference between adjacent numbers. ;

[0096] Consecutive number determination: Traverse the difference sequence Identify a subsequence with t-1 consecutive differences equal to d, and record the corresponding range of invoice numbers.

[0097] Violation flag: Mark all invoices in the subsequence that meet the conditions as "batch consecutive number violation".

[0098] 3) Result Update

[0099] Record violations in the annual violation results table and establish a temporary identifier linked to the "document number and invoice number" for subsequent image location.

[0100] IV. Automatic acquisition of electronic images of irregular invoices, the process is as follows: Figure 4 As shown, its specific implementation process includes the following steps.

[0101] S11: After the consecutive number review task is completed, the electronic image acquisition module is automatically started. The image positioning unit reads all the violation data and corresponding temporary identifiers of the violation results database that have not been acquired, and obtains the combined query conditions of "document number + invoice number" through the association of the temporary identifiers.

[0102] S12: The RPA robot returns to the enterprise's financial system with the query conditions, automatically enters the "Invoice Details Query" interface, enters the document number and clicks query, locates the record corresponding to the "Invoice Number" in the result list and clicks the "Details" button, and jumps to the electronic image display page; the robot simulates a human clicking the "Download Electronic Image" button, obtains the image file, and saves it to the local temporary directory.

[0103] S13: The automatic download and sorting unit reads the image files in the local temporary directory, renames them according to the naming rule of "year-month-invoice number-violation type" (such as "2024-10-12345678-batch consecutive numbers"), and automatically uploads them to the corresponding directory of the intranet file server; at the same time, it updates the "image storage path" and "image acquisition status (success)" of the violation data in the "violation result database" and establishes an association index between the image and the violation information.

[0104] S14: If the exception handling unit detects situations such as "no file is generated within 10 seconds after clicking the download button (download timeout)", "file size is less than 10KB (file is corrupted)", or "system prompts that the image does not exist", it will immediately mark the data image status as missing in the violation result database and automatically call the notification push module to send an alert message containing the document number, invoice number, violation type, and reason for the exception to the audit specialist.

[0105] V. Automatic push notification of violation results, the specific implementation process includes the following steps.

[0106] S15: The result push module is automatically activated according to the preset push cycle. It reads all new violation data from the previous day from the violation result database and groups them according to the department to which the person seeking reimbursement belongs.

[0107] S16: The module automatically generates a standardized violation review report (Excel format). The report includes fields such as "serial number, temporary identifier, document number, invoice code, invoice number, claimant, department, issuance date, violation type, image acquisition status, and image storage path". For violation data with successfully acquired images, the corresponding image files are automatically compressed into a ZIP package.

[0108] S17: Based on the department-audit specialist correspondence configured in the initialization, the module pushes information to the corresponding specialist via WeChat or email: an Excel file of the violation review report, and if there are image files, a download link for the internal network compressed package is provided simultaneously.

[0109] S18: After the push is completed, the module automatically records log information such as push time, recipient, push channel, and whether it was successful. If the push fails, an alarm message is immediately sent to the RPA administrator to ensure that no information is missed.

[0110] This application, based on RPA technology and combined with a custom consecutive number comparison algorithm, constructs a fully automated solution for the entire process of "automatic invoice data collection - intelligent review of consecutive number compliance - automatic reminder of violation information": An RPA robot simulates manual operation, automatically logs into the company's financial system, and collects invoice data for the current year, achieving non-intrusive data acquisition; the consecutive number comparison algorithm analyzes the collected invoice numbers to identify non-compliant consecutive invoices, while simultaneously using RPA to collect electronic images of the corresponding non-compliant invoices; finally, through a preset notification channel, the violation information is automatically sent to the corresponding responsible personnel, achieving unmanned, efficient, and accurate invoice compliance review.

[0111] Figure 5 A structural block diagram of a computer device according to a specific embodiment of this application is shown. Figure 5As shown, the computer device includes a memory and a processor, the memory storing instructions executable on the processor. When the processor executes the instructions, it implements the methods described in the above embodiments. The number of memories and processors can be one or more. This computer device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The computer device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0112] The computer device may also include a communication interface for communicating with external devices and exchanging data. The devices are interconnected using different buses and can be mounted on a common motherboard or otherwise as needed. The processor can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as a display device coupled to the interface). In other embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). The bus can be divided into address buses, data buses, control buses, etc. For ease of illustration, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0113] Optionally, in a specific implementation, if the memory, processor, and communication interface are integrated on a single chip, then the memory, processor, and communication interface can communicate with each other through an internal interface.

[0114] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting advanced RISC machines (ARM) architecture.

[0115] This application provides a computer-readable storage medium (such as the memory described above) storing computer instructions that, when executed by a processor, implement the method provided in this application.

[0116] Optionally, the memory may include a stored program area and a stored data area, wherein the stored program area may store the operating system and application programs required for at least one function; the stored data area may store data created based on the use of the computer device for mapping. Furthermore, the memory may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the computer device for mapping via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A non-intrusive invoice data collection and intelligent identification and early warning system for consecutive number violations, characterized in that: include: Data acquisition module: Collects raw invoice data from the enterprise's financial system using an RPA robot; Data storage and verification module: This module stores the raw invoice data collected by the data acquisition module into the database and cleans the data using RPA's built-in verification rules during the storage process. The consecutive number comparison module includes a real-time traversal unit, a rule configuration unit, and a result marking unit, wherein: Timed traversal unit: The RPA robot periodically reads the verified invoice data for the current year from the database and traverses the invoice numbers one by one; Rule configuration unit: includes a basic rule library, which contains general audit rules. The RPA robot can call the basic rule library to calculate the adjacent differences of each pair of invoice numbers after traversal and mark the non-compliant data in real time. Result Marking Unit: Synchronizes the violation data marked by the real-time traversal unit to the result database, and generates a unique temporary identifier for each violation data. This identifier is associated with the core information of the invoice and serves as the location index for subsequent electronic image acquisition. Electronic image acquisition module: works in conjunction with the consecutive number comparison module to achieve accurate acquisition and standardized storage of images of non-compliant invoices.

2. The non-intrusive invoice data collection and consecutive number violation intelligent identification and early warning system according to claim 1, characterized in that: It also includes a results push module, which is used to push result documents and image files to the audit specialist on a regular basis. The result documents store the violation data, and the impact files store images of the violation invoices associated with the violation data.

3. A non-intrusive invoice data collection and consecutive number violation intelligent identification and early warning system according to claim 1 or 2, characterized in that: The electronic image acquisition module includes: Image positioning unit: The RPA robot reads the unique temporary identifier of the violation data in the result database, obtains the combined query conditions of document number and invoice number, and automatically returns to the corresponding enterprise financial system; Automatic download and sorting unit: The RPA robot simulates the operation of manually clicking the "Download Electronic Image" button, obtains electronic image files in PDF or JPG format, automatically names them according to the standardized naming rule of "Year-Month-Invoice Number-Violation Type", and stores them in a specified path on the intranet. At the same time, it establishes an association index between the image file storage path and the violation data in the results database, realizing one-click association retrieval of image files and violation information. Anomaly Handling Unit: Set up a real-time download status monitoring mechanism. If anomalies such as missing image files, download timeout, or file corruption are detected, the system will automatically mark the invoice as "image missing" in the results database and send an anomaly alert containing invoice information to the auditors through preset channels.

4. The non-intrusive invoice data collection and consecutive number violation intelligent identification and early warning system according to claim 3, characterized in that: The rule configuration unit also includes a visual configuration interface, which allows auditors to operate without coding and directly adjust the consecutive number judgment criteria and expand the comparison dimensions.

5. The non-intrusive invoice data collection and consecutive number violation intelligent identification and early warning system according to claim 4, characterized in that: The basic rule base includes a consecutive number comparison algorithm, which automatically identifies batches of consecutive numbered invoices that violate regulations, as detailed below: For the cleaned structured invoice data belonging to the target review year, sort the invoice numbers in ascending order to generate a globally ordered invoice number sequence: ； in Let i be the i-th invoice number, and The date of issuance is not distinguished. Obtain the currently effective consecutive number determination threshold t and number difference threshold d from the rule configuration unit; For a globally ordered sequence S, calculate the difference between adjacent numbers. ; Traversing the difference sequence Identify subsequences with t-1 consecutive differences equal to d, and record the corresponding range of invoice numbers; All invoices in the subsequence that meet the conditions are marked as "batch consecutive number violation"; Record violations in the annual violation results table and establish a temporary identifier linked to "document number and invoice number" for subsequent image location.

6. A non-intrusive method for invoice data collection and intelligent identification and early warning of consecutive number violations, characterized in that: The non-intrusive invoice data collection and consecutive number violation intelligent identification and early warning system as described in any one of claims 1-5 includes the following steps: I. System initialization configuration; II. Automatic collection and verification of invoice data; III. Automatic comparison of invoice data; IV. Automatic acquisition of electronic images of irregular invoices.

7. The non-intrusive invoice data collection and intelligent identification and early warning method for consecutive number violations according to claim 6, characterized in that: Also includes: V. Results of violations will be automatically pushed out.

8. A non-intrusive invoice data collection and intelligent identification and early warning method for consecutive number violations according to claim 6 or 7, characterized in that: Step three, the automatic comparison of invoice data, specifically includes: S7: The data acquisition module transmits the original invoice data list to the data storage and verification module in real time for data cleaning and storage; S8: After the data collection for the day is completed, the built-in consecutive number comparison algorithm is called periodically to automatically determine the consecutive number relationship of all extracted invoice numbers; S9: After traversing and reading the invoice data, call the basic rule library in the rule configuration unit, sort the invoice numbers in ascending order by the issuance date, and calculate the difference between adjacent invoice numbers; if data that meets the consecutive number determination rules is detected, mark the group of invoices as "batch consecutive number violation"; S10: The result marking unit completes the information of the identified violation data, adds fields such as violation type, review time, and rule number, and generates a unique temporary identifier for each violation data; it synchronously writes the violation data into the violation result database and establishes a relationship between the temporary identifier and "document number and invoice number" as a positioning index for subsequent image acquisition, and at the same time generates a review log and uploads it to the RPA control center.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method of claim 8.

10. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that: When the computer program / instructions are executed by the processor, they implement the steps of the method of claim 8.

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