RPA-based full-automatic all-electricity invoice issuing method and system

Abstract

The invention discloses an RPA-based full-automatic all-electricity invoice issuing method and system. The method comprises the following steps: constructing an RPA system: creating an RPA user, creating a business process, and defining a process rule; data acquisition and preprocessing: capturing basic transaction data, and performing data preprocessing on the captured data; invoicing and intelligent inspection: logging in a full-electricity invoice service platform, filling and submitting data information in an invoicing interface, and verifying various contents of an invoice; invoice circulation and signing determination: sending the issued full-electricity invoice to the user, and monitoring the invoice signing condition; and storage and filing management: uploading the issued all-electricity invoice to a management system. According to the invention, by introducing the RPA technology, the full-automatic processing of the full-electricity invoices from data acquisition and issuing to circulation and archiving is realized, the time consumption of manual operation is greatly reduced, and the invoice issuing efficiency is improved compared with the traditional manual or semi-automatic process.

Description

Technical Field

[0001] The present invention belongs to the field of software development technology, and more specifically, particularly relates to a method for fully automatically issuing all-electric invoices based on RPA. At the same time, the present invention also relates to a system for fully automatically issuing all-electric invoices based on RPA. Background Art

[0002] The all-electronic invoice is a fully digital invoice form with the same legal effect as the paper invoice. It does not exist in paper form, does not require physical media support, and does not need to be applied for. The main features of the all-electronic invoice include: Information digitization: The all-electronic invoice converts the face information of the traditional paper invoice into digital form and stores and manages it electronically. Electronic signature: The electronic signature technology is used to sign the invoice, ensuring the uniqueness, non-repudiation and tamper-proof nature of the electronic invoice. Automatic circulation and delivery: After the all-electronic invoice data file is issued, it will be automatically sent to the tax digital account of the issuer and the recipient, which is convenient for delivery and accounting, and reduces the manual sending and receiving links. The emergence of the all-electronic invoice conforms to the development trend of the digital economy. It has significant advantages over paper invoices in terms of security and convenience.

[0003] However, there are some problems with the existing technology: the existing all-electric invoice issuance process first requires manual collection and extraction of transaction data, and then organizes it into a format that meets the requirements for all-electric invoice issuance, manually checks and fills in the various contents of the invoice, and the relevant personnel then review the invoice information to check whether there are any violations of tax laws. If necessary, multi-level approval is required. After the review is correct, the invoice will be sent to the customer, and the customer also needs to sign and confirm online. This process may also involve multiple communications and coordination. Once an error occurs, manual intervention is required to correct it. The entire process not only consumes a lot of manpower and material resources, but also has the problem of low efficiency, easy errors and high labor costs due to the high probability of errors caused by human factors. Therefore, we propose a method and system for fully automatic issuance of all-electric invoices based on RPA. Summary of the invention

[0004] In view of the problems existing in the prior art, the purpose of the present invention is to provide a method and system for fully automatic issuance of all-electronic invoices based on RPA. The recommended method introduces RPA technology to achieve fully automated processing of all-electronic invoices from data collection, issuance to circulation and archiving, which greatly reduces the time consumption of manual operations and improves the efficiency of invoice issuance compared with traditional manual or semi-automated processes.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for fully automatically issuing all-electric invoices based on RPA, comprising the following steps:

[0006] S1, build the RPA system: create RPA users, create business processes, define process rules, write interaction and exception handling programs, and perform detection and optimization adjustments;

[0007] S2, data collection and preprocessing: Connect to the enterprise system through the RPA robot, capture basic transaction data, and preprocess the captured data;

[0008] S3, invoice issuance and intelligent verification: log in to the all-electric invoice service platform, fill in and submit data information on the invoice interface based on the processed data, and verify the invoice contents through the rules and tax control logic preset by the RPA robot;

[0009] S4, invoice circulation and receipt confirmation: The RPA robot sends the issued all-electric invoice to the user, monitors the invoice receipt in real time, and automatically records the receipt information;

[0010] S5, storage and filing management: upload the issued all-electric invoices to the management system, store and file them according to preset standards and index rules, and automatically process invoice events and update the ledger.

[0011] Optionally, in step S1, the RPA system architecture includes:

[0012] S11, determine business processes and rules: determine automated processes and process execution rules, wherein the automated processes include reading transaction information, capturing data text, verifying data rationality and integrity, data preprocessing, and logging;

[0013] S12, set up the automation process: design the automation flow chart in the RPA tool, including the logical sequence of each step, decision points, and exception handling mechanism;

[0014] S13, configure RPA robots: configure robots in the RPA tool so that they can perform tasks according to preset automation processes, including setting triggers, defining operation steps, and integrating data input and output;

[0015] S14, Testing and Optimization: Test the automated process in the actual environment to ensure that it can perform all tasks correctly and adjust and optimize the automated process based on the test results to improve the stability and efficiency of the process;

[0016] S15, exception handling and self-optimization: The system will alarm when an abnormal situation occurs in the RPA process, prioritize the activation of the backup plan and notify the operation and maintenance personnel, and then use the RPA model learning ability to update and improve the automation process.

[0017] Optionally, in step S12, the automation process includes:

[0018] 1) Data extraction: RPA robots read transaction data in ERP enterprise resource planning and CRM customer relationship management systems in real time, including purchase orders and sales contracts, and automatically extract data text in invoices through pre-set mapping rules;

[0019] 2) Data processing: The RPA robot pre-processes the data text to convert the data text into a data format that the robot can directly process;

[0020] 3) Invoice issuance: The RPA robot logs into the all-electric invoice service platform, fills in the invoice information according to the tax law, and submits the issuance application;

[0021] 4) Invoice circulation: The RPA robot delivers the issued all-electric invoice to the buyer, monitors the receipt status of the invoice, and automatically updates the invoice circulation record;

[0022] 5) Storage and archiving: The RPA robot synchronizes the issued all-electric invoice data to the company's internal financial management system for classification, numbering and encrypted storage.

[0023] Optionally, the data extraction step includes: defining mapping rules, automatically capturing data, and extracting key information;

[0024] The mapping rules are based on the correspondence between the source data format and the target data format, and adopt a commonly used fixed template;

[0025] Automatic data capture: The RPA robot will automatically log into the ERP and CRM systems according to preset rules, locate transaction data, and then simulate manual operations to obtain the required transaction data;

[0026] Extracting key information: The RPA robot reads transaction data and extracts data text according to mapping rules.

[0027] Optionally, the data processing steps include: data cleaning, data verification, data conversion and data storage;

[0028] The data cleaning includes reviewing the content and quality of transaction data, screening required data information, identifying and deleting duplicate information, and processing missing values ​​and outliers;

[0029] Data validation: checking the accuracy and completeness of data according to predetermined rules and logic, and generating reports and alerts;

[0030] The data conversion: converting the corresponding data into a format according to the internal format of the invoice template;

[0031] The data storage: storing the converted data in a database;

[0032] The data cleaning adopts OCR and AI algorithms;

[0033] The OCR is an optical character recognition technology used to recognize various types of documents, including scanned documents, pictures, PDFs, audio and video;

[0034] The AI ​​algorithm is used to process unstructured text, which is based on deep learning and natural language processing technology. Optionally, the deep learning includes a convolutional neural network, and the convolutional layer calculation formula is:

[0035] Output feature map size:

[0036] Where W is the size of the input feature map;

[0037] K is the convolution kernel size;

[0038] P is for filling;

[0039] S is the step size;

[0040] Parameter quantity: K 2 ×C in yimesC out , C in and C out are the number of input and output channels respectively;

[0041] Calculation amount: K 2 ×C in ×O 2 yimesC out .

[0042] Optionally, the convolutional neural network is constructed by converting the input into a large matrix and performing matrix multiplication, including:

[0043] 1) Input transformation: transform the input data into a large matrix;

[0044] 2) Weight matrix construction: reshape the convolution kernel into a matrix, where the number of rows is the size of the convolution kernel multiplied by the number of input channels, and the number of columns is the number of output channels;

[0045] 3) Matrix multiplication: Use the GEMM algorithm to multiply the weight matrix and the input matrix to obtain an intermediate matrix;

[0046] 4) Output reshaping: Reshape the intermediate matrix into the form of output feature map.

[0047] Optionally, in step S3, the invoice issuance and intelligent verification include:

[0048] S31, the RPA robot automatically logs into the store-wide invoice service platform;

[0049] S32, create a new invoice template and fill the pre-processed data text into the invoice;

[0050] S33, automatically submits invoicing applications to ensure correct data transmission;

[0051] S34, verify the invoice according to the preset rules and tax control logic;

[0052] S35, for any anomalies found during verification, notify relevant personnel and record logs according to the settings;

[0053] S36, archive the confirmed correct invoice and push it to the next process;

[0054] The invoice verification includes: checking the invoice amount, verifying the tax rate of the goods, checking the invoice filling status, checking the accuracy of the information of the buyer and seller, and the unique identification of the invoice number.

[0055] A system for fully automatic issuance of all-electric invoices based on RPA, the system being used to implement the above-mentioned method for fully automatic issuance of all-electric invoices based on RPA, comprising a server cluster, a control module, an interaction module, a tracking module, a storage unit and a terminal device;

[0056] The server cluster is used to deploy RPA automation software, ERP system and CRM system;

[0057] The acquisition module is used to identify and pre-process the data;

[0058] The control module is used to execute the automated invoice filling process;

[0059] The interaction module is used to send and store data text;

[0060] The tracking module is used to track the receipt status of the invoice;

[0061] The storage module is used to deploy databases and rule bases, and record data texts and historical data;

[0062] The terminal device includes a PC and a mobile device, which is used to receive the circulating all-electric invoice.

[0063] The technical effects and advantages of the present invention are as follows: By introducing RPA technology, the fully automated processing of all-electric invoices from data collection, issuance to circulation and archiving is realized, which greatly reduces the time consumption of manual operations and greatly improves the efficiency of invoice issuance. Since RPA can perform accurate data processing and invoice verification according to preset rules and algorithms, it reduces errors caused by human negligence and reduces the tax risks of enterprises. The automated process significantly reduces labor costs, reduces dependence on human resources, and reduces ineffective labor and repetitive work. The present invention not only simplifies the issuance process of all-electric invoices and improves the financial management efficiency of enterprises, but also brings multiple beneficial effects such as significant cost savings, enhanced compliance, and environmental protection.

[0064] Secondly, in the data cleaning process of RPA, the convolution operation is implemented by using the matrix multiplication algorithm based on the original AI algorithm, taking advantage of the modern hardware architecture and the highly optimized linear algebra library to accelerate the calculation and improve the computational efficiency of the convolutional neural network.

[0065] Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a schematic flow chart of the method provided by the present invention;

[0067] Figure 2 is an operation flow chart provided by an embodiment of the present invention;

[0068] Figure 3 It is a schematic block diagram of the system architecture provided by the present invention. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0070] like Figures 1 to 3 As shown, a method and system for fully automatically issuing all-electronic invoices based on RPA provided by an embodiment of the present invention includes the following steps:

[0071] S1, build the RPA system: create RPA users, create business processes, define process rules, write interaction and exception handling programs, and perform detection and optimization adjustments;

[0072] S2, data collection and preprocessing: Connect to the enterprise system through the RPA robot, capture basic transaction data, and preprocess the captured data;

[0073] S3, invoice issuance and intelligent verification: log in to the all-electric invoice service platform, fill in and submit data information on the invoice interface based on the processed data, and verify the invoice contents through the rules and tax control logic preset by the RPA robot;

[0074] S4, invoice circulation and receipt confirmation: The RPA robot sends the issued all-electric invoice to the user, monitors the invoice receipt in real time, and automatically records the receipt information;

[0075] S5, storage and filing management: upload the issued all-electric invoices to the management system, store and file them according to preset standards and index rules, and automatically process invoice events and update the ledger.

[0076] Furthermore, during the automation process, when the RPA process encounters an abnormal situation that cannot be handled, such as network failure, data anomaly, etc., the system will immediately activate the backup plan or issue an alarm signal to notify the operation and maintenance personnel to intervene in time, and continuously optimize and improve the process through machine learning.

[0077] Furthermore, by real-time monitoring of various activities in the process, any possible anomalies or errors can be quickly identified. Once an anomaly is detected, the RPA robot will record detailed anomaly information and immediately notify the relevant responsible person. Based on the type and severity of the anomaly, the RPA robot can make decisions according to preset rules, such as retrying, skipping, waiting for manual intervention, etc.

[0078] Furthermore, RPA robots collect and analyze process data to identify performance bottlenecks or common problems, and use machine learning algorithms to identify patterns of success and failure, thereby finding potential optimization points. RPA robots can continuously learn and adapt to new situations through machine learning technology to improve their intelligence level and processing capabilities. Based on the above analysis, robots or managers can adjust the automated process to improve efficiency, reduce the probability of errors, and respond to changing business needs.

[0079] For example, the RPA robot reads transaction data in the ERP enterprise resource planning and CRM customer relationship management systems in real time, including purchase orders and sales contracts, automatically extracts the data text in the invoice, and then pre-processes the data text to convert the data text into a data format that the robot can directly process. The robot then delivers the issued all-electric invoice to the buyer, monitors the receipt status of the invoice, automatically updates the invoice circulation record, and synchronizes the issued all-electric invoice data to the company's internal financial management system for classification, numbering and encrypted storage.

[0080] In step S1, the RPA system architecture includes:

[0081] S11, determine business processes and rules: determine automated processes and process execution rules, wherein the automated processes include reading transaction information, capturing data text, verifying data rationality and integrity, data preprocessing, and logging;

[0082] The process execution rules are:

[0083] Data entry and validation rules, including definition of data sources used for invoicing (e.g. ERP system, order management system);

[0084] Verify the completeness and accuracy of input data to ensure that all necessary information has been provided and is correct;

[0085] Convert input data into the format required for invoicing;

[0086] Rules for filling in invoice information:

[0087] Define the filling rules for each field of the invoice according to tax law regulations and invoice format requirements;

[0088] Set the filling logic for key information such as invoice number, invoice date, purchaser information, product details, etc.

[0089] Handle special situations, such as filling in multiple lines of product details, discounts, notes, etc.

[0090] Compliance rules:

[0091] Ensure that the execution of the process complies with tax laws, accounting standards and relevant regulations;

[0092] Regular compliance reviews and audits of processes to verify compliance;

[0093] Timely adjust process rules according to regulatory changes to ensure continuous compliance;

[0094] Process control rules:

[0095] Define the execution order and conditional branches of the process to ensure that the process can be executed according to the predetermined path;

[0096] Set up wait points, checkpoints, and decision points in the process to pause the process, validate data, or make decisions when necessary;

[0097] Manage and execute tasks to ensure efficient use of resources and smooth processes.

[0098] S12, set up the automation process: design the automation flow chart in the RPA tool, including the logical sequence of each step, decision points, and exception handling mechanism;

[0099] In step S12, the automation process includes:

[0100] 1) Data extraction: RPA robots read transaction data in ERP enterprise resource planning and CRM customer relationship management systems in real time, including purchase orders and sales contracts, and automatically extract data text in invoices through pre-set mapping rules;

[0101] 2) Data processing: The RPA robot pre-processes the data text to convert the data text into a data format that the robot can directly process;

[0102] 3) Invoice issuance: The RPA robot logs into the all-electric invoice service platform, fills in the invoice information according to the tax law, and submits the issuance application;

[0103] 4) Invoice circulation: The RPA robot delivers the issued all-electric invoice to the buyer, monitors the receipt status of the invoice, and automatically updates the invoice circulation record;

[0104] 5) Storage and archiving: The RPA robot synchronizes the issued all-electric invoice data to the company's internal financial management system for classification, numbering and encrypted storage.

[0105] Furthermore, to build an automated process through RPA software, you first need to sort out the specific process of issuing invoices, determine the operational processes that can be automated, select the appropriate RPA software according to the process needs, and then open the RPA software creation tool to design the logical structure of the automated process, including task sequence, decision points and exception handling mechanisms, use the RPA tool's scripting language or graphical user interface to write the specific steps of the automated task, and finally test the automated process in a controlled environment, deploy the tested automated process to the production environment, and monitor its performance in real time.

[0106] Furthermore, when the RPA robot pre-processes the data text, it first needs to identify the data source. These data sources may include PDF files, Word documents, spreadsheets, web pages and other formats. The robot needs to be able to identify and extract data from these different data sources. In the data source, the data may be distributed in different locations, such as tables, paragraphs, and specific tags. The RPA robot needs to accurately locate the location of these data and ensure the integrity of the data when extracting data to avoid omissions or duplicate extractions.

[0107] Secondly, the extracted data text may contain some irrelevant information, such as advertisements, comments, etc. The RPA robot needs to be able to identify and remove this information, format the data in different formats from different data sources, unify the format standards, and facilitate subsequent processing. Ensure that the encoding format of the data text is consistent with the encoding format processed by the RPA robot to avoid garbled or unrecognizable situations;

[0108] Finally, the RPA robot needs to convert unstructured data text into structured data formats, such as JSON, XML, etc., so that the RPA robot can process it more efficiently. During the data conversion process, it is necessary to verify the accuracy and consistency of the data to ensure that the converted data meets expectations.

[0109] Specifically, the data extraction steps include: defining mapping rules, automatically capturing data, and extracting key information;

[0110] The mapping rules are based on the correspondence between the source data format and the target data format, and adopt a commonly used fixed template;

[0111] Automatic data capture: The RPA robot will automatically log into the ERP and CRM systems according to preset rules, locate transaction data, and then simulate manual operations to obtain the required transaction data;

[0112] Extracting key information: The RPA robot reads transaction data and extracts data text according to mapping rules.

[0113] The steps of data processing include: data cleaning, data verification, data conversion and data storage;

[0114] The data cleaning includes reviewing the content and quality of transaction data, screening required data information, identifying and deleting duplicate information, and processing missing values ​​and outliers;

[0115] Furthermore, data cleaning is a crucial step in the data processing process, which is used to improve the quality and accuracy of data and provide a reliable basis for subsequent data analysis and decision support. Since invoice data involves the accuracy and compliance of financial transactions, data cleaning is particularly important when processing transaction data;

[0116] When cleaning data, it is necessary to check whether each item in the transaction data conforms to the expected format and scope, and ensure the integrity and consistency of the evaluation data;

[0117] Completeness refers to whether the data contains all necessary fields without omissions;

[0118] Consistency refers to whether the data is consistent between different records or different data sources;

[0119] Filter out data related to the current analysis or processing task according to business needs;

[0120] Use database queries, hash tables, or specialized deduplication algorithms to identify and delete duplicate data. For example, in transaction data, duplicate records may appear due to various reasons (such as system errors, duplicate data entry, etc.). These duplicate records will interfere with the results of data analysis, so they need to be identified and deleted.

[0121] Handling missing values ​​and outliers:

[0122] In transaction data, some fields may have missing values ​​due to various reasons (such as missing data entry, system failure, etc.). The methods include filling in default values, estimating missing values ​​using interpolation, or filling in according to business rules.

[0123] Outliers are data points that behave abnormally in a data set. They may be caused by errors, fraud, or extreme circumstances. In transaction data, outliers may appear as abnormally high transaction amounts, impossible transaction times or locations, etc. Methods for dealing with outliers include deleting outliers, treating outliers as missing values, or using statistical methods to identify and deal with outliers.

[0124] Data validation: checking the accuracy and completeness of data according to predetermined rules and logic, and generating reports and alerts;

[0125] The data conversion: converting the corresponding data into a format according to the internal format of the invoice template;

[0126] The data storage: storing the converted data in a database;

[0127] The data cleaning adopts OCR and AI algorithms;

[0128] The OCR is an optical character recognition technology used to recognize various types of documents, including scanned documents, pictures, PDFs, audio and video;

[0129] The AI ​​algorithm is used to process unstructured text, which is based on deep learning and natural language processing technology. The deep learning includes convolutional neural network, and the calculation formula of its convolution layer is:

[0130] Output feature map size:

[0131] Where W is the size of the input feature map;

[0132] K is the convolution kernel size;

[0133] P is for filling;

[0134] S is the step size;

[0135] Parameter quantity: K 2 ×C in yimesC out , C in and C out are the number of input and output channels respectively;

[0136] Calculation amount: K 2 ×C in ×O 2 yimesC out .

[0137] The convolutional neural network works by converting the input into a large matrix and performing matrix multiplication, including:

[0138] 1) Input transformation: transform the input data into a large matrix;

[0139] 2) Weight matrix construction: reshape the convolution kernel into a matrix, where the number of rows is the size of the convolution kernel multiplied by the number of input channels, and the number of columns is the number of output channels;

[0140] 3) Matrix multiplication: Use the GEMM algorithm to multiply the weight matrix and the input matrix to obtain an intermediate matrix;

[0141] 4) Output reshaping: Reshape the intermediate matrix into the form of output feature map.

[0142] For example, suppose there is an input matrix A with a size of (mtimesn×c in ), where m and n are the height and width of the input feature map, respectively, and c in is the input channel; there is a weight matrix K, whose size is (f×f×c in timesc out ), where f is the size of the convolution kernel, c out The number of output channels also includes:

[0143] Weight matrix reshaping: Reshape the weight matrix K into a (f 2 ×c in )timesc out Matrix of

[0144] Input matrix reshape: Reshape the input matrix A into a (m×n)×(f 2 timesc in ), where each (f×f) block is multiplied by a column vector of the weight matrix;

[0145] Matrix multiplication: Perform matrix multiplication operations to obtain the intermediate result matrix B, whose size is (m×n)×c out;

[0146] Biased addition: Add the bias term b (l+2) (The bias term is a c out Dimensional vector) is added to each channel of matrix B;

[0147] Activation function: Apply the activation function to each element of the matrix B to obtain the final output feature map;

[0148] In this way, the convolution operation can be converted into an efficient matrix multiplication, thereby taking advantage of modern hardware architecture and highly optimized linear algebra libraries to accelerate calculations, and this process is usually performed in parallel on the GPU to take advantage of its efficient matrix operation capabilities. In addition, deep learning frameworks usually further optimize this process, using algorithms to reduce the number of multiplication and addition operations, or to reduce computational complexity.

[0149] S13, configure RPA robots: configure robots in the RPA tool so that they can perform tasks according to preset automation processes, including setting triggers, defining operation steps, and integrating data input and output;

[0150] Furthermore, a trigger is a mechanism that monitors specific events or conditions in a system or application and performs predefined actions or responses when these events or conditions occur. It is used to start or trigger the automation process of the RPA robot, ensuring that the robot can automatically perform the preset tasks when specific conditions or events occur;

[0151] For example, in an automated process, events such as the completion of invoice issuance, changes in invoice status (such as sent, received, accounted, etc.), and the arrival of a specific time point (such as the monthly invoice summary time) can all be used as trigger conditions;

[0152] And according to the trigger type, triggers can be divided into time triggers (such as scheduled tasks), event triggers (such as invoice status change events), data triggers (such as changes in specific data in the invoice database), etc.

[0153] If it is a time trigger, you need to set the specific time point and frequency; if it is an event trigger, you need to specify the event type and source to be monitored; if it is a data trigger, you need to define the conditions and query statements for data changes;

[0154] Set the response actions that should be performed when the trigger conditions are met. These actions include sending notifications (such as emails, SMS messages), updating database records, calling other automated processes, etc.

[0155] S14, Testing and Optimization: Test the automated process in the actual environment to ensure that it can perform all tasks correctly and adjust and optimize the automated process based on the test results to improve the stability and efficiency of the process;

[0156] S15, exception handling and self-optimization: The system will alarm when an abnormal situation occurs in the RPA process, prioritize the activation of the backup plan and notify the operation and maintenance personnel, and then use the RPA model learning ability to update and improve the automation process.

[0157] In step S3, the invoice issuance and intelligent verification include:

[0158] S31, the RPA robot automatically logs into the store-wide invoice service platform;

[0159] S32, create a new invoice template and fill the pre-processed data text into the invoice;

[0160] S33, automatically submits invoicing applications to ensure correct data transmission;

[0161] S34, verify the invoice according to the preset rules and tax control logic;

[0162] S35, for any anomalies found during verification, notify relevant personnel and record logs according to the settings;

[0163] S36, archive the confirmed correct invoice and push it to the next process;

[0164] The invoice verification includes: checking the invoice amount, verifying the tax rate of the goods, checking the invoice filling status, checking the accuracy of the information of the buyer and seller, and the unique identification of the invoice number.

[0165] A system for fully automatic issuance of all-electric invoices based on RPA, the system being used to implement the method for fully automatic issuance of all-electric invoices based on RPA as described in any one of claims 1 to 8, comprising a server cluster, a control module, an interaction module, a tracking module, a storage unit and a terminal device;

[0166] The server cluster is used to deploy RPA automation software, ERP system and CRM system;

[0167] The acquisition module is used to identify and pre-process the data;

[0168] The control module is used to execute the automated invoice filling process;

[0169] The interaction module is used to send and store data text;

[0170] The tracking module is used to track the receipt status of the invoice;

[0171] The storage module is used to deploy databases and rule bases, and record data texts and historical data;

[0172] The terminal device includes a PC and a mobile device, which is used to receive the circulating all-electric invoice.

[0173] In summary, the present invention, by introducing RPA technology, realizes the fully automated processing of all-electric invoices from data collection, issuance to circulation and archiving, greatly reducing the time consumption of manual operations and greatly improving the efficiency of invoice issuance. Since RPA can perform accurate data processing and invoice verification according to preset rules and algorithms, it reduces errors caused by human negligence and reduces the tax risks of enterprises. The automated process significantly reduces labor costs, reduces dependence on human resources, and reduces ineffective labor and repetitive work. The present invention not only simplifies the issuance process of all-electric invoices and improves the financial management efficiency of enterprises, but also brings multiple beneficial effects such as significant cost savings, enhanced compliance, and environmental protection.

[0174] Secondly, in the data cleaning process of RPA, the convolution operation is implemented by using the matrix multiplication algorithm based on the original AI algorithm, taking advantage of the modern hardware architecture and the highly optimized linear algebra library to accelerate the calculation and improve the computational efficiency of the convolutional neural network.

[0175] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for fully automatic issuance of electronic invoices based on RPA, characterized in that: The steps include: S1, build the RPA system: create RPA users, create business processes, define process rules, write interaction and exception handling programs, and perform detection and optimization adjustments; S2, data collection and preprocessing: Connect to the enterprise system through the RPA robot, capture basic transaction data, and preprocess the captured data; S3, invoice issuance and intelligent verification: log in to the all-electric invoice service platform, fill in and submit data information on the invoice interface based on the processed data, and verify the invoice contents through the rules and tax control logic preset by the RPA robot; S4, invoice circulation and receipt confirmation: The RPA robot sends the issued all-electric invoice to the user, monitors the invoice receipt in real time, and automatically records the receipt information; S5, storage and filing management: upload the issued all-electric invoices to the management system, store and file them according to preset standards and index rules, and automatically process invoice events and update the ledger.

2. According to claim 1, a method for fully automatic issuance of all-electronic invoices based on RPA is characterized by: In step S1, the RPA system architecture includes: S11, determine business processes and rules: determine automated processes and process execution rules, wherein the automated processes include reading transaction information, capturing data text, verifying data rationality and integrity, data preprocessing, and logging; S12, set up the automation process: design the automation flow chart in the RPA tool, including the logical sequence of each step, decision points, and exception handling mechanism; S13, configure RPA robots: configure robots in the RPA tool so that they can perform tasks according to preset automation processes, including setting triggers, defining operation steps, and integrating data input and output; S14, Testing and Optimization: Test the automated process in the actual environment to ensure that it can perform all tasks correctly and adjust and optimize the automated process based on the test results to improve the stability and efficiency of the process; S15, exception handling and self-optimization: The system will alarm when an abnormal situation occurs in the RPA process, prioritize the activation of the backup plan and notify the operation and maintenance personnel, and then use the RPA model learning ability to update and improve the automation process.

3. According to claim 2, a method for fully automatic issuance of all-electronic invoices based on RPA is characterized in that: In step S12, the automation process includes: 1) Data extraction: RPA robots read transaction data in ERP enterprise resource planning and CRM customer relationship management systems in real time, including purchase orders and sales contracts, and automatically extract data text in invoices through pre-set mapping rules; 2) Data processing: The RPA robot pre-processes the data text to convert the data text into a data format that the robot can directly process; 3) Invoice issuance: The RPA robot logs into the all-electric invoice service platform, fills in the invoice information according to the tax law, and submits the issuance application; 4) Invoice circulation: The RPA robot delivers the issued all-electric invoice to the buyer, monitors the receipt status of the invoice, and automatically updates the invoice circulation record; 5) Storage and archiving: The RPA robot synchronizes the issued all-electric invoice data to the company's internal financial management system for classification, numbering and encrypted storage.

4. According to claim 3, a method for fully automatic issuance of all-electronic invoices based on RPA is characterized in that: The steps of data extraction include: defining mapping rules, automatically grabbing data and extracting key information; The mapping rules are based on the correspondence between the source data format and the target data format, and adopt a commonly used fixed template; Automatic data capture: The RPA robot will automatically log into the ERP and CRM systems according to preset rules, locate transaction data, and then simulate manual operations to obtain the required transaction data; Extracting key information: The RPA robot reads transaction data and extracts data text according to mapping rules.

5. According to claim 3, a method for fully automatic issuance of electronic invoices based on RPA is characterized in that: The steps of data processing include: data cleaning, data verification, data conversion and data storage; The data cleaning includes reviewing the content and quality of transaction data, screening required data information, identifying and deleting duplicate information, and processing missing values ​​and outliers; Data validation: checking the accuracy and completeness of data according to predetermined rules and logic, and generating reports and alerts; The data conversion: converting the corresponding data into a format according to the internal format of the invoice template; The data storage: storing the converted data in a database; The data cleaning adopts OCR and AI algorithms; The OCR is an optical character recognition technology used to recognize various types of documents, including scanned documents, pictures, PDFs, audio and video; The AI ​​algorithm is used to process unstructured text, which is based on deep learning and natural language processing technology.

6. According to claim 5, a method for fully automatic issuance of electronic invoices based on RPA is characterized in that: The deep learning includes a convolutional neural network, and the calculation formula of the convolutional layer is: Output feature map size: Where W is the size of the input feature map; K is the convolution kernel size; P is for filling; S is the step size; Parameter quantity: K 2 ×C in yimesC out , C in and C out are the number of input and output channels respectively; Calculation amount: K 2 ×C in ×O 2 yimesC out .

7. The method for fully automatic issuance of electronic invoices based on RPA according to claim 6 is characterized by: The convolutional neural network works by converting the input into a large matrix and performing matrix multiplication, including: 1) Input transformation: transform the input data into a large matrix; 2) Weight matrix construction: reshape the convolution kernel into a matrix, where the number of rows is the size of the convolution kernel multiplied by the number of input channels, and the number of columns is the number of output channels; 3) Matrix multiplication: Use the GEMM algorithm to multiply the weight matrix and the input matrix to obtain an intermediate matrix; 4) Output reshaping: Reshape the intermediate matrix into the form of output feature map.

8. According to claim 1, a method for fully automatic issuance of electronic invoices based on RPA is characterized by: In step S3, the invoice issuance and intelligent verification include: S31, the RPA robot automatically logs into the store-wide invoice service platform; S32, create a new invoice template and fill the pre-processed data text into the invoice; S33, automatically submits invoicing applications to ensure correct data transmission; S34, verify the invoice according to the preset rules and tax control logic; S35, for any anomalies found during verification, notify relevant personnel and record logs according to the settings; S36, archive the confirmed correct invoice and push it to the next process; The invoice verification includes: checking the invoice amount, verifying the tax rate of the goods, checking the invoice filling status, checking the accuracy of the information of the buyer and seller, and the unique identification of the invoice number.

9. A system for fully automatic issuance of electronic invoices based on RPA, characterized by: The system is used to implement the method for fully automatically issuing all-electric invoices based on RPA as described in any one of claims 1 to 8, including a server cluster, a control module, an interaction module, a tracking module, a storage unit and a terminal device; The server cluster is used to deploy RPA automation software, ERP system and CRM system; The acquisition module is used to identify and pre-process the data; The control module is used to execute the automated invoice filling process; The interaction module is used to send and store data text; The tracking module is used to track the receipt status of the invoice; The storage module is used to deploy databases and rule bases, and record data texts and historical data; The terminal device includes a PC and a mobile device, which is used to receive the circulating all-electric invoice.

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