Invoice image management method and device for financial industry and computer equipment

Abstract

The invention discloses an invoice image management method and device for the financial industry and computer equipment. The method comprises the following steps: acquiring invoice data; generating an invoice image according to the type corresponding to the invoice data in combination with a preset invoice image template library; collecting the invoice images, and generating an index and a label for each invoice image; and performing invoice data retrieval according to the index, and performing invoice data statistics according to the tag. By implementing the method provided by the invention, full-process automatic management can be realized, the personnel and management efficiency is effectively improved, and meanwhile, the problems of bill diversification, non-standard management and data falsification are solved.

Description

Technical Field

[0001] The present invention relates to an invoice management method, and more specifically to an invoice image management method, device and computer equipment for the financial industry. Background Art

[0002] The bill ERP management system improves the efficiency and standardization of bill management by centrally managing all bills, including paper and electronic bills. The specific process is: relevant personnel submit paper or electronic bills to the system. After being sorted and summarized by responsible personnel, the data is entered into the system for unified management and operation.

[0003] However, the current system relies on manual bill submission, leading to certain vulnerabilities in the management process. For example, bills may be incomplete, and certain information may be omitted due to improper operation. Furthermore, the risk of bill forgery or counterfeiting is high, and manual review cannot completely eliminate these issues, affecting the reliability and accuracy of the system. Due to the complex steps involved in bill submission, organization, and data entry, the management process is complex, resulting in low overall management efficiency. During the management process, bills may become poorly categorized and data may be disorganized, making information storage and search difficult. Furthermore, the system may also encounter unclear information during retrieval, making it difficult for managers to retrieve bill information and affecting work efficiency. Generating summary reports and statistical data is also challenging, preventing the rapid and accurate provision of relevant information. Because the system requires certain professional knowledge and operational skills, it places high demands on operators. Relevant personnel must not only accurately process bill information but also master the system's operational procedures. This increases training and management costs and the risk of errors. This is especially true when personnel turnover occurs, which can impact system usage and data accuracy.

[0004] In summary, the current bill ERP management system has certain problems in manual review, information organization, query and report generation. These defects limit the system's potential in improving management efficiency, reducing error rates and ensuring data security.

[0005] Therefore, it is necessary to design a new method to achieve full-process automated management, effectively improve personnel and management efficiency, and at the same time solve the problems of bill diversification, irregular management and data falsification. Summary of the Invention

[0006] The purpose of the present invention is to overcome the shortcomings of the prior art and provide an invoice image management method, device and computer equipment for the financial industry.

[0007] To achieve the above objectives, the present invention adopts the following technical solutions: an invoice image management method for the financial industry, comprising:

[0008] Get invoice data;

[0009] Generate an invoice image according to the type corresponding to the invoice data and a preset invoice image template library;

[0010] Collecting the invoice images and generating an index and a label for each invoice image;

[0011] Invoice data is retrieved according to the index, and invoice data statistics are performed according to the tag.

[0012] A further technical solution is: obtaining invoice data includes:

[0013] Create a business directory, connect it, and authorize the corresponding account to obtain invoice data;

[0014] Use the account number to obtain invoice data and store it in the enterprise directory.

[0015] Its further technical solution is: the invoice data includes invoice code number, invoice type, amount, date, and specific commodity.

[0016] A further technical solution is that the invoice image is generated according to the type corresponding to the invoice data in combination with a preset invoice image template library, including:

[0017] Selecting an invoice image template from the invoice image template library according to the invoice type and the preset purpose corresponding to the specific commodity;

[0018] The invoice data is combined with the invoice image template to generate an invoice image.

[0019] A further technical solution is: selecting an invoice image template from the invoice image template library according to the invoice type and the preset purpose corresponding to the specific commodity includes:

[0020] Determine the intended use based on the business scope of the upstream and downstream enterprises involved in the specific product, including main business, refueling, business trips, and reimbursement;

[0021] An invoice image template is selected from the invoice image template library according to the invoice type and the preset purpose.

[0022] A further technical solution is that the collecting of the invoice images and generating an index and a label for each invoice image includes:

[0023] An index and a label of the invoice image are constructed based on the invoice type and the preset purpose of the invoice image.

[0024] A further technical solution is: performing invoice data retrieval according to the index and performing invoice data statistics according to the tag, including:

[0025] Invoice data is retrieved through multi-dimensional indexing; statistical analysis is performed on the invoice data corresponding to each of the tags, and the content of the statistical analysis includes the total amount of the invoice, amount, purpose, and risk level.

[0026] The present invention also provides an invoice image management device for the financial industry, comprising:

[0027] A data acquisition unit, used for acquiring invoice data;

[0028] An image generating unit, configured to generate an invoice image according to the type corresponding to the invoice data and a preset invoice image template library;

[0029] a generating unit, configured to collect the invoice images and generate an index and a label for each invoice image;

[0030] The retrieval statistics unit is used to retrieve the invoice data according to the index and to perform invoice data statistics according to the tag.

[0031] Its further technical solution is: the data acquisition unit includes:

[0032] The catalog creation subunit is used to create the enterprise catalog, connect to it, and authorize the corresponding account to obtain invoice data;

[0033] The invoice data acquisition subunit is used to obtain invoice data using the account number and store it in the enterprise directory.

[0034] The present invention further provides a computer device, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the computer program.

[0035] The beneficial effects of the present invention compared with the existing technology are as follows: the present invention automatically obtains invoice data and combines it with a preset image template library to automatically generate standardized invoice images; the system automatically collects all invoice images and generates a unique index and label for each invoice to achieve standardized management of invoices; based on the index, the system can quickly retrieve invoice data and improve work efficiency; through label management, the system can also perform intelligent statistics on invoice data to support financial analysis and decision-making; the entire process does not require human intervention, avoiding problems such as data falsification, invoice loss and irregular management, and significantly improving personnel and management efficiency.

[0036] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 A schematic diagram of an application scenario of the invoice image management method for the financial industry provided by an embodiment of the present invention;

[0039] Figure 2 A flowchart of an invoice image management method for the financial industry provided by an embodiment of the present invention;

[0040] Figure 3 Schematic diagram of the sub-process of the invoice image management method for the financial industry provided by the embodiment of the present invention Figure 1 ;

[0041] Figure 4 Schematic diagram of the sub-process of the invoice image management method for the financial industry provided by the embodiment of the present invention Figure 2 ;

[0042] Figure 5 Schematic diagram of the sub-process of the invoice image management method for the financial industry provided by the embodiment of the present invention Figure 3 ;

[0043] Figure 6 A schematic block diagram of an invoice image management device for the financial industry provided by an embodiment of the present invention;

[0044] Figure 7 A schematic block diagram of a data acquisition unit of an invoice image management device for the financial industry provided by an embodiment of the present invention;

[0045] Figure 8 A schematic block diagram of the **** unit of the invoice image management device for the financial industry provided by an embodiment of the present invention;

[0046] Figure 9 A schematic block diagram of the **** unit of the invoice image management device for the financial industry provided by an embodiment of the present invention;

[0047] Figure 10 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0049] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0050] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0051] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0052] See also Figure 1 and Figure 2 , Figure 1 A schematic diagram of an application scenario of the invoice image management method for the financial industry provided by an embodiment of the present invention. Figure 2This is a schematic flow chart of an invoice image management method for the financial industry provided by an embodiment of the present invention. This invoice image management method for the financial industry is applied to a server that interacts with a terminal, automatically acquiring invoice data and storing it in an enterprise directory, achieving standardized and systematized information collection. Based on the invoice type and specific commodity, it automatically selects an appropriate image template from a template library to generate a standard invoice image, reducing manual intervention. A unique index and label are generated for each invoice image, and image aggregation and information identification are achieved through preset rules to ensure data accuracy and traceability. Efficient data retrieval is performed through multi-dimensional indexing to quickly locate the required invoice information, improving retrieval efficiency. Data statistical analysis is performed based on labels, including invoice total amount, purpose, and risk level, automatically generating reports to assist in decision-making. Through templateization, automatic aggregation, precise labeling, and intelligent statistical analysis, this method addresses the problems of invoice document diversity, irregular management, and fraud, improving management efficiency and reducing the risk of manual operation. This method achieves full digitization of invoice management through automated processes, effectively improving personnel work efficiency and management standardization.

[0053] Figure 2 FIG. 1 is a flow chart of an invoice image management method for the financial industry provided by an embodiment of the present invention. Figure 2 As shown, the method includes the following steps S110 to S140.

[0054] S110: Obtain invoice data.

[0055] In this embodiment, invoice data refers to official voucher information related to corporate financial transactions, typically provided by tax authorities or sellers. It includes, but is not limited to, the following elements: invoice code number, amount, invoice type, date, specific product name, seller information, buyer information, etc. This data plays an important role in credit risk assessment, corporate qualification review, credit management, and archiving in the financial industry.

[0056] In one embodiment, if Figure 3 As shown, the above-mentioned step S110 may include steps S111 to S112.

[0057] S111. Create a business directory, connect it, and authorize the corresponding account for obtaining invoice data.

[0058] In this embodiment, the enterprise directory refers to a logical storage space or database entry set up for each target enterprise, which is used to centrally manage and store all relevant invoice data of the enterprise. This directory not only helps to organize and classify invoice information, but also facilitates subsequent data retrieval and analysis.

[0059] First, a dedicated company directory is created in the system for each company that needs to process invoices. Then, by binding and authorizing the company's account corresponding to the national tax platform and local invoice platform, the system can legally access and automatically obtain the company's invoice information from these platforms.

[0060] S112. Obtain invoice data using the account number and store it in the enterprise directory.

[0061] In this embodiment, once the enterprise directory is created and account authorization is complete, the system uses these accounts to log in to certain platforms to synchronize the latest invoice data, either manually or automatically at a pre-set interval (e.g., daily or weekly). This includes receiving electronic invoice file uploads, scanning paper invoice images, and extracting invoice-related data from the enterprise's internal business systems. The acquired invoice data is then stored in the corresponding enterprise directory for further processing, such as generating invoice images and performing data analysis.

[0062] Specifically, by binding the corporate account, the financial industry's invoice image management system automatically passes the company's invoice information from the national tax and local invoice platforms, including invoice code number, amount, date, specific goods and other invoice elements, and automatically generates invoice electronic image files based on these invoice elements based on the invoice image template library. This electronic image can be used by the financial industry for corporate credit business risk control, corporate qualification review, corporate credit management and archiving needs.

[0063] In summary, step S110 describes the process of acquiring invoice data, namely, how to create an enterprise directory, authorize necessary account permissions, and automatically collect invoice data from multiple channels through these accounts, and finally store it securely in the enterprise directory, laying the foundation for subsequent invoice image management and application.

[0064] S120: Generate an invoice image according to the type corresponding to the invoice data and a preset invoice image template library.

[0065] In this embodiment, an invoice image is an electronic representation of the invoice generated based on the original invoice information using specific algorithms and technical means (such as AI beautification). This image fully reproduces the actual invoice appearance and can be stored in various formats, such as PDF, JPG, and PNG, for easy viewing, printing, or archiving. Invoice images not only retain all the information of the original invoice but also enhance its visual quality and professionalism, ensuring compliance with tax, risk management, and regulatory requirements.

[0066] In one embodiment, see Figure 4 , the above-mentioned step S120 may include steps S121 to S122.

[0067] S121. Select an invoice image template from the invoice image template library according to the invoice type and the preset purpose corresponding to the specific commodity.

[0068] In this example, invoice image templates refer to digital templates used to generate invoice images. These templates are based on the actual invoice format, using technologies such as CSS, JavaScript, and HTML to fully reproduce the actual invoice format. AI-enhanced templates ensure compliance with relevant financial and tax regulations. The template library contains 16 templates, covering over 99% of invoice types on the market. A variety of image templates for different invoice types are provided to meet the needs of various business scenarios.

[0069] In one embodiment, see Figure 5 , the above-mentioned step S121 may include steps S1211 to S1212.

[0070] S1211. Determine the preset purpose based on the business scope of the upstream and downstream enterprises involved in the specific commodity, and the preset purpose includes main business, refueling, business trip, and reimbursement.

[0071] In this embodiment, the system uses artificial intelligence classification technology to automatically categorize the primary uses of invoices based on product type and the business scope of upstream and downstream businesses, such as invoices related to core business activities, fuel invoices, travel expense invoices, and employee reimbursement invoices. This step helps more accurately match the most appropriate invoice image template, ensuring that the generated invoice image is the most realistic.

[0072] S1212: Select an invoice image template from the invoice image template library according to the invoice type and the preset purpose.

[0073] In this embodiment, once the specific purpose of an invoice is determined, the system selects the most appropriate template from the invoice image template library based on the invoice type (e.g., special VAT invoice, ordinary VAT invoice, hand-torn invoice, etc.) and the intended purpose. These templates are designed according to professional standards in terms of layout, fonts, and colors, ensuring that the generated invoice image is both standardized and professional.

[0074] S122: Generate an invoice image by combining the invoice data with the invoice image template.

[0075] In this embodiment, after selecting the appropriate invoice image template, the system combines the previously acquired invoice data (step S110) and uses the selected template to generate the final invoice image. This process involves operations such as data filling and formatting to ensure that all necessary invoice information is accurately presented on the generated invoice image. The generated invoice image can be further used by the financial industry for purposes such as controlling corporate credit risk, verifying corporate qualifications, managing corporate credit, and archiving.

[0076] Specifically, the verified invoice information is populated into the selected template, ensuring all required fields are correctly filled. Artificial intelligence technology is used to optimize the invoice image, adjusting the layout, font, and color. This ensures that the final invoice image not only retains all the original invoice information but also looks more beautiful and professional. Generated invoice images can be stored in multiple formats, such as PDF, JPG, and PNG, to facilitate user-defined use.

[0077] After completing the above steps, the system will generate the final invoice image file. According to the user's actual needs, the invoice image can be saved to a designated location or integrated into an existing document management system for future query, printing or archiving.

[0078] In summary, step S120 describes how to select the most appropriate template from a pre-set invoice image template library based on the invoice data type and the intended use of the specific product, and then combine it with the invoice data to generate a professional invoice image. This process ensures that the invoice image not only reflects all the details of the original invoice but also meets the specific needs of different application scenarios.

[0079] S130: Collect the invoice images and generate an index and a label for each invoice image.

[0080] In this embodiment, an index is a structure used to quickly locate and retrieve data in a database or information system. For the invoice image management system, an index is one or more identifiers created within the system for each invoice image, and these identifiers are associated with specific characteristics of the invoice (such as purpose, type, label, etc.). Through these indexes, users can efficiently find invoice images under specific conditions. For example, if a company wants to view all invoices related to business trips, it only needs to query the index corresponding to the purpose of "business trip" to quickly obtain a list of related invoice images. Indexes can also help to refine classifications, such as different types of invoices, different statuses, different financial business types (credit business invoices), different risk levels, and different tax compliance categories (compliant, suspected violations, violations to be verified)

[0081] Tags are descriptive words or phrases attached to invoice images to provide additional information and make invoices easier to understand and manage. Tags can be automatically generated by the system, manually added, or a combination of the two. In this embodiment, tags are constructed based on the following categories:

[0082] Invoice Purpose: Various purposes are automatically classified after analyzing the business scope of commodities and upstream and downstream enterprises through artificial intelligence algorithms, such as main business, refueling, business trips, reimbursement, etc.

[0083] Invoice type refers to the specific type of invoice, such as special VAT invoice, general VAT invoice, hand-torn invoice, etc.

[0084] Pre-set labels include, but are not limited to, platform risk assessment results, manual annotation results, and intelligently learned risk levels, specific uses, and other labels. This allows each invoice image to be assigned rich, semantically informative labels, facilitating internal management and retrieval, while also enabling the rapid and accurate communication of key invoice information when interacting with external regulators or partners.

[0085] Specifically, an index and a label of the invoice image are constructed based on the invoice type and preset purpose of the invoice image.

[0086] In actual operation, when invoice images are collected into the system, they are first preliminarily classified according to their invoice type and preset purpose. Then, the system uses AI technology to deeply analyze the invoice content, automatically determine the actual purpose of the invoice, and create corresponding labels as needed. At the same time, factors such as platform risks, manual labeling, and intelligent learning are also considered to further refine the content of the labels. Ultimately, the system will generate one or more groups of indexes for each invoice image. These indexes directly reflect all the key attributes of the invoice, such as invoice type, status, financial business type, risk level, tax compliance, etc. In addition, a series of labels will be generated for each invoice image so that users can more intuitively understand the background information and important features of the invoice. In this way, whether it is internal review or external communication, efficient processing and accurate transmission of invoice image data can be achieved.

[0087] Specifically, we first need to build a large invoice database containing invoice samples from different industries, different types of goods, and upstream and downstream companies. These samples should cover as many possible situations as possible to ensure the generalization ability of the model after training.

[0088] For each invoice sample, it is necessary to have an expert-annotated or clearly identified "primary purpose" as a training label. This step is crucial because it directly affects the learning effect of the subsequent model.

[0089] Useful features are extracted from each invoice, including but not limited to product name, specification model, quantity, amount, supplier information, buyer information, etc. At the same time, key words in the invoice text description also need to be considered.

[0090] Depending on the nature of the problem, you can choose a supervised learning classification algorithm, such as decision trees, random forests, and support vector machines (SVMs), or more advanced deep learning methods, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), their LSTM / GRU variants, and the recently popular Transformer architecture. For unstructured text data, you can also use pretrained language models such as BERT for transfer learning. Train the selected model using a pre-processed dataset. During training, optimize model performance by adjusting hyperparameters and use techniques such as cross-validation to prevent overfitting.

[0091] Build or introduce an existing industry knowledge graph that includes upstream and downstream relationships across industries and information about typical products. This helps understand the position of specific products within the entire industry chain.

[0092] NLP technology is used to analyze the business scope fields in the business licenses of upstream and downstream companies, extracting key business areas and service types. Combined with industry knowledge graphs, the general context of inter-company transactions can be inferred.

[0093] By combining the invoice information with the business scope of upstream suppliers and downstream customers to create a richer context, we can use the trained AI model to predict the primary purpose of the invoice.

[0094] In addition to relying on pure AI models, you can also set up rule engines to supplement model deficiencies. For example, if a product clearly belongs to a specific category (such as food or medicine), you can directly use rules to determine its primary use. Or, when the probability distribution of the model output is relatively dispersed, you can use rules to further filter the most likely options.

[0095] Establish user feedback channels to allow manual correction of misclassification results. These new cases are then added to the training set to retrain the model, gradually improving accuracy. As the market changes and new products emerge, regularly update the industry knowledge graph and model parameters to ensure the system always accurately reflects the latest business activities.

[0096] In summary, through a carefully designed index and labeling system, the invoice image management system not only improves the availability and processing efficiency of invoice data, but also provides users with a more convenient operating experience.

[0097] S140: Retrieve invoice data according to the index, and perform invoice data statistics according to the tag.

[0098] In this embodiment, invoice data is retrieved through multi-dimensional indexing; statistical analysis is performed on the invoice data corresponding to each tag, and the content of the statistical analysis includes the total invoice amount, amount, purpose, and risk level.

[0099] Specifically, in this embodiment, the index is one of the core components for building an invoice information database. These indexes not only cover the basic attributes of invoices (such as invoice type and status), but also include more complex classifications, such as financial business types (credit business invoices), risk levels, and tax compliance categories (compliant, suspected violations, violations to be verified). By adopting advanced data indexing technology, the system can support multi-dimensional invoice data retrieval. This means that financial institutions or corporate users can query invoice information based on a combination of multiple standards, such as precise searches based on invoice purpose (main business, refueling, business trips, reimbursement, etc.), amount range, time period, specific corporate trading partners, and other conditions. This flexibility enables users to quickly locate the required invoice records, greatly improving work efficiency.

[0100] In addition, the system is connected to multiple invoice platforms and automatically collects invoice data from different channels, which further enhances the breadth and depth of the index system and makes the retrieval function more comprehensive.

[0101] For each invoice image, the system automatically generates a series of tags containing key invoice information, such as total amount, amount, purpose, risk level, etc. Based on these tags, the system can perform detailed statistical analysis and provide users with a comprehensive view of invoice data. Specifically:

[0102] Total Invoice Amount: Summarizes the total amount of all invoices within a certain period to help users understand the overall financial flow.

[0103] Amount: You can count the amount of a single invoice or a group of invoices according to different standards, such as classifying them by purpose to view the expenditure under each category.

[0104] Purpose: To count the number and amount of invoices for different purposes (such as main business, refueling, business trips, and reimbursements) to assist users in evaluating the cost structure of various business activities.

[0105] Risk level: Collect statistics on the risk tags of each invoice to identify potential risk points, provide decision support for financial institutions, and help enterprises better manage financial risks.

[0106] These statistical results can not only be presented to users in intuitive formats, such as charts or reports, but can also be directly accessed through the partitioned statistical tag content in step 4, including total amount, amount, purpose, and other tags, allowing users to clearly assess the current situation of the enterprise. For financial institutions, this means that they can more conveniently and quickly grasp and evaluate the invoice data and risks of enterprises. For enterprises, it means achieving integrated and electronic management of invoices and accounts, improving invoice processing efficiency, reducing the possibility of errors and fraud caused by manual operations, and saving labor costs.

[0107] In summary, this embodiment utilizes a carefully designed indexing and tagging system, combined with advanced data indexing and retrieval technologies, to not only make invoice data retrieval efficient and convenient, but also provides users with a powerful method for rapid query, statistics, and analysis of invoice information through tagged statistical analysis. This significantly improves the efficiency of invoice information management for businesses and financial institutions, strengthens risk control capabilities, and ensures data security and integrity. Furthermore, the system supports integration with internal systems and autonomous uploads, ensuring diversity and fault tolerance to meet the needs of diverse scenarios.

[0108] The above method can automatically collect corporate invoice data and generate corresponding electronic vouchers, reducing manual intervention and realizing fully automatic management; the system automatically obtains invoice data without manual submission or review, effectively eliminating problems such as data falsification and invoice leakage; using the invoice image template library, standardized invoice images are generated according to the invoice type and commodity purpose to ensure the uniformity of bill format; by connecting to the corporate account, invoice data can be automatically obtained and stored in the corporate directory, simplifying the operation process; indexes and labels are generated for each invoice image to achieve efficient invoice management and subsequent queries; support for rapid retrieval through multi-dimensional indexing improves the retrieval efficiency of invoice data; invoice data statistics are performed according to labels to facilitate analysis of the amount, purpose, risk, etc. of the invoice; the use of standardized management methods solves the problem of a wide variety of invoices and difficult management in the industry; significantly improves personnel work efficiency and saves a lot of manpower time and resources; the automation and standardized management of the entire system effectively improves the efficiency and accuracy of financial and invoice management.

[0109] The above-mentioned invoice image management method for the financial industry automatically obtains invoice data and automatically generates standardized invoice images in combination with a preset image template library; the system automatically collects all invoice images and generates a unique index and label for each invoice to achieve standardized management of invoices; based on the index, the system can quickly retrieve invoice data and improve work efficiency; through label management, the system can also perform intelligent statistics on invoice data to support financial analysis and decision-making; the entire process does not require human intervention, avoiding problems such as data falsification, invoice loss and irregular management, and significantly improving personnel and management efficiency.

[0110] Figure 6 FIG is a schematic block diagram of an invoice image management device 300 for the financial industry provided by an embodiment of the present invention. Figure 6 As shown, corresponding to the above invoice image management method for the financial industry, the present invention also provides an invoice image management device 300 for the financial industry. The invoice image management device 300 for the financial industry includes a unit for executing the above invoice image management method for the financial industry, and the device can be configured in a server. Figure 6 The invoice image management device 300 for the financial industry includes a data acquisition unit 301 , an image generation unit 302 , a generation unit 303 and a retrieval statistics unit 304 .

[0111] The data acquisition unit 301 is used to acquire invoice data; the image generation unit 302 is used to generate an invoice image based on the type corresponding to the invoice data and a preset invoice image template library; the generation unit 303 is used to collect the invoice images and generate an index and label for each invoice image; the retrieval and statistics unit 304 is used to retrieve invoice data based on the index and to collect invoice data statistics based on the label.

[0112] In one embodiment, if Figure 7 As shown, the data acquisition unit 301 includes: a directory creation subunit 3011 and an invoice data acquisition subunit 3012.

[0113] The directory creation subunit 3011 is used to create an enterprise directory, connect to and authorize the corresponding account for obtaining invoice data; the invoice data acquisition subunit 3012 is used to use the account to obtain invoice data and store it in the enterprise directory.

[0114] In one embodiment, if Figure 8 As shown, the image generation unit 302 includes a template selection subunit 3021 and an invoice image generation subunit 3022.

[0115] The template selection subunit 3021 is used to select an invoice image template from the invoice image template library according to the invoice type and the preset purpose corresponding to the specific commodity; the invoice image generation subunit 3022 is used to generate an invoice image by combining the invoice data with the invoice image template.

[0116] In one embodiment, if Figure 9 As shown, the template selection subunit 3021 includes a usage determination module 30211 and a selection module 30212 .

[0117] The purpose determination module 30211 is used to determine the preset purpose based on the business scope of the upstream and downstream enterprises involved in the specific commodity, and the preset purpose includes main business, refueling, business trip, and reimbursement; the selection module 30212 is used to select an invoice image template from the invoice image template library based on the invoice type and the preset purpose.

[0118] In one embodiment, the generating unit 303 is configured to construct an index and a label for the invoice image based on the invoice type and the preset purpose of the invoice image.

[0119] In one embodiment, the retrieval statistics unit 304 is used to retrieve invoice data through multi-dimensional indexes; perform statistical analysis on the invoice data corresponding to each tag, and the content of the statistical analysis includes the total amount of the invoice, amount, purpose, and risk level.

[0120] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned invoice image management device 300 and each unit for the financial industry can refer to the corresponding description in the aforementioned method embodiment. For the convenience and brevity of the description, it will not be repeated here.

[0121] The invoice image management device 300 for the financial industry can be implemented in the form of a computer program. The computer program can be used in Figure 10 Runs on the computer device shown.

[0122] See also Figure 10 , Figure 10 1 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 may be a server, wherein the server may be an independent server or a server cluster composed of multiple servers.

[0123] See Figure 10 The computer device 500 includes a processor 502 , a memory, and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0124] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, which, when executed, can enable the processor 502 to execute an invoice image management method for the financial industry.

[0125] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500.

[0126] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute an invoice image management method for the financial industry.

[0127] The network interface 505 is used to communicate with other devices through the network. Figure 10 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0128] The processor 502 is configured to execute a computer program 5032 stored in the memory to implement the following steps:

[0129] Acquire invoice data; generate invoice images based on the type corresponding to the invoice data and a preset invoice image template library; aggregate the invoice images and generate an index and label for each invoice image; retrieve invoice data based on the index and perform invoice data statistics based on the label.

[0130] The invoice data includes the invoice code number, invoice type, amount, date, and specific commodity.

[0131] In one embodiment, when the processor 502 implements the step of obtaining invoice data, it specifically implements the following steps:

[0132] Create an enterprise directory, connect to it, and authorize the corresponding account to obtain invoice data; use the account to obtain invoice data and store it in the enterprise directory.

[0133] In one embodiment, when implementing the step of generating an invoice image based on the type corresponding to the invoice data and a preset invoice image template library, the processor 502 specifically implements the following steps:

[0134] An invoice image template is selected from the invoice image template library according to the invoice type and the preset purpose corresponding to the specific commodity; and the invoice image is generated by combining the invoice data with the invoice image template.

[0135] In one embodiment, when the processor 502 implements the step of selecting an invoice image template from the invoice image template library based on the invoice type and the preset purpose corresponding to the specific commodity, the processor 502 specifically implements the following steps:

[0136] The preset purpose is determined according to the business scope of the upstream and downstream enterprises involved in the specific commodity, and the preset purpose includes main business, refueling, business trip, and reimbursement; an invoice image template is selected from the invoice image template library according to the invoice type and the preset purpose.

[0137] In one embodiment, the processor 502 implements the following steps when implementing the steps of aggregating the invoice images and generating an index and a label for each invoice image:

[0138] An index and a label of the invoice image are constructed based on the invoice type and the preset purpose of the invoice image.

[0139] In one embodiment, when the processor 502 performs the steps of retrieving invoice data according to the index and collecting invoice data statistics according to the tag, the processor 502 specifically performs the following steps:

[0140] Invoice data is retrieved through multi-dimensional indexing; statistical analysis is performed on the invoice data corresponding to each of the tags, and the content of the statistical analysis includes the total amount of the invoice, amount, purpose, and risk level.

[0141] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0142] Those skilled in the art will appreciate that all or part of the steps in the method of the above-described embodiment can be implemented by instructing the relevant hardware through a computer program. The computer program includes program instructions, which can be stored in a storage medium that is computer-readable. The program instructions are executed by at least one processor in the computer system to implement the steps in the method of the above-described embodiment.

[0143] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein when the computer program is executed by a processor, the processor performs the following steps:

[0144] Acquire invoice data; generate invoice images based on the type corresponding to the invoice data and a preset invoice image template library; aggregate the invoice images and generate an index and label for each invoice image; retrieve invoice data based on the index and perform invoice data statistics based on the label.

[0145] The invoice data includes the invoice code number, invoice type, amount, date, and specific commodity.

[0146] In one embodiment, when the processor executes the computer program to implement the step of obtaining invoice data, the processor specifically implements the following steps:

[0147] Create an enterprise directory, connect to it, and authorize the corresponding account to obtain invoice data; use the account to obtain invoice data and store it in the enterprise directory.

[0148] In one embodiment, when the processor executes the computer program to implement the step of generating an invoice image based on the type corresponding to the invoice data and a preset invoice image template library, the processor specifically implements the following steps:

[0149] An invoice image template is selected from the invoice image template library according to the invoice type and the preset purpose corresponding to the specific commodity; and the invoice image is generated by combining the invoice data with the invoice image template.

[0150] In one embodiment, when the processor executes the computer program to implement the step of selecting an invoice image template from the invoice image template library based on the invoice type and the preset purpose corresponding to the specific commodity, the processor specifically implements the following steps:

[0151] The preset purpose is determined according to the business scope of the upstream and downstream enterprises involved in the specific commodity, and the preset purpose includes main business, refueling, business trip, and reimbursement; an invoice image template is selected from the invoice image template library according to the invoice type and the preset purpose.

[0152] In one embodiment, when the processor executes the computer program to implement the steps of collecting the invoice images and generating an index and a label for each invoice image, the processor specifically implements the following steps:

[0153] An index and a label of the invoice image are constructed based on the invoice type and the preset purpose of the invoice image.

[0154] In one embodiment, when the processor executes the computer program to implement the steps of retrieving invoice data according to the index and performing invoice data statistics according to the tag, the processor specifically implements the following steps:

[0155] Invoice data is retrieved through multi-dimensional indexing; statistical analysis is performed on the invoice data corresponding to each of the tags, and the content of the statistical analysis includes the total amount of the invoice, amount, purpose, and risk level.

[0156] The storage medium may be any computer-readable storage medium that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk.

[0157] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0158] In the several embodiments provided herein, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the various units is merely a logical functional division, and actual implementation may employ other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not implemented.

[0159] The steps in the methods of the embodiments of the present invention may be adjusted in order, combined, or deleted as needed. The units in the devices of the embodiments of the present invention may be combined, divided, or deleted as needed. Furthermore, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0160] If this integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the existing technology, or all or part 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 a number of instructions for causing a computer device (which can be a personal computer, terminal, or network device, etc.) to execute all or part of the steps of the method described in various embodiments of the present invention.

[0161] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. The invoice image management method for the financial industry is characterized by: include: Get invoice data; Generate an invoice image according to the type corresponding to the invoice data and a preset invoice image template library; Collecting the invoice images and generating an index and a label for each invoice image; Invoice data is retrieved according to the index, and invoice data statistics are performed according to the tag.

2. The invoice image management method for the financial industry according to claim 1, characterized in that: The obtaining of invoice data includes: Create a business directory, connect it, and authorize the corresponding account to obtain invoice data; Use the account number to obtain invoice data and store it in the enterprise directory.

3. The invoice image management method for the financial industry according to claim 1, characterized in that: The invoice data includes invoice code number, invoice type, amount, date, and specific goods.

4. The invoice image management method for the financial industry according to claim 1, characterized in that: The generating of the invoice image according to the type corresponding to the invoice data and in combination with a preset invoice image template library includes: Selecting an invoice image template from the invoice image template library according to the invoice type and the preset purpose corresponding to the specific commodity; The invoice data is combined with the invoice image template to generate an invoice image.

5. The invoice image management method for the financial industry according to claim 4, characterized in that: The selecting of an invoice image template from the invoice image template library according to the invoice type and the preset purpose corresponding to the specific commodity includes: Determine the intended use based on the business scope of the upstream and downstream enterprises involved in the specific product, including main business, refueling, business trips, and reimbursement; An invoice image template is selected from the invoice image template library according to the invoice type and the preset purpose.

6. The invoice image management method for the financial industry according to claim 1, characterized in that: The collecting of the invoice images and generating an index and a label for each invoice image includes: An index and a label of the invoice image are constructed based on the invoice type and the preset purpose of the invoice image.

7. The invoice image management method for the financial industry according to claim 1, characterized in that: The retrieving invoice data according to the index and performing invoice data statistics according to the tag include: Invoice data is retrieved through multi-dimensional indexing; statistical analysis is performed on the invoice data corresponding to each of the tags, and the content of the statistical analysis includes the total amount of the invoice, amount, purpose, and risk level.

8. An invoice image management device for the financial industry, characterized in that: include: A data acquisition unit, used for acquiring invoice data; An image generating unit, configured to generate an invoice image according to the type corresponding to the invoice data and a preset invoice image template library; a generating unit, configured to collect the invoice images and generate an index and a label for each invoice image; The retrieval statistics unit is used to retrieve the invoice data according to the index and to perform invoice data statistics according to the tag.

9. The invoice image management device for the financial industry according to claim 8, characterized in that: The data acquisition unit includes: The catalog creation subunit is used to create the enterprise catalog, connect to it, and authorize the corresponding account to obtain invoice data; The invoice data acquisition subunit is used to obtain invoice data using the account number and store it in the enterprise directory.

10. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

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