An approval process construction method, device, equipment, storage medium and program product

Abstract

This application provides a method, apparatus, device, storage medium, and program product for constructing an approval process, relating to the fields of financial technology or data processing technology. The method includes: generating a business approval process using a graphical configuration tool, the business approval process including application form fields, approval nodes, and business rules; validating the application data using a natural language processing model and generating a validation result; and outputting approval assistance prompts based on the validation result and business rules. This method can support flexible adaptation to multiple business scenarios, reduce compliance risks, and improve business approval efficiency.

Description

Technical Field

[0001] This application relates to the fields of financial technology or data processing technology, and in particular to an approval process construction method, apparatus, equipment, storage medium and program product. Background Technology

[0002] In the business operations of financial institutions and other industries, the approval process is a core element to ensure business compliance, risk control, and efficient execution.

[0003] In related technologies, approval process construction relies on fixed templates, which makes it difficult to cope with the dynamic adjustment needs of diverse business scenarios; while customized approval process construction systems based on code development have pain points such as long development cycles, high costs, and difficult maintenance. Summary of the Invention

[0004] This application provides a method, apparatus, device, storage medium, and program product for constructing an approval process, which can support flexible adaptation to multiple business scenarios, reduce compliance risks, and improve business approval efficiency.

[0005] Firstly, this application provides a method for constructing an approval process, including: generating a business approval process through a graphical configuration tool, the business approval process including application form fields, approval nodes and business rules; validating the application data using a natural language processing model and generating a validation result; and outputting approval assistance prompts based on the validation result and business rules.

[0006] In one possible embodiment, generating a business approval process using a graphical configuration tool includes: selecting application form fields by dragging and dropping in the graphical interface to generate an application form; selecting approval nodes and business rules by dragging and dropping in the graphical interface to generate a flowchart; and binding the flowchart with the business rule base to form an executable business approval process.

[0007] In one possible embodiment, verifying application data using a natural language processing model includes: using the natural language processing model to perform at least one of the following: verifying typos and grammatical errors in the application data; detecting sensitive information in the application data; and generating compliance recommendations based on business rules.

[0008] In one possible embodiment, after verifying the application data using a natural language processing model, the approval process construction method further includes: identifying business context information in the application data using a natural language processing model; calling business rules from a business rule base based on the business context information; and processing the application data for approval based on the business rules to obtain a business rule approval result.

[0009] In one possible embodiment, the output approval assistance prompt includes: determining a text summary of the application data using a natural language processing model; associating the application data with historical cases using a knowledge graph to obtain historical case association results; and generating approval assistance prompts based on the text summary, business rule approval results, and historical case association results.

[0010] In one possible embodiment, before using a natural language processing model to verify the application data and generate the verification result, the approval process construction method further includes: receiving natural language instructions input by the user through an intelligent question-and-answer interface; and generating application form fields based on the natural language instructions.

[0011] In one possible embodiment, before using a natural language processing model to verify the application data and generate verification results, the approval process construction method further includes: clustering multiple application data; performing batch processing operations on the clustered application data through a batch processing interface, the batch processing operations including generating verification results and / or approval processing.

[0012] In one possible embodiment, after verifying the application data using a natural language processing model, the approval process construction method further includes: detecting abnormal events in the business approval process; and triggering a predefined repair strategy based on the abnormal events.

[0013] Secondly, this application provides an approval process construction device, including: a business approval process generation module, used to generate a business approval process through a graphical configuration tool, the business approval process including application form fields, approval nodes and business rules; a verification module, used to verify the application data using a natural language processing model and generate verification results; and a prompting module, used to output approval assistance prompts based on the verification results and business rules.

[0014] Thirdly, this application provides an electronic device, including: a processor and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method as described in any of the first aspects.

[0015] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any of the first aspects.

[0016] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method of any one of the first aspects.

[0017] In this embodiment, the electronic device generates a business approval process through a graphical configuration tool. This tool allows users to quickly construct application form fields, approval nodes, and business rules via drag-and-drop, eliminating the need for code development and thus lowering the technical barrier and improving response speed. The electronic device uses a natural language processing (NLP) model to validate the application data and generate validation results. The NLP model performs in-depth analysis of the application data, identifying text errors and sensitive information, and matching it with business rules to generate validation results, ensuring the compliance of the application data. Based on the validation results and configured business rules, the electronic device outputs approval assistance prompts for applicants to correct their application data or for approvers to use as a reference for decision-making, thereby reducing the burden of manual judgment.

[0018] In summary, the approval process construction method of this application embodiment employs a graphical configuration tool to ensure rapid customization of business approval processes, a natural language processing model to achieve intelligent verification of application data, and approval assistance prompts that combine verification results with business rules to form corrective suggestions for applicants and decision support for approvers. This achieves lightweight, intelligent, and compliant control of business approval processes, solving the problems of low efficiency and insufficient rule verification in manual approvals. Furthermore, its dynamic configuration capabilities adapt to diverse business needs, thereby improving the efficiency and compliance of business process approvals. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] Figure 1 This is a schematic diagram illustrating an application scenario of the approval process construction method according to an embodiment of this application;

[0021] Figure 2 This is a flowchart illustrating the approval process construction method in an embodiment of this application.

[0022] Figure 3 This is a flowchart illustrating an approval process construction method according to another embodiment of this application;

[0023] Figure 4 This is a schematic diagram illustrating the selection of application form fields in an embodiment of this application to generate an application form;

[0024] Figure 5 This is a schematic diagram illustrating the selection of approval nodes and business rules in an embodiment of this application to generate a flowchart;

[0025] Figure 6 This is a schematic diagram illustrating an approval process construction method according to another embodiment of this application;

[0026] Figure 7This is a schematic diagram of an approval process construction device according to an embodiment of this application;

[0027] Figure 8 This is a schematic diagram of an electronic device according to an embodiment of this application.

[0028] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0029] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0030] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of the relevant data all comply with the relevant laws, regulations, and standards of the relevant countries and regions, have taken necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation access points for users to choose to authorize or refuse.

[0031] Furthermore, the technical solution involved in this application, which involves big data analysis of user information (including but not limited to personal biometrics, identity data, consumption data, asset data, electronic terminal operation data, etc.) and the use of artificial intelligence technology for automated decision-making, and makes decisions that have a significant impact on personal rights based on the results of automated decision-making, provides users with corresponding operation entry points for users to choose to agree to or reject the results of automated decision-making; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0032] It should be noted that the approval process construction method, apparatus, equipment, storage medium and program products provided in this application can be used in the fields of financial technology or data processing technology, or in any field other than the fields of financial technology or data processing technology. This application does not limit the application fields of the approval process construction method, apparatus, equipment, storage medium and program products.

[0033] Figure 1 This is a schematic diagram illustrating the application of the approval process construction method in this application embodiment.

[0034] like Figure 1 As shown, user 1 accesses the approval process construction entry 3 through terminal device 2 to construct the approval process. Server 4 can execute the approval process construction method of this application embodiment to provide approval process construction services to terminal device 2. Server 4 may be equipped with a no-code platform to push a graphical interface to terminal device 2, assisting user 1 in intuitively constructing the approval process.

[0035] In related technology 1, the approval process relies on the transmission of paper documents and manual approval at each level. Approver must complete the approval process in a fixed office location, which suffers from geographical limitations and poor timeliness. For example, if an approver is traveling, the process may stall, or delays in document transmission may extend the approval cycle, resulting in overall low efficiency and a high risk of errors. In addition, the storage and management costs of paper materials are high, and it is difficult to achieve real-time data tracking and compliance verification.

[0036] Related technology 2 overcomes the physical limitations of paper-based document delivery through electronic approval, supports cross-departmental and cross-regional approvals, and reduces omissions through a to-do reminder function. Approver can handle pending tasks anytime via a personal computer (PC) or mobile device, and the process automatically moves to the next stage after approval, significantly shortening the approval cycle. However, this approach suffers from at least one of the following problems: rigid process customization, insufficient rule validation, bottlenecks in approval efficiency, difficulties in process integration, long development cycles, and high maintenance costs.

[0037] Rigid process customization: Approval nodes and permission configurations rely on developers to write code, which cannot be flexibly adjusted and is difficult to adapt to the needs of rapidly changing business.

[0038] Insufficient rule validation refers to the lack of intelligent validation of the format and content compliance of application data, such as the inability to automatically identify typos, leakage of sensitive information, or missing attachments.

[0039] The bottleneck in approval efficiency refers to the fact that approvers need to read each item of the application content one by one, and there is a lack of intelligent auxiliary tools to support them, which leads to long approval cycles and a high risk of errors.

[0040] Difficulty in process integration refers to the need to develop independent approval processes for different business scenarios, making it impossible to quickly reuse or merge similar applications through a unified platform. Instead, the process logic needs to be adjusted through code development, resulting in long development cycles and high maintenance costs.

[0041] The approval process construction method, apparatus, equipment, storage medium, and program products provided in this application aim to enable the rapid construction and release of business approval processes by leveraging the drag-and-drop process customization capabilities of a no-code platform. At the same time, they improve problems such as rigid rules, low efficiency of manual approval, and insufficient compliance by using natural language models to intelligently verify application data, match rules, and assist in decision-making.

[0042] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0043] Figure 2 This is a flowchart illustrating an approval process construction method according to an embodiment of this application. The approval process construction method of this embodiment can be executed by an electronic device, specifically by, for example... Figure 1 The server shown is executing.

[0044] like Figure 2 As shown, the approval process construction method of this application embodiment includes steps S110 to S130.

[0045] S110. Generate business approval processes through graphical configuration tools.

[0046] The business approval process includes application form fields, approval nodes, and business rules.

[0047] Application form fields refer to the data items that users need to fill in when submitting a business application, including field types, validation rule fields, and sensitive information fields. Data types include text, numbers, and attachments. Validation rule fields include format validation and mandatory attachment validation. Sensitive information fields are used, for example, to indicate which data should be hidden from display.

[0048] Graphical configuration tools are software tools that allow users to configure application form fields, approval nodes, and business rules by dragging and dropping without writing code, such as no-code platforms.

[0049] The graphical configuration tool allows users to configure application form fields, approval nodes, and business rules.

[0050] S120. Use a natural language processing model to verify the application data and generate verification results.

[0051] Application data refers to the content of the approval application submitted by the user, including text, attachments, and metadata. For example, application data includes expense details, invoice attachments, and business trip locations from a financial reimbursement form.

[0052] Natural Language Processing (NLP) models refer to text analysis models based on deep learning. For example, a NLP module can be used to identify typos, grammatical errors, sensitive information, and match business rules in application data.

[0053] S130. Based on the verification results and business rules, output approval assistance prompts.

[0054] Approval assistance prompts refer to approval suggestions generated based on verification results and business rules, used to assist applicants and approvers in their decision-making. For example, approval assistance prompts may suggest that a supplementary security assessment report is needed or that the current application meets the travel travel standards.

[0055] In this embodiment, the electronic device generates a business approval process through a graphical configuration tool. This tool allows users to quickly construct application form fields, approval nodes, and business rules via drag-and-drop, eliminating the need for code development and thus lowering the technical barrier and improving response speed. The electronic device uses a natural language processing (NLP) model to validate the application data and generate validation results. The NLP model performs in-depth analysis of the application data, identifying text errors and sensitive information, and matching it with business rules to generate validation results, ensuring the compliance of the application data. Based on the validation results and configured business rules, the electronic device outputs approval assistance prompts for applicants to correct their application data or for approvers to use as a reference for decision-making, thereby reducing the burden of manual judgment.

[0056] In summary, the approval process construction method of this application embodiment employs a graphical configuration tool to ensure rapid customization of business approval processes, a natural language processing model to achieve intelligent verification of application data, and approval assistance prompts that combine verification results with business rules to form corrective suggestions for applicants and decision support for approvers. This achieves lightweight, intelligent, and compliant control of business approval processes, solving the problems of low efficiency and insufficient rule verification in manual approvals. Furthermore, its dynamic configuration capabilities adapt to diverse business needs, thereby improving the efficiency and compliance of business process approvals.

[0057] Figure 3 This is a flowchart of an approval process construction method according to another embodiment of this application.

[0058] like Figure 3 As shown, in one possible embodiment, step S110, which generates a business approval process template through a graphical configuration tool, includes steps S111 to S113.

[0059] S111. In the graphical interface, select application form fields by dragging and dropping to generate the application form.

[0060] Drag and drop refers to the interaction method of moving predefined components to the configuration area using mouse or touch operations.

[0061] An application form is a form that users need to fill out when submitting a business application. The application form includes fields selected by the user. Applicants can provide corresponding application data based on these fields.

[0062] Figure 4 This is a schematic diagram illustrating the selection of application form fields in an embodiment of this application to generate an application form.

[0063] like Figure 4 As shown, users can access the function of selecting application form fields through the form design component. This function provides specific application form fields in the form of components, such as text, numerical values, attachments, drop-down menus, required fields, and sensitive fields. Users can generate an application form by selecting the corresponding application form field.

[0064] S112. In the graphical interface, select approval nodes and business rules by dragging and dropping to generate a flowchart.

[0065] Figure 5 This is a schematic diagram illustrating the selection of approval nodes and business rules in an embodiment of this application to generate a flowchart.

[0066] like Figure 5 As shown, users access the function of selecting approval nodes and business rules through the process building component. This function provides operations for creating, editing, and deleting processes in a component format. Additionally, this function also provides the ability to select approval nodes and business rules in a component format. Selecting approval nodes includes, for example, setting up approval personnel groups; selecting business rules includes, for example, setting approval permissions, setting approval processes, and setting approval comments.

[0067] exist Figure 5 In the example, after the user selects the approval node and business rules, the user can also set the scope of the flowchart publication, initiate the business approval process, withdraw or terminate the business approval process, and set the flowchart to be enabled or disabled.

[0068] In this embodiment of the application, users can configure the application form and flowchart by dragging and dropping application form fields in the graphical interface.

[0069] S113. Bind the flowchart to the business rule base to form an executable approval process template.

[0070] A business rule base is a database that stores predefined business rules. For example, a business rule base might contain rules such as requiring invoices for financial reimbursements to be less than 5,000 yuan.

[0071] For example, when a user drags and drops the approval node "Department Manager Approval" into the flowchart, the electronic device automatically binds the business rule of "Department Manager Approval Permission," thereby generating an executable approval process template.

[0072] In this embodiment, the electronic device generates an application form by dragging and dropping application form fields in a graphical interface, and generates a flowchart by dragging and dropping approval nodes and business rules in the same interface. This allows users to visually select and configure application forms and flowcharts. The drag-and-drop method lowers the technical barrier, enabling non-technical personnel to quickly configure approval processes. By binding the flowchart to a business rule base, an executable business approval process is formed, ensuring a strong correlation and consistency between the application form, approval nodes, and business rules. This avoids data omissions or conflicts between the application form and the flowchart, thereby improving the compliance and executableness of the business approval process.

[0073] In one possible embodiment, the generation of the flowchart in step S112 includes at least one of the following: using flowchart symbols to represent the approval process; distinguishing the priority of approval nodes by color; and connecting approval nodes by arrows to indicate the process sequence.

[0074] Flowchart symbols are standardized graphic symbols, such as start nodes and approval nodes. For example, a diamond symbol can be used to represent an approval node.

[0075] Color coding refers to using colors to identify the attributes of approval nodes, such as priority. For example, red indicates an urgent approval node, meaning that the approval node has the highest priority.

[0076] In this embodiment, the flowchart uses standardized flowchart symbols, color differentiation, and arrow connections to intuitively display the structure and sequence of the approval process, thereby improving the visualization of the approval process. Furthermore, it helps users quickly understand the structure and priority of the approval process, reduces the complexity of approval process configuration, and ultimately achieves more intuitive approval process design and management.

[0077] like Figure 3 As shown, in one possible embodiment, step S120, which verifies the application data using a natural language processing model, includes step S121.

[0078] S121. Utilize a natural language processing model to perform at least one of the following: verify typos and grammatical errors in the application data; detect sensitive information in the application data; and generate compliance recommendations based on business rules.

[0079] For example, a natural language processing model may include at least one sub-network, each sub-network being used to verify typos and grammatical errors in the application data, detect sensitive information in the application data, and generate compliance recommendations based on business rules.

[0080] To detect typos and grammatical errors in the application data, such as an application for travel expenses of 5000, the natural language model might detect a grammatical error indicating that the currency symbol is missing. The electronic device can then output a prompt message requiring the currency symbol to be entered.

[0081] For sensitive information in application data, such as un-anonymized ID card numbers, natural language models can detect this sensitive information. Electronic devices can then output a notification indicating that the application data includes this sensitive information.

[0082] For compliance suggestions generated based on business rules, such as application data including travel expenses, the natural language model can call the business rules for travel expenses. These business rules indicate that the travel expenses do not exceed a preset threshold. If the travel expenses in the application data exceed the preset threshold, the electronic device can also output a prompt message that the travel expenses exceed the preset threshold and asks the user to resubmit the travel expenses.

[0083] The above verification results and related business rules can be displayed through a floating window.

[0084] In this embodiment, by utilizing a natural language processing model to perform at least one of the following: verifying typos and grammatical errors in the application data; detecting sensitive information in the application data; and generating compliance suggestions based on business rules, multi-dimensional verification of the application data can be performed, reducing the problem of false positives or omissions in single-dimensional verification and lowering compliance risks. For example, in the loan approval process, the electronic device can use a natural language processing model to simultaneously detect the compliance of the collateral information indicated in the application data, text format errors, and leakage of sensitive information. The electronic device can also prompt the applicant to improve the application data through a floating window.

[0085] like Figure 2 As shown, in one possible embodiment, before step S120 verifies the application data using a natural language processing model and generates the verification result, the approval process construction method further includes steps S141 to S142.

[0086] S141. Cluster the data from multiple applications.

[0087] For example, electronic devices can cluster multiple application data using clustering rules or clustering algorithms. For instance, a clustering rule might instruct the clustering of multiple applications submitted by the same applicant with the same next approval stage. The clustering rule might specify a clustering algorithm such as K-Means clustering.

[0088] S142. Perform batch processing operations on the clustered application data through the batch processing interface.

[0089] Batch processing operations include generating verification results and / or approval processing.

[0090] A batch processing interface refers to a user interface that supports batch generation of validation results and / or approval processing. For example, a batch processing interface may also support one-click approval, batch rejection, or return operations. For instance, approvers can use the batch processing interface to uniformly view clustered application data and quickly complete approval decisions.

[0091] In this embodiment, approval efficiency can be improved by clustering multiple application data and performing batch processing operations on the clustered application data through a batch processing interface. For example, in a financial reimbursement scenario, electronic devices can merge multiple reimbursement applications from the same department for processing, eliminating the need for approvers to click on each application individually, thus reducing the complexity of the approval process. This optimizes the allocation of approval resources, reduces repetitive operations by approvers, and improves the processing speed of business approval workflows.

[0092] like Figure 2 As shown, in one possible embodiment, after step S120 verifies the application data using a natural language processing model, the approval process construction method further includes steps S151 to S153.

[0093] S151. Identify business context information in the application data through a natural language processing model.

[0094] Business context information refers to the business scenario characteristics implicit in the application data. For a business trip expense reimbursement approval scenario, the corresponding business context information includes, for example, international travel and domestic travel. For instance, if the application data contains an international travel field, the corresponding business context information is international travel.

[0095] For example, based on the above embodiments, the natural language processing model may further include a sub-network for identifying business context information in the application data.

[0096] S152. Based on the business context information, retrieve the business rules from the business rule library.

[0097] For example, the business rule base includes a complete set of business rules, each with a business context information tag. Based on the business context information, the electronic device can retrieve the business rule with the corresponding tag from the business rule base.

[0098] S153. Based on business rules, process the application data for approval to obtain the business rule approval result.

[0099] Dynamic validation refers to adjusting business rules and executing approval processes based on real-time identified business context information. For example, the business context information of international business travel triggers the business rule that foreign exchange vouchers must be entered, while the business context information of domestic business travel triggers the business rule that transportation allowance caps must be set.

[0100] For example, an electronic device can perform automated approval processing of application data based on business rules using a built-in rule engine. The rule engine refers to a logic processing module that performs business rule matching; for instance, it detects whether application data conforms to business rules to achieve approval processing of the application data based on those rules.

[0101] In this embodiment, the natural language processing model first parses the business context information in the application data, then accurately calls the corresponding business rules from the business rule base, and finally performs adaptive and automated approval processing on the application data based on the business rules, thereby improving the efficiency of business rule approval processing.

[0102] For example, for relatively simple business rules such as granting permissions, electronic devices can automatically execute the corresponding approval process, reducing the workload of manual approval.

[0103] like Figure 3 As shown, in one possible embodiment, the output approval assistance prompt of step S130 includes steps S131 to S133.

[0104] S131. Determine the text summary of the application data using a natural language processing model.

[0105] A text summary is a text that contains the main information of the application data, which is converted from the application data.

[0106] For example, based on the above embodiments, the natural language processing model may further include a sub-network for determining a text summary of the application data.

[0107] S132. Use knowledge graphs to associate application data with historical cases to obtain historical case association results.

[0108] A knowledge graph is a graph database that stores historical cases and their business relationships.

[0109] For example, after approving historical application data, the corresponding approval result (such as whether the approval was granted) can be saved as a historical approval result. The electronic device can analyze and process the historical approval result (such as statistically analyzing the approval results of multiple historical application data to obtain the approval pass rate of application data of the same category) and obtain historical analysis results. The electronic device can use historical application data and historical analysis results to update the knowledge graph. In this embodiment of the application, for the current application data, the electronic device can associate the current application data with historical cases in the knowledge graph. Specifically, it can associate the corresponding historical application data and use the corresponding historical analysis results and historical approval results as historical case association results.

[0110] S133. Generate approval assistance prompts based on text summaries, business rule approval results, and historical case association results.

[0111] To illustrate with a concrete example: An electronic device uses a natural language processing model to determine that the text summary of the application data indicates the business trip location is a high-risk area, and the business rule approval result is that a security assessment report was not provided. The electronic device then uses a knowledge graph to perform historical case association on the application data, obtaining historical case association results such as failed approvals and the approval rates of similar application data. Therefore, the electronic device combines the text summary, the business rule approval result, and the historical case association results to generate an approval assistance prompt, such as: "A security assessment report is required."

[0112] Therefore, this application embodiment uses multi-model collaboration of natural language processing model, knowledge graph (and rule engine) to cover multiple dimensions such as text verification, business rule approval, and historical case association to generate approval assistance prompts, which can improve the comprehensiveness and reliability of approval assistance prompts.

[0113] like Figure 2 As shown, in one possible embodiment, before generating the business approval process template through the graphical configuration tool in step S110, the approval process construction method further includes steps S101 to S102.

[0114] S101. Receive natural language commands input by the user through the intelligent question-and-answer interface.

[0115] S102. Dynamically generate application form fields based on natural language instructions.

[0116] Intelligent question-answering interfaces refer to dialogue systems that support natural language interaction. For example, if a user inputs the natural language command "Help me create a business trip application," the electronic device will generate an application form based on this command, including fields such as the business trip location and the duration of the business trip.

[0117] Dynamic generation refers to automatically creating application form fields based on real-time natural language commands input by the user. For example, after a user inputs the natural language command "Create a business trip application for me" and also outputs the natural language command "Transportation for business trip", the electronic device can generate the application form field for the business trip location and duration, and then generate the application form field for the transportation.

[0118] For example, after receiving natural language instructions input by the user through an intelligent question-and-answer interface, the electronic device can parse the natural language instructions to obtain the keywords of the natural language instructions, and dynamically generate application form fields based on the keywords of the natural language instructions.

[0119] This application embodiment reduces the burden of manual input for users and improves user experience by using an intelligent question-and-answer interface and dynamically generated application form fields. Furthermore, it ensures a strong correlation between application form fields and business approval processes, thereby achieving more efficient business approval process initiation and compliance control.

[0120] like Figure 2 As shown, in one possible embodiment, after step S120 verifies the application data using a natural language processing model, the approval process construction method further includes steps S161 to S162.

[0121] S161. Abnormal events in the approval process for testing business.

[0122] For example, electronic devices can detect abnormal events in business approval processes through deep learning models, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks.

[0123] For example, LSTM can be used to continuously monitor data such as the processing time of approval nodes and whether business rules conflict, in order to identify abnormal events.

[0124] Abnormal events include approval failure to process within the time limit, business approval process timeout, and incorrect application data.

[0125] S162. Trigger a predefined repair strategy based on the abnormal event.

[0126] The remediation strategy refers to the automatic handling logic for abnormal events. For example, if an approver fails to process an application within the specified time, the corresponding remediation strategy includes a timeout reminder. This timeout reminder specifically involves triggering a pop-up notification to the approver's PC and mobile devices. For business approval processes that time out, the corresponding remediation strategy includes recalling the approval process. For incorrect application data, the corresponding remediation strategy includes invalidating the business approval process.

[0127] In this embodiment, the electronic device improves the stability and automation level of the business approval process by detecting abnormal events in the process and triggering predefined repair strategies based on these events. This mitigates bottlenecks in the business approval process caused by single points of failure, while automated repair strategies reduce the need for manual intervention, ensuring efficient operation in complex business scenarios.

[0128] Figure 6 This is a schematic diagram of an approval process construction method according to another embodiment of this application.

[0129] like Figure 6 As shown, applicants enter the business approval process by submitting a business application. The business approval process includes submitting application data, verifying the application data, and processing approvals according to business rules. Figure 6 In the example, after a business approval process is submitted to the approver, the electronic device can output approval assistance prompts to aid the approver's decision-making and allow the applicant to correct application data. If the approver fails to process the application within the specified time, the process can be set to automatically approve or reject it.

[0130] Figure 7 This is a schematic diagram of the structure of the approval process construction device according to an embodiment of this application. Figure 7 As shown, the approval process construction device provided in this application embodiment includes: a business approval process generation module 210, a verification module 220, and a prompting module 230.

[0131] The business approval process generation module 210 is used to generate business approval processes through a graphical configuration tool. The business approval process includes application form fields, approval nodes, and business rules.

[0132] The verification module 220 is used to verify the application data using a natural language processing model and generate verification results.

[0133] The prompt module 230 is used to output approval assistance prompts based on the verification results and business rules.

[0134] In one possible embodiment, the business approval process generation module includes: an application form generation submodule, used to select application form fields by dragging and dropping in a graphical interface to generate an application form; a flowchart generation submodule, used to select approval nodes and business rules by dragging and dropping in a graphical interface to generate a flowchart; and a binding submodule, used to bind the flowchart with a business rule base to form an executable business approval process.

[0135] In one possible embodiment, the verification module includes: a verification submodule, configured to utilize a natural language processing model to perform at least one of the following: verifying typos and grammatical errors in the application data; detecting sensitive information in the application data; and generating compliance recommendations based on business rules.

[0136] In one possible embodiment, the approval process construction device further includes: a context information recognition module, used to identify business context information in the application data through a natural language processing model; a business rule invocation module, used to invoke business rules from the business rule base according to the business context information; and a business rule approval processing module, used to process the application data for approval based on the business rules to obtain the business rule approval result.

[0137] In one possible embodiment, the prompting module includes: a text summary determination submodule, used to determine the text summary of the application data through a natural language processing model; an association submodule, used to associate the application data with historical cases through a knowledge graph to obtain historical case association results; and a prompting submodule, used to generate approval assistance prompts based on the text summary, business rule approval results, and historical case association results.

[0138] In one possible embodiment, the approval process construction device further includes: a natural language instruction receiving module, used to receive natural language instructions input by the user through an intelligent question-and-answer interface; and an application form field generation module, used to generate application form fields based on the natural language instructions.

[0139] In one possible embodiment, the approval process construction apparatus further includes: a clustering module for clustering multiple application data; and a batch processing module for performing batch processing operations on the clustered application data through a batch processing interface, wherein the batch processing operations include generating verification results and / or approval processing.

[0140] In one possible embodiment, the approval process construction apparatus further includes: an anomaly detection module for detecting abnormal events in the business approval process; and a repair module for triggering a predefined repair strategy based on the abnormal events.

[0141] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 8 As shown, this application embodiment provides an electronic device including a processor 301 and a memory 302. Optionally, the device further includes a communication component 303. The processor 301, memory 302, and communication component 303 are connected via a bus 304.

[0142] In the specific implementation process, the memory 302 stores code, and the processor 301 runs the code stored in the memory 302 to execute the method of the above method embodiment.

[0143] The specific implementation process of processor 301 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0144] In the above Figure 8 In the illustrated embodiments, it should be understood that the processor 301 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0145] The memory 302 may include high-speed RAM memory, and may also include non-volatile memory (NVM), such as at least one disk storage.

[0146] Bus 304 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Bus 304 can be divided into address bus, data bus, control bus, etc. For ease of illustration, the bus 304 in the accompanying drawings of this application is not limited to only one bus or one type of bus.

[0147] This application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods described in the above-described method embodiments.

[0148] The aforementioned computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0149] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0150] This application provides a computer program product, including a computer program that, when executed by a processor, implements the methods provided in any of the embodiments described above.

[0151] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0152] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0153] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0154] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0155] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0156] If the integrated unit / module is implemented as a software program module and sold or used as an independent financial product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software financial product. This computer software financial product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0157] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0158] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0159] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for constructing an approval process, characterized in that, include: A business approval process is generated using a graphical configuration tool. The business approval process includes application form fields, approval nodes, and business rules. The application data is validated using a natural language processing model, and validation results are generated. Based on the verification results and the business rules, output approval assistance prompts.

2. The method according to claim 1, characterized in that, The process of generating business approval workflows through graphical configuration tools includes: In the graphical interface, select application form fields by dragging and dropping to generate the application form; In the graphical interface, approval nodes and business rules can be selected by dragging and dropping to generate a flowchart; The flowchart is then bound to the business rule base to form an executable business approval process.

3. The method according to claim 2, characterized in that, The verification of the application data using a natural language processing model includes: Perform at least one of the following using the natural language processing model: Verify the application data for typos and grammatical errors; Detect sensitive information in the application data; Compliance recommendations are generated based on the aforementioned business rules.

4. The method according to claim 3, characterized in that, After verifying the application data using a natural language processing model, the method further includes: The natural language processing model is used to identify business context information in the application data. Based on the business context information, the business rule is retrieved from the business rule base; The application data is processed for approval based on the aforementioned business rules, resulting in the business rule approval result.

5. The method according to claim 4, characterized in that, The output approval assistance prompts include: The text summary of the application data is determined using a natural language processing model; The application data is linked to historical cases using a knowledge graph to obtain the historical case association results; The approval assistance prompt is generated based on the text summary, the business rule approval result, and the historical case association result.

6. The method for constructing an approval process according to any one of claims 1-5, characterized in that, Before using a natural language processing model to verify the application data and generate the verification result, the method further includes: Receive natural language commands input by the user through an intelligent question-and-answer interface; The application form fields are generated based on the natural language instructions.

7. The method according to claim 4, characterized in that, Before using a natural language processing model to verify the application data and generate the verification result, the method further includes: Clustering of multiple application data; Batch processing operations are performed on the clustered application data through a batch processing interface. The batch processing operations include generating verification results and / or the approval process.

8. The method for constructing an approval process according to any one of claims 1-5, characterized in that, After verifying the application data using a natural language processing model, the method further includes: Detect abnormal events in the business approval process; The abnormal event triggers a predefined repair strategy.

9. An approval process construction device, characterized in that, The device includes: The business approval process generation module is used to generate business approval processes through a graphical configuration tool. The business approval process includes application form fields, approval nodes, and business rules. The verification module is used to verify the application data using a natural language processing model and generate verification results. The prompt module is used to output approval assistance prompts based on the verification results and the business rules.

10. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 9.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 8.

12. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 8.

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