Intelligent process approval method and device based on dynamic rule engine and medium

Abstract

The invention discloses an intelligent process approval method and device based on a dynamic rule engine and a medium, and relates to the technical field of informatization management, and the method comprises the steps: obtaining a business document of an initial approval scene, and obtaining an initial approval data model through scene field configuration based on the business document of the initial approval scene; according to the initial approval data model, determining an initial approval process through an approval process definition of node configuration; performing self-evolution analysis of a dynamic rule engine on the initial approval process to obtain an updated dynamic rule engine; obtaining submission approval, and performing process node approval instantiation on the submission approval based on the updated dynamic rule engine to determine approval instance information; and based on the approval instance information, obtaining subsequent approval nodes through state analysis of the approval nodes. Through the method, the technical problem that the rule type and the approval node cannot be automatically updated in the existing process approval technology is solved.

Description

Technical Field

[0001] This application relates to the field of information management technology, and in particular to an intelligent process approval method, device and medium based on a dynamic rule engine. Background Technology

[0002] As enterprises become increasingly information-driven, automated approval processes have become crucial for improving work efficiency and reducing human error. Existing enterprise approval processes are primarily implemented through methods such as role-based access control (RBAC) models, parallel or sequential path approvals, and workflow engines. Because existing technologies typically employ static design, they cannot adapt to dynamically changing business needs, leading to the development of dynamic engine-based design methods.

[0003] Dynamic rule engines can parse and execute rules stored in a rule base, but these rules still need to be edited and updated by users or developers. Existing dynamic rule engines allow users to customize the definition of business processes, the addition of approval nodes, and the storage of rule types, but they cannot automatically update rule types and approval nodes based on changes in business processes and approval nodes. Summary of the Invention

[0004] This application provides an intelligent process approval method, device, and medium based on a dynamic rule engine, which solves the technical problem that existing process approval technologies cannot update rule types and approval nodes on their own.

[0005] In a first aspect, embodiments of this application provide an intelligent process approval method based on a dynamic rule engine. The method includes: acquiring business documents for an initial approval scenario, and obtaining an initial approval data model based on these documents and by configuring scenario fields; determining an initial approval process based on the initial approval data model and by defining the approval process through node configuration; performing self-evolution analysis of the initial approval process using a dynamic rule engine to obtain an updated dynamic rule engine; acquiring submitted approvals, and instantiating process node approvals for the submitted approvals based on the updated dynamic rule engine to determine approval instance information; and obtaining subsequent approval nodes based on the approval instance information and by analyzing the approval node status.

[0006] In one implementation of this application, an initial approval data model is obtained based on the business documents of the initial approval scenario through scenario field configuration. Specifically, this includes: sorting out the data fields of the business documents of the initial approval scenario to obtain condition setting items; setting condition relationships for the condition setting items to determine the judgment conditions of branch nodes; and obtaining the initial approval data model through data model pre-preparation based on the judgment conditions of branch nodes.

[0007] In one implementation of this application, the initial approval process is determined based on the initial approval data model and the approval process definition through node configuration. Specifically, this includes: determining the approval process framework by drawing the approval process based on the initial approval data model; configuring approval node strategies on the approval process framework to obtain approval termination conditions; determining the first approval configuration based on the approval termination conditions and by selecting the type of approver; configuring node features on the approval process framework to obtain the second approval configuration; wherein the types of node feature configurations include: node form parameters, assignment information, and callback phase; and determining the initial approval process based on the first and second approval configurations.

[0008] In one implementation of this application, a self-evolutionary analysis of the dynamic rule engine is performed on the initial approval process to obtain an updated dynamic rule engine. Specifically, this includes: determining the approval process change data based on the initial approval process, and calculating the change impact value of the approval process change data to obtain weighted impact rule parameters; performing cosine similarity historical node template matching on the weighted impact rule parameters to determine the self-evolutionary approval process node configuration; and obtaining an updated dynamic rule engine through self-updating of approval rules based on the self-evolutionary approval process node configuration.

[0009] In one implementation of this application, historical node template matching with cosine similarity is performed on the weighted influence rule parameters to determine the configuration of the self-evolving approval process nodes. Specifically, this includes: vector standardization of the weighted influence rule parameters, and dynamic weighted similarity calculation of the business entity feature vectors after vector standardization to determine the weighted template similarity; matching the weighted template similarity with the historical node templates to determine the configuration of the self-evolving approval process nodes.

[0010] In one implementation of this application, based on an updated dynamic rule engine, the submitted approval is instantiated as a process node to determine the approval instance information. Specifically, this includes: determining the selected approval process based on the updated dynamic rule engine; obtaining business document data and instantiating the selected approval process and business document data to determine the approval instance information; wherein, the approval instance information includes: approval flow node information and a multi-level JSON structure.

[0011] In one implementation of this application, subsequent approval nodes are obtained based on approval instance information and approval node status analysis. Specifically, this includes: using an updated dynamic rule engine to determine node information in the approval instance information and determine the node pass status; and obtaining subsequent approval nodes based on the node pass status and branch node association judgment.

[0012] In one implementation of this application, after obtaining subsequent approval nodes based on approval instance information and through approval node status analysis, the method further includes: obtaining node approval parameter data, constructing parameter nodes from the node approval parameter data to obtain signing nodes; setting the signing nodes as subsequent approval nodes and prioritizing the approval of subsequent approval nodes; obtaining assignment information, and replacing the participants of subsequent approval nodes with the assignment information to construct new participants.

[0013] Secondly, embodiments of this application also provide an intelligent process approval device based on a dynamic rule engine, characterized in that the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: acquire business documents for an initial approval scenario, and based on the business documents for the initial approval scenario, obtain an initial approval data model through scenario field configuration; determine an initial approval process according to the initial approval data model and through the approval process definition configured by the node; perform self-evolution analysis of the initial approval process using a dynamic rule engine to obtain an updated dynamic rule engine; acquire submitted approvals, and based on the updated dynamic rule engine, instantiate process node approvals for the submitted approvals to determine approval instance information; and based on the approval instance information, obtain subsequent approval nodes through approval node status analysis.

[0014] Thirdly, embodiments of this application also provide a non-volatile computer storage medium for intelligent process approval based on a dynamic rule engine, storing computer-executable instructions. The computer-executable instructions are characterized by: acquiring business documents for an initial approval scenario, and obtaining an initial approval data model based on the business documents of the initial approval scenario through scenario field configuration; determining an initial approval process based on the approval process definition configured by the nodes according to the initial approval data model; performing self-evolutionary analysis of the initial approval process using a dynamic rule engine to obtain an updated dynamic rule engine; acquiring submitted approvals, and instantiating process node approvals for the submitted approvals based on the updated dynamic rule engine to determine approval instance information; and obtaining subsequent approval nodes based on the approval instance information through approval node status analysis.

[0015] This application provides an intelligent process approval method, device, and medium based on a dynamic rule engine. Through the self-evolution analysis and collaborative efficiency mechanism of the dynamic rule engine, it solves the technical problem that existing process approval technologies cannot update rule types and approval nodes on their own, realizes dynamic self-updating of process approval rules, and improves the efficiency and node scalability of collaborative approval. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 A flowchart of an intelligent process approval method based on a dynamic rule engine provided for embodiments of this application;

[0018] Figure 2 This is a schematic diagram of the internal structure of an intelligent process approval device based on a dynamic rule engine, provided for an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] This application provides an intelligent process approval method, device, and medium based on a dynamic rule engine. Through the self-evolution analysis and collaborative efficiency mechanism of the dynamic rule engine, it solves the technical problem that existing process approval technologies cannot update rule types and approval nodes on their own, realizes dynamic self-updating of process approval rules, and improves the efficiency and node scalability of collaborative approval.

[0021] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0022] Figure 1 This document provides a flowchart of an intelligent workflow approval method based on a dynamic rule engine, as illustrated in an embodiment of this application. Figure 1 As shown in the figure, the intelligent process approval method based on a dynamic rule engine provided in this application embodiment specifically includes the following steps:

[0023] Step 101: Obtain the business documents for the initial approval scenario, and based on the business documents for the initial approval scenario, obtain the initial approval data model through scenario field configuration.

[0024] For example, in order to meet the business documents of different approval scenarios, this application configures the approval scenario for the judgment conditions of branch nodes in the approval process through scenario field configuration, and obtains the initial approval data model.

[0025] Specifically, based on the business documents of the initial approval scenario, the initial approval data model is obtained through scenario field configuration, including: sorting out the data fields of the business documents of the initial approval scenario to obtain condition setting items; setting the condition relationship of the condition setting items to determine the judgment conditions of the branch node; and obtaining the initial approval data model through data model pre-production based on the judgment conditions of the branch node.

[0026] In one embodiment, for business documents in different approval scenarios, data fields are organized and data models are pre-built to be used as judgment conditions for branch nodes during the approval process. For example, in the order confirmation approval scenario, fields such as store name, total order amount, and the organization to which the purchase belongs can be pre-built.

[0027] The data interface returns the hierarchical structure of the organization by calling the interface. It returns the structure based on a combination of parent ID, level, and whether it is a detail field. The returned structure includes the organization name and supports filtering by organization ID, user ID, and user name. It returns a paginated list of users, including user ID, user name, and pagination information.

[0028] Finally, by returning the list of organizational roles, the system returns a list containing role number, role name, and pagination information.

[0029] Step 102: Based on the initial approval data model, determine the initial approval process through the approval process definition configured in the node configuration.

[0030] For example, in order to ensure that different approvers can complete the approval process, this application achieves standardized creation of the approval architecture through the definition of approval process in node configuration, thereby improving the configuration support coverage of approval nodes.

[0031] Specifically, based on the initial approval data model, the initial approval process is determined through the node configuration and approval process definition, including: determining the approval process framework by drawing the approval process based on the initial approval data model; configuring approval node strategies on the approval process framework to obtain the approval termination conditions; determining the first approval configuration by selecting the type of approver based on the approval termination conditions; configuring node features on the approval process framework to obtain the second approval configuration; wherein, the types of node feature configurations include: node form parameters, assignment information, and callback stage; and determining the initial approval process based on the first and second approval configurations.

[0032] In one embodiment, an approval process is defined for a specific approval scenario, including selecting the approval scope, adding new nodes, configuring node approval strategies, selecting node participants, setting special functions for nodes, branching conditions, different paths, and triggering conditions. This configuration is performed via a graphical interface, facilitating design participation for non-technical personnel.

[0033] The approval workflow requires the following basic information: workflow name and scope of application (this scope is the organization in the procurement mall so that different organizations can set up different approval workflows).

[0034] The approval workflow requires drawing a process model. The front end displays the start and end nodes by default, and the administrator can draw the process in the middle and choose to add approval nodes or conditional branches.

[0035] The basic settings for the approval node include: filling in the node name (such as department leader, finance personnel, etc. for easy differentiation), selecting the approval node completion strategy, the type of approver, and adding participants (user, position, and department are returned by the corresponding query interface of the user module, and the initiator is the user who submitted the approval).

[0036] Advanced settings for approval nodes include: selecting the node form URL (to pass the PC form URL address when sending approval messages to approvers, facilitating message redirection), selecting the node mobile form URL (if a mobile version exists), and selecting whether to allow assignment and post-signature (for supported special functions; for example, if approval node A allows assignment, when the approval flow reaches node A, the special function interface will return that assignment is allowed, and the front-end will then add the assignment function to modify the approval participants of node A during the approval process).

[0037] If approval node B allows subsequent signatures, then when node B approves, the special function interface will return that it allows subsequent signatures. The front end will then add the signature function after making a judgment. The approver of node B can choose to add a signature or not. After the signature is added, an approval node will be added next after node B approves. Configure the node start and finish callback URLs (if there are special business processing when the node starts or finishes, the URL can be configured. When the node starts or finishes, if the URL is configured, it will be called. The parameters are uniform and include: approval instance, source business document, node start or node end, node approval status, and node processing time).

[0038] Finally, conditional branching allows for flexible construction of approval workflows, such as when order amounts exceeding 10,000 require additional financial approval. The preferred approach is to pre-define branch data field models based on the approval scenario, including field names and descriptions for order amount, store name, and purchasing organization name.

[0039] Furthermore, the approval system supports left and right parentheses, operators (greater than, less than, equal to, greater than or equal to, less than or equal to, beginning with "is", ending with "is", etc.

[0040] Step 103: Perform a self-evolution analysis of the initial approval process using the dynamic rule engine to obtain an updated dynamic rule engine.

[0041] For example, in order to enable the workflow approval to automatically update rule types and approval nodes, this application improves the adaptability of the workflow approval through the self-evolution analysis of a dynamic rule engine, reduces the need for manual modification, and improves approval efficiency.

[0042] Specifically, the initial approval process undergoes a self-evolution analysis of the dynamic rule engine to obtain an updated dynamic rule engine. This includes: determining the approval process change data based on the initial approval process, calculating the change impact value of the approval process change data to obtain weighted impact rule parameters; performing cosine similarity historical node template matching on the weighted impact rule parameters to determine the self-evolutionary approval process node configuration; and obtaining an updated dynamic rule engine through self-updating of approval rules based on the self-evolutionary approval process node configuration.

[0043] Furthermore, cosine similarity is used to match historical node templates for the weighted influence rule parameters to determine the configuration of the self-evolving approval process nodes. Specifically, this includes: vector standardization of the weighted influence rule parameters, and dynamic weighted similarity calculation of the business entity feature vectors after vector standardization to determine the weighted template similarity; and matching the weighted template similarity with the historical node templates to determine the configuration of the self-evolving approval process nodes.

[0044] In one embodiment, CDC listeners are deployed at key nodes of the business process to capture relevant data on process structure changes and business attribute updates in real time, and the data is standardized into JSON Schema format (i.e., structured change timeline).

[0045] Based on the structured change timeline, a rule dependency graph is constructed. By identifying affected rules, quantifying the scope of impact, and analyzing high-risk changes, the change impact value is calculated.

[0046] Furthermore, the scope of impact can be analyzed using the PageRank algorithm. High-risk change analysis selects change times with impact values ​​greater than a preset threshold for labeling, thereby obtaining a list of impact rules.

[0047] Then, business feature vectors are extracted, and weighted template similarity is determined by cosine similarity calculation to retrieve similar historical change processes.

[0048] Finally, the weighted template similarity is matched with historical node templates to determine the configuration of the self-evolving approval process nodes.

[0049] Step 104: Obtain the submitted approval and, based on the updated dynamic rule engine, instantiate the submitted approval for the process node approval to determine the approval instance information.

[0050] Specifically, based on the updated dynamic rule engine, the submitted approval is instantiated for process node approval to determine the approval instance information, including: determining the selected approval process based on the updated dynamic rule engine; obtaining business document data and instantiating the selected approval process and business document data to determine the approval instance information; wherein, the approval instance information includes: approval flow node information and multi-level JSON structure.

[0051] In one embodiment, after the page draws the approval flow nodes, the front end assembles them into a multi-level node JSON, which is then passed to the back end for intelligent JSON parsing. The hierarchy and node list are assigned sequentially according to the multi-level structure. The back end stores the node fields as follows: process definition ID, node type (user, condition), node participant type (user, role), node number, parent node number, child node number, node name, whether assignment is allowed, whether signature is allowed, node form URL, node movement form URL, node completion strategy, whether it is a detail-level node, node level, whether it is a branch node, node start callback URL, and node completion callback URL.

[0052] Step 105: Based on the approval instance information, obtain the subsequent approval nodes through approval node status analysis.

[0053] For example, in order to provide precise guidance for the next approval node, this application uses approval node status analysis to achieve correlation analysis of the next node under different instances and different needs, thereby improving the accuracy and intelligence of the approval process.

[0054] Specifically, based on the approval instance information, subsequent approval nodes are obtained through approval node status analysis. This includes: using an updated dynamic rule engine to judge the node information of the approval instance and determine the node pass status; and based on the node pass status, subsequent approval nodes are obtained through branch node association judgment.

[0055] Specifically, after obtaining subsequent approval nodes based on approval instance information and through approval node status analysis, the method further includes: obtaining node approval parameter data, constructing parameter nodes from the node approval parameter data to obtain signing nodes; setting the signing nodes as subsequent approval nodes and prioritizing the approval of subsequent approval nodes; obtaining assignment information and replacing the participants of subsequent approval nodes with the assignment information to construct new participants.

[0056] In one embodiment, the first pending approval node of the business instantiation approval flow node is found according to the dynamic rule engine, and the pending approval node and the specific approval user configured on the node are also instantiated.

[0057] If a node is configured for automatic approval, its information (name, policy, processing time) is recorded in the approval process node table. After an automatic node approves, the next pending approval node is found based on the node hierarchy and the next node number in the approval flow. If the participant in the next approval node is a specific user, the next approval node and the user are assembled into the approval flow node table and the approval flow node log table. Approval pending messages are then sent to these users to notify them. The node requiring approval is then marked as pending, awaiting user approval.

[0058] Furthermore, if the participants in the next approval node are specific roles, the corresponding users are located in real time based on their roles. The next approval node and its users are then assembled into the approval flow node table and approval flow node log table. These users are then notified that approval is pending, and the node status is set to "pending," awaiting user approval. The same applies to roles. For subsequent calls to the next approval node instance, if a node start callback URL exists, an API call is performed.

[0059] After the node is approved, a) based on the approval instance, approval node, and approving user information, find the corresponding approval node and node log, modify the node status, record the node log operation time, record the node log approval status, and record the node log approval comments. According to the node strategy, dynamically determine whether the node status is complete (approved). If the node is complete, find the next node based on the initial instantiated approval process node.

[0060] If it is a branch node, the expression is assembled based on the business document data, data model, and conditional branch configuration. The conditional judgment unit of the QLExpress rule engine determines which branch node to follow, and then finds the next approval node associated with the branch node. After finding the node to be approved, the node log (node ​​approval user) is assembled, the node and node log are stored, and a message is sent to the next node user.

[0061] Furthermore, if the status after approval is "Approved and Signed," then additional signing information parameters (signing node name, node strategy, node participants) are passed during approval. Upon receiving this information, the approval system prioritizes analyzing the signing portion, constructing a new node in the approval process based on the signing information, and modifying the original node's associated node numbers, hierarchy, etc., before inserting the new signing node. Then, the original approval process is processed.

[0062] If node A approves and assigns a participant, the assignment information (node ​​participant) is added as a parameter upon approval. Upon receiving this information, the approval system replaces the assignment information with the new participant information when searching for the next approval node, creating a new participant in real time and integrating it into the existing approval process. The approval process then continues.

[0063] The above are embodiments of the method proposed in this application. Based on the same inventive concept, embodiments of this application also provide an intelligent process approval device based on a dynamic rule engine, the structure of which is as follows: Figure 2 As shown.

[0064] Figure 2 This is a schematic diagram of the internal structure of an intelligent workflow approval device based on a dynamic rule engine, provided as an embodiment of this application. Figure 2 As shown, the device includes:

[0065] At least one processor 201;

[0066] And a memory 202 that is communicatively connected to at least one processor;

[0067] The memory 202 stores instructions executable by at least one processor, which are executed by at least one processor 201 to enable at least one processor 201 to:

[0068] The process involves: acquiring business documents for the initial approval scenario; obtaining the initial approval data model based on these documents and configuring scenario fields; determining the initial approval process based on the approval process definition configured in the node configuration, performing self-evolution analysis of the initial approval process using a dynamic rule engine to update the dynamic rule engine; acquiring submitted approval documents and instantiating process node approvals based on the updated dynamic rule engine to determine approval instance information; and obtaining subsequent approval nodes based on the approval instance information and approval node status analysis.

[0069] Some embodiments of this application provide corresponding to Figure 1 A non-volatile computer storage medium for intelligent process approval based on a dynamic rule engine, storing computer-executable instructions, wherein the computer-executable instructions are configured as follows:

[0070] The process involves: acquiring business documents for the initial approval scenario; obtaining the initial approval data model based on these documents and configuring scenario fields; determining the initial approval process based on the approval process definition configured in the node configuration, performing self-evolution analysis of the initial approval process using a dynamic rule engine to update the dynamic rule engine; acquiring submitted approval documents and instantiating process node approvals based on the updated dynamic rule engine to determine approval instance information; and obtaining subsequent approval nodes based on the approval instance information and approval node status analysis.

[0071] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for IoT devices and media are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0072] The systems, media, and methods provided in this application are one-to-one correspondences. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0073] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0074] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0075] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0076] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0077] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0078] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0079] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0080] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0081] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. An intelligent process approval method based on a dynamic rule engine, characterized in that, The method includes: Obtain the business documents for the initial approval scenario, and based on the business documents for the initial approval scenario, obtain the initial approval data model through scenario field configuration; Based on the initial approval data model, the initial approval process is determined through the approval process definition configured in the nodes; The initial approval process is subjected to self-evolution analysis of the dynamic rule engine to obtain an updated dynamic rule engine; Obtain the submitted approval, and based on the updated dynamic rule engine, instantiate the submitted approval into a process node to determine the approval instance information; Based on the approval instance information, subsequent approval nodes are obtained through approval node status analysis.

2. The intelligent process approval method based on a dynamic rule engine according to claim 1, characterized in that, Based on the business documents of the initial approval scenario, the initial approval data model is obtained through scenario field configuration, specifically including: The data fields of the business documents in the initial approval scenario are sorted out to obtain the condition setting items; The condition settings are configured to determine the conditions for branch node judgment. Based on the branch node judgment conditions, the initial approval data model is obtained through data model pre-construction.

3. The intelligent process approval method based on a dynamic rule engine according to claim 1, characterized in that, Based on the initial approval data model, the initial approval process is determined through the approval process definition configured in the nodes, specifically including: Based on the initial approval data model, the approval process framework is determined by drawing the approval process. Configure approval node strategies for the aforementioned approval process framework to obtain approval termination conditions; Based on the aforementioned approval termination conditions, the first approval configuration is determined by selecting the type of approver. The approval process framework is configured with node features to obtain a second approval configuration; wherein, the types of node feature configuration include: node form parameters, assignment information, and callback stage; The initial approval process is determined based on the first approval configuration and the second approval configuration.

4. The intelligent process approval method based on a dynamic rule engine according to claim 1, characterized in that, The initial approval process is subjected to a self-evolutionary analysis of the dynamic rule engine to obtain an updated dynamic rule engine, specifically including: Based on the initial approval process, change data for the approval process is determined, and the impact value of the change data is calculated to obtain weighted impact rule parameters. The weighted influence rule parameters are matched with historical node templates using cosine similarity to determine the configuration of the self-evolving approval process nodes. Based on the self-evolving approval process node configuration, the updated dynamic rule engine is obtained through self-updating of approval rules.

5. The intelligent process approval method based on a dynamic rule engine according to claim 4, characterized in that, The weighted influence rule parameters are subjected to cosine similarity historical node template matching to determine the self-evolving approval process node configuration, specifically including: The influence rule parameters of the weights are vectorized, and the similarity of the business entity feature vectors after vector standardization is dynamically weighted to determine the weighted template similarity. The weighted template similarity is matched with historical node templates to determine the configuration of the self-evolving approval process nodes.

6. The intelligent process approval method based on a dynamic rule engine according to claim 1, characterized in that, Based on the updated dynamic rule engine, the submitted approval is instantiated as a process node to determine the approval instance information, specifically including: Based on the updated dynamic rule engine, the selected approval process is determined; Obtain business document data, and instantiate the selected approval process and the business document data to determine the approval instance information; wherein, the approval instance information includes: approval flow node information and a multi-level JSON structure.

7. The intelligent process approval method based on a dynamic rule engine according to claim 1, characterized in that, Based on the aforementioned approval instance information, subsequent approval nodes are obtained through approval node status analysis, specifically including: Based on the updated dynamic rule engine, the node information of the approval instance is judged to determine the node pass status; Based on the status of the node, the subsequent approval node is obtained by determining the association with the branch node.

8. The intelligent process approval method based on a dynamic rule engine according to claim 1, characterized in that, After obtaining subsequent approval nodes based on the approval instance information and through approval node status analysis, the method further includes: Obtain the node approval parameter data, and construct the parameter-added node based on the node approval parameter data to obtain the signature node; Set the signing node as the subsequent approval node, and give priority to approving the subsequent approval node; Obtain the assignment information and replace the participants in the subsequent approval nodes with the assignment information to construct new participants.

9. An intelligent workflow approval device based on a dynamic rule engine, characterized in that, The device includes: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to: Obtain the business documents for the initial approval scenario, and based on the business documents for the initial approval scenario, obtain the initial approval data model through scenario field configuration; Based on the initial approval data model, the initial approval process is determined through the approval process definition configured in the nodes; The initial approval process is subjected to self-evolution analysis of the dynamic rule engine to obtain an updated dynamic rule engine; Obtain the submitted approval, and based on the updated dynamic rule engine, instantiate the submitted approval into a process node to determine the approval instance information; Based on the approval instance information, subsequent approval nodes are obtained through approval node status analysis.

10. A non-volatile computer storage medium for intelligent process approval based on a dynamic rule engine, storing computer-executable instructions, characterized in that, The computer-executable instructions are set as follows: Obtain the business documents for the initial approval scenario, and based on the business documents for the initial approval scenario, obtain the initial approval data model through scenario field configuration; Based on the initial approval data model, the initial approval process is determined through the approval process definition configured in the nodes; The initial approval process is subjected to self-evolution analysis of the dynamic rule engine to obtain an updated dynamic rule engine; Obtain the submitted approval, and based on the updated dynamic rule engine, instantiate the submitted approval into a process node to determine the approval instance information; Based on the approval instance information, subsequent approval nodes are obtained through approval node status analysis.

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