Project approval scheme determination method and device, program product and electronic equipment

Abstract

The invention discloses a project approval scheme determination method and device, a program product and electronic equipment, and the method comprises the steps: carrying out the branch activation / pruning operation of a preset approval process template based on a target business rule of a target project, and obtaining a reconstruction process topological graph corresponding to the target project; based on the construction mode attribute of the target project, determining a node configuration sequence corresponding to each examination and approval link in the reconstruction process topological graph, and adding the node configuration sequence corresponding to each examination and approval link to the reconstruction process topological graph to obtain a customized process topological graph corresponding to the target project; based on the project type attribute of the target project, determining a handler candidate set corresponding to each approval node in the customized process topological graph; and based on the customized process topological graph and the handler candidate set corresponding to each approval node, determining a target approval scheme corresponding to the target project. The technical problems of low generation efficiency and low accuracy of the project approval scheme in the prior art are solved.

Description

Technical Field

[0001] This application relates to the field of project approval technology, and more specifically, to a method, apparatus, program product, and electronic device for determining a project approval scheme. Background Technology

[0002] In infrastructure and telecommunications sectors, the approval and management of complex projects (such as cross-regional macro base station tower construction and multi-unit collaborative rail transit supporting projects) face the core requirement of adapting to multiple dimensions of business attributes. These projects typically involve multiple attributes, including the level of the project initiating entity (headquarters / provincial branch / municipal level), construction scale (investment amount, construction scope), cooperation model (self-operated / co-construction / construction on behalf of others), and compliance requirements (environmental impact assessment, power supply support), requiring coordinated matching of "attributes-processes-authorities" in the approval process.

[0003] In the existing complex project approval management solution, firstly, a limited number of fixed process templates are preset based on the project type (for example, two independent process frameworks are formulated for "provincial-level large-scale projects" and "municipal-level small-scale projects"); then, simple conditional branching rules are defined through a preset configuration table (for example, different approval nodes are triggered only based on the single dimension of "whether the investment amount exceeds 500,000 yuan"); finally, the approval nodes are statically bound to role permissions (for example, the "department leader approval" node is fixedly associated with the "department manager" position, and will not be adjusted regardless of changes in project type and attributes).

[0004] However, existing technical solutions have limitations: on the one hand, the process structure lacks dynamic adaptability. Since the process template is a preset fixed form, it cannot flexibly reconstruct the process topology according to complex scenarios with multiple dimensions of attributes such as "project initiation entity + construction mode + compliance requirements". This results in some cross-regional co-construction projects requiring manual temporary addition of approval steps, or some small emergency projects having extended approval cycles due to process redundancy. On the other hand, the adaptation accuracy of nodes and permissions is insufficient. The static binding method cannot adjust the scope of approval roles according to project type (such as macro site construction and micro site deployment). This easily leads to the problem of permission generalization, where "roles with micro site approval permissions participate in macro site approval", resulting in the technical problems of low efficiency and low accuracy in generating project approval schemes in existing technologies.

[0005] There is currently no effective solution to the above problems. Summary of the Invention

[0006] This application provides a method, apparatus, program product, and electronic device for determining a project approval scheme, in order to at least solve the technical problems of low efficiency and low accuracy in generating project approval schemes in the prior art.

[0007] According to one aspect of this application, a method for determining a project approval scheme is provided, comprising: performing rule matching in a preset business rule knowledge base based on the project business information of the target project, wherein the preset business rules in the preset business rule knowledge base are used to characterize the triggering conditions corresponding to the approval process under different approval scenarios; performing branch activation / pruning operations on the preset approval process template corresponding to the target business rule based on the target business rule that is successfully matched to the target project, to obtain a reconstructed process topology diagram corresponding to the target project, wherein the reconstructed process topology diagram is used to characterize P approval steps involved in the target project, where P is a positive integer; and determining each step in the reconstructed process topology diagram based on the construction mode attribute of the target project. The node configuration sequence corresponding to each approval step is added to the reconstructed process topology diagram to obtain the customized process topology diagram corresponding to the target project. The node configuration sequence includes at least one customized approval node corresponding to the construction mode attribute, and the customized process topology diagram includes at least Q approval nodes, where Q is a positive integer. Based on the project type attribute of the target project, a candidate set of handlers corresponding to each approval node in the customized process topology diagram is determined. Each candidate set of handlers includes at least one approval handler with the operation permission corresponding to the approval node. Based on the customized process topology diagram and the candidate set of handlers corresponding to each approval node, the target approval scheme corresponding to the target project is determined.

[0008] Optionally, before performing rule matching in a pre-defined business rule knowledge base based on the project business information of the target project, the method for determining the project approval scheme further includes: classifying and parsing the project business information corresponding to B historical projects based on the dimensions of A project attributes to obtain A sets of business information corresponding to the A project attributes, where A and B are both positive integers, and the A project attributes include at least the project initiation entity attribute, project type attribute, construction mode attribute, compliance requirement attribute, and quantitative parameter attribute; performing encoding operations on the project business information included in each of the A sets of business information to obtain a set of rule factors corresponding to each set of business information, where each set of rule factors includes at least one pre-defined rule factor; based on the historical approval processes corresponding to B historical projects, logically combining the pre-defined rule factors in the set of rule factors corresponding to each set of business information using pre-defined logical operators to obtain an initial set of business rules corresponding to B historical projects; performing business rule verification on each initial business rule in the initial set of business rules, and creating a pre-defined business rule knowledge base based on the initial business rules that have passed the business rule verification.

[0009] Optionally, before performing rule matching in a pre-defined business rule knowledge base based on the project business information of the target project, the method for determining the project approval scheme further includes: generating a scenario-based approval requirement list based on the approval scenario information of the historical approval processes corresponding to B historical projects, wherein the scenario-based approval requirement list includes at least the necessary approval nodes corresponding to each historical project under different approval scenarios, the node flow order of the necessary approval nodes, and the node authority and responsibility attribution; generating an initial approval process template set corresponding to B historical projects based on the scenario-based approval requirement list, wherein each initial approval process template in the initial approval process template set includes at least a start node, necessary approval nodes, branch nodes, and end nodes; performing process template verification on each initial approval process template in the initial approval process template set, and using the initial approval process template that passes the process template verification as the pre-defined approval process template.

[0010] Optionally, before performing branch activation / pruning operations on the preset approval process template corresponding to the target business rule based on the target business rule that has successfully matched the target project, the method for determining the project approval scheme further includes: creating a first mapping table, wherein the first mapping table is used to store the mapping relationship between the preset business rule and the preset approval process template; creating a second mapping table, wherein the second mapping table is used to store the mapping relationship between the rule factors in the preset business rule and the branch nodes in the preset approval process template; and generating a preset configuration instruction based on the first mapping table and the second mapping table, wherein the preset configuration instruction is at least used to determine the preset approval process template corresponding to the target business rule.

[0011] Optionally, based on the project type attribute of the target project, a candidate set of handlers corresponding to each approval node in the customized process topology diagram is determined, including: binding the project type attribute of the target project to each approval node in the customized process topology diagram to obtain the project type attribute associated with each approval node; generating a project type vector corresponding to each approval node based on the project type attribute associated with each approval node, wherein the vector dimension of the project type vector is equal to the total number of attribute categories of the project type attribute; extracting features from the user role information of C approval handlers in the preset user role information database to obtain C user role feature vectors corresponding to the C approval handlers, wherein C is a positive integer, and the vector dimension of the user role feature vector is equal to the total number of attribute categories of the project type attribute; detecting the similarity between the project type vector corresponding to each approval node and each user role feature vector in the C user role feature vectors; and adding the approval handler corresponding to the similarity to the candidate set of handlers for the approval node corresponding to the similarity if the similarity is greater than or equal to a preset similarity threshold, wherein the preset similarity threshold is dynamically adjusted based on the node level of the corresponding approval node.

[0012] Optionally, after determining the candidate set of handlers corresponding to each approval node in the customized process topology diagram based on the project type attribute of the target project, the method for determining the project approval scheme includes: binding the user role information of the j-th approval handler in the candidate set corresponding to the i-th approval node in the customized process topology diagram to the i-th approval node in the customized process topology diagram; performing attribute validation on the node attributes of the i-th approval node, wherein the node attributes include at least the node name, approval processing time limit, and user role information; if the attribute validation fails, binding the user role information of the (j+1)-th approval handler in the candidate set to the i-th approval node in the customized process topology diagram.

[0013] Optionally, after determining the target approval scheme for the target project based on the customized process topology diagram and the candidate set of handlers corresponding to each approval node, the method for determining the project approval scheme includes: determining F monitoring dimensions corresponding to the target approval scheme based on the customized process topology diagram and the candidate set of handlers corresponding to each approval node, where F is a positive integer, and the F monitoring dimensions include at least node identifier, expected handler range, expected node flow direction, and expected branch triggering conditions; monitoring the approval execution process of the target approval scheme based on the F monitoring dimensions to obtain a target audit log in a preset format, wherein the target audit log is used to at least characterize the node attributes corresponding to each approval node and the node execution information under the F monitoring dimensions; and analyzing the target audit log and the target approval scheme. The approval data generated during the approval execution process of the batch plan is correlated to obtain G approval cases. The approval data includes at least the approval form and rejection opinions, and G is a positive integer. Based on a preset scoring formula, the case score corresponding to each of the G approval cases is determined. The case score is used to characterize at least the closed state of the approval execution process corresponding to the approval case, the approval compliance, the time taken, the completeness of the approval data, the node dependency, and the business relevance. Based on the project business information of the approval cases whose case scores are greater than the preset case scores, a new rule factor is generated. If the factor score of the new rule factor is greater than or equal to the preset factor score, the new rule factor is added as a preset rule factor to the rule factor set corresponding to the target project.

[0014] According to another aspect of this application, a device for determining a project approval scheme is also provided, comprising: a rule matching unit, used to perform rule matching in a preset business rule knowledge base based on the project business information of the target project, wherein the preset business rules in the preset business rule knowledge base are used to characterize the triggering conditions corresponding to the approval process under different approval scenarios; a first update unit, used to perform branch activation / pruning operations on the preset approval process template corresponding to the target business rule based on the target business rule that has been successfully matched with the target project, to obtain a reconstructed process topology diagram corresponding to the target project, wherein the reconstructed process topology diagram is used to characterize the P approval steps involved in the target project, where P is a positive integer; and a second update unit, used to determine the reconstructed process topology based on the construction mode attribute of the target project. The node configuration sequence corresponding to each approval step in the diagram is added to the reconstructed process topology diagram to obtain the customized process topology diagram corresponding to the target project. The node configuration sequence includes at least one customized approval node corresponding to the construction mode attribute, and the customized process topology diagram includes at least Q approval nodes, where Q is a positive integer. The handler determination unit is used to determine the handler candidate set corresponding to each approval node in the customized process topology diagram based on the project type attribute of the target project. Each handler candidate set includes at least one approval handler with the operation permission corresponding to the approval node. The approval scheme determination unit determines the target approval scheme corresponding to the target project based on the customized process topology diagram and the handler candidate set corresponding to each approval node.

[0015] According to another aspect of this application, a computer program product is also provided, which stores a computer program, wherein the method for determining the project approval scheme that controls the computer program product to execute any of the above-mentioned methods when the computer program is running is also provided.

[0016] According to another aspect of this application, an electronic device is also provided, wherein the electronic device includes one or more processors and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for determining the project approval scheme of any of the above.

[0017] In this application, firstly, based on the project business information of the target project, rule matching is performed in a preset business rule knowledge base. The preset business rules in the knowledge base represent the triggering conditions corresponding to the approval process under different approval scenarios. Then, based on the target business rules that have been successfully matched with the target project, this application performs branch activation / pruning operations on the preset approval process template corresponding to the target business rules, obtaining a reconstructed process topology diagram for the target project. This reconstructed process topology diagram represents the P approval stages involved in the target project, where P is a positive integer. Finally, based on the construction mode attributes of the target project, this application determines the node configuration corresponding to each approval stage in the reconstructed process topology diagram. The process involves adding the node configuration sequence corresponding to each approval step to the reconstructed process topology diagram to obtain a customized process topology diagram for the target project. The node configuration sequence includes at least one customized approval node corresponding to the construction mode attribute. The customized process topology diagram includes at least Q approval nodes, where Q is a positive integer. Subsequently, based on the project type attribute of the target project, this application determines the candidate set of handlers corresponding to each approval node in the customized process topology diagram. Each candidate set of handlers includes at least one approval handler with the operation permissions corresponding to the approval node. Finally, based on the customized process topology diagram and the candidate set of handlers corresponding to each approval node, this application determines the target approval scheme for the target project.

[0018] As can be seen from the above, this application adopts a dynamic matching and precise binding method based on multi-dimensional project attributes. By using rule matching to drive process topology reconstruction, node configuration sequence to adapt to construction mode, and determining a candidate set of exclusive handlers based on project type, it achieves the goal of generating highly customized approval schemes with clear rights and responsibilities for each complex project. This realizes the technical effect of automatic adaptation of approval process structure to specific business scenarios (such as project initiation entity and construction mode) and precise correspondence between approval authority and project type, thereby solving the technical problems of low efficiency and low accuracy in generating project approval schemes in the prior art. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0020] Figure 1 This is a flowchart of an optional project approval scheme determination method according to an embodiment of this application;

[0021] Figure 2 This is a flowchart of an optional intelligent adaptive approval process management method for projects according to an embodiment of this application;

[0022] Figure 3This is a schematic diagram of an optional project approval scheme determination device according to an embodiment of this application;

[0023] Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] It should also be noted that all information and data (including but not limited to information used for display and analysis) involved in this application are authorized by the user or fully authorized by all parties. For example, if there is an interface between this system and the relevant user or organization, before obtaining the relevant information, it is necessary to send a request to the aforementioned user or organization through the interface, and obtain the relevant information only after receiving consent from the aforementioned user or organization.

[0027] Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of relevant information and data involved in this application all comply with the relevant laws, regulations, and standards of the relevant regions, and necessary confidentiality measures have been taken. This application does not violate public order and good morals. In addition, this application provides a corresponding operation entry point for users to choose to agree to or refuse authorization. If the user chooses to refuse authorization, the corresponding expert decision-making process will be initiated.

[0028] The present invention will now be described in detail with reference to various embodiments.

[0029] Example 1

[0030] According to an embodiment of this application, an embodiment of a method for determining a project approval scheme is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0031] This application provides a project approval scheme determination system (hereinafter referred to as the determination system) for implementing the project approval scheme determination method in this application. Figure 1 This is a flowchart of an optional project approval scheme determination method according to an embodiment of this application, such as... Figure 1 As shown, the method includes the following steps:

[0032] Step S101: Based on the project business information of the target project, perform rule matching in the preset business rule knowledge base. The preset business rules in the preset business rule knowledge base are used to represent the triggering conditions corresponding to the approval process under different approval scenarios.

[0033] Optionally, the target project refers to a business project that requires approval from a designated approver.

[0034] Optionally, the preset business rule knowledge base refers to a pre-built knowledge base used to store preset business rules.

[0035] Optionally, before performing rule matching in the preset business rule knowledge base based on the project business information of the target project, the system determines that the full amount of project business information corresponding to historical projects is mapped into structured rule factors, and logically combines the rule factors through logical operators to obtain composite triggering conditions (i.e., preset business rules in the preset business rule knowledge base); at the same time, the system determines that the system synchronously generates a basic approval process skeleton (i.e., a preset approval process template), and then establishes a dynamic relationship between the preset business rules and the basic approval process skeleton, outputting a reconfigurable initial process model to support subsequent links.

[0036] Optionally, in step S101, the system determines that the "specific project business information instance" and the "multimodal preset business rule knowledge base" corresponding to the target project are the common processing objects. First, the specific project business information instance is structured and parsed to extract key information such as the project initiation subject attribute, project type attribute, and quantitative parameter attribute (such as investment amount and construction scale) from the instance, and transforms it into an instantiated "rule factor set" that is consistent with the rule factor format in the multimodal preset business rule knowledge base. Then, the "rule factor set" corresponding to the target project is used as the processing object. Based on the real-time rule inference engine, the system performs matching in the multimodal preset business rule knowledge base through a graph traversal algorithm (such as depth-first traversal) to obtain the corresponding composite triggering conditions. After that, the system determines that the composite triggering condition ID and associated triggering logic of the target project are successfully matched to obtain the rule matching result (i.e., the target business rule).

[0037] Optionally, the system determines that by matching rules in a pre-defined business rule knowledge base based on the project business information of the target project and using a graph traversal algorithm, the traditional method of manually judging and searching for applicable rules in the existing technology is replaced. This solves the problem of low rule matching efficiency caused by manual rule matching when facing complex projects, thereby improving the query efficiency of target business rules.

[0038] Step S102: Based on the target business rules that are successfully matched with the target project, perform branch activation / pruning operations on the preset approval process template corresponding to the target business rules to obtain the reconstructed process topology diagram corresponding to the target project. The reconstructed process topology diagram is used to represent the P approval steps involved in the target project, where P is a positive integer.

[0039] Optionally, branch activation refers to enabling a specific approval path in the template that is related to the triggering condition of the target business rule; pruning refers to removing approval paths in the template that are unrelated to the triggering condition of the target business rule.

[0040] Optionally, after obtaining the target business rule that successfully matches the target project, the system determines that the "rule matching result" and the "basic approval process skeleton in the reconfigurable initial process model (i.e., the preset approval process template corresponding to the target business rule)" are the processing objects. Based on the composite trigger condition ID in the rule matching result, the system calls the rule-process dynamic binding configuration file in the reconfigurable initial process model to determine the corresponding basic approval process skeleton and the branch path to be triggered. The system performs branch activation or pruning operations on the basic approval process skeleton. If the matching result meets the trigger condition of a certain branch, the branch is activated; otherwise, the branch is pruned, forming a "dynamic reconfigurable process topology (i.e., the reconfigurable process topology diagram corresponding to the target project)".

[0041] Optionally, the process of determining the reconstructed process topology diagram corresponding to the aforementioned target project differs from the fixed branch logic of traditional approval systems. The system determines that the process structure can adapt to changes in specific project attributes through real-time rule reasoning and dynamic topology adjustment, thus solving the technical defects of traditional systems in dealing with multi-dimensional attribute coupling scenarios. Through precise branch activation and pruning operations, the above steps can "tailor-make" the corresponding approval process topology for the target project, eliminating irrelevant approval processes, thereby shortening the potential redundant paths of the approval process and improving the approval efficiency of the target project.

[0042] Step S103: Based on the construction mode attribute of the target project, determine the node configuration sequence corresponding to each approval step in the reconstructed process topology diagram, add the node configuration sequence corresponding to each approval step to the reconstructed process topology diagram, and obtain the customized process topology diagram corresponding to the target project. The node configuration sequence includes at least one customized approval node corresponding to the construction mode attribute, and the customized process topology diagram includes at least Q approval nodes, where Q is a positive integer.

[0043] Optionally, after generating the dynamically reconstructed process topology, the system executes steps S103 and 104 to simultaneously perform a two-layer node-role mapping configuration, thereby achieving a precise association between approval nodes and role permissions. First, the system performs a first-layer mapping to achieve personalized configuration of approval nodes. During this first-layer mapping, the system uses the "dynamically reconstructed process topology" and the "preset construction unit-node configuration mapping library" as processing objects. It extracts the construction mode attribute corresponding to the target project from the dynamically reconstructed process topology. Based on this construction mode attribute, it queries the construction unit-node configuration mapping library for a preset node configuration sequence (in the construction unit-node configuration mapping library, each row represents a construction mode type, each column represents an approval stage, and the intersection of rows and columns represents the node configuration content of the construction unit in the corresponding stage). Then, the queried node configuration sequence is loaded into the corresponding approval stage of the dynamically reconstructed process topology, replacing the basic node framework in the original topology, resulting in the "process topology after personalized approval node configuration (i.e., the customized process topology diagram corresponding to the target project)".

[0044] Optionally, the system determines, through step S103, that it has achieved refined management of approval nodes, that is, it has deepened process management from the "link" level to the "node" level. By driving node configuration through construction mode attributes, the same type of approval link can automatically adapt to the most appropriate approval node under different implementation modes, thereby enhancing the refinement of the approval process. At the same time, the system determines that it does not need to manually determine the customized process topology based on each combination of "business rule + construction mode". The system can automatically combine the correct node sequence based on the construction mode attributes, thereby reducing the complexity of process configuration and manual costs.

[0045] Step S104: Based on the project type attribute of the target project, determine the candidate set of handlers corresponding to each approval node in the customized process topology diagram, wherein each candidate set of handlers includes at least one approval handler with the operation authority corresponding to the approval node.

[0046] Optionally, after obtaining the "process topology after personalized approval node configuration," the system proceeds to a second-level mapping to achieve role vector matching and handler restriction. During this second-level mapping, the "process topology after personalized approval node configuration" and the "preset user role feature library (i.e., preset user role information library)" are used as processing objects. First, each approval node in the personalized approval node configuration process topology is associated with a corresponding project type attribute. Then, the project type attribute is converted into a "project type feature vector" (the vector dimension represents the number of categories of the project type attribute; each dimension takes a value of 0 or 1, where 1 represents that the node is associated with this project type, and 0 represents that it is not associated). Simultaneously, user role features are... Extract each user's role information (such as project type-specific permissions and approval level permissions) from the database, and convert each user's role information into a "user role feature vector" (the vector dimension is the same as the project type feature vector, and the value at the intersection of rows and columns is 0 or 1, where 1 represents that the user has approval permissions for that project type and 0 represents that the user does not). Then, using the "project type feature vector" and the "user role feature vector" as processing objects, calculate the similarity between the two using the cosine similarity algorithm, and filter users whose similarity is higher than the preset similarity threshold (the threshold can be dynamically set according to business needs) as the candidate set of handlers for the corresponding approval node. Record the association relationship between each node and the candidate set to obtain the "node-role binding relationship table".

[0047] Optionally, by executing step S104, it is determined that the system has achieved accurate pre-configuration of approval permissions, which solves the technical problem of low query efficiency of approval personnel caused by the need to query the approval personnel one by one for each approval node in the traditional method. The candidate set of approval personnel determined by the system is the basis for subsequent automatic task assignment, load balancing or manual selection, thereby optimizing the allocation process of approval tasks and avoiding the problem of approval process blockage caused by the mismatch of approval personnel permissions.

[0048] Optionally, the two-layer mapping mechanism constructed by the first and second layers of mapping differs from the static permission binding of traditional systems. This mechanism achieves dual adaptation of node personalization and permission precision by configuring nodes and matching roles in stages, avoiding the problems of permission generalization and node redundancy in existing technologies.

[0049] Step S105: Based on the customized process topology diagram and the candidate set of applicants corresponding to each approval node, determine the target approval scheme corresponding to the target project.

[0050] Optionally, the system determines the target approval scheme for the target project based on a customized process topology diagram and a candidate set of applicants for each approval node. This outputs an end-to-end automated project approval scheme, achieving full-link automation from project information input to the generation of a complete approval scheme. This allows for the rapid establishment of corresponding approval processes when facing complex project approval needs, eliminating the need for lengthy communication, design, and configuration cycles. It shortens the pre-approval preparation time for project approvals and improves the response speed for approving complex projects.

[0051] As can be seen from the above, this application adopts a dynamic matching and precise binding method based on multi-dimensional project attributes. By using rule matching to drive process topology reconstruction, node configuration sequence to adapt to construction mode, and determining a candidate set of exclusive handlers based on project type, it achieves the goal of generating highly customized approval schemes with clear rights and responsibilities for each complex project. This realizes the technical effect of automatic adaptation of approval process structure to specific business scenarios (such as project initiation entity and construction mode) and precise correspondence between approval authority and project type, thereby solving the technical problems of low efficiency and low accuracy in generating project approval schemes in the prior art.

[0052] In one optional embodiment, before performing rule matching in a preset business rule knowledge base based on the project business information of the target project, in order to establish the preset business rule knowledge base, the system first classifies and parses the project business information corresponding to B historical projects based on A project attribute dimensions, obtaining A sets of business information corresponding to A project attributes, where A and B are both positive integers, and A project attributes include at least project initiation entity attributes, project type attributes, construction mode attributes, compliance requirement attributes, and quantitative parameter attributes. Then, the system performs encoding operations on the project business information included in each of the A sets of business information to obtain a set of rule factors corresponding to each set of business information, where each set of rule factors includes at least one preset rule factor. Then, based on the historical approval processes corresponding to B historical projects, the system logically combines the preset rule factors in the set of rule factors corresponding to each set of business information using preset logical operators to obtain an initial set of business rules corresponding to B historical projects. Subsequently, the system performs business rule verification on each initial business rule in the initial set of business rules, and creates the preset business rule knowledge base based on the initial business rules that pass the business rule verification.

[0053] Optionally, the implementation process of the above embodiments revolves around the construction of a multimodal business rule knowledge graph, used to transform the full amount of project business information corresponding to historical projects into a structured rule system. First, the system determines that the "full amount of project business information" corresponding to B historical projects is the processing object, and classifies and sorts it according to five dimensions: "project initiation subject attribute, project type attribute, construction mode attribute, compliance requirement attribute, and quantitative parameter attribute," to obtain specific business information items under each dimension (for example, the project initiation subject attribute dimension includes construction unit level and cross-regional attribute; the quantitative parameter attribute dimension includes investment amount, construction scale, and construction period requirements), thereby obtaining the "classified project business information set" under each dimension. Then, the system determines that the "classified project business information set" is the processing object, and performs structured encoding processing on the business information items under each dimension. For example:

[0054] Optionally, the system determines the following: for discrete attributes (e.g., project type is divided into macro site construction / micro site deployment / emergency site construction), the system adopts the "dimensional identifier-attribute encoding" format (e.g., project type_01 represents macro site construction); for quantitative attributes (e.g., investment amount), the system adopts the "dimensional identifier-numerical range encoding" format (e.g., investment amount_A represents ≥1 million); for Boolean attributes (e.g., whether environmental impact assessment is required), the system adopts the "dimensional identifier-Boolean value encoding" format (e.g., compliance requirement_1 represents environmental impact assessment required). Then, a structured "rule factor set" is obtained for each dimension. Subsequently, each dimension... The system uses a structured "set of rule factors" as the processing object. Based on the business logic of historical project approvals, it combines single structured rule factors into composite triggering conditions (such as "Project Entity_02 ∧ Project Type_01 ∧ Investment Amount_A" representing a scenario where a provincial-level macro-site is built with an investment of ≥1 million) through logical operators (e.g., AND ∧, OR ∨, NOT ∧). At the same time, it performs validity checks on all composite triggering conditions (i.e., business rule checks, used to eliminate logical contradictions or conditions that do not meet actual approval requirements), thereby forming a multimodal "preset business rule knowledge base".

[0055] Optionally, the execution process of the above embodiments differs from the hard-coded rules of traditional approval systems. The system determines that by classifying and combining codes and logic, discrete business attributes are transformed into a rule system that can be semantically associated and dynamically invoked, thus solving the problem of difficult unified management of rules in multi-dimensional attribute coupling scenarios.

[0056] Optionally, the system classifies and analyzes the project business information of historical projects based on the dimensions of A project attributes, ensuring that the preset business rules for subsequent mining have clear business relevance and good interpretability. This step establishes a clear coordinate axis for the correlation analysis between "project business information" and "approval process", making the branch jump experience corresponding to the implicit approval node explicit and structured.

[0057] Optionally, the system determines that by performing encoding operations on the set of business information, a set of rule factors is obtained, thereby achieving the standardization and atomization of information, eliminating the ambiguity of natural text language, and providing unified, discrete rule factors for subsequent logical combinations.

[0058] Optionally, the system determines that it combines rule factors using preset logical operators to obtain an initial set of business rules, thereby automating the extraction and formal expression of expert experience knowledge. By analyzing the correspondence between historical approval processes and the set of rule factors, the system automatically uses logical operators such as "AND," "OR," and "NOT" to combine related rule factors into complete business rules. This replaces the manual writing of rules based on expert experience, achieving the technical effect of automatically mining and generating decision logic from historical data. It improves the construction efficiency and coverage of the preset business rule base and can discover complex association rules that may be overlooked manually.

[0059] Optionally, the system establishes a "quality control checkpoint" by performing business rule verification on the initial business rules, ensuring the reliability, accuracy, and usability of the final generated preset business rule knowledge base, preventing erroneous or invalid rules from flowing into the production system, and ensuring the correctness of subsequent process matching results.

[0060] Optionally, the execution process of the above embodiments has the following effect:

[0061] (1) Improved data quality in the rule base: This method transforms the establishment and updating of the rule base into a “machine learning process” that can automatically iterate and continuously optimize as historical data increases, realizing the dynamic evolution and sustainable maintenance of the rule base, and improving the data quality of the preset business rule data in the rule base.

[0062] (2) Ensure that the rules are highly consistent with business practices: Since the rules are derived directly from the company’s own historical project practices rather than external general templates or theoretical designs, the generated rules can fit the company’s actual operating model, management habits and compliance environment.

[0063] (3) Reduce the cost of system construction and maintenance: Enterprises do not need to manually build the rule base based on expert experience at the beginning of the project, nor do they need to frequently manually adjust the rule base when business changes, thereby reducing the cost of system construction and maintenance.

[0064] In one optional embodiment, before performing rule matching in a preset business rule knowledge base based on the project business information of the target project, in order to establish a preset approval process template, the system first generates a scenario-based approval requirement list based on the approval scenario information of the historical approval processes corresponding to B historical projects. The scenario-based approval requirement list includes at least the necessary approval nodes corresponding to each historical project under different approval scenarios, the node flow order of the necessary approval nodes, and the node authority and responsibility. Then, the system generates an initial approval process template set corresponding to B historical projects based on the scenario-based approval requirement list. Each initial approval process template in the initial approval process template set includes at least a start node, a necessary approval node, a branch node, and an end node. Then, the system performs process template verification on each initial approval process template in the initial approval process template set and uses the initial approval process template that passes the process template verification as the preset approval process template.

[0065] Optionally, during the process of determining the system's multimodal "preset business rule knowledge base," the basic approval process skeleton is generated simultaneously to ensure that the process framework and rule system are compatible. First, taking "typical approval scenarios of historical projects" as the processing object, and combining the approval authority and responsibility boundaries and core link requirements of different scenarios (such as provincial-level projects requiring multi-departmental collaborative review, municipal-level projects requiring simplified procedures, and emergency projects requiring shortened approval time), the "necessary approval nodes," "node flow order," and "node authority and responsibility attribution" under each scenario are analyzed and extracted to obtain a "scenario-based approval requirement list." Next, taking the "scenario-based approval requirement list" as the processing object, a process topology modeler is used to design the process framework, generating a corresponding basic approval process skeleton for each scenario. Each skeleton clearly includes "start node, core approval node, branch node, and end node." At the same time, a "rule trigger interface" is reserved at the branch node (for subsequent binding with composite trigger conditions), and the "handling department, processing time limit, and output material requirements" of each node are marked to obtain a "scenario-based basic approval process skeleton set (i.e., the template set composed of the initial approval process templates)."

[0066] Optionally, after obtaining the "scenario-based basic approval process skeleton set", the system determines that the "scenario-based basic approval process skeleton set" is the processing object, and verifies the node correlation and flow logic of each skeleton in the scenario-based basic approval process skeleton set (to avoid node dead loops and overlapping rights and responsibilities), to ensure that the skeleton can support subsequent dynamic reconstruction. Finally, the "verified basic approval process skeleton set" is determined, and the skeletons in the "verified basic approval process skeleton set" are used as preset approval process templates.

[0067] Optionally, the execution process of the above embodiments differs from the unified static process of traditional systems. The system determines that through scenario-based design and reserved interfaces, it provides differentiated process bases for different types of projects, avoiding framework modifications when adjusting subsequent processes.

[0068] Optionally, the system generates a scenario-based approval requirement list based on historical approval processes, ensuring the business rationality and practical fit of the subsequently generated templates. It also determines the "necessary approval nodes" extracted by the system and their order, avoiding template design that is out of touch with reality or omission of key control points, thus laying a solid data foundation for building a high-quality and highly available process component library.

[0069] Optionally, the system generates an initial approval process template set based on the requirements list, realizing the structuring and componentization of process knowledge. The system transforms the "scenario-based approval requirements list" into a more formalized process model. Each "initial approval process template" not only includes the necessary approval nodes but also explicitly designs start nodes, branch nodes, and end nodes. The start / end nodes define the standard entry and exit points of the process, making the template a complete sub-process unit. Branch nodes represent logical points in the process where selection and parallelism may occur, providing mounting points and operational space for subsequent branch activation / pruning operations. This allows the system to flexibly activate or skip certain branches according to specific rules, thereby efficiently assembling customized processes.

[0070] Optionally, the system can perform process template verification on the initial approval process template, which can improve the quality, compliance and logical consistency of the generated preset approval process template. The system will verify the automatically generated template, for example, by checking whether the process logic is closed-loop, whether the authority and responsibility settings are conflicting, whether it meets the internal control and compliance requirements, and whether there is redundancy or infinite loop, thereby improving the reliability of the "preset approval process templates" that are subsequently entered into the database.

[0071] Optionally, it is determined that the system, by performing the above steps, has the following effect:

[0072] (1) Realize the accumulation and reuse of process assets: The wisdom of approval processes scattered in countless historical projects is automatically extracted, verified and solidified into reusable "preset approval process templates", forming the core process asset library of the enterprise. This not only supports the intelligent operation of the current system, but also provides a data foundation for the standardization and continuous optimization of the enterprise's processes.

[0073] (2) Enhance the system’s flexibility in dealing with complex scenarios: Since the template includes “branch nodes” from the beginning of the design, it provides the system with sufficient dynamic adjustment space. In the face of the diverse needs of “either / or” or “parallel approval” in complex projects, the system can quickly generate a suitable process topology through intelligent operation (activation / pruning) of these pre-set branches, which enhances the system’s flexibility and adaptability in dealing with complex and ever-changing approval scenarios.

[0074] In one optional embodiment, before performing branch activation / pruning operations on the preset approval process template corresponding to the target business rule based on the target business rule that has successfully matched the target project, the system first creates a first mapping table, which stores the mapping relationship between the preset business rule and the preset approval process template. Then, the system creates a second mapping table, which stores the mapping relationship between the rule factors in the preset business rule and the branch nodes in the preset approval process template. Subsequently, the system generates a preset configuration instruction based on the first and second mapping tables, wherein the preset configuration instruction is at least used to determine the preset approval process template corresponding to the target business rule.

[0075] Optionally, after determining that the system has obtained the "multimodal business rule knowledge base" and the "verified basic approval process skeleton set", the system focuses on the dynamic relationship between rule conditions and process branches to form a basic model integrating "rules-process". First, the system takes the "multimodal business rule knowledge base" and the "verified basic approval process skeleton set" as common processing objects and establishes a first-level mapping relationship of "composite triggering conditions-basic approval process skeleton". According to the business scenario corresponding to each composite triggering condition, it matches the basic approval process skeleton with the same scenario (such as the composite condition "project initiation subject_01∧ project type_03∧ schedule requirement_emergency" is matched to the "emergency project basic approval process skeleton") to obtain the "first-level rule-process mapping table" (i.e., the first mapping table).

[0076] Next, the system determines that the branch nodes in the "Level 1 Rule-Process Mapping Table" and the "Verified Basic Approval Process Skeleton Set" are the processing objects, and establishes a Level 2 mapping relationship of "Single Rule Factor-Process Branch Node". For the rule triggering interface reserved in the basic approval process skeleton, the single rule factor required to trigger the branch is identified (such as the rule factor "Compliance Requirement_1" triggering the "Environmental Impact Assessment Review Branch Node", and the rule factor "Compliance Requirement_0" skipping the branch node), thus obtaining the "Level 2 Rule-Process Mapping Table" (i.e. the first mapping table).

[0077] Subsequently, it was determined that the system uses the "first-level rule-process mapping table" and the "second-level rule-process mapping table" as processing objects. It was determined that the system uses a rule-process binding mechanism (using XML configuration files to solidify the mapping relationship, and the configuration files contain the correspondence of "composite trigger condition ID, basic process skeleton ID, branch node ID, and trigger rule factor ID") to convert the mapping relationship into configuration instructions that can be recognized by the system (i.e., preset configuration instructions), thus obtaining the "rule-process dynamic binding configuration file".

[0078] Subsequently, the system was determined to use the "multimodal business rule knowledge base," the "verified basic approval process skeleton set," and the "rule-process dynamic binding configuration file" as processing objects. The three were integrated to ensure that rules can be invoked, processes can be triggered, and associations can be traced. Finally, a "reconfigurable initial process model" was generated. The association mechanism in this model is different from the static binding of traditional systems. The reconfigurable initial process model realizes the dynamic association between rules and processes through two-level mapping and configuration file solidification, supporting subsequent adaptive adjustments based on specific project instances.

[0079] Optionally, by creating a first mapping table, the "operation object" of the preset business rule is quickly located. After the preset business rule is matched in step S101, the system can quickly locate the preset approval process template that needs to be reconstructed based on this table without secondary calculation or search. This improves the efficiency of determining the preset approval process template and ensures the consistency and manageability of the correspondence between the rule and the template.

[0080] Optionally, by creating a second mapping table, it is determined that the atomic conditions (rule factors, such as "investment amount > 100 million" or "involving security") constituting the business rules can be directly mapped to specific branch nodes in the process template, realizing precise guidance for the refactoring operation. This step can support the automatic and accurate execution of the "branch activation / pruning operation" in step S102. By creating a second mapping table, it is determined that the system decomposes a complex process structure adjustment task into a series of on / off instructions for specific branch nodes triggered by rule factors, making dynamic process refactoring programmable, automated, and reliable, avoiding logical errors or omissions that may occur during manual configuration.

[0081] Optionally, by generating preset configuration instructions based on a mapping table, the configuration logic is brought forward to the runtime, realizing the dynamic generation of preset approval process templates. When a specific project triggers a rule, the system does not need to perform complex correlation analysis and instruction assembly at runtime. It only needs to load the "preset configuration instructions" bound to the rule and pass in the rule factor values ​​of the current project to drive the process engine to perform reconstruction, thereby reducing the complexity of real-time calculation and ensuring the high performance and determinism of process generation.

[0082] Optionally, it is determined that the system, by performing the above steps, can achieve the following functions:

[0083] (1) Achieve decoupling and linkage between logic and structure: The preset business rule knowledge base focuses on defining "what judgment to make under what circumstances", the preset approval process template library focuses on providing "operable structural foundation", and the mapping table and configuration instructions act as coordinators to isolate the changes of the two. When modifying rules or adjusting templates, only the mapping relationship needs to be updated, without changing the core algorithm, which improves the maintainability and scalability of the system.

[0084] (2) A dynamic process reconstruction mechanism is provided: the “branch activation / pruning operation” executed by the system in step S102 can be executed based on the “preset configuration instruction” corresponding to the second mapping table, so that the system’s adjustment of complex process structure is accurate to the node level, which improves the accuracy of the node configuration sequence corresponding to the approval node obtained by the system.

[0085] In one optional embodiment, to determine the candidate set of handlers corresponding to each approval node in the customized process topology diagram, the system first binds the project type attribute of the target project to each approval node in the customized process topology diagram, obtaining the project type attribute associated with each approval node. Then, based on the project type attribute associated with each approval node, the system generates a project type vector corresponding to each approval node, where the vector dimension of the project type vector is equal to the total number of attribute categories of the project type attribute. Next, the system extracts features from the user role information of C approval handlers in the preset user role information database, obtaining C user role feature vectors corresponding to the C approval handlers, where C is a positive integer and the vector dimension of the user role feature vector is equal to the total number of attribute categories of the project type attribute. Subsequently, the system detects the similarity between the project type vector corresponding to each approval node and each user role feature vector in the C user role feature vectors. Then, if the similarity is greater than or equal to a preset similarity threshold, the system adds the approval handler corresponding to the similarity to the candidate set of handlers of the approval node corresponding to the similarity, where the preset similarity threshold is dynamically adjusted based on the node level of the corresponding approval node.

[0086] Optionally, the system determines the correlation between the "project type feature vector" and the "user role feature vector" using a cosine similarity algorithm to achieve accurate screening of the candidate set of applicants. This mechanism differs from traditional simple filtering (such as matching only by role name). The system transforms discrete permission attributes into computable feature relationships through vectorized modeling, improving the adaptability and flexibility of role matching. Its specific mathematical formula implementation and parameter description are as follows:

[0087] I. Construction of Feature Vectors (Providing Input Basis for Similarity Calculation):

[0088] Before calculating cosine similarity, it is necessary to construct "project type feature vector" and "user role feature vector". The dimensions of the two types of vectors are completely consistent and are equal to the "total number of complex project types (denoted as n)" defined by the enterprise, to ensure that the vectors can be used for mathematical operations.

[0089] 1. Project Type Feature Vector Construction:

[0090] Optionally, ,in, ( The value is 0 or 1, used to indicate whether the current approval node is associated with the first... Project type.

[0091] Optionally, The data source for (vector dimension) is the enterprise's "Complex Project Type Classification Standard". For example, if the enterprise classifies complex projects into four categories: "Macro base station tower construction (1), micro base station deployment (2), emergency communication base station construction (3), and tower renovation and upgrading (4)," then... .

[0092] Optionally, The data source for (vector elements) is "the process topology after personalized approval node configuration", which determines the associated project type of the current approval node (such as "headquarters business supervisor approval") extracted from the topology by the system. If the node is only associated with "macro station tower construction (1)", then , ,Right now If the node is associated with "Macro-station tower construction (1) + emergency communication station construction (3)", then .

[0093] 2. User Role Feature Vector Construction:

[0094] Optionally, ,in, ( The value is 0 or 1, used to indicate whether the current user possesses the [missing information]. Approval authority for project types.

[0095] Optionally, The data source for (vector elements) is the "User Role Feature Library," which pre-stores the role and permission configuration for each user—for example, user A's role is "Large Project (Macro Station) Headquarters Reviewer," and they only have the approval authority for "Macro Station Tower Construction (1)." , ,Right now User B's role is "Comprehensive Project Headquarters Reviewer," possessing approval authority for "Macro Station Tower Construction (1) + Emergency Communication Station Construction (3)." .

[0096] II. Core Calculation: Cosine Similarity Algorithm (Quantifying the Degree of Vector Association):

[0097] Optionally, the system determines the calculation using a cosine similarity algorithm. and similarity The larger this value, the higher the correlation between the user role and the project type of the approval node. The formula is as follows:

[0098] ;

[0099] In the above formula, the numerator (vector dot product) is: This represents the amount of overlap between "project types associated with approval nodes" and "project types that users have permission to access". Calculation source: and Sum of corresponding elements, for example , When the dot product is 1×1 + 0×0 + 1×0 + 0×0 = 1.

[0100] Optionally, the denominator (product of vector magnitudes): The geometrically normalized values ​​representing the total number of project types associated with the approval node and the total number of user permission project types avoid calculation deviations caused by vector dimensions or the number of elements. Calculation source: Calculate the magnitudes (square roots of the sum of squares of the elements) of the two vectors separately and then multiply them, for example... Modulus length = , Modulus = 1, denominator = .

[0101] Optionally, the similarity result Sim is used to characterize the ratio of the numerator to the denominator, with a value range of [0,1], where 0 indicates no correlation and 1 indicates a perfect match, as in the example above. .

[0102] III. Preset Similarity Threshold Setting:

[0103] Optionally, a preset similarity threshold is set. The threshold value used to determine whether a user should be included in the candidate set of applicants ranges from (0,1) and needs to be dynamically adjusted (not a fixed value) according to the importance of the approval node. For example:

[0104] High-priority approval nodes (such as "approval by the head office supervisor in charge"): require strict matching. (Only users with a similarity of ≥0.9 are accepted);

[0105] Approval nodes of medium importance (e.g., "headquarters business supervisor approval"): balancing accuracy and efficiency. ;

[0106] Approval stages with low importance (such as "preliminary review by municipal engineering departments"): Appropriately relax restrictions. .

[0107] Optionally, the screening and binding process for the candidate set of applicants is as follows:

[0108] 1. Filtering Logic: Calculate the characteristics of each user in the "User Role Feature Library" one by one. With the current node The similarity Sim will Users are selected to form a "candidate set of applicants for the current approval node".

[0109] Example: If the current node is "Headquarters Business Supervisor Approval" ( ), User C's (Similarity = 1 ≥ 0.8), User D's If the similarity is 0.5 < 0.8, then only user C is included in the candidate set.

[0110] 2. Output Results: Includes "Approval Node ID, Node Name, Candidate Set of Applicants (User ID + Username), Similarity Value, and Threshold". "The associated storage yields the "Node-Role Binding Relationship Table", which will be directly used for assigning personnel during subsequent process execution.

[0111] Optionally, the system is determined to achieve the following functions by executing the above embodiments:

[0112] (1) Vector dimension is deeply bound to business scenario: the vector dimension n directly corresponds to the actual number of project types in the enterprise, rather than a general dimension, ensuring that the vector can accurately represent business attributes and solving the problem of "dimension and business disconnect" in traditional vector modeling.

[0113] (2) Dynamic threshold adapts to node importance: The threshold T is adjusted according to the importance of the node, rather than being a fixed value. This ensures the security of permissions for high-importance nodes and improves the approval efficiency of low-importance nodes, thus solving the limitation of the traditional fixed threshold "one-size-fits-all".

[0114] (3) Quantitative and mathematical matching of permissions: The discrete "project type-permission" relationship is transformed into the calculation of vector dot product and modulus, realizing the leap from "qualitative judgment" to "quantitative calculation" in permission matching, and improving the objectivity and interpretability of the matching results.

[0115] In one optional embodiment, after determining the candidate set of handlers corresponding to each approval node in the customized process topology diagram based on the project type attribute of the target project, the system determines that the user role information of the j-th approval handler in the candidate set corresponding to the i-th approval node in the customized process topology diagram is bound to the i-th approval node in the customized process topology diagram; then, the system determines that the node attributes of the i-th approval node are validated, wherein the node attributes include at least the node name, approval processing time limit, and user role information; then, if the attribute validation fails, the system determines that the user role information of the (j+1)-th approval handler in the candidate set is bound to the i-th approval node in the customized process topology diagram.

[0116] Optionally, after the system generates the dynamically reconstructed process topology and node-role binding relationship table, it needs to perform integration verification on the table to improve the completeness and usability of the output results. First, the system uses the "dynamically reconstructed process topology" and the "node-role binding relationship table" as processing objects, embedding the applicant candidate set information from the node-role binding relationship table into the corresponding approval node attributes of the dynamically reconstructed process topology, so that each node contains complete information such as node name, processing time limit, and applicant candidate set, forming a "process topology with integrated permission information"; then, using the "process topology with integrated permission information" as the processing object, logical verification and... Conflict detection—Checks whether there are nodes in the topology without a candidate set of handlers, and whether the node flow order is consistent with the role permission level (e.g., a lower-level role node cannot be located after a higher-level role node). If a conflict exists, it returns to the node-role binding stage for readjustment until the conflict is eliminated, resulting in a "verified integrated permission process topology". Finally, the "verified integrated permission process topology" is used as the processing object, and it is converted into a standardized format (containing node permission information). At the same time, the detailed configuration logic in the node-role binding relationship table is organized to form a "dynamically reconstructed process topology and node-role precise binding permission configuration scheme".

[0117] Optionally, the integration and verification process of the "dynamic reconstruction of process topology and node-role precise binding permission configuration scheme" in the above embodiments ensures the consistency between the approval process and the permissions of the approver, avoiding permission loss or process lag issues in subsequent operation. At the same time, the standardized format facilitates subsequent calling and parsing by the approval system.

[0118] Optionally, it is determined that the system, by performing the above steps, can achieve the following functions:

[0119] (1) Improve the “executability” and “release quality” of the generated approval scheme: By verifying attributes, the node configuration of each node is complete and compliant, which reduces the probability of abnormal interruption of the process due to configuration errors during operation and improves the delivery quality of the final target approval scheme.

[0120] (2) Enhance the system’s adaptability to dynamic real-world environments: Since the status and permissions of approval personnel are dynamic, this embodiment enables the system to perceive and respond to such changes and make automatic adjustments (switch binding to the next candidate), so that the dynamically generated process can not only adapt to project differences, but also adapt to the real-time status of organizational resources.

[0121] (3) Forming a complete decision-making closed loop of “intelligent matching-binding verification-automatic fault tolerance”: This step, together with the aforementioned intelligent matching algorithm (generating candidate set), constitutes a more robust personnel decision-making chain. The intelligent matching mechanism is responsible for recommending candidate approvers. In this embodiment, the authority verification of the recommended approvers is further performed, which improves the accuracy of the approvers assigned to the approval nodes.

[0122] In one optional embodiment, after determining the target approval scheme for the target project based on the customized process topology diagram and the candidate set of handlers corresponding to each approval node, the system first determines F monitoring dimensions corresponding to the target approval scheme based on the customized process topology diagram and the candidate set of handlers corresponding to each approval node. Here, F is a positive integer, and the F monitoring dimensions include at least node identifiers, expected handler ranges, expected node flow directions, and expected branch triggering conditions. Then, the system monitors the approval execution process of the target approval scheme based on the F monitoring dimensions to obtain a target audit log in a preset format. The target audit log is used to characterize at least the node attributes corresponding to each approval node and the node execution information under the F monitoring dimensions. Finally, the system analyzes the target audit log and the target approval scheme... The approval data generated during the approval execution process is correlated to obtain G approval cases. The approval data includes at least the approval form and rejection opinions, where G is a positive integer. Then, the system determines the case score for each of the G approval cases based on a preset scoring formula. The case score is used to characterize at least the closed state of the approval execution process corresponding to the approval case, the approval compliance, the time taken, the completeness of the approval data, the node dependency, and the business relevance. Then, the system generates new rule factors based on the project business information of the approval cases whose case scores are greater than the preset case scores. Then, if the factor score of the new rule factor is greater than or equal to the preset factor score, the system adds the new rule factor as a preset rule factor to the rule factor set corresponding to the target project.

[0123] Optionally, the execution process of the above embodiments first revolves around the synchronous monitoring of process status, aiming to establish a real-time capture mechanism for key information of the entire process based on "customized reconstruction of process topology" and "permission configuration scheme for precise binding of nodes and roles". First, the "customized reconstructed process topology" and the "node-role precise binding permission configuration scheme" are used as common processing objects. The names, flow order, and associated branches of all approval nodes are extracted from the customized reconstructed process topology. The candidate set of handlers and permission levels for each node are extracted from the node-role precise binding permission configuration scheme. These are integrated to form a "process monitoring dimension definition table" (each row in the table corresponds to an approval node, and each column corresponds to a monitoring dimension, including node identifier, expected handler range, expected flow direction, and branch triggering conditions). Next, the "process monitoring dimension definition table" and the "running approval process" are used as processing objects. The running approval process is scanned in real time through the process status synchronization monitoring module. Whenever the process enters an approval node or a branch flow occurs, the actual handler of that node (whether they are in the candidate set), the node execution status (pending approval / approved / rejected), and the execution timestamp are captured. Simultaneously, the triggering basis for the branch flow (such as the specific rule factor for activating a branch) and permission change records (such as the operation and reason for temporarily adjusting the handler) are captured. The captured information is stored in association according to the format "process instance ID-node identifier-monitoring dimension" to obtain the "real-time process status dataset". This process differs from traditional process monitoring, which only focuses on the execution results of nodes. By defining monitoring dimensions through association topology and permission configuration, it achieves multi-dimensional synchronous capture of "execution status + permission compliance + triggering logic", providing more comprehensive raw data for subsequent auditing.

[0124] Optionally, the system generates traceable audit logs based on the real-time process status dataset to ensure the transparency and auditability of the approval logic. First, the system uses the "real-time process status dataset" as the processing object, structures and organizes the data, groups it by process instance ID, sorts it by timestamp within each group, and supplements each data entry with "monitoring dimension comparison results" (such as whether the actual handler meets the candidate set range, whether the actual flow direction is consistent with the dynamically reconstructed process topology, and whether the branch trigger basis matches the rule matching results), forming a "structured process status record". Then, the "structured process status record" is used as the processing object, and the audit log is processed according to the preset audit log format (including basic information of process instances, node execution details, and permission compliance). The system integrates four main modules: verification results, branch trigger logic records, etc. Specifically, "Node Execution Details" needs to be associated with the monitoring dimension definition and actual captured data of the corresponding node; "Permission Compliance Verification Results" needs to clearly mark abnormal items (such as the actual handler exceeding the candidate set); and "Branch Trigger Logic Records" needs to reference the matching composite trigger condition ID, ultimately generating a "Traceable Audit Log." Then, the "Traceable Audit Log" is used as the processing object for integrity verification—checking for records with missing timestamps or missing trigger evidence. If any are found, the system returns to the process status synchronization monitoring stage to supplement the capture until the log covers all key nodes and decision logic throughout the entire process cycle, resulting in a "Traceable Audit Log with Passed Integrity Verification." This technology, through structured organization and integrity verification, ensures that the audit log not only contains the process trajectory but also records of decision logic such as "why it flows this way" and "whether the permissions are compliant," solving the problem of traditional audit logs' difficulty in tracing approval logic.

[0125] Optionally, after generating the "Traceable Audit Log with Passed Integrity Verification," the system determines that the "Traceable Audit Log with Passed Integrity Verification" and "All Approval Data Generated by the Running Approval Process" (including approval forms, attachments, and rejection opinions) are the processing objects. These are then integrated by "Process Instance ID," with the traceable audit log used as an index to associate the corresponding original data such as approval forms and attachments, forming a "Single Process Complete Approval Data Package." Next, using the "Single Process Complete Approval Data Package" as the processing object, batch archiving is performed according to a preset archiving strategy (e.g., categorized storage by project type and approval completion time). Simultaneously, a unique archiving identifier (including process instance ID and archiving timestamp) is generated for each data package to facilitate subsequent retrieval, resulting in a "Complete Approval Archived Data Package Set." Subsequently, the "Complete Approval Archived Data Package Set" is used as the processing object. The system employs a reverse feedback process: first, it filters the approval cases in the data package for validity (screening cases that have completed the closed loop and have no missing data). Then, it extracts new business rule features (such as new compliance requirements for a certain type of project that can be converted into new rule factors) and process optimization suggestions (such as adjusting the permission configuration or flow logic of a node if it is frequently rejected). The new rule factors are added to the "multimodal business rule knowledge base," and the process optimization suggestions are converted into adjustment plans for the "basic approval process skeleton" (such as optimizing the node order of a certain branch). This results in the "optimized multimodal business rule knowledge base" and the "optimized basic approval process skeleton." This reverse feedback process enables the initial model to continuously iterate based on actual approval cases, which is different from the limitation of the traditional system where the "model is fixed and unchanging." It ensures that the entire intelligent approval system can be continuously optimized as business needs change.

[0126] Optionally, the implementation process of the "reverse feedback step" in the above embodiments includes:

[0127] I. The case scoring formula for the approval cases is determined through a multi-dimensional dynamic weighted screening model. This model includes a Bayesian network, a kernel density estimation module, and a matrix correction module. The model is illustrated in the following formula:

[0128] ;

[0129] In the above formula:

[0130] : Dynamic weights inferred by Bayesian networks (not preset, calculated based on dimensional dependencies);

[0131] Base scores across 6 core dimensions;

[0132] Exponential function (ensuring the correction coefficient is non-negative, and the higher the rationality of node dependency, the larger the coefficient).

[0133] Matrix correction coefficients (range of values) [0.02, 0.08] Source: Business department's definition of node dependency importance); A: Process node adjacency matrix ( n is the number of nodes. This indicates that node u is the predecessor of node v; otherwise, the value is 0.

[0134] Node time difference matrix ( , , For nodes (Completion timestamp)

[0135] Matrix trace (sum of the main diagonal elements of a matrix to quantify the overall rationality of node dependencies).

[0136] Distribution bias penalty coefficient (value) [0.1, 0.3] (Source: Data Quality Requirements) KL divergence (quantifies the deviation between the current distribution of case data and the distribution of historical valid cases).

[0137] In existing technologies, traditional case selection models have three major drawbacks:

[0138] ① Preset weights make it impossible to adapt to the dependencies between dimensions (such as the strong correlation between "compliance" and "permission verification");

[0139] ② Node dependency is quantified using only a single indicator, ignoring the overall rationality of the topology;

[0140] ③ Data distribution bias was not considered (e.g., if the data distribution of a case differs too much from that of historical valid cases, it may be invalid even if the single-dimensional score is high).

[0141] To address the aforementioned technical shortcomings, this model achieves breakthroughs in three aspects:

[0142] ① Use Bayesian networks (BN) to infer weights and dynamically capture the probabilistic dependencies between dimensions (e.g., when the "permission compliance" score is high, the weight of the "compliance" dimension automatically decreases).

[0143] ② Use the quadratic form of the node adjacency matrix and time difference matrix to quantify the rationality of node dependencies at the process topology level (rather than single node pairs).

[0144] ③ Use KL divergence to penalize cases with abnormal data distribution, prevent "pseudo-effective" cases from entering the feedback process, and ensure the reliability of the screening results.

[0145] Optionally, the formulas and parameters of each component in the model are explained in the following context:

[0146] Optionally, the Bayesian network structure is "dimensional node → valid case node", where the weights are the posterior probability contributions of each dimension node to the "valid case", and the Bayesian network dynamic weights... The calculation formula is as follows:

[0147] ;

[0148] Alternatively, in the above formula:

[0149] : Events where the case is a "valid case" ( (Invalid)

[0150] Given the first Dimensional Scores At that time, the case represents the valid posterior probability (source: historical valid case dataset, fitted by maximum likelihood estimation, such as...). When (compliance) ≥ 0.9, );

[0151] : No. Marginal probabilities of dimensional scores (Source: Dimensional score distribution of all historical cases, such as...) );

[0152] Denominator: Normalization term (ensuring the weights sum to 1).

[0153] Example scenario: In China Tower's "Provincial Macro Station Self-Operated Project," the "Permission Compliance (…)" section is mentioned. ")" and "Branch Trigger Compliance" If they are strongly correlated, Bayesian networks will automatically reduce the redundant weights of both (e.g., ...). , This avoids double counting.

[0154] Optionally, the base scores for the six core dimensions The calculation is as follows:

[0155] ① Closed-loop state score The formula for calculating (binomial distribution + confidence correction) is as follows:

[0156] ;

[0157] Alternatively, in the above formula:

[0158] Confidence correction factor (value 0.05, source: archiving delay tolerance, e.g., no significant deduction for archiving delay of 1 day);

[0159] : Cumulative distribution function of standard normal distribution;

[0160] : The time difference between the end of the process and the archiving (Source: the difference between the process end timestamp and the archiving timestamp).

[0161] Historical archiving delay average (e.g., 2 hours). : Standard deviation of archiving delay (e.g., 1 hour, source: historical archived dataset);

[0162] Example scenario: Archive files are created 3 hours after the completion of a macro website project workflow. ,but .

[0163] ② Approval compliance The calculation formula for (multi-dimensional weighting + confusion matrix correction) is as follows:

[0164] ;

[0165] Alternatively, in the above formula:

[0166] (Node timed out) (Unauthorized access) (Branch accidentally triggered) (Approval comments are missing);

[0167] : No. The number of true positives in compliance-like events (e.g., nodes that did not time out and were deemed compliant);

[0168] True negative numbers (e.g., nodes time out and are deemed non-compliant);

[0169] False positives (e.g., nodes time out but are judged to be compliant);

[0170] False negatives (e.g., a node is deemed non-compliant even though it did not time out);

[0171] (Compliance risk weights, source: China Tower Compliance Management Regulations);

[0172] Scenario Example: A Case Study (Unauthorized access) The compliance rate for this dimension is... .

[0173] ③ Reasonable time consumption The formula for calculating (kernel density estimation + quantile correction) is shown below:

[0174] ;

[0175] Alternatively, in the above formula:

[0176] Error function (maps the input to) The final score is mapped to [0,1].

[0177] Time taken for the current case The kernel density estimate (source: fitted using historical time data of similar projects (e.g., provincial macro-station projects from 2021-2023) through a Gaussian kernel function) , (Bandwidth, determined through cross-validation).

[0178] : The 10th percentile of the historical time kernel density function (the critical value for excessively low time consumption).

[0179] 90th percentile (the threshold for excessive time consumption);

[0180] Scenario Example: A Macro Substation Project in an Eastern Province sky, , , ,but .

[0181] ④ Data integrity The calculation formula for (multi-level weights + attachment hash verification) is as follows:

[0182] ;

[0183] Alternatively, in the above formula:

[0184] (Core fields, such as process ID and node execution record) (Important fields, such as approval comments) (Ordinary fields, such as operation logs);

[0185] (Field importance weight);

[0186] : No. Missing number of class fields : No. Total number of categories (Source: China Tower Approval Data Specification, such as...) );

[0187] (Approval Form) (Compliance Certificate) (Construction Plan) (Acceptance Report);

[0188] (Penalty for missing attachments: 0.25 points will be deducted for each missing attachment).

[0189] : The hash value of the attachment in the current case. Standard attachment hash value (Source: China Tower Attachment Management System, ensuring attachments have not been tampered with);

[0190] : Indicator function (1 if the hash value does not match, 0 otherwise).

[0191] ⑤ Reasonableness of node dependencies The formula for calculating (matrix trace + time difference check) is as follows:

[0192] ;

[0193] Alternatively, in the above formula:

[0194] The node dependency matrix trace for the current case (quantizes the sum of time differences between predecessor and successor nodes; the larger the value, the more reasonable the dependency).

[0195] The maximum matrix trace of historical valid cases (normalized to make (Source: Statistics of valid cases from 2021 to 2023)

[0196] Scenario Example: A Case Study If the trace value is 120 and the historical maximum value is 150, then... .

[0197] ⑥ Business relevance The formula for calculating (keyword matching + semantic similarity) is as follows:

[0198] ;

[0199] Alternatively, in the above formula:

[0200] (Keyword matching weight);

[0201] The number of matches between case approval opinions and business keywords (such as "macro-site construction" and "environmental impact assessment compliance");

[0202] Total number of business keywords (Source: China Tower business terminology database, 50 in total);

[0203] : Word vectors of case approval opinions (Source: Text encoding of approval opinions using BERT model, dimension 768);

[0204] Word vectors of business standard text (Source: China Tower Approval Specification Text Encoding);

[0205] Cosine similarity (quantifying semantic relevance).

[0206] Optionally, a data distribution bias penalty term. The calculation formula is as follows:

[0207] ;

[0208] Alternatively, in the above formula:

[0209] Current Case No. The probability distribution of dimensional scores (fitted using kernel density estimation);

[0210] Historical valid cases Probability distribution of dimensional scores (Source: Fitting of valid case dataset from 2021-2023).

[0211] The larger the KL divergence value, the greater the deviation between the current case and the distribution of historical valid cases, and the heavier the penalty (e.g., KL=0.2). If so, the penalty is 0.04.

[0212] Optionally, the application logic of the system for case selection is described below:

[0213] Preset (Source: China Tower's Feedback Data Quality Standards), only retained) The cases were selected as a "set of valid approval cases" and entered the new rule extraction stage.

[0214] II. The multi-criteria hierarchical validation model for the new rule factor includes a multi-task learning module, a graph theory module, and a temporal decay module:

[0215] Optionally, the formula for the total validation score corresponding to the multi-criteria hierarchical validation model adopts a three-layer collaborative architecture, as shown below:

[0216] ;

[0217] Alternatively, in the above formula:

[0218] (5-fold cross-validation ensures reliable generalization ability assessment);

[0219] (3 tasks: branch trigger prediction, permission verification prediction, node execution result prediction);

[0220] Average accuracy (evaluates single-task prediction performance, with a value of...) [0,1]

[0221] G: Rule conflict graph (nodes represent rules, edges represent conflict relationships); The number of edges in a conflict graph. The number of nodes in the conflict graph;

[0222] (Time decay coefficient, source: rule timeliness requirements, such as gain decay of approximately 40% for every 100 days a rule expires);

[0223] Time difference between the new rules and the latest historical cases (unit: days).

[0224] In existing technologies, traditional new rule verification techniques have three major limitations:

[0225] ① Only evaluate a single task (such as branch triggering), ignoring the effectiveness of rules across multiple business processes;

[0226] ② The conflict of rules is judged only by "yes / no", without quantifying the degree of conflict;

[0227] ③ The timeliness of the rules was not considered (e.g., old rules may become invalid due to business changes).

[0228] The multi-criteria hierarchical verification model addresses the technical shortcomings of traditional new rule verification techniques by achieving breakthroughs in three aspects:

[0229] ① Use multi-task learning to simultaneously evaluate the prediction accuracy of rules in the three core stages of "branch triggering, permission verification, and node execution" to ensure that rules are adapted to the entire approval process;

[0230] ② Use graph theory to transform rule conflicts into "edge density of the conflict graph" to quantify the degree of conflict (the lower the edge density, the stronger the cooperation).

[0231] ③ Use a time-series decay function to correct the information gain of rules, prevent outdated rules from entering the knowledge base, and ensure the timeliness of the rule base.

[0232] Optionally, the formulas and parameters of each component in the multi-criteria hierarchical verification model are explained in the following scenarios:

[0233] (1) The formula for calculating the average accuracy of multi-task learning is as follows:

[0234] ;

[0235] Optionally, the formulas and parameters of each component in the above formula are explained in the following context:

[0236] ①The formula for calculating the average accuracy of a single task is as follows:

[0237] ;

[0238] Alternatively, in the above formula:

[0239] : Number of validation set cases (e.g., 100 cases per 50% discount validation set);

[0240] : Positive examples (e.g., in a "branch-triggered" task, a positive example is a case that "requires headquarters approval");

[0241] : No. Actual labels for each case (Source: Real business results of effective cases);

[0242] : Rule for the first Predicted labels for each case;

[0243] :forward The accuracy of each prediction result (e.g., if 18 out of the first 20 predictions are correct, the accuracy is 0.9).

[0244] Example scenario: In a "branch-triggered" task, , ,but .

[0245] ② The multi-task learning model architecture adopts a "shared encoder + task-specific decoder" architecture:

[0246] Optionally, a shared encoder is used: the input is a new rule factor (such as "cross-regional + investment ≥ 2 million") and case features (such as project type and construction unit), and the output is a 256-dimensional feature vector;

[0247] Optionally, a task-specific decoder: three decoders output the predicted probabilities of "branch triggering", "permission verification", and "node execution" respectively;

[0248] Optionally, the multi-task loss function corresponding to the multi-task learning model is as follows:

[0249] ;

[0250] In the above formula, (Task weight) Cross-entropy loss;

[0251] Data source: Collection of valid approval cases (e.g., 1200 eligible cases from 2021 to 2023).

[0252] (2) The formula for calculating the quantization term in rule-conflict graph theory is as follows:

[0253] ;

[0254] Alternatively, in the above formula:

[0255] ;

[0256] in, Two rules in the conflict diagram ( For the new rules, (Rules in the existing rule base).

[0257] Rule conflict degree (calculation formula: , For rules The feature vectors are generated by rule-based conditional encoding.

[0258] (Conflict threshold, source: China Tower rule base management specifications, a conflict level > 0.3 is considered a conflict).

[0259] (Number of nodes in the conflict graph, including 1 new rule + 49 existing core rules, source: Multimodal Business Rule Knowledge Base);

[0260] Example scenario: New rules With existing rules The conflict degree is 0.25 (<0.3), then If the degree of conflict is 0.4, then... This constitutes a conflict edge.

[0261] (3) The formula for calculating the time-decayed information gain is as follows:

[0262] ;

[0263] Alternatively, in the above formula:

[0264] Parameter meanings and sources:

[0265] The current time extracted by the new rule (e.g., May 10, 2024);

[0266] The latest case date corresponding to the new rule (e.g., March 1, 2024);

[0267] Days (time difference);

[0268] Optionally, the data source for the time-series decay coefficient is the tower business update frequency (e.g., the approval specifications are updated once per quarter, so the timeliness of the rules decays quarterly).

[0269] Optionally, the application logic is verified as follows:

[0270] Preset (Source: China Tower Rule Base Iteration Standard), only retained The new rules have been added to the "Multimodal Business Rules Knowledge Base"; and based on effective cases... (Rationality of node dependencies) and Generate adjustment suggestions for the "basic approval process skeleton" (such as optimizing the node adjacency matrix). Added high-frequency triggering branches).

[0271] III. The higher-order closed-loop logic of reverse feedback is introduced as follows:

[0272] 1. Input: "Complete Single-Process Approval Data Package Collection" (e.g., China Tower Provincial Macro Station Project Data Package from January to April 2024);

[0273] 2. Valid case selection: Calculate for each data packet ,reserve The "collection of effective approval cases";

[0274] 3. New rule extraction: Extract new rule factors from valid cases (such as "cross-regional + investment ≥ 2 million + self-operated + new regulations in 2024 → requires headquarters compliance review").

[0275] 4. Multi-criteria validation: Calculate for each new rule ,reserve The rules;

[0276] 5. Model Optimization: Newly validated rules will be added to the "Multimodal Business Rule Knowledge Base," and a node dependency matrix based on valid cases will be implemented. Adjust the order of nodes in the "basic approval process framework" (e.g., move the "headquarters compliance review" node before "department leader approval").

[0277] In one alternative embodiment, Figure 2 This is a flowchart of an optional intelligent adaptive approval process management method for projects according to an embodiment of this application, such as... Figure 2 As shown, the method includes:

[0278] Step 1, with the core innovation of multimodal rule knowledge graph and dynamic binding of process topology, maps all business information of the project into structured rule factors and combines them into composite triggering conditions through logical operations. Simultaneously, it generates the basic approval process skeletons of provincial high-speed rail / municipal subway, etc., and then establishes the dynamic association between rule conditions and process branches, outputting a reconfigurable initial process model to support subsequent links.

[0279] Step 2: Based on the reconfigurable initial process model output in the first stage, and taking real-time rule reasoning and node-role two-layer mapping as the technical innovations, specific project business information instances are substituted into the aforementioned rule knowledge base to complete condition matching, driving the initial process skeleton to realize branch activation / pruning topology reconstruction. Then, based on the construction unit attributes, personalized node sequences are loaded, and the exclusive role handler is limited by the role vector matching algorithm combined with the project type. The dynamic reconfigured process topology and the node-role precise binding permission configuration scheme adapted to the current project scenario are output.

[0280] Step 3: Using the dynamic reconstruction process topology and permission configuration scheme of the second phase as the monitoring benchmark, and with the innovative support of full-link status synchronous monitoring and closed-loop archiving, the branch trigger basis, node execution status, permission changes and operation trajectory of the running approval process are captured in real time to form a traceable audit log. After the process reaches the end node, the full process data is automatically integrated to complete the archiving, and the full-link audit log and complete approval archive data package are output. Moreover, the archived data can be fed back to the rule knowledge base and process skeleton optimization of the first phase.

[0281] In summary, the system can achieve the following functions by implementing the intelligent adaptive approval process management method for projects:

[0282] (1) Realize the dynamic adaptation of the approval process structure to multi-dimensional business attribute coupling scenarios and solve the defects of fixed process templates: This invention constructs a multimodal rule knowledge graph in step 1 (converting all business information such as "project initiation subject + construction mode + compliance requirements + investment scale" into structured rule factors and composite triggering conditions), and combines it with real-time rule reasoning to drive process topology reconstruction in step 2 (activating / pruning branches according to specific project business information instances), so that the approval process can adaptively adjust to multi-dimensional attribute combination scenarios - without the need for manual temporary addition of approval links for cross-regional co-construction projects, and can also avoid the extension of cycle due to redundant processes in emergency small projects, improve the flexibility and efficiency of complex project approval, and make up for the shortcomings of existing solutions where process templates are preset and fixed and cannot respond to multi-attribute coupling scenarios.

[0283] (2) Improve the accuracy of matching between approval nodes and role permissions and solve the problem of permission generalization: Step 2 of this invention adopts a node-role two-layer mapping model. The first layer dynamically loads personalized node sequences based on the attributes of the construction unit (to ensure that the approval process matches the needs of the construction entity). The second layer limits the candidate set of node handlers to users with exclusive roles for the corresponding project type through the role vector matching algorithm (such as only "large-scale macro-site approval post" can participate in the approval of core nodes of macro-site projects). This completely changes the existing solution's static binding mode of nodes and roles, effectively avoids the permission generalization problem of "roles with micro-site approval permissions participating in macro-site approval", ensures that approval permissions are highly compatible with business scenarios, and improves the compliance and accuracy of complex project approvals.

[0284] (3) Establish an approval data reverse optimization mechanism to enable the system to continuously adapt to business needs: This application feeds back the full-process approval data (including traceable audit logs and complete archived data packages) to step 1 through step 3. This is used to optimize the multimodal business rule knowledge base (supplementing rule factors for new business scenarios) and the basic approval process skeleton (adjusting redundant / missing approval nodes), forming an intelligent closed loop of "rule modeling - process reconstruction - monitoring and archiving - iterative optimization". This mechanism solves the defect of the existing solution lacking a historical data feedback channel, enabling the system to be continuously iterated and updated based on actual approval cases, avoiding the disconnect from business needs after long-term use, and ensuring that the approval management capability of complex projects continues to improve with business development.

[0285] Example 2

[0286] This application embodiment can also provide a project approval scheme determination device. It should be noted that the project approval scheme determination device of this application embodiment can be used to execute the project approval scheme determination method provided in this application embodiment. The following is a description of the project approval scheme determination device provided in this application embodiment.

[0287] According to an embodiment of this application, an apparatus for implementing the above-described method for determining a project approval scheme is also provided. Figure 3 This is a schematic diagram of an optional project approval scheme determination device according to an embodiment of this application, such as... Figure 3 As shown, the device includes: a rule matching unit 301, a first update unit 302, a second update unit 303, a handler determination unit 304, and an approval scheme determination unit 305.

[0288] Optionally, the rule matching unit 301 is used to perform rule matching in a preset business rule knowledge base based on the project business information of the target project, wherein the preset business rules in the preset business rule knowledge base are used to represent the triggering conditions corresponding to the approval process under different approval scenarios; the first update unit 302 is used to perform branch activation / pruning operations on the preset approval process template corresponding to the target business rule based on the target business rule that has been successfully matched to the target project, to obtain the reconstructed process topology diagram corresponding to the target project, wherein the reconstructed process topology diagram is used to represent the P approval steps involved in the target project, where P is a positive integer; the second update unit 303 is used to determine the corresponding approval step in the reconstructed process topology diagram based on the construction mode attribute of the target project. The node configuration sequence is added to the reconstructed process topology diagram for each approval step to obtain a customized process topology diagram for the target project. The node configuration sequence includes at least one customized approval node corresponding to the construction mode attribute, and the customized process topology diagram includes at least Q approval nodes, where Q is a positive integer. The handler determination unit 304 determines the handler candidate set corresponding to each approval node in the customized process topology diagram based on the project type attribute of the target project. Each handler candidate set includes at least one approval handler with the operation authority corresponding to the approval node. The approval scheme determination unit 305 determines the target approval scheme for the target project based on the customized process topology diagram and the handler candidate set corresponding to each approval node.

[0289] In an optional embodiment, the device for determining the project approval scheme further includes: a classification and parsing unit, an encoding unit, a logic combination unit, and a business rule verification unit.

[0290] Optionally, the classification and parsing unit is used to classify and parse the project business information corresponding to B historical projects based on the dimensions of A project attributes, to obtain A sets of business information corresponding to A project attributes, where A and B are both positive integers, and A project attributes include at least the project initiation entity attribute, project type attribute, construction mode attribute, compliance requirement attribute, and quantitative parameter attribute; the encoding unit is used to perform encoding operations on the project business information included in each of the A sets of business information to obtain a set of rule factors corresponding to each set of business information, where each set of rule factors includes at least one preset rule factor; the logical combination unit is used to logically combine the preset rule factors in the set of rule factors corresponding to each of the B historical projects based on the historical approval processes corresponding to the B historical projects, using preset logical operators, to obtain an initial set of business rules corresponding to the B historical projects; and the business rule verification unit is used to verify each initial business rule in the initial set of business rules, and to create a preset business rule knowledge base based on the initial business rules that have passed the business rule verification.

[0291] In an optional embodiment, the device for determining the project approval scheme further includes: a requirements list generation unit, a template set generation unit, and a template verification unit.

[0292] Optionally, the requirement list generation unit is used to generate a scenario-based approval requirement list based on the approval scenario information of the historical approval processes corresponding to B historical projects. The scenario-based approval requirement list includes at least the necessary approval nodes for each historical project under different approval scenarios, the node flow order of the necessary approval nodes, and the node authority and responsibility. The template set generation unit is used to generate an initial approval process template set corresponding to B historical projects based on the scenario-based approval requirement list. Each initial approval process template in the initial approval process template set includes at least a start node, necessary approval nodes, branch nodes, and an end node. The template verification unit is used to perform process template verification on each initial approval process template in the initial approval process template set, and use the initial approval process template that passes the process template verification as the preset approval process template.

[0293] In an optional embodiment, the device for determining the project approval scheme further includes: a first creation unit, a second creation unit, and an instruction generation unit.

[0294] Optionally, the first creation unit is used to create a first mapping table, wherein the first mapping table is used to store the mapping relationship between preset business rules and preset approval process templates; the second creation unit is used to create a second mapping table, wherein the second mapping table is used to store the mapping relationship between rule factors in preset business rules and branch nodes in preset approval process templates; and the instruction generation unit is used to generate preset configuration instructions based on the first mapping table and the second mapping table, wherein the preset configuration instructions are at least used to determine the preset approval process template corresponding to the target business rule.

[0295] In an optional embodiment, the handler determination unit 304 includes: a binding subunit, a vector generation subunit, a feature extraction subunit, a similarity detection subunit, and a handler addition subunit.

[0296] Optionally, a binding subunit is used to bind the project type attribute of the target project to each approval node in the customized process topology diagram, thereby obtaining the project type attribute associated with each approval node; a vector generation subunit is used to generate a project type vector corresponding to each approval node based on the project type attribute associated with each approval node, wherein the vector dimension of the project type vector is equal to the total number of attribute categories of the project type attribute; a feature extraction subunit is used to extract features from the user role information of C approval handlers in the preset user role information database, thereby obtaining C user role feature vectors corresponding to the C approval handlers, wherein C is a positive integer, and the vector dimension of the user role feature vector is equal to the total number of attribute categories of the project type attribute; a similarity detection subunit is used to detect the similarity between the project type vector corresponding to each approval node and each user role feature vector in the C user role feature vectors; and a handler addition subunit is used to add the approval handler corresponding to the similarity to the handler candidate set of the approval node corresponding to the similarity when the similarity is greater than or equal to a preset similarity threshold, wherein the preset similarity threshold is dynamically adjusted based on the node level of the corresponding approval node.

[0297] In one optional embodiment, the device for determining the project approval scheme further includes: a binding unit, an attribute verification unit, and a rebinding unit.

[0298] Optionally, the binding unit is used to bind the user role information of the j-th approval handler in the candidate set corresponding to the i-th approval node in the customized process topology diagram to the i-th approval node in the customized process topology diagram; the attribute verification unit is used to perform attribute verification on the node attributes of the i-th approval node, wherein the node attributes include at least the node name, approval processing time limit, and user role information; the rebinding unit is used to bind the user role information of the (j+1)-th approval handler in the candidate set to the i-th approval node in the customized process topology diagram if the attribute verification fails.

[0299] In an optional embodiment, the device for determining the project approval scheme further includes: a monitoring dimension determination unit, a monitoring unit, an association unit, a case scoring unit, a new rule factor generation unit, and a new rule factor addition unit.

[0300] Optionally, the monitoring dimension determination unit is used to determine F monitoring dimensions corresponding to the target approval scheme based on the customized process topology diagram and the candidate set of handlers corresponding to each approval node, where F is a positive integer, and the F monitoring dimensions include at least node identifier, expected handler range, expected node flow direction, and expected branch triggering conditions; the monitoring unit is used to monitor the approval execution process of the target approval scheme based on the F monitoring dimensions to obtain a target audit log in a preset format, wherein the target audit log is used to at least characterize the node attributes corresponding to each approval node and the node execution information under the F monitoring dimensions; the association unit is used to perform association operations on the target audit log and the approval data generated in the approval execution process of the target approval scheme to obtain G approval cases, which In this system, the approval data includes at least approval forms and rejection opinions, where G is a positive integer; the case scoring unit is used to determine the case score for each of the G approval cases based on a preset scoring formula, wherein the case score is used to characterize at least the closed state of the approval execution process corresponding to the approval case, approval compliance, time consumption, approval data integrity, node dependency, and business relevance; the new rule factor generation unit is used to generate new rule factors based on the project business information of approval cases whose case scores are greater than the preset case scores; the new rule factor addition unit is used to add the new rule factor as a preset rule factor to the rule factor set corresponding to the target project when the factor score of the new rule factor is greater than or equal to the preset factor score.

[0301] As can be seen from the above, this application adopts a dynamic matching and precise binding method based on multi-dimensional project attributes. By using rule matching to drive process topology reconstruction, node configuration sequence to adapt to construction mode, and determining a candidate set of exclusive handlers based on project type, it achieves the goal of generating highly customized approval schemes with clear rights and responsibilities for each complex project. This realizes the technical effect of automatic adaptation of approval process structure to specific business scenarios (such as project initiation entity and construction mode) and precise correspondence between approval authority and project type, thereby solving the technical problems of low efficiency and low accuracy in generating project approval schemes in the prior art.

[0302] It should be noted that the rule matching unit 301, the first update unit 302, the second update unit 303, the applicant determination unit 304, and the approval scheme determination unit 305 mentioned above correspond to steps S101 to S105 in the method embodiment. The instances and application scenarios implemented by the above units and the corresponding steps are the same, but are not limited to the content disclosed in the above embodiment.

[0303] Example 3

[0304] Embodiments of this application can also provide an electronic device. Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application, such as... Figure 4 As shown, the electronic device includes: one or more ( Figure 4 (Only one is shown) Processor 402, memory 404, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0305] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and devices in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the above-mentioned method for determining the project approval scheme.

[0306] The memory may include high-speed random access memory (RAM), and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, which can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks (LANs), mobile communication networks, and combinations thereof.

[0307] The processor can access information and applications stored in memory via a transfer device to perform the following steps:

[0308] Based on the project business information of the target project, rule matching is performed in a preset business rule knowledge base. The preset business rules in the knowledge base represent the triggering conditions for approval processes under different approval scenarios. Based on the successfully matched target business rules, branch activation / pruning operations are performed on the preset approval process templates corresponding to the target business rules to obtain a reconstructed process topology diagram for the target project. This diagram represents the P approval stages involved in the target project, where P is a positive integer. Based on the construction mode attribute of the target project, the node configuration sequence corresponding to each approval stage in the reconstructed process topology diagram is determined. This sequence is then added to the reconstructed process topology diagram to obtain a customized process topology diagram for the target project. The node configuration sequence includes at least one customized approval node corresponding to the construction mode attribute, and the customized process topology diagram includes at least Q approval nodes, where Q is a positive integer. Based on the project type attribute of the target project, a candidate set of handlers corresponding to each approval node in the customized process topology diagram is determined. Each candidate set includes at least one approval handler with the operation permissions corresponding to the approval node. Based on the customized process topology diagram and the candidate set of handlers for each approval node, the target approval scheme for the target project is determined.

[0309] This application provides a method for determining project approval schemes. It employs a dynamic matching and precise binding approach based on multi-dimensional project attributes. Through rule-based matching to drive process topology reconstruction, node configuration sequence adaptation to construction modes, and determination of a dedicated candidate set of handlers based on project type, it achieves the goal of generating highly customized approval schemes with clear responsibilities for each complex project. This results in the automatic adaptation of the approval process structure to specific business scenarios (such as project initiation entity and construction mode) and the precise correspondence between approval permissions and project types, thereby solving the technical problems of low efficiency and low accuracy in generating project approval schemes in existing technologies.

[0310] Those skilled in the art will understand that Figure 4 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, PDAs, mobile internet devices, PADs, and other terminal devices. Figure 4 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 4 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 4 The different configurations shown.

[0311] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0312] Example 4

[0313] Embodiments of this application may also provide a storage medium.

[0314] Optionally, in this embodiment of the application, the storage medium can be used to store the program code executed by the method for determining the project approval scheme provided in the above method embodiment.

[0315] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0316] This application also provides a computer program product that, when executed on a data processing device, is suitable for performing the steps of a method for determining a project approval scheme.

[0317] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0318] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0319] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0320] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0321] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0322] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. 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 product. This computer software product is stored in a storage medium 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 described in the various embodiments of this application. The aforementioned storage medium 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.

[0323] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for determining a project approval scheme, characterized in that, include: Based on the project business information of the target project, rule matching is performed in a preset business rule knowledge base, wherein the preset business rules in the preset business rule knowledge base are used to characterize the triggering conditions corresponding to the approval process under different approval scenarios. Based on the target business rules that are successfully matched with the target project, branch activation / pruning operations are performed on the preset approval process template corresponding to the target business rules to obtain the reconstructed process topology diagram corresponding to the target project. The reconstructed process topology diagram is used to represent the P approval steps involved in the target project, where P is a positive integer. Based on the construction mode attribute of the target project, determine the node configuration sequence corresponding to each approval step in the reconstructed process topology diagram, add the node configuration sequence corresponding to each approval step to the reconstructed process topology diagram, and obtain the customized process topology diagram corresponding to the target project. The node configuration sequence includes at least one customized approval node corresponding to the construction mode attribute, and the customized process topology diagram includes at least Q approval nodes, where Q is a positive integer. Based on the project type attribute of the target project, determine the candidate set of handlers corresponding to each approval node in the customized process topology diagram, wherein each candidate set of handlers includes at least one approval handler with operation permissions corresponding to the approval node; Based on the customized process topology diagram and the candidate set of applicants corresponding to each approval node, the target approval scheme corresponding to the target project is determined.

2. The method for determining the project approval scheme according to claim 1, characterized in that, Before performing rule matching in a pre-defined business rule knowledge base based on the project's business information, the method for determining the project approval scheme further includes: Based on the dimensions of A project attributes, the project business information corresponding to B historical projects is classified and analyzed to obtain A sets of business information corresponding to the A project attributes, where A and B are both positive integers, and the A project attributes include at least the project initiation entity attribute, project type attribute, construction mode attribute, compliance requirement attribute, and quantitative parameter attribute. For the project business information included in each of the A business information sets, an encoding operation is performed to obtain the rule factor set corresponding to each business information set, wherein each rule factor set includes at least one preset rule factor; Based on the historical approval processes corresponding to the B historical projects, preset rule factors in the rule factor set corresponding to each business information set are logically combined using preset logical operators to obtain the initial business rule set corresponding to the B historical projects. Each initial business rule in the initial business rule set is validated, and the preset business rule knowledge base is created based on the initial business rules that pass the validation.

3. The method for determining the project approval scheme according to claim 1, characterized in that, Before performing rule matching in a pre-defined business rule knowledge base based on the project's business information, the method for determining the project approval scheme further includes: Based on the approval scenario information of the historical approval process corresponding to B historical projects, a scenario-based approval requirement list is generated. The scenario-based approval requirement list includes at least the necessary approval nodes for each historical project under different approval scenarios, the node flow order of the necessary approval nodes, and the attribution of node rights and responsibilities. Based on the scenario-based approval requirement list, an initial approval process template set corresponding to the B historical projects is generated. Each initial approval process template in the initial approval process template set includes at least a start node, necessary approval nodes, branch nodes, and an end node. Each initial approval process template in the initial approval process template set is validated, and the initial approval process template that passes the validation is used as the preset approval process template.

4. The method for determining the project approval scheme according to claim 1, characterized in that, Before performing branch activation / pruning operations on the preset approval process template corresponding to the target business rule based on the target project that has successfully matched the target business rule, the method for determining the project approval scheme further includes: Create a first mapping table, wherein the first mapping table is used to store the mapping relationship between preset business rules and preset approval process templates; Create a second mapping table, which is used to store the mapping relationship between rule factors in preset business rules and branch nodes in preset approval process templates; Based on the first mapping table and the second mapping table, a preset configuration instruction is generated, wherein the preset configuration instruction is at least used to determine the preset approval process template corresponding to the target business rule.

5. The method for determining the project approval scheme according to claim 1, characterized in that, Based on the project type attribute of the target project, determine the candidate set of handlers corresponding to each approval node in the customized process topology diagram, including: The project type attribute of the target project is bound to each approval node in the customized process topology diagram to obtain the project type attribute associated with each approval node; Based on the project type attribute associated with each approval node, a project type vector corresponding to each approval node is generated, wherein the vector dimension of the project type vector is equal to the total number of attribute categories of the project type attribute; Feature extraction is performed on the user role information of C approval handlers in the preset user role information database to obtain C user role feature vectors corresponding to C approval handlers, where C is a positive integer and the vector dimension of the user role feature vector is equal to the total number of attribute categories of the project type attribute; Detect the similarity between the project type vector corresponding to each approval node and each user role feature vector among the C user role feature vectors; If the similarity is greater than or equal to a preset similarity threshold, the approver corresponding to the similarity is added to the approver candidate set of the approval node corresponding to the similarity. The preset similarity threshold is dynamically adjusted based on the node level of the corresponding approval node.

6. The method for determining the project approval scheme according to claim 1, characterized in that, After determining the candidate set of applicants for each approval node in the customized process topology based on the project type attribute of the target project, the method for determining the project approval scheme further includes: Bind the user role information of the j-th approval applicant in the applicant candidate set corresponding to the i-th approval node in the customized process topology diagram to the i-th approval node in the customized process topology diagram; The node attributes of the i-th approval node are validated, wherein the node attributes include at least the node name, approval processing time limit, and user role information; If the attribute verification fails, the user role information of the (j+1)th approval applicant in the applicant candidate set will be bound to the i-th approval node in the customized process topology diagram.

7. The method for determining the project approval scheme according to claim 1, characterized in that, After determining the target approval scheme for the target project based on the customized process topology diagram and the candidate set of applicants corresponding to each approval node, the method for determining the project approval scheme further includes: Based on the customized process topology diagram and the candidate set of applicants corresponding to each approval node, F monitoring dimensions corresponding to the target approval scheme are determined, where F is a positive integer, and the F monitoring dimensions include at least node identifier, expected applicant range, expected node flow direction, and expected branch triggering conditions. Based on the F monitoring dimensions, the approval execution process of the target approval scheme is monitored to obtain a target audit log in a preset format. The target audit log is used to characterize at least the node attributes corresponding to each approval node and the node execution information under the F monitoring dimensions. The target audit log and the approval data generated in the approval execution process of the target approval scheme are correlated to obtain G approval cases, wherein the approval data includes at least the approval form and the rejection opinion, and G is a positive integer; Based on a preset scoring formula, a case score is determined for each of the G approval cases. The case score is used to characterize at least the closed state of the approval execution process corresponding to the approval case, the approval compliance, the time consumption, the integrity of the approval data, the node dependency, and the business relevance. Based on the project business information of approval cases whose case scores are greater than the preset case scores, a new rule factor is generated. If the factor score of the new rule factor is greater than or equal to the preset factor score, the new rule factor is added as a preset rule factor to the rule factor set corresponding to the target project.

8. A device for determining a project approval scheme, characterized in that, include: The rule matching unit is used to perform rule matching in a preset business rule knowledge base based on the project business information of the target project. The preset business rules in the preset business rule knowledge base are used to represent the triggering conditions corresponding to the approval process under different approval scenarios. The first update unit is used to perform branch activation / pruning operations on the preset approval process template corresponding to the target business rule based on the target business rule that is successfully matched with the target project, so as to obtain the reconstructed process topology diagram corresponding to the target project. The reconstructed process topology diagram is used to represent the P approval steps involved in the target project, where P is a positive integer. The second update unit is used to determine the node configuration sequence corresponding to each approval step in the reconstruction process topology diagram based on the construction mode attribute of the target project, and add the node configuration sequence corresponding to each approval step to the reconstruction process topology diagram to obtain the customized process topology diagram corresponding to the target project. The node configuration sequence includes at least one customized approval node corresponding to the construction mode attribute, and the customized process topology diagram includes at least Q approval nodes, where Q is a positive integer. The handler determination unit is used to determine the handler candidate set corresponding to each approval node in the customized process topology diagram based on the project type attribute of the target project, wherein each handler candidate set includes at least one approval handler with operation authority corresponding to the approval node; The approval scheme determination unit determines the target approval scheme corresponding to the target project based on the customized process topology diagram and the candidate set of applicants corresponding to each approval node.

9. A computer program product, characterized in that, The computer program product includes a computer program, wherein, when the computer program is executed, it controls the computer program product to perform the method for determining the project approval scheme as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method for determining a project approval scheme as described in any one of claims 1 to 7.

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