A workflow management method and system for government daily office scene

Abstract

The application relates to workflow management, and discloses a workflow management method and system for government daily office scenes, which significantly improves the efficiency, accuracy and compliance of government affairs approval through intelligent policy analysis and automatic workflow adjustment, reduces the complexity and error rate of manual operation, and provides a more efficient and reliable workflow management solution for government daily office.

Description

Technical Field

[0001] This application relates to the field of workflow management technology, and more specifically, to a workflow management method and system for government daily office scenarios. Background Technology

[0002] In the daily office environment of government, existing workflow management systems are typically designed for handling routine, well-defined tasks. However, when faced with sudden, time-sensitive policy changes, these rigid process definitions often struggle to quickly adapt to newly added, complex approval paths and cross-departmental collaboration requirements. For example, a higher-level authority might suddenly issue an urgent new policy requiring significant adjustments to the existing public service application process, introducing entirely new eligibility review standards, mandating joint reviews by multiple previously uninvolved departments, and setting stricter deadlines for final report submissions. Such sudden policy changes render the inherent logic of existing workflows incapable of directly adapting to the new, complex approval paths and cross-departmental collaboration requirements. The system cannot identify new approval nodes, automatically determine new eligibility standards, or coordinate parallel or sequential relationships between newly added departments. Summary of the Invention

[0003] This application discloses a workflow management method and system for government daily office scenarios, aiming to solve the problems that existing workflow management systems have difficulty adapting quickly to new complex approval paths and cross-departmental collaboration requirements when faced with sudden and time-sensitive policy adjustments, as well as the resulting problems such as excessive manual intervention, data inconsistency, slow system response and compliance risks.

[0004] The technical solution of this application is as follows: Firstly, this application discloses a workflow management method for daily government office scenarios, the method comprising: Policy documents can be obtained through government data interfaces or document upload channels; The policy documents are then subjected to format standardization and semantic structuring processes to obtain policy texts with a standardized structure. By using a pre-trained dependency parser and combining it with a pre-defined government affairs domain dictionary, syntactic dependency analysis is performed on policy texts with a normative structure to obtain a policy syntax tree. The policy syntax tree is matched with a pre-built set of policy semantic parsing rules to identify and extract policy elements from the policy syntax tree. The policy elements include at least the policy object, policy conditions, and policy actions. The policy elements are mapped and matched with the predefined task nodes in the workflow engine to establish the correspondence between policy elements and task nodes; Based on the corresponding relationship, the workflow engine automatically adjusts or generates an executable workflow template. The automatic adjustment or generation includes at least one of the following operations: inserting a new task node, modifying the judgment logic of an existing task node, adjusting the execution priority of an existing task node, or creating a parallel task flow or a serial task flow. Deploy the adjusted or generated workflow template to the workflow engine and activate it, so that the workflow engine can start and execute the government approval workflow instance based on the workflow template; The process of standardizing the format and activating the workflow template is executed by computer equipment deployed on the government intranet or government cloud platform.

[0005] Secondly, this application also discloses a workflow management system for daily government office scenarios, the system comprising: The policy document acquisition module is used to acquire policy documents through government data interfaces or document upload channels. The content organization module is used to perform format standardization and semantic structuring on policy documents in sequence to obtain policy texts with a standardized structure. The syntactic analysis module is used to perform syntactic dependency analysis on policy texts with a normative structure using a pre-trained dependency parser and a pre-defined government domain dictionary, to obtain a policy syntax tree. The element extraction module is used to match the policy syntax tree with a pre-built set of policy semantic parsing rules, identify and extract policy elements from the policy syntax tree, wherein the policy elements include at least the policy object, policy conditions and policy actions; The element mapping module is used to map and match policy elements with predefined task nodes in the workflow engine, and establish the correspondence between policy elements and task nodes. The template generation module is used to automatically adjust or generate executable workflow templates by the workflow engine based on the corresponding relationship. The automatic adjustment or generation includes at least one of the following operations: inserting new task nodes, modifying the judgment logic of existing task nodes, adjusting the execution priority of existing task nodes, and creating parallel task flows or serial task flows. The template deployment module is used to deploy the adjusted or generated workflow template to the workflow engine and activate it, so that the workflow engine can start and execute the government approval workflow instance based on the workflow template.

[0006] This application discloses a workflow management method and system for daily government office scenarios. By automatically acquiring policy documents and standardizing their format and semantic structuring, combined with dependency parsing and policy semantic analysis rules, it can accurately identify and extract key elements such as policy objects, policy conditions, and policy actions from complex policy texts. Subsequently, these policy elements are intelligently mapped to predefined task nodes in the workflow engine, establishing a correspondence between policies and processes. Based on this correspondence, the workflow engine can automatically adjust or generate executable workflow templates, including inserting new task nodes, modifying decision logic, adjusting execution priorities, and even creating parallel or serial task flows. This allows the workflow system to quickly and flexibly adapt to sudden and time-sensitive policy adjustments. Finally, the adjusted or generated workflow template is deployed and activated to initiate and execute government approval workflow instances.

[0007] Through the aforementioned technical solution, this application effectively addresses the problems of existing workflow management systems in adapting to policy changes, such as rigid processes and difficulty in quickly adapting to new, complex approval paths and cross-departmental collaboration requirements. It avoids deviations in task flow paths and inconsistencies in key data caused by manual intervention, as well as the resulting slow system response and compliance risks. Through intelligent policy analysis and automated workflow adjustment, this application significantly improves the efficiency, accuracy, and compliance of government approvals, reduces the complexity and error rate of manual operations, and provides a more efficient and reliable workflow management solution for daily government operations. Attached Figure Description

[0008] Figure 1 This is a flowchart illustrating a workflow management method for daily government office scenarios provided by an embodiment of the present invention; Figure 2 This is a schematic diagram of a process for constructing the policy semantic parsing rule set provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a workflow management system for daily office scenarios in government, provided by an embodiment of the present invention. Detailed Implementation

[0009] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0010] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0011] In the daily office environment of government, existing workflow management systems are typically designed for handling routine, well-defined tasks. However, when faced with sudden, time-sensitive policy changes, these rigid process definitions often struggle to quickly adapt to newly added complex approval paths and cross-departmental collaboration requirements. For example, a higher-level authority might suddenly issue an urgent new policy requiring significant adjustments to the existing public service application process, introducing entirely new eligibility review standards, mandating joint reviews by multiple previously uninvolved departments, and setting stricter deadlines for final report submissions. This sudden policy change renders the inherent logic of the existing workflow incompatible with the new, complex approval paths and cross-departmental collaboration requirements. The system cannot identify new approval nodes, automatically determine new eligibility standards, or coordinate parallel or sequential relationships between newly added departments. Given the urgency of the new policy and the inability of the existing system to undergo deep restructuring in a short time, system administrators and operational staff are forced to adopt temporary workarounds. They overuse the system's "free-flow" mode, urgently assign tasks, and manually create parallel temporary sub-processes. This manual intervention causes discrepancies between the actual task flow path and the path recorded by the system, increasing the complexity and uncertainty of task scheduling. Simultaneously, due to manual input errors, delayed information transmission, or misunderstandings of policies, inconsistencies in key data across different departments' systems when processing the same application directly undermine the data consistency of process instances. These data inconsistencies and the increasing manual coordination further lengthen the entire approval process, leaving a large backlog of tasks requiring manual verification and correction. To manually track and correct these data discrepancies, staff must frequently perform cross-module data queries and comparisons. These unexpected and complex query requests place enormous and continuous computational and I / O pressure on the server's core processing units and system data storage devices, resulting in slow system response, operational lag, and severely impacting the normal work efficiency of all users. Furthermore, in subsequent annual audits or special policy compliance checks, the fact that many processes were completed through manual coordination or non-standard methods leads to incomplete operation logs or broken chains, making it impossible for the system to clearly display the complete task flow path, approval basis, and clearly defined responsible parties, exposing serious compliance risks.

[0012] In this regard, refer to Figure 1 , Figure 1 This is a flowchart illustrating a workflow management method for daily government office scenarios provided by an embodiment of the present invention. The method includes: S11, obtain policy documents through government data interfaces or document upload channels; S12, The policy document is subjected to format standardization and semantic structuring in sequence to obtain a policy text with a standardized structure; S13, using a pre-trained dependency parser and a pre-set government affairs domain dictionary, perform syntactic dependency analysis on the policy text of the normative structure to obtain a policy syntax tree; S14, Match the policy syntax tree with a pre-built set of policy semantic parsing rules, identify and extract policy elements from the policy syntax tree, wherein the policy elements include at least policy objects, policy conditions and policy actions; S15, map and match the policy elements with predefined task nodes in the workflow engine to establish a correspondence between the policy elements and the task nodes; S16. Based on the correspondence, the workflow engine automatically adjusts or generates an executable workflow template. The automatic adjustment or generation includes at least one of the following operations: inserting a new task node, modifying the judgment logic of an existing task node, adjusting the execution priority of an existing task node, or creating a parallel task flow or a serial task flow. S17, Deploy the adjusted or generated workflow template to the workflow engine and activate it, so that the workflow engine starts and executes the government approval workflow instance according to the workflow template; The process of standardizing the format and activating the workflow template is performed by computer equipment deployed on the government intranet or government cloud platform.

[0013] This application involves in-depth analysis of policy documents to extract key policy elements and intelligently mapping them to workflow task nodes, thereby automating the adjustment or generation of workflow templates. This effectively addresses the problems of rigid processes and slow adaptation in existing systems when facing policy changes, significantly improving the response speed and accuracy of government approvals while reducing errors and compliance risks associated with manual intervention.

[0014] The method proposed in this application is primarily applied to routine government office scenarios, aiming to optimize the management and execution of government approval workflows. Here, "policy documents" refer to documents issued by government departments, including various rules and regulations, notices, announcements, and guidelines, the content of which directly affects the procedures and conditions for government approvals. "Government data interface" refers to a standard interface used for data exchange and sharing between different government systems, such as an API interface; "file upload channel" refers to a functional module that allows users to upload local policy documents through the interface.

[0015] "Format standardization processing" refers to preprocessing the original policy documents to eliminate format differences caused by the diversity of document sources (such as PDF, Word, scanned copies, etc.), so that the content can be effectively recognized and processed by subsequent automated programs. "Semantic structuring processing" refers to further parsing the semantic content of the policy text based on format standardization, transforming it into structured data that machines can understand, such as identifying paragraphs, clauses, and citation relationships.

[0016] A pre-trained dependency parser is a tool built on natural language processing technology that identifies dependency relationships between words in a sentence, thereby revealing the sentence's grammatical structure. Combined with a pre-defined government domain dictionary, this parser can more accurately understand terminology and expressions specific to the government domain. The "policy syntax tree" is the output of syntactic dependency analysis. It represents the dependency relationships of words in policy texts in a tree structure, providing a foundation for subsequent semantic parsing.

[0017] The "Policy Semantic Parsing Rule Set" is a predefined set of rules used to identify and extract "policy elements" with specific meanings from the policy syntax tree. These policy elements are the core content of the policy; for example, "policy object" refers to the subject or matter to which the policy applies, "policy condition" refers to the preconditions that trigger a specific policy action, and "policy action" refers to the specific operation or behavior that the policy requires to be performed.

[0018] The "workflow engine" is the core component responsible for managing and executing workflows, driving the flow of tasks based on predefined workflow templates. "Task nodes" are the basic execution units in a workflow, representing a specific approval step or operation. By "mapping and matching" policy elements with task nodes, a connection can be established between policy requirements and actual workflow execution.

[0019] A "workflow template" is a blueprint for a workflow, defining the sequence, conditions, participants, and other elements of a task. The key to this application is its ability to "automatically adjust or generate" these templates to adapt to policy changes. The adjusted or generated templates are then "deployed" to the workflow engine and "activated," thereby initiating and executing specific "government approval workflow instances." The entire process is executed on a "government intranet or government cloud platform," ensuring data security and system stability.

[0020] In practice, the first step is to obtain policy documents. One approach is to manually upload these documents to the system, for example, by submitting PDF or Word documents through a file upload channel. Another approach is for the system to automatically retrieve the latest policy documents from the government document publishing platform periodically via a government data interface.

[0021] After obtaining the policy document, it needs to undergo format standardization and semantic structuring to obtain a policy text with a standardized structure. One implementation method is that after receiving the policy document, the system first calls a document parsing service. This service can identify and unify the character encoding of different file formats (such as PDF and DOCX), remove layout elements (such as headers, footers, and watermarks), and convert the table content in the document into structured data (such as JSON or XML). Subsequently, the service further segments the text into paragraphs, identifies the policy clause numbers, and extracts the reference relationships between clauses, such as "refer to Article 3 of these Measures".

[0022] Next, a pre-trained dependency parser, combined with a pre-defined government domain dictionary, performs syntactic dependency analysis on the policy text with a standardized structure, resulting in a policy syntax tree. One implementation involves inputting the semantically structured policy text into a deep learning-based pre-trained dependency parsing model. This model is trained with a government domain dictionary containing a large number of government professional terms and expressions. The parser identifies the subject-verb-object, attributive, and complement relationships between words in the text and represents them in a tree structure. For example, in the sentence "The applicant should submit complete materials within ten working days," the parser identifies "applicant" as the subject, "submit" as the verb, "materials" as the object, "within ten working days" as an adverbial of time, and "complete" as an attributive of "materials."

[0023] Then, the policy syntax tree is matched with a pre-built set of policy semantic parsing rules to identify and extract policy elements from the policy syntax tree. These policy elements include at least policy objects, policy conditions, and policy actions. One implementation involves the system maintaining a set of policy semantic parsing rules, which contains a series of rules based on syntactic dependency patterns and domain term definitions. For example, a rule might be defined as: "If the syntax tree contains the pattern '(subject)-(predicate: requirement / regulation)-(object: submission / providation)-(modifier: material / proof)', then 'subject' is identified as the policy object, 'submission / providation' is identified as the policy action, and 'material / proof' is identified as the policy condition." The system traverses the policy syntax tree, matching the syntactic structure in the tree with the rule set. Once a match is successful, the corresponding policy object, policy condition, and policy action are extracted. For example, from the statement "Enterprises applying for high-tech certification must meet the condition that R&D investment accounts for more than 15%", we can extract that the policy target is "enterprises", the policy condition is "R&D investment accounts for more than 15%", and the policy action is "applying for high-tech certification".

[0024] Subsequently, policy elements are mapped and matched with predefined task nodes in the workflow engine to establish a correspondence between policy elements and task nodes. One implementation approach is to predefine various general task nodes in the workflow engine, such as "material submission," "qualification review," "departmental countersigning," and "approval decision," with each task node accompanied by metadata describing its function and required inputs and outputs. The system performs fuzzy matching or semantic similarity-based matching in the workflow engine's task node library based on the type and content of the extracted policy elements. For example, if a policy element includes the action of "applicant submits application materials," the system will map it to a task node named "submit application" in the workflow engine; if the policy condition is "applicant needs to provide identity verification," it may be mapped to the "identity verification" task node.

[0025] Based on the aforementioned correspondence, the workflow engine automatically adjusts or generates executable workflow templates. This automatic adjustment or generation includes at least one of the following operations: inserting new task nodes, modifying the judgment logic of existing task nodes, adjusting the execution priority of existing task nodes, or creating parallel or sequential task flows. One implementation involves the workflow engine receiving the correspondence between policy elements and task nodes and first loading a basic workflow template. If a new approval step is identified in the policy element, such as "environmental assessment," and there is no corresponding task node in the basic template, the workflow engine will automatically insert an "environmental assessment" task node at an appropriate location. If policy conditions change, such as "approval amounts exceeding 1 million require approval from higher-level departments," the workflow engine will modify the judgment logic of the "approval decision" task node to include the new amount judgment condition. If the policy requires certain tasks to be executed with priority, such as "urgent matters must be handled with priority," the workflow engine will adjust the execution priority of the relevant task nodes. Furthermore, if the policy requires multiple departments to sign off in parallel, or if certain tasks must be executed sequentially in a specific order, the workflow engine will create corresponding parallel or sequential task flows.

[0026] Finally, the adjusted or generated workflow template is deployed to and activated by the workflow engine, enabling the workflow engine to start and execute the government approval workflow instance based on the workflow template. One implementation involves saving the workflow template to the workflow template library and marking its status as "pending deployment" once it has been adjusted or generated. After administrator confirmation, the deployment operation is triggered through the management interface, and the workflow engine loads and activates the template in its runtime environment. Subsequently, when a new government approval application is submitted, the workflow engine starts a new workflow instance based on this newly activated workflow template and automatically executes it according to the processes, conditions, and task sequences defined in the template.

[0027] The workflow management method proposed in this application for government daily office scenarios effectively solves the problems of slow response and rigid processes in traditional government workflow systems when faced with policy changes by introducing intelligent policy document parsing and workflow template adaptive adjustment mechanisms.

[0028] In some embodiments of this application, to ensure that policy documents can be accurately understood and applied by subsequent automated processing flows, it is necessary to perform format standardization and semantic structuring on the original policy documents. Specifically, the following methods can be used when performing format standardization and semantic structuring on policy documents.

[0029] The format standardization process includes at least one of the following: character encoding standardization, layout element stripping, and table to structured data conversion; the semantic structuring process includes at least one of the following: paragraph segmentation, clause number recognition, and citation extraction.

[0030] Specifically, character encoding standardization refers to converting policy documents with different encoding formats into a unified standard encoding format to avoid garbled characters or parsing errors caused by encoding inconsistencies. Layout element stripping involves identifying and removing non-content layout elements from policy documents, such as headers, footers, watermarks, background images, page numbers, tables of contents, and heading styles, retaining only the core text content and simplifying subsequent processing. Table-to-structured data conversion involves parsing the tabular content contained in policy documents and converting it into a standardized structured data format, such as JSON, XML, or database tables, to facilitate data extraction, querying, and utilization.

[0031] Paragraph segmentation refers to dividing the policy text into independent logical paragraphs according to rules such as semantic integrity or punctuation, providing basic units for subsequent semantic analysis. Clause number identification refers to automatically identifying various clause numbers in the policy text and establishing their hierarchical relationships, which helps in understanding the policy's organizational structure. Citation extraction refers to identifying citation relationships between different clauses in the policy text or between the policy document and other external documents, thereby constructing a network of connections between policy clauses.

[0032] The proposed solution, through meticulous format standardization and semantic structuring of policy documents, effectively eliminates heterogeneity in format, encoding, layout, and content organization. This technical solution significantly improves the automation and accuracy of policy document preprocessing. Format standardization ensures the consistency and cleanliness of input data, greatly reducing the error rate caused by data quality issues in subsequent processing stages. Semantic structuring makes the deep semantic information of the policy text explicit, providing clear and standardized input for the accurate identification and extraction of policy elements. This not only improves the efficiency of policy analysis but also enhances the depth and breadth of policy understanding, providing high-quality and reliable data support for the workflow engine to automatically adjust or generate executable workflow templates. This effectively avoids workflow configuration errors caused by misunderstandings of policy text, thereby improving the intelligence and compliance of government approval processes.

[0033] In the above methods, refer to Figure 2 , Figure 2 This is a schematic diagram of a process for constructing the policy semantic parsing rule set according to an embodiment of the present invention. S18 is: constructing the policy semantic parsing rule set, specifically as follows: S181, perform syntactic dependency annotation on historical policy document samples to obtain an annotated corpus; S182, Extract high-frequency syntactic dependency patterns from the annotated corpus; S183, the high-frequency syntactic dependency patterns are associated and mapped with the domain entries in the government affairs domain dictionary to obtain pattern-entry association rules; S184, perform conflict resolution and merging on the pattern-term association rules to obtain the policy semantic parsing rule set.

[0034] Among these, policy elements are key components of policy texts that carry core semantic information. Specifically, the policy object can be understood as the subject or object to which the policy is applied, such as "enterprise," "individual," or "specific project"; policy conditions refer to the preconditions required to trigger or meet a specific policy action, such as "meeting specific qualifications," "completing within a specified period," or "meeting certain standards"; and policy actions are the specific behaviors or operations required by the policy, such as "submitting an application," "conducting approval," or "issuing subsidies." Accurate identification of these elements is fundamental to automating government approval workflows.

[0035] Furthermore, the process of constructing the policy semantic parsing rule set aims to improve the accuracy and adaptability of policy element extraction. Specifically, syntactic dependency annotation of historical policy document samples refers to conducting linguistic analysis on a large number of published and implemented historical policy documents through manual or semi-automated methods to identify and mark the syntactic dependency relationships between words, thereby forming a high-quality annotated corpus. This annotated corpus forms the basis for subsequent rule learning. Extracting high-frequency syntactic dependency patterns from the annotated corpus refers to using statistical or machine learning methods to discover frequently occurring syntactic structure patterns from the annotated corpus. These patterns usually correspond to the typical expressions of policy elements in the text. For example, the "subject-verb-object" structure may correspond to "policy object-policy action-policy recipient". Associating and mapping the high-frequency syntactic dependency patterns with domain entries in the government affairs domain dictionary refers to binding these abstract syntactic patterns with professional terms specific to the government affairs domain (such as "application", "approval", "filing", "subsidy", etc.) to form specific pattern-entry association rules. For example, when “enterprise” is identified as the subject and “application” is the predicate, it can be inferred that “enterprise” is the policy target and “application” is the policy action.

[0036] Finally, conflict resolution and merging of the pattern-term association rules refers to deduplication, priority sorting, and logical integration of the generated rules to eliminate ambiguity or conflict between rules, ensure the internal consistency and effectiveness of the rule set, and ultimately obtain a rule set that can be used to efficiently and accurately parse policy semantics.

[0037] This application's solution overcomes the limitations of traditional manual rule set construction by systematically building a policy semantic parsing rule set. Through this technical solution, the application significantly improves the accuracy and automation level of policy element extraction. This method utilizes historical policy document samples for learning and induction, making the construction process of the policy semantic parsing rule set more systematic, automated, and intelligent. This significantly improves the accuracy and coverage of policy element identification, especially when facing complex and ever-changing government policy texts. Furthermore, by continuously updating historical policy document samples and domain dictionaries, this rule set can dynamically adapt to changes in policy language and the introduction of new policies, thereby enhancing the responsiveness and adaptability of the entire workflow management system to policy changes, reducing the cost and error rate of manual rule maintenance, and ultimately improving the efficiency and compliance of government approval processes.

[0038] In some preferred embodiments, it is assumed that a set of semantic parsing rules for policy documents related to "enterprise subsidy applications" needs to be constructed. First, a large number of historical policy documents related to "enterprise subsidy applications" are collected as samples. These documents are then manually or semi-automatically annotated with syntactic dependencies, for example, annotating syntactic structures such as "enterprise (policy target) applies (policy action) for subsidies that meet the conditions (policy conditions)". From these annotated corpora, high-frequency syntactic dependency patterns such as "noun + verb + adjective + noun" can be extracted. Subsequently, these patterns are associated with entries in the government domain dictionary for the fields of "enterprise", "application", "subsidy", and "meeting the conditions", forming pattern-entry association rules such as "when 'enterprise' is used as the subject, 'application' as the predicate, and 'meeting the conditions' modifier appears, it is identified as a policy target, policy action, and policy condition." Finally, these rules are deduplicated and prioritized; for example, if two rules can both identify "application", but one is more specific, it is given higher priority. In this way, an efficient and accurate set of policy semantic parsing rules can be obtained, which can be used to automatically extract policy objects, policy conditions, and policy actions from new policy documents.

[0039] In some of the embodiments described above in this application, an automatic adjustment or generation of executable workflow templates through a workflow engine is proposed. However, in actual implementation, the lack of a systematic and refined adjustment or generation mechanism may lead to insufficient accuracy in template adjustments, making it difficult to fully cover all requirements of policy documents. Furthermore, when faced with complex and ever-changing policy rules, the adjustment efficiency may be low, and new logical errors may even be introduced. Failure to address these issues may result in government approval processes being inconsistent with the latest policy requirements, affecting the compliance and efficiency of government services.

[0040] In this regard, this application further proposes the automatic adjustment or generation of executable workflow templates, including: Obtain the currently deployed workflow template in the workflow engine as the baseline template; Based on the correspondence, the differences between the baseline template and the policy elements are determined; For each of the aforementioned differences, perform one of the following operations: When the difference point indicates that the baseline template lacks a task node corresponding to the policy element, a new task node is inserted into the baseline template; When the difference point indicates that the judgment logic of the existing task node in the baseline template is inconsistent with the policy element, the judgment logic of the existing task node is modified. When the difference point indicates that the execution priority of an existing task node in the baseline template is inconsistent with the policy element, the execution priority of the existing task node shall be adjusted. When the difference point indicates that the policy element requires the existence of multiple parallel or serially executed task nodes in the baseline template, a parallel task flow or a serial task flow is created in the baseline template.

[0041] Specifically, "obtaining the currently deployed workflow template in the workflow engine as a baseline template" means that before adjusting or generating a workflow template, the currently used template related to the pending government approval item is first retrieved and loaded from the workflow engine. This template is considered a reference benchmark for subsequent comparison with new policy elements. The baseline template can be understood as a digital representation of the existing government approval process, aiming to provide a starting point for modification and expansion.

[0042] The phrase "determining the differences between the baseline template and the policy elements based on the correspondence" refers to analyzing the baseline template after obtaining it, using the previously established correspondence between policy elements and task nodes, to identify discrepancies between the baseline template and the requirements of the new policy elements. These discrepancies may include missing nodes in the baseline template, inconsistencies between existing node logic and policy requirements, or improper node execution order. In practical applications, this process can be automated using comparison algorithms. For example, set operations can be performed between the set of task nodes mapped to policy elements and the set of task nodes in the baseline template to identify newly added, deleted, or modified elements.

[0043] Furthermore, the phrase "for each of the aforementioned discrepancies, perform one of the following operations" means that after identifying all discrepancies, the system will adopt a corresponding automated adjustment strategy based on the specific type of the discrepancy. Specifically: When the discrepancy indicates that the baseline template lacks a task node corresponding to the policy element, the system will insert a new task node into the baseline template. For example, if the new policy introduces an additional approval step, a corresponding task node will be added to the template.

[0044] When the discrepancy indicates that the judgment logic of an existing task node in the baseline template is inconsistent with the policy element, the system will modify the judgment logic of the existing task node. For example, if the policy adjusts the threshold or judgment rule of a certain approval condition, the condition expression of the corresponding task node will be updated.

[0045] When the discrepancy indicates that the execution priority of an existing task node in the baseline template is inconsistent with the policy element, the system will adjust the execution priority of the existing task node. For example, if a new policy requires that a certain approval step must be completed before other steps, its priority will be increased.

[0046] When the difference point indicates that the policy element requires multiple task nodes to be executed in parallel or sequentially in a specific order in the baseline template, the system will create a parallel task flow or a sequential task flow in the baseline template. For example, if the policy stipulates that certain approval items can be performed simultaneously or must be executed in a strict order, parallel or sequential process branches will be constructed accordingly.

[0047] This application's solution uses the deployed current workflow template as a baseline and systematically identifies differences between the baseline template and policy elements based on the correspondence between policy elements and task nodes. This difference identification mechanism enables the system to selectively perform operations such as insertion, modification, adjustment, or creation, ensuring that the adjustment or generation process of the workflow template is accurate and comprehensive. This refined adjustment based on differences avoids blindly reconstructing the entire template, improves the efficiency and accuracy of adjustments, and effectively solves the omissions or errors that may occur in traditional methods when facing complex policy changes.

[0048] This application further proposes that after the workflow template is activated, the method also includes: The workflow engine's logging module and status listener continuously collect the task execution status and key data field change records of the workflow instance; The actual workflow path of the workflow instance is determined from the task execution status, and the expected workflow path of the workflow instance is determined from the workflow template. The actual workflow path and the expected workflow path are compared for path consistency. When the comparison result fails, a path conflict event is generated. The key data fields entered or updated by different departments when processing the same workflow instance are compared with the data validation rules loaded from the workflow template. When the comparison result fails, a data conflict event is generated.

[0049] Specifically, the logging module refers to the component within the workflow engine used to record all task execution events, status changes, user operations, and other information. The status listener can be understood as a real-time monitoring mechanism configured to continuously monitor the running status of workflow instances, such as task startup, completion, pause, and rejection, and capture the entry, modification, or deletion operations of key data fields across different task nodes. Its purpose is to provide comprehensive and real-time foundational data for subsequent path comparison and data verification.

[0050] The actual workflow path of a workflow instance refers to the sequence in which task nodes are actually triggered and completed during the execution of the workflow instance. The expected workflow path refers to the standard sequence of task nodes that the workflow instance should follow under a specific policy version, as defined by the aforementioned workflow template. Path consistency comparison involves comparing the actual workflow path with the expected workflow path node by node and sequence by sequence to determine if they are completely consistent. When the comparison fails, i.e., any deviation is found between the actual path and the expected path (e.g., skipping a necessary node, executing a node that should not be executed, or incorrect sequence), a path conflict event is generated. The purpose is to promptly detect and mark abnormal or non-compliant behaviors during process execution.

[0051] In practical applications, the key data fields refer to data items that have a decisive impact on the approval result or process flow during the government approval process, such as applicant information, approval amount, and approval conditions. The data validation rules refer to a predefined set of rules used to verify the legality, completeness, and consistency of key data fields, such as data type, value range, format requirements, and logical relationships with other fields. Field consistency comparison involves checking each key data field entered or updated by each department in the workflow instance against the loaded data validation rules. When the comparison result fails, i.e., a key data field is found to be inconsistent with the preset validation rules, a data conflict event is generated. The purpose is to ensure the accuracy and compliance of government approval data.

[0052] This application's solution effectively addresses potential path deviations and data inconsistencies in government approval processes by introducing continuous monitoring and comparison mechanisms after the workflow template is activated. Through this technical solution, the application significantly improves the transparency, compliance, and data quality of government approval workflows. Compared to basic solutions that rely solely on template generation and deployment, this application, through real-time monitoring and intelligent comparison, proactively detects and warns of path deviations and data inconsistencies during workflow execution, effectively preventing violations and data errors caused by human negligence or system errors. This not only helps ensure the seriousness and fairness of government approvals and reduces administrative risks but also improves the efficiency and credibility of government services. Furthermore, by generating path conflict events and data conflict events, it provides clear evidence for subsequent conflict resolution and process optimization, enabling more refined and intelligent government management.

[0053] In some preferred embodiments, a specific example is given below. Suppose a local government issues a new "business start-up subsidy" policy. This policy stipulates that businesses applying for subsidies must go through five core stages: "online application submission" -> "preliminary review of materials" -> "on-site verification" -> "approval and public announcement" -> "fund disbursement." Furthermore, in the "preliminary review of materials" stage, the applicant company's registered capital must be greater than or equal to 1 million yuan. Based on the above policy, the workflow engine generates and activates a corresponding workflow template.

[0054] In practice, one company submitted a subsidy application.

[0055] Scenario 1 (Path Conflict): Due to an operational error by a staff member, after completing the "Initial Material Review," the process directly jumps to the "Fund Disbursement" stage, skipping the "On-site Verification" and "Approval and Public Announcement" stages. At this point, the workflow engine's log recording module and status listener continuously collect task execution status data. The system will determine the actual workflow path as "Online Application Submission" -> "Initial Material Review" -> "Fund Disbursement" based on the collected task execution status, while the expected workflow path is "Online Application Submission" -> "Initial Material Review" -> "On-site Verification" -> "Approval and Public Announcement" -> "Fund Disbursement." When a path consistency comparison is performed and the actual path is found to be inconsistent with the expected path, the system will immediately generate a path conflict event and trigger a corresponding early warning mechanism to notify the relevant personnel to intervene.

[0056] Scenario 2 (Data Conflict): During the "Initial Material Review" stage, a staff member enters the company's registered capital as 800,000 yuan, while policy stipulates that the registered capital must be greater than or equal to 1 million yuan. At this point, the system loads a preset data validation rule, which includes the validation logic for "registered capital >= 1 million yuan". When the staff member submits this data, the system compares the entered key data field (registered capital 800,000 yuan) with the data validation rule for consistency. If it finds a discrepancy, the system immediately generates a data conflict event and prompts the staff member to correct the data entry or undergo a special approval process.

[0057] As can be seen from the above examples, the solution proposed in this application can effectively detect and mark process deviations and data errors during workflow execution, thereby ensuring the compliance and accuracy of government approvals.

[0058] In some of the embodiments described above in this application, a scheme is proposed to detect path conflicts by comparing the actual workflow path of a workflow instance with the expected workflow path. However, in actual government office scenarios, policy documents may exist in multiple versions, and different versions may take effect at different times. This can lead to different expected workflow paths that the same type of government approval item should follow when it is initiated at different times. If the policy version corresponding to the workflow instance is not accurately identified and the correct expected workflow path is not determined accordingly, the path consistency comparison results may be biased, affecting the accuracy and effectiveness of conflict detection.

[0059] In this regard, this application further proposes methods for determining the aforementioned expected circulation path, including: Assign a unique version identifier and effective time range to each policy version; A policy version timeline is constructed based on a graph database, where nodes represent policy version identifiers and edges represent the effective time range corresponding to the policy version identifiers. When a workflow instance starts, the policy version identifier that the workflow instance should follow is determined from the policy version timeline based on the workflow instance's start time. Based on the policy version identifier, retrieve the adapted workflow template corresponding to the policy version from the workflow template library, and use the sequence of task nodes defined in the adapted workflow template as the expected workflow path.

[0060] Specifically, assigning a unique version identifier and effective time range to each policy version means that when a policy document is released or updated, the system automatically generates a globally unique identifier for it, clearly specifying the start and end dates or time periods of its validity. For example, if a policy document is released on January 1, 2023, and replaced by a new version on January 1, 2024, its effective time range is [2023-01-01, 2023-12-31]. This measure aims to ensure that each policy version has clear boundaries and traceability in the time dimension.

[0061] The construction of a policy version timeline based on a graph database refers to utilizing the characteristics of graph databases to store and manage policy version information. Nodes in a graph database can represent different policy version identifiers, while the edges connecting these nodes can represent the effective time range corresponding to each policy version identifier. This structure can intuitively display the evolution history and time validity of policy versions, facilitating efficient time range queries.

[0062] In practical applications, when a workflow instance starts, the system obtains the instance's precise start time. Then, using this start time as a query condition, it searches within a pre-built policy version timeline to determine which policy version is currently in effect and should be followed at that start time. This allows for the accurate identification of the policy version identifier associated with the workflow instance.

[0063] Furthermore, once the policy version identifier that the workflow instance should follow is determined, the system will use this identifier as a search criterion to find a precisely matching adapted workflow template in the pre-set workflow template library. This adapted workflow template is pre-configured and adjusted according to a specific policy version, and contains the complete task node sequence and logic of the government approval process under that policy version. Finally, the task node sequence defined in the adapted workflow template is extracted as the expected flow path of the workflow instance for subsequent path consistency comparison.

[0064] This application's solution effectively addresses the challenge of accurately obtaining the expected workflow path of workflow instances in a dynamically changing policy environment by introducing policy version management and a time-axis-based expected workflow path determination mechanism. Through this technical solution, the application ensures that the expected workflow path of a government approval workflow instance is determined based on the most accurate and effective policy version at the time of initiation. This significantly improves the accuracy of path consistency comparison and effectively avoids misjudgments or omissions caused by policy version iterations or differences in effective dates. Consequently, it not only enhances the compliance of government approval processes, ensuring that each workflow instance strictly adheres to the policy provisions corresponding to its initiation, but also improves the robustness and adaptability of the entire workflow management system, enabling it to better cope with complex and ever-changing policy environments and providing more accurate and reliable process monitoring and conflict management capabilities for daily government operations.

[0065] In some preferred embodiments, a specific example is given below. Suppose a local government issues a policy document regarding "business start-up subsidies." The first version (V1.0) took effect on January 1, 2023, and stipulated three approval stages: A, B, and C. Subsequently, on July 1, 2023, the policy document was revised, and a new version (V2.0) was released, adjusting the approval stages to A, D, and C. At this time, the system assigns a version identifier "Policy_EnterpriseSubsidy_V1.0" and an effective time range [2023-01-01, 2023-06-30] to V1.0, and an effective time range "Policy_EnterpriseSubsidy_V2.0" and an effective time range [2023-07-01, ∞] to V2.0. This information is stored in a policy version timeline built based on a graph database.

[0066] When a company submits an application for "Business Start-up Subsidy" on May 15, 2023, and initiates a workflow instance, the system retrieves the instance's start time as May 15, 2023. Based on this start time, the system queries the policy version timeline to determine that the policy version identifier that this instance should follow is "Policy_EnterpriseSubsidy_V1.0". Subsequently, the system retrieves the adapted workflow template corresponding to "Policy_EnterpriseSubsidy_V1.0" from the workflow template library. This template defines the task node sequence as "A -> B -> C". This sequence is then determined as the expected workflow path for this workflow instance.

[0067] If another enterprise submits the same application on August 10, 2023, and starts a different workflow instance, the system will determine the policy version identifier to be followed as "Policy_EnterpriseSubsidy_V2.0" from the policy version timeline based on its start time of August 10, 2023. At this time, the system will retrieve the adapted workflow template corresponding to "Policy_EnterpriseSubsidy_V2.0", whose task node sequence is "A -> D -> C", and use this as the expected workflow path for that workflow instance. In this way, even for the same application type, due to different start times, the system can accurately match the correct policy version and expected workflow path, thereby ensuring the accuracy of subsequent path consistency comparisons.

[0068] In some embodiments described above, the workflow engine's log recording module and status listener are used to continuously collect task execution status and key data field change records of workflow instances. Furthermore, the key data fields entered or updated by different departments when processing the same workflow instance are compared with the data validation rules loaded from the workflow template to generate data conflict events. Specifically, the step of loading data validation rules can be further refined to ensure the accuracy and adaptability of data validation.

[0069] The method further includes: loading data validation rules, specifically: Based on the application type of the workflow instance, a set of basic key data fields is loaded from a preset business type data definition library, wherein the set of basic key data fields is defined using a JSONSchema data structure; Based on the policy version followed by the workflow instance, obtain the definitions of key data fields added, modified, or deprecated in that policy version from the policy version data revision log; The newly added, modified, or obsolete key data field definitions are merged into the basic key data field set to obtain an extended key data field set; Based on the current process stage of the workflow instance, load the process stage-specific key data fields from the process stage data requirement configuration; The key data fields specific to the process stage are integrated into the extended key data field set to obtain a contextualized key data field set; All fields in the contextualized key data field set are used as the range of fields to be verified, and the corresponding data verification rules are loaded.

[0070] Specifically, when loading data validation rules, the system first loads a set of basic key data fields from a pre-defined business type data definition library based on the application type of the current workflow instance. This set of basic key data fields defines the core data structures and validation rules required for a specific business type, and can be defined using a JSONSchema data structure to provide flexible and standardized data structure descriptions and validation capabilities.

[0071] Furthermore, considering the dynamic changes in policies, the system retrieves the definitions of key data fields added, modified, or deprecated in the policy version data revision log based on the policy version followed by the current workflow instance. This revision information reflects changes in data field requirements due to policy updates. Subsequently, these added, modified, or deprecated key data field definitions are merged into the previously loaded basic key data field set, resulting in an extended key data field set. This set contains all relevant data field definitions for a specific business type under a specific policy version.

[0072] Furthermore, government approval processes often have multiple stages, and the data field requirements may differ at each stage. Therefore, this application will also load key data fields specific to the current process stage from the process stage data requirement configuration, based on the current process stage of the workflow instance. These fields are essential for the business requirements specific to the current process stage.

[0073] Finally, these process-stage-specific key data fields are integrated into an extended key data field set, forming a contextualized key data field set. This set comprehensively considers the characteristics of application type, policy version, and process stage, and includes all key data fields that need to be validated in the current context. Finally, all fields in the contextualized key data field set are used as the scope of fields to be validated, and corresponding data validation rules are loaded to ensure the comprehensiveness and accuracy of data validation.

[0074] This application's solution achieves refined management and validation of key data fields in government approval workflow instances by dynamically loading data validation rules in a layered manner. First, a basic field set is loaded based on the application type of the workflow instance, ensuring coverage of common business needs. Second, by incorporating policy version revision logs, the data validation rules can respond promptly to policy changes, avoiding data non-compliance due to policy updates. Third, the introduction of process-stage-specific fields ensures that only the data required for the current stage is validated in different approval processes, improving validation efficiency and accuracy. Therefore, by comprehensively considering various contextual factors such as application type, policy version, and process stage, a highly contextualized set of key data fields and corresponding validation rules are constructed, enabling accurate identification and correction of inconsistencies in data entry or updates, effectively avoiding government risks caused by data errors or non-compliance.

[0075] Through the aforementioned technical solution, this application enables dynamic adaptation and precise loading of data verification rules, significantly improving the accuracy and efficiency of data consistency comparison in government approval workflows. This solution avoids the false alarms or omissions that may result from using static, general verification rules, ensuring a high degree of alignment between data verification and the current business context, policy requirements, and process stages. This not only helps to promptly identify and resolve data conflict events and reduces the workload of manual review, but also effectively guarantees the compliance, rigor, and timeliness of government approvals, thereby improving the intelligence level and management efficiency of daily government operations.

[0076] In some of the embodiments described above in this application, by continuously collecting the task execution status and change records of key data fields of workflow instances, and performing path consistency comparison and field consistency comparison, path conflict events or data conflict events can be effectively identified and generated. However, simply identifying conflict events cannot completely solve the problems that may arise in the government approval process. If these conflicts are not handled in a timely and effective manner, it may lead to the stagnation of the approval process, data inconsistency, and even affect the efficiency and credibility of government services.

[0077] In this regard, this application further proposes that after generating the path conflict event or the data conflict event, the method further includes: The system queries authoritative policy interpretation logs, which record the final policy intent and approval conditions of all policy texts after semantic parsing, time-range anchoring, and intent conformity pre-verification. These logs are generated through cross-validation using dual-track semantic parsing. When the results of two independent semantic parsings are consistent, the policy intent and approval conditions of the policy document are written into the authoritative policy interpretation log; when the results of two independent semantic parsings are inconsistent, it is marked as pending manual review. Referring to the preset conflict resolution instruction set, and based on the policy intent and approval conditions in the authoritative policy interpretation log, the conflict is arbitrated through the rule engine to obtain the arbitration result; Based on the arbitration result, the future task sequence of the current workflow instance is adjusted to jump from the incorrect flow path to the correct starting node; Based on the arbitration result, the key data fields in the workflow instance are updated, and the updated key data fields are synchronized to the departmental business systems of all relevant departments through the data synchronization interface.

[0078] Specifically, the authoritative policy interpretation log can be understood as a rigorously verified and confirmed policy knowledge base. Its content not only includes the original semantic analysis results of the policy text but also incorporates time-range anchoring and intent conformity pre-verification, ensuring the accuracy and authoritativeness of the recorded policy intent and approval conditions. The log's generation mechanism is particularly crucial. It employs a dual-track semantic analysis cross-validation method: two independent semantic analysis systems analyze the same policy text. Only when the two analysis results are consistent are the policy intent and approval conditions written into the authoritative policy interpretation log, thereby minimizing the errors that might arise from a single analysis system. If the two analysis results are inconsistent, it is marked for manual review to ensure that the final policy interpretation entered into the log is accurate.

[0079] The conflict resolution instruction set is a predefined set of rules or strategies used to guide the rule engine in making judgments and decisions based on policy intent and approval conditions when facing different types of conflict events. These instructions may include priority rules, default processing logic, and exception handling procedures. The rule engine is an automated decision-making system that can perform logical reasoning and judgment based on the input conflict event type, the policy intent and approval conditions in the authoritative policy interpretation log, and the pre-defined conflict resolution instruction set, thereby outputting a clear arbitration result.

[0080] In practical applications, adjusting the future task sequence of the current workflow instance based on the arbitration result means that when a path conflict is detected, the system no longer continues execution along the incorrect workflow path. Instead, based on the arbitration result, it jumps the execution pointer or status of the workflow instance to a correct starting node. This node is a task node on the correct path that conforms to the policy intent, determined based on authoritative policy interpretation and conflict resolution instruction sets. For example, if an approval step is found to have been incorrectly skipped, the arbitration result may instruct the workflow to backtrack to the skipped step or jump directly to the subsequent correct step.

[0081] Furthermore, based on the arbitration result, key data fields in the workflow instance are updated, and the updated key data fields are synchronized to the departmental business systems of all relevant departments through a data synchronization interface, aiming to resolve data conflict issues. When a data conflict occurs, the arbitration result will indicate which data fields need to be corrected. The corrected data will be synchronized through the data synchronization interface to ensure data consistency in all relevant departmental business systems involved in this workflow instance, avoiding subsequent problems caused by data inconsistency.

[0082] This application's solution effectively addresses the issues of path deviation or data inconsistency during the execution of government approval workflows by introducing authoritative policy interpretation logs and a conflict arbitration mechanism. Through these technical solutions, this application significantly enhances the intelligence and automation level of government approval workflows. Compared to basic solutions that only identify conflicts, this application further provides automatic conflict arbitration and correction capabilities, effectively preventing approval delays and errors caused by process deviations or data inconsistencies. Specifically, the introduction of authoritative policy interpretation logs ensures that the basis for conflict resolution is accurate and credible, avoiding biases caused by subjective judgment. The automated arbitration by the rules engine greatly shortens conflict handling time and improves the responsiveness of government services. Furthermore, adjustments to future task sequences and synchronous updates of key data fields ensure that workflow instances always flow along the correct policy path and ensure cross-departmental data consistency, thereby improving the compliance and accuracy of government approvals and reducing the cost and risk of manual intervention.

[0083] In some preferred embodiments, a specific example is given below. Suppose a local government issues a new policy for "Enterprise High-Tech Enterprise Certification," which stipulates that applicant enterprises must complete an "Environmental Compliance Self-Assessment" and pass a "Technological Innovation Capability Assessment" by a third-party organization before submitting application materials. However, in actual implementation, due to system configuration errors or operator negligence, a certain enterprise's workflow instance directly enters the "Technological Innovation Capability Assessment" stage without completing the "Environmental Compliance Self-Assessment," thus triggering a path conflict event.

[0084] At this point, the proposed solution will come into play. First, the system will query the authoritative policy interpretation log, which clearly records the final policy intent and approval conditions for the "Enterprise High-Tech Enterprise Certification" policy, namely, "Environmental Compliance Self-Inspection" is a prerequisite for "Technological Innovation Capability Assessment." Next, referring to the preset conflict resolution instruction set, such as "If the prerequisite is not met, backtrack to the prerequisite task node," the rule engine will arbitrate based on the policy intent and approval conditions in the authoritative policy interpretation log, and the arbitration result will be: this workflow instance should backtrack to the "Environmental Compliance Self-Inspection" task node.

[0085] Based on this arbitration result, the system will automatically adjust the future task sequence of the current workflow instance, jumping it from the incorrect "Technological Innovation Capability Assessment" stage back to the "Environmental Compliance Self-Inspection" task node, and suspending the execution of subsequent tasks until the "Environmental Compliance Self-Inspection" is completed and passed. Simultaneously, if, during this process, certain key data fields (such as "Environmental Compliance Assessment Results") are incorrectly entered or missing due to process errors, the arbitration result will also instruct the system to update these key data fields and synchronize the correct or supplementary data information to all relevant departmental business systems, such as the environmental protection bureau's business system and the science and technology bureau's business system, through a data synchronization interface, ensuring data consistency and accuracy. Through this series of automated processes, the company's workflow instance is corrected, avoiding policy non-compliance and approval delays caused by process errors.

[0086] In some of the embodiments described above in this application, after the workflow template is activated, the workflow engine's log recording module and status listener continuously collect the task execution status and key data field change records of the workflow instance, and perform path consistency comparison or field consistency comparison to generate path conflict events or data conflict events. However, simply generating conflict events cannot completely solve the problem. If these conflict events are not subsequently assessed for their impact and notified in a timely manner, the conflicts may not receive effective attention and timely handling, thereby affecting the normal flow, compliance, and public interest of government affairs.

[0087] In response, this application further proposes a method for assessing the impact of a path conflict or data conflict and sending an early warning notification after the conflict is generated.

[0088] After generating the path conflict event or the data conflict event, the method further includes: According to preset conflict impact assessment rules, the impact degree of the path conflict event or the data conflict event is assessed, wherein the impact degree assessment considers at least the following three assessment dimensions: The potential damage to public interests caused by the conflict, the degree of violation of policy compliance, and the delay in the timeliness of government affairs completion are assessed. Based on the assessed impact, a corresponding early warning level is selected from a preset early warning level configuration, wherein the early warning levels include at least low-level, medium-level, and high-level early warnings, and each early warning level corresponds to different response timeliness requirements. Based on the warning level, a corresponding list of notification objects is obtained from a preset notification object mapping table; a warning notification containing conflict details and the warning level is generated, and the warning notification is sent to the recipients in the list of notification objects through at least one channel, such as an SMS gateway, an email server, or an instant messaging interface.

[0089] Specifically, conflict impact assessment rules refer to a series of predefined logical judgment conditions and scoring standards used to quantify the potential negative impacts of path conflict events or data conflict events. These rules can be constructed based on historical data, expert experience, or policies and regulations. For example, it can be defined that when a path conflict occurs in a key approval process, the delay in the timeliness of government affairs completion will be automatically assessed as "high." The "potential damage value of the conflict to the public interest" in the assessment dimensions refers to the degree of negative impact that the conflict may have on the public interest. For example, conflicts involving people's livelihood and public safety usually have a higher potential damage value. The "degree of violation of policy compliance value" refers to the severity of the conflict event's inconsistency with existing policies, regulations, and rules. For example, conflicts that violate mandatory provisions have a higher degree of violation value. The "delay in the timeliness of government affairs completion" refers to the degree to which the conflict event may cause the government approval or service processing time to exceed the expected or prescribed time limit.

[0090] The alert level configuration refers to a pre-defined mapping table or rule set that associates different levels of impact with specific alert levels (e.g., low, medium, and high alerts). Each alert level corresponds to a different response time requirement; for example, a high alert may require a response within 1 hour, a medium alert within 4 hours, and a low alert may allow a response within 24 hours. The notification object mapping table is a database or configuration item that stores the correspondence between different alert levels and their corresponding recipient lists. For example, a high-level alert may require notification to department heads and compliance managers, while a low-level alert may only require notification to the specific personnel in charge. The alert notification content typically includes a detailed description of the conflict, the time of occurrence, the involved workflow instance, the assessed impact level, and the corresponding alert level, so that recipients can quickly understand the situation and take action. The choice of notification channel can be configured according to the urgency of the alert and the recipient's preferences. For example, for a high-level alert, it can be sent simultaneously via SMS and instant messaging interfaces to ensure timely delivery of information.

[0091] This application's solution introduces a conflict impact assessment mechanism, enabling the system to quantitatively analyze the severity, scope, and urgency of a conflict upon detection, based on preset rules. Based on the assessment results, the system intelligently selects an appropriate warning level and automatically identifies the relevant responsible persons or departments that need to be notified. Subsequently, the system generates a notification containing detailed conflict information and the warning level, and sends it to the designated recipients through multiple communication channels. This series of operations ensures that conflict events are not only detected but also promptly and accurately communicated to the correct personnel, thereby prompting relevant parties to take appropriate response measures based on the actual impact of the conflict, avoiding the escalation of potential risks due to information delays or improper handling.

[0092] refer to Figure 3 , Figure 3 This is a schematic diagram of a workflow management system for daily government office scenarios provided by an embodiment of the present invention. The system includes: The policy document acquisition module is used to acquire policy documents through government data interfaces or document upload channels. The content organization module is used to perform format standardization and semantic structuring on the policy documents in sequence to obtain policy texts with a standardized structure. The syntactic analysis module is used to perform syntactic dependency analysis on the policy text of the normative structure using a pre-trained dependency parser and in conjunction with a preset government domain dictionary, to obtain a policy syntax tree; The element extraction module is used to match the policy syntax tree with a pre-built set of policy semantic parsing rules, identify and extract policy elements from the policy syntax tree, wherein the policy elements include at least policy objects, policy conditions and policy actions; The element mapping module is used to map and match the policy elements with predefined task nodes in the workflow engine, and establish the correspondence between the policy elements and the task nodes. The template generation module is used to automatically adjust or generate an executable workflow template by the workflow engine according to the correspondence. The automatic adjustment or generation includes at least one of the following operations: inserting a new task node, modifying the judgment logic of an existing task node, adjusting the execution priority of an existing task node, or creating a parallel task flow or a serial task flow. The template deployment module is used to deploy the adjusted or generated workflow template to the workflow engine and activate it, so that the workflow engine can start and execute the government approval workflow instance based on the workflow template.

[0093] The workflow management system proposed in this application, through the collaborative operation of its various internal functional modules, achieves intelligent parsing of policy documents, accurate extraction of policy elements, and automated adjustment and deployment of workflow templates. Therefore, this system can effectively address the problem of process rigidity caused by frequent policy adjustments in daily government operations, significantly improve the response speed, accuracy, and compliance of government approvals, thereby reducing errors and risks caused by manual intervention.

[0094] In response, the workflow management system proposed in this application for government daily office scenarios significantly overcomes the shortcomings of existing technologies through its intelligent policy document parsing, policy element extraction, and workflow template adaptive adjustment mechanism. This system can automatically identify key information from policy documents and transform it into executable workflow logic, thereby achieving rapid and accurate updates to workflow templates. Compared to traditional systems that rely on manual understanding and adjustments, this application's system can significantly improve the response speed and accuracy of government approvals, reduce the uncertainty and errors caused by manual operations, and effectively ensure the compliance of government processes. Therefore, this application provides government departments with a more flexible, intelligent, and efficient daily office environment.

[0095] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A workflow management method for daily government office scenarios, characterized in that, The method includes: Policy documents can be obtained through government data interfaces or document upload channels; The policy documents are then subjected to format standardization and semantic structuring processes in sequence to obtain policy texts with a standardized structure. By using a pre-trained dependency parser and combining it with a pre-defined government affairs domain dictionary, the policy text of the normative structure is subjected to syntactic dependency analysis to obtain a policy syntax tree. The policy syntax tree is matched with a pre-built set of policy semantic parsing rules to identify and extract policy elements from the policy syntax tree, wherein the policy elements include at least policy objects, policy conditions, and policy actions; The policy elements are mapped and matched with predefined task nodes in the workflow engine to establish a correspondence between the policy elements and the task nodes; Based on the correspondence, the workflow engine automatically adjusts or generates an executable workflow template. The automatic adjustment or generation includes at least one of the following operations: inserting a new task node, modifying the judgment logic of an existing task node, adjusting the execution priority of an existing task node, or creating a parallel task flow or a serial task flow. Deploy the adjusted or generated workflow template to the workflow engine and activate it, so that the workflow engine starts and executes the government approval workflow instance based on the workflow template; The process of standardizing the format and activating the workflow template is performed by computer equipment deployed on the government intranet or government cloud platform.

2. The method according to claim 1, characterized in that, The format standardization process includes at least one of the following: character encoding standardization, layout element stripping, and table to structured data conversion; the semantic structuring process includes at least one of the following: paragraph segmentation, clause number recognition, and citation extraction.

3. The method according to claim 1, characterized in that, The policy elements include policy objects, policy conditions, and policy actions, wherein the policy objects represent the types of matters to which the policy applies, the policy conditions represent the preconditions that trigger the policy actions, and the policy actions represent the specific operations that need to be performed in the workflow template. The method further includes: constructing the policy semantic parsing rule set, specifically: Syntactic dependency annotation was performed on samples of historical policy documents to obtain an annotated corpus; Extract high-frequency syntactic dependency patterns from the annotated corpus; The high-frequency syntactic dependency patterns are associated and mapped with domain entries in the government affairs domain dictionary to obtain pattern-entry association rules; The pattern-term association rules are conflict-resolved and merged to obtain the policy semantic parsing rule set.

4. The method according to claim 1, characterized in that, The automatic adjustment or generation of executable workflow templates includes: Obtain the currently deployed workflow template in the workflow engine as the baseline template; Based on the correspondence, the differences between the baseline template and the policy elements are determined; For each of the aforementioned differences, perform one of the following operations: When the difference point indicates that the baseline template lacks a task node corresponding to the policy element, a new task node is inserted into the baseline template; When the difference point indicates that the judgment logic of the existing task node in the baseline template is inconsistent with the policy element, the judgment logic of the existing task node is modified. When the difference point indicates that the execution priority of an existing task node in the baseline template is inconsistent with the policy element, the execution priority of the existing task node shall be adjusted. When the difference point indicates that the policy element requires multiple parallel processes in the baseline template, a parallel task flow or a serial task flow is created in the baseline template.

5. The method according to claim 1, characterized in that, After the workflow template is activated, the method further includes: The workflow engine's logging module and status listener continuously collect the task execution status and key data field change records of the workflow instance; The actual workflow path of the workflow instance is determined from the task execution status, and the expected workflow path of the workflow instance is determined from the workflow template. The actual workflow path and the expected workflow path are compared for path consistency. When the comparison result fails, a path conflict event is generated. The key data fields entered or updated by different departments when processing the same workflow instance are compared with the data validation rules loaded from the workflow template. When the comparison result fails, a data conflict event is generated.

6. The method according to claim 5, characterized in that, Determining the expected workflow path of the workflow instance from the workflow template includes: Each policy version shall be assigned a unique version identifier and effective time range; A policy version timeline is constructed based on a graph database, where nodes represent policy version identifiers and edges represent the effective time range corresponding to the policy version identifiers. When the workflow instance starts, the policy version identifier that the workflow instance should follow is determined from the policy version timeline based on the startup time of the workflow instance; Based on the policy version identifier, retrieve the adapted workflow template corresponding to the policy version from the workflow template library, and use the task node sequence defined in the adapted workflow template as the expected workflow path.

7. The method according to claim 5, characterized in that, The method further includes: loading data validation rules, specifically: Based on the application type of the workflow instance, a set of basic key data fields is loaded from a preset business type data definition library, wherein the set of basic key data fields is defined using a JSONSchema data structure; Based on the policy version followed by the workflow instance, obtain the definitions of key data fields added, modified, or deprecated in the policy version data revision log; The newly added, modified, or obsolete key data field definitions are merged into the basic key data field set to obtain an extended key data field set; Based on the current process stage of the workflow instance, load the process stage-specific key data fields from the process stage data requirement configuration; The key data fields specific to the process stage are integrated into the extended key data field set to obtain a contextualized key data field set; All fields in the contextualized key data field set are used as the range of fields to be verified, and the corresponding data verification rules are loaded.

8. The method according to claim 5, characterized in that, After generating the path conflict event or the data conflict event, the method further includes: The system queries authoritative policy interpretation logs, which record the final policy intent and approval conditions of all policy texts after semantic parsing, time-range anchoring, and intent conformity pre-verification. These logs are generated through cross-validation using dual-track semantic parsing. When the results of two independent semantic parsings are consistent, the policy intent and approval conditions of the policy document are written into the authoritative policy interpretation log; when the results of two independent semantic parsings are inconsistent, it is marked as pending manual review. Referring to the preset conflict resolution instruction set, and based on the policy intent and approval conditions in the authoritative policy interpretation log, the conflict is arbitrated through the rule engine to obtain the arbitration result; Based on the arbitration result, the future task sequence of the current workflow instance is adjusted to jump from the incorrect flow path to the correct starting node; Based on the arbitration result, the key data fields in the workflow instance are updated, and the updated key data fields are synchronized to the departmental business systems of all relevant departments through the data synchronization interface.

9. The method according to claim 5, characterized in that, After generating the path conflict event or the data conflict event, the method further includes: assessing the impact of the path conflict event or the data conflict event according to a preset conflict impact assessment rule, wherein the impact assessment considers at least three assessment dimensions, namely the potential damage value of the conflict to the public interest, the degree of violation of policy compliance, and the delay time in the timeliness of completing government affairs. Based on the assessed degree of impact, a corresponding warning level is selected from the preset warning level configuration, wherein the warning levels include at least low-level warning, medium-level warning and high-level warning, and each warning level corresponds to different response time requirements; Based on the warning level, a corresponding list of notification objects is obtained from a preset notification object mapping table; a warning notification containing conflict details and the warning level is generated, and the warning notification is sent to the recipients in the list of notification objects through at least one channel, such as an SMS gateway, an email server, or an instant messaging interface.

10. A workflow management system for daily government office scenarios, characterized in that, The system includes: The policy document acquisition module is used to acquire policy documents through government data interfaces or document upload channels. The content organization module is used to perform format standardization and semantic structuring on the policy documents in sequence to obtain policy texts with a standardized structure. The syntactic analysis module is used to perform syntactic dependency analysis on the policy text of the normative structure using a pre-trained dependency parser and in conjunction with a preset government domain dictionary, to obtain a policy syntax tree; The element extraction module is used to match the policy syntax tree with a pre-built set of policy semantic parsing rules, identify and extract policy elements from the policy syntax tree, wherein the policy elements include at least policy objects, policy conditions and policy actions; The element mapping module is used to map and match the policy elements with predefined task nodes in the workflow engine, and establish the correspondence between the policy elements and the task nodes. The template generation module is used to automatically adjust or generate an executable workflow template by the workflow engine according to the correspondence. The automatic adjustment or generation includes at least one of the following operations: inserting a new task node, modifying the judgment logic of an existing task node, adjusting the execution priority of an existing task node, or creating a parallel task flow or a serial task flow. The template deployment module is used to deploy the adjusted or generated workflow template to the workflow engine and activate it, so that the workflow engine can start and execute the government approval workflow instance based on the workflow template.

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