Method, device and electronic equipment for generating a knowledge base of a visualization workflow

Abstract

The application relates to a knowledge system generation method and device for visualizing a workflow and electronic equipment. The method comprises the following steps: performing structural depth analysis on the visualized workflow to obtain a static structure and dynamic logic of the visualized workflow, and encapsulating the static structure and the dynamic logic into a structured semantic model; extracting all knowledge entities in the structured semantic model and the correlation relationship between the knowledge entities, and performing aggregation and deduction on the knowledge entities and the correlation relationship to construct a knowledge graph; extracting corresponding information from the knowledge graph according to a preset document template and a preset chart rule, and generating a knowledge system corresponding to the visualized workflow; and displaying the knowledge graph to a user on an interactive graphical interface, and performing dependency relationship analysis or change comparison analysis on any knowledge entity in the knowledge graph when detecting that the user performs a selection or change operation on the knowledge entity. The application solves the technical problem that knowledge of a visualized workflow is difficult to automatically deposit and inherit.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, and electronic device for generating a knowledge system for a visual workflow. Background Technology

[0002] While the widely adopted visual development model in low-code / no-code platforms and various AI workflow systems has significantly lowered the barrier to entry for system construction and improved development efficiency, it has also gradually exposed prominent knowledge management issues in practical applications. As business complexity increases, workflows often contain a large number of key business rules, data processing logic, and system integration details. This core knowledge is mainly implicit in the configuration and connection relationships of visual nodes, lacking a unified and systematic way of expression, and relying heavily on developers' personal experience and fragmented understanding. On the one hand, in scenarios with personnel changes or frequent workflow iterations, design ideas and implicit logic are difficult to preserve completely, and manually maintained documents are severely out of sync with the actual workflow status, posing a significant risk of knowledge asset loss. On the other hand, existing platforms generally lack in-depth analysis tools for the internal structure, data flow, and dependencies of workflows. New members struggle to quickly understand the overall system and cannot efficiently retrieve, analyze, or intelligently answer questions about workflows, severely restricting the maintainability and inheritability of the system. The root cause is that existing technologies have failed to deeply analyze the workflow topology, node semantics, and data and control flow and transform them into structured knowledge, resulting in knowledge management remaining at the stage of manual organization. Summary of the Invention

[0003] This application provides a method, apparatus, and electronic device for generating a knowledge system for a visual workflow, in order to solve the technical problem that visual workflow knowledge is difficult to automatically accumulate and pass on.

[0004] Firstly, this application provides a method for generating a knowledge system for a visualized workflow, comprising: performing a deep structural analysis of the visualized workflow to obtain its static structure and dynamic logic, and encapsulating the static structure and dynamic logic into a structured semantic model, wherein the static structure includes node information of each node and the connection relationships between nodes, and the dynamic logic includes the transmission and transformation relationships of data between nodes; extracting all knowledge entities and the relationships between knowledge entities from the structured semantic model, and aggregating and deriving the knowledge entities and relationships based on preset aggregation rules to construct a knowledge graph; extracting corresponding information from the knowledge graph according to preset document templates and preset chart rules to generate a knowledge system corresponding to the visualized workflow, wherein the knowledge system includes at least document content and / or visual display content generated based on the knowledge graph; displaying the knowledge graph to the user in an interactive graphical interface, and performing dependency analysis or change comparison analysis on any knowledge entity in the knowledge graph when the user performs a selection or change operation.

[0005] Secondly, this application provides a knowledge system generation device for a visualized workflow, comprising: an encapsulation module, used to perform deep structural analysis on the visualized workflow to obtain its static structure and dynamic logic, and encapsulate the static structure and dynamic logic into a structured semantic model, wherein the static structure includes node information of each node and connection relationships between nodes, and the dynamic logic includes data transmission and transformation relationships between nodes; a construction module, used to extract all knowledge entities and relationships between knowledge entities from the structured semantic model, and aggregate and deduce the knowledge entities and relationships based on preset aggregation rules to construct a knowledge graph; a generation module, used to extract corresponding information from the knowledge graph according to preset document templates and preset chart rules to generate a knowledge system corresponding to the visualized workflow, wherein the knowledge system includes at least document content and / or visual display content generated based on the knowledge graph; and an analysis module, used to display the knowledge graph to a user in an interactive graphical interface, and perform dependency analysis or change comparison analysis on any knowledge entity in the knowledge graph when the user performs a selection or change operation.

[0006] As an optional example, the above encapsulation module includes: a first identification unit, used to abstract the above-mentioned visual workflow into a directed graph structure, analyze the connection relationships between nodes, and identify the process structure of sequential execution, branch execution, loop execution, and parallel execution; a first analysis unit, used to use natural language processing technology to perform semantic analysis on the name information, description information, and configuration parameters of each node, and use a node function classification model to map each node to the corresponding function category, while extracting key semantic information related to node functions from the configuration parameters to obtain the static structure of the above-mentioned visual workflow; and a first construction unit, used to parse the data transmission relationship and condition control relationship between each node, track the flow and transformation process of data between different nodes to construct data flow relationships, and parse the condition judgment logic to determine the execution path of the process under different conditions to obtain the dynamic logic of the above-mentioned visual workflow.

[0007] As an optional example, the above encapsulation module includes: a mapping unit, used to map the node information, connection relationships and node types in the above static structure into semantic entities, and to map the data transmission relationships, data transformation relationships and condition control rules in the above dynamic logic into semantic relationships; and a second construction unit, used to construct a unified structured semantic representation based on the above semantic entities and the above semantic relationships, and to organize each node and its associated inputs, outputs, logical conditions and functional information into a data structure to form the above structured semantic model.

[0008] As an optional example, the above-mentioned construction module includes: a second identification unit, used to identify knowledge entities representing workflow nodes, data items, functional components and configuration parameters from the above-mentioned structured semantic model, and to identify the relationships between entities representing the execution order, data transmission, condition triggering and logical dependencies between nodes; a merging unit, used to merge knowledge entities that implement the same business rule based on the above-mentioned preset aggregation rules, and to deduce the implicit relationships to form higher-order knowledge entities and relationships; and an integration unit, used to integrate all knowledge entities and all relationships into the graph database to form the above-mentioned knowledge graph.

[0009] As an optional example, the above generation module includes: a first generation unit, used to extract the required knowledge entities and relationships from the knowledge graph according to the above preset document template, and fill them into the corresponding positions of the above preset document template to generate corresponding document content; and a second generation unit, used to call a graphic layout algorithm according to the above preset chart rules, and generate corresponding visual display content based on the knowledge entities and relationships in the above knowledge graph.

[0010] As an optional example, the analysis module includes: a third identification unit, configured to, upon detecting that the user performs a selection operation on the target knowledge entity, traverse the knowledge graph, identify and graphically highlight all knowledge entities that directly or indirectly depend on the target knowledge entity, wherein the target knowledge entity is any knowledge entity in the knowledge graph; and a second analysis unit, configured to, upon detecting that the user performs a change operation on the target knowledge entity, update the knowledge graph, and perform comparative analysis on the versions of the knowledge graph before and after the update, displaying the comparative analysis results in a visual diagram or annotation, wherein the change operation includes adding, deleting, and modifying.

[0011] As an optional example, the above apparatus further includes: a first retrieval module, configured to, after displaying the knowledge graph to a user in an interactive graphical interface, filter the knowledge entities in the knowledge graph when detecting that the user performs a retrieval operation on a target knowledge entity type in the knowledge graph, and only display the knowledge entities and their relationships that match the selected target knowledge entity type, wherein the target knowledge entity type is any one of the knowledge entity types in the knowledge graph; and a second retrieval module, configured to, when detecting that the user performs a retrieval operation on a target attribute keyword in the knowledge graph, match the knowledge entities and their relationships in the knowledge graph, and only display the knowledge entities and their relationships that contain the target attribute keyword, wherein the target attribute keyword is any one of the attribute keywords in the knowledge graph.

[0012] Thirdly, this application provides a storage medium storing a computer program, wherein the computer program is executed by a processor to perform the knowledge system generation method for the visualization workflow described above.

[0013] Fourthly, this application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the above-described knowledge system generation method for visual workflow through the computer program.

[0014] The technical solutions provided in this application have the following advantages compared with the prior art: This application employs a deep structural analysis of the visualized workflow to obtain its static structure and dynamic logic. The static structure and dynamic logic are then encapsulated into a structured semantic model. The static structure includes node information and connections between nodes, while the dynamic logic includes data transmission and transformation relationships between nodes. All knowledge entities and their relationships within the structured semantic model are extracted. Based on preset aggregation rules, these knowledge entities and relationships are aggregated and deduced to construct a knowledge graph. According to preset document templates and graph rules, corresponding information is extracted from the knowledge graph to generate a knowledge system corresponding to the visualized workflow. The knowledge system includes at least document content and / or visual display content generated based on the aforementioned knowledge graph. It presents the knowledge graph to the user in an interactive graphical interface and performs dependency analysis or change comparison analysis when the user selects or modifies any knowledge entity in the knowledge graph. This method involves deep structural analysis of the visualized workflow to obtain its static structure and dynamic logic, encapsulating it into a structured semantic model. Based on this, it automatically extracts knowledge entities and their relationships to construct a knowledge graph, and then generates document content and visual display content synchronized with the workflow based on the knowledge graph. Dependency analysis and change comparison analysis are supported in the interactive graphical interface. This achieves the goal of automatically transforming the business logic and technical details originally implicit in the visualized process into an understandable, searchable, and inheritable knowledge system, effectively preventing knowledge loss, reducing system understanding and maintenance costs, and improving the maintainability, scalability, and collaboration efficiency of the workflow system. Ultimately, it solves the technical problem of the difficulty in automatically accumulating and inheriting knowledge from visualized workflows. Attached Figure Description

[0015] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0018] Figure 1 This is a flowchart of an optional knowledge system generation method for a visual workflow according to an embodiment of this application; Figure 2 This is a flowchart illustrating a specific implementation of an optional knowledge system generation method for a visual workflow according to an embodiment of this application. Figure 3 This is a schematic diagram of the structure of an optional knowledge system generation device for visual workflow according to an embodiment of this application; Figure 4 This is a schematic diagram of an optional electronic device according to an embodiment of this application. Detailed Implementation

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

[0020] The following disclosure provides numerous different embodiments or examples for implementing various structures of this application. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of this application. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0021] According to a first aspect of the embodiments of this application, a method for generating a knowledge system for a visualized workflow is provided, optionally, as follows: Figure 1 As shown, the above method includes: S102 performs in-depth structural analysis of the visualized workflow to obtain its static structure and dynamic logic, and encapsulates the static structure and dynamic logic into a structured semantic model. The static structure includes the node information of each node and the connection relationship between nodes, while the dynamic logic includes the data transmission and transformation relationship between each node. S104: Extract all knowledge entities and the relationships between them from the structured semantic model, and aggregate and deduce the knowledge entities and relationships based on preset aggregation rules to construct a knowledge graph. S106. Based on the preset document template and preset chart rules, extract the corresponding information from the knowledge graph to generate the knowledge system corresponding to the visual workflow. The knowledge system includes at least the document content and / or visual display content generated based on the knowledge graph. S108: Display the knowledge graph to the user through an interactive graphical interface, and perform dependency analysis or change comparison analysis on any knowledge entity in the knowledge graph when the user performs a selection or change operation.

[0022] Optionally, this embodiment proposes a method for generating a knowledge system for visual workflows, applicable to low-code / no-code platforms and various AI workflow systems. This method addresses the issues of implicit business logic and difficulty in knowledge accumulation and inheritance under visual development models. By systematically analyzing and modeling the visual workflow, the process structure, originally only used for execution, is automatically transformed into an understandable, queryable, and reusable knowledge system, achieving structured expression and intelligent management of workflow knowledge.

[0023] Specifically, such as Figure 2 The flowchart shown first provides an in-depth analysis of the visualized workflow execution structure, including topology analysis, semantic understanding, and data / control flow tracing, to obtain the workflow's static structure and dynamic logic. The static structure describes the overall topology of the workflow, including node information, node types, and connections between nodes. The dynamic logic describes the workflow's behavioral characteristics during operation, including data transmission paths between different nodes, data processing and transformation methods, and the control rules for process flow based on conditional judgments. Through comprehensive analysis of node connections, data flow, and control logic, the workflow is transformed from a graphical representation into computer-processable semantic information.

[0024] Building upon this foundation, the parsed static structure and dynamic logic are further encapsulated into a unified structured semantic model. This structured semantic model maps node information and connection relationships to semantic entities, and data transmission relationships, data transformation relationships, and condition control rules to semantic relations. This allows for a complete expression of the workflow's design intent, execution logic, and internal dependencies in structured data form, providing a standardized foundation for subsequent knowledge extraction and analysis.

[0025] Subsequently, knowledge entities and their relationships are automatically extracted from the structured semantic model. Knowledge entities include, but are not limited to, workflow nodes, data items, functional components, and configuration parameters; relationships include execution order relationships, data dependencies, conditional trigger relationships, and logical dependencies. Based on this, multiple functionally or business-related knowledge entities are aggregated according to preset aggregation rules, and implicit relationships are deduced to form a higher-level knowledge representation. By uniformly organizing and storing the above knowledge entities and their relationships, a knowledge graph reflecting the complete knowledge structure of the workflow is constructed.

[0026] Furthermore, using the knowledge graph as the sole source of facts, and based on preset document templates and chart rules, corresponding information is extracted from the knowledge graph to automatically generate a knowledge system corresponding to the visualized workflow. The knowledge system includes at least document content and / or visual display content generated based on the knowledge graph. The document content can be used to describe the overall design, functional specifications, and operational steps of the workflow, including but not limited to technical design documents, user manuals, API reference documents, and architecture diagrams. The visual display content intuitively presents the data flow, dependencies, and logical structure of the workflow in chart form, such as PNG and SVG formats, thereby ensuring the consistency and real-time nature of knowledge representation.

[0027] Furthermore, this embodiment provides an interactive platform that displays the knowledge graph to users through an interactive graphical interface. It uses visual charts (such as data flow diagrams and dependency graphs) and tools like intelligent navigation, change impact analysis, and version comparison to intuitively present knowledge, supporting exploration and question-and-answer sessions to lower the barrier to understanding. Users can also select or modify any knowledge entity in the knowledge graph. Upon detecting a user action, the system can automatically perform dependency analysis or change comparison analysis on the target knowledge entity, identifying and presenting its direct or indirect impact scope, or displaying the differences in the knowledge structure before and after the change. This assists users in understanding the workflow structure, assessing the impact of changes, and guiding subsequent optimizations.

[0028] Optionally, in this embodiment, by performing deep analysis and semantic modeling on the visualized workflow, the automated extraction and structured expression of workflow knowledge are achieved, avoiding reliance on manual memorization of core knowledge; workflow knowledge is managed uniformly based on knowledge graphs, keeping documents synchronized with actual processes and effectively preventing knowledge disconnection and loss; by automatically generating documents and visual charts, the system understanding and maintenance costs are significantly reduced, and the efficiency of collaboration and inheritance is improved; through interactive analysis capabilities, intuitive insights into workflow dependencies and the impact of changes are achieved, enhancing the security and controllability of system evolution.

[0029] As an optional example, a deep structural analysis of the visual workflow is performed to obtain its static structure and dynamic logic, including: The visualized workflow is abstracted into a directed graph structure, and the connection relationships between nodes are analyzed to identify the process structure of sequential execution, branch execution, loop execution, and parallel execution. Using natural language processing technology, semantic analysis is performed on the name information, description information and configuration parameters of each node. Then, using a node function classification model, each node is mapped to the corresponding function category. At the same time, key semantic information related to node functions is extracted from the configuration parameters to obtain the static structure of the visualized workflow. The data transmission and condition control relationships between nodes are analyzed to track the flow and transformation of data between different nodes in order to construct data flow relationships. The condition judgment logic is also analyzed to determine the execution path of the process under different conditions, thus obtaining the dynamic logic of the visualized workflow.

[0030] Optionally, in this embodiment, a deep structural analysis of the visualized workflow is performed to obtain its static structure and dynamic logic. Specifically, this may include the following process: First, the system abstracts the visualized workflow into a directed graph structure, where each functional node is a node in the graph, and the connections between nodes are directed edges. By analyzing the connection relationships between nodes and edges, the system automatically identifies basic process structures such as sequential execution, branch execution, loop execution, and parallel execution in the workflow, thereby clarifying the overall control framework and key execution paths. Second, regarding the semantic information of each node, the system uses natural language processing technology to perform semantic analysis on the node's name, description, and configuration parameters. Combined with a node function classification model, the system maps nodes to predefined functional categories, such as data input, logical judgment, data processing, or external service calls. Simultaneously, it extracts key information related to the node's function from the configuration parameters to form a unified characterization of each node's functional attributes, thus obtaining the static structure of the visualized workflow. Building upon this foundation, the system further analyzes the data transfer and condition control relationships between nodes, tracing the data flow paths and processing and transformation processes across different nodes. This constructs a complete data flow relationship chain, and the system analyzes the condition judgment logic to clarify the execution direction and branching rules of the process when different conditions are met or not met, thereby obtaining the dynamic logic of the visualized workflow. Through this structured analysis process, the workflow, originally presented only in graphical form, can be transformed into a computable representation that simultaneously contains structural information and behavioral logic, providing a reliable foundation for subsequent semantic modeling and knowledge extraction.

[0031] As an optional example, encapsulating static structure and dynamic logic into a structured semantic model includes: Map node information, connection relationships, and node types in static structures to semantic entities, and map data transmission relationships, data transformation relationships, and condition control rules in dynamic logic to semantic relationships. Based on semantic entities and semantic relationships, a unified structured semantic representation is constructed, and each node and its associated inputs, outputs, logical conditions and functional information are organized into a data structure to form a structured semantic model.

[0032] Optionally, in this embodiment, the static structure and dynamic logic in the original workflow or system are uniformly abstracted and encapsulated to construct a computable and reasonable structured semantic model. Specifically, the static structure in the system is first semantically mapped, and the node information, node types, and connections between nodes are abstracted into semantic entities. Node information describes the functional attributes and configuration parameters of nodes, node types identify the role of nodes in the overall process, and connections between nodes characterize the structural dependencies between different nodes, thus forming a set of entities with clear semantic boundaries.

[0033] Based on this, semantic modeling is performed on the dynamic logic manifested during system operation. Dynamic logic includes, but is not limited to, data transmission relationships between nodes, data processing and transformation relationships, and control rules based on conditional judgments. By mapping the above dynamic logic to semantic relationships, the flow path, processing method, and triggering conditions of data between different semantic entities can be clearly described, thereby achieving semantic characterization of the process behavior level.

[0034] Furthermore, based on the obtained semantic entities and semantic relationships, a unified structured semantic representation is constructed. This representation organizes each semantic entity and its associated inputs, outputs, logical conditions, and functional information in a structured manner, forming a standardized data structure. This structured semantic model not only fully reflects the system's topology and execution logic but also provides a unified data foundation for subsequent analysis, retrieval, reasoning, and automated processing.

[0035] As an optional example, all knowledge entities and their relationships within a structured semantic model are extracted. Based on predefined aggregation rules, the knowledge entities and their relationships are aggregated and deduced to construct a knowledge graph, including: Identify knowledge entities representing workflow nodes, data items, functional components, and configuration parameters from the structured semantic model, and identify the relationships between entities representing execution order, data transfer, condition triggering, and logical dependencies between nodes; Based on preset aggregation rules, knowledge entities that implement the same business rule are merged, and implicit relationships are derived to form higher-order knowledge entities and relationships. All knowledge entities and all relationships are integrated into a graph database to form a knowledge graph.

[0036] Optionally, in this embodiment, based on the completed structured semantic model, the knowledge information contained therein is further systematically extracted, aggregated, and deduced to form a knowledge graph oriented towards workflow and business logic understanding. This process achieves a unified expression and high-level abstraction of complex process knowledge by identifying and modeling the associations of various semantic elements in the structured semantic model.

[0037] Specifically, the process begins by extracting various knowledge entities from the structured semantic model to represent workflow nodes, data items, functional components, and configuration parameters. Workflow node entities represent execution units within the process, data item entities describe the data flowing between nodes, functional component entities characterize the processing capabilities of a node, and configuration parameter entities reflect the control and constraints during node operation. Simultaneously, the relationships between these knowledge entities are identified and extracted, including execution order relationships, data transfer relationships, condition-based triggering relationships, and logical dependencies between nodes, thereby constructing a complete entity-relationship set.

[0038] Based on this, semantic-level aggregation and derivation are performed on the extracted knowledge entities and their relationships according to pre-defined aggregation rules. Aggregation rules are used to identify multiple knowledge entities that jointly participate in achieving the same business goal or business rule, and merge them into a unified higher-order knowledge entity to reduce redundant expressions and improve the semantic abstraction level. Simultaneously, through the combination, transmission, and constraint analysis of existing relationships, implicit relationships that are not explicitly described but objectively exist in the structured semantic model are derived, thereby completing the logical connections between entities.

[0039] Furthermore, all knowledge entities and their relationships, after aggregation and derivation, are uniformly written into a graph database and stored and managed using nodes and edges, ultimately forming a structured and computable knowledge graph. This knowledge graph can fully reflect the workflow structure, data flow paths, and business logic dependencies.

[0040] As an optional example, based on preset document templates and preset chart rules, corresponding information is extracted from the knowledge graph to generate a knowledge system corresponding to the visualized workflow, including: Based on the preset document template, extract the required knowledge entities and relationships from the knowledge graph, and fill them into the corresponding positions in the preset document template to generate the corresponding document content; Based on preset chart rules, the graph layout algorithm is invoked to generate corresponding visual content according to the knowledge entities and relationships in the knowledge graph.

[0041] Optionally, in this embodiment, the completed knowledge graph is used as the sole source of facts for the visualization workflow, providing on-demand multi-form knowledge output to ensure consistency and traceability among different forms of knowledge expression.

[0042] Specifically, regarding template-based document generation, the system pre-builds various document templates for different use cases, including but not limited to technical design documents, system architecture documentation, and user manuals. The document templates define the content structure, semantic placeholders, and presentation specifications for each document type. Based on the template instructions, the document generation engine retrieves knowledge entities and their relationships from the knowledge graph that match the semantic placeholders, automatically filling the corresponding positions in the template with the search results, and finally rendering and generating a formatted document file, such as Markdown, PDF, or other configurable formats. In this way, multiple related node entities in the knowledge graph can be automatically organized into coherent operational steps or logical descriptions.

[0043] In terms of automatic generation of visualization charts, the system analyzes subgraphs composed of entities and their relationships in the knowledge graph for different types of visualization content, such as architecture diagrams, data flow diagrams, and dependency graphs, and automatically calls preset graphic layout algorithms for rendering. Graphic layout algorithms include, but are not limited to, hierarchical layout algorithms and force-directed layout algorithms, to ensure that the generated charts conform to the execution logic and data flow of the workflow in terms of spatial structure, ultimately producing understandable graphical displays, such as PNG or SVG formats.

[0044] By using knowledge graphs as the sole and accurate data source, the system can automatically generate corresponding documents and charts based on different usage needs, and synchronously update relevant knowledge outputs when changes occur in the visualization workflow. This effectively avoids inconsistencies caused by manual document maintenance and improves the accuracy, consistency, and maintainability of knowledge expression.

[0045] As an optional example, when a user is detected to have performed a selection or modification operation on any knowledge entity in the knowledge graph, dependency analysis or change comparison analysis is performed, including: When a user selects a target knowledge entity, the knowledge graph is traversed to identify and graphically highlight all knowledge entities that are directly or indirectly dependent on the target knowledge entity, where the target knowledge entity is any knowledge entity in the knowledge graph. When a user performs a change operation on a target knowledge entity, the knowledge graph is updated, and a comparative analysis is performed on the versions of the knowledge graph before and after the update. The results of the comparative analysis are displayed in a visual diagram or with annotations. The change operation includes adding, deleting, and modifying.

[0046] Optionally, in this embodiment, during the process of displaying and interacting with the knowledge system of the visualized workflow based on the knowledge graph, when the system detects that the user performs a selection or change operation on any knowledge entity in the knowledge graph, it further triggers dependency analysis or change comparison analysis for the target knowledge entity to assist the user in gaining a deeper understanding of the workflow structure and its scope of influence and risk assessment.

[0047] Specifically, when the system detects that a user has selected a target knowledge entity, it uses that entity as the starting point for analysis. It traverses and analyzes the knowledge graph, tracing the relationships between knowledge entities layer by layer, representing execution order, data transfer, condition triggering, and logical dependencies. This identifies all downstream knowledge entities that directly depend on the target knowledge entity, as well as related knowledge entities that indirectly depend on it through multi-level relationships. The system then highlights or marks the identified dependent knowledge entities and their relationships graphically in an interactive interface, allowing users to intuitively understand the target knowledge entity's role in the overall workflow and its impact on other nodes, data, or business rules.

[0048] Furthermore, upon detecting a user's modification operation on a target knowledge entity, the system first updates the knowledge graph based on the modification operation, which includes adding, deleting, or modifying attributes of knowledge entities. Subsequently, the system compares and analyzes the pre-update and post-update versions of the knowledge graph to identify changes in knowledge entities, relationships, and dependencies caused by the modification operation. The system displays the comparative analysis results in the form of visual diagrams, difference annotations, or change lists. For example, by identifying newly added entities, highlighting modified entity attributes, and annotating affected relationships, the system visually presents the differences in the knowledge structure before and after the change.

[0049] Through the aforementioned dependency analysis and change comparison analysis mechanism, users can promptly grasp the scope of influence of key knowledge entities and the chain reaction caused by changes when understanding, maintaining, or adjusting visualized workflows, thereby reducing the risk of misoperation and improving the controllability and security of complex workflows during the evolution process.

[0050] As an alternative example, after displaying the knowledge graph to the user through an interactive graphical interface, the above method also includes: When a user performs a retrieval operation on a target knowledge entity type in the knowledge graph, the knowledge entities in the knowledge graph are filtered, and only knowledge entities that match the selected target knowledge entity type and their relationships are displayed. The target knowledge entity type can be any type of knowledge entity in the knowledge graph. When a user performs a search operation on a target attribute keyword in the knowledge graph, the knowledge entities and their relationships in the knowledge graph are matched, and only the knowledge entities and their relationships containing the target attribute keyword are displayed. The target attribute keyword can be any attribute keyword in the knowledge graph.

[0051] Optionally, in this embodiment, the interactive graphical interface provides user-oriented intelligent search and filtering functions, enabling users to quickly locate the required knowledge entities and their associated information, thereby improving the efficiency of understanding and applying visualized workflow knowledge. Specifically, when the system detects that a user is performing a search operation on a target knowledge entity type in the knowledge graph, the system filters all knowledge entities in the knowledge graph based on the selected target knowledge entity type, retaining only the knowledge entities that match the selected type and their associated relationships, and dynamically displays the filtering results in the interactive interface. Through this operation, users can focus on specific categories of workflow nodes, data items, or business rules, thereby quickly identifying key knowledge points and their dependency chains, reducing analysis complexity.

[0052] Furthermore, when the system detects a user performing a search operation on a target attribute keyword in the knowledge graph, it matches the attributes of all knowledge entities and their relationships within the knowledge graph, identifies knowledge entities containing the target attribute keyword and their related relationships, and dynamically presents the matching results graphically. Target attribute keywords can cover any searchable attribute, such as entity name, function description, data identifier, and configuration parameters. Through this function, users can accurately locate nodes or data items of interest using keywords and immediately obtain their position within the overall knowledge system, their upstream and downstream dependencies, and their business logic relationships, thereby significantly improving the accuracy and efficiency of knowledge query and analysis.

[0053] Combining the two retrieval methods described above, this approach enables multi-dimensional filtering and visualization based on type and attributes. This allows users to quickly obtain relevant information, analyze dependencies, and support decision-making and adjustments when faced with complex visual workflows and large-scale knowledge graphs. Simultaneously, this functionality provides a fundamental guarantee for knowledge transfer, collaborative sharing, and dynamic document updates, significantly reducing the learning cost for new members and improving the maintainability and operational efficiency of the entire system.

[0054] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0055] According to another aspect of the embodiments of this application, a knowledge system generation device for visual workflow is also provided, such as... Figure 3 As shown, it includes: Encapsulation module 302 is used to perform in-depth structural analysis of the visual workflow to obtain the static structure and dynamic logic of the visual workflow, and encapsulate the static structure and dynamic logic into a structured semantic model. The static structure includes the node information of each node and the connection relationship between nodes, and the dynamic logic includes the data transmission and transformation relationship between each node. Module 304 is used to extract all knowledge entities and the relationships between them in the structured semantic model, and to aggregate and deduce the knowledge entities and relationships based on preset aggregation rules in order to construct a knowledge graph. The generation module 306 is used to extract corresponding information from the knowledge graph according to the preset document template and preset chart rules, and generate a knowledge system corresponding to the visual workflow. The knowledge system includes at least the document content and / or visual display content generated based on the knowledge graph. The analysis module 308 is used to display the knowledge graph to the user in an interactive graphical interface, and to perform dependency analysis or change comparison analysis on any knowledge entity in the knowledge graph when it detects that the user has performed a selection or change operation.

[0056] It should be noted that the encapsulation module 302 in this embodiment can be used to execute step S102 in this application embodiment, the construction module 304 in this embodiment can be used to execute step S104 in this application embodiment, the generation module 306 in this embodiment can be used to execute step S106 in this application embodiment, and the analysis module 308 in this embodiment can be used to execute step S108 in this application embodiment.

[0057] As an optional example, the encapsulation module includes: The first identification unit is used to abstract the visualized workflow into a directed graph structure, analyze the connection relationship between each node, and identify the process structure of sequential execution, branch execution, loop execution and parallel execution. The first analysis unit is used to perform semantic analysis on the name information, description information and configuration parameters of each node using natural language processing technology, and to map each node to the corresponding function category using a node function classification model. At the same time, it extracts key semantic information related to the node function from the configuration parameters to obtain the static structure of the visualized workflow. The first building unit is used to analyze the data transmission relationship and condition control relationship between each node, track the flow and transformation process of data between different nodes, build data flow relationship, and analyze the condition judgment logic to determine the execution path of the process under different conditions, so as to obtain the dynamic logic of the visualized workflow.

[0058] As an optional example, the encapsulation module includes: The mapping unit is used to map node information, connection relationships and node types in the static structure to semantic entities, and to map data transmission relationships, data transformation relationships and condition control rules in the dynamic logic to semantic relationships. The second building unit is used to construct a unified structured semantic representation based on semantic entities and semantic relationships, organizing each node and its associated inputs, outputs, logical conditions and functional information into a data structure to form a structured semantic model.

[0059] As an optional example, the building blocks include: The second identification unit is used to identify knowledge entities representing workflow nodes, data items, functional components and configuration parameters from the structured semantic model, and to identify the relationships between entities representing the execution order, data transfer, condition triggering and logical dependencies between nodes. The merging unit is used to merge knowledge entities that implement the same business rule based on preset aggregation rules, and deduce the implicit relationships to form higher-order knowledge entities and relationships. Integration units are used to integrate all knowledge entities and all relationships into a graph database to form a knowledge graph.

[0060] As an optional example, the generated modules include: The first generation unit is used to extract the required knowledge entities and relationships from the knowledge graph according to the preset document template, and fill them into the corresponding positions of the preset document template to generate the corresponding document content. The second generation unit is used to call the graph layout algorithm according to the preset graph rules, and generate corresponding visual display content based on the knowledge entities and relationships in the knowledge graph.

[0061] As an optional example, the analysis module includes: The third identification unit is used to traverse the knowledge graph and identify and graphically highlight all knowledge entities that are directly or indirectly dependent on the target knowledge entity when the user performs a selection operation on the target knowledge entity. The target knowledge entity is any knowledge entity in the knowledge graph. The second analysis unit is used to update the knowledge graph when it detects that a user has performed a change operation on the target knowledge entity, and to compare and analyze the versions of the knowledge graph before and after the update, and to display the comparison and analysis results in the form of visual diagrams or annotations. The change operation includes adding, deleting and modifying.

[0062] As an optional example, the above-described apparatus further includes: The first retrieval module is used to filter the knowledge entities in the knowledge graph after the knowledge graph is displayed to the user in the interactive graphical interface, and only display the knowledge entities and their relationships that match the selected target knowledge entity type when the user performs a retrieval operation on the target knowledge entity type in the knowledge graph. The second retrieval module is used to match knowledge entities and their relationships in the knowledge graph when it detects that a user has performed a retrieval operation on the target attribute keyword in the knowledge graph, and to display only the knowledge entities and their relationships containing the target attribute keyword, where the target attribute keyword can be any attribute keyword in the knowledge graph.

[0063] For other examples of this embodiment, please refer to the examples above, which will not be repeated here.

[0064] Figure 4 This is a schematic diagram of an optional electronic device according to an embodiment of this application, such as... Figure 4 As shown, it includes a processor 402, a communication interface 404, a memory 406, and a communication bus 408. The processor 402, communication interface 404, and memory 406 communicate with each other via the communication bus 408. Memory 406 is used to store computer programs; When processor 402 executes a computer program stored in memory 406, it performs the following steps: The visualization workflow is subjected to in-depth structural analysis to obtain its static structure and dynamic logic. The static structure and dynamic logic are then encapsulated into a structured semantic model. The static structure includes the node information of each node and the connection relationship between nodes, while the dynamic logic includes the data transmission and transformation relationship between nodes. Extract all knowledge entities and their relationships from the structured semantic model, and aggregate and deduce the knowledge entities and relationships based on preset aggregation rules to construct a knowledge graph; Based on preset document templates and preset chart rules, extract corresponding information from the knowledge graph to generate a knowledge system corresponding to the visual workflow. The knowledge system includes at least document content and / or visual display content generated based on the knowledge graph. The knowledge graph is displayed to the user through an interactive graphical interface, and when the user performs a selection or modification operation on any knowledge entity in the knowledge graph, dependency analysis or change comparison analysis is performed on it.

[0065] Optionally, in this embodiment, the communication bus can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single thick line, but this does not indicate that there is only one bus or one type of bus. The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0066] The memory may include RAM, or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0067] As an example, the memory 406 described above may include, but is not limited to, the encapsulation module 302, the construction module 304, the generation module 306, and the analysis module 308 from the knowledge system generation device for the visualization workflow described above. Furthermore, it may include, but is not limited to, other module units from the knowledge system generation device for the visualization workflow described above, which will not be elaborated upon in this example.

[0068] The processor mentioned above can be a general-purpose processor, including but not limited to: CPU (Central Processing Unit), NP (Network Processor), etc.; it can also be DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0069] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments, and will not be repeated here.

[0070] Those skilled in the art will understand that Figure 4The structure shown is for illustrative purposes only. The device that implements the knowledge system generation method of the above-described visualization workflow can be a terminal device, such as a smartphone (e.g., Android phone, iOS phone), tablet computer, handheld computer, mobile internet device (MID), PAD, etc. Figure 4 This does not limit the structure of the aforementioned electronic devices. For example, the electronic device may also include components that are more... Figure 4 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 4 The different configurations shown.

[0071] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, ROM, RAM, disk or optical disk, etc.

[0072] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer program, which, when executed by a processor, performs the steps in the knowledge system generation method of the above-described visualization workflow.

[0073] Optionally, in this embodiment, those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0074] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0075] If the integrated units in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause one or more computer devices (which may be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0076] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed client can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between units or modules, and may be electrical or other forms.

[0078] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0079] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0080] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for generating a knowledge system for a visual workflow, characterized in that, include: The visualization workflow is subjected to in-depth structural analysis to obtain its static structure and dynamic logic. The static structure and dynamic logic are then encapsulated into a structured semantic model. The static structure includes node information of each node and the connection relationship between nodes, while the dynamic logic includes the data transmission and transformation relationship between nodes. Extract all knowledge entities and the relationships between them from the structured semantic model, and aggregate and deduce the knowledge entities and relationships based on preset aggregation rules to construct a knowledge graph; Based on preset document templates and preset chart rules, corresponding information is extracted from the knowledge graph to generate a knowledge system corresponding to the visualization workflow. The knowledge system includes at least document content and / or visualization content generated based on the knowledge graph. The knowledge graph is displayed to the user through an interactive graphical interface, and when the user performs a selection or change operation on any knowledge entity in the knowledge graph, dependency analysis or change comparison analysis is performed on it.

2. The method according to claim 1, characterized in that, Perform in-depth structural analysis on the visualized workflow to obtain its static structure and dynamic logic, including: The visualized workflow is abstracted into a directed graph structure, and the connection relationships between nodes are analyzed to identify the process structures of sequential execution, branch execution, loop execution, and parallel execution. Using natural language processing technology, semantic analysis is performed on the name information, description information and configuration parameters of each node, and a node function classification model is used to map each node to the corresponding function category. At the same time, key semantic information related to the node function is extracted from the configuration parameters to obtain the static structure of the visualization workflow. The data transmission and condition control relationships between nodes are analyzed to track the flow and transformation of data between different nodes in order to construct data flow relationships. The condition judgment logic is also analyzed to determine the execution path of the process under different conditions, thus obtaining the dynamic logic of the visualized workflow.

3. The method according to claim 1, characterized in that, Encapsulating the static structure and the dynamic logic into a structured semantic model includes: Map the node information, connection relationships, and node types in the static structure to semantic entities, and map the data transmission relationships, data transformation relationships, and condition control rules in the dynamic logic to semantic relationships. Based on the semantic entities and semantic relationships, a unified structured semantic representation is constructed, and each node and its associated inputs, outputs, logical conditions, and functional information are organized into a data structure to form the structured semantic model.

4. The method according to claim 1, characterized in that, Extracting all knowledge entities and their relationships from the structured semantic model, and then aggregating and deriving the knowledge entities and relationships based on preset aggregation rules to construct a knowledge graph includes: Identify knowledge entities representing workflow nodes, data items, functional components, and configuration parameters from the structured semantic model, and identify the relationships between entities representing execution order, data transfer, condition triggering, and logical dependencies between nodes; Based on the preset aggregation rules, knowledge entities that implement the same business rule are merged, and implicit relationships are derived to form higher-order knowledge entities and relationships. All knowledge entities and all relationships are integrated into a graph database to form the knowledge graph.

5. The method according to claim 1, characterized in that, Based on preset document templates and preset chart rules, corresponding information is extracted from the knowledge graph to generate the knowledge system corresponding to the visualization workflow, including: Based on the preset document template, the required knowledge entities and relationships are extracted from the knowledge graph and filled into the corresponding positions of the preset document template to generate the corresponding document content; According to the preset chart rules, the graphic layout algorithm is invoked to generate corresponding visual display content based on the knowledge entities and relationships in the knowledge graph.

6. The method according to claim 1, characterized in that, When the user is detected to have performed a selection or modification operation on any knowledge entity in the knowledge graph, dependency analysis or change comparison analysis is performed, including: When the user is detected to have selected a target knowledge entity, the knowledge graph is traversed to identify and graphically highlight all knowledge entities that are directly or indirectly dependent on the target knowledge entity, wherein the target knowledge entity is any knowledge entity in the knowledge graph. When the user performs a change operation on the target knowledge entity, the knowledge graph is updated, and a comparative analysis is performed on the versions of the knowledge graph before and after the update. The comparative analysis results are displayed in a visual diagram or annotation. The change operation includes adding, deleting, and modifying.

7. The method according to any one of claims 1 to 6, characterized in that, After displaying the knowledge graph to the user through an interactive graphical interface, the method further includes: When the user performs a retrieval operation on the target knowledge entity type in the knowledge graph, the knowledge entities in the knowledge graph are filtered, and only the knowledge entities that match the selected target knowledge entity type and their relationships are displayed. The target knowledge entity type can be any knowledge entity type in the knowledge graph. When the system detects that the user performs a search operation on the target attribute keyword in the knowledge graph, it matches the knowledge entities and their relationships in the knowledge graph and only displays the knowledge entities and their relationships containing the target attribute keyword, wherein the target attribute keyword is any attribute keyword in the knowledge graph.

8. A knowledge system generation device for visual workflow, characterized in that, include: An encapsulation module is used to perform in-depth structural analysis of the visualized workflow to obtain the static structure and dynamic logic of the visualized workflow, and to encapsulate the static structure and the dynamic logic into a structured semantic model. The static structure includes node information of each node and the connection relationship between nodes, and the dynamic logic includes the data transmission and transformation relationship between each node. The construction module is used to extract all knowledge entities and the relationships between them in the structured semantic model, and to aggregate and deduce the knowledge entities and the relationships based on preset aggregation rules in order to construct a knowledge graph. The generation module is used to extract corresponding information from the knowledge graph according to the preset document template and preset chart rules, and generate the knowledge system corresponding to the visualization workflow. The knowledge system includes at least the document content and / or visualization display content generated based on the knowledge graph. The analysis module is used to display the knowledge graph to the user in an interactive graphical interface, and to perform dependency analysis or change comparison analysis on any knowledge entity in the knowledge graph when the user performs a selection or change operation.

9. A computer-readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to perform the method described in any one of claims 1 to 7.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method described in any one of claims 1 to 7 through the computer program.

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