Interactive management method and system for bridging digital spaces for research

Abstract

This invention relates to the field of data processing technology, specifically including an interactive management method and system for cross-digital spaces in research and learning, comprising: setting up a knowledge graph and an application scenario network; establishing multi-level associations between the knowledge graph and the application scenario network to set up cross-knowledge links; and setting up a cross-digital space for digital interactive management. It addresses the technical problems of a lack of connection and interaction between science and technology exhibitions, science and technology learning, science and technology research and development, and application, resulting in low research and learning efficiency, disjointed understanding of the entire process of science and technology products, and an inability to meet the core needs of integrating theory and practice in digital research and learning. It achieves the technical effect of setting up a cross-digital space to accurately map theoretical knowledge to practical cases, meeting the research and learning needs of the entire process from basic learning to scientific research exploration, connecting and interacting between science and technology exhibitions, science and technology learning, science and technology research and development, and application, improving knowledge transfer efficiency and interactive experience, and providing research and learning groups with a coherent and three-dimensional cognitive path for science and technology products.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to an interactive management method and system for research and learning that bridges digital spaces. Background Technology

[0002] Science and technology exhibitions, science and technology learning, science and technology research and development and application are isolated from each other, lacking connection and interaction. Science popularization is just visiting and experiencing limited science and technology products, science education is just learning science and technology knowledge courses, and scientific research is only carried out within research and development units. It is difficult to form a synergy and to transform science and technology experience into knowledge acquisition and innovation.

[0003] Current science popularization, technology exhibitions, and their websites are small in scale, with limited product display capacity, low level, and separated from science education and research. They are unable to support the needs of full-process and in-depth research and learning. Knowledge and scenarios are disconnected. Existing science and technology product research and learning often presents the basic principles of science and technology products in isolated documents or simple models, without establishing a connection with actual application scenarios such as deployment cases and troubleshooting in research and learning scenarios. This makes it difficult for users to combine theoretical knowledge with practical scenarios, resulting in knowledge silos. Summary of the Invention

[0004] This application provides an interactive management method and system for bridging digital spaces in research and learning, aiming to solve the technical problems in existing technologies where science and technology exhibitions, science and technology learning, science and technology research and development and application are mutually closed, lack connection and interaction, resulting in the formation of knowledge silos by separating knowledge from practice scenarios, making it difficult to meet the needs of in-depth research and learning, causing low research and learning efficiency, inconsistent understanding of the entire process of science and technology products, and failing to meet the core needs of integrating theory and practice in digital research and learning.

[0005] In view of the above problems, the technical solution to achieve the present application is as follows: In its first aspect, this application provides an interactive management method for cross-digital spaces in research and study, wherein the method includes: constructing a technology digital simulation model based on big data of technology products and setting up a knowledge graph; constructing an application scenario network based on deployment cases, industry solutions, typical fault handling, and modification examples of the technology products in research and study scenarios; establishing multi-level associations between the knowledge graph and the application scenario network to set up cross-knowledge links; integrating an online experience interface, a linking learning interface, and a research interface, and setting up a cross-digital space for digital interactive management based on the cross-knowledge links.

[0006] Preferably, a classification governance mechanism is set up, which classifies and labels the big data of technology products through knowledge entity semantic clustering.

[0007] Preferably, in the technology digital simulation model, the technology product is used as the core node, and knowledge entities including the basic principles, key technologies, design parameters, manufacturing processes, and evolution history of the technology product are associated to obtain a mapping path from the technology product to the knowledge graph.

[0008] Preferably, the map update mechanism is triggered when the technological iteration of the technology product or the expansion of the research and study scenario is detected.

[0009] Preferably, the graph update mechanism identifies the semantic relationship with newly added knowledge nodes through an incremental learning model and performs timeliness verification in conjunction with an expert verification process.

[0010] Preferably, the system receives a first interactive instruction, which triggers entry into the online experience interface; in response to the first interactive instruction, it extracts the static three-dimensional display features of the technology product and uses the static three-dimensional display features to perform a static three-dimensional display of the technology product.

[0011] Preferably, a second interactive instruction is received, which is used to trigger entry into the online experience interface; in response to the second interactive instruction, dynamic immersive scene features of the technology product are extracted, and the technology product is dynamically immersive scene displayed using the dynamic immersive scene features.

[0012] In a second aspect, this application provides an interactive management system for research and study that bridges digital spaces. The system includes: a knowledge graph setting module for constructing a digital simulation model of science and technology products based on big data of these products and setting up a knowledge graph; an application scenario network construction module for constructing an application scenario network based on deployment cases, industry solutions, typical fault handling, and modification examples of the science and technology products in research and study scenarios; a hierarchical association module for establishing multi-level associations between the knowledge graph and the application scenario network, and setting up cross-connected knowledge links; and a digital interaction management module for integrating an online experience interface, linking a learning interface and a research interface, and managing digital interactions across digital spaces based on the cross-connected knowledge links.

[0013] In summary, one or more technical solutions provided in this application enable the construction of digital simulation models and knowledge graphs, the creation of application scenario networks in conjunction with research and learning scenarios, the establishment of cross-connected knowledge links, the integration of multiple interfaces to form a cross-connected digital space, the accurate mapping of theoretical knowledge to practical cases, the fulfillment of the research and learning needs throughout the entire process from basic learning to scientific research exploration, the connection and interaction between science and technology exhibitions, science and technology learning, science and technology research and development and application, the improvement of knowledge transfer efficiency and interactive experience, and the provision of a coherent and three-dimensional cognitive path for science and technology products for research and learning groups.

[0014] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating an interactive management method for bridging digital spaces in one embodiment of a learning and research project.

[0017] Figure 2 This is an architecture diagram of an interactive management system for bridging digital spaces in one embodiment of a learning and research program.

[0018] Figure labeling: Knowledge graph setting module 11, application scenario network construction module 12, hierarchical association module 13, digital interaction management module 14. Detailed Implementation

[0019] This application provides an interactive management method and system for bridging digital spaces in research and learning, which solves the technical problems in the prior art where science and technology exhibitions, science and technology learning, science and technology research and development and application are mutually closed, lack connection and interaction, resulting in knowledge and practice scenarios being separated to form knowledge islands, the interactive experience being unable to meet the needs of in-depth research and learning, resulting in low research and learning efficiency, inconsistent understanding of the entire process of science and technology products, and inability to adapt to the core needs of integrating theory and practice in digital research and learning.

[0020] 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 a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0021] It should be noted that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such process, method, product, or device.

[0022] Example 1: The present application will be described in detail below with reference to the accompanying drawings, as follows... Figure 1 As shown, this application provides an interactive management method for research and learning that bridges digital spaces, the method comprising: Based on big data of technological products, a digital simulation model of technology is constructed and a knowledge graph is set up; based on the deployment cases, industry solutions, typical fault handling and transformation examples of the technological products in the research and study scenario, an application scenario network is constructed.

[0023] In one embodiment, big data on technology products refers to massive amounts of data related to technology products, including product design parameters, manufacturing processes, user feedback, and fault records, which form the basis for building digital simulation models and knowledge graphs. A technology digital simulation model is a virtual model that simulates the performance and behavior of technology products using computers, capable of demonstrating the product's operating status under different conditions. A knowledge graph is a structured semantic knowledge base that describes entities and their relationships through a graph structure, used for knowledge organization and management. An application scenario network is a network covering the actual application of technology products in different research and learning scenarios, including deployment cases, industry solutions, typical fault handling, and modification examples, used to demonstrate the actual application of technology products.

[0024] Optionally, a large amount of data related to technological products can be collected, including design parameters, manufacturing processes, and user feedback. This data can be used to construct a digital simulation model of the technology, simulating the operational status of the product under different conditions. Simultaneously, a knowledge graph can be built based on this data, organizing knowledge entities related to the technological product through semantic association. Based on the actual application of the technological product in research and learning scenarios, such as deployment cases, industry solutions, typical fault handling, and modification examples, an application scenario network can be constructed. The knowledge graph and the application scenario network can be linked at multiple levels to form cross-connected knowledge links. Preferably, constructing the digital simulation model and knowledge graph can structurally integrate the knowledge of technological products, providing users with systematic knowledge support. Specifically, through the knowledge graph, users can clearly understand the entire process of technological products from basic principles to their evolutionary history. Constructing the application scenario network can combine theoretical knowledge with practical application scenarios, solving the problem of knowledge silos and significantly improving the depth and efficiency of research and learning.

[0025] The knowledge graph is associated with the application scenario network at multiple levels, and cross-knowledge links are set up; an online experience interface, a linked learning interface, and a research interface are integrated, and a cross-digital space is set up for digital interaction management based on the cross-knowledge links.

[0026] In one embodiment, multi-level association refers to establishing multiple layers of connections between the knowledge graph and the application scenario network. These connections can be semantic, logical, or functional. Through multi-level association, accurate mapping from knowledge to application scenarios can be achieved. Cross-knowledge links refer to dynamic connection paths established between the knowledge graph and the application scenario network. These paths can reflect changes in the relationship between knowledge and application scenarios in real time, supporting dynamic updates of knowledge and expansion of application scenarios. Online experience interfaces refer to interactive interfaces accessible via the internet, providing static and dynamic displays of technological products, allowing users to intuitively experience the functions and features of these products. Linked learning interfaces refer to interactive interfaces that support deep learning, providing learning resources and courses related to technological products to help users systematically learn about them. Research interfaces refer to interactive interfaces that support researchers in conducting research and exploration, providing research tools and data support to help researchers conduct innovative research. Cross-digital spaces refer to virtual spaces that integrate online experience, linked learning, and research functions. By connecting knowledge links, seamless integration between different functional interfaces is achieved, supporting users' interactive needs at different stages of learning.

[0027] Optionally, semantic analysis and association matching can be performed on knowledge entities in the knowledge graph and application instances in the application scenario network using natural language processing and machine learning technologies to establish multi-level associations. Specifically, the "fault handling" entity in the knowledge graph can be associated with "typical fault handling cases" in the application scenario network to form a cross-connected knowledge link. An integrated digital platform can be developed, which includes an online experience interface, a linked learning interface, and a research interface. Based on the cross-connected knowledge link, a cross-connected digital space can be set up to realize dynamic interactive management between different interfaces. Furthermore, when a user triggers a fault handling case in the online experience interface, they can be automatically redirected to the linked learning interface through the cross-connected knowledge link to provide relevant fault handling knowledge, or redirected to the research interface to provide relevant research tools and data support. Preferably, by setting up multi-level associations and cross-knowledge links, a precise mapping between theoretical knowledge and practical application scenarios is achieved. Specifically, in the case of science and technology product research and learning, by crossing knowledge links, users can quickly jump from troubleshooting cases to related knowledge learning and scientific research exploration, which significantly improves the continuity and efficiency of research and learning. The dynamic interactive management function that crosses digital space can automatically adjust the interactive content and method according to the user's needs and behaviors, thereby improving the user experience and satisfaction.

[0028] Furthermore, the method described in this application includes: A classification governance mechanism is set up, which classifies and labels the big data of technology products through knowledge entity semantic clustering.

[0029] In one embodiment, the classification governance mechanism is used to classify and label big data of technology products, dividing and managing the data according to certain categories to better organize and utilize the data; knowledge entity semantic clustering refers to clustering knowledge entities into different categories based on their semantic similarity. Knowledge entities can be technology products, technical concepts, or application scenarios. Semantic clustering analyzes the semantic relationships between entities and groups entities with similar semantics into one category; classification labels are the identifiers after data classification, used to identify different categories in big data of technology products, quickly identify and retrieve data, and facilitate subsequent processing and application.

[0030] Optionally, the classification and governance mechanism first preprocesses the big data of technology products, extracting knowledge entities; then, it uses natural language processing technology to analyze the semantic features of these knowledge entities; finally, through a knowledge entity semantic clustering algorithm, it groups entities with similar semantic features into one category and assigns a classification label to each category. Specifically, all knowledge entities related to artificial intelligence technology are grouped into one category, labeled "artificial intelligence." Classification labels facilitate data organization and management, providing structured input for subsequent digital simulation model construction and knowledge graph generation. Preferably, by classifying and labeling the big data of technology products, the classification and governance mechanism quickly locates and retrieves relevant data, reducing data retrieval time and effectively improving the efficiency and accuracy of data processing. Classification labels provide clear structured input for the construction of the knowledge graph, enabling the knowledge graph to more accurately reflect the knowledge system of technology products, thereby better supporting knowledge acquisition and application during the research and learning process.

[0031] Furthermore, this application provides a method for constructing a digital simulation model of technology based on big data of technology products and setting up a knowledge graph, the method comprising: In the aforementioned digital simulation model of science and technology, the science and technology product is used as the core node, and knowledge entities including the basic principles, key technologies, design parameters, manufacturing processes, and evolution history of the science and technology product are associated to obtain the mapping path from the science and technology product to the knowledge graph.

[0032] In one embodiment, a core node in a technology digital simulation model refers to a technology product itself as the center of the model, around which related knowledge and information are associated; a knowledge entity refers to an information unit with clear semantics and independent meaning, such as the basic principles, key technologies, design parameters, manufacturing processes, and evolution history of a technology product; and a mapping path refers to the connection path from the technology product to the knowledge graph, through which knowledge entities related to the technology product can be located and associated in the knowledge graph.

[0033] Optionally, when constructing a digital simulation model of a technology product, the technology product is identified as the core node. Around this core node, knowledge entities related to the technology product are extracted, including basic principles, key technologies, design parameters, manufacturing processes, and evolutionary history. Through knowledge graph technology, these knowledge entities are associated with the core node, forming a mapping path from the technology product to the knowledge graph. Specifically, for a new type of drone product, the core node is the drone itself, and the associated knowledge entities include flight principles, navigation technology, material parameters, manufacturing processes, and technological evolutionary history. These knowledge entities form a network structure in the knowledge graph through semantic association, allowing users to quickly obtain comprehensive knowledge about the drone. Preferably, constructing a mapping path with the technology product as the core node provides a structured knowledge display method that effectively supports the needs of different stages in the learning process. It provides precise knowledge support from basic learning to advanced applications, significantly improving the continuity and efficiency of learning, helping users quickly understand and master the overall picture of the technology product, promoting in-depth learning and application of knowledge, and achieving a systematic integration and structured display of technology product knowledge.

[0034] Furthermore, this application provides a method for establishing multi-level associations between the knowledge graph and the application scenario network, and setting up cross-connected knowledge links. The method includes: When a technological iteration of the technology product or an expansion of the research and study scenario is detected, the map update mechanism is triggered.

[0035] In one embodiment, technological iteration refers to the updating and upgrading of technological products at the technical level, such as the addition of new functions, performance improvement, or design improvement; research and study scenario expansion refers to the expansion of the application scenarios or scope of research and study activities, such as expanding from a single laboratory environment to a real industrial application environment; the knowledge graph update mechanism is used to update the knowledge graph when technological iteration or research and study scenario expansion is detected, so as to ensure that the information in the graph remains up-to-date and accurate.

[0036] Optionally, a pre-set monitoring module can monitor the technological iteration of scientific and technological products and the expansion of research and learning scenarios in real time. Once relevant changes are detected, such as the release of a new version or the emergence of a new application scenario, the knowledge graph update mechanism will be triggered. The knowledge graph update mechanism is used to identify newly added or changed knowledge nodes, including new technical parameters and new application scenario cases. Through incremental learning models, the semantic relationship between these new nodes and nodes in the existing knowledge graph is analyzed, and the knowledge graph is automatically updated. Combined with an expert verification process, the updated content is verified for timeliness to ensure the accuracy of the updated knowledge. Specifically, when a scientific and technological product is upgraded from the first generation to the second generation and an energy-saving function is added, the knowledge graph update mechanism will automatically identify this change, add the energy-saving function as a new knowledge node to the knowledge graph, and establish a connection with relevant key technology nodes. Preferably, the knowledge graph updated in a timely manner by the knowledge graph update mechanism can provide the most accurate guidance for research and learning activities, reflect the latest developments of scientific and technological products and technologies in real time, and avoid outdated and incorrect knowledge. Through the expert verification process, the accuracy of the updated content is guaranteed, significantly improving the reliability and efficiency of research and learning.

[0037] Furthermore, the method described in this application includes: The graph update mechanism identifies the semantic relationship between the newly added knowledge nodes and the graph through an incremental learning model, and performs timeliness verification in conjunction with an expert verification process.

[0038] In one embodiment, the incremental learning model is a machine learning model that can update and expand the knowledge system by continuously learning new data based on existing knowledge. Furthermore, the incremental learning model is used to identify the semantic relationships between newly added knowledge nodes and nodes in the existing knowledge graph. Semantic relationships refer to the semantic connections between different knowledge entities, such as similarity, relevance, or subordinate relationships. By analyzing these relationships, the appropriate position of the new knowledge node in the knowledge graph is determined. The expert verification process refers to the process by which domain experts review and verify the updated knowledge graph to ensure the accuracy and timeliness of the updated content, including evaluating the accuracy, completeness, and logic of the newly added knowledge nodes.

[0039] Optionally, when the knowledge graph update mechanism is triggered, the incremental learning model first performs semantic analysis on the newly added knowledge nodes to identify their semantic relationships with nodes in the existing knowledge graph. Based on these relationships, the incremental learning model appropriately inserts the new nodes into the knowledge graph. Then, an expert verification process is initiated, where domain experts review the new nodes and their relationships to ensure accuracy and timeliness. The expert verification process evaluates the description of the new nodes, the accuracy of the relationships, and whether they conform to the latest technological developments until the updated content meets the standards. Preferably, the incremental learning model utilizes its learning capabilities to quickly identify the semantic relationships of new knowledge nodes, improving update efficiency. Simultaneously, the expert verification process ensures the high quality of the updated content and reduces the error rate. The combination of the incremental learning model and the expert verification process guarantees the timeliness and accuracy of the knowledge graph, enhances user trust and reliance on the knowledge graph, and provides reliable theoretical support for research and learning activities, enabling efficient and accurate knowledge graph updates through the knowledge graph update mechanism.

[0040] Furthermore, this application provides an integrated online experience interface, linking the learning interface and the research interface, and the method further includes: Receive a first interactive instruction, which is used to trigger entry into the online experience interface; respond to the first interactive instruction, extract the static three-dimensional display features of the technology product, and perform a static three-dimensional display of the technology product using the static three-dimensional display features.

[0041] In one embodiment, the first interactive instruction refers to the instruction issued by the user through the interactive interface to trigger entry into the online experience interface, which can be a button click, voice command, or other form of user operation; the static stereoscopic display features refer to the features used to display the three-dimensional structure and appearance of the technology product, including the product's shape, size, color, and material, through which a three-dimensional static model of the technology product can be generated; static stereoscopic display refers to displaying the technology product in the form of a three-dimensional static model using computer graphics technology, allowing users to view the product's appearance and structure from different angles, but without involving dynamic interaction or animation effects.

[0042] Optionally, upon receiving the first interactive command from the user, an operation to enter the online experience interface is triggered. This interface displays a 3D model of the technological product. After responding to the first interactive command, it extracts static 3D display features related to the technological product from the database, including the product's 3D dimensions, appearance color, and material texture. Using these static 3D display features, a 3D static model of the technological product is generated and displayed in the online experience interface. Specifically, if a user wants to view the appearance of a new drone, they issue a first interactive command by clicking the "View Appearance" button. Upon response, the system extracts the drone's static 3D display features, generates a 3D static model of the drone, and displays it in the online experience interface. Users can view various details of the drone through rotation, zoom, and other operations. Preferably, the static 3D display provides users with intuitive information about the appearance and structure of the technological product, helping them quickly understand the product's basic features. Simultaneously, it provides a foundation for dynamic interaction and deep learning, enabling users to further explore the product's functions and application scenarios based on their understanding of its appearance.

[0043] Furthermore, the method described in this application includes: Receive a second interactive instruction, which is used to trigger entry into the online experience interface; respond to the second interactive instruction, extract the dynamic immersive scene features of the technology product, and display the technology product in a dynamic immersive scene using the dynamic immersive scene features.

[0044] In one embodiment, the second interactive instruction refers to an instruction issued by the user through the interactive interface to trigger entry into the online experience interface and display a dynamic immersive scene. This instruction can be a button click, a voice command, or other form of user operation. The dynamic immersive scene features refer to the dynamic characteristics used to display the technological product in actual operation or use scenarios, including the product's motion state, interactive effects, and environmental background. These features can be used to generate a dynamic immersive scene of the technological product. The dynamic immersive scene display refers to using computer graphics technology to display the effects of the technological product in actual operation or use scenarios in the form of a dynamic 3D model, enabling users to experience the product's functions and performance in an immersive way. This typically involves animation effects, interactive operations, and environmental simulation.

[0045] Optionally, upon receiving a second interactive command from the user, an operation to enter the online experience interface is triggered. This interface is used to display dynamic immersive scenes of the technology product. After responding to the second interactive command, dynamic immersive scene features related to the technology product are extracted from the database, including the product's motion trajectory, interactive effects, and environmental background. These features are used to generate a dynamic immersive scene of the technology product, which is then displayed on the online experience interface. Specifically, if a user wants to see the performance of a new drone in actual flight, they issue a second interactive command by clicking the "View Flight Demonstration" button. Upon response, the drone's dynamic immersive scene features are extracted, a dynamic model of the drone in the flight scene is generated, and displayed on the online experience interface. The user can control the drone's flight direction and altitude through the interface to experience the drone's actual flight effects. Preferably, the dynamic immersive scene display significantly enhances the user's learning effect and experience, providing a more realistic and vivid product experience. This helps users deeply understand the product's functions and performance. Furthermore, the dynamic immersive scene display supports interactive operations, allowing users to actually operate the product in a simulated environment, further enhancing user participation and learning outcomes.

[0046] In summary, the embodiments of this application have at least the following technical effects: This application provides an interactive management method and system for a cross-disciplinary digital space in research and learning. It utilizes big data on technological products to construct a digital simulation model and knowledge graph, builds an application scenario network based on deployment cases, industry solutions, typical fault handling, and modification examples of technological products in research and learning scenarios, establishes multi-level associations between the knowledge graph and the application scenario network, and integrates online experience interfaces, learning interfaces, and research interfaces. This allows for digital interactive management by creating a cross-disciplinary digital space for research and learning. It achieves the construction of a digital simulation model and knowledge graph, the establishment of an application scenario network and cross-disciplinary knowledge links in conjunction with research and learning scenarios, and the integration of multiple interfaces to form a cross-disciplinary digital space. This enables precise mapping from theoretical knowledge to practical cases, meeting the needs of the entire research and learning process from basic learning to scientific research exploration. It connects and interacts between science and technology exhibitions, science and technology learning, science and technology research and development, and applications, improving the efficiency of knowledge transfer and the interactive experience, and providing a coherent and comprehensive cognitive path for research and learning groups.

[0047] Example 2, based on the same inventive concept as the interactive management method for bridging digital spaces in the foregoing examples, such as... Figure 2 As shown, this application provides an interactive management system for research and learning that bridges digital spaces, the system comprising: The knowledge graph setting module 11 is used to construct a digital simulation model of science and technology products based on big data of science and technology products, and to set up a knowledge graph.

[0048] Application scenario network construction module 12 is used to construct an application scenario network based on deployment cases, industry solutions, typical fault handling, and transformation examples of the technology products in research and study scenarios.

[0049] The hierarchical association module 13 is used to perform multi-level association between the knowledge graph and the application scenario network, and to set up cross-knowledge links.

[0050] The digital interaction management module 14 is used to integrate the online experience interface, link the learning interface and the scientific research interface, and set up a cross-digital space for digital interaction management according to the cross-knowledge link.

[0051] Furthermore, the knowledge graph setting module 11 is used to perform the following method: A classification governance mechanism is set up, which classifies and labels the big data of technology products through knowledge entity semantic clustering.

[0052] Furthermore, the knowledge graph setting module 11 is also used to perform the following method: In the aforementioned digital simulation model of science and technology, the science and technology product is used as the core node, and knowledge entities including the basic principles, key technologies, design parameters, manufacturing processes, and evolution history of the science and technology product are associated to obtain the mapping path from the science and technology product to the knowledge graph.

[0053] Furthermore, the hierarchical association module 13 is used to perform the following method: When a technological iteration of the technology product or an expansion of the research and study scenario is detected, the map update mechanism is triggered.

[0054] Furthermore, the hierarchical association module 13 is also used to perform the following method: The graph update mechanism identifies the semantic relationship between the newly added knowledge nodes and the graph through an incremental learning model, and performs timeliness verification in conjunction with an expert verification process.

[0055] Furthermore, the digital interaction management module 14 is also used to perform the following methods: Receive a first interactive instruction, which is used to trigger entry into the online experience interface; respond to the first interactive instruction, extract the static three-dimensional display features of the technology product, and perform a static three-dimensional display of the technology product using the static three-dimensional display features.

[0056] Furthermore, the digital interaction management module 14 is also used to perform the following methods: Receive a second interactive instruction, which is used to trigger entry into the online experience interface; respond to the second interactive instruction, extract the dynamic immersive scene features of the technology product, and display the technology product in a dynamic immersive scene using the dynamic immersive scene features.

[0057] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0058] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0059] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. An interactive management method for research and learning that bridges digital spaces, characterized in that, The method includes: Based on big data of technological products, a digital simulation model of technology is constructed, and a knowledge graph is set up; Based on the deployment cases, industry solutions, typical fault handling, and transformation examples of the aforementioned technological products in research and study scenarios, an application scenario network is constructed. The knowledge graph and the application scenario network are associated at multiple levels, and cross-connect knowledge links are set up; It integrates an online experience interface, links learning interfaces and research interfaces, and sets up a cross-connected digital space for digital interactive management based on the aforementioned cross-connected knowledge links.

2. The method as described in claim 1, characterized in that, The method includes: A classification governance mechanism is set up, which classifies and labels the big data of technology products through knowledge entity semantic clustering.

3. The method as described in claim 2, characterized in that, Based on big data of technological products, a digital simulation model of technology is constructed, and a knowledge graph is set up. The method includes: In the aforementioned digital simulation model of science and technology, the science and technology product is used as the core node, and knowledge entities including the basic principles, key technologies, design parameters, manufacturing processes, and evolution history of the science and technology product are associated to obtain the mapping path from the science and technology product to the knowledge graph.

4. The method as described in claim 1, characterized in that, The method includes establishing multi-level associations between the knowledge graph and the application scenario network, and setting up cross-connected knowledge links. When a technological iteration of the technology product or an expansion of the research and study scenario is detected, the map update mechanism is triggered.

5. The method as described in claim 4, characterized in that, The method includes: The graph update mechanism identifies the semantic relationship between the newly added knowledge nodes and the graph through an incremental learning model, and performs timeliness verification in conjunction with an expert verification process.

6. The method as described in claim 5, characterized in that, The method integrates an online experience interface, links learning interfaces and research interfaces, and further includes: Receive a first interactive instruction, which is used to trigger entry into the online experience interface; In response to the first interactive command, the static three-dimensional display features of the technology product are extracted, and the technology product is statically displayed in three dimensions using the static three-dimensional display features.

7. The method as described in claim 6, characterized in that, The method includes: Receive a second interactive instruction, which is used to trigger entry into the online experience interface; In response to the second interactive command, the dynamic immersive scene features of the technology product are extracted, and the technology product is displayed in a dynamic immersive scene using the dynamic immersive scene features.

8. An interactive management system for research and learning that bridges digital spaces, characterized in that: The system is used to implement the interactive management method for cross-digital spaces for research and learning as described in any one of claims 1-7, the system comprising: The knowledge graph setup module is used to build digital simulation models of science and technology products based on big data of science and technology products, and to set up knowledge graphs. The application scenario network construction module is used to construct an application scenario network based on the deployment cases, industry solutions, typical fault handling, and transformation examples of the technology products in the research and study scenario. The hierarchical association module is used to perform multi-level associations between the knowledge graph and the application scenario network, and to set up cross-knowledge links. The digital interaction management module is used to integrate the online experience interface, link the learning interface and the scientific research interface, and manage the digital interaction by setting up a cross-digital space based on the cross-knowledge link.

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