LIC remote cooperation management method and system based on VR multiple digital contents

Abstract

The invention provides an LIC remote cooperation management method and system based on VR multiple digital contents, and relates to the technical field of data processing. The method comprises the steps of obtaining a database corresponding to a target culture item, and constructing a virtual display model corresponding to the target culture item based on the database; obtaining a cooperation request of each researcher, and determining a cooperation permission of each researcher based on the cooperation request; obtaining a feedback data set of each researcher on the virtual display model based on the cooperation permission; generating a corresponding interaction graph based on each feedback data set; and generating a plurality of target display models of the virtual display model based on the interaction graph. By implementing the technical scheme provided by the invention, the cooperation efficiency of the cultural project can be improved.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and in particular to a LIC remote collaboration management method and system based on VR multi-digital content. Background Art

[0002] With the development of globalization and the continuous expansion of the academic research and design fields, cross-regional collaboration has become increasingly important in the business related to cultural parks and exhibition halls. In academic research, the research on cultural heritage often requires the joint participation of experts and scholars from all over the world. They need to conduct in-depth discussions and analyses on digital models of cultural relics, etc. The traditional face-to-face communication mode is restricted by time and space, and there is often a delay in the communication between researchers.

[0003] In the design field of cultural parks and exhibition halls, projects usually invite designers and reviewers from different regions to participate. At present, designers mainly communicate design plans through methods such as email and video conferencing. However, this traditional remote communication method greatly affects the reviewers' perception of the design effect and the depth of discussion on design details, resulting in low collaboration efficiency of cultural projects. Summary of the Invention

[0004] This application provides a LIC remote collaboration management method and system based on VR multi-digital content, which can improve the collaboration efficiency of cultural projects.

[0005] In a first aspect, this application provides a LIC remote collaboration management method based on VR multi-digital content, and the method includes: Obtain a database corresponding to a target cultural project, and construct a virtual display model corresponding to the target cultural project based on the database; Obtain collaboration requests of each researcher, and determine the collaboration permissions of each researcher based on the collaboration requests; Obtain feedback data sets of each researcher on the virtual display model based on the collaboration permissions; Generate corresponding interaction graphs based on each of the feedback data sets; Generate multiple target display models of the virtual display model based on the interaction graphs.

[0006] By adopting the above technical solutions, first, by obtaining the database of the target cultural project and constructing a virtual display model, a digital research environment is provided for researchers, breaking through the time and space limitations of traditional face-to-face communication; second, by obtaining the collaboration requests of researchers and determining the corresponding collaboration permissions, hierarchical management of researchers is achieved, making the research process more standardized and orderly; third, by obtaining the feedback data set of researchers on the virtual display model based on the collaboration permissions and generating the corresponding interaction graph, various types of information generated during the research process are effectively recorded and utilized; finally, multiple target display models are generated based on the interaction graph, enabling the virtual display model to be dynamically adjusted according to different research needs, significantly improving the collaboration efficiency of cultural projects.

[0007] In the second aspect of the present application, a LIC remote collaboration management system based on VR multi-digital content is provided. The system includes: A reservation information acquisition module, configured to acquire the reservation information of each visitor in the cultural park on the current day, where the reservation information includes identity information, an interest theme set, and a visiting time period; A visiting area determination module, configured to, for any one of the visitors, determine a plurality of target visiting areas in the cultural park based on the interest theme set; A visiting strategy determination module, configured to determine a target visiting strategy based on each of the target visiting areas and the visiting time period; A backup strategy determination module, configured to expand the target visiting strategy based on the historical visiting records of the cultural park and the identity information to obtain a backup visiting strategy; A strategy sending module, configured to send the target visiting strategy and the backup visiting strategy to the smart terminals of the corresponding visitors.

[0008] In the third aspect of the present application, a computer storage medium is provided. The computer storage medium stores multiple instructions, and the instructions are adapted to be loaded and executed by a processor to perform the above method steps.

[0009] In the fourth aspect of the present application, an electronic device is provided, including: a processor and a memory; wherein, the memory stores a computer program, and the computer program is adapted to be loaded and executed by the processor to perform the above method steps.

[0010] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: First, this application provides a digital research environment for researchers by obtaining the database of the target cultural project and constructing a virtual display model, breaking through the time and space limitations of traditional face-to-face communication. Second, by obtaining the collaboration requests of researchers and determining the corresponding collaboration permissions, hierarchical management of researchers is achieved, making the research process more standardized and orderly. Third, by obtaining the feedback data set of researchers on the virtual display model based on the collaboration permissions and generating the corresponding interaction graph, various types of information generated during the research process are effectively recorded and utilized. Finally, multiple target display models are generated based on the interaction graph, enabling the virtual display model to be dynamically adjusted according to different research needs, significantly improving the collaboration efficiency of cultural projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 is a schematic flowchart of a LIC remote collaboration management method based on VR multi-digital content provided by an embodiment of the present application; Figure 2 is a schematic block diagram of a LIC remote collaboration management system based on VR multi-digital content provided by an embodiment of the present application; Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

[0012] Description of the reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments.

[0014] In the description of the embodiments of the present application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design solution described as "for example" or "for instance" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "for example" or "for instance" is intended to present related concepts in a specific manner.

[0015] In the description of the embodiments of the present application, the term "plural" means two or more. For example, plural systems refer to two or more systems, and plural screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the technical features indicated. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "include", "comprise", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0016] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0017] Please refer to Figure 1 , and a flowchart of a LIC remote collaboration management method based on VR multi-digital content is specifically proposed. This method can be implemented depending on a computer program, can be implemented depending on a single-chip microcomputer, or can run on a LIC remote collaboration management system based on VR multi-digital content. This computer program can be integrated in a computer device or can run as an independent tool-class application. Specifically, this method includes steps 10 to 50, and the above steps are as follows: Step 10: Obtain the database corresponding to the target cultural project, and build a virtual display model corresponding to the target cultural project based on the database.

[0018] In the embodiments of the present application, the target cultural project refers to specific project contents such as cultural heritages, exhibition hall layouts, and cultural park plans that need to be studied, designed, or displayed. For example, the restoration project of an archaeological site, the design of a new exhibition area in a museum, or the overall planning scheme of a cultural park, etc.

[0019] In the embodiments of the present application, the database refers to a collection of various types of digital materials related to the target cultural project, including but not limited to various types of digital information such as historical literature records, physical image data, three-dimensional scan data, archaeological excavation records, restoration records, audio materials, and expert research reports.

[0020] In the embodiments of the present application, the virtual display model refers to a virtual reality scene generated by means of three-dimensional modeling, scene construction, etc. based on the digital information in the database for displaying the target cultural project. This model has an interactive function, supports researchers to perform operations such as observation, analysis, and annotation in the virtual environment, and can present the spatial layout, detailed features, and relevant historical information of the target cultural project.

[0021] Specifically, since the target cultural project contains a large amount of digital materials, it is necessary to first obtain the database corresponding to the target cultural project. Specifically, the database includes multiple data sub-libraries, and each data sub-library corresponds to different types of digital materials. For example, the first data sub-library is used to store the historical literature materials of the target cultural project, the second data sub-library is used to store the physical image materials of the target cultural project, and the third data sub-library is used to store the audio materials of the target cultural project. During the process of obtaining the database, the system standardizes and stores different types of materials through a preset unified data format. After obtaining the database, the system constructs a virtual display model corresponding to the target cultural project based on the database. First, data processing is performed on various types of materials in the database to extract the feature information of the materials. For example, the system uses text recognition technology to extract key information such as time and location from historical literature, uses image processing technology to extract feature data such as shape and size from physical images, and uses audio analysis technology to extract information such as speech content from audio materials. Subsequently, the system constructs the basic framework of the virtual display model based on the feature information. Specifically, the system first generates a three-dimensional geometric model based on the feature data of the physical image as the main structure of the virtual display model; then adds the key information extracted from the historical literature to the corresponding positions in the form of annotations; finally, establishes an association between the audio content and a specific area. During this process, the system uses a preset spatial layout algorithm to ensure the reasonable distribution of various types of information in the virtual environment.

[0022] On the basis of the above embodiments, as an optional embodiment, the step of constructing a virtual display model corresponding to the target cultural project based on the database may further include the following steps: Step 101: Classify and screen the historical literature, physical images, and audio data in the database, and extract the characteristic attribute information of each type of data.

[0023] Specifically, the historical documents, physical images, and audio data included in the target cultural project have different data formats and content attributes, and need to be systematically processed to facilitate the subsequent construction of a virtual display model. Therefore, the system first classifies and filters the data in the database. Specifically, based on the preset data classification rules, the system classifies historical documents according to dimensions such as age, theme, and source; classifies physical images according to dimensions such as shooting time, shooting location, and shooting object; and classifies audio data according to dimensions such as recording time, content type, and language. After the classification is completed, the system uses a feature extraction algorithm to extract the feature attribute information of various types of data. For historical documents, the system uses natural language processing technology to extract feature attributes such as time information (such as dynasties, specific years), geographical location information (such as place names, directions), and event description information (such as historical events, people's activities) in the documents; for physical images, the system uses computer vision technology to extract feature attributes such as geometric features (such as contour lines, shape features), texture features (such as surface materials, texture distributions), and color features (such as color distributions, hue changes) in the images; for audio data, the system uses audio analysis technology to extract feature attributes such as sound features (such as audio waveforms, spectral features) and speech content (such as oral history, expert commentary).

[0024] Step 102: Based on the preset spatio-temporal association rules, perform temporal arrangement and spatial mapping on the feature attribute information to construct a spatio-temporal display framework for the target cultural project.

[0025] Specifically, the system performs temporal arrangement and spatial mapping on the extracted feature attribute information based on the preset spatio-temporal association rules. In terms of temporal arrangement, the system first establishes a unified time axis, maps the time information in different types of data onto the time axis to form a time series; in terms of spatial mapping, based on geographic information system (GIS) technology, the system maps the data containing location information into a three-dimensional spatial coordinate system to determine the spatial distribution relationship of each data element. Through temporal arrangement and spatial mapping, the system generates a spatio-temporal display framework for the target cultural project, which clearly shows the distribution rules of various types of data in the time and space dimensions.

[0026] Step 103: Generate the basic grid of the virtual scene according to the hierarchical structure of the spatio-temporal display framework.

[0027] Specifically, based on the spatial distribution relationship, the three-dimensional space is divided into multiple grid units; then, according to the time series, a time attribute is assigned to each grid unit; finally, based on the hierarchical relationship of the data type, different hierarchical identifiers are set for the grid units. For example, data related to the building structure is assigned to the basic level, data related to the decorative pattern is assigned to the detail level, and data related to the literature annotation is assigned to the description level. The basic grid generated in this way contains both spatial structure information and time dimension and data hierarchy information.

[0028] Step 104: implanting characteristic attribute information into the basic grid to form a scene element containing cultural characteristics.

[0029] Specifically, first establish the correspondence between the feature attribute information and the basic grid unit, and determine the precise position of each feature attribute information in the basic grid through the spatial positioning algorithm. For the feature attribute information of the physical image, the system uses 3D reconstruction technology to construct a geometric model in the corresponding grid unit, and uses UV mapping technology to accurately map the texture information to the model surface, while restoring the material effect of the object through the material rendering algorithm. For text information in historical documents, the system converts it into interactive spatial annotations, sets annotation icons of different sizes and styles according to the importance of the information, and fixes the annotations at the corresponding grid positions. For audio data, the system establishes a sound trigger area, associates the audio information with a specific grid space, and achieves the spatial positioning effect of the sound.

[0030] Step 105: Dynamically optimize the layout of scene elements to generate an interactive virtual display model.

[0031] Specifically, after completing the implantation of characteristic attribute information, the system dynamically optimizes the layout of scene elements to enhance the interactive experience. First, a visual optimization algorithm is used to calculate the spatial relationship between the elements in the scene, including line of sight occlusion, information density, and spatial balance. When visual interference is detected between elements, the system automatically adjusts the position parameters of the elements, such as changing the display position of annotations to avoid overlap, or adjusting the scale of the model to ensure that the visual focus is prominent. At the same time, the system configures interactive parameters for each scene element, including click response, hover effect, and linkage display rules. For example, when a user selects a cultural relic model, the system automatically highlights the model and simultaneously displays the relevant literature annotations and audio descriptions; when a user views a building component, the system can link and display the morphological changes of the component in different historical periods.

[0032] Step 20: Obtain collaboration requests from each researcher, and determine collaboration permissions for each researcher based on the collaboration requests.

[0033] Specifically, since the research of the target cultural project requires the joint participation of multiple researchers, to ensure the orderly progress of the research work and data security, the system needs to systematically manage the collaborative behaviors of the researchers. Specifically, the system first obtains the collaboration requests submitted by the researchers through the collaboration management module. Each collaboration request includes the identity information, professional field, type of application permission, and description of the research purpose of the researcher. The identity information includes basic information such as the employee number and affiliated institution of the researcher; the professional field includes research directions such as archaeology and cultural relics protection; the type of application permission includes operation permissions such as data viewing, annotation adding, and model editing; the description of the research purpose includes the research plan and expected results. A multi-level evaluation mechanism is used to process the collaboration requests. First, by calling the identity authentication database, the identity information of the researcher is verified to verify their academic background and research qualifications. Second, the system uses a semantic matching algorithm to calculate the relevance between the professional field of the researcher and the project research direction, and generates a professional matching score. The system compares the professional matching score with a preset threshold to determine the professional suitability of the researcher. At the same time, the system analyzes the key information in the description of the research purpose and evaluates the feasibility of the research plan and the expected value of the research results.

[0034] Based on the evaluation results, the system assigns collaboration permissions to the researchers according to the preset permission grading rules. The permission grading includes three levels: the first level is the basic permission, which allows the researcher to view public data and add research notes; the second level is the intermediate permission, which allows the researcher to perform data annotation and comments; the third level is the advanced permission, which allows the researcher to participate in model editing and data modification. The system comprehensively determines the permission level of each researcher based on the identity verification results, professional matching score, and research plan evaluation. For example, for senior researchers with a high professional matching score and a clear research plan, the system grants them advanced permissions; for junior researchers with a general professional relevance, the system grants them basic permissions. The embodiment of the present application also implements a dynamic management mechanism for permissions. By recording the operation behaviors of the researchers, the system evaluates their permission usage in real time. The system sets up an operation monitoring module to record information such as data access frequency, editing operations, and research progress. When it detects that a researcher has made an important contribution in a certain field, it automatically raises their corresponding operation permissions; when abnormal operations are found, the system will lower the permission level and trigger a security warning.

[0035] On the basis of the above embodiments, as an optional embodiment, the step of determining the collaboration permissions of each researcher based on the collaboration request may further include the following steps: Step 201: Extract the historical collaboration records of each researcher, and generate a professional ability index for each researcher based on the historical collaboration records.

[0036] Specifically, to more accurately evaluate the collaboration qualifications of researchers and allocate reasonable collaboration permissions, the system needs to conduct a systematic analysis of the historical performance and professional capabilities of researchers. First, extract the historical collaboration records of each researcher from the collaboration database, including data such as the number of projects participated in, the quality of research results, and the collaboration feedback scores. For the quality of research results, the system uses text analysis algorithms to evaluate the professional depth and innovation of research reports; for collaboration feedback scores, the system collects evaluation data from project leaders and partners. The system uses a multi-dimensional evaluation model to calculate the professional ability index of researchers. This model comprehensively considers various indicators in the historical collaboration records and sets weight coefficients according to the importance of the indicators. For example, the system assigns a higher weight to the influence of research results, a medium weight to the number of projects participated in, and a basic weight to the collaboration feedback score. Through weighted calculation, the system obtains the professional ability index of each researcher, which reflects the comprehensive research strength and collaboration performance of the researcher.

[0037] Step 202: Group each researcher according to the professional ability index and determine the initial collaboration level.

[0038] Specifically, based on the professional ability index, the system uses a clustering analysis algorithm to group researchers. The system presets multiple collaboration level thresholds and divides researchers with similar professional ability indices into the same collaboration level group. For example, researchers with professional ability indices in the highest range are classified as expert level, those in the medium range are classified as backbone level, and those in the basic range are classified as ordinary level. Through this grouping method, the initial collaboration levels of researchers are formed.

[0039] Step 203: Calculate the research fit based on the degree of match between the research direction in the collaboration request and the target cultural project.

[0040] Specifically, the system analyzes the degree of match between the research direction in the collaboration request and the target cultural project. The system first extracts the keywords of the research direction from the collaboration request and simultaneously obtains the research field characteristics of the target cultural project. Through a semantic similarity calculation algorithm, the system calculates the similarity between the research direction keywords and the project characteristics and generates a research fit score. This score reflects the degree of coincidence between the professional direction of the researcher and the project requirements.

[0041] Step 204: Combine the initial collaboration level and the research fit to determine the collaboration permissions of each researcher.

[0042] Specifically, the embodiment of the present application uses a decision matrix model to combine the initial collaboration level with the research fit to determine the final collaboration permission. The decision matrix presets corresponding permission levels according to different combinations of the initial collaboration level and the research fit. For example, when a researcher has an expert-level initial collaboration level and a high research fit, the system grants the highest-level collaboration permission, allowing them to participate in the editing of core data and the making of key decisions; when a researcher is at the backbone level and has a medium research fit, the system grants them medium-level collaboration permission, allowing them to participate in data analysis and model optimization; when a researcher is at the general level and has a low research fit, the system grants them basic collaboration permission, allowing them to view public data and provide research suggestions.

[0043] Step 30: Obtain the feedback data sets of each researcher on the virtual display model based on their collaboration permissions.

[0044] Specifically, in order to continuously optimize the virtual display model and improve the research effect, the system needs to collect the feedback data of researchers. The feedback collection module can be used to obtain the feedback data sets generated by each researcher during the use of the virtual display model based on their collaboration permissions. For researchers with high-level permissions, the system collects their suggestions for modifying the model structure and key parameters; for researchers with medium-level permissions, the system collects their comments on data annotation and analysis results; for researchers with basic permissions, the system collects their usage experiences of the model display effect. The system records these feedback data in real time, including information such as feedback type, feedback content, and feedback time, to form a structured feedback data set. In this way, the system establishes a multi-level feedback collection mechanism, providing effective data support for the continuous optimization of the virtual display model and improving the pertinence and accuracy of research work.

[0045] Based on the above embodiment, as another alternative embodiment, the step of obtaining the feedback data sets of each researcher on the virtual display model based on their collaboration permissions may further include the following steps: Step 301: Monitor the interaction operation trajectories of each researcher in the virtual display model and generate a behavior heat map.

[0046] Specifically, in order to comprehensively understand the research behavior characteristics of researchers and obtain valuable feedback information, the system needs to systematically monitor and analyze the activities of researchers in the virtual display model. Specifically, the system first uses the behavior tracking module to record the interaction operation trajectories of researchers in real time, including spatial-temporal data such as the perspective movement path, the dwell time point, and the operation trigger position. The system uses a trajectory analysis algorithm to process these raw data, converts the discrete operation points into continuous behavior trajectories, and calculates the operation frequency and dwell duration at each spatial position to generate a behavior heat map representing the attention distribution of researchers.

[0047] Step 302: Extract the annotation information and modification suggestions of each researcher for specific areas of the virtual display model.

[0048] Specifically, the system obtains the annotation information and modification suggestions added by researchers for specific areas of the virtual display model through the annotation collection module. The annotation information includes content such as text annotations, references, research findings, etc.; the modification suggestions include aspects such as model structure adjustment, parameter optimization, and display effect improvement. The system performs semantic analysis on this information, extracts keywords and core viewpoints, and establishes associations with the spatial coordinates of the virtual display model to form structured annotation data.

[0049] Step 303: Perform hierarchical filtering on the behavior heat map, annotation information, and modification suggestions according to the collaboration permission levels of each researcher.

[0050] Specifically, according to the collaboration permission levels of researchers, perform hierarchical filtering on the obtained behavior heat map, annotation information, and modification suggestions. For researchers with high-level permissions, the system completely retains their behavior heat map and all feedback information; for researchers with medium-level permissions, the system retains their behavior heat map and basic annotation information, but filters out suggestions involving core parameter modifications; for researchers with basic permissions, the system mainly retains their behavior heat map and feedback on the usage experience. The system uses a weight calculation model to set different information weights according to the permission levels to ensure that the feedback of high-permission researchers has higher reference value.

[0051] Step 304: Integrate the information after hierarchical filtering to obtain the feedback data set of each researcher for the virtual display model.

[0052] Specifically, after completing the hierarchical filtering, the system performs fusion processing on various types of information through the data integration module. First, align the behavior heat map and annotation information in the spatial dimension and analyze the corresponding relationship between the focus points of researchers and the feedback content. Then, the system constructs a multi-dimensional feedback data structure according to the type of annotation information and the level of modification suggestions. Finally, the system organizes this information according to dimensions such as researchers, time sequence, and spatial location to generate a complete feedback data set.

[0053] Step 40: Generate corresponding interaction graphs based on each feedback data set.

[0054] Specifically, to visually display the feedback characteristics of researchers and discover research patterns, the system visualizes the feedback dataset as an interactive graph. First, the data processing module structures the feedback dataset and extracts key information such as the operation location, annotation content, and modification suggestions. The system uses a graph construction algorithm, sets researchers as graph nodes, sets feedback behaviors as connecting edges, and determines the node size according to the collaboration permission level and feedback quantity of researchers. The weight and direction of the edges are calculated based on the feedback type, frequency, and time interval. The system identifies the theme characteristics of the feedback content through cluster analysis, uses different visual attributes to represent different types of feedback themes, and supports interactive operations in dimensions such as time period, feedback type, and research theme. Through this visualization method, the system transforms complex feedback data into an intuitive interactive graph, effectively showing the collaboration relationship and feedback characteristics among researchers, and providing a clear visual reference for the optimization of the virtual display model.

[0055] Based on the above embodiments, as another alternative embodiment, the step of generating a corresponding interactive graph based on each feedback dataset may further include the following steps: Step 401: Analyze each feedback dataset in terms of spatial and temporal dimensions to generate an event sequence containing interaction locations and timestamps.

[0056] Specifically, the spatial and temporal analysis module analyzes each feedback dataset in terms of dimensions, extracts the spatial coordinates and time information of each interaction event, and the system organizes these discrete interaction records in chronological order to generate a structured event sequence containing interaction locations and timestamps.

[0057] Step 402: Calculate the attention index of each interaction event in the event sequence, where the attention index is determined according to the residence duration and operation frequency of the researcher.

[0058] Specifically, an attention calculation model is used to evaluate the importance of each interaction event in the event sequence, calculate the residence duration of the researcher at each interaction location, and obtain the duration value through the residence time within the cumulative time window; at the same time, count the number of operations of the researcher at each location, and record the trigger frequencies of interaction behaviors including clicks, drags, zooms, etc. The system uses a weighted calculation method, takes the residence duration and operation frequency as key indicators, and combines the preset weight coefficients to calculate the attention index of each interaction event. This index reflects the degree of attention of the researcher to a specific area and the research investment.

[0059] Step 403: Perform hierarchical screening on the interaction events based on the attention index to obtain a core set of interaction nodes.

[0060] Specifically, based on the calculated attention index, the system performs hierarchical screening on interaction events through a threshold screening mechanism. The system presets multiple attention thresholds, marks interaction events with an attention index exceeding the high threshold as core nodes, marks events with an attention index in the medium range as secondary nodes, and filters out events with an attention index lower than the basic threshold. Through this screening mechanism, the system obtains a core interaction node set containing key interaction information.

[0061] Step 404: Analyze the correlation relationships between the nodes in the core interaction node set and generate node connection weights.

[0062] Specifically, the system calculates the time correlation between nodes through temporal correlation analysis to identify node combinations that frequently appear within a specific time window; calculates the distance correlation between nodes through spatial correlation analysis to identify node groups that are adjacent in the virtual display model; calculates the operation correlation between nodes through behavior correlation analysis to identify sets of nodes with similar interaction patterns. Based on these multi-dimensional correlation features, the system calculates the connection weights between nodes, and the weight values reflect the association strength between nodes.

[0063] Step 405: Hierarchically organize the core interaction node set according to the node connection weights to generate an interaction graph.

[0064] Specifically, a hierarchical relationship is constructed according to the node connection weights. Nodes with higher connection weights are organized as upper-layer nodes, nodes with medium connection weights are organized as middle-layer nodes, and nodes with lower connection weights are organized as basic nodes. The system converts this hierarchical structure into an intuitive interaction graph through visual rendering, where the size of the nodes represents the attention index, the thickness of the lines connecting the nodes represents the connection weights, and the spatial positions of the nodes reflect the hierarchical relationship.

[0065] Step 50: Generate multiple target display models of the virtual display model based on the interaction graph.

[0066] Specifically, to optimize the virtual display effect for different research needs, the system needs to generate multiple target display models based on the interaction graph. Specifically, the system first identifies the core node clusters in the interaction graph through the graph analysis module, and these node clusters reflect the main areas of concern of the researchers. The system calculates the spatial distribution characteristics and association strengths of each node cluster, and extracts characteristic parameters including node density, connection weight, and hierarchical relationship. Based on these parameters, the system uses a model generation algorithm to construct targeted target display models, where high-concern areas obtain more detailed display effects and more flexible interaction methods, while low-concern areas are simplified to improve rendering efficiency. In this way, the system generates a series of target display models suitable for different research scenarios, which not only ensures the display quality of key areas but also realizes the reasonable allocation of computing resources and improves the overall utilization efficiency of the virtual display model.

[0067] Based on the above embodiments, as another optional embodiment, the step of generating the corresponding interaction graph based on each feedback data set may further include the following steps: Step 501: Extract the key attribute labels of each level of nodes from the interaction graph and construct a model feature index.

[0068] Specifically, extract the key attribute labels from each level of nodes in the interaction graph, including information such as the spatial position, attention index, and interaction type of the nodes, and organize these attribute labels in a structured manner to construct a model feature index containing complete feature descriptions.

[0069] Step 502: Divide the virtual display model into multiple sub-model regions according to the model feature index.

[0070] Specifically, based on the constructed model feature index, the system uses a region division algorithm to spatially divide the virtual display model. First, identify the spatial aggregation features in the feature index to determine the distribution range of the key nodes; then, according to the node density and attribute similarity, divide the virtual display model into multiple sub-model regions with relatively independent features. Each sub-model region has specific functional attributes and display requirements.

[0071] Step 503: Calculate the association degree coefficient of each sub-model region based on the node connection weight in the interaction graph.

[0072] Specifically, the relationship between each sub-model region is analyzed through the association degree calculation module, and based on the connection weight between nodes in the interaction graph, the association degree coefficient between sub-model regions is calculated. The calculation process considers the interaction frequency, operation flow direction, and timing characteristics between regions, and obtains a numerical index reflecting the regional association strength through weighted calculation. The higher the association degree coefficient, the closer the functional association between the two sub-model regions.

[0073] Step 504: Reorganize and match each sub-model region according to the correlation coefficient to generate multiple groups of model combination solutions.

[0074] Specifically, based on the calculated correlation coefficient, the system uses a recombination matching algorithm to generate multiple groups of model combination schemes, giving priority to combining sub-model areas with high correlation to form functionally tight model units; then, according to different combination rules, such as functional complementarity, spatial adjacency, operational coherence, etc., these model units are further combined to generate multiple candidate model combination schemes.

[0075] Step 505: Optimize the scenarios for each model combination scheme to generate multiple target display models.

[0076] Specifically, the model combination scheme is adjusted in a targeted manner according to the needs of different application scenarios, such as research analysis, teaching demonstration, collaborative design, etc. The optimization process includes adjusting the regional display ratio, optimizing the interactive interface layout, setting the scene switching effect, etc., and finally generating a series of target display models that meet the needs of different scenarios. Through the above processing mechanism, the system realizes the intelligent conversion from the interactive map to the target display model. This model generation method based on feature indexing and association analysis can not only maintain the core functional characteristics of the original model, but also flexibly combine and optimize according to actual usage needs, significantly improving the applicability and use effect of the virtual display model. At the same time, the generation of multiple target display models provides more targeted support for research work in different scenarios.

[0077] Based on the above embodiment, as another optional embodiment, a LIC remote collaborative management method based on VR multi-digital content may also include the following process: Specifically, historical research materials of the target cultural project are collected, including historical documents such as archaeological reports, research papers, and expert interpretations. At the same time, real-time data such as current research results, academic discussions, and the latest discoveries are obtained. The system performs structured processing on these data, extracts key research elements and research relationships, and constructs a knowledge association graph reflecting the research evolution process. Based on the constructed knowledge association graph, the system analyzes the distribution characteristics of the research content through a hot spot identification module. The system calculates the citation frequency, association strength, and time distribution of each node in the graph to identify areas with higher research heat. At the same time, through the path analysis algorithm, the system traces the evolution route of research results and identifies key research paths that have important impacts on the research development. These paths usually reflect important research breakthroughs and method innovations. A priority ranking algorithm is used to evaluate and rank the identified research hot spots. Specifically, the system comprehensively considers factors such as the duration, influence range, and innovation degree of the research hot spots, and calculates the importance index of each hot spot area. Based on the calculation results, the system highlights the high-priority hot spot areas in the virtual display model, such as using special visual effects, adding interaction prompts, or setting detailed information annotations, to guide researchers to pay priority attention to these important areas. According to the identified key research paths, the system adjusts the content organization of the virtual display model through a display optimization module. The system first analyzes the temporal characteristics and logical relationships of the research paths, and then adjusts the display order of each component in the model accordingly to ensure that the displayed content can reflect the development context of the research. At the same time, based on the association relationships reflected in the research paths, the system optimizes the connection methods and interaction logics between the components in the model to strengthen the display of the relevance between important research findings.

[0078] Please refer to Figure 2 , which is a schematic diagram of the modules of an LIC remote collaboration management system based on VR multi-digital content provided by an embodiment of this application. The LIC remote collaboration management system based on VR multi-digital content may include: a model construction module, a permission determination module, a feedback acquisition module, a graph generation module, and a model display module, where: The model construction module is used to obtain the database corresponding to the target cultural project and construct a virtual display model corresponding to the target cultural project based on the database; The permission determination module is used to obtain the collaboration requests of each researcher and determine the collaboration permissions of each researcher based on the collaboration requests; The feedback acquisition module is used to obtain the feedback data set of each researcher on the virtual display model based on the collaboration permissions; The graph generation module is used to generate a corresponding interactive graph based on each feedback data set; The model display module is used to generate multiple target display models of the virtual display model based on the interactive graph.

[0079] Optionally, the model construction module is further configured to classify and screen historical documents, physical images, and audio data in the database, and extract feature attribute information of various types of data; Based on a preset spatio-temporal association rule, perform temporal sorting and spatial mapping on the feature attribute information to construct a spatio-temporal display framework for the target cultural project; Generate a basic grid of the virtual scene according to the hierarchical structure of the spatio-temporal display framework; Implant the feature attribute information into the basic grid to form scene elements containing cultural features; Perform dynamic layout optimization on the scene elements to generate an interactive virtual display model.

[0080] Optionally, the permission determination module is further configured to extract the historical collaboration records of each researcher, and generate a professional ability index for each researcher based on the historical collaboration records; Group each researcher according to the professional ability index to determine the initial collaboration level; Calculate the research fit degree based on the matching degree between the research direction in the collaboration request and the target cultural project; Combine the initial collaboration level and the research fit degree to determine the collaboration permissions of each researcher.

[0081] Optionally, the feedback acquisition module is further configured to monitor the interaction operation trajectories of each researcher in the virtual display model, and generate a behavior heat map; Extract the annotation information and modification suggestions of each researcher for a specific area of the virtual display model; Perform hierarchical filtering on the behavior heat map, the annotation information, and the modification suggestions according to the collaboration permission levels of each researcher; Integrate the information after hierarchical filtering to obtain a feedback data set of each researcher for the virtual display model.

[0082] Optionally, the atlas generation module is further configured to perform spatio-temporal dimension analysis on each feedback data set to generate an event sequence including interaction positions and timestamps; Calculate the attention index of each interaction event in the event sequence, where the attention index is determined according to the residence time and operation frequency of the researcher; Perform hierarchical screening on the interaction events based on the attention index to obtain a core interaction node set; Analyze the association relationship between each node in the core interaction node set to generate a node connection weight; Hierarchically organize the core interaction node set according to the node connection weights to generate the interaction graph.

[0083] Optionally, the model display module is further configured to extract the key attribute labels of the nodes at each level from the interaction graph and construct a model feature index; Divide the virtual display model according to the model feature index to obtain multiple sub-model regions; Calculate the correlation coefficient of each sub-model region based on the node connection weights in the interaction graph; Reorganize and match each sub-model region according to the correlation coefficient to generate multiple groups of model combination schemes; Optimize the scenarios of each model combination scheme to generate multiple target display models.

[0084] Optionally, the model display module is further configured to collect the historical research data and current research data of the target cultural project and construct a knowledge association graph; Identify the research hot spots and key research paths based on the knowledge association graph; Sort the research hot spots by priority and highlight them in the virtual display model; Adjust the display order and association relationship of the virtual display model according to the key research path.

[0085] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above function modules is used as an example. In actual applications, the above functions can be allocated to different function modules according to needs, that is, the internal structure of the device is divided into different function modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be seen in the method embodiment, which will not be repeated here.

[0086] The embodiment of the present application also provides a computer storage medium, which can store multiple instructions. The instructions are suitable for being loaded and executed by a processor to execute a method for LIC remote collaboration management based on VR multi-digital content in the above embodiment. The specific execution process can refer to the specific description in the above embodiment and will not be repeated here.

[0087] Please refer to Figure 3 The present application also discloses an electronic device. Figure 3 It is a schematic structural diagram of an electronic device disclosed in the embodiment of the present application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0088] Among them, the communication bus 302 is used to realize the connection and communication between these components.

[0089] Among them, the user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may further include a standard wired interface and a wireless interface.

[0090] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0091] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire server using various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling the data stored in the memory 305, it performs various functions of the server and processes data. Optionally, the processor 301 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 301 may integrate one or several combinations of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs, etc.; the GPU is responsible for the rendering and drawing of the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 301 and may be implemented separately by a single chip.

[0092] Among them, the memory 305 may include a Random Access Memory (RAM), or may also include a Read-Only Memory. Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area can store the data involved in the above-mentioned method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a LIC remote collaboration management method based on VR multi-digital content.

[0093] In Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program of a LIC remote collaboration management method stored in the memory 305. When executed by one or more processors 301, the electronic device 300 is caused to execute the method as described in one or more of the above embodiments. It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0094] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0095] In several implementation manners provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0096] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0097] In addition, each functional unit in various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0098] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present 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 memory and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present application. And the aforementioned memory includes: various media such as USB flash drives, mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0099] The above are only exemplary embodiments of the present disclosure and should not be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. Those skilled in the art will easily think of other implementation schemes of the present disclosure after considering the specification and the disclosure of the practical truth.

[0100] The present application aims to cover any variations, uses, or adaptive changes of the present disclosure. These variations, uses, or adaptive changes follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A LIC remote collaborative management method based on VR multi-digital content, applied to a sharing platform, characterized in that: The method comprises: Acquire a database corresponding to a target cultural project, and construct a virtual display model corresponding to the target cultural project based on the database; Acquire collaboration requests from various researchers, and determine collaboration rights of various researchers based on the collaboration requests; Obtaining a feedback data set of each of the researchers on the virtual display model based on the collaboration authority; Generate a corresponding interaction graph based on each of the feedback data sets; A plurality of target display models of the virtual display model are generated based on the interaction graph.

2. The LIC remote collaborative management method based on VR multi-digital content according to claim 1 is characterized in that: The step of constructing a virtual display model corresponding to the target cultural project based on the database includes: Classify and screen the historical documents, physical images and audio data in the database to extract characteristic attribute information of each type of data; Based on the preset spatiotemporal association rules, the characteristic attribute information is temporally arranged and spatially mapped to construct a spatiotemporal display framework of the target cultural project; Generating a basic grid of a virtual scene according to the hierarchical structure of the spatiotemporal presentation framework; implanting the characteristic attribute information into the basic grid to form a scene element containing cultural characteristics; Dynamically optimize the layout of the scene elements to generate an interactive virtual display model.

3. The LIC remote collaborative management method based on VR multi-digital content according to claim 1 is characterized in that: Determining the collaboration authority of each researcher based on the collaboration request includes: Extracting historical collaboration records of each of the researchers, and generating a professional capability index of each of the researchers based on the historical collaboration records; Grouping the researchers according to the professional capability index to determine the initial collaboration level; Calculate the research fit based on the matching degree between the research direction in the collaboration request and the target cultural project; The collaboration authority of each researcher is determined based on the initial collaboration level and the research compatibility.

4. The LIC remote collaborative management method based on VR multi-digital content according to claim 1 is characterized in that: The obtaining of a feedback dataset of each researcher on the virtual display model based on the collaboration authority includes: Monitoring the interactive operation trajectories of each researcher in the virtual display model and generating a behavior heat map; Extracting annotation information and modification suggestions of each researcher on a specific area of ​​the virtual display model; According to the collaboration authority level of each of the researchers, the behavior heat map, the annotation information and the modification suggestions are graded and filtered; The information after hierarchical filtering is integrated to obtain a feedback data set of each researcher on the virtual display model.

5. The LIC remote collaborative management method based on VR multi-digital content according to claim 1 is characterized in that: The generating a corresponding interaction graph based on each feedback data set includes: Performing spatiotemporal dimension analysis on each of the feedback data sets to generate an event sequence including interaction locations and timestamps; Calculating the attention index of each interactive event in the event sequence, wherein the attention index is determined according to the researcher's stay time and operation frequency; Based on the attention index, the interaction events are graded and screened to obtain a core interaction node set; Analyze the association relationship between the nodes in the core interaction node set and generate node connection weights; The core interaction node set is hierarchically organized according to the node connection weights to generate the interaction graph.

6. The LIC remote collaborative management method based on VR multi-digital content according to claim 1 is characterized in that: The generating of the multiple target display models of the virtual display model based on the interaction graph includes: Extract key attribute labels of nodes at each level from the interaction graph and construct a model feature index; Dividing the virtual display model into regions according to the model feature index to obtain a plurality of sub-model regions; Calculate the correlation coefficient of each sub-model region based on the node connection weight in the interaction graph; Recombining and matching each of the sub-model regions according to the correlation coefficient to generate multiple groups of model combination schemes; Scenario optimization is performed on each of the model combination schemes to generate multiple target display models.

7. The LIC remote collaborative management method based on VR multi-digital content according to claim 1 is characterized in that: The method further comprises: Collect historical research data and current research data of the target cultural project to construct a knowledge association map; Identify research hotspots and key research paths based on the knowledge association graph; Prioritizing the research hotspots and marking them in the virtual display model; The display order and association relationship of the virtual display model are adjusted according to the key research path.

8. A LIC remote collaboration management system based on VR multi-digital content, characterized in that: Applied to a sharing platform, the system includes: A model building module, used to obtain a database corresponding to a target cultural project, and build a virtual display model corresponding to the target cultural project based on the database; The authority determination module is used to obtain the collaboration request of each researcher and determine the collaboration authority of each researcher based on the collaboration request; A feedback acquisition module, used for acquiring a feedback data set of each researcher on the virtual display model based on the collaboration authority; A graph generation module, used to generate a corresponding interaction graph based on each feedback data set; A model display module is used to generate multiple target display models of the virtual display model based on the interaction map.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

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