File travel immersive experience construction system based on digital text creation interaction

Abstract

The invention relates to the technical field of man-machine interaction data processing, and particularly discloses a digital text and creative interaction-based text and creative immersive experience construction system, which is characterized in that a narrative dependency graph for recording user experience progress is dynamically generated by constructing a narrative logic network comprising a plurality of narrative nodes; when experience is interrupted, the current state is stored, and after restarting, the experience progress of the user before interruption is completely recovered based on the stored data; and finally, in combination with the real-time position of the user, intelligently screening subsequent narrative nodes and generating a matched digital creative content activation scheme. The technical problem that an existing system cannot keep narrative continuity after experience interruption is solved, and seamless splicing and personalized path planning of immersive experience are achieved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology for human-computer interaction, and specifically to a system for constructing an immersive cultural tourism experience based on digital cultural and creative interaction. Background Technology

[0002] With the deep integration of digital technology and the cultural tourism industry, creating immersive cultural tourism experiences using digital cultural and creative interactive methods such as augmented reality and virtual reality has become an important direction for industry innovation and development. Existing immersive cultural tourism experience systems typically provide users with location-based or image-recognition-based digital content through mobile terminal applications or fixed interactive devices. For example, in museums or historical sites, users can scan specific exhibits or locations to trigger corresponding 3D models, audio guides, or video animations, thereby enhancing the interactivity and enjoyment of their visit.

[0003] The existing technology has the following shortcomings:

[0004] When existing immersive cultural and tourism experience systems are restarted due to application switching or unexpected interruption, they cannot fully restore the multiple narrative nodes that the user has triggered and the logical connections and semantic context between them. This results in logical contradictions between the digital cultural and creative content pushed later and the user's experience history before the interruption, which seriously undermines the coherence of the narrative and the user's sense of immersion. Summary of the Invention

[0005] The purpose of this invention is to provide a system for constructing an immersive cultural tourism experience based on digital cultural and creative interaction, so as to solve the problems mentioned above.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A system for constructing immersive cultural tourism experiences based on digital cultural and creative interactions, comprising:

[0008] The narrative logic network construction module is used to build a directed graph structure containing multiple narrative nodes, where each narrative node is associated with corresponding digital cultural and creative content, and the nodes are connected by directed edges to form a complete narrative logic network.

[0009] The dynamic dependency analysis module, based on the narrative logic network and combined with the narrative nodes triggered by the user in real time, dynamically analyzes and records the dependency relationship between triggered nodes and nodes to be triggered, and generates a narrative dependency graph of the user's current experience progress.

[0010] The state continuity reconstruction module saves the current narrative dependency graph when the system detects an experience interruption; after restarting, it restores the user's experience progress state at the time of the interruption based on the saved narrative dependency graph.

[0011] The intelligent scene continuation module, based on the reconstructed experience progress status and combined with the user's real-time location information, selects subsequent narrative nodes that meet the conditions from the narrative logic network, generates digital cultural and creative content activation schemes that match the current progress, and achieves seamless continuation of immersive experience.

[0012] As a further aspect of the present invention: the establishment of a directed graph structure containing multiple narrative nodes specifically includes:

[0013] The core thematic elements of each narrative node are extracted through semantic analysis, and the semantic correlation strength between nodes is calculated.

[0014] The connection rules for directed edges are defined based on the semantic association strength. When the association strength exceeds a set threshold, a directed connection between nodes is established.

[0015] After constructing the initial narrative logic network based on the connection rules, a dynamic priority parameter is introduced to adjust the connection strength between nodes in real time.

[0016] Through iterative optimization, a final stable narrative logic network structure is formed, in which the out-degree and in-degree of each node meet the preset narrative coherence requirements.

[0017] As a further aspect of the present invention: after constructing the initial narrative logic network based on connection rules, a dynamic priority parameter is introduced to adjust the connection strength between nodes in real time, specifically including:

[0018] Collect real-time behavioral data of users during the narrative experience, including dwell time, interaction frequency, and task completion rate at each narrative node;

[0019] The dynamic priority parameters of each narrative node are calculated based on real-time behavioral data. The dynamic priority parameters include: dwell time and interaction frequency.

[0020] Based on the calculated dynamic priority parameters, the connection strength of directed edges in the initial narrative logic network is adjusted in real time, prioritizing the strengthening of connections between high-priority nodes.

[0021] Through a continuous iterative adjustment process, a narrative logic network that adapts to user behavior characteristics is formed.

[0022] As a further aspect of the present invention: the generation of the narrative dependency graph of the user's current experience progress specifically includes:

[0023] When a user triggers a new narrative node, the state information of the corresponding narrative node is recorded in real time, and the corresponding narrative node is marked as activated in the narrative logic network.

[0024] Based on the directed edge connections in the narrative logic network, extract all nodes to be triggered that can be directly reached from the activated nodes, forming the current user's activation state set.

[0025] The dependency strength between each node to be triggered and the already activated nodes in the set of active states is analyzed. The dependency strength is calculated based on the semantic association between nodes and the path distance.

[0026] Based on the dependency strength analysis results, a narrative dependency graph containing activated nodes, nodes to be triggered, and their dependencies is constructed.

[0027] As a further aspect of the present invention: the dependency strength is calculated based on a combination of semantic correlation and path distance between nodes, specifically including:

[0028] Extract the topic elements of activated nodes and nodes to be triggered, and obtain the initial semantic relevance by calculating the topic element matching degree;

[0029] Analyze the context of two nodes in the narrative logic network and correct the initial semantic relevance based on the number of shared adjacent nodes;

[0030] In the narrative logic network, traverse all paths from the activated node to the node to be triggered, and count the number of intermediate nodes in the path as the path distance.

[0031] The corrected semantic relevance and path distance are fused together according to preset weights to obtain the final dependency strength value.

[0032] As a further aspect of the present invention: after restarting, restoring the user's experience progress state at the time of interruption based on the saved narrative dependency graph specifically includes:

[0033] Perform integrity verification on the saved narrative dependency graph and identify all narrative nodes that are in an active state.

[0034] Based on the dependencies between nodes recorded in the narrative dependency graph, re-establish the logical connection order between activated nodes;

[0035] By analyzing the positional relationships of each activated node in the original narrative logic network, the contextual semantic associations between nodes are restored.

[0036] Based on the restored semantic associations and logical connection order, the complete narrative progress state at the time of user interruption is reconstructed.

[0037] As a further aspect of the present invention: the step of restoring the contextual semantic associations between nodes by analyzing the positional relationships of each activated node in the original narrative logic network specifically includes:

[0038] Identify the connection paths of each activated node in the original narrative logic network, and count the direct and indirect connections between nodes;

[0039] Analyze the weight distribution of each connection path in the network topology to determine the main semantic propagation paths between nodes;

[0040] Calculate the semantic connectivity between nodes based on the main semantic propagation path;

[0041] Based on the calculation results of semantic connectivity, the semantic association network between activated nodes is reconstructed, restoring the complete contextual semantic relationship before the interruption.

[0042] As a further aspect of the present invention: the step of filtering subsequent narrative nodes that meet the conditions from the narrative logic network specifically includes:

[0043] Based on the reconstructed experience progress status, identify all currently active narrative nodes and their associated digital cultural and creative content;

[0044] Obtain the user's real-time location information, calculate the spatial topological relationship between the location and each node to be triggered, and determine the location matching degree;

[0045] By combining the dependency strength between nodes recorded in the narrative dependency graph, the narrative coherence coefficient of each node to be triggered is calculated.

[0046] Based on the weighted evaluation results of location matching degree and narrative coherence coefficient, the optimal set of subsequent narrative nodes is selected.

[0047] As a further aspect of the present invention: the process of obtaining the narrative coherence coefficient is as follows:

[0048] Extract the dependency strength data between the node to be triggered and the already activated node from the narrative dependency graph;

[0049] Analyze the topological position of the node to be triggered in the narrative logic network and calculate its minimum path distance to the already activated nodes;

[0050] By combining dependency strength data and minimum path distance, the narrative relevance of the node to be triggered is calculated;

[0051] Based on the quantitative results of narrative relevance, the final narrative coherence coefficient is obtained through preset transformation rules.

[0052] As a further aspect of the present invention: generating a digital cultural and creative content activation scheme that matches the current progress, achieving seamless continuation of the immersive experience, specifically including:

[0053] Analyze the content characteristics of each node in the selected set of subsequent narrative nodes to determine the type distribution and correlation of digital cultural and creative content;

[0054] The activation priority order is determined based on the strength of the dependencies between nodes, while potential conflicts between digital cultural and creative content on different nodes are detected.

[0055] An activation scheme is generated based on the priority order and conflict detection results, which includes the content loading order and the presentation timing.

[0056] Digital cultural and creative content is preloaded according to the activation plan, and the presentation parameters are dynamically adjusted based on the user's real-time location.

[0057] Generate transitional content that connects the current progress with the subsequent narrative, and integrate digital cultural and creative content with real-world scenes through real-time rendering.

[0058] The beneficial effects of this invention are:

[0059] (1) By constructing a narrative logic network and a narrative dependency graph, the system not only records the nodes triggered by the user, but also more accurately captures and stores the logical connection order and contextual semantic association between nodes. Upon recovery, the system can fully reconstruct the user's experience progress state before interruption by analyzing semantic connectivity and path weights based on the saved dependency graph, including all activated nodes, their trigger order, and their semantic relationships. This allows virtual characters and subsequent plot to seamlessly connect with the user's previous actions, fundamentally solving the problem of narrative disjointness and emotional detachment caused by interruption, and providing users with a highly coherent and complete immersive narrative experience.

[0060] (2) This invention breaks through the preset, fixed experience paths of traditional systems. By introducing dynamic priority parameters, the system can collect and analyze user behavior data such as dwell time and interaction frequency in real time, dynamically adjusting the connection strength between nodes in the narrative logic network, making the network structure adaptable to user interests and preferences. In the continuation stage, the system does not simply recommend the nearest node, but comprehensively calculates the location matching degree and narrative coherence coefficient, intelligently selecting the optimal set of nodes that are spatially close and narratively coherent from many nodes to be triggered, and generating an activation scheme that matches them. This ensures that each experience continuation simultaneously conforms to the user's physical location and past browsing history, thereby planning a personalized browsing path for each user. Attached Figure Description

[0061] The invention will now be further described with reference to the accompanying drawings.

[0062] Figure 1 This is a flowchart of the system of the present invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] Please see Figure 1 As shown, this invention is a system for constructing an immersive cultural tourism experience based on digital cultural and creative interaction, comprising:

[0065] The narrative logic network construction module is used to build a directed graph structure containing multiple narrative nodes, where each narrative node is associated with corresponding digital cultural and creative content, and the nodes are connected by directed edges to form a complete narrative logic network.

[0066] The dynamic dependency analysis module, based on the narrative logic network and combined with the narrative nodes triggered by the user in real time, dynamically analyzes and records the dependency relationship between triggered nodes and nodes to be triggered, and generates a narrative dependency graph of the user's current experience progress.

[0067] The state continuity reconstruction module saves the current narrative dependency graph when the system detects an experience interruption; after restarting, it restores the user's experience progress state at the time of the interruption based on the saved narrative dependency graph.

[0068] The intelligent scene continuation module, based on the reconstructed experience progress status and combined with the user's real-time location information, selects subsequent narrative nodes that meet the conditions from the narrative logic network, generates digital cultural and creative content activation schemes that match the current progress, and achieves seamless continuation of immersive experience.

[0069] In the narrative logic network construction module, core thematic elements of each narrative node are extracted through semantic analysis. Specifically, text analysis is performed on the digital cultural and creative content associated with each narrative node to extract keywords and thematic features. Methods based on word frequency statistics and semantic similarity calculation are used to determine the main content features of each node. For each node, a set of thematic elements is established, containing the main concepts, characters, and event semantic elements involved in that node.

[0070] Calculate the semantic association strength between nodes. Specifically, for any two narrative nodes, calculate the similarity of their set of topic elements. A similarity calculation method based on a vector space model is used. First, construct the topic vectors for all narrative nodes, then calculate the cosine similarity between the topic vectors of every two nodes. The calculated cosine similarity value is used as the semantic association strength between nodes, and this strength value ranges from 0 to 1.

[0071] The connection rules for directed edges are defined based on the semantic association strength. A semantic association strength threshold is set, which is determined through multiple experimental tests, with a preferred value of 0.7. When the semantic association strength between two nodes exceeds this threshold, a directed connection is established between the two nodes. The direction of the directed edge is determined by the logical sequence relationship between the nodes, specifically based on the temporal order or causal relationship of the node content.

[0072] An initial narrative logic network is constructed based on connection rules. All narrative nodes are treated as vertices in the network, and directed edges are established according to the aforementioned connection rules to form the initial narrative logic network structure. This network structure records the logical connections between all narrative nodes.

[0073] A dynamic priority parameter is introduced to adjust the connection strength between nodes in real time. Real-time user behavior data is collected during the narrative experience, including dwell time, interaction frequency, and task completion rate at each narrative node. Dwell time is calculated by recording the time difference between the user entering and leaving the node; interaction frequency is the number of times the user interacts with the node content per unit time; and task completion rate is determined by the proportion of the user who completes the tasks set for that node.

[0074] Dynamic priority parameters for each narrative node are calculated based on real-time behavioral data. Specifically, the three metrics—dwell time, interaction frequency, and task completion rate—are normalized separately, and then weighted and summed according to preset weighting coefficients. These weighting coefficients are determined through expert evaluation: dwell time has a weight of 0.4, interaction frequency has a weight of 0.3, and task completion rate has a weight of 0.3. The calculated dynamic priority parameters reflect the importance of each node in the user experience process.

[0075] Based on the calculated dynamic priority parameters, the connection strength of directed edges in the initial narrative logic network is adjusted in real time. For directed edges connecting two high-priority nodes, their connection strength is appropriately increased; for directed edges connecting low-priority nodes, their connection strength is correspondingly decreased. The adjustment range of connection strength is proportional to the change in the node priority parameters.

[0076] Through a continuous iterative adjustment process, a narrative logic network that adapts to user behavior characteristics is formed. Each time a user interacts with a node, the priority parameters of the relevant nodes are recalculated, and the connection strength in the network is adjusted accordingly. After multiple iterations, the network structure gradually adapts to the user's experience habits and preferences.

[0077] The final stable narrative logic network structure is formed through an iterative optimization process. After each adjustment, the out-degree and in-degree of each node in the network are checked to see if they meet the preset narrative coherence requirements. The out-degree requirement ensures that each node has sufficient potential for further development, while the in-degree requirement ensures that each node can be effectively triggered. For nodes that do not meet the requirements, optimization is performed by adjusting their connections with other nodes until all nodes meet the coherence requirements.

[0078] In the dynamic dependency analysis module, the system records the state information of the corresponding narrative node in real time when a user triggers it. Specifically, after a user triggers a narrative node through interaction, the system records the node's identifier, trigger time, and dwell time, among other state information. Simultaneously, within the established narrative logic network, the node's state is marked as active. This marking process is achieved by modifying the node's state attributes, changing the node's state value from the default inactive state to an active state. The recorded state information is stored in the user's experience progress record, providing a data foundation for subsequent dependency analysis.

[0079] Based on the directed edge connections in the narrative logic network, all nodes directly reachable from activated nodes are extracted for triggering. Specifically, the system traverses all directed edges in the narrative logic network, identifying all edges whose starting point is an activated node and whose ending point is an inactive node. The endpoint nodes corresponding to these directed edges are collected to form the current user's activation state set. During this process, the system excludes nodes that are connected to activated nodes but are temporarily unreachable due to other constraints. The establishment of the activation state set provides a clear analytical scope for subsequent dependency strength analysis.

[0080] The dependency strength between each node to be triggered and the already activated nodes in the active state set is analyzed. The dependency strength is calculated based on a combination of semantic relevance and path distance between nodes. The specific calculation process includes several steps: First, the topic elements of the activated nodes and the nodes to be triggered are extracted. The initial semantic relevance is obtained by calculating the topic element matching degree. The method for calculating the topic element matching degree is to count the number of identical elements in the topic element sets of the two nodes, and then divide by the square root of the number of elements in the union of the two sets. This calculation method can effectively balance matching accuracy and computational complexity.

[0081] This paper analyzes the context of two nodes within a narrative logic network and adjusts the initial semantic relevance based on the number of shared neighboring nodes. Specifically, it identifies the set of nodes adjacent to both nodes and calculates the number of nodes in this set. The ratio of the number of shared neighboring nodes to the average number of neighboring nodes per node is used as a correction coefficient to adjust the initial semantic relevance. This adjustment process better reflects the actual degree of relevance between nodes within the network structure.

[0082] In the narrative logic network, all paths from activated nodes to nodes to be triggered are traversed, and the number of intermediate nodes in each path is counted as the path distance. For cases with multiple paths, the path with the fewest intermediate nodes is selected. The path distance reflects the proximity of two nodes in the narrative logic network; a smaller path distance indicates a more direct connection between the two nodes.

[0083] The corrected semantic relevance and path distance are fused together with preset weights to obtain the final dependency strength value. The preset weights are determined based on experimental data analysis, with a weight of 0.6 for semantic relevance and 0.4 for path distance. The path distance is normalized before calculation, converting it to a value between 0 and 1, with smaller values ​​indicating closer distances. The dependency strength is calculated as follows: dependency strength equals the corrected semantic relevance multiplied by 0.6, plus the normalized path distance multiplied by 0.4. This calculation method comprehensively considers the semantic relevance and structural proximity of nodes.

[0084] Based on the dependency strength analysis results, a narrative dependency graph is constructed, containing activated nodes, nodes to be triggered, and their dependencies. During construction, activated and nodes to be triggered are used as vertices of the graph, and the thickness of the connections between nodes is determined by the calculated dependency strength values. A higher dependency strength value results in a thicker connection, indicating a closer relationship between the two nodes. Simultaneously, different colors are used in the graph to distinguish activated and nodes to be triggered, allowing users to intuitively understand the current progress of their experience. The completed narrative dependency graph will serve as an important record of the user experience status, providing a basis for subsequent experience recovery and scene continuation.

[0085] In the state continuity reconstruction module, the integrity of the saved narrative dependency graph is verified, identifying all narrative nodes in an active state. Specifically, the saved narrative dependency graph data is read, and its data structure integrity is checked. All nodes in the graph are traversed, and nodes marked as active are selected. For each active node, its necessary attributes are verified to be complete, including node identifier, activation time, and interaction records. If data corruption or missing information is found, data recovery is performed through a verification and repair mechanism. Finally, a complete list of active nodes is generated, containing information on all narrative nodes that need to be recovered.

[0086] Based on the dependencies between nodes recorded in the narrative dependency graph, the logical connection order between activated nodes is re-established. Specifically, all connections between activated nodes are extracted from the narrative dependency graph. The temporal and causal information recorded in these connections is analyzed to determine the order of nodes. In cases with multiple connection paths, the primary connection path is determined based on the dependency strength value. Through this process, the triggering order and logical associations of activated nodes in the original experience are reconstructed, forming an ordered sequence of nodes.

[0087] By analyzing the positional relationships of each activated node in the original narrative logic network, the contextual semantic associations between nodes are restored. This process includes several specific steps: First, the connection paths of each activated node in the original narrative logic network are identified, and the direct and indirect connections between nodes are statistically analyzed. A direct connection refers to a directed edge connecting two nodes directly, while an indirect connection refers to two nodes being connected through an intermediate node. A depth-first search algorithm is then used to traverse the original narrative logic network to find all connection paths between activated nodes.

[0088] Analyze the weight distribution of each connection path in the network topology to determine the main semantic propagation paths between nodes. The weight distribution analysis is based on the weight value of each edge on the path, and the product of all weight values ​​on the path is taken as the weight of that path. Compare the weight values ​​of different paths and select the path with the largest weight value as the main semantic propagation path. In the case of multiple main paths, record the top three paths with the largest weight values.

[0089] Based on the primary semantic propagation paths, the semantic connectivity between nodes is calculated. The method for calculating semantic connectivity is as follows: for every two activated nodes, all primary semantic propagation paths between them are counted, the weights of these paths are summed, and then divided by the shortest path length between the two nodes. This calculation method considers both the quantity and quality of connection paths and the distance between nodes. The semantic connectivity value ranges from 0 to 1; a higher value indicates a stronger semantic association between the two nodes.

[0090] Based on the semantic connectivity calculation results, a semantic association network among activated nodes is reconstructed. Node pairs with a semantic connectivity greater than 0.5 are connected, and the connection strength is equal to the semantic connectivity value. This resulting semantic association network accurately reflects the semantic relationships of activated nodes in the original narrative logic network, restoring the complete contextual semantic relationships before the interruption.

[0091] Based on the restored semantic associations and logical connection order, the complete narrative progress state at the time of user interruption is reconstructed. The re-established logical connection order is fused with the restored semantic association network to generate a complete narrative progress state record. This record includes not only basic information about each activated node, but also the logical order relationship and semantic association strength between nodes. Through this complete narrative progress state, the system can accurately understand the user's experience progress before interruption, providing complete state information for subsequent experience continuation.

[0092] In the intelligent scene continuation module, based on the reconstructed experience progress status, all currently active narrative nodes and their associated digital cultural and creative content are identified. Specifically, the system reads information about all narrative nodes marked as active from the reconstructed experience progress status record. For each active node, basic information about its associated digital cultural and creative content is extracted, including content type, file size, and presentation format. Simultaneously, the positional information of these nodes within the narrative logic network, as well as the logical connections between them, are recorded. This step provides foundational data support for subsequent node selection.

[0093] The system acquires the user's real-time location information, calculates the spatial topological relationship between the location and each node to be triggered, and determines the location matching degree. The user's current latitude and longitude coordinates are obtained through the mobile device's positioning function. For each node to be triggered, its preset trigger location coordinates are obtained. The straight-line distance between the user's current location and the trigger locations of each node is calculated and converted into a location matching degree. The conversion method is as follows: when the distance is less than 50 meters, the location matching degree is 1; when the distance is between 50 meters and 200 meters, the location matching degree decreases linearly from 1 to 0.5; when the distance exceeds 200 meters, the location matching degree is 0. This calculation method ensures that the location matching degree accurately reflects the spatial proximity between the user and the nodes.

[0094] Based on the dependency strength between nodes recorded in the narrative dependency graph, the narrative coherence coefficient of each node to be triggered is calculated. First, the dependency strength data between the node to be triggered and the activated nodes is extracted from the narrative dependency graph, and the largest dependency strength value is taken as the baseline value. Then, the topological position of the node to be triggered in the narrative logic network is analyzed, and the minimum path distance between it and each activated node is calculated using a breadth-first search algorithm, and the minimum path distance value is taken. The narrative relevance is calculated by multiplying the dependency strength baseline value by a path distance decay coefficient, which is equal to 1 divided by the square of the minimum path distance. Finally, the narrative relevance is converted into a narrative coherence coefficient using a preset conversion rule: when the narrative relevance is greater than 0.8, the narrative coherence coefficient is 1; when the narrative relevance is between 0.5 and 0.8, the narrative coherence coefficient is 0.8; when the narrative relevance is between 0.3 and 0.5, the narrative coherence coefficient is 0.5; and when the narrative relevance is less than 0.3, the narrative coherence coefficient is 0.

[0095] Based on the weighted evaluation results of location matching degree and narrative coherence coefficient, the optimal set of subsequent narrative nodes is selected. The weighting scheme is as follows: location matching degree weight 0.4, narrative coherence coefficient weight 0.6. For each node to be triggered, its comprehensive score is calculated using the formula: location matching degree multiplied by 0.4 plus narrative coherence coefficient multiplied by 0.6. All nodes to be triggered are sorted from highest to lowest comprehensive score, and the top 5 nodes are selected as the optimal set of subsequent narrative nodes. If there are fewer than 5 nodes, all nodes with a comprehensive score greater than 0.6 are selected.

[0096] The analysis of the content characteristics of each node in the selected set of subsequent narrative nodes determines the type distribution and relationships of digital cultural and creative content. For each selected node, the specific characteristics of its digital cultural and creative content are analyzed, including content format (such as 3D models, audio, video, text and images), data size, and estimated loading time. Simultaneously, the relationships between different node contents are analyzed, including the relevance of content themes and the complementarity of presentation methods. This analytical process provides a content-level basis for the subsequent activation plan development.

[0097] The activation priority order is determined based on the strength of dependencies between nodes, while potential conflicts between digital cultural and creative content on different nodes are detected. The activation priority order is primarily determined by the dependency strength value between nodes; nodes with higher dependency strength values ​​have higher priority. For nodes with the same dependency strength, their overall score is then considered for ranking. Conflict detection mainly checks for resource conflicts between the digital cultural and creative content on different nodes, including conflicts in video memory usage, audio channel conflicts, and network bandwidth conflicts. Detected conflicts are resolved by adjusting the content loading order or reducing the quality of some content.

[0098] An activation scheme is generated based on priority order and conflict detection results, including content loading order and presentation sequence. Content loading order is arranged from highest to lowest activation priority, but dependencies between content are considered to ensure that basic content is loaded first, followed by derivative content. The presentation sequence plan specifies the start time, duration, and end time of each digital cultural and creative content item, ensuring a reasonable temporal rhythm for content presentation. The scheme also includes an exception handling mechanism; when content fails to load, backup content is automatically activated or the presentation sequence of subsequent content is adjusted.

[0099] Digital cultural and creative content is preloaded according to the activation scheme, and presentation parameters are dynamically adjusted based on the user's real-time location. The digital cultural and creative content is preloaded into memory according to the loading order specified in the activation scheme. During content presentation, the system continuously monitors changes in the user's real-time location and dynamically adjusts presentation parameters, including content size, orientation, and transparency. For example, as the user approaches a virtual object, the object's display size is increased and its display precision is improved accordingly.

[0100] Transitional content is generated to connect the current progress with the subsequent narrative, merging digital cultural and creative content with the real-world scene through real-time rendering. This transitional content includes scene gradations, character dialogue, and plot explanations, used to connect the experience progress before the interruption with the subsequent narrative. The duration of the transitional content is controlled between 3 and 5 seconds to ensure a natural transition without affecting the smoothness of the experience. Finally, through real-time rendering technology, all digital cultural and creative content is merged with the user's current real-world scene, ensuring that the virtual content and the real environment remain consistent in terms of perspective, lighting, and shadows, achieving dual continuity in both visuals and narrative.

[0101] The working principle of this invention is as follows: A narrative logic network is formed by establishing a directed graph structure containing multiple narrative nodes, where nodes are associated with digital cultural and creative content and connected by directed edges. Based on the narrative nodes triggered by the user in real time, the dependency relationships between triggered nodes and nodes to be triggered are dynamically analyzed to generate a narrative dependency graph. When the experience is interrupted, this graph is saved, and upon restarting, the user's experience progress at the time of interruption is restored based on the saved graph. Finally, based on the reconstructed experience progress and the user's real-time location information, subsequent narrative nodes that meet the conditions are selected from the narrative logic network to generate a matching digital cultural and creative content activation scheme, achieving seamless continuation of the immersive experience. This system calculates the node association strength through semantic analysis, introduces dynamic priority parameters to adjust the connection strength in real time, and uses dependency strength calculation and semantic connectivity analysis to ensure narrative coherence. Ultimately, intelligent scene continuation achieves the fusion presentation of digital content and real-world scenes.

[0102] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A digital creative interaction-based travel and culture immersive experience construction system, characterized in that, The application relates to a narrative logic network construction module for establishing a directed graph structure containing a plurality of narrative nodes, wherein each narrative node is associated with corresponding digital creative content, and the nodes are connected by directed edges to form a complete narrative logic network. A dynamic dependency analysis module dynamically analyzes and records the dependency relationship between triggered nodes and to-be-triggered nodes based on the narrative logic network and in combination with narrative nodes triggered by a user in real time, and generates a narrative dependency graph of the current experience progress of the user. A state continuity reconstruction module saves the current narrative dependency graph when the system detects an interruption in experience. After restarting, the experience progress state of the user at the time of interruption is restored based on the saved narrative dependency graph. An intelligent scene continuation module filters subsequent narrative nodes that meet the conditions from the narrative logic network based on the reconstructed experience progress state and in combination with real-time location information of the user, generates a digital creative content activation scheme matched with the current progress, and realizes seamless continuation of immersive experience. The establishment of the directed graph structure containing a plurality of narrative nodes specifically comprises the following steps.

2. The digital creative interaction-based immersive travel experience construction system according to claim 1, characterized in that, Core theme elements of each narrative node are extracted through semantic analysis, and the semantic correlation strength between the nodes is calculated. The connection rules of the directed edges are defined according to the semantic correlation strength, and the directed connection between the nodes is established when the correlation strength exceeds a set threshold. After the initial narrative logic network is constructed based on the connection rules, a dynamic priority parameter is introduced to adjust the connection strength between the nodes in real time. Through an iterative optimization process, a final stable narrative logic network structure is formed, wherein the out-degree and in-degree of each node meet the preset narrative continuity requirement. After the initial narrative logic network is constructed based on the connection rules, a dynamic priority parameter is introduced to adjust the connection strength between the nodes in real time, specifically comprising the following steps.

3. The digital creative interaction-based immersive travel experience construction system according to claim 2, characterized in that, Real-time behavior data of the user in the narrative experience process is collected, including the stay duration, interaction frequency and task completion degree at each narrative node. Based on the real-time behavior data, the dynamic priority parameter of each narrative node is calculated, and the dynamic priority parameter comprises the stay duration and the interaction frequency. According to the calculated dynamic priority parameter, the connection strength of the directed edges in the initial narrative logic network is adjusted in real time, and the connection relationship between high-priority nodes is preferentially strengthened. Through a continuous iterative adjustment process, a narrative logic network adaptive to the behavior characteristics of the user is formed. The generation of the narrative dependency graph of the current experience progress of the user specifically comprises the following steps.

4. The digital creative interaction-based immersive travel experience construction system according to claim 1, characterized in that, When the user triggers a new narrative node, the state information of the corresponding narrative node is recorded in real time, and the corresponding narrative node is marked as an activated state in the narrative logic network. According to the directed edge connection relationship in the narrative logic network, all to-be-triggered nodes directly reachable from the activated node are extracted to form an activated state set of the current user. The dependency strength between each to-be-triggered node and the activated node in the activated state set is analyzed, and the dependency strength is comprehensively calculated based on the semantic correlation degree and path distance between the nodes. Based on the dependency strength analysis result, a narrative dependency graph containing the activated node, the to-be-triggered node and the dependency relationship therebetween is constructed. ​ 5. The digital creative interaction-based immersive travel experience construction system according to claim 4, characterized in that, ​ extracting theme elements of the activated node and the to-be-triggered node, obtaining an initial semantic correlation degree by calculating a matching degree of the theme elements; analyzing a context environment of the two nodes in the narrative logic network, and correcting the initial semantic correlation degree based on a number of shared adjacent nodes; traversing all paths from the activated node to the to-be-triggered node in the narrative logic network, and counting a number of intermediate nodes in the paths as path distances; fusing and calculating the corrected semantic correlation degree and the path distances according to preset weights to obtain a final dependence strength value.

6. The digital creative interaction-based travel and culture immersive experience construction system according to claim 1, characterized in that, After the restart, the experience progress state at the time of the user interruption is restored based on the saved narrative dependence graph, and specifically includes: performing integrity verification on the saved narrative dependence graph to identify all narrative nodes in an activated state in the narrative dependence graph; reestablishing a logical connection order between the activated nodes according to a dependence relationship between the nodes recorded in the narrative dependence graph; restoring a context semantic correlation between the nodes by analyzing a positional relationship of each activated node in an original narrative logic network; reconstructing a complete narrative progress state at the time of the user interruption based on the restored semantic correlation and the logical connection order.

7. The digital creative interaction-based immersive travel experience construction system according to claim 6, characterized in that, The restoring of the context semantic correlation between the nodes by analyzing the positional relationship of each activated node in the original narrative logic network specifically includes: identifying a connection path of each activated node in the original narrative logic network, and counting a direct connection and an indirect connection relationship between the nodes; analyzing a weight distribution of each connection path in a network topology structure to determine a main semantic propagation path between the nodes; calculating a semantic connectivity between the nodes based on the main semantic propagation path; reconstructing a semantic correlation network between the activated nodes according to a calculation result of the semantic connectivity to restore a complete context semantic relationship before the interruption.

8. The digital creative interaction-based travel and culture immersive experience construction system according to claim 1, characterized in that, The filtering of the subsequent narrative nodes that meet the conditions from the narrative logic network specifically includes: identifying all narrative nodes in an activated state and associated digital creative content based on the reconstructed experience progress state; obtaining real-time location information of a user, calculating a spatial topology relationship between the location and each to-be-triggered node to determine a location matching degree; calculating a narrative coherence coefficient of each to-be-triggered node in combination with a dependence strength between the nodes recorded in the narrative dependence graph; screening an optimal subsequent narrative node set according to a weighted evaluation result of the location matching degree and the narrative coherence coefficient.

9. The digital creative interaction-based travel and culture immersive experience construction system according to claim 8, characterized in that, The obtaining process of the narrative coherence coefficient is: extracting dependence strength data between a to-be-triggered node and an activated node from the narrative dependence graph; analyzing a topological position of the to-be-triggered node in the narrative logic network to calculate a minimum path distance between the to-be-triggered node and the activated node; calculating a narrative correlation degree of the to-be-triggered node in combination with the dependence strength data and the minimum path distance; obtaining a final narrative coherence coefficient through a preset conversion rule based on a quantitative result of the narrative correlation degree.

10. The digital creative interaction-based immersive travel experience construction system according to claim 1, characterized in that, generating a digital creative content activation scheme matched with a current progress to realize seamless continuation of the immersive experience, and specifically includes: analyzing content features of each node in the screened subsequent narrative node set to determine a type distribution and an association relationship of the digital creative content; According to the dependence relationship between the nodes, the activation priority order is determined, and potential conflicts between different node digital creative content are detected; Based on the priority order and the conflict detection result, an activation scheme containing content loading order and presentation timing is generated; According to the activation scheme, the digital creative content is preloaded, and the presentation parameters are dynamically adjusted based on the real-time location of the user; Transition content connecting the current progress and the subsequent narrative is generated, and the digital creative content is fused with the real scene through real-time rendering.

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