Intelligent generation method and system for visitor tasks in digital and intelligent space

Abstract

This disclosure provides an intelligent generation method and system for visitor tasks in a digital space. The intelligent generation method includes obtaining the identity information of the target visitor and the spatial information of the target digital space selected by the target visitor; generating corresponding visitor demand analysis results and spatial structure analysis results based on the identity information and spatial information, respectively, and generating corresponding target visit plan information based on the visitor demand analysis results and spatial structure analysis results; generating initial itinerary scripts under multiple differentiated experience dimensions based on the target visit plan information, scoring each initial script scheme, and selecting the target itinerary script from multiple initial itinerary scripts based on the scoring results; and completing the structured construction of the visit itinerary based on the target itinerary script to generate the target visitor task in the target digital space, thereby improving the flexibility and adaptability of generating visitor tasks.

Description

Technical Field

[0001] This disclosure relates to the field of digital space technology, and in particular to a method and system for intelligently generating visitor tasks in digital spaces. Background Technology

[0002] As an intelligent carrier integrating digital technology and physical space, digital spaces are widely used in various scenarios such as smart exhibition halls, digital twins, and intelligent interactions, providing visitors with customized access services. Currently, most methods for generating visitor tasks in digital spaces are based on fixed templates and preset rules, and can only generate single task schemes. This results in a lack of flexibility and adaptability, making it difficult to accurately match the personalized needs of different visitors or fully adapt to the spatial characteristics of digital spaces. This seriously affects the overall visitor experience and limits the further improvement of the intelligent service capabilities of digital spaces. Summary of the Invention

[0003] The main objective of this disclosure is to propose an intelligent generation method and system for digital space visitor tasks, which can improve the flexibility and adaptability of generating visitor tasks.

[0004] To achieve the above objectives, a first aspect of this disclosure proposes an intelligent generation method for digital space visitor tasks, comprising: Obtain the identity information of the target visitor and the spatial information of the target digital space selected by the target visitor to be accessed; Based on the identity information and the spatial information, corresponding visitor demand analysis results and spatial structure analysis results are generated respectively, and corresponding target visit plan information is generated based on the visitor demand analysis results and spatial structure analysis results; Based on the target visit plan information, initial itinerary scripts under multiple differentiated experience dimensions are generated, and each initial script is scored. Based on the scoring results, the target itinerary script is selected from multiple initial itinerary scripts. Based on the target itinerary arrangement script, the structured construction of the visit itinerary is completed, and the target visitor task within the target digital space is generated.

[0005] In some embodiments, generating corresponding target visit plan information based on the visitor demand analysis results and the spatial structure analysis results includes: Based on the visitor demand analysis results and the spatial structure analysis results, a target visit path adapted to the target visitors is generated. Each circulation node in the target visitor path is decomposed into a corresponding specific task, forming a correspondence between each circulation node and the specific task; Based on the visitor demand analysis results, a matching time budget is allocated to the specific tasks corresponding to each of the movement nodes, and the execution logic of the specific tasks under each of the movement nodes is analyzed to determine the dependencies between the specific tasks. Based on the target visit path, the correspondence between the target movement nodes and the specific tasks, the time budget of each specific task, and the dependency relationship, the target visit plan information is constructed.

[0006] In some embodiments, generating an initial itinerary script based on the target visit plan information across multiple differentiated experience dimensions includes: A spatial director syntax tree is constructed. Based on the spatial director syntax tree, the target visit plan information is transformed into a spatial scheduling scheme with a narrative structure. Based on the target visit plan information, time allocation strategies and experience design styles of various differentiated experience dimensions, including conservative, innovative, balanced, fast, and in-depth, are used to adapt the target visit plan information according to the station scheduling parameters, depth parameters, and movement trajectory parameters in the spatial director syntax tree, and generate the initial itinerary script corresponding to each experience dimension.

[0007] In some embodiments, scoring each of the initial script proposals and selecting a target itinerary script from the plurality of initial itinerary scripts based on the scoring results includes: The corresponding evaluation indicators for each initial itinerary script are extracted from the dimensions of time efficiency, experience quality, and resource consumption, and then quantitatively scored. Configure the weights for each dimension of quantitative scoring, and calculate the comprehensive score for each of the initial itinerary scripts based on the weights; The comprehensive scores of each initial itinerary script are filtered for compliance based on the constraints of the target digital intelligence space to obtain the filtered comprehensive score. The initial itinerary scripts are sorted from highest to lowest according to the filtered comprehensive score, and the initial itinerary script with the highest score is selected as the target itinerary script.

[0008] In some embodiments, the step of constructing a structured visit itinerary based on the target itinerary scheduling script and generating a target visitor task for the target visitor within the target digital space includes: The target itinerary script, the spatial information, and the demand analysis results are stored as context information, and historical data of historical visit itinerary construction are retrieved. Based on the context information and the historical data, a structured access itinerary that is adapted to the target visitor and the target digital space is inferred. Based on the target structured structure, the access itinerary is constructed in a structured manner, and the structured construction results are integrated to generate the target visitor task within the target digital space.

[0009] In some embodiments, the step of constructing a structured access itinerary based on the target structured structure and integrating the structured construction results to generate the target visitor task within the target digital space includes: Based on the target structured structure, the timeline of the access process is generated, the flow nodes are refined, the executor is assigned, the multi-sensory experience is designed, and various mapping relationships are generated in sequence, forming a standardized structured construction result of the access process; Based on the structured construction results, a target visitor task is generated for the target visitor within the target digital intelligence space.

[0010] In some embodiments, after generating the target visitor task within the target digital space, the intelligent generation method for the digital space visitor task further includes: The target visitor task is deployed to the target digital space for execution, and the execution progress of the target visitor task and the running status of each node are monitored in real time. Based on the preset evaluation indicators, the effectiveness of the visitor experience, space resource utilization and task completion after the task is completed is evaluated. Based on the results of the performance evaluation, the time allocation, path planning, or experience design in the target visitor task are iteratively optimized and adjusted. When an abnormality is detected in the execution of the target visitor's task or when the experience effect deviates from the threshold, an abnormality handling process is triggered for real-time correction.

[0011] To achieve the above objectives, a second aspect of this disclosure provides an intelligent generation system for digital space visitor tasks, comprising: The data acquisition module is used to acquire the identity information of the target visitor and the spatial information of the target digital space selected by the target visitor to be visited; The planning module is used to generate corresponding visitor demand analysis results and spatial structure analysis results based on the identity information and spatial information, respectively, and to generate corresponding target visit plan information based on the visitor demand analysis results and spatial structure analysis results; The route exploration module is used to generate initial itinerary scripts with multiple differentiated experience dimensions based on the target visit plan information, score each initial script scheme, and select the target itinerary script from multiple initial itinerary scripts based on the scoring results; The task creation module is used to construct the structured visit itinerary based on the target itinerary arrangement script, and generate the target visitor task within the target digital space.

[0012] To achieve the above objectives, a third aspect of this disclosure provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the intelligent generation method for digital space visitor tasks described in the first aspect of the embodiment.

[0013] To achieve the above objectives, a fourth aspect of this disclosure provides a storage medium, which is a computer-readable storage medium storing a computer program that, when executed by a processor, implements the intelligent generation method for digital space visitor tasks described in the first aspect of the embodiment.

[0014] The beneficial effects of the embodiments disclosed herein include: This embodiment first obtains the identity information of the target visitor and the spatial information of the target digital space, then performs visitor needs analysis and spatial structure analysis respectively, and generates target visit plan information accordingly. Based on the plan, it generates initial itinerary scripts with multiple differentiated experience dimensions. The optimal target itinerary script is selected through scoring. Finally, the visit itinerary is structurally constructed based on the target script to generate target visitor tasks. This breaks away from the limitations of fixed templates, preset rules and single solutions. It fully combines the personalized needs of different visitors and effectively adapts to the spatial characteristics of the digital space itself, thus effectively solving the problem of insufficient flexibility and adaptability of the original method, and improving the flexibility and adaptability of visitor task generation. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the intelligent generation method for visitor tasks in a digital space provided in this embodiment of the disclosure; Figure 2 yes Figure 1 A flowchart further includes step S102; Figure 3 yes Figure 1 A flowchart further includes step S103; Figure 4 yes Figure 1 A flowchart further includes step S104; Figure 5 yes Figure 4 A flowchart further includes step S403; Figure 6 yes Figure 1A flowchart illustrating the further steps following step S104; Figure 7 This is a schematic diagram of the functional modules of the intelligent generation system for digital space visitor tasks provided in this embodiment of the disclosure; Figure 8 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this disclosure. Detailed Implementation

[0016] The accompanying drawings in the embodiments clearly and completely describe the technical solutions in the embodiments of this disclosure. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0017] It is understood that in the specific embodiments of this disclosure, the retrieval of identity information, spatial information and related data is involved. When the above embodiments of this disclosure are applied to specific products or technologies, user permission or consent can be obtained, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards.

[0018] Furthermore, when this embodiment of the disclosure needs to retrieve identity information, spatial information, and related data, separate permission or consent to the identity information, spatial information, and related data can be obtained through pop-up windows or redirection to a confirmation page. After clearly obtaining separate permission or consent to the identity information, spatial information, and related data, the necessary identity information, spatial information, and related data for the normal operation of this embodiment of the disclosure can be obtained.

[0019] In this disclosure, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0020] Please see Figure 1 , Figure 1 This is a flowchart illustrating the intelligent generation method for digital space visitor tasks provided in this embodiment. This intelligent generation method for digital space visitor tasks can be applied to a server, or jointly executed by a terminal and a server. The intelligent generation method for digital space visitor tasks includes steps S101 to S104: Step S101: Obtain the identity information of the target visitor and the spatial information of the target digital space selected by the target visitor; Step S102: Generate corresponding visitor demand analysis results and spatial structure analysis results based on identity information and spatial information respectively, and generate corresponding target visit plan information based on visitor demand analysis results and spatial structure analysis results. Step S103: Generate initial itinerary scripts with multiple differentiated experience dimensions based on the target visit plan information, score each initial script scheme, and select the target itinerary script from multiple initial itinerary scripts based on the scoring results; Step S104: Based on the target itinerary arrangement script, complete the structured construction of the visit itinerary and generate the target visitor task in the target digital space.

[0021] Regarding step S101 above, the target visitor is an individual user about to enter the digital space for a visit and experience. Their identity information is a data set describing the visitor's basic characteristics and preferences. From the perspective of cross-temporal and spatial reuse, this identity information is not limited to a single visitor at the current moment and in the current region, but can include one or more geographically dispersed visitor groups, or a set of visitor profiles built based on historical data. Specifically, the identity information includes the visitor's basic information such as age, gender, occupation, and educational background; the visitor's preference information such as areas of interest, preference types, and preference intensity; the visitor's ability information such as language ability, mobility ability, cognitive ability, and interaction ability; and the visitor's historical data such as historical visit records, historical behavioral data, and historical feedback data.

[0022] Furthermore, the identity information in this embodiment may be the visitor ID or other identity information of the target visitor. Based on this message, other required identity information can be found from the database, such as the visitor's basic information, such as age, gender, occupation, and educational background, and the visitor's preference information, such as areas of interest, preference type, and preference intensity. This embodiment does not impose specific limitations on this.

[0023] The target digital intelligent space is an intelligent carrier that integrates digital technology and physical space, such as intelligent exhibition halls, digital twin scenarios, and intelligent interactive spaces. Its spatial information is a data set describing the physical structure and functional configuration of the space. In order to support cross-temporal and spatial task scheduling and reuse, this spatial information can refer not only to the single physical space to be accessed, but also to multiple digital intelligent spaces located in different geographical locations, or snapshots of the same space at different time dimensions. Specifically, the spatial information includes basic spatial information such as space ID, space type, space size, and spatial layout; spatial area information such as area division, area type, area location, and area connection relationship; spatial facility information such as facility type, facility location, facility function, and facility status; spatial sensor information such as sensor type, sensor location, and sensor capability; and historical spatial data such as historical access records, historical activity data, and historical optimization records.

[0024] Furthermore, embodiments of this disclosure can also introduce time attributes and regional identifiers to adapt visitor tasks generated for one spatiotemporal scenario to another spatiotemporal scenario, thereby achieving cross-temporal scheduling and reuse of task templates.

[0025] Similarly, the spatial information in this embodiment may be the spatial ID or other identity information of the target digital space. Based on this message, other required spatial information, such as spatial type, spatial size, spatial layout, and spatial area information such as area division, area type, area location, and area connection relationship, can be found from the database. This embodiment does not impose specific limitations on these aspects.

[0026] It should be noted that this embodiment of the disclosure constructs a complete profile of visitor needs and spatial characteristics by acquiring the identity information of the target visitor and the spatial information of the target digital space, providing data support for subsequent personalized and precise itinerary planning. Visitor identity information reflects individual differences and subjective preferences, while spatial information reflects objective conditions and capability boundaries. The combination of the two is the foundation for realizing personalized services and optimized allocation of spatial resources, avoiding the extensive service model in traditional methods that ignores individual differences or spatial characteristics.

[0027] Regarding step S102 above, the visitor demand analysis result is conclusive data obtained through in-depth mining and reasoning of visitor identity information. For example, the visitor demand analysis result may include at least one of the visitor's primary interest areas, secondary interest areas, preferred experience types, expected visit duration, preferred interaction methods, and accessibility requirements. The spatial structure analysis result is conclusive data obtained through structured processing and analysis of spatial information. For example, the spatial structure analysis structure may include at least one of the following: area identification and classification results, area connection relationship diagram, facility distribution map, sensor coverage, and historical visitor flow patterns. The target visit plan information is a preliminary itinerary plan generated based on visitor demand and spatial structure. Further, the target visit plan information may include the optimal visit route, specific tasks corresponding to each circulation node, time budget for each task, and dependencies between tasks.

[0028] Furthermore, the visitor needs analysis process may include: acquiring basic visitor information, preference information, ability information, historical data, and real-time data; conducting preference analysis to identify primary and secondary areas of interest; conducting ability assessment to evaluate language ability, mobility ability, cognitive ability, and interaction ability levels; and conducting needs inference to infer expected visit duration, preferred path type, interaction level requirements, and accessibility requirements.

[0029] In the above steps, the core of visitor needs analysis is to extract visitors' explicit and implicit needs from multi-source data. Preference analysis uses collaborative filtering or content recommendation algorithms, combined with historical behavioral data, to identify the types of exhibits and experience methods that visitors are most interested in. Ability assessment uses a graded scale; for example, language ability is divided into monolingual, bilingual, and multilingual levels, and mobility ability is divided into normal, assistive, and wheelchair categories. Needs inference integrates preferences and abilities, using a rule engine or machine learning model to predict visitors' reasonable needs.

[0030] For example, a 35-year-old engineer visitor whose history shows multiple visits to technology exhibitions, and whose current mood is "interested" based on real-time data, can be inferred to have a primary interest in technology, an expected visit duration of 60-90 minutes, and a preference for interactive experiences.

[0031] Furthermore, the spatial structure analysis process may include: acquiring basic spatial information, regional information, facility information, sensor information, and historical data; identifying and classifying regions, such as the introductory hall, theme hall, and concluding hall; analyzing regional connectivity relationships and constructing a regional connectivity map; analyzing facility distribution and establishing a mapping relationship between facilities and regions; analyzing sensor coverage and establishing a mapping relationship between sensors and regions; and analyzing historical data to identify patterns such as popular routes, average dwell time, and peak hours.

[0032] The core of spatial structure analysis is transforming physical space into a computable data model. Area identification, based on spatial layout data, divides continuous space into areas with clearly defined functional boundaries. Connectivity analysis, based on physical pathways between areas such as doors and corridors, constructs directed or undirected graph models. Facility distribution analysis associates each facility, such as booths, screens, and robots, with its corresponding area. Sensor coverage analysis associates each sensor, such as cameras and microphones, with its monitored area. Historical data analysis extracts information such as pedestrian flow patterns, hotspots, and congestion points from past visit records.

[0033] For example, the spatial structure analysis results of a certain exhibition hall show that: the introductory hall has an area of ​​50 square meters and connects theme hall A and theme hall B; theme hall A has 3 interactive booths and is covered by 2 cameras; historical data shows that the average stay time in theme hall A is 20 minutes, with the peak period being from 2 to 4 pm.

[0034] It should be noted that the embodiments of this disclosure quantify subjective visitor preferences and objective spatial characteristics into a computable data model through systematic visitor demand analysis and spatial structure analysis. Visitor demand analysis enables the system to understand the unique needs of each visitor, while spatial structure analysis enables the system to grasp the capacity boundaries and operational rules of the space. The combination of the two lays the foundation for generating truly personalized and feasible visitor tasks, avoiding the drawbacks of personalized design exceeding spatial capabilities or wasting spatial resources in areas of no interest to visitors.

[0035] Regarding step S103 above, differentiated experience dimensions refer to experience styles designed from different perspectives. For example, a conservative approach emphasizes stability and reliability, an innovative approach emphasizes novelty and uniqueness, a balanced approach considers the needs of all parties, a fast approach emphasizes efficiency and efficiency, and an in-depth approach emphasizes immersive experience. The initial itinerary arrangement script is generated by adjusting various script options based on the target visit plan information, according to the time allocation strategy and experience design style of different experience dimensions. Scoring refers to quantitatively evaluating each script option from multiple dimensions such as time efficiency, experience quality, and resource consumption to form a comprehensive score. The target itinerary arrangement script is the optimal solution selected from multiple initial scripts, serving as the foundation for subsequent structured construction.

[0036] Regarding step S104 above, structured construction refers to the process of transforming the selected itinerary script into detailed, executable task instructions. This includes the precise generation of the timeline, detailed description of movement nodes, specific allocation of executors, detailed design of multi-sensory experiences, and the establishment of various mapping relationships. The target visitor task is the final generated set of task instructions that can be directly deployed and executed. It contains the complete activity flow and interaction specifications of visitors within the digital space and can be recognized and executed by various execution devices within the space, such as digital humans, robots, and bio-humans.

[0037] In summary, this embodiment of the present disclosure, through the intelligent generation method for digital space visitor tasks in steps S101 to S104, first obtains the identity information of the target visitor and the spatial information of the target digital space, performs visitor needs analysis and spatial structure analysis respectively, and generates target visit plan information accordingly. Then, based on the plan, it generates initial itinerary scripts with multiple differentiated experience dimensions. The optimal target itinerary script is selected through scoring. Finally, the visit itinerary structure is constructed based on the target script to generate target visitor tasks. This breaks away from the limitations of fixed templates, preset rules and single solutions, fully combines the personalized needs of different visitors and effectively adapts to the spatial characteristics of the digital space itself, thereby effectively solving the problem of insufficient flexibility and adaptability of the original method and improving the flexibility and adaptability of visitor task generation.

[0038] The following is a detailed description of the further contents included in steps S101 to S104 in the embodiments of this disclosure.

[0039] Please see Figure 2 , Figure 2 yes Figure 1 The flowchart further includes step S102. In some embodiments, the process of generating corresponding target visit plan information based on visitor demand analysis results and spatial structure analysis results may also include steps S201 to S204: Step S201: Based on the visitor demand analysis results and spatial structure analysis results, generate a target visit path that is suitable for the target visitors; Step S202: Decompose each circulation node in the target visit path into corresponding specific tasks, and form a correspondence between each circulation node and the specific task. Step S203: Based on the visitor demand analysis results, allocate matching time budgets to the specific tasks corresponding to each traffic flow node, analyze the execution logic of the specific tasks under each traffic flow node, and determine the dependency relationship between the specific tasks. Step S204: Based on the correspondence between the target visit path, the target movement nodes and specific tasks, the time budget of each specific task and the dependency relationship, construct the target visit plan information.

[0040] In the above steps, the generation of the target visitor path is a multi-factor comprehensive optimization process. Its core lies in selecting a continuous and minimal subset of spatial units from the spatial structure based on the actual needs of visitors, rather than occupying the entire physical space. In this embodiment of the disclosure, this minimal continuous subset can be defined as the minimum spatial unit, that is, the minimum set of regions that can meet the specific needs of visitors and maintain the topological continuity of the space.

[0041] This embodiment of the disclosure can filter candidate points related to visitor needs from all physical points in a spatial structure based on the visitor's main areas of interest. For example, if an exhibition hall has 10 physical points and the visitor's interest is technology, then 5 points related to the technology theme will be selected. Next, based on the regional connectivity graph of the spatial structure, a graph search algorithm is used to generate a continuous sequence of points from the selected candidate points. This sequence must meet the continuity requirement of the spatial topology, meaning that adjacent points in the sequence are directly connected in physical space or reachable through a reasonable path. Finally, combining the visitor's time budget and historical visitor flow data, the efficiency of this continuous sequence of points is optimized to reduce unnecessary backtracking and waiting time, forming the final target visit path. It is worth noting that this path only occupies a portion of the continuous points in the spatial structure; the remaining unselected physical points, such as the other 5 points in the exhibition hall, are still idle and can be used concurrently by other visitor tasks, thereby achieving fine-grained sharing and efficient reuse of spatial resources.

[0042] For example, if a visitor's interests are technology and art, and the spatial structure includes a lobby, Technology Hall A, Technology Hall B, Art Hall, and Concluding Hall, totaling 10 physical locations, with Technology Hall A having 3 locations, Technology Hall B having 2 locations, the Art Hall having 3 locations, and the Lobby and Concluding Hall each having 1 location. If the visitor's interests only cover Technology Hall A (3 locations) and Technology Hall B (2 locations), the generated target visit path might be: Lobby, Technology Hall A Location 1, Technology Hall A Location 2, Technology Hall A Location 3, Technology Hall B Location 1, Technology Hall B Location 2, Concluding Hall. This path only occupies 7 consecutive locations (including the Lobby and Concluding Hall), while the 3 locations in the Art Hall are unoccupied and can be used by other visitors simultaneously.

[0043] The process of decomposing visitor flow nodes into specific tasks is based on the facility distribution and functional positioning of each node. Since the target visitor path only includes nodes in the smallest spatial unit, task decomposition is only performed on these selected nodes. For the introductory hall node, tasks can be decomposed into welcome tasks, guided tour tasks, and equipment collection tasks; for the themed exhibition hall node, tasks can be decomposed into exhibit viewing tasks, interactive experience tasks, and knowledge learning tasks; for the closing hall node, tasks can be decomposed into feedback collection tasks, souvenir collection tasks, and farewell tasks. Each task needs to clearly define its content description, execution method, expected results, and other attributes.

[0044] For example, node A of the Science and Technology Hall includes screen exhibits and interactive devices. The tasks are broken down into five minutes: watching a film on the history of science and technology development, 10 minutes of participating in a virtual reality interactive experience, and 5 minutes of learning basic principles.

[0045] The allocation of the time budget needs to comprehensively consider factors such as the visitor's expected total time, the importance of each node, and the complexity of the tasks. Since the target visit path only includes consecutive points in the smallest spatial unit, the time budget is allocated only to these selected points. More time is allocated to nodes of high visitor interest, and less time is allocated to less important nodes. It is also necessary to analyze the logical dependencies between tasks to determine which tasks can be executed in parallel, which tasks must be executed sequentially, and which tasks can only begin after a specific task is completed.

[0046] For example, in Science and Technology Hall A, the video viewing task is the knowledge foundation for the subsequent interactive experience task, thus creating a dependency, and must be executed sequentially. While the interactive experience task and the knowledge learning task can be designed in parallel, they are usually designed sequentially considering visitor attention limitations. The time budget is allocated as 5 minutes for video viewing, 10 minutes for the interactive experience, and 5 minutes for knowledge learning.

[0047] The construction of the target visit plan information involves integrating all the above elements into a structured data object. This data object includes path information (path list), a node-to-task mapping table (task list corresponding to each node), a time budget table (time allocation for each task), and a dependency graph (directed dependencies between tasks). This plan information is designed only for consecutive points within the smallest spatial unit, ensuring the non-exclusivity of spatial resources and enabling the same physical space to simultaneously accommodate the personalized tasks of multiple visitors.

[0048] It should be noted that the embodiments disclosed herein transform abstract visitor needs and spatial structures into specific and operable visit plan information. This process realizes the mapping transformation from user intent to preliminary itinerary, providing a standardized basic template for subsequent multi-path exploration and optimization. This ensures that the various script schemes generated subsequently are designed differently within a reasonable framework, guaranteeing the diversity of schemes while avoiding ineffective designs that are divorced from reality.

[0049] In some embodiments, step S103, in the process of generating an initial itinerary script based on multiple differentiated experience dimensions according to the target visit plan information, may further include: (1) Construct a spatial director syntax tree. The spatial director syntax tree uses the frame as the interface and establishes spatial order and power relations through axis logic. Its structure includes position scheduling, depth layer and motion trajectory. Position scheduling is used to define the position relationship and distance parameters of the actors in the planar space. Depth layer is used to define the depth control parameters of the foreground, middle ground and background. Motion trajectory is used to define the movement path parameters of the camera and the actors. (2) Based on the spatial director syntax tree, the target visit plan information is transformed into a spatial scheduling scheme with a narrative structure; (3) Based on the target visit plan information, the target visit plan information is adapted to generate the initial itinerary script corresponding to each experience dimension by combining the time allocation strategies and experience design styles of various differentiated experience dimensions, including conservative, innovative, balanced, fast and in-depth, and the station scheduling parameters, depth level parameters and movement trajectory parameters in the space director syntax tree.

[0050] In the steps described above, the Spatial Director's Syntax Tree uses a frame as its interface, establishing spatial order and power relationships through axial logic. Its structure includes positioning, depth, and movement trajectories. Positioning defines the actors—including biological beings, digital beings, and robots—and their positional relationships and distance parameters in planar space. By adjusting the relative positions and spacing between actors, different narrative effects of social relationships are constructed. Depth defines the depth-of-field control parameters for the foreground, middle ground, and background. By controlling the visibility and interaction priority of different spatial levels, a rich sense of spatial hierarchy is created. Movement trajectories define the movement paths of the camera and actors. By designing smooth or abrupt movement methods, the viewer's focus and emotional direction are guided. The Spatial Director's Syntax Tree transforms the abstract spatial structure into a computable model with narrative logic, providing a spatial scheduling foundation for subsequent script generation.

[0051] Secondly, based on the aforementioned spatial director syntax tree, the target visit plan information is transformed into a spatial scheduling scheme with a narrative structure. This scheduling scheme clarifies the executor's positioning, depth of field division, and movement path design at each spatial point, upgrading the original visit plan, which only contained a task list, into a narrative spatial script with a director's mindset.

[0052] Finally, based on the target visit plan information, and according to the time allocation strategies and experience design styles of various differentiated experience dimensions, including conservative, innovative, balanced, fast, and in-depth, and combined with the station scheduling parameters, depth parameters, and movement trajectory parameters in the space director syntax tree, the target visit plan information is adapted to generate the initial itinerary script corresponding to each experience dimension.

[0053] In this regard, the script design for the conservative experience dimension emphasizes stability, reliability, and minimal risk. For example, the time allocation strategy is to add a buffer time of 10%-20% to the initial time budget to cope with possible delays; the experience design style adopts traditional and mature interaction methods, such as screen viewing and voice explanation, avoiding the use of experimental technologies.

[0054] The script design for the innovative experience dimension emphasizes novelty, uniqueness, and breaking with convention. The time allocation strategy adopts a flexible adjustment mechanism, with key moments able to be compressed by 5% or extended by 15%; the experience design style incorporates cutting-edge technologies, such as augmented reality, holographic projection, and robot interaction.

[0055] The script design, which balances various aspects of the experience, strikes a compromise between conservatism and innovation. The time allocation strategy fluctuates by 5% from the initial budget; the experience design style blends traditional and innovative elements, offering both stable and reliable explanations as well as moderately novel interactions.

[0056] The script design for the fast-paced experience emphasizes efficiency, conciseness, and time-saving. The overall time allocation strategy is compressed by 20%, eliminating secondary tasks and retaining the core experience; the experience design style is streamlined and efficient, reducing waiting and redundant steps.

[0057] The script design for the in-depth experience dimension emphasizes immersion and full engagement. The overall time allocation strategy is extended by 30% to increase the depth and detail of the experience; the experience design style is rich and diverse, providing an immersive experience from multiple angles and levels.

[0058] For example, based on the same visit plan information, the conservative approach increases the buffer time by 10% and uses the standard explanation method; the innovative approach adds AR interactive elements to the science and technology hall and dynamically adjusts the time allocation; the balanced approach maintains the original time budget and mixes explanations and light interactions; the fast approach is compressed to visiting only the core exhibits; and the in-depth approach adds expert-guided tours and in-depth discussion sessions in each exhibition hall.

[0059] It should be noted that this embodiment combines a spatial director syntax tree with differentiated experience dimensions, enabling the generated initial itinerary script to not only include time allocation and task arrangement but also incorporate professional directorial thinking, achieving a qualitative leap from a task list to a narrative space. Furthermore, by generating script schemes with five differentiated experience dimensions, it covers a complete spectrum from pursuing efficiency to pursuing depth, and from pursuing stability to pursuing innovation. While meeting the requirements of this embodiment, more different experience dimensions can be set, and this embodiment does not impose specific limitations on this. This multi-scheme generation strategy breaks the limitations of traditional single-scheme approaches, providing users with diversified choices and a rich candidate pool for subsequent evaluation and selection. Moreover, these schemes are reasonable variations on the basic visitor plan information, maintaining respect for visitor needs and spatial characteristics while reflecting the differentiated design philosophy of different experience dimensions.

[0060] Please see Figure 3 , Figure 3 yes Figure 1The flowchart further includes step S103. In some embodiments, the process of scoring each initial script scheme and selecting the target itinerary script from multiple initial itinerary scripts based on the scoring results may also include steps S301 to S304: Step S301: Extract the corresponding evaluation indicators for each initial itinerary script from the dimensions of time efficiency, experience quality, and resource consumption, and then quantify and score them respectively. Step S302: Configure the weights of the quantitative scores for each dimension, and calculate the comprehensive score of each initial itinerary script based on the weights; Step S303: Combine the constraints of the target digital intelligence space to perform compliance filtering on the comprehensive score of each initial itinerary script to obtain the filtered comprehensive score; Step S304: Sort each initial itinerary script from high to low according to the filtered comprehensive score, and select the initial itinerary script with the highest score as the target itinerary script.

[0061] In the above steps, the evaluation indicators for the time efficiency dimension include whether the total duration meets visitor expectations, the proportion of effective time to total time, wasted time (waiting time and repetitive time), and the rationality of time allocation at each node. The evaluation indicators for the experience quality dimension include visitor satisfaction prediction based on historical data and machine learning models of similar scenarios, interaction participation (quantity and quality of interactive elements), learning effectiveness (knowledge transfer effectiveness evaluation), and emotional experience (emotional change curve and emotional peak). The evaluation indicators for the resource consumption dimension include equipment utilization (equipment usage time and frequency), energy consumption (electricity and network consumption), human resource costs (number of human resources required), and maintenance costs (equipment maintenance and replacement costs). Furthermore, each indicator can be quantified into a score between 0 and 1 for subsequent weighted calculations.

[0062] For example, an innovative solution has a time efficiency score of 0.75 because the total duration is moderate but the time utilization rate is slightly low, an experience quality score of 0.85 because the interactive design is novel, and a resource consumption score of 0.70 because it uses a lot of high-energy-consuming equipment.

[0063] The weighting of each dimension needs to be adjusted according to the specific application scenario. In general scenarios, the highest weight can be set for experience quality, such as 0.4, followed by time efficiency and resource consumption, such as 0.3 each. In efficiency-first scenarios, the weight of time efficiency can be increased. In resource-constrained scenarios, the weight of resource consumption can be increased.

[0064] Constraint filtering is a crucial step in ensuring the feasibility of selected solutions in actual implementation. Constraints include time limits (total duration cannot exceed the maximum expected visitor duration), resource limits (limits on the number of devices, energy consumption, manpower), technical feasibility (whether certain technologies relied upon by the solution are available in the current space), and security requirements (whether certain high-risk designs are permitted). Compliance filtering checks whether each solution meets all hard constraints; only solutions that meet all constraints can proceed to the next round of ranking.

[0065] For example, if a certain in-depth program has a total duration of 120 minutes, but the visitor expects a maximum duration of 90 minutes, then this program will be filtered out; if an innovative program requires a holographic projection device, but it is not currently deployed in the space, then this program will be filtered out.

[0066] The sorting and selection process involves ranking the remaining filtered options from highest to lowest overall score, and choosing the option with the highest score as the target itinerary script. This option represents the best overall performance in terms of time efficiency, experience quality, and resource consumption, while meeting all hard constraints. It best suits the visitor's needs and space requirements under the current conditions, providing a personalized itinerary arrangement.

[0067] It should be noted that the embodiments disclosed herein construct a scientific, objective, and configurable scheme evaluation and screening mechanism through multi-dimensional quantitative scoring, weight configuration, constraint filtering, and ranking selection. This mechanism avoids the drawbacks of subjective judgment and simple voting, ensuring that the finally selected scheme performs excellently across multiple target dimensions. Furthermore, by adjusting weights and constraints, the system can flexibly adapt to the needs of different scenarios, such as increasing the weight of time efficiency during peak hours and increasing the weight of resource consumption in energy-saving mode, achieving scenario-adaptive evaluation standards.

[0068] Please see Figure 4 , Figure 4 yes Figure 1 The flowchart further includes step S104. In some embodiments, the process of generating target visitor tasks within the target digital space by completing the structured construction of the visit itinerary based on the target itinerary scheduling script may also include steps S401 to S403: Step S401: Store the target itinerary script, spatial information, and demand analysis results as contextual information, and retrieve historical data of historical visit itinerary construction; Step S402: Based on contextual information and historical data, reason to obtain the target structure of the access journey of the target visitor and the target digital space. Step S403: Based on the target structured structure, complete the structured construction of the access itinerary, and integrate the structured construction results to generate the target visitor task in the target digital space.

[0069] In the steps described above, storing contextual information provides the foundation for subsequent reasoning. The target itinerary scheduling script includes the selected path, tasks, time allocation, and dependencies; spatial information includes the latest facility status and sensor coverage; and the demand analysis results include detailed visitor preferences and capabilities. Historical data consists of successful and unsuccessful cases extracted from past visits, including itinerary design, execution effectiveness, and feedback evaluations for various visitor types in different spaces. This data is stored in the Memory module, forming a searchable knowledge base. For example, the Memory module stores data from the most recent 1000 visitor itineraries, including itinerary ID, visitor profile, space type, scheme type, and execution effectiveness rating.

[0070] The reasoning behind the target-oriented structured structure is an intelligent decision-making process based on context and historical data. The reasoning algorithm first extracts successful patterns from historical data; for example, for visitors with a strong interest in technology, arranging more interactive activities in the technology exhibition hall has a higher success rate. Then, it matches the current visitor and spatial characteristics to find the most similar historical success stories. Finally, it optimizes and adjusts the script based on the characteristics of the current target itinerary, generating a suitable structured structure. The structured structure includes the timeline framework design, detailed plans for movement nodes, the allocation of executor types, and the overall direction of the multi-sensory experience.

[0071] For example, the executor in this disclosure embodiment may include spatial objects such as biological humans, digital humans, and robots, and the multi-sensory data includes visual, auditory, tactile, olfactory, gustatory, electrocardiogram, electroencephalogram, electromyogram, electrooculogram, etc., collected in four-dimensional multi-sensory spatiotemporal data.

[0072] For example, the target structure obtained through reasoning is as follows: the timeline adopts a three-part design with a 10-minute introduction, a 70-minute main body, and a 10-minute conclusion; the detailed plan for the circulation nodes adds a personalized greeting to the entrance hall; the executor allocation is that a digital human is responsible for guiding the tour and a robot is responsible for interactive assistance; the multi-sensory experience direction is to highlight the sense of technology with visuals and to complement the immersive sound effects with auditory elements.

[0073] The execution of structured construction involves transforming the reasoned structured framework into concrete, executable task instructions. This process includes: precise generation of time segments in seconds along the timeline; detailed descriptions of movement nodes, including the specific location of each node, required equipment, and interaction flow; precise allocation of executors, assigning each task to a specific digital human or robot; detailed design of multi-sensory experiences, including visual content, auditory background, and tactile feedback for each node; and the establishment of various mapping relationships, such as time-executor mapping, executor-node mapping, and node-sensor mapping. The final generated target visitor task is a complete instruction set containing all execution details, which can be directly invoked and executed by various execution devices within the space.

[0074] It should be noted that this embodiment of the disclosure incorporates historical data retrieval and intelligent reasoning mechanisms to integrate past successes and failures into the current itinerary design, achieving knowledge reuse and inheritance. This design enables the system to learn, continuously optimizing and improving from practice, avoiding repeated mistakes, and gradually improving the quality of itinerary design. Simultaneously, structured construction transforms the abstract script into concrete execution instructions, achieving a complete closed loop from design to execution.

[0075] Please see Figure 5 , Figure 5 yes Figure 4 The flowchart further includes step S403. In some embodiments, during the process of completing the structured construction of the access itinerary based on the target structured structure and integrating the structured construction results to generate the target visitor task in the target digital space, steps S501 to S502 may also be included: Step S501: Based on the target structured structure, the timeline of the access process is generated, the flow nodes are refined, the executor is assigned, the multi-sensory experience is designed, and various mapping relationships are generated in sequence to form a standardized structured construction result of the access process; Step S502: Generate target visitor tasks within the target digital space based on the structured construction results.

[0076] In the above steps, timeline generation involves creating a timeline structure accurate to the second based on the total duration and time segment framework. For example, this may include setting start and end times, dividing the total duration into several time segments, each corresponding to a specific task, and setting the start and end times of each segment to ensure a continuous and conflict-free timeline. Node refinement involves detailing the specific arrangement of each node, including its precise spatial coordinates, a list of required equipment such as screens, speakers, and interactive devices, the task sequence within the node, and switching conditions. Performer assignment assigns a specific entity to each task; for example, digital human A is responsible for the welcoming task, robot B for the guiding task, and biological human C for the interaction task. Assignment needs to consider the performer's capabilities, location, current state, and load balancing. Multi-sensory experience design involves designing detailed sensory stimulation schemes for each node. Visual design includes screen display content, lighting effects, and visual objects; auditory design includes background music tracks, voice content, and environmental sound effects; tactile design includes temperature settings, tactile feedback modes, and physical contact methods; olfactory design includes odor type, concentration, and release timing; and gustatory design is implemented in scenarios that provide gustatory experiences. Mapping relationship generation establishes the association between various elements, including time-executor mapping which time period is executed, executor-movement node mapping which nodes each executor is responsible for, node-sensor mapping which sensors each node is associated with for monitoring, and task-resource mapping which equipment resources each task requires.

[0077] The generation of target visitor tasks involves integrating all the above structured construction results into a complete and standardized task instruction set. This instruction set can be stored in JSON or XML format for easy program parsing and execution. The instruction set can include the following main parts: Task metadata (task ID, visitor ID, space ID, generation time); Timeline task list (timestamp and content of each task); Space node list (coordinates and attributes of each node); Executor assignment list (task arrangement for each executor); Multi-sensory experience configuration (multi-sensory design parameters for each node); Mapping relationship table (correspondence of various mappings); and Exception handling contingency plan (response measures for various exceptions).

[0078] For example, the generated JSON-formatted target visitor task contains 2,000 lines of code that detail every action, interaction, and sensory experience of the visitor over the next 90 minutes, as well as the collaborative work arrangements of 10 execution devices within the space.

[0079] It should be noted that, in this embodiment, the abstract structured structure is transformed into executable, specific task instructions. Precise generation of the timeline ensures the orderly progress of the itinerary, the refinement of movement nodes eliminates ambiguity during execution, the allocation of executors achieves optimal resource configuration, the design of multi-sensory experiences creates an immersive visitor experience, and the establishment of mapping relationships guarantees collaborative work between various elements. The resulting target visitor task is a complete, precise, and executable action guide that can be directly deployed to run in the digital space.

[0080] Please see Figure 6 , Figure 6 yes Figure 1 The flowchart further includes steps S104. In some embodiments, after generating the target visitor task within the target digital space, steps S601 to S604 may also be included: Step S601: Deploy the target visitor task to the target digital space for execution, and monitor the execution progress of the target visitor task and the running status of each node in real time. Step S602: Based on preset evaluation indicators, evaluate the visitor experience, space resource utilization, and task completion after task execution. Step S603: Based on the results of the effectiveness evaluation, iteratively optimize and adjust the time allocation, path planning, or experience design in the target visitor task; Step S604: When an abnormality is detected in the execution of the target visitor's task or the experience deviates from the threshold, the abnormality handling process is triggered for real-time correction.

[0081] In the above steps, task deployment involves sending the generated target visitor task instruction set to various execution devices within the digital space, including digital human servers, robot controllers, the intelligent booth control system where the bio-human resides, and environmental control systems such as lighting and sound. After deployment, the system enters real-time monitoring mode, collecting execution progress and operational status data in real time through a sensor network within the space, including cameras, microphones, position sensors, and device status sensors. Monitoring includes time progress (device time deviation from planned time), task completion (number of completed tasks), executor status (working status of each digital human and robot), visitor status (visitor's current location, behavior, and emotions), resource usage (device and energy consumption), etc. Furthermore, the monitoring data is updated once per second, forming a real-time status stream.

[0082] Effectiveness evaluation is a comprehensive assessment conducted after the task is completed or after a phase of execution. Visitor experience effectiveness evaluation is obtained through various methods, including behavioral analysis during execution (such as dwell time, interaction frequency, facial expression recognition), post-execution questionnaires (such as satisfaction ratings, open feedback), and physiological indicator measurements (such as heart rate and skin conductance). Space resource utilization evaluation includes equipment utilization rate, energy consumption data, and space occupancy rate. Task completion evaluation includes the percentage of planned tasks completed, time deviations, and the number of abnormal events. All evaluation indicators are summarized to form a comprehensive evaluation report, providing a basis for subsequent optimization.

[0083] For example, after a visitor's trip, the evaluation report showed a satisfaction score of 4.8 / 5.0, equipment utilization rate of 75%, and task completion rate of 100%. However, the interactive session in Science and Technology Hall A was a bit short, and the visitor expressed that he was not satisfied.

[0084] Iterative optimization and adjustments are made to improve the original task based on evaluation results. If the evaluation finds that a certain step is under-timed, the time budget for that step will be appropriately increased when generating tasks for similar visitors in the future. If an interaction design is found to be ineffective, the experience design will be adjusted. If congestion points are found in the path planning, the path selection will be optimized. This iterative optimization allows the system to learn from each execution and continuously improve the quality of task generation. Optimization adjustments can be applied to the subsequent itineraries of the current visitor if they are still in progress, or to the itinerary design of future visitors.

[0085] Anomaly handling is a crucial mechanism for ensuring the stability of task execution. Anomaly detection is based on real-time monitoring data. When significant delays (e.g., exceeding 5 minutes), executor malfunctions (e.g., robot offline), abnormal visitor status (e.g., sudden mood swings), or equipment failures are detected, the system automatically triggers the anomaly handling process. Handling strategies include activating backup plans (e.g., switching to backup equipment), mitigation plans (e.g., simplifying subsequent steps), and emergency stop (e.g., terminating the trip and guiding the visitor to a safe area). The handling process comprises five stages: anomaly confirmation, anomaly classification, strategy selection, processing execution, and recovery confirmation, ensuring timely and accurate handling of anomalies.

[0086] For example, when the detection system detects that the guide robot is offline, it immediately triggers an exception handling mechanism, switches the guide task handled by the robot to the nearest digital human to continue execution, and notifies maintenance personnel to handle the robot malfunction.

[0087] It should be noted that this disclosed embodiment constructs a complete closed-loop task lifecycle management system by introducing four stages: execution detection, effect evaluation, iterative optimization, and exception handling. Execution detection ensures that tasks proceed as planned, effect evaluation provides data support for optimization, iterative optimization enables the system to have continuous learning capabilities, and exception handling ensures the robustness and reliability of the system. This closed-loop design enables this technical solution not only to generate high-quality visitor tasks but also to continuously improve and refine them in actual execution, truly realizing intelligent, adaptive, and self-optimizing digital space service capabilities.

[0088] Please see Figure 7 This disclosure also provides an intelligent generation system for digital space visitor tasks, which can implement the above-mentioned intelligent generation method for digital space visitor tasks. The intelligent generation system for digital space visitor tasks includes: Data acquisition module 701 is used to acquire the identity information of the target visitor and the spatial information of the target digital space selected by the target visitor to be visited; The planning module 702 is used to generate corresponding visitor demand analysis results and spatial structure analysis results based on identity information and spatial information, respectively, and to generate corresponding target visit plan information based on the visitor demand analysis results and spatial structure analysis results. The path exploration module 703 is used to generate initial itinerary scripts under multiple differentiated experience dimensions based on the target visit plan information, score each initial script scheme, and select the target itinerary script from multiple initial itinerary scripts based on the scoring results; The task creation module 704 is used to construct a structured visit itinerary based on the target itinerary arrangement script, and generate target visitor tasks for the target visitors within the target digital space.

[0089] In summary, the intelligent generation system for digital space visitor tasks, through the intelligent generation method for digital space visitor tasks in the above embodiments, first obtains the identity information of the target visitor and the spatial information of the target digital space, then performs visitor needs analysis and spatial structure analysis respectively, and generates target visit plan information accordingly. Based on this plan, it generates initial itinerary scripts with multiple differentiated experience dimensions, selects the optimal target itinerary script through scoring, and finally completes the structured construction of the visit itinerary based on the target script to generate the target visitor task. This overcomes the limitations of fixed templates, preset rules, and single solutions, fully combining the personalized needs of different visitors and effectively adapting to the spatial characteristics of the digital space itself. This effectively solves the problem of insufficient flexibility and adaptability of the original method, and improves the flexibility and adaptability of visitor task generation.

[0090] It should be noted that the planning module 702 can also be called the ReAct planning module, the path exploration module 703 can also be called the ToT exploration module, and the task creation module 704 can also be called the CoALA module. In addition, a Planning module is also provided to perform the processes of detection, effect evaluation, iterative optimization, and anomaly handling. The intelligent generation method for digital space visitor tasks provided in this embodiment can be understood as an instantiation of the intelligent generation method for director scripts based on the fusion of ReAct+ToT+CoALA in the specific scenario of digital space. The two are essentially the same; the visitor tasks generated by the former are the specific manifestation of the director scripts generated by the latter in digital space.

[0091] Specifically, the ReAct planning module, through a cycle of thinking, action, and observation, systematically acquires spatial information and visitor identity information, essentially analyzing the stage and understanding the main characters. It performs spatial structure analysis and visitor needs analysis, generating preliminary target visit plan information based on this, equivalent to producing the first draft of the script. Secondly, the ToT exploration module generates initial itinerary scripts based on the preliminary plan, offering five differentiated experience dimensions: conservative, innovative, balanced, fast, and in-depth. This allows for multi-path exploration of different storylines and quantitative evaluation and selection of each option based on time efficiency, experience quality, and resource consumption, ultimately choosing the target itinerary. The process involves scriptwriting, specifically the optimal script solution. Finally, the CoALA modular creation architecture uses the Memory module to store visitor needs, spatial information, and historical data as context. The Reasoning module then performs deep reasoning on the target script to generate a suitable structured structure. The Action module uses this structure to accurately generate a timeline, refine movement nodes, assign specific executors, design multi-sensory experiences, and establish various mapping relationships, thereby completing the structured construction of the target visitor's task. The Planning module then forms a complete task lifecycle management closed loop through execution detection, effect evaluation, iterative optimization, and exception handling.

[0092] Therefore, the final generated target visitor task fully encompasses all the core elements of the director's script. The timeline defines the start and end times of each stage, the movement nodes clarify every spatial point the visitor needs to traverse, the executor assignment specifies which executor is responsible for each task, the multi-sensory design details the visual, auditory, and tactile sensory stimulation schemes for each node, and the mapping relationship establishes a collaborative connection between tasks and resources, time and executors. This means that every action and sensory experience of the visitor in the digital space is executed according to the "script" carefully written by the system, the "director." The process of generating visitor tasks is essentially the process of writing and executing this director's script.

[0093] The specific implementation of the intelligent generation system for digital space visitor tasks is basically the same as the specific embodiment of the intelligent generation method for digital space visitor tasks described above, and will not be repeated here. Subject to meeting the requirements of the embodiments of this disclosure, the intelligent generation system for digital space visitor tasks may also be equipped with other functional modules to implement the intelligent generation method for digital space visitor tasks in the above embodiments.

[0094] This disclosure also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned intelligent generation method for digital space visitor tasks. This electronic device can be any intelligent terminal, including tablet computers, in-vehicle computers, etc.

[0095] Please see Figure 8 , Figure 8 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 801 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this disclosure. The memory 802 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store operating devices and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 802, and the processor 801 calls and executes the intelligent generation method for digital space visitor tasks according to the embodiments of this disclosure. The 803 input / output interface is used to implement information input and output. The communication interface 804 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 805 transmits information between various components of the device (e.g., processor 801, memory 802, input / output interface 803, and communication interface 804); The processor 801, memory 802, input / output interface 803, and communication interface 804 are connected to each other within the device via bus 805.

[0096] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described intelligent generation method for digital space visitor tasks.

[0097] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0098] The embodiments described in this disclosure are for the purpose of more clearly illustrating the technical solutions of this disclosure and do not constitute a limitation on the technical solutions provided by this disclosure. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by this disclosure are also applicable to similar technical problems.

[0099] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this disclosure, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0100] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0101] Those skilled in the art will understand that all or some of the steps, apparatuses, or functional modules / units in the methods disclosed above can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0102] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in this disclosure and the foregoing drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0103] It should be understood that in this disclosure, "at least one item" means one or more, and "more than one" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0104] In the several embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0105] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0106] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0107] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0108] The preferred embodiments of the present disclosure have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present disclosure. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and spirit of the present disclosure shall be within the scope of the claims of the present disclosure.

Claims

1. A method for intelligently generating visitor tasks in a digital space, characterized in that, include: Obtain the identity information of the target visitor and the spatial information of the target digital space selected by the target visitor to be accessed; Based on the identity information and the spatial information, corresponding visitor demand analysis results and spatial structure analysis results are generated respectively, and corresponding target visit plan information is generated based on the visitor demand analysis results and spatial structure analysis results; Based on the target visit plan information, initial itinerary scripts under multiple differentiated experience dimensions are generated, and each initial script is scored. Based on the scoring results, the target itinerary script is selected from multiple initial itinerary scripts. Based on the target itinerary arrangement script, the structured construction of the visit itinerary is completed, and the target visitor task within the target digital space is generated.

2. The intelligent generation method for digital space visitor tasks according to claim 1, characterized in that, The generation of corresponding target visit plan information based on the visitor demand analysis results and the spatial structure analysis results includes: Based on the visitor demand analysis results and the spatial structure analysis results, a target visit path adapted to the target visitors is generated. Each circulation node in the target visitor path is decomposed into a corresponding specific task, forming a correspondence between each circulation node and the specific task; Based on the visitor demand analysis results, a matching time budget is allocated to the specific tasks corresponding to each of the movement nodes, and the execution logic of the specific tasks under each of the movement nodes is analyzed to determine the dependencies between the specific tasks. Based on the target visit path, the correspondence between the target movement nodes and the specific tasks, the time budget of each specific task, and the dependency relationship, the target visit plan information is constructed.

3. The intelligent generation method for digital space visitor tasks according to claim 1, characterized in that, The initial itinerary script, generated based on the target visit plan information, encompasses multiple differentiated experience dimensions and includes: A spatial director syntax tree is constructed. Based on the spatial director syntax tree, the target visit plan information is transformed into a spatial scheduling scheme with a narrative structure. Based on the target visit plan information, time allocation strategies and experience design styles of various differentiated experience dimensions, including conservative, innovative, balanced, fast, and in-depth, are used to adapt the target visit plan information according to the station scheduling parameters, depth parameters, and movement trajectory parameters in the spatial director syntax tree, and generate the initial itinerary script corresponding to each experience dimension.

4. The intelligent generation method for digital space visitor tasks according to claim 1, characterized in that, The step of scoring each of the initial script proposals and selecting the target itinerary script from the multiple initial itinerary scripts based on the scoring results includes: The corresponding evaluation indicators for each initial itinerary script are extracted from the dimensions of time efficiency, experience quality, and resource consumption, and then quantitatively scored. Configure the weights for each dimension of quantitative scoring, and calculate the comprehensive score for each of the initial itinerary scripts based on the weights; The comprehensive scores of each initial itinerary script are filtered for compliance based on the constraints of the target digital intelligence space to obtain the filtered comprehensive score. The initial itinerary scripts are sorted from highest to lowest according to the filtered comprehensive score, and the initial itinerary script with the highest score is selected as the target itinerary script.

5. The intelligent generation method for digital space visitor tasks according to claim 1, characterized in that, The structured construction of the visit itinerary based on the target itinerary arrangement script, generating the target visitor task within the target digital space, includes: The target itinerary script, the spatial information, and the demand analysis results are stored as context information, and historical data of historical visit itinerary construction are retrieved. Based on the context information and the historical data, a structured access itinerary that is adapted to the target visitor and the target digital space is inferred. Based on the target structured structure, the access itinerary is constructed in a structured manner, and the structured construction results are integrated to generate the target visitor task within the target digital space.

6. The intelligent generation method for digital space visitor tasks according to claim 5, characterized in that, The process of constructing a structured visit itinerary based on the target structured structure, and integrating the structured construction results to generate the target visitor task within the target digital space, includes: Based on the target structured structure, the timeline of the access process is generated, the flow nodes are refined, the executor is assigned, the multi-sensory experience is designed, and various mapping relationships are generated in sequence, forming a standardized structured construction result of the access process; Based on the structured construction results, a target visitor task is generated for the target visitor within the target digital intelligence space.

7. The intelligent generation method for digital space visitor tasks according to claim 1, characterized in that, After generating the target visitor's task within the target digital space, the intelligent generation method for the digital space visitor task further includes: The target visitor task is deployed to the target digital space for execution, and the execution progress of the target visitor task and the running status of each node are monitored in real time. Based on the preset evaluation indicators, the effectiveness of the visitor experience, space resource utilization and task completion after the task is completed is evaluated. Based on the results of the performance evaluation, the time allocation, path planning, or experience design in the target visitor task are iteratively optimized and adjusted. When an abnormality is detected in the execution of the target visitor's task or when the experience effect deviates from the threshold, an abnormality handling process is triggered for real-time correction.

8. An intelligent generation system for visitor tasks in a digital space, characterized in that, include: The data acquisition module is used to acquire the identity information of the target visitor and the spatial information of the target digital space selected by the target visitor to be visited; The planning module is used to generate corresponding visitor demand analysis results and spatial structure analysis results based on the identity information and spatial information, respectively, and to generate corresponding target visit plan information based on the visitor demand analysis results and spatial structure analysis results; The route exploration module is used to generate initial itinerary scripts with multiple differentiated experience dimensions based on the target visit plan information, score each initial script scheme, and select the target itinerary script from multiple initial itinerary scripts based on the scoring results; The task creation module is used to construct the structured visit itinerary based on the target itinerary arrangement script, and generate the target visitor task within the target digital space.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the intelligent generation method for digital space visitor tasks as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the intelligent generation method for digital space visitor tasks as described in any one of claims 1 to 7.

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