An offline and online integrated tourism scene scripting task guiding play system

Abstract

The present application belongs to the technical field of smart tourism, and discloses a tourism scene script task guiding playing system combining offline and online, which comprises an offline scene sensing module, a scene feature analysis module, an online and offline linkage matching module, a path optimization module, a task progress tracking module, a multi-terminal data synchronization module and a script task management module. The multi-source positioning signal fusion technology is used to realize accurate positioning in the whole scene. The feature vector is constructed based on the tourist behavior mode and the scene dynamic characteristics. The task correlation directed graph is used to ensure the logicality of the recommendation. The navigation path is optimized in combination with the tourist intention analysis. The interactive mechanism of preheating guidance and core triggering is adopted to enhance the experience immersion. The spatial and time sequence double verification is implemented to ensure the real completion of the task. The cross-terminal real-time synchronization is established to ensure the data consistency. The task parameters are continuously optimized through analysis. The present application can balance the exploration freedom and task guidance, reduce the decision fatigue, and realize the intelligent dispersion and accurate flow guiding of the tourists.

Description

Technical Field

[0001] This invention relates to the field of smart tourism technology, and more specifically, to a scripted task-guided travel system that integrates offline and online tourism scenarios. Background Technology

[0002] Existing tourism guidance technologies suffer from a fundamental flaw: a severe disconnect between online and offline experiences. Information presented on online platforms struggles to synchronize with real-time offline scenarios, often leading to awkward situations for tourists: restaurants guided by navigation are full or temporarily closed, recommended exhibitions have ended, or activity areas have been temporarily relocated due to weather. This information lag continuously diminishes tourists' trust in online guidance, severely impacting their travel experience. Secondly, traditional tourism guidance content is often monotonous and unappealing, limited to basic map navigation and simple information displays, failing to effectively stimulate tourists' desire to explore and participate. Tourists within scenic areas are often passively receiving information, lacking immersive experiences and interactive fun, resulting in low engagement and difficulty in creating lasting travel memories. Most critically, existing systems generally lack a complete task management mechanism, failing to achieve closed-loop management of the entire process from task design, release, personalized recommendations, task receipt, execution guidance, completion verification, and reward feedback. Tourists lack a consistent and guided experience, businesses struggle to accurately reach their target audience, and platforms lack effective data analytics capabilities. This fragmented approach to tourism results in isolated information silos, wasted resources, and disjointed experiences. This fragmented travel experience severely hinders the digital transformation and service upgrades of the cultural and tourism industry, failing to meet the deeper needs of modern tourists for personalized, immersive, and socialized tourism experiences.

[0003] In view of this, the present invention proposes a scripted task-guided tour system that integrates offline and online elements to solve the above problems. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a scripted task-guided travel system that integrates offline and online elements in a tourism scenario, comprising:

[0005] The offline scene perception module is used to acquire multi-source positioning signal sequences from tourists' mobile terminals and scene status time-series data from surrounding businesses, perform fusion filtering on the multi-source positioning signal sequences, and generate fused positioning coordinates.

[0006] The scene feature analysis module is used to construct a dynamic feature vector of the scene based on the trajectory features of the fused positioning coordinates and the time series data of the scene state;

[0007] The online and offline linkage matching module is used to match the scripted task library with the scene dynamic feature vector as the query condition, extract the candidate task set and construct the task association directed graph, and filter and generate the target task sequence based on the task association directed graph.

[0008] The path optimization module is used to extract the execution location and effective time window of each task in the target task sequence and plan a navigation path that meets the time window constraints.

[0009] The task progress tracking module is used to monitor the location of tourists along the navigation path, push task guidance content when tourists enter the trigger area of ​​the task execution location, and receive task completion voucher data submitted by tourists.

[0010] The multi-terminal data synchronization module is used to perform spatial inclusion and temporal rationality checks on task completion voucher data, update the task status based on the check results, and synchronize them to the merchant and management ends.

[0011] The scripted task management module is used to extract tourist behavior feature parameters based on synchronously completed task execution data, and to reverse correct the trigger condition parameters of the corresponding tasks in the scripted task library.

[0012] The modules are connected via wired and / or wireless means to enable data transmission between them.

[0013] The technical effects and advantages of this invention's scripted task-guided travel system that integrates offline and online elements in tourism scenarios are as follows:

[0014] This invention's scenario-based, task-guided travel system comprehensively enhances the immersion and personalization of the travel experience. Tourists enjoy precise guidance; the system cleverly balances the relationship between freedom of exploration and task-based guidance, allowing tourists to maintain a natural pace while not missing out on surrounding attractions. For scenic spots and businesses, this invention achieves intelligent dispersion and precise flow control of visitor traffic, eliminating overcrowding during peak periods and providing reasonable exposure for off-season and less popular areas, significantly improving overall operational efficiency and tourist satisfaction. Real-time verification and reward mechanisms for task completion stimulate tourists' enthusiasm for exploration and sense of accomplishment. This invention can continuously optimize recommendation strategies, allowing the scenic spot experience to continuously improve over time, forming a virtuous cycle. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of a scripted task-guided travel system that integrates offline and online tourism scenarios according to the present invention. Detailed Implementation

[0016] 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.

[0017] This application provides a scripted task-guided tour system that integrates offline and online elements for tourism scenarios. The system's execution entities include, but are not limited to, those running the system: smart tourism platforms, scenic area management systems, tourist mobile applications, and merchant service terminals, which can be considered general computing nodes in this application. The guided tour system includes, but is not limited to, at least one of the following: a cloud-based task coordination engine, a distributed location awareness system, and an intelligent path planner.

[0018] Please see Figure 1 In this embodiment of the invention, a scripted task-guided travel system that integrates offline and online elements for tourism scenarios includes:

[0019] The offline scene perception module acquires multi-source positioning signal sequences from tourists' mobile terminals and time-series data of scene status from surrounding businesses. It then performs fusion filtering on the multi-source positioning signal sequences to generate fused positioning coordinates. The multi-source positioning signal sequences include key information such as satellite positioning coordinates, Bluetooth beacon signal strength, Wi-Fi access point information, and inertial sensor data, acquired in real-time through a multi-channel data acquisition interface. Satellite positioning coordinates provide a global positioning benchmark, but their accuracy decreases indoors or in urban canyons; Bluetooth beacon signals provide high-precision positioning supplementation within short range; Wi-Fi access point information assists indoor positioning; and inertial sensor data records the relative changes in movement trajectories. The scene status time-series data includes dynamic parameters such as business operating status, customer density, activity periods, and promotional information. This data provides comprehensive raw material for subsequent analysis, ensuring the integrity and accuracy of positioning and scene perception.

[0020] It is important to note that this invention collects multi-source positioning signal sequences from tourists via mobile terminals (including satellite positioning coordinates, Bluetooth beacon signals, Wi-Fi access point information, and inertial sensor data) and obtains time-series data on the status of surrounding businesses through network interfaces. These technical measures are essential for providing precise tourism guidance services. Upon initial installation or use, the system displays a detailed privacy policy and data collection authorization page, clearly informing tourists of the types, purposes, scope of use, and security measures for data collection.

[0021] All collected location information and behavioral data undergo real-time encryption and partitioned storage processing, strictly limited to task matching, route planning, and experience optimization necessary for providing this service, and will not be used for unauthorized third-party analysis or commercial promotion. The image data in the task completion voucher uses local hash verification technology, eliminating the need to upload original images and further protecting user privacy. This invention complies with relevant laws and regulations, adopting strict data minimization principles, tiered access control, and data lifecycle management measures to ensure the security of tourists' personal information.

[0022] The system features a built-in data collection transparency control panel, allowing users to view and manage their data at any time, and providing convenient authorization adjustments and data deletion functions. Users can choose to opt out of specific types of data collection without affecting core service usage, and the system also supports periodic reminders for users to review their privacy settings. For historical data that is no longer needed, the system will automatically anonymize or securely delete it within a preset period (usually no more than six months).

[0023] Merchant data is collected based on legally authorized agreements, containing only necessary business status information and not involving trade secrets. All data transmission employs end-to-end encryption technology, and multiple security measures are implemented during storage to ensure the full protection of the legitimate rights and interests of all parties in the entire service ecosystem.

[0024] The scene feature analysis module is used to construct a dynamic feature vector for the scene based on trajectory features from fused positioning coordinates and time-series data of scene states. This dynamic feature vector includes dimensions such as visitor movement status labels, merchant status values, status freshness coefficients, and relative distances. Movement status labels describe the visitor's movement patterns, such as "walking quickly," "stopping to observe," and "hesitating and wandering"; merchant status values ​​reflect the real-time status of surrounding merchants; the status freshness coefficient quantifies the timeliness of merchant status updates; and relative distances reflect the spatial relationship between the visitor and various points of interest. These multi-dimensional features collectively constitute the scene's "spatiotemporal imprint," providing a foundation for subsequent task matching and path planning.

[0025] The online-offline integrated matching module is used to match a scripted task library with scene dynamic feature vectors as query conditions, extract a set of candidate tasks, and construct a directed graph representing task associations. Based on the directed graph, it then filters and generates a sequence of target tasks. This module first retrieves tasks from the scripted task library that match the features of the current scene, forming a candidate set; then it analyzes the pre-dependencies, mutual exclusions, and sequence constraints between tasks, constructing a directed graph structure representing task associations; finally, through graph analysis algorithms, it selects the optimal task execution sequence, providing a task framework for path optimization.

[0026] The path optimization module extracts the execution locations and effective time windows of each task in the target task sequence and plans navigation paths that meet the time window constraints. This module first analyzes tourists' short-term behavioral intentions and predicts their natural movement trends; then, it constructs an initial visit sequence by combining the spatial distribution of task execution locations and time window constraints; finally, by considering path smoothness and tourist comfort, it optimizes the navigation route to generate a final navigation path that balances task completion efficiency and experience comfort.

[0027] The task progress tracking module monitors the visitor's location along the navigation path. When a visitor enters the trigger area of ​​the task execution location, it pushes task guidance content and receives task completion verification data submitted by the visitor. This module continuously tracks the visitor's location to achieve precise task triggering and interactive guidance. When a visitor approaches the task point, the system first sends a pre-warm-up prompt; when the visitor has fully entered the trigger area, it pushes the complete task content and interactive interface; finally, it verifies the task completion status, collects feedback data, and provides a basis for subsequent task recommendations.

[0028] The multi-terminal data synchronization module performs spatial inclusion and temporal sequence validation on task completion voucher data. Based on the validation results, it updates the task status and synchronizes it to the merchant and management ends. This module ensures the authenticity and validity of task completion by verifying whether the collection location and time of the task voucher meet preset conditions to prevent cheating. At the same time, it synchronizes the validation results to relevant merchant and platform management systems in real time to maintain data consistency and support multi-party collaboration.

[0029] The scripted task management module extracts visitor behavior characteristic parameters based on synchronously completed task execution data, and then corrects the trigger condition parameters of corresponding tasks in the scripted task library. By analyzing a large amount of task execution history, this module learns visitor behavior patterns and preferences, dynamically adjusts task matching conditions and reward weights, enabling the system to continuously self-optimize as it is used, thereby improving the accuracy of task recommendations and user satisfaction.

[0030] The modules are connected via wired and / or wireless means to enable data transmission between them.

[0031] In this embodiment of the invention, the detailed implementation steps for performing fusion filtering on multi-source positioning signal sequences to generate fused positioning coordinates include:

[0032] Satellite positioning coordinates in a multi-source positioning signal sequence are divided into sliding windows, and the Euclidean distance between the sampling point within each window and the geometric center of the window is calculated. Sliding window partitioning is a fundamental method for processing time-series positioning data, improving the stability of anomaly detection through local analysis. The partitioning process first determines an appropriate window size. (Typically 10-20 sampling points) and a sliding step size S (typically 1 / 4 to 1 / 3 of the window size), then the window is moved sequentially along the time axis to extract the satellite positioning coordinate subsequence within each window. For the coordinate set within each window, the geometric center is calculated as a reference, using the following formula:

[0033] ;

[0034] in, For window The geometric center coordinates, and For the first in the window Latitude and longitude coordinates of each sampling point This represents the number of sampling points within the window. Then, the Euclidean distance from each sampling point to the geometric center is calculated using the following formula:

[0035] ;

[0036] in, For the first Euclidean distance from each sampling point to the geometric center of the window and These are the longitude and latitude coordinates of the geometric center, respectively. These distance values ​​constitute the distribution characteristics of the sampling points within the window, providing a basic metric for subsequent anomaly identification.

[0037] Sampling points whose Euclidean distance exceeds a preset multiple of the standard deviation of the distances within the window are marked as candidate outliers. Candidate outlier screening is the initial step in identifying potential location jump points, using statistical tests to exclude samples that significantly deviate from the population. The screening process first calculates the mean μ and standard deviation of all Euclidean distances within the window. Then set an abnormal threshold. ,in The preset multiplier (usually 2-3) reflects the stringency of the anomaly detection. When the Euclidean distance of the sampling points... At this point, the point is marked as a candidate anomaly and proceeds to the next verification stage. The choice of the standard deviation factor k takes into account the complexity of the positioning environment; a higher value can be set in open environments, while a lower threshold is used in complex environments to improve detection sensitivity.

[0038] For candidate anomalies, the angle between the displacement vectors formed by the candidate anomaly and its adjacent sampling points is detected. Anomalies with an angle less than a preset acute angle threshold are identified and removed. Displacement vector angle analysis is a crucial method for verifying the rationality of anomalies, eliminating unnatural jumps through assessment of the continuity of the motion trajectory. The analysis process first constructs a forward displacement vector for each candidate anomaly. (From the previous sampling point to the current point) and the backward displacement vector (From the current point to the next sampling point), then calculate the angle between the two vectors. The formula is:

[0039] ;

[0040] in, For vector dot product, and The vector magnitude. When the included angle... Less than the preset acute angle threshold When the angle (typically set to 30° to 45°) is considered to be a sudden change in the direction of motion, which does not conform to the characteristics of a natural movement trajectory, this candidate point is identified as an abnormal jump point and removed from the sequence. This verification method based on motion continuity effectively distinguishes between genuine motion steering and abnormal fluctuations generated by the positioning system, improving the accuracy of anomaly detection.

[0041] Triangulation is performed on the Bluetooth beacon signal strength in a multi-source positioning signal sequence to obtain the estimated beacon coordinates. Bluetooth beacon positioning is an important supplement to indoor precise positioning, and the location coordinates are calculated by inverting the signal strength. The calculation process first collects the identifiers and signal strength (RSSI) values ​​of surrounding Bluetooth beacons, and filters out a subset of beacons with signal strength exceeding a minimum threshold (usually -85dBm); then, the RSSI values ​​are converted into distance estimates using a signal propagation model, as shown in the formula:

[0042] ;

[0043] in, To estimate the distance, This represents the current received signal strength value. The signal strength at a reference distance (usually the strength at 1 meter). and Environmental parameters are obtained through on-site calibration. When three or more beacons are detected, weighted least squares method is used for triangulation. Based on the known location and estimated distance of each beacon, the most probable location of the mobile terminal is calculated.

[0044] In cases of insufficient beacon coverage, location prediction compensation is performed by combining historical trajectory data to ensure the continuity and reliability of positioning.

[0045] Positioning weights are calculated based on the signal quality parameters of the satellite positioning coordinates and beacon estimated coordinates after removing outlier points, and then weighted fusion is performed to generate fused positioning coordinates. Positioning fusion is a key step in integrating the advantages of multiple source signals, combining the strengths of different positioning methods through adaptive weight allocation. The fusion process first evaluates the quality parameters of each signal source. For satellite positioning, the main considerations are the number of satellites, geometric distribution (PDOP), and signal-to-noise ratio; for Bluetooth beacon positioning, the number of effective beacons and signal strength stability are considered. Based on these parameters, the positioning weights are calculated using the following formula:

[0046] ;

[0047] ;

[0048] in, and These are the weights for satellite positioning and beacon positioning, respectively. and The corresponding signal quality is scored. The final fused positioning coordinates are calculated using a weighted average:

[0049] ;

[0050] in, and These are the coordinates for satellite positioning and beacon positioning, respectively. In special environments, the system also combines inertial sensor data for short-term trajectory prediction to compensate for temporary loss of positioning signals, ensuring the continuity and smoothness of positioning. This multi-source fusion positioning method significantly improves positioning accuracy and reliability in various complex environments, providing an accurate location basis for subsequent scene analysis and task triggering.

[0051] In this embodiment of the invention, the detailed implementation steps for constructing a scene dynamic feature vector based on the trajectory features of fused positioning coordinates and scene state time series data include:

[0052] The displacement vectors of adjacent coordinate points in the fused positioning coordinate sequence are calculated, and the motion state labels of tourists are identified based on the difference in direction angle and the rate of change of magnitude of the displacement vectors. Motion state recognition is a fundamental step in understanding the behavioral intentions of tourists, and behavioral patterns are inferred by analyzing the dynamic characteristics of the movement trajectory. The recognition process first calculates the displacement vector sequence between adjacent fused coordinate points. Each vector contains two key attributes: magnitude (velocity) and orientation angle. Then, the variation characteristics of continuous displacement vectors are analyzed, and the orientation angle difference sequence is calculated. and the sequence of modulus change rate The formula is:

[0053] ;

[0054] ;

[0055] in, For the first The direction angle of each displacement vector Its modulus. By combining and analyzing the statistical characteristics of these two sequences, the system uses an improved decision tree algorithm to identify various fine-grained motion state labels, including "stationary" ( , "Straight line walking" , "Accelerate" , "Turning to observe" , "Hesitation and wavering" , These tags directly reflect the real-time behavioral characteristics of tourists, providing key information on the behavioral dimension of the scene feature vector.

[0056] The scene state time-series data is grouped by merchant entity and differential operations are performed to identify the time and type of state transitions for each merchant. State transition analysis is the core method for capturing dynamic changes in the scene, identifying key events by detecting significant changes in the state sequence. The analysis process first groups the scene state time-series data by merchant ID, forming independent state sequences for each merchant; then, differential operations are performed on each sequence to calculate the state difference between adjacent times; finally, by setting an appropriate threshold, the moment when the difference is significantly non-zero is identified as the state transition point. For discrete states (such as business status), changes in state values ​​are directly detected; for continuous states (such as customer flow density), an adaptive threshold method is used to dynamically adjust the detection sensitivity based on historical fluctuation levels. Each transition point, in addition to recording the time information, is also labeled with the transition type, such as "open for business," "peak customer flow," or "start of special event." This transition information provides a temporal perspective for understanding the dynamic evolution of merchant states and is the foundation for constructing a state freshness coefficient.

[0057] The time difference between the current moment and the most recent state transition moment for each merchant is calculated and mapped to a state freshness coefficient. The freshness coefficient is a key indicator for quantifying the timeliness of information, reflecting the recentity of merchant state updates. The calculation process first determines the current system time. Then, for each merchant, query the time of its most recent state transition. Calculate the time difference Then, the time difference is converted into a freshness coefficient in the interval [0,1] using a nonlinear mapping function. The formula is:

[0058] ;

[0059] in, This is a decay parameter that controls the rate at which freshness decays over time, and is typically set dynamically based on the business type and status. For example, the customer traffic status of restaurants decays relatively quickly (…). The decline in the openness of tourist attractions was relatively slow. (Relatively small). The freshness coefficient directly reflects the timeliness and reference value of state information. The closer the value is to 1, the more up-to-date the information and the greater its value for decision-making. The closer the value is to 0, the more likely the information is outdated and should be used with caution.

[0060] The current status value, status freshness coefficient, relative distance between merchants and tourists, and motion status labels of each merchant are vectorized, concatenated, and normalized to generate a scene dynamic feature vector. Feature vector construction is a key step in integrating multi-dimensional scene information, providing a unified format for subsequent analysis through vectorization. The construction process first standardizes each original feature to eliminate the influence of differences in units.

[0061] For categorical features such as state values ​​and motion labels, one-hot encoding is used to convert them into numerical vectors. For numerical features such as freshness coefficients and relative distances, Min-Max normalization is used to map them to the [0,1] interval. The processed features are then concatenated into a complete vector in a predefined order to form the final scene dynamic feature vector. The resulting scene dynamic feature vector is a high-dimensional feature representation that comprehensively describes the spatiotemporal characteristics and behavioral context of the current tourist's environment, providing rich feature conditions for subsequent task matching.

[0062] In this embodiment of the invention, the detailed implementation steps of matching a scripted task library with scene dynamic feature vectors as query conditions, extracting a candidate task set, and constructing a task association directed graph include:

[0063] The process iterates through the trigger condition features of each task in the scripted task library, filtering tasks whose trigger conditions intersect with the scene's dynamic feature vector to form a candidate task set. Task filtering is the initial step in the matching process, eliminating obviously mismatched tasks through feature similarity evaluation. The filtering process first extracts the trigger condition feature vector for each task. This vector has the same structure and dimensions as the scene feature vector, representing the ideal scene conditions suitable for triggering the task. Then, it calculates the similarity between the trigger condition feature vector and the current scene feature vector, using cosine similarity as the primary metric. For certain key dimensions (such as merchant type, business status, etc.), necessary condition markers are set; these dimensions must meet an exact match before similarity calculation can continue. When the similarity exceeds a preset threshold... When the hash rate is typically 0.6-0.7, the corresponding task is added to the candidate set. To improve retrieval efficiency, the system uses Locality Sensitive Hash (LSH) technology to construct the index structure, dividing the task library into multiple buckets to quickly locate potentially similar task subsets, significantly reducing the traversal cost of a large-scale task library.

[0064] Extracting the lists of prerequisite task identifiers and mutually exclusive task identifiers declared for each task in the candidate task set. Task relationship extraction is a fundamental step in constructing the task graph structure, establishing ordered constraints by analyzing the logical connections between tasks. The extraction process accesses the metadata information of each candidate task to obtain two types of key relationships: prerequisite task relationships and mutually exclusive task relationships. The prerequisite task identifier list records the preceding tasks that the task depends on, which must be completed before the current task; the mutually exclusive task identifier list records tasks that conflict with the current task, which cannot coexist with the current task in the execution sequence. Relationship extraction considers not only directly declared relationships but also implicit connections through reasoning mechanisms. For example, if task A is a prerequisite task of task B, and task B is a prerequisite task of task C, the system will deduce that task A is also an indirect prerequisite task of task C, forming a transitive closure; similarly, if task X is mutually exclusive with task Y, and task Y is mutually exclusive with task Z, it does not mean that task X and task Z are also mutually exclusive; the system will maintain the independence of mutually exclusive relationships. These fine-grained relationship definitions ensure the logical consistency and executability of the final task sequence.

[0065] Using each task as a node, dependency edges are created based on a list of preceding task identifiers, and mutual exclusion edges are created based on a list of mutual exclusion task identifiers. Graph construction is the core step in forming a task-related network, visually representing the complex relationships between tasks through a graph structure. The construction process begins with creating the graph. ,in This is a set of nodes containing all candidate tasks; Let be the set of edges, containing two types of directed edges: dependent edges and mutually exclusive edges. For each pair of tasks... If the task Appear in the mission Add the dependency edge to the list of prerequisite tasks. , indicating task Must be in the task Completed previously; if task Appear in the mission In the list of mutually exclusive tasks, add a mutual exclusion edge. and , indicating task and They cannot be executed simultaneously. Each edge also has a weight attribute, which depends on the edge's weight. The weight of mutual exclusion edges is calculated based on the strength of logical connections between tasks. The assessment is based on the degree of conflict. This double-edge design allows the graph structure to simultaneously express sequential and exclusive constraints, providing complete constraints for subsequent path planning.

[0066] The system checks for cycles in the directed graph formed by dependent edges. If a cycle exists, the edge with the lowest weight in the cycle is removed. Cycle detection is a crucial step in ensuring the feasibility of the task sequence, avoiding logical contradictions by eliminating circular dependencies. The detection process uses a depth-first search (DFS) algorithm, starting from each node without a predecessor and exploring all reachable nodes, recording the visit status. When the algorithm finds that a node's successor contains a node already in the current path, it indicates the existence of a cycle. Once a cycle is detected, the system needs to break the circular dependency by selecting the edge with the lowest weight in the cycle. To delete, that is, to remove the edge from the edge set of the graph. Removed from the list. The weight calculation considers both the task's importance score and dependency strength; the formula is:

[0067] ;

[0068] in, and Tasks and Importance rating The strength of the dependency between tasks, , , The weighting coefficients and Importance scoring is based on the reward value of the task and player feedback, while dependency strength reflects the tightness of the logical connections between tasks. This weighted loop elimination strategy ensures that critical task dependencies are maintained while minimizing the impact on the overall task structure.

[0069] Merging dependent edges and mutually exclusive edges after eliminating loops generates a task-associative directed graph. Graph merging is the final step in forming a complete constraint network, integrating multiple relation types to construct a unified task graph structure. The merging process first involves merging the set of dependent edges after eliminating loops. and the set of mutually exclusive edges Merge into a unified set of edges. Then, a consistency check is performed on the merged graph structure to ensure that no edge combinations violate basic constraints. For example, if task A depends on task B, and tasks A and B are mutually exclusive, this contradictory constraint combination needs to be identified and adjusted. The graph structure also records multiple attributes of each node (task), such as task type, difficulty level, expected completion time, and reward value, providing rich decision-making basis for subsequent task selection. The final generated task association directed graph is a complete constraint expression model, including both the execution order relationship between tasks and the mutual exclusion conflict relationship between tasks, providing a structured foundation for the generation of the target task sequence.

[0070] In this embodiment of the invention, the detailed implementation steps for generating a target task sequence based on task-association directed graph filtering include:

[0071] The task-dependent directed graph is traversed, and tasks that satisfy pre-dependencies and do not have mutual exclusion conflicts are selected to form an initial task set. This initial task selection is the first step in generating the sequence, filtering out tasks that do not meet the conditions using graph constraints. The selection process first identifies the initial task set in the graph; these tasks have no nodes with in-degree dependencies and can be executed directly. Then, a topological sorting algorithm is used to traverse the entire graph structure level by level, processing each task node... The process checks two core conditions: prerequisite dependency satisfaction and mutual exclusion. Prerequisite dependency satisfaction requires that all prerequisite tasks for a node are included in the currently constructed sequence; mutual exclusion requires that the node has no mutually exclusive edges connecting it to any existing tasks in the sequence. Tasks that simultaneously meet both conditions are added to the initial selection set, ensuring that the tasks in the set can form a logically consistent execution sequence. The selection process employs an incremental construction strategy, dynamically updating candidate tasks that meet the conditions as the sequence expands, avoiding the computational overhead of repeatedly traversing the entire graph.

[0072] The initial task set is sorted according to the product of the matching degree of each task's trigger condition and its reward weight. A predetermined number of tasks at the top of the sorting are then selected to form the initial task sequence. Task sorting is a crucial step in optimizing the selection process, and the most valuable subset of tasks is determined through comprehensive scoring. The sorting process first calculates a comprehensive score for each task. The score is determined by two key factors: the degree of matching of the triggering conditions. and reward weight Trigger condition matching reflects the degree of fit between the task and the current scenario, calculated through the similarity between the scenario feature vector and the task trigger condition vector; reward weight quantifies the value return upon task completion, including virtual rewards, physical discounts, and experiential value, among other dimensions. The comprehensive score calculation formula is as follows:

[0073] ;

[0074] in, This represents the matching degree value (range 0-1). This represents the normalized reward weight (range 0-1). This product-based scoring mechanism ensures that only tasks with both high matching degree and high reward can obtain the highest score, avoiding the one-sidedness of single-dimensional optimization. The initial selection set is sorted in descending order according to the score, and the top K tasks are selected to form the initial task sequence. The value of K is dynamically adjusted according to the tourist type and play duration, usually between 3 and 5, ensuring a moderate number and diversity of tasks.

[0075] The trajectory segments within the most recent preset time period are extracted from the fused positioning coordinate sequence, and the principal direction vector is calculated. Principal direction analysis is a key step in understanding tourist movement trends, inferring future intentions through historical trajectories.

[0076] The analysis process first extracts trajectory data from the fused positioning coordinate sequence over a recent period (usually 5-15 minutes); then, using a weighted linear regression method, it fits the overall trend of the trajectory points to obtain the principal direction vector. The weighted scheme assigns higher weight to recent coordinate points, ensuring that direction calculations focus more on the latest movement trend. The formula for calculating the principal direction vector is:

[0077] ;

[0078] in, For the first in the trajectory displacement vectors As time weight, satisfying Furthermore, the more recent the time period, the greater the weight; an exponential decay function is typically used. calculate, The vector normalization operation ensures that the principal direction vector is a unit vector. The principal direction vector intuitively reflects the movement trend and intended direction of tourists, providing a spatial reference benchmark for task selection.

[0079] Calculate the angle between the azimuth vector and the principal direction vector of each task execution location relative to the tourist's current position in the initial task sequence, denoted as the azimuth deflection angle. Azimuth deflection angle calculation is a core step in evaluating the spatial matching degree of the tasks, measuring the coordination between the tasks and the movement trend through the angle. The calculation process first determines the tourist's current position. and the locations of each mission Constructing the orientation vector Then calculate the relationship between this vector and the principal direction vector. The included angle The formula is:

[0080] ;

[0081] in," " represents the dot product of vectors. It is an inverse cosine function. Azimuth deflection. The range is A smaller value indicates that the task execution location is closer to the tourist's natural movement direction, the path offset required to execute the task is smaller, and the interference with the tourist's original itinerary is smaller. This angular metric provides a spatial dimension assessment of task adaptability, offering a geometrically intuitive decision-making basis for the final task selection.

[0082] Tasks with azimuth deviations exceeding a preset wraparound threshold are eliminated, generating a target task sequence. Target sequence generation is the final step in task selection, ensuring spatial execution coherence through geometric constraints. The generation process begins by setting the wraparound threshold. (usually) arrive This represents the maximum permissible azimuth offset angle; then, the azimuth deflection angle is evaluated for each task in the initial task sequence. ,when When a task is removed from the sequence, it indicates that its location deviates from the tourist's intended movement, requiring a significant change of direction to reach, and is therefore unsuitable for the current recommendation. The remaining tasks maintain their relative order from their original sequence, forming the final target task sequence. The loopback threshold is set considering the characteristics of different scenarios; the threshold can be appropriately relaxed in open scenic areas, while the restriction is tightened on linear tour routes to ensure consistency between task recommendations and spatial layout. The target task sequence guarantees overall optimization of tasks in terms of logical relationships, value returns, and spatial layout, providing a clear task framework for subsequent path planning.

[0083] In this embodiment of the invention, the detailed implementation steps for extracting the execution location and effective time window of each task in the target task sequence and planning a navigation path that satisfies the time window constraints include:

[0084] The system extracts directional inertial features and velocity rhythm features from the fused positioning coordinate sequence of tourists' recent trajectories, and constructs a short-term intention vector based on these features. Intention vector construction is the core step in predicting tourist behavior, inferring short-term movement trends through historical trajectory analysis. The construction process first extracts two key dynamic features from the fused positioning sequence: directional inertial features and velocity rhythm features. Directional inertial features quantify the stability and change pattern of the trajectory direction, calculated using the angular autocorrelation function of continuous displacement vectors; velocity rhythm features describe the periodicity and fluctuation pattern of movement speed, obtained through spectral analysis of velocity time series. Based on these two types of features, the system constructs a short-term intention vector for tourists. This vector not only contains the direction component, but also encodes the velocity expectation and possible dwell tendency, represented as:

[0085] ;

[0086] in, and The unit vector component is the expected direction of movement. The expected speed value, The probability of stay is calculated using a weighted moving average, assigning higher weight to recent trajectories; speed expectation is estimated using an ARIMA time-series prediction model; and the probability of stay is obtained through Bayesian inference based on the distribution of historical stop points and current environmental characteristics. The intent vector comprehensively expresses the tourist's short-term movement intentions, providing a multi-dimensional behavioral basis for trajectory prediction.

[0087] Starting from the tourist's current location, the trajectory is extrapolated along the direction of the tourist's short-term intention vector to generate an intention trajectory prediction line. Trajectory prediction is a key step in understanding natural movement paths, simulating possible routes for tourists without intervention through intention extension. The prediction process begins with the current location... Starting from the intention vector The directional components are initially extrapolated linearly; then, environmental perception correction is introduced, considering factors such as terrain obstacles, road network constraints, and the attractiveness of points of interest, to dynamically adjust the predicted trajectory. The corrected trajectory prediction line L consists of a series of prediction points at equal time intervals. Composition, each point represents a future moment. The expected location. The recursive formula for the predicted point is:

[0088] ;

[0089] in, For time step, For position The environmental correction vector comprehensively considers road network guidance, point-of-interest attraction, and obstacle repulsion. The prediction duration typically covers 30-60 minutes of future activity range, ensuring it includes most possible task execution locations. The intent trajectory prediction line provides a benchmark for subsequent task evaluation, representing the visitor's "natural flow" path and serving as a crucial basis for measuring task deviation.

[0090] The vertical projection distance from each task execution location in the column to the intended trajectory prediction line is defined as the task unexpectedness of the corresponding task. Task unexpectedness calculation is a key method for quantifying the degree of path deviation, assessing the fit between the task and the natural path through geometric distance. The calculation process first determines the execution location of each task. Then, the shortest distance from it to the intended trajectory prediction line L is calculated. The specific implementation uses a point-to-polyline shortest distance algorithm. For each segment of the predicted line, the distance from the point to the segment is calculated, and the minimum of all distances is taken as the final result. (Task unexpectedness) This shortest distance value is defined by the formula:

[0091] ;

[0092] in, Let be the distance function from a point to a line segment. This represents the line segment between two adjacent points on the prediction line. This represents the total number of predicted points. The task unexpectedness intuitively reflects the degree to which the task requires deviating from the natural path. The smaller the value, the closer the task is to the tourist's natural travel route, the lower the additional path cost required to perform the task, and the less interference with the tourist's original itinerary.

[0093] Tasks are sorted in ascending order based on their task unexpectedness (U). Tasks are then selected sequentially from this sorted list until the cumulative unexpectedness value of the selected tasks reaches a preset unexpectedness threshold, forming a comfortable task set. Constructing the comfortable task set is a crucial step in balancing task completeness and user experience comfort, controlling the overall deviation through accumulated unexpectedness. The construction process first sorts tasks in ascending order based on their unexpectedness (U), forming a sequence of tasks from closest to furthest. Then, tasks are selected sequentially from the beginning of the sequence, and the cumulative unexpectedness value of each selected task is recorded. ;when First time exceeding the preset unexpectedness threshold Selection stops when the time is right. The unexpectedness threshold is dynamically set based on visitor type and play mode. Exploratory visitors have a higher threshold, allowing them to explore more paths; while efficient visitors have a lower threshold, preferring to complete tasks along the main route. The comfort task set balances task value and path comfort, ensuring that the overall path deviation of visitors is controlled within a reasonable range when completing recommended tasks, avoiding fatigue and confusion caused by frequent and significant path changes, and significantly improving the overall play experience.

[0094] The effective time windows of each task in the comfortable task set are extracted, and tasks whose effective time windows intersect with the current time are selected to form a temporally feasible task subset. Temporal feasibility screening is a crucial step in ensuring that task time constraints are met; unexecutable tasks are eliminated through time window analysis. The screening process first extracts the effective time window for each task. This window defines the timeframe within which tasks can be triggered and completed; then it calculates the timeframe from the current moment. Starting from the destination, considering the tourist's normal moving speed v, the minimum time required to reach the destination. When the conditions are met If a visitor can reach the task location before the deadline, the task is considered time-feasible and is retained in the time-feasible task subset; otherwise, the task is discarded because the visitor cannot arrive on time. This spatiotemporal constraint-based filtering mechanism ensures the time feasibility of the final planned task sequence and avoids the user experience degradation caused by recommending unachievable tasks.

[0095] Tasks in the temporally feasible task subset are sorted according to their projection positions along the intention trajectory prediction line to generate an initial visit sequence. Spatial sorting is a key method for generating a natural visit sequence, determining the task order that aligns with the direction of movement through projection positions. The sorting process first calculates the projection point of each task execution location on the intention trajectory prediction line L, where the projection point is the point on the prediction line with the minimum distance to the task execution location. Then, the parameter position t of the projection point on the prediction line is recorded, representing the proportion of the distance traveled along the prediction line from the starting point. Finally, the tasks are sorted in ascending order according to the magnitude of parameter t, forming a task visit sequence from near to far along the prediction line. This projection-based sorting method ensures that the task visit order is consistent with the natural movement direction of tourists, minimizing unnecessary back-and-forth travel, improving path efficiency, and reducing visitor fatigue.

[0096] The algorithm calculates the sequence of angles between the lines connecting adjacent task execution locations in the initial access sequence and the intention trajectory prediction line. When any angle in the sequence exceeds a preset turning threshold, a transition guide point located on the intention trajectory prediction line is inserted between the corresponding adjacent tasks. Path smoothing optimization is a key step in improving the navigation experience, mitigating the discomfort caused by sharp turns through guide points. The optimization process first calculates the angles between each pair of adjacent tasks in the initial sequence. Execution location connection vector Tangent vector to the line of the intended trajectory prediction at this location The included angle The formula is:

[0097] ;

[0098] When the included angle Exceeding the preset steering threshold (usually) arrive When a large turn occurs, it indicates that the path segment has a significant deviation, which may lead to a poor navigation experience. The system inserts a transition guide point G between the two tasks, located on an appropriate position on the predicted line segment between the projection points of the two tasks, prioritizing points of interest or rest areas on the predicted line. The introduction of the transition guide point breaks down a large-angle turn into two smaller-angle turns, significantly improving the smoothness and naturalness of the path. It also serves as an additional stop for rest or scenic viewing, enriching the overall experience.

[0099] The initial access sequence after inserting transition guide points is mapped to the geographic road network to generate a navigation path. Road network mapping is the final step in forming the final navigation path, transforming the abstract sequence into a concrete, walkable route. The mapping process first obtains the coordinate set of all points in the access sequence (task execution locations and transition guide points). Then, using the path planning API of a Geographic Information System (GIS), the optimal road network path between adjacent points is calculated, considering multiple factors such as road type, congestion, and pedestrian friendliness. Finally, these road segments are connected into a complete navigation path R, including detailed turn prompts, road type, and estimated travel time information. The road network mapping not only considers the shortest distance principle but also the comfort and safety of the path, prioritizing scenic roads, pedestrian streets, and dedicated visitor routes while avoiding congested sections and unsafe areas. The final generated navigation path satisfies the spatial sequence requirements of the task execution while considering the comfort and feasibility of actual walking, providing tourists with a clear, intuitive, and easy-to-follow travel guide.

[0100] In this embodiment of the invention, the detailed implementation steps for monitoring the tourist's location along the navigation path and pushing task guidance content when the tourist enters the trigger area of ​​the task execution location include:

[0101] The system continuously calculates the distance between the tourist's current location and the next execution location on the navigation path. Location monitoring is the fundamental step in task triggering; the triggering timing is determined through real-time distance calculation. The monitoring process is based on continuous location data from the mobile terminal, employing a sliding window averaging method to eliminate location jitter and obtain a stable current location. Then, the location of the next task to be executed is extracted from the navigation path, and the straight-line distance between the two points is calculated. The calculation frequency is dynamically adjusted based on the tourist's movement speed. The update frequency is reduced when the tourist is stationary or moving at low speed to save power, and increased when moving quickly to ensure trigger accuracy. The system also maintains a record of distance change trends. By comparing multiple consecutive measurement results, it determines whether the tourist is approaching the task point, providing a trend basis for preheating triggering.

[0102] When the distance is first less than the pre-warm-up distance threshold, the pre-warm-up guidance content for the task corresponding to that execution location is extracted and pushed to the mobile terminal. Pre-warm-up triggering is the first stage of progressive task guidance, enhancing anticipation and readiness through advance notice. The triggering process first sets the pre-warm-up distance threshold. (Typically 2-3 times the radius of the task point), when measuring distance First less than Furthermore, when the distance trend shows a continuous decrease, the pre-warm-up trigger mechanism is activated. Then, pre-warm-up guidance content for the task is retrieved from the task library, including a brief task description, directional guidance, and estimated arrival time. Finally, the content is sent to the visitor's mobile device via a push notification system, using a low-intrusion notification format to avoid interrupting the current activity. The design of the pre-warm-up content considers the appropriate amount of information and the creation of anticipation, providing sufficient contextual introduction without revealing complete task details, stimulating the visitor's desire to explore and their curiosity, effectively improving the final task participation rate and completion quality.

[0103] The core triggering area is defined with the execution location as the center and a preset trigger radius. Area delineation is a crucial step for precise triggering, defining a clearly defined task activation space through geometric boundaries. The delineation process first determines the core coordinate points of the task. This is typically the center of the Point of Interest (POI) or a specific interaction point; then, an appropriate trigger radius is set. (Dynamically adjusted based on task type and venue size, typically between 5-20 meters), a virtual trigger area circle is drawn with the core point as the center. For venues with special shapes, the system supports using polygonal or elliptical trigger areas for more accurate matching of the actual spatial layout. The design of the trigger area balances the technical limitations of positioning accuracy with the practical needs of user experience, ensuring trigger accuracy while avoiding repeated triggering issues caused by boundary jitter.

[0104] When a visitor enters the core trigger area, the complete onboarding content and interactive components for the task are loaded and pushed to the mobile device. The core trigger is a crucial stage in task guidance, initiating formal task interaction through the display of complete content. The triggering process continuously monitors the visitor's location. The positional relationship with the core triggering area, when the conditions are met. Upon confirmation that the visitor has entered the triggered area, the system immediately loads the complete task's guidance package, including a detailed task description, background story, interaction guide, completion conditions, and reward information. Finally, the complete content is presented to the visitor via in-app push notifications or a foreground window, activating relevant interactive components such as photo upload, QR code scanning, and Q&A verification. The design of the complete guidance content emphasizes narrative and immersion, enhancing user engagement and increasing the enjoyment and satisfaction of task completion through scenario-based task presentation. Interactive components provide intuitive interfaces based on task type, reducing the cognitive burden of task understanding and completion.

[0105] The system displays complete guidance content on mobile devices, awaiting visitors to submit task completion voucher data. Voucher collection is the final step in the task interaction, verifying task completion through user submission. The collection process begins by clearly displaying the task completion conditions and voucher requirements on the mobile device, such as "taking a photo of the building's facade," "answering historical knowledge questions," or "collecting specific product information." Then, corresponding interactive interfaces are provided to guide users in generating and submitting voucher data as required. Finally, the system receives the submitted vouchers, attaches metadata such as current location coordinates and timestamps, forming a complete task completion voucher package. Voucher types are designed to be diverse based on the nature of the task, including image vouchers (photos taken by the user or scanned QR codes), text vouchers (answers to questions or observation records), and interactive vouchers (specific interaction records with scene elements). The system also provides real-time auxiliary functions, such as photo composition guidelines, answer hints, and interactive guidance animations, to help users generate compliant vouchers more efficiently, improving task completion rates and user satisfaction.

[0106] In this embodiment of the invention, the detailed implementation steps for performing spatial inclusion and temporal rationality checks on task completion voucher data, and updating the task status based on the check results, include:

[0107] The system extracts the embedded voucher collection location coordinates and timestamp from the task completion voucher data. Metadata extraction is a preparatory step before verification, obtaining key verification information by parsing the voucher data. The extraction process parses two types of core metadata from the task completion voucher package: spatial location information and timestamp information. Spatial location information includes the precise geographic coordinates (longitude and latitude) and accuracy estimates at the time of voucher collection, automatically embedded by the mobile terminal's positioning system during voucher generation. Timestamp information records the precise time of voucher generation, using a unified time format and time zone setting to ensure system time consistency. For image vouchers, the system also extracts additional metadata from the EXIF ​​information, such as shooting direction and lighting conditions, providing supplementary evidence for advanced verification. Metadata extraction employs a secure parsing mechanism to prevent data tampering and forgery, ensuring the authenticity and reliability of the basic verification data and laying the foundation for subsequent spatial and temporal verification.

[0108] The distance between the credential collection location coordinates and the center point of the current task trigger area is calculated. If the distance exceeds the radius of the trigger area, the spatial inclusion verification fails. Spatial verification is a key mechanism to prevent location fraud, ensuring that the credential is collected within the valid area through boundary judgment. The verification process first determines the coordinates of the center point and the valid radius r of the task trigger area, and then calculates the distance d between the credential collection location and the center point.

[0109] When the distance *d* exceeds the radius *r* of the trigger area, the system determines that the spatial inclusion verification has failed, indicating that the credential was not collected within the designated task area and may involve "remote completion" fraud. The verification algorithm considers positioning error factors and introduces a fault-tolerance mechanism: when *d* only slightly exceeds *r* and is within the error tolerance range (usually 1-2 times the positioning accuracy), the system will downgrade to a "warning" instead of direct failure and require the user to provide additional verification information. For trigger areas with special shapes, the system uses a point-to-polygon or point-to-ellipse distance algorithm for more accurate boundary determination, ensuring the accuracy and fairness of the verification.

[0110] The time sequence verification fails if the difference between the voucher collection timestamp and the task receipt time is negative or exceeds the task's preset maximum completion time. Time sequence verification is an effective means of preventing time manipulation, ensuring the task's genuine completion through time window limitations. The verification process first obtains two key time points: the task receipt time (the time the user receives the complete task content) and the voucher collection time (the time the user generates the task voucher); then, the time difference is calculated. Finally, a comparison is made with preset conditions to determine the outcome. If the voucher generation time is earlier than the task retrieval time, it indicates a logical contradiction and is directly judged as a failure; when hour, The preset maximum completion time for a task indicates that the user's time consumption exceeds the reasonable completion range, and is therefore judged as a failure. The maximum completion time is dynamically set according to the task type and complexity; simple observation tasks are typically 5-15 minutes, while complex interactive tasks can be extended to 30-60 minutes. Time-sequence verification effectively prevents cheating behaviors in the time dimension, such as pre-prepared credentials or delayed submission, ensuring the timeliness and authenticity of task completion.

[0111] When both checks pass, the task status is updated to "Completed" and a completion timestamp is recorded. Status update is a crucial operation after successful verification, modifying the latest task status record in the database. The update process first checks the results of spatial inclusion and temporal sequence rationality; only when both pass does the system execute the status update operation. Then, the task record's status field is changed from "In Progress" to "Completed," and the accurate completion timestamp is recorded. Finally, the actual completion time of the task is calculated as reference data for performance evaluation. Status updates employ a transaction processing mechanism to ensure data consistency and prevent status chaos caused by concurrent operations. The updated status information becomes an important basis for task reward distribution, visitor achievement recording, and system data analysis, supporting the normal operation and continuous optimization of the entire game system.

[0112] When any verification fails, the task status remains in progress, and a verification feedback message containing the reason for the failure is returned. Failure feedback is a user-friendly design element in the verification process, helping users understand and correct problems through clear prompts. The feedback process first analyzes the specific reasons for the failure, distinguishing between spatial verification failure and temporal verification failure; then, it generates targeted feedback messages, including the failure type, reason explanation, and improvement suggestions; finally, it presents the feedback content intuitively through the user interface, using a design language that is eye-catching but not overly discouraging. For spatial verification failures, the system prompts "Please complete the task within the specified area," and may include simplified map guidance; for temporal verification failures, it prompts "Task completion time is abnormal" or "Task time limit exceeded," and provides corresponding solutions, depending on the specific situation. The system also records detailed logs of failed attempts for subsequent behavior analysis and rule optimization, helping to distinguish between genuine operational errors and intentional rule testing, and continuously improving the intelligence and adaptability of the verification mechanism.

[0113] In this embodiment of the invention, the detailed implementation steps for synchronizing between the merchant's end and the management end include:

[0114] The system monitors for changes in task status. When a status change is detected, a synchronization data packet carrying the status change details and a sequence number is generated. Event monitoring is the trigger mechanism for data synchronization, initiating the real-time synchronization process through change detection. The monitoring process adopts a publish-subscribe model, setting change triggers for the task status table. When a modification to a key field (such as task status, completion time, voucher information, etc.) is detected, a synchronization event is automatically triggered. The system then constructs a standardized synchronization data packet containing three key pieces of information: the changed content (a comparison of field values ​​before and after the modification), a sequence number (a globally unique, incrementing identifier to ensure synchronization order), and metadata (timestamp, operation type, source identifier, etc.). The data packet uses a compact binary format encoding, ensuring both transmission efficiency and content integrity. The monitoring mechanism supports multi-level change granularity configuration, allowing for the filtering of synchronization content based on the needs of different endpoints, avoiding unnecessary data transmission, and optimizing network resource usage and response time.

[0115] Synchronization data packets are distributed to both merchant and management terminals via long-lived connection channels. Data distribution is the core of the synchronization process, enabling real-time data transmission across multiple terminals through efficient channels. The distribution process first determines the target terminal list, including the relevant merchant's business terminals and platform management system; then, synchronization data packets are pushed to each target terminal through a pre-established WebSocket long-lived connection channel. The long-lived connection mechanism avoids the overhead of frequent connection establishment, significantly reducing synchronization latency and improving real-time performance; simultaneously, priority-based message queue management ensures that important state changes (such as task completion) are delivered first. For terminals that are temporarily offline, the system maintains a persistent message queue to ensure that the terminal can receive all changes during the offline period upon reconnection. The distribution process also implements load balancing and traffic control strategies to prevent system pressure caused by sudden high concurrency, ensuring the stability and reliability of the synchronization service.

[0116] The system receives confirmation responses from each endpoint, carrying the local data version number. Response processing is the verification step confirming synchronization completion, ensuring data consistency through version comparison. The process involves receiving confirmation messages from each target endpoint after successfully applying the synchronization data packet. These messages contain the endpoint's current local data version number (usually the sequence number of the last applied synchronization packet). The system then records and tracks the version status of each endpoint, establishing an end-to-end version mapping table to monitor the overall system's data synchronization status in real time. The confirmation mechanism employs a timeout retry strategy; if no response is received from the endpoint within a predetermined time, the system automatically performs a limited number of retransmission attempts. Response processing also includes error identification and handling, such as version conflicts and application failures. The system automatically handles or marks these as exceptions requiring manual intervention based on preset conflict resolution strategies, ensuring the reliability and eventual consistency of data synchronization.

[0117] The version numbers returned by each end are compared with the version number of the sending end. Incremental data resending is performed on ends with inconsistent version numbers. Version comparison is a crucial step in ensuring synchronization integrity, accurately filling in missing data through difference analysis. The comparison process first analyzes the version numbers returned by each end. Compared with the latest version number of the current system The differences between them; when When the client's data lags behind the server's, incremental compensation is required. The compensation method employs an efficient differential transmission strategy, sending only incremental changes between versions instead of the full dataset, significantly reducing network traffic. Specifically, it utilizes a version chain storage mechanism, where the system retains all version change records within a certain time window, enabling precise construction of differential data packets between any two versions. For cases where the version difference is too large (exceeding the retention window), the system initiates a full synchronization mechanism to reconstruct the client's complete data state. This intelligent incremental synchronization strategy ensures eventual data consistency while maximizing synchronization efficiency, adapting to synchronization needs under different network conditions and terminal states.

[0118] If no acknowledgment response is received within the preset timeout period, the synchronization data packet is retransmitted until the data version numbers on all ends are consistent. The retransmission mechanism is a fault-tolerant design to address network instability, ensuring eventual synchronization completion through continuous attempts. The mechanism is implemented by first setting a monitoring timer for each sent synchronization data packet. When the timer exceeds the preset timeout threshold (usually 3-5 seconds) and no acknowledgment response is received, the retransmission process is triggered. The system then reconstructs the synchronization data packet (keeping the original sequence number unchanged) and retransmits it. Simultaneously, a backoff algorithm is used to adjust the interval of subsequent retransmissions to avoid network congestion. The maximum number of retries is typically set to 3-5. Exceeding this limit marks the synchronization task as abnormal, initiating manual intervention. The retransmission strategy considers the network conditions and importance of different ends, employing a more aggressive retransmission strategy for key merchants and management ends to ensure timely synchronization of core business data. The entire synchronization process is only completed when the system confirms that the data version numbers on all target ends are consistent with the server, or when an unrecoverable synchronization failure is explicitly recorded, ensuring the global consistency and reliability of system data.

[0119] In this embodiment of the invention, the detailed implementation steps for extracting tourist behavior feature parameters based on synchronously completed task execution data and reversing the trigger condition parameters of the corresponding tasks in the scripted task library include:

[0120] The system extracts the times when tourists enter and leave each task trigger area from the task execution data and calculates the dwell time. Dwell time analysis is a fundamental method for understanding the depth of user interaction, evaluating task attractiveness and difficulty through time metrics. The analysis process first extracts two key time points from the task execution log: the entry time (the time when the tourist first enters the trigger area) and the exit time (the time when the tourist finally leaves the trigger area); then, it calculates the complete dwell time D, comprehensively reflecting the time tourists spend at the task location. For cases with multiple entry and exit records, the system uses a cumulative calculation method to merge multiple dwell time records for the same task. Dwell time is an important indicator of task interaction quality; a longer dwell time usually indicates that the task is highly attractive or interactive; while a too short dwell time may suggest problems with task design, such as unclear guidance, insufficient content attractiveness, or unreasonable difficulty settings. This basic time metric provides key input data for subsequent statistical analysis and parameter optimization.

[0121] This study calculates the mean and variance of dwell time for the same task across multiple tourist samples. Statistical analysis is a scientific method for identifying task characteristics, revealing the general performance of a task through population data. The analysis process first groups data by task ID and collects the dwell time dataset for each task across a large sample of tourists; then, it calculates two key statistics: the mean and the variance. and variance The mean reflects the typical completion time of a task and serves as a benchmark for evaluating the rationality of task design; the variance quantifies the dispersion of completion time, reflecting the adaptability of task difficulty and the consistency of user experience. The calculation formula is:

[0122] ;

[0123] ;

[0124] in, For the first The duration of a tourist's stay on this mission This represents the total sample size. The statistical process employs a sliding window method, focusing on data from the most recent period (typically 7-30 days) to ensure the analysis reflects the current situation rather than historical averages. The system also performs stratified analysis based on factors such as tourist type, time period, and weather conditions to identify performance differences under different conditions, providing a basis for fine-tuning task parameters.

[0125] When the average dwell time is lower than a preset proportion of the task's preset completion time, the trigger condition matching threshold for that task is increased. Threshold adjustment is a key mechanism for optimizing task triggering accuracy, improving the targeting of recommendations through feedback learning. The adjustment process first compares the actual average dwell time μ with the expected completion time during task design. proportional relationship; when hour( The preset proportional coefficient (usually 0.6-0.8) indicates that most tourists' actual stay on this task is significantly shorter than expected, possibly because the task is not attractive enough to the current tourists or the relevance is insufficient; then the system increases the relevance threshold of the task's trigger conditions. This allows for more stringent screening of eligible target tourists. Threshold adjustments employ a gradual strategy, increasing them slightly each time (typically 5%-10%) to avoid a sudden drop in task exposure due to over-adjustment. Through this reverse adjustment based on actual interaction data, the system can progressively optimize the target audience for tasks, ensuring that tasks are recommended to tourists most likely to have a positive interaction experience, thereby improving overall task participation quality and completion satisfaction.

[0126] The spatial deviation integral value between the tourist's actual travel trajectory and the navigation path is extracted from the task execution data. Path deviation analysis is an important method for evaluating the rationality of navigation, quantifying the difference between the planned and actual paths through trajectory comparison. The analysis process first extracts two paths: the navigation path planned by the system. and the actual travel path of tourists Then, the spatial deviation between these two paths is calculated using an integral metric, accumulating the instantaneous distance between the actual trajectory and the planned path along the actual trajectory. The calculation formula is as follows:

[0127] ;

[0128] in, For a moment The actual location, This represents the position of the actual trajectory projected onto the planned path. As a spatial distance metric, the integration interval covers the entire task execution process. In the discrete implementation, the integral value is approximated by a weighted sum of sampling points:

[0129] ;

[0130] in, For the actual trajectory of the first One sampling point, Let it be the nearest projection point on the planned path. This is the weighting coefficient. The deviation score directly reflects the degree to which tourists follow the planned route. A low value indicates that the navigation plan matches the actual travel tendency of tourists, while a high value suggests that the plan may not be reasonable enough or the task is not attractive enough, providing a quantitative basis for subsequent adjustment of reward weights.

[0131] When the spatial deviation score exceeds a preset deviation threshold, the reward weight of the task is reduced and it is added to the scripted task library. Reward adjustment is an effective means of optimizing task influence, guiding user behavior through value reshaping. The adjustment process first involves calculating the deviation score... With preset threshold Compare; when If this occurs, it indicates that the tourist has deviated significantly from the path while performing the task, and the actual attractiveness of the task may not be sufficient to support its current reward setting; then the system proportionally reduces the reward weight W of the task, calculated as follows:

[0132] ;

[0133] in, To adjust the weights (typically 0.1-0.3), the degree of adjustment is controlled. The adjusted new weights are securely written to the scripted task library via database transactions, recording the adjustment history and basis, supporting subsequent auditing and rollback operations. The dynamic adjustment mechanism of reward weights enables the system to continuously optimize the value allocation of tasks based on actual user behavior feedback, strengthening popular tasks that align with visitor behavior habits, and weakening tasks with high deviation or low participation, forming a positive cycle of self-optimization and continuously improving the overall play experience and the health of the task ecosystem.

[0134] This invention realizes a scripted task guidance system for tourism scenarios that integrates offline and online elements through location fusion, scene feature analysis, task matching and planning, path optimization, task interaction, and self-optimization learning. The intelligent guidance method of this invention can accurately identify tourist intentions, match the optimal task sequence, and provide a natural and smooth navigation experience, offering a systematic solution for immersive interaction in tourism scenarios.

[0135] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0136] It should be noted that all formulas in this manual are calculated by removing dimensions and taking their numerical values. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0137] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A scripted, task-guided travel system that integrates offline and online elements for tourism scenarios, characterized in that... include: The offline scene perception module is used to acquire multi-source positioning signal sequences from tourists' mobile terminals and scene status time-series data from surrounding businesses, perform fusion filtering on the multi-source positioning signal sequences, and generate fused positioning coordinates; the scene status time-series data includes business operating status, customer flow density, activity periods, and promotional information; The scene feature analysis module is used to construct a scene dynamic feature vector based on the trajectory features of the fused positioning coordinates and the scene state time series data, including: Calculate the displacement vectors of adjacent coordinate points in the fused positioning coordinate sequence, and identify the tourist's motion status label based on the difference in the direction angle of the displacement vectors and the rate of change of the magnitude. The scenario state time series data is grouped by merchant entity and differential operation is performed to identify the state transition time and transition type of each merchant. Calculate the time difference between the current moment and the moment of the most recent state transition for each merchant, and map it to the state freshness coefficient; The current status value of each merchant, the status freshness coefficient, the relative distance between the merchant and the tourist, and the motion status label are vectorized, concatenated, and normalized to generate the scene dynamic feature vector. The online and offline linkage matching module is used to match the scripted task library with the scene dynamic feature vector as the query condition, extract the candidate task set and construct the task association directed graph, and filter and generate the target task sequence based on the task association directed graph. The path optimization module is used to extract the execution location and effective time window of each task in the target task sequence, and plan a navigation path that meets the time window constraints. The task progress tracking module is used to monitor the tourist's location along the navigation path, push task guidance content when the tourist enters the trigger area of ​​the task execution location, and receive task completion certificate data submitted by the tourist. The multi-terminal data synchronization module is used to perform spatial inclusion verification and temporal rationality verification on the task completion voucher data, update the task status according to the verification results and synchronize it to the merchant end and the management end. The scripted task management module is used to extract tourist behavior feature parameters based on synchronously completed task execution data, and to reverse correct the trigger condition parameters of the corresponding tasks in the scripted task library.

2. The system according to claim 1, characterized in that, The step of performing fusion filtering on the multi-source positioning signal sequence to generate fused positioning coordinates includes: The satellite positioning coordinates in the multi-source positioning signal sequence are divided into sliding windows, and the Euclidean distance between the sampling point in each window and the geometric center of the window is calculated. Sampling points whose Euclidean distance exceeds a preset multiple of the standard deviation of the distance within the window are marked as candidate outliers; For each candidate anomaly point, the angle between its displacement vector and the adjacent sampling points before and after it is detected. When the angle is less than a preset acute angle threshold, it is confirmed as an abnormal jump point and removed. Triangulation is performed on the Bluetooth beacon signal strength in the multi-source positioning signal sequence to obtain the estimated beacon coordinates; The positioning weights are calculated based on the signal quality parameters of the satellite positioning coordinates after removing abnormal jump points and the beacon estimated coordinates, and then weighted and fused to generate the fused positioning coordinates.

3. The system according to claim 1, characterized in that, The step of matching the scenario dynamic feature vector as a query condition to a scripted task library, extracting a candidate task set, and constructing a task association directed graph includes: The trigger condition features of each task in the scripted task library are traversed, and tasks whose trigger conditions intersect with the scene dynamic feature vector are selected to form a candidate task set. Extract the list of preceding task identifiers and the list of mutually exclusive task identifiers for each task declaration in the candidate task set; Using each task as a node, create dependency edges based on the list of preceding task identifiers, and create mutual exclusion edges based on the list of mutual exclusion task identifiers; Detect whether there is a cycle in the directed graph formed by dependent edges. If there is, delete the dependent edge with the lowest weight in the cycle. Merge the dependent edges and mutually exclusive edges after eliminating loops to generate the task-related directed graph.

4. The system according to claim 1, characterized in that, The step of generating a target task sequence based on the task-associated directed graph includes: Traverse the directed graph of task associations and filter tasks that satisfy prerequisite dependencies and do not have mutual exclusion conflicts to form a preliminary task set; The initial task set is sorted according to the product of the matching degree of the trigger conditions of each task and the reward weight, and a preset number of tasks at the top of the sort are selected to form the initial task sequence. Extract the trajectory segment within the most recent preset time period from the fused positioning coordinate sequence, and calculate the main direction vector; Calculate the angle between the azimuth vector of each task execution location in the initial task sequence relative to the tourist's current position and the main direction vector, and denot it as the azimuth deflection angle; Tasks with azimuth deviations exceeding a preset loop threshold are removed, and the target task sequence is generated.

5. The system according to claim 1, characterized in that, The step of extracting the execution location and effective time window of each task in the target task sequence and planning a navigation path that satisfies the time window constraint includes: The directional inertial features and velocity rhythm features of the tourist's recent trajectory are extracted from the fused positioning coordinate sequence, and a short-term intention vector of the tourist is constructed based on the directional inertial features and velocity rhythm features. Starting from the tourist's current location, extrapolate the trajectory along the direction of the tourist's short-term intention vector to generate an intention trajectory prediction line; Calculate the vertical projection distance from the execution location of each task in the target task sequence to the intention trajectory prediction line, and define the vertical projection distance as the task unexpectedness of the corresponding task. The tasks are sorted in ascending order according to their unexpectedness. Tasks are selected sequentially from the sorting results until the cumulative unexpectedness of the selected tasks reaches the preset unexpectedness threshold. The selected tasks are then used to form a set of comfortable tasks. Extract the effective time window of each task in the comfortable task set, and filter the tasks whose effective time window has a reachable intersection with the current time to form a temporally feasible task subset; The tasks in the temporally feasible task subset are sorted according to the projection position of their execution locations along the intention trajectory prediction line to generate an initial access sequence; Calculate the angle sequence between the line connecting adjacent task execution locations in the initial access sequence and the intention trajectory prediction line. When there is an angle in the angle sequence that exceeds a preset turning threshold, insert a transition guide point located on the intention trajectory prediction line between the corresponding adjacent tasks. The initial access sequence after the insertion of the transition guide point is mapped to the geographic road network to generate the navigation path.

6. The system according to claim 1, characterized in that, The system monitors the tourist's location along the navigation path and pushes task guidance content when the tourist enters the trigger area of ​​the task execution location, including: Continuously calculate the distance between the tourist's current location and the next execution location on the navigation path; When the distance is less than the preheating distance threshold for the first time, the preheating guidance content of the task corresponding to the execution location is extracted and pushed to the mobile terminal; The core triggering area is defined with the execution location as the center and a preset triggering radius. When a visitor enters the core triggering area, the complete tutorial content and interactive components for the task are loaded and pushed to the mobile terminal. The complete guide is displayed on the mobile device, waiting for the visitor to submit the task completion certificate data.

7. The system according to claim 1, characterized in that, The step of performing spatial inclusion and temporal sequence rationality checks on the task completion voucher data, and updating the task status based on the check results, includes: Extract the voucher collection location coordinates and voucher collection timestamp embedded in the task completion voucher data; Calculate the distance between the coordinates of the credential collection location and the center point of the current task triggering area. If the distance exceeds the radius of the triggering area, the spatial inclusion check is deemed to have failed. Calculate the difference between the timestamp of the voucher collection and the time of task receipt. If the difference is negative or exceeds the preset maximum completion time of the task, the time sequence rationality check is deemed to have failed. When both checks pass, update the task status to "completed" and record the completion timestamp. If any verification fails, the task status remains in progress and a verification feedback message containing the reason for the failure is returned.

8. The system according to claim 1, characterized in that, The synchronization to the merchant and management ends includes: Listen for changes in task status, and when a status change is detected, generate a synchronization data packet carrying the status change content and a sequence number; The synchronization data packets are distributed to the merchant and management terminals via a long-connection channel. Receive confirmation responses from each terminal, each carrying the local data version number; Compare the data version number returned by each end with the version number of the sending end, and perform incremental data resending for ends with inconsistent version numbers; If no acknowledgment response is received within the preset timeout period, the synchronization data packet will be retransmitted until the data version numbers of all terminals are consistent.

9. The system according to claim 1, characterized in that, The process of extracting tourist behavior feature parameters based on synchronously completed task execution data and then reversing the trigger condition parameters of the corresponding tasks in the scripted task library includes: Extract the times when tourists enter and leave each task triggering area from the task execution data, and calculate the duration of stay. The mean and variance of the length of stay for the same task were statistically analyzed across multiple tourist samples. When the average dwell time is lower than a preset proportion of the preset completion time of the task, the trigger condition matching threshold of the task is increased. Extract the spatial deviation integral value between the tourist's actual travel trajectory and the navigation path from the task execution data; When the spatial deviation integral value exceeds the preset deviation threshold, the reward weight of the task is reduced and written into the scripted task library.

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