Any-time adjustable personal tourism guidance management method, device and equipment and storage medium

Abstract

The invention discloses a anytime adjustable personal tourism guidance management method, device and equipment and a storage medium, and the method comprises the steps: generating an initial tourism task recommendation list through tourism role selection, behavior data, geographic position data, time data and interest preference data inputted by a tourist; in the travel task execution process, task completion data are collected in real time, and point rewards are calculated; the dynamic data of the current environment of the tourist is detected, and when the environment change is detected, the follow-up tourism content is re-planned according to the environment change quantity, so that the tourism experience of the tourist can be highly personalized and dynamically adapted, and the tourism experience of the tourist is improved by responding to the environment change in real time and integrating a gamification incentive mechanism. According to the method, the smoothness, interestingness and participation depth of the tourism process are remarkably improved, the sense of contusion caused by plan stiffness in traditional tourism is effectively avoided, more pleasant, more unforgettable and achievement-sense travel experience is finally brought, the intelligent level of tourism management is improved, and digitized upgrading of scenic spots is achieved.

Description

Technical Field

[0001] This invention relates to the field of tourism management technology, and in particular to a method, device, equipment, and storage medium for managing personal travel guides that can be adjusted at any time. Background Technology

[0002] The scenic area meets tourists' leisure and entertainment needs by providing natural landscapes and cultural experiences.

[0003] However, if tourists are not adequately prepared in advance, they often lack sufficient gains and experiences during the trip, and their knowledge expansion and enjoyment are insufficient.

[0004] Tourists exist in cognitive cocoons, which hinder the improvement of their tourism capabilities.

[0005] During a trip, due to subjective and objective reasons, it may be necessary to adjust the itinerary on the spot, making it difficult to provide automatic recommendations and guidance. Summary of the Invention

[0006] The main objective of this invention is to provide a method, device, equipment, and storage medium for managing personalized travel guidance that can be adjusted at any time. This aims to solve the technical problems in the prior art, such as the lack of personalized role customization in travel planning, the inability to dynamically adjust travel content based on tourist behavior and the environment in real time, and insufficient user participation leading to a monotonous experience and low user stickiness.

[0007] In a first aspect, the present invention provides a method for managing personal travel guidelines that can be adjusted at any time, the method comprising the following steps: Based on the tourist's input of tourist role selection, behavioral data, geographical location data, time data, and interest preference data, an initial list of recommended tourist tasks is generated; During the execution of tourism tasks, task completion data is collected in real time and points rewards are calculated. The system detects dynamic data of the tourist's current environment, and when environmental changes are detected, it re-plans subsequent tourism content based on the magnitude of the environmental changes.

[0008] Optionally, the step of generating an initial tourism task recommendation list based on the tourist's input of tourism role selection, behavioral data, geographical location data, time data, and interest preference data includes: Based on tourists' input of their chosen tourist roles, behavioral data, geographic location data, time data, and interest preference data, a role-matching algorithm is used to perform multi-dimensional data fusion processing to obtain the data fusion results. Based on the data fusion results, an initial list of recommended travel tasks that meets the personalized needs of tourists is dynamically filtered and generated.

[0009] Optionally, the step of performing multi-dimensional data fusion processing based on the tourist's input of tourist role selection, behavioral data, geographical location data, time data, and interest preference data, using a role-matching algorithm, to obtain the data fusion result includes: Based on the tourist's input of tourist role selection, behavioral data, geographical location data, time data, and interest preference data, the role adaptation algorithm performs feature extraction, normalization processing, and dynamic weight allocation on the data of each dimension to generate a structured data fusion result. The data fusion result includes quantitative indicators of role matching degree, environmental adaptability, and time sensitivity.

[0010] Optionally, based on the tourist's input of tourist role selection, behavioral data, geographic location data, time data, and interest preference data, a role-matching algorithm is used to extract features, normalize, and dynamically assign weights to the data across each dimension, generating a structured data fusion result. This data fusion result includes quantitative indicators of role matching degree, environmental adaptability, and time sensitivity, including: Acquire tourist input data including their chosen tourist role, behavior, geographic location, time, and interests. Based on the selected tourist role, the tourist's role is mapped to a preset role feature library to obtain a role feature vector; Quantitative features are extracted from the behavioral data, the geographic location data, the time data, and the interest preference data; The eigenvalues ​​of the quantized features are scaled to the [0,1] interval using Min-Max normalization, and the feature weights are calculated based on environmental factors and dynamic coefficients using the following formula:

[0011] in, For the first Feature weights of each feature For the first One environmental factor, For dynamic coefficients; The normalized features are weighted using the feature weights to obtain a weighted feature vector. The role feature vector and the weighted feature vector are then fused using a dot product to obtain a structured data fusion result.

[0012] Optionally, the step of collecting task completion data and calculating points rewards in real time during the execution of the tourism task includes: During the execution of the tourism task selected by the tourist through the initial tourism task recommendation list, task completion data is collected in real time through the positioning module, task status sensor and user interaction interface of the tourist terminal; The task completion data is transmitted to the system management platform, where it is calculated in real time based on the points acquisition formula to obtain task completion points, and a points reward is generated based on the task completion points.

[0013] Optionally, the step of transmitting the task completion data to the system management platform, calculating the task completion data in real time based on the points acquisition formula to obtain task completion points, and generating points rewards based on the task completion points includes: The task completion data is transmitted to the system management platform, and the task completion score is calculated in real time based on the following integral formula:

[0014] in, Points are awarded for completing the task. The basic points reward for completing the sub-tasks The difficulty level of the subtask. The basic points reward for daily tasks. The base points reward for each upgrade. Based on the visitor's current rating, The basic points reward for arena challenges. For the number of wins, The base points reward per unit of time. For the duration of the trip; The points reward is dynamically generated based on the points earned from completing the task, and sound effects and interface feedback are triggered simultaneously.

[0015] Optionally, the step of detecting dynamic data of the tourist's current environment, and when an environmental change is detected, replanning subsequent tourism activities based on the magnitude of the environmental change, includes: Continuously collect dynamic data on tourists, scenic spots, and the environment through multi-source data interfaces; Based on the tourist dynamic data, the scenic area dynamic data, and the environmental dynamic data, the current observed value is quantitatively compared with the preset benchmark value of the tourism plan node, and the environmental change is calculated using the following formula:

[0016] in, For environmental change, Due to the tourist's location deviation, Due to the deviation in the density of people in the scenic area, Due to environmental and weather deviations, , , Preset weighting coefficients; The optimal route planning and time redistribution scheme are output based on the environmental changes, and subsequent tourism content is updated in real time based on the optimal route planning and the time redistribution scheme.

[0017] Secondly, to achieve the above objectives, the present invention also proposes a personal travel guidance management device that can be adjusted at any time, the personal travel guidance management device comprising: The task recommendation module is used to generate an initial list of recommended travel tasks based on the tourist's selected travel role, behavioral data, geographical location data, time data, and interest preference data. The points calculation module is used to collect task completion data in real time and calculate points rewards during the execution of tourism tasks; The replanning module is used to detect dynamic data of the tourist's current environment. When an environmental change is detected, the subsequent tourism content is replanned based on the amount of environmental change.

[0018] Thirdly, to achieve the above objectives, the present invention also proposes a readily adjustable personal travel guidance management device, the readily adjustable personal travel guidance management device comprising: a memory, a processor, and a readily adjustable personal travel guidance management program stored in the memory and executable on the processor, the readily adjustable personal travel guidance management program being configured to implement the steps of the readily adjustable personal travel guidance management method described above.

[0019] Fourthly, to achieve the above objectives, the present invention also proposes a storage medium storing a readily adjustable personal travel guide management program, wherein the readily adjustable personal travel guide management program, when executed by a processor, implements the steps of the readily adjustable personal travel guide management method described above.

[0020] This invention proposes a customizable personal travel guidance management method that generates an initial travel task recommendation list based on the tourist's input of travel role selection, behavioral data, geographical location data, time data, and interest preference data. During the execution of travel tasks, it collects task completion data in real time and calculates points rewards. It also detects dynamic data of the tourist's current environment; when environmental changes are detected, it re-plans subsequent travel content based on the magnitude of these changes. This enables tourists to achieve a highly personalized and dynamically adaptable travel experience. By responding to environmental changes in real time and incorporating gamified incentive mechanisms, it significantly improves the smoothness, fun, and depth of participation in the travel process, effectively avoiding the frustration caused by rigid planning in traditional tourism. Ultimately, it brings a more enjoyable, memorable, and fulfilling travel experience, improves the intelligence level of tourism management, realizes the digital upgrade of scenic spots, effectively improves tourist satisfaction, and promotes scenic spot construction and tourism resource development. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of the present invention; Figure 2 This is a flowchart illustrating the first embodiment of the adjustable personal travel guidance management method of the present invention; Figure 3 This is a flowchart illustrating the second embodiment of the adjustable personal travel guidance management method of the present invention; Figure 4 This is a flowchart illustrating the third embodiment of the adjustable personal travel guidance management method of the present invention; Figure 5 This is a schematic diagram of the role-playing, points-based, and readily adjustable personal travel guide and management system of the present invention. Figure 6 This is a functional block diagram of the first embodiment of the adjustable personal travel guidance management device of the present invention.

[0022] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0024] The solution of this invention mainly involves: generating an initial list of recommended travel tasks based on the tourist's input of their chosen travel role, behavioral data, geographic location data, time data, and interest preference data; collecting task completion data and calculating reward points in real time during the execution of travel tasks; detecting dynamic data of the tourist's current environment, and replanning subsequent travel content based on environmental changes when detected. This enables tourists to achieve a highly personalized and dynamically adapted travel experience. By responding to environmental changes in real time and incorporating gamified incentive mechanisms, the smoothness, fun, and depth of participation in the travel process are significantly improved. This effectively avoids the frustration caused by rigid planning in traditional tourism, ultimately leading to a more enjoyable, memorable, and fulfilling travel experience. It also improves the level of intelligent tourism management, achieves digital upgrades of scenic spots, effectively increases tourist satisfaction, promotes scenic spot construction and tourism resource development, and solves the technical problems in existing technologies such as the lack of personalized role customization in tourism planning, the inability to dynamically adjust travel content based on tourist behavior and environment in real time, and insufficient user participation leading to a monotonous experience and low stickiness.

[0025] Reference Figure 1 , Figure 1 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of the present invention.

[0026] like Figure 1As shown, the device may include: a processor 1001, such as a CPU; a communication bus 1002; a user interface 1003; a network interface 1004; and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0027] Those skilled in the art will understand that Figure 1 The device structure shown does not constitute a limitation on the device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0028] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating device, a network communication module, a user interface module, and a personal travel guide management program that can be adjusted at any time.

[0029] The device of this invention calls the adjustable personal travel guide management program stored in the memory 1005 via the processor 1001 and performs the following operations: Based on the tourist's input of tourist role selection, behavioral data, geographical location data, time data, and interest preference data, an initial list of recommended tourist tasks is generated; During the execution of tourism tasks, task completion data is collected in real time and points rewards are calculated. The system detects dynamic data of the tourist's current environment, and when environmental changes are detected, it re-plans subsequent tourism content based on the magnitude of the environmental changes.

[0030] The device of the present invention, through processor 1001 calling the adjustable personal travel guide management program stored in memory 1005, also performs the following operations: Based on tourists' input of their chosen tourist roles, behavioral data, geographic location data, time data, and interest preference data, a role-matching algorithm is used to perform multi-dimensional data fusion processing to obtain the data fusion results. Based on the data fusion results, an initial list of recommended travel tasks that meets the personalized needs of tourists is dynamically filtered and generated.

[0031] The device of the present invention, through processor 1001 calling the adjustable personal travel guide management program stored in memory 1005, also performs the following operations: Based on the tourist's input of tourist role selection, behavioral data, geographical location data, time data, and interest preference data, the role adaptation algorithm performs feature extraction, normalization processing, and dynamic weight allocation on the data of each dimension to generate a structured data fusion result. The data fusion result includes quantitative indicators of role matching degree, environmental adaptability, and time sensitivity.

[0032] The device of the present invention, through processor 1001 calling the adjustable personal travel guide management program stored in memory 1005, also performs the following operations: Acquire tourist input data including their chosen tourist role, behavior, geographic location, time, and interests. Based on the selected tourist role, the tourist's role is mapped to a preset role feature library to obtain a role feature vector; Quantitative features are extracted from the behavioral data, the geographic location data, the time data, and the interest preference data; The eigenvalues ​​of the quantized features are scaled to the [0,1] interval using Min-Max normalization, and the feature weights are calculated based on environmental factors and dynamic coefficients using the following formula:

[0033] in, For the first Feature weights of each feature For the first One environmental factor, For dynamic coefficients; The normalized features are weighted using the feature weights to obtain a weighted feature vector. The role feature vector and the weighted feature vector are then fused using a dot product to obtain a structured data fusion result.

[0034] The device of the present invention, through processor 1001 calling the adjustable personal travel guide management program stored in memory 1005, also performs the following operations: During the execution of the tourism task selected by the tourist through the initial tourism task recommendation list, task completion data is collected in real time through the positioning module, task status sensor and user interaction interface of the tourist terminal; The task completion data is transmitted to the system management platform, where it is calculated in real time based on the points acquisition formula to obtain task completion points, and a points reward is generated based on the task completion points.

[0035] The device of the present invention, through processor 1001 calling the adjustable personal travel guide management program stored in memory 1005, also performs the following operations: The task completion data is transmitted to the system management platform, and the task completion score is calculated in real time based on the following integral formula:

[0036] in, Points are awarded for completing the task. The basic points reward for completing the sub-tasks The difficulty level of the subtask. The basic points reward for daily tasks. The base points reward for each upgrade. Based on the visitor's current rating, The basic points reward for arena challenges. For the number of wins, The base points reward per unit of time. For the duration of the trip; The points reward is dynamically generated based on the points earned from completing the task, and sound effects and interface feedback are triggered simultaneously.

[0037] The device of the present invention, through processor 1001 calling the adjustable personal travel guide management program stored in memory 1005, also performs the following operations: Continuously collect dynamic data on tourists, scenic spots, and the environment through multi-source data interfaces; Based on the tourist dynamic data, the scenic area dynamic data, and the environmental dynamic data, the current observed value is quantitatively compared with the preset benchmark value of the tourism plan node, and the environmental change is calculated using the following formula:

[0038] in, For environmental change, Due to the tourist's location deviation, Due to the deviation in the density of people in the scenic area, Due to environmental and weather deviations, , , Preset weighting coefficients; The optimal route planning and time redistribution scheme are output based on the environmental changes, and subsequent tourism content is updated in real time based on the optimal route planning and the time redistribution scheme.

[0039] This embodiment, through the above-described scheme, generates an initial list of recommended travel tasks based on the tourist's input of their chosen travel role, behavioral data, geographic location data, time data, and interest preference data. During the execution of these tasks, task completion data is collected in real time, and reward points are calculated. Dynamic data of the tourist's current environment is detected; when environmental changes are detected, subsequent travel content is replanned based on the magnitude of these changes. This enables a highly personalized and dynamically adaptable travel experience. By responding to environmental changes in real time and incorporating gamified incentive mechanisms, the smoothness, fun, and depth of participation in the travel process are significantly enhanced. This effectively avoids the frustration caused by rigid planning in traditional tourism, ultimately leading to a more enjoyable, memorable, and fulfilling travel experience. It also improves the intelligence level of tourism management, achieves digital upgrades of scenic spots, effectively increases tourist satisfaction, and promotes scenic spot construction and tourism resource development.

[0040] Based on the above hardware structure, an embodiment of the adjustable personal travel guidance management method of the present invention is proposed.

[0041] Reference Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the adjustable personal travel guidance management method of the present invention.

[0042] In the first embodiment, the method for managing readily adjustable personal travel guidance includes the following steps: Step S10: Generate an initial list of recommended travel tasks based on the tourist's selected travel role, behavioral data, geographical location data, time data, and interest preference data.

[0043] It should be noted that, based on the tourist's selected travel role, behavioral data, geographical location data, time data, and interest preference data, this personalized information can be intelligently integrated to automatically generate an initial travel task recommendation list that highly matches the tourist's needs, providing the tourist with an accurate and smooth starting point experience.

[0044] Step S20: During the execution of the tourism task, collect task completion data in real time and calculate points rewards.

[0045] It should be understood that during the execution of tourism tasks, task completion data can be collected in real time, and corresponding points can be calculated and rewards can be given. This real-time incentive allows users to feel a continuous sense of accomplishment and fun in every step of the exploration, significantly improving their enthusiasm and enjoyment in the tourism process, making travel no longer monotonous and boring, but full of motivation and anticipation, and effectively enhancing users' engagement and satisfaction with tourism activities.

[0046] Step S30: Detect the dynamic data of the tourist's current environment. When an environmental change is detected, replan the subsequent tourism content based on the amount of environmental change.

[0047] Understandably, by monitoring the dynamic data of the tourist's current environment in real time, the system can automatically optimize subsequent itinerary arrangements and replan subsequent travel content when environmental changes are detected. This ensures that the journey is always smooth, comfortable, and full of surprises, completely avoiding the frustration and itinerary interruptions caused by rigid planning in traditional tourism. It transforms every trip into a relaxed, enjoyable, and unforgettable experience with a sense of control.

[0048] This embodiment, through the above-described scheme, generates an initial list of recommended travel tasks based on the tourist's input of their chosen travel role, behavioral data, geographic location data, time data, and interest preference data. During the execution of these tasks, task completion data is collected in real time, and reward points are calculated. Dynamic data of the tourist's current environment is detected; when environmental changes are detected, subsequent travel content is replanned based on the magnitude of these changes. This enables a highly personalized and dynamically adaptable travel experience. By responding to environmental changes in real time and incorporating gamified incentive mechanisms, the smoothness, fun, and depth of participation in the travel process are significantly enhanced. This effectively avoids the frustration caused by rigid planning in traditional tourism, ultimately leading to a more enjoyable, memorable, and fulfilling travel experience. It also improves the intelligence level of tourism management, achieves digital upgrades of scenic spots, effectively increases tourist satisfaction, and promotes scenic spot construction and tourism resource development.

[0049] Furthermore, Figure 3 This is a flowchart illustrating the second embodiment of the adjustable personal travel guidance management method of the present invention, as shown below. Figure 3 As shown, based on the first embodiment, a second embodiment of the adjustable personal travel guidance management method of the present invention is proposed. In this embodiment, step S10 specifically includes the following steps: Step S11: Based on the tourist's input of tourist role selection, behavior data, geographical location data, time data, and interest preference data, perform multi-dimensional data fusion processing through role adaptation algorithm to obtain data fusion results.

[0050] It should be noted that by intelligently integrating tourists' travel role preferences and behavioral data (i.e., historical behavioral habits), geographical location data (i.e., real-time geographical location), time data (i.e., itinerary schedule), and interest preference data (i.e., interest tags), and through role-adaptation algorithms, multi-dimensional data fusion processing can be performed to obtain data fusion results.

[0051] Furthermore, step S11 specifically includes the following steps: Based on the tourist's input of tourist role selection, behavioral data, geographical location data, time data, and interest preference data, the role adaptation algorithm performs feature extraction, normalization processing, and dynamic weight allocation on the data of each dimension to generate a structured data fusion result. The data fusion result includes quantitative indicators of role matching degree, environmental adaptability, and time sensitivity.

[0052] It should be understood that, based on tourists' input of their chosen travel roles, behavioral data, geographic location data, time data, and interest preference data, a role-matching algorithm can extract features, normalize, and dynamically assign weights to the data across these dimensions. This generates structured data fusion results, including quantitative indicators such as role matching degree, environmental adaptability, and time sensitivity. This ensures that travel recommendations accurately match tourists' individual characteristics and real-time environment, guaranteeing a smooth, comfortable, and anticipated journey from the very beginning. It effectively avoids the experience gaps and frustrations caused by mismatched planning in traditional tourism, bringing a highly personalized and enjoyable start to the trip.

[0053] Furthermore, based on the tourist's input of tourist role selection, behavioral data, geographical location data, time data, and interest preference data, the steps described herein use a role-matching algorithm to extract features, normalize, and dynamically assign weights to the data across each dimension, generating a structured data fusion result. This data fusion result includes quantitative indicators of role matching degree, environmental adaptability, and time sensitivity, specifically comprising the following steps: Acquire tourist input data including their chosen tourist role, behavior, geographic location, time, and interests. Based on the selected tourist role, the tourist's role is mapped to a preset role feature library to obtain a role feature vector; Quantitative features are extracted from the behavioral data, the geographic location data, the time data, and the interest preference data; The eigenvalues ​​of the quantized features are scaled to the [0,1] interval using Min-Max normalization, and the feature weights are calculated based on environmental factors and dynamic coefficients using the following formula:

[0054] in, For the first Feature weights of each feature For the first One environmental factor, For dynamic coefficients; The normalized features are weighted using the feature weights to obtain a weighted feature vector. The role feature vector and the weighted feature vector are then fused using a dot product to obtain a structured data fusion result.

[0055] It should be noted that the system receives tourist input via the tourist terminal, including tourist role selection (e.g., "knowledge-seeking"), behavioral data (historical attraction visit records), geographic location data (real-time GPS coordinates), time data (current time period and travel duration constraints), and interest preference data (e.g., the confidence score of the keyword "culture" is 0.85). Based on a pre-defined role feature library, tourist roles are mapped to structured feature vectors (e.g., "knowledge-seeking" corresponds to [0.9, 0.7, 0.5]). Quantitative features are extracted from each data source (behavioral data generates an attraction type distribution vector [0.7, 0.3], geographic location data is input...). The regional attribute code is [0.8, 0.2], the time sensitivity coefficient for time data is calculated to be 0.9, and the keyword confidence score for interest preference data is 0.85. Min-Max normalization is used to uniformly scale the feature values ​​to the [0,1] interval (e.g., mapping the stay duration [10,120] minutes to [0.1,1.0]). Based on environmental factors (such as weather index, congestion level) and dynamic coefficients, the weights of each feature are dynamically calculated using the above formula. The normalized features and weights are fused together to generate a weighted feature vector, and the dot product operation between the role feature vector and the weighted feature vector is performed, for example:

[0056] The output is a structured data fusion result, including quantitative indicators such as role matching degree (0.87), environmental adaptability (0.73) and time sensitivity (0.68), which provides accurate technical input for subsequent tourism task recommendations.

[0057] Step S12: Dynamically filter and generate an initial tourism task recommendation list that meets the personalized needs of tourists based on the data fusion results.

[0058] Understandably, the initial travel task recommendation list is dynamically filtered and generated based on the data fusion results to meet the personalized needs of tourists. This ensures that the initial travel task recommendation list accurately matches the tourists' current needs and expectations, effectively avoiding the "uniformity" problem in traditional travel planning, and providing tourists with a smooth, tailored, and exciting start to their personalized travel experience.

[0059] This embodiment utilizes the above-described scheme to perform multi-dimensional data fusion processing using a role-matching algorithm on the tourist's input of tourist role selection, behavioral data, geographical location data, time data, and interest preference data, thereby obtaining a data fusion result. Based on the data fusion result, an initial tourism task recommendation list that meets the tourist's personalized needs is dynamically filtered and generated. This enables tourists to obtain a highly personalized experience, with each recommendation precisely matching their interests and needs, making the trip full of anticipation and pleasure from the very beginning. It effectively avoids the disappointment caused by rigid planning in traditional tourism, bringing a smooth, comfortable, and unforgettable start to the trip.

[0060] Furthermore, Figure 4 This is a flowchart illustrating the third embodiment of the adjustable personal travel guidance management method of the present invention, as shown below. Figure 4 As shown, based on the first embodiment, a third embodiment of the adjustable personal travel guidance management method of the present invention is proposed. In this embodiment, step S20 specifically includes the following steps: Step S21: During the execution of the tourism task selected by the tourist through the initial tourism task recommendation list, task completion data is collected in real time through the positioning module, task status sensor and user interaction interface of the tourist terminal.

[0061] It should be noted that during the process of tourists performing the tourism tasks selected through the initial tourism task recommendation list, the precise location coordinates of tourists are obtained in real time through the positioning module of the tourist terminal (which may be a module corresponding to the Global Positioning System (GPS), Beidou, or other positioning systems; this embodiment does not limit this). The task execution progress (such as the completion status of scenic spot check-ins or the duration of stay) is monitored using task status sensors, and specific operation data of task completion are collected through user interaction interfaces (such as clicking to confirm or inputting ratings), thereby realizing real-time and continuous data collection of task completion status.

[0062] Step S22: Transmit the task completion data to the system management platform, calculate the task completion data in real time based on the points acquisition formula, obtain task completion points, and generate points rewards based on the task completion points.

[0063] Understandably, the task completion data (including task status, actual dwell time, environmental feedback score, and user behavior trajectory) is transmitted to the system management platform in real time through the communication module; after receiving the data, the platform immediately calculates and obtains task completion points; subsequently, the system dynamically generates point rewards (such as coins or points) based on the point value.

[0064] Furthermore, step S22 specifically includes the following steps: The task completion data is transmitted to the system management platform, and the task completion score is calculated in real time based on the following integral formula:

[0065] in, Points are awarded for completing the task. The basic points reward for completing the sub-tasks The difficulty level of the subtask. The basic points reward for daily tasks. The base points reward for each upgrade. Based on the visitor's current rating, The basic points reward for arena challenges. For the number of wins, The base points reward per unit of time. For the duration of the trip; The points reward is dynamically generated based on the points earned from completing the task, and sound effects and interface feedback are triggered simultaneously.

[0066] It should be understood that the task completion data (including task status, actual stay time, environmental feedback score, and user behavior trajectory) collected in real time by the tourist terminal is transmitted to the system management platform through the communication module. After receiving the data, the platform immediately calls the points acquisition formula to perform real-time calculation and dynamically generates task completion points based on the calculation results. Subsequently, the system triggers the point reward (such as gold coins and points) distribution logic in real time according to the point value, and simultaneously visualizes the reward through sound effects (such as completion sound effects) and interface feedback (such as screen flashing and level upgrade animations) to ensure that users receive perceptible incentive feedback in real time during task execution.

[0067] Accordingly, step S30 specifically includes the following steps: Continuously collect dynamic data on tourists, scenic spots, and the environment through multi-source data interfaces; Based on the tourist dynamic data, the scenic area dynamic data, and the environmental dynamic data, the current observed value is quantitatively compared with the preset benchmark value of the tourism plan node, and the environmental change is calculated using the following formula:

[0068] in, For environmental change, Due to the tourist's location deviation, Due to the deviation in the density of people in the scenic area, Due to environmental and weather deviations, , , Preset weighting coefficients; The optimal route planning and time redistribution scheme are output based on the environmental changes, and subsequent tourism content is updated in real time based on the optimal route planning and the time redistribution scheme.

[0069] It should be noted that dynamic data of tourists (such as real-time location coordinates, duration of stay, and behavioral trajectory), dynamic data of the scenic area (such as visitor density, facility opening status, and service capacity), and dynamic environmental data (such as weather index, traffic congestion rate, and emergency event markers) are continuously collected through multi-source data interfaces (including tourist terminal GPS positioning modules, scenic area real-time monitoring systems, and meteorological service application programming interfaces (APIs)). The current observed values ​​are quantitatively compared with preset benchmark values ​​for the tourism plan nodes (such as planned route coordinates, expected visitor density, and ideal weather conditions), and the tourist location deviation (i.e., the absolute value of the Euclidean distance between the current GPS coordinates and the planned route) and the scenic area visitor density deviation (i.e., the absolute value of the current visitor density compared with the planned value) are calculated. The system dynamically calculates environmental changes using the above formula, based on preset weighting coefficients (w1, w2, w3, e.g., w1=0.4, w2=0.3, w3=0.3). When the environmental change exceeds a preset threshold (e.g., ≥0.5), the system outputs optimal route planning (e.g., generating alternative attractions based on the state deviation) and time reallocation plans (e.g., adjusting the duration of stay at subsequent attractions). It also updates subsequent travel content in real time (including attraction order, task recommendations, and time allocation) to ensure the travel itinerary seamlessly adapts to environmental changes. In the specific implementation, see Figure 5 , Figure 5 This is a schematic diagram of the role-playing, points-based, and readily adjustable personal travel guidance and management system of the present invention, as shown below. Figure 5 As shown, the system includes a tourist terminal for receiving tourist input, displaying tourism tasks, and processing points rewards; a scenic area construction terminal for providing key scenic area information; a merchant terminal for providing recommendations for merchandise sales, catering, and entertainment facilities; an information editing terminal for editing key scenic area information; and a system management platform that communicates with the tourist terminal, scenic area construction terminal, merchant terminal, and information editing terminal via the Internet to manage tourism data, execute tourism guidance algorithms, and dynamically adjust algorithms.

[0070] Tourism Editing Terminal: A public-participatory open platform for collecting key information about scenic spots. Users can modify system entries at any time.

[0071] On the editing page, you can modify, add, or delete the text content of the entry. After making changes, click the "Submit" button and wait for system review.

[0072] Once selected, you can earn corresponding points or coins.

[0073] 1. Scan the QR code at the entrance of the scenic area, or scan the QR code on the paper or electronic ticket, or use the WeChat mini-program, or download the APP, etc.

[0074] 2. Visitors select their role type.

[0075] Based on the visitor's operation history, visitor behavior data can be obtained, and combined with the tags selected by the visitor, roles can be recommended to the visitor, and the suitability of each role can be displayed.

[0076] 3. Pre-trip advice.

[0077] Provide information on the destination's climate, food, lifestyle, sun protection and mosquito prevention measures, and offer suggestions on local cultural customs and rules.

[0078] 4. Enjoy the travel experience.

[0079] After completing each small task during the journey, the system evaluates the quality of the completion based on a set algorithm model. Tourists can receive rewards such as coins and points, accompanied by sound effects to enhance the gameplay.

[0080] 5. Travel routes are variable; the result of a previous action will lead to changes in the subsequent route. Temporary changes to settings will cause the system to adjust subsequent planning.

[0081] 6. Unexpected events are inevitable during travel, and the system can provide relevant suggestions.

[0082] 7. Review and record the travel process, such as saving photos, videos, travel footprints, etc., and give a travel evaluation to improve travel skills and enjoy the results of the trip at any time.

[0083] Algorithm model: 1. Points-based reward algorithm model: The points acquisition formula, during its use, incentivizes tourists to take specific actions by reasonably setting points rewards and level upgrades, and provides a suitable channel for tourists to use real currency to upgrade their levels.

[0084] 2. Travel guidance and an algorithm model that can be adjusted in real time: In tourism scenarios, user needs, scenic area status (crowds, facilities), and external environment (weather, transportation) all exhibit dynamic changes, making it difficult for traditional static recommendation or planning models to cope with immediate needs.

[0085] This embodiment uses time-series data to update the perception of changes in the environment and needs in real time, enabling dynamic activation of tags and employing algorithmic logic to output the optimal adjustment strategy, thereby achieving dynamic adaptation to the tourism process.

[0086] (1) Raw data: It includes an original preference vector S, a tourism role M formed based on the original preference vector S using a specific algorithm, and a tourism plan Nt formed using a specific algorithm model.

[0087] The original preference vector S = {the preference vector formed by tourists before their trip}; Traveler roles M = {Relaxed type, Knowledge seeker, Adventurer, Foodie, Parent-child type, Anti-influencer type, Group tour type, Personalized travel type, Casual type, etc.}; The set of nodes for the travel plan is Nt = {(T1, X1, S1), (T2, X2, S2), ..., (T...} n X n S n In the context of the tourism plan, T represents time, X represents tourism activities such as accommodation, dining, and sightseeing, and S represents relevant statuses such as weather and traffic conditions; weights are assigned to the statuses of each node in the tourism plan.

[0088] (2) Collect observed variables: Observations are collected through "time slices" (e.g., every 30 minutes, time slice t=0, 1, 2...), and mapped to the corresponding tourism plan node Nt, forming the observation state Yt of the tourism plan node.

[0089] Collection of Tourism Plan Nodes (Observations): Yt={(Ty1, Xy1, Sy1), (Ty2, Xy2, Sy2),..., (Ty n , Xy n Sy n )} The observed variables mainly include the following dynamic data: Visitor dynamic data includes visitors' real-time location, stay duration, current behavior (queuing, sightseeing, resting), changes in preferences (temporary favorites, cancellations), and feedback (negative reviews, complaints). Data sources include mobile apps, mini-programs, and IoT devices.

[0090] Scenic Area Dynamic Data: Real-time visitor density (at each attraction and passageway), facility status (equipment malfunctions and maintenance), service capacity (parking lot and restaurant vacancies), and event changes (performance cancellations and temporary exhibitions). Data sources include the scenic area's Internet of Things (IoT) system, ticketing system, and staff reports.

[0091] Environmental dynamics data: real-time weather (rainfall, strong winds, high temperatures), traffic conditions (road congestion, bus delays), and emergencies (road closures, epidemic warnings). Data sources include meteorological APIs, transportation department interfaces, and news alerts.

[0092] (3) Define weights As needed, the weights of each variable and state, as well as the tourism strategy, can be defined. Multiple weights can be set to form a matrix, and priority levels can be defined at the same time.

[0093] (4) Variable comparison As time progresses, tourism planning node (observation) Yt data is generated and compared with tourism planning node Nt to form the comparison result Nt:Yt.

[0094] (5) Algorithm decision on optimal adjustment strategy: When Nt: Yt≠1, decision-making is carried out through multi-level mathematical model calculation.

[0095] It should be noted that this embodiment utilizes modern technology and a game-like approach to create several tourist roles (MT) through overall planning and design, such as the laid-back type, the knowledge-seeking type, the adventure type, the gourmet type, the parent-child type, the anti-internet celebrity type, the group tour type, the personalized type, and the casual type.

[0096] The system integrates multi-source information such as tourist behavior data, geographical location data, and time data, and recommends suitable tourism tasks based on their interests, preferences, and task completion status.

[0097] After selecting a role, tourists follow the system's instructions to complete their tour and earn points and rewards.

[0098] This embodiment provides abundant geographical and cultural tourism resources, as well as practical skills such as photography and wellness. Tourists do not need to collect any tourism information in advance. They can simply select tags to fully immerse themselves in and deeply participate in the tourism process. This breaks the traditional single sightseeing model of tourism, enhances the tourist experience and enjoyment, and improves their tourism appreciation ability.

[0099] This embodiment allows tourists to gain a deeper understanding and experience of the destination's culture, history, and nature, resulting in a unique and unforgettable travel experience that satisfies their desire for novelty and personalized experiences. It also cultivates tourists' travel skills, such as photography, appreciation, and general travel abilities.

[0100] This embodiment establishes a dynamic adjustment mechanism for task matching, which adjusts the task matching strategy in real time based on tourists' feedback on tasks and environmental changes.

[0101] Tourists can modify their travel tags at any time, or the system can replan their travel itinerary based on a pre-defined algorithm model after detecting changes in factors such as the tourist's route, weather, road conditions, or congestion at upcoming attractions. For example, if an unforeseen event necessitates shortening the travel time, the system will use the remaining time to carefully select and replan the itinerary after the tourist modifies their time tag.

[0102] After completing each small task during the journey, the system evaluates the quality of the completion based on a set algorithm model. Tourists can receive rewards such as coins and points, accompanied by sound effects to enhance the gameplay.

[0103] This embodiment enables automatic check-in at scenic spots, allowing users to share photos on social media, invite like-minded companions to travel together, and receive meaningful souvenirs.

[0104] This embodiment comprehensively archives tourists' travel records, allowing for easy access and visualization, and summarizes them to form annual travel route maps, travel experience points, and travel growth metrics.

[0105] This embodiment can obtain tourist behavior data based on the tourist's operation history, combine it with the tags selected by the tourist, recommend roles to the tourist, and display the suitability of each role.

[0106] This embodiment provides a comprehensive and systematic recommendation of goods sales, catering, and entertainment facilities to promote economic development.

[0107] This embodiment is applicable to a single scenic area, but other scenic areas can be added to form a tourist destination selection.

[0108] It includes tourist terminals, scenic area construction terminals, merchant terminals, information editing terminals, and a system management platform. The system management platform maintains real-time data communication with the tourist terminals, scenic area construction terminals, merchant terminals, and information editing terminals via the Internet. This invention forms a complete system by setting up intelligent control terminals, scenic area construction terminals, tourist platforms, and personnel management platforms. The system management platform manages, visualizes, and stores tourism management service data and corresponding analysis results, improving the level of intelligent tourism management, realizing the digital upgrade of scenic areas, effectively improving tourist satisfaction, and promoting scenic area construction and tourism resource development.

[0109] 1. Through overall planning and design, based on dimensions such as travel experience, behavior patterns, consumption concepts, and travel goals, several virtual tourist characters are created. Through customized services, tourists are trained in a targeted manner to enhance their travel capabilities and reduce the impact of tourists' cognitive cocoons.

[0110] 2. During the trip, tourists can modify their travel tag settings at any time. Alternatively, the system can replan subsequent travel content based on a specific algorithm model after detecting changes in tourist attractions, weather, road conditions, or congestion at upcoming attractions.

[0111] 3. After completing each small task during the journey, the system evaluates the quality of the completion based on a specific algorithm model. Tourists can receive rewards such as coins or points, accompanied by sound effects to enhance the gameplay.

[0112] By setting up activities such as dropping equipment, gaining gold coins, and leveling up, along with screen flashing and sound effects, the system continuously stimulates dopamine secretion, creating a conditioned reflex in the brain that "doing tasks = gaining pleasure," thus leading to a desire to repeat this behavior.

[0113] The completion of each small goal brings a sense of accomplishment as the progress bar advances, driving tourists to continue pursuing the next goal, thus profoundly influencing the travel experience and enhancing their travel capabilities.

[0114] In the specific implementation, relevant information can be collected, such as: 1. Collect key information about the scenic area: (1) Natural scenery, history and culture, themed scenes, etc. Including local legends, allusions, poems, etc.

[0115] (2) Amusement projects, folk experience, handicrafts, role-playing, light shows, live performances, etc.

[0116] (3) Transportation conditions, accommodation, local delicacies, toilets, mother and baby rooms, etc.

[0117] (4) Ticket prices, transparency of consumption, and the matching degree between service and price.

[0118] (5) Security and rescue facilities, tourist reviews and word-of-mouth, friend recommendations, etc. These can be listed by scenic spots or merchants.

[0119] 2. Collect key personal information: travel preferences, travel companion preferences, budget, time, season, transportation convenience, accommodation suitability, freedom of movement, hidden costs, special equipment, etc. Tourists can then select tags.

[0120] 3. Collect information on the tourist journey: tourist attractions along the way, weather conditions, road conditions, traffic congestion at upcoming attractions, etc.

[0121] (1) Uploading photos, videos, and audio recordings of tourist attractions; (2) Automatic check-in at tourist attractions, displaying the time and location of arrival and departure; (3) Describe your feelings using voice or text; (4) Photographs of tickets or passes.

[0122] 4. Security Information: (1) Local emergency contact information (scenic area customer service, consulate phone number, car rental company rescue phone number).

[0123] This embodiment, through the above-described scheme, collects task completion data in real time via the positioning module, task status sensor, and user interface of the tourist terminal during the execution of the tourist task selected by the tourist through the initial tourist task recommendation list. The task completion data is then transmitted to the system management platform, where task completion points are calculated in real time based on the points acquisition formula. Point rewards are generated based on these points, allowing tourists to receive immediate and vivid feedback of points rewards for each task completed during the trip. This makes the exploration journey full of a sense of accomplishment and fun, significantly enhancing the enjoyment and enthusiasm of the trip, completely breaking away from the monotony of traditional tourism, and transforming the entire journey into an unforgettable experience full of motivation, surprises, and anticipation.

[0124] Accordingly, the present invention further provides a personal travel guidance management device that can be adjusted at any time.

[0125] Reference Figure 6 , Figure 6 This is a functional block diagram of the first embodiment of the adjustable personal travel guidance management device of the present invention.

[0126] In a first embodiment of the adjustable personal travel guidance management device of the present invention, the adjustable personal travel guidance management device includes: The task recommendation module 10 is used to generate an initial list of recommended travel tasks based on the tourist's input of travel role selection, behavior data, geographical location data, time data, and interest preference data.

[0127] The points calculation module 20 is used to collect task completion data in real time and calculate points rewards during the execution of tourism tasks.

[0128] The replanning module 30 is used to detect dynamic data of the tourist's current environment. When an environmental change is detected, the subsequent tourism content is replanned based on the amount of environmental change.

[0129] The task recommendation module 10 is further configured to perform multi-dimensional data fusion processing based on the tourist's input of tourist role selection, behavioral data, geographical location data, time data, and interest preference data, and obtain data fusion results through a role adaptation algorithm; and dynamically filter and generate an initial tourism task recommendation list that meets the tourist's personalized needs based on the data fusion results.

[0130] The task recommendation module 10 is also used to extract features, normalize and dynamically assign weights to the data in each dimension based on the tourist's input of tourist role selection, behavior data, geographical location data, time data and interest preference data, and generate structured data fusion results. The data fusion results include quantitative indicators of role matching degree, environmental adaptability and time sensitivity.

[0131] The task recommendation module 10 is also used to obtain tourist role selection, behavior data, geographical location data, time data and interest preference data input by tourists; Based on the selected tourist role, the tourist's role is mapped to a preset role feature library to obtain a role feature vector; Quantitative features are extracted from the behavioral data, the geographic location data, the time data, and the interest preference data; The eigenvalues ​​of the quantized features are scaled to the [0,1] interval using Min-Max normalization, and the feature weights are calculated based on environmental factors and dynamic coefficients using the following formula:

[0132] in, For the first Feature weights of each feature For the first One environmental factor, For dynamic coefficients; The normalized features are weighted using the feature weights to obtain a weighted feature vector. The role feature vector and the weighted feature vector are then fused using a dot product to obtain a structured data fusion result.

[0133] The points calculation module 20 is also used to collect task completion data in real time through the positioning module, task status sensor and user interaction interface of the tourist terminal during the execution of the tourist task selected by the tourist through the initial tourist task recommendation list; transmit the task completion data to the system management platform, calculate the task completion data in real time based on the points acquisition formula, obtain task completion points, and generate points rewards based on the task completion points.

[0134] The integration calculation module 20 is also used to transmit the task completion data to the system management platform, and to perform real-time calculation on the task completion data based on the following integration formula to obtain the task completion score:

[0135] in, Points are awarded for completing the task. The basic points reward for completing the sub-tasks The difficulty level of the subtask. The basic points reward for daily tasks. The base points reward for each upgrade. Based on the visitor's current rating, The basic points reward for arena challenges. For the number of wins, The base points reward per unit of time. For the duration of the trip; The points reward is dynamically generated based on the points earned from completing the task, and sound effects and interface feedback are triggered simultaneously.

[0136] The replanning module 30 is also used to continuously collect dynamic data of tourists, dynamic data of scenic spots and dynamic data of the environment through multi-source data interfaces; Based on the tourist dynamic data, the scenic area dynamic data, and the environmental dynamic data, the current observed value is quantitatively compared with the preset benchmark value of the tourism plan node, and the environmental change is calculated using the following formula:

[0137] in, For environmental change, Due to the tourist's location deviation, Due to the deviation in the density of people in the scenic area, Due to environmental and weather deviations, , , Preset weighting coefficients; The optimal route planning and time redistribution scheme are output based on the environmental changes, and subsequent tourism content is updated in real time based on the optimal route planning and the time redistribution scheme.

[0138] The steps for implementing each functional module of the adjustable personal travel guidance management device can be referred to in the various embodiments of the adjustable personal travel guidance management method of the present invention, and will not be repeated here.

[0139] Furthermore, this embodiment of the invention also proposes a storage medium storing a readily adjustable personal travel guide management program, which, when executed by a processor, performs the following operations: Based on the tourist's input of tourist role selection, behavioral data, geographical location data, time data, and interest preference data, an initial list of recommended tourist tasks is generated; During the execution of tourism tasks, task completion data is collected in real time and points rewards are calculated. The system detects dynamic data of the tourist's current environment, and when environmental changes are detected, it re-plans subsequent tourism content based on the magnitude of the environmental changes.

[0140] Furthermore, when the adjustable personal travel guidance management program is executed by the processor, it also performs the following operations: Based on tourists' input of their chosen tourist roles, behavioral data, geographic location data, time data, and interest preference data, a role-matching algorithm is used to perform multi-dimensional data fusion processing to obtain the data fusion results. Based on the data fusion results, an initial list of recommended travel tasks that meets the personalized needs of tourists is dynamically filtered and generated.

[0141] Furthermore, when the adjustable personal travel guidance management program is executed by the processor, it also performs the following operations: Based on the tourist's input of tourist role selection, behavioral data, geographical location data, time data, and interest preference data, the role adaptation algorithm performs feature extraction, normalization processing, and dynamic weight allocation on the data of each dimension to generate a structured data fusion result. The data fusion result includes quantitative indicators of role matching degree, environmental adaptability, and time sensitivity.

[0142] Furthermore, when the adjustable personal travel guidance management program is executed by the processor, it also performs the following operations: Acquire tourist input data including their chosen tourist role, behavior, geographic location, time, and interests. Based on the selected tourist role, the tourist's role is mapped to a preset role feature library to obtain a role feature vector; Quantitative features are extracted from the behavioral data, the geographic location data, the time data, and the interest preference data; The eigenvalues ​​of the quantized features are scaled to the [0,1] interval using Min-Max normalization, and the feature weights are calculated based on environmental factors and dynamic coefficients using the following formula:

[0143] in, For the first Feature weights of each feature For the first One environmental factor, For dynamic coefficients; The normalized features are weighted using the feature weights to obtain a weighted feature vector. The role feature vector and the weighted feature vector are then fused using a dot product to obtain a structured data fusion result.

[0144] Furthermore, when the adjustable personal travel guidance management program is executed by the processor, it also performs the following operations: During the execution of the tourism task selected by the tourist through the initial tourism task recommendation list, task completion data is collected in real time through the positioning module, task status sensor and user interaction interface of the tourist terminal; The task completion data is transmitted to the system management platform, where it is calculated in real time based on the points acquisition formula to obtain task completion points, and a points reward is generated based on the task completion points.

[0145] Furthermore, when the adjustable personal travel guidance management program is executed by the processor, it also performs the following operations: The task completion data is transmitted to the system management platform, and the task completion score is calculated in real time based on the following integral formula:

[0146] in, Points are awarded for completing the task. The basic points reward for completing the sub-tasks The difficulty level of the subtask. The basic points reward for daily tasks. The base points reward for each upgrade. Based on the visitor's current rating, The basic points reward for arena challenges. For the number of wins, The base points reward per unit of time. For the duration of the trip; The points reward is dynamically generated based on the points earned from completing the task, and sound effects and interface feedback are triggered simultaneously.

[0147] Furthermore, when the adjustable personal travel guidance management program is executed by the processor, it also performs the following operations: Continuously collect dynamic data on tourists, scenic spots, and the environment through multi-source data interfaces; Based on the tourist dynamic data, the scenic area dynamic data, and the environmental dynamic data, the current observed value is quantitatively compared with the preset benchmark value of the tourism plan node, and the environmental change is calculated using the following formula:

[0148] in, For environmental change, Due to the tourist's location deviation, Due to the deviation in the density of people in the scenic area, Due to environmental and weather deviations, , , Preset weighting coefficients; The optimal route planning and time redistribution scheme are output based on the environmental changes, and subsequent tourism content is updated in real time based on the optimal route planning and the time redistribution scheme.

[0149] Those skilled in the art will understand that all or part of the steps in the methods described above can be implemented by a program instructing related hardware. The program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium is a computer-readable storage medium, including: USB flash drive, mobile hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other media that can store program code.

[0150] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0151] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0152] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for managing a personal travel guide at any time, characterized by, The instant-adjustable personal travel guide management method comprises: According to the travel role selection, behavior data, geographical location data, time data and interest preference data input by the tourists, an initial travel task recommendation list is generated; During the execution of the travel task, task completion data is collected in real time and an integral reward is calculated; Dynamic data of the environment in which the tourists are currently located is detected, and when an environmental change is detected, subsequent travel content is re-planned according to the environmental change amount.

2. The on-the-spot adjustable personal travel guide management method according to claim 1, wherein, According to the travel role selection, behavior data, geographical location data, time data and interest preference data input by the tourists, an initial travel task recommendation list is generated, comprising: According to the travel role selection, behavior data, geographical location data, time data and interest preference data input by the tourists, multi-dimensional data fusion processing is performed through a role adaptation algorithm to obtain a data fusion result; According to the data fusion result, an initial travel task recommendation list that meets the personalized needs of the tourists is dynamically screened and generated.

3. The on-the-spot adjustable personal travel guide management method according to claim 2, wherein, According to the travel role selection, behavior data, geographical location data, time data and interest preference data input by the tourists, multi-dimensional data fusion processing is performed through a role adaptation algorithm to obtain a data fusion result, comprising: According to the travel role selection, behavior data, geographical location data, time data and interest preference data input by the tourists, feature extraction, normalization processing and dynamic weight distribution are performed on the data in each dimension through a role adaptation algorithm to generate a structured data fusion result, wherein the data fusion result contains quantified indicators of role matching degree, environmental adaptability and time sensitivity.

4. The method of claim 3, wherein the management of the personal tour guide is performed in real time. According to the travel role selection, behavior data, geographical location data, time data and interest preference data input by the tourists, multi-dimensional data fusion processing is performed through a role adaptation algorithm to obtain a data fusion result, comprising: Obtain the travel role selection, behavior data, geographical location data, time data and interest preference data input by the tourists; Map the role of the tourists to a preset role feature library according to the travel role selection to obtain a role feature vector; Quantitative features are extracted from the behavior data, the geographical location data, the time data and the interest preference data; The feature values of the quantitative features are scaled to the interval [0, 1] by Min-Max normalization, and the feature weight is calculated based on environmental factors and dynamic coefficients by the following formula: wherein, is a feature weight for the th feature, is a feature weight for the th environmental factor, is a dynamic coefficient; The normalized features are weighted by the feature weight to obtain a weighted feature vector, and the role feature vector and the weighted feature vector are dot product fused to obtain a structured data fusion result.

5. The on-the-spot adjustable personal travel guide management method of claim 1, wherein, During the execution of the travel task, task completion data is collected in real time and an integral reward is calculated, comprising: During the execution of the travel task selected by the tourists through the initial travel task recommendation list, task completion data is collected in real time through the positioning module, task state sensor and user interaction interface of the tourist terminal; The task completion data is transmitted to a system management platform, the task completion data is calculated in real time based on an integral acquisition formula, task completion points are obtained, and an integral reward is generated according to the task completion points.

6. The on-the-spot adjustable personal travel guide management method according to claim 5, wherein, The task completion data is transmitted to a system management platform, the task completion data is calculated in real time based on an integral acquisition formula, task completion points are obtained, and an integral reward is generated according to the task completion points. The task completion data is transmitted to a system management platform, the task completion data is calculated in real time based on an integral acquisition formula, task completion points are obtained, and an integral reward is generated according to the task completion points. wherein, is a task completion score, is a base score reward for completing a subtask, is a difficulty coefficient of a subtask, is a base score reward for a daily task, is a base score reward for each upgrade, is a current level of a visitor, is a base score reward for a tournament challenge, is a number of victories, is a base score reward per unit of time, is a length of a tour; An integral reward is dynamically generated according to the task completion points, and sound effect prompting and interface feedback are triggered synchronously.

7. The on-the-spot adjustable personal travel guide management method of claim 1, wherein, The dynamic data of the environment in which the tourist is currently located is detected, and when an environmental change is detected, subsequent travel content is re-planned according to an environmental change amount. Tourist dynamic data, scenic spot dynamic data and environmental dynamic data are continuously collected through a multi-source data interface; The current observation value is quantitatively compared with a benchmark value preset for a travel plan node according to the tourist dynamic data, the scenic spot dynamic data and the environmental dynamic data, and an environmental change amount is calculated through the following formula: wherein, is an environmental change amount, is a tourist position deviation, is a scenic area flow density deviation, is an environmental weather deviation, , , is a preset weight coefficient; An optimal path planning and time reallocation scheme are output according to the environmental change amount, and subsequent travel content is updated in real time according to the optimal path planning and the time reallocation scheme.

8. A personal travel guidance management device that can be adjusted at any time, characterized in that, The real-time adjustable personal travel guide management device comprises: A task recommendation module is configured to generate an initial travel task recommendation list according to travel role selection, behavior data, geographical location data, time data and interest preference data input by a tourist; An integral calculation module is configured to collect task completion data and calculate an integral reward in real time during travel task execution; A re-planning module is configured to detect dynamic data of the environment in which the tourist is currently located, and when an environmental change is detected, subsequent travel content is re-planned according to an environmental change amount.

9. A personal tour guide management device capable of being adjusted at any time, characterized by The real-time adjustable personal travel guide management device comprises a memory, a processor and a real-time adjustable personal travel guide management program stored on the memory and executable on the processor, and the real-time adjustable personal travel guide management program is configured to implement the steps of the real-time adjustable personal travel guide management method according to any one of claims 1 to 7.

10. A storage medium, characterized by The storage medium stores a real-time adjustable personal travel guide management program, and the real-time adjustable personal travel guide management program implements the steps of the real-time adjustable personal travel guide management method according to any one of claims 1 to 7 when executed by a processor.

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