Park tourist dynamic regulation and control method, device and equipment based on real-time load state and medium
By monitoring park visitor load and user experience data in real time, and combining this with historical user preferences, personalized park management strategies are generated, which solves the problem of poor visitor experience in the park and improves visitor satisfaction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SUZHOU VOCATIONAL INSTITUTE OF INDUSTRIAL TECHNOLOGY
- Filing Date
- 2025-12-29
- Publication Date
- 2026-05-01
AI Technical Summary
Current technology cannot provide personalized park visit recommendations, resulting in a poor experience for visitors in parks.
By monitoring the park's visitor load and user experience data in real time, and combining this with users' historical preferences, dynamic control strategies are generated, including route optimization, alternative park recommendations, and off-peak visit strategies, and then pushed to the user's terminal.
This enhances the visitor experience by providing personalized suggestions and avoiding negative experiences during peak hours.
Smart Images
Figure CN121958367A_ABST
Abstract
Description
Methods, devices, equipment, and media for dynamic control of park visitors based on real-time load status. Technical Field
[0001] This application relates to tourism management, and in particular to a method, device, equipment, and medium for dynamic control of park visitors based on real-time load status. Background Technology
[0002] Kernel density analysis is mainly based on the estimation method of unknown data intervals using the dataset density function clustering algorithm. With the rapid development of computers and geographic information systems, this model is widely used in the spatial processing of regional economic and population data. The applied research mainly focuses on the following aspects: (1) Parameter correction, model verification, spatial mapping and visualization research of point-based data spatial estimation models. (2) Research on human behavior influencing factors, activity space and infrastructure service level combined with POI (Point of Interest) data and mobile phone signaling data. (3) Research on the mechanism of urban environmental carrying capacity, scenic area tourism carrying capacity and urban economic development driving force based on professional models.
[0003] The number and density of visitors to a park are related to many factors, including but not limited to area, type, environmental conditions, and level; the completeness of the park's infrastructure; and the POI data analysis of surrounding facilities. It is also related to time, such as different seasons, summer and winter vacations, and holidays.
[0004] Since excessive visitor density can lead to a poor park experience, and different visitors may have different perceptions of the park experience even at the same density, providing visitors with customized park visit suggestions is an important measure to improve their park experience. Currently, there is no similar solution in existing technology. Summary of the Invention
[0005] This application provides a method, apparatus, equipment, and medium for dynamic control of park visitors based on real-time load, which solves the technical problem that existing technologies cannot provide customized park visit suggestions to ensure the park visit experience.
[0006] The first aspect of this application provides a method for dynamic control of park visitors based on real-time load, comprising: real-time monitoring of the real-time visitor load status of a target park, wherein the real-time visitor load status is calculated based at least on the fusion of real-time visitor distribution data of the park and real-time visitor experience data of users; dynamically constructing and pushing a combination of control strategies adapted to the historical preferences of the visitor load status through a strategy generation engine according to the real-time visitor load status and the historical preferences of users for visitor load status, wherein the combination of control strategies includes multiple related types of travel control strategies; and pushing at least one of the control strategies to a user terminal.
[0007] Preferably, the real-time monitoring of the target park's real-time visitor load status is calculated based at least on the fusion of the park's real-time visitor distribution data and users' real-time visitor experience data, including: performing spatiotemporal kernel density analysis on the real-time visitor distribution data to obtain a visitor distribution heatmap; and fusion of users' real-time visitor experience data to calculate a dynamic load index reflecting the spatial carrying capacity and visitor experience comfort through a pre-trained visitor experience-load coupling model.
[0008] Preferably, the user's real-time park experience data is obtained through the following methods: in response to the user's request for a park suggestion terminal, the park suggestion terminal is pre-deployed in public places in various parks; a park suggestion terminal is provided and associated with the user and worn by the user; the park suggestion terminal collects the user's emotional state in real time as the real-time park experience data.
[0009] Preferably, the various types of travel regulation strategies include: route optimization strategies for guiding the spatial distribution of tourists within the target park; alternative park recommendation strategies for guiding tourists to other parks; and time-space combination travel strategies for guiding tourists to visit different parks during off-peak hours.
[0010] Preferably, the step of dynamically constructing a combination of control strategies adapted to the historical preferences of the visitor load status through a strategy generation engine, based on the visitor load status and the user's historical preferences for the park load status, includes: when the visitor load status exceeds the upper limit of the visitor load status in the user's historical preferences, the travel control strategy prioritizes recommending alternative park recommendation strategies and time-space combination travel strategies; when the visitor load status does not exceed the upper limit of the park load status in the user's historical preferences but the internal spatial distribution is uneven, the travel control strategy prioritizes recommending a tour route optimization strategy, with alternative park recommendation strategies as alternatives.
[0011] Preferably, the alternative park recommendation strategy is obtained by: acquiring the real-time visitor load status of the alternative parks and the accessibility of the visitor's current location to the alternative parks; filtering alternative parks whose real-time visitor load status is lower than the upper limit of the historical preferred visitor load status, and attaching transportation options from the current location to each alternative park, generating a recommendation plan and pushing it to the user terminal.
[0012] Generate extended recommendation schemes for each alternative park, including suggestions for optimizing internal routes.
[0013] Preferably, the tour route optimization strategy is obtained by: obtaining the real-time load status of each zone within the park; taking the visitor's current location as the starting point and aiming to maximize the overall real-time experience data of the park visitor, generating a dynamically recommended tour route through a path planning algorithm, wherein the overall real-time experience data of the park visitor is the average value of the real-time experience data of each visitor.
[0014] A second aspect of this application provides a dynamic control device for park visitors based on real-time load status, comprising: a monitoring module for real-time monitoring of the real-time visitor load status of a target park, wherein the real-time visitor load status is calculated based at least on the fusion of real-time visitor distribution data of the park and real-time visitor experience data of users; a strategy generation module for dynamically constructing and pushing a combination of control strategies adapted to the historical preferences of the visitor load status through a strategy generation engine, based on the real-time visitor load status and the historical preferences of users for visitor load status, wherein the combination of control strategies includes multiple related types of travel control strategies; and a push module for pushing at least one of the control strategies to a user terminal.
[0015] A third aspect of this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it causes the electronic device to perform the method described in the first aspect of this application.
[0016] A fourth aspect of this application is a computer-readable storage medium for storing a computer program that, when run on a computer, causes the computer to perform the method described in the first aspect.
[0017] In this embodiment, a method for dynamic control of park visitors based on real-time load is adopted, comprising: real-time monitoring of the real-time visitor load status of the target park, wherein the real-time visitor load status is calculated based at least on the fusion of the park's real-time visitor distribution data and the user's real-time visitor experience data; dynamically constructing and pushing a combination of control strategies adapted to the historical preferences of the visitor load status through a strategy generation engine according to the real-time visitor load status and the user's historical preferences for visitor load status, wherein the combination of control strategies includes multiple related types of travel control strategies; and pushing at least one of the control strategies to the user terminal.
[0018] This application solves the problem of providing appropriate park management suggestions in real time based on users' real-time park visit experience data, thereby helping visitors to have a better park visit experience. Attached Figure Description
[0019] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 is a flowchart of a method for dynamic control of park visitors based on real-time load according to an embodiment of this application.
[0020] Figure 2 is a schematic diagram of a park visitor dynamic control device based on real-time load according to an embodiment of this application.
[0021] Figure 3 is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation
[0022] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.
[0023] The present application will now be described in detail with reference to the accompanying drawings and embodiments.
[0024] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.
[0025] This application provides a method for generating dynamic control strategies for park visitors based on real-time load status. As shown in Figure 1, which is a flowchart of a method for generating dynamic control strategies for park visitors based on real-time load status according to an embodiment of this application, the method includes the following steps: Step S102: Real-time monitoring of the real-time visitor load status of the target park, wherein the real-time visitor load status is calculated based at least on the fusion of the park's real-time visitor distribution data and the user's real-time park experience data; Step S104: Based on the real-time visitor load status and the user's historical preference for visitor load status, dynamically constructing and pushing a combination of control strategies adapted to the historical preference for visitor load status through a strategy generation engine, wherein the combination of control strategies includes multiple related types of travel control strategies; Step S106: Pushing at least one of the control strategies to the user terminal.
[0026] The above embodiments obtain the real-time visitor load status by combining real-time visitor distribution data and users' real-time park experience data. This real-time visitor load status is customized data for individual users, taking into account both objective data and users' subjective feelings. Therefore, by further comparing this data with the user's historical preferences for visitor load status, control strategies can be given to specific users and pushed to the user terminal to suggest subsequent travel itineraries, thereby improving the user's travel experience.
[0027] Specifically, some users prefer locations with a population density of at least the first density, experiencing a negative experience if the density exceeds the first density; others prefer locations with a population density greater than or equal to the second density but less than or equal to the third density, experiencing a negative experience if the density is lower than the second density or higher than the third density; still others are insensitive to population density, meaning it doesn't significantly impact their experience. Given these different user preferences, in actual park visits, due to numerous influencing factors, the specific value of population density affecting a user's real-time experience is not entirely determined by user preferences. However, for a given user, their preferences are generally relatively stable; that is, users with low tolerance for high population density are usually more likely to experience negative experiences due to increased population density than users with high tolerance. Therefore, the historical preferences for visitor load status in this embodiment reflect the baseline response of users to visitor load status. This can serve as a benchmark for assessing the level of real-time visitor load status for a specific user and whether it is likely to trigger a negative reaction. Corresponding park management strategies can be generated based on these historical preferences to provide suggestions that allow users to obtain a park experience that aligns with their historical preferences.
[0028] In the above scheme, real-time visitor distribution data is dynamically changing data that is objective and exhibits certain developmental patterns; similarly, real-time user experience data is also dynamically changing data that reflects users' subjective feelings. Objective factors such as visitor density, distribution, and other subjective and objective factors all influence the real-time visitor experience data. Therefore...
[0029] In embodiments of the present invention, the two sets of data mentioned above are continuously monitored. In the above scheme, the two sets of data within a predetermined period, for example, every 10 minutes, are fused. During fusion, the average value of the park experience data within that period and the average value of the corresponding personnel density at the visitor's location are taken to obtain the real-time visitor load status, which includes personnel density minus real-time experience.
[0030] In the above scheme, the historical preference of tourist load status is obtained by comprehensively considering historical data on individual personnel density and real-time experience. For example, the average data of personnel density and real-time experience recorded during historical park visits under the condition that the similarity of gardens and the similarity of travel time meet the threshold are captured. From this data, the personnel density limit corresponding to a good real-time experience is selected as the historical preference of tourist load status.
[0031] In some preferred embodiments, the real-time monitoring of the target park's real-time visitor load status is calculated based at least on the fusion of the park's real-time visitor distribution data and users' real-time visitor experience data, including: performing spatiotemporal kernel density analysis on the real-time visitor distribution data to obtain a visitor distribution heatmap; and fusion of users' real-time visitor experience data to calculate a dynamic load index reflecting the spatial carrying capacity and visitor experience comfort through a pre-trained visitor experience-load coupling model.
[0032] In some preferred embodiments, the user's real-time park experience data is obtained by: responding to the user's request for a park suggestion terminal, the park suggestion terminal is pre-deployed in public places in various parks; a park suggestion terminal is provided and associated with the user and worn by the user; the park suggestion terminal collects the user's emotional state in real time as the real-time park experience data.
[0033] For example, the park visit suggestion terminal is in the form of a wristband, equipped with various biosensors. It detects the user's biosignals by being worn on the wrist, thereby indirectly inferring the user's emotional state. Specifically, in some embodiments, the park visit suggestion terminal monitors heart rate and heart rate variability via photoplethysmography (PPG). Decreased heart rate variability is often associated with stress and anxiety; specific patterns of heart rate acceleration may be related to excitement or tension. In some embodiments, the park visit suggestion terminal measures skin conductivity, a parameter highly sensitive to emotional arousal (such as tension, excitement, and stress). In some embodiments, the park visit suggestion terminal uses an accelerometer / gyroscope to identify the intensity of physical activity, gestures, and postures to assist in judging emotions or eliminating motion interference. By combining these physiological signals with contextual data, deep learning models can be used to classify or regress emotional labels or continuous values such as "calm," "pleasant," "excited," "irritable," and "anxious."
[0034] The aforementioned park suggestion terminals can be borrowed and returned after user registration with their real names. They are usable across multiple parks, providing convenience for users to borrow and return them at various locations. These terminals record data collected during user usage, including location, park, time, and real-time park experience data. The terminals periodically upload this data to the cloud for subsequent fusion calculations and as historical user data.
[0035] By utilizing the aforementioned park visit suggestion terminal, user emotions can be promptly fed back and suggestions for adjustments to the park visit can be provided, making park management more scientific and avoiding complaints caused by the accumulation of emotions over a long period of time, thereby improving the user's park visit experience.
[0036] Using multiple of the aforementioned park visit suggestion terminals simultaneously can reflect the emotions of the visitor group. This emotional state typically excludes individual visitor preferences and is more related to the park experience influenced by objective factors such as crowd density. Therefore, it is possible to promptly understand the emotions of the visitor group within the park, thereby enabling timely group-level adjustments.
[0037] In some optional embodiments, sentiment labels or numerical data of park visit suggestion terminals in use within the same park at the same time are collected, and group sentiment indicators are generated by mapping pre-trained machine learning data.
[0038] In some optional embodiments, the various types of travel control strategies include: when the negative emotion index in the group emotion index exceeds a threshold, the travel control strategy prioritizes alleviating the negative emotion index. Specific strategies are generated based on a comprehensive judgment of personnel distribution data, the presence of unforeseen events, and feedback data from on-site staff. For example, if long queues are caused solely by excessive personnel density, temporary flow control measures can be implemented to reduce the number of subsequent visitors entering the park, announcements can be made about less crowded areas to achieve a more balanced distribution of people, and fast lanes can be provided to reduce queues.
[0039] In some preferred embodiments, in addition to the above-mentioned group emotion-based regulation strategies, for optimizing an individual's park visit experience, the various types of travel regulation strategies include: route optimization strategies for guiding the spatial distribution of visitors within the target park; alternative park recommendation strategies for guiding visitors to other parks; and time-space combination travel strategies for guiding visitors to visit different parks during off-peak hours.
[0040] In some preferred embodiments, the step of dynamically constructing a combination of control strategies adapted to the historical preferences of the visitor load status through a strategy generation engine, based on the visitor load status and the user's historical preferences for the park load status, includes: when the visitor load status exceeds the upper limit of the visitor load status in the user's historical preferences, the travel control strategy prioritizes recommending alternative park recommendation strategies and time-space combination travel strategies; when the visitor load status does not exceed the upper limit of the park load status in the user's historical preferences but the internal spatial distribution is uneven, the travel control strategy prioritizes recommending a tour route optimization strategy, with alternative park recommendation strategies as alternatives.
[0041] In some preferred embodiments, the alternative park recommendation strategy is obtained by: acquiring the real-time visitor load status of the alternative parks and the traffic accessibility between the visitor's current location and the alternative parks; filtering alternative parks whose real-time visitor load status is lower than the upper limit of the historically preferred visitor load status, attaching a transportation plan from the current location to each alternative park, and generating a recommendation plan to push to the user terminal.
[0042] In some preferred embodiments, the tour route optimization strategy is obtained by: obtaining the real-time load status of each zone within the park; starting from the current location of the visitor and aiming to maximize the overall real-time experience data of the park visitor, generating a dynamically recommended tour route through a path planning algorithm, wherein the overall real-time experience data of the park visitor is the average value of the real-time experience data of each visitor.
[0043] In some optional embodiments, the spatiotemporal combination play strategy is obtained by: analyzing the historical and real-time visitor flow patterns of the target park and surrounding parks, constructing a visitor flow time sequence complementarity model, generating a combination scheme that includes the low-load period of the target park and the suitable period of the surrounding parks, and estimating the comfort level of each period.
[0044] In some optional embodiments, when pushing the control strategy, dynamic ticket price information or preferential policies that match the strategy content are also pushed.
[0045] In some optional embodiments, the method further includes the steps of: collecting user feedback data on the control strategy, and optimizing the strategy combination and sorting rules of the strategy generation engine based on the feedback data to suit the preferences of specific users.
[0046] Based on the same inventive concept as the above-described method embodiments, embodiments of this application also provide a dynamic control device for park visitors based on real-time load status, comprising: a monitoring module for real-time monitoring of the real-time visitor load status of a target park, wherein the real-time visitor load status is calculated at least based on the fusion of real-time visitor distribution data of the park and real-time visitor experience data of users; a strategy generation module for dynamically constructing and pushing a combination of control strategies adapted to the historical preferences of the visitor load status through a strategy generation engine based on the real-time visitor load status and the historical preferences of users for visitor load status, wherein the combination of control strategies includes multiple related types of travel control strategies; and a push module for pushing at least one of the control strategies to a user terminal.
[0047] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.
[0048] Based on the same inventive concept as the above method embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it enables the electronic device to implement the control method described in the above embodiments.
[0049] In one embodiment, the electronic device may be a server. In this embodiment, the structure of the electronic device may be as shown in FIG3, including a memory 2001, a communication module 2003, and one or more processors 2002.
[0050] The memory 2001 is used to store computer programs executed by the processor 2002. The memory 2001 may mainly include a program storage area and a data storage area. The program storage area may store the operating system and programs required to run instant messaging functions, etc.; the data storage area may store various instant messaging information and operation instruction sets, etc.
[0051] Memory 2001 can be volatile memory, such as random-access memory (RAM); memory 2001 can also be non-volatile memory, such as read-only memory, flash memory, hard disk drive (HDD), or solid-state drive (SSD); or memory 2001 can be used to carry or store information with pointers.
[0052] The desired computer program in the form of an instruction or data structure and any other medium accessible by a computer, but not limited thereto.
[0053] Memory 2001 can be a combination of the above-mentioned memories.
[0054] Processor 2002 may include one or more central processing units (CPUs) or digital processing units, etc. Processor 2002 is used to implement the above-mentioned audio data processing method when calling computer programs stored in memory 2001.
[0055] The communication module 2003 is used to communicate with terminal devices and other servers.
[0056] This application embodiment does not limit the specific connection medium between the memory 2001, communication module 2003, and processor 2002. In this application embodiment, the memory 2001 and processor 2002 are connected via a bus 2004 in Figure 3, which is described by arrows in Figure 3. The connection methods between other components are only illustrative and are not intended to be limiting.
[0057] The Bus 2004 can be divided into address bus, data bus, control bus, etc. For ease of description, only one arrow is used to describe it in Figure 3, but it does not mean that there is only one bus or one type of bus.
[0058] Based on the same inventive concept as the above-described method embodiments, embodiments of the present invention also provide a computer-readable storage medium for storing a computer program. When the computer program is run on a computer, it enables the electronic device to implement the control method described in the above embodiments. The computer-readable storage medium can be a readable signal medium or a readable storage medium. A readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.
[0059] Based on the same inventive concept as the above-described method embodiments, embodiments of the present invention also provide a computer program product, which includes a computer program that, when run on an electronic device, causes the electronic device to perform the steps of the control methods described above according to various exemplary embodiments of this application. The program product may take the form of any combination of one or more readable media. These computer program commands can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the commands, which execute via the processor of the computer or other programmable data processing device, create means for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.
[0060] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.
Claims
1. A method for dynamic control of park visitors based on real-time load status, characterized in that, Includes the following steps: The system monitors the real-time visitor load status of the target park, which is calculated based on the fusion of the park's real-time visitor distribution data and the user's real-time visitor experience data. Based on the real-time visitor load status and the user's historical preferences for visitor load status, the system dynamically constructs and pushes a combination of control strategies that are adapted to the historical preferences for visitor load status through a strategy generation engine. The combination of control strategies includes multiple related types of travel control strategies. At least one of the control strategies is pushed to the user terminal.
2. The method according to claim 1, characterized in that, The real-time monitoring of the target park's real-time visitor load status is calculated based on at least the fusion of the park's real-time visitor distribution data and users' real-time park experience data. This includes: performing spatiotemporal kernel density analysis on the real-time visitor distribution data to obtain a visitor distribution heatmap; and fusion of users' real-time park experience data to calculate a dynamic load index reflecting the spatial carrying capacity and visitor comfort level using a pre-trained visitor experience-load coupling model.
3. The method according to claim 2, characterized in that, The user's real-time park experience data is obtained through the following methods: in response to the user's request for a park suggestion terminal, the park suggestion terminal is pre-deployed in public places in various parks; a park suggestion terminal is provided and associated with the user and worn by the user; the park suggestion terminal collects the user's emotional state in real time as the real-time park experience data.
4. The method according to claim 3, characterized in that, The various types of travel regulation strategies include: route optimization strategies to guide the spatial distribution of tourists within the target park; alternative park recommendation strategies to guide tourists to other parks; and time-space combination travel strategies to guide tourists to visit different parks during off-peak hours.
5. The method according to claim 4, characterized in that, The process involves dynamically constructing a combination of control strategies adapted to the historical preferences of the visitor load status through a strategy generation engine, based on the visitor load status and the user's historical preferences for park load status. This includes: when the visitor load status exceeds the upper limit of the visitor load status in the user's historical preferences, the travel control strategy prioritizes recommending alternative park recommendation strategies and time-space combination travel strategies; when the visitor load status does not exceed the upper limit of the park load status in the user's historical preferences but the internal spatial distribution is uneven, the travel control strategy prioritizes recommending a tour route optimization strategy, with alternative park recommendation strategies as alternatives.
6. The method according to claim 5, characterized in that, The alternative park recommendation strategy is obtained by acquiring the real-time visitor load status of the alternative parks and the traffic accessibility between the visitor's current location and the alternative parks. The system filters out alternative parks whose real-time visitor load is below the upper limit of historically preferred visitor load levels, and provides transportation options from the current location to each alternative park, generating recommended routes that are then pushed to the user's device. For each alternative park, an expanded recommended route including internal route optimization suggestions is generated.
7. The method according to claim 4, characterized in that, The tour route optimization strategy is obtained through the following methods: obtaining the real-time load status of each zone within the park; taking the visitor's current location as the starting point and aiming to maximize the overall real-time experience data of the park visitor, generating a dynamically recommended tour route through a path planning algorithm, wherein the overall real-time experience data of the park visitor is the average value of the real-time experience data of each visitor.
8. A dynamic control device for park visitors based on real-time load status, characterized in that, include: The monitoring module is used to monitor the real-time visitor load status of the target park. The real-time visitor load status is calculated based on the fusion of the park's real-time visitor distribution data and the user's real-time visitor experience data. The strategy generation module is used to dynamically construct and push a combination of control strategies that are adapted to the historical preferences of the tourist load status based on the real-time tourist load status and the user's historical preferences for the tourist load status through the strategy generation engine. The combination of control strategies includes multiple related types of travel control strategies. The push module is used to push at least one of the control strategies to the user terminal.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it causes the electronic device to implement the method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program that, when run on a computer, causes the computer to perform the method as described in any one of claims 1 to 7.