A method for recommending scenic spot tour routes, terminal equipment and storage medium

Abstract

The present invention relates to a method for recommending scenic spot tour routes, a terminal device and a storage medium, the method comprising: S1: collecting scenic spot data and user preference behavior data; S2: initially setting the number of user actions t=1; S3: sequentially calculating the probability of the user selecting each scenic spot when the user takes the tth action; S4: extracting the maximum value from the probability of the user selecting each scenic spot when the user takes the tth action as the optimal probability when the user takes the tth action, and taking the scenic spot corresponding to the optimal probability as the optimal scenic spot selected to arrive when the user takes the tth action; S5: judging whether the number of user actions t is equal to the maximum number of actions N, if so, constructing the user's scenic spot tour route planning in the scenic spot based on the optimal scenic spot selected to arrive when the user takes the Nth action; otherwise, setting t to be incremented by 1 and returning to S3. The present invention can provide users with personalized tour route recommendations, thereby improving user experience.

Description

Technical Field

[0001] The present invention relates to the field of route planning, and in particular to a method for recommending a scenic spot tour route, a terminal device and a storage medium. Background Art

[0002] In the cultural and tourism industry, with the improvement of people's living standards and the increase in tourism demand, intelligent and personalized tourism services are gaining more and more attention. There are some problems with traditional tourism planning methods. For example, fixed linear route planning cannot meet the personalized needs of different users, and cannot adapt to the real-time changes in scenic spots. At present, there are some intelligent tourism planning systems based on recommendation algorithms and geographic information, but most systems still have some limitations. For example, some systems may only recommend attractions based on user preferences without considering the real-time situation of the scenic spot; other systems may only generate fixed linear routes and cannot be adjusted based on real-time feedback from users. Summary of the invention

[0003] In order to solve the above problems, the present invention proposes a method for recommending scenic spot tour routes, a terminal device and a storage medium.

[0004] The specific plan is as follows:

[0005] A method for recommending a scenic spot tour route comprises the following steps:

[0006] S1: Collect scenic spot data and user preference behavior data;

[0007] S2: Initially set the number of user actions t=1;

[0008] S3: Calculate the probability of the user selecting each scenic spot in the tth action:

[0009]

[0010] in, represents the probability that the user chooses to go to attraction i during the tth action; represents the optimal probability of the user's t-1th action, , represents the user's preference for scenic spot i calculated based on scenic spot data and user preference behavior data;

[0011] S4: Extract the maximum value from the probability of selecting each scenic spot when the user takes the tth action as the optimal probability when the user takes the tth action , the scenic spot corresponding to the optimal probability is taken as the optimal scenic spot that the user chooses to reach when taking the tth action;

[0012]

[0013] in, It means to find the maximum value;

[0014] S5: Determine whether the number of user actions t is equal to the maximum number of actions N. If so, proceed to S6; otherwise, increment t by 1 and return to S3;

[0015] S6: Construct a route plan for the user's scenic spot tour in the scenic area based on the optimal scenic spot selected to be reached during N actions.

[0016] Furthermore, the user's preference for scenic spot i is calculated based on the scenic spot data and user preference behavior data The calculation formula is:

[0017]

[0018] in, , , , , , and are weight coefficients, Indicates the matching degree between the user's interest tag and the tag of scenic spot i, Indicates the matching degree between the scenic spots that the user wants to visit and the opening status of scenic spot i, Indicates the matching degree between the user's planned tour time period and the opening hours of scenic spot i, Indicates the degree of match between the time period when the user plans to dine and whether there is a restaurant at scenic spot i. represents the travel time from attraction k to attraction i, k represents the optimal attraction chosen by the user at the t-1th action, represents the flow of people at scenic spot i, Indicates that the user plans to watch a performance at attraction i.

[0019] Furthermore, the scenic spot data includes: the label of each scenic spot, the opening status of each scenic spot, the opening hours of each scenic spot, whether each scenic spot has a restaurant, the time required to travel between two scenic spots, and the flow of people at the scenic spots.

[0020] Furthermore, the user preference behavior data includes: user's interest tags, attractions the user wants to visit, the time period the user plans to browse, the time period the user plans to dine, and whether the user plans to watch performances at each attraction.

[0021] Furthermore, step S1 also includes preprocessing, feature extraction and data normalization of the acquired scenic spot data and user preference behavior data; the preprocessing includes processing of incomplete data and abnormal data.

[0022] A terminal device for recommending scenic spot tour routes comprises a processor, a memory and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method described above in the embodiment of the present invention are implemented.

[0023] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described above in an embodiment of the present invention are implemented.

[0024] The present invention adopts the above technical solution, and can provide users with personalized tour route recommendations, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Shown is a flow chart of a method according to a first embodiment of the present invention. DETAILED DESCRIPTION

[0026] To further illustrate various embodiments, the present invention provides drawings. These drawings are part of the disclosure of the present invention, which are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in this field should be able to understand other possible implementations and advantages of the present invention.

[0027] The present invention will now be further described with reference to the accompanying drawings and specific implementation methods.

[0028] Embodiment 1:

[0029] The embodiment of the present invention provides a method for recommending scenic spot tour routes. Figure 1 As shown, the method comprises the following steps:

[0030] S1: Collect scenic spot data and user preference behavior data.

[0031] In this embodiment, the scenic spot data includes: the label of each scenic spot, the opening status of each scenic spot, the opening hours of each scenic spot, whether each scenic spot has a restaurant, the time required between two scenic spots, and the flow of people in the scenic spot. The scenic spot data can be statistically uploaded to the system by the scenic spot management personnel for subsequent calculation.

[0032] User preference behavior data includes: user's interest tags, the attractions the user wants to visit, the time period the user plans to browse, the time period the user plans to dine, and whether the user plans to watch a performance at each attraction. User preference behavior data can be filled in by the user in the client user interface and uploaded to the system for subsequent calculation.

[0033] Furthermore, after collecting scenic spot data and user preference behavior data, this embodiment also includes preprocessing, feature extraction and data normalization of the collected data. Preprocessing includes processing incomplete data and abnormal data (such as removing such data) to ensure the accuracy and completeness of the data. Feature extraction is to convert the collected data into feature vectors. The specific conversion method can be set by technicians in this field and is not limited here. Data normalization is used to uniformly map feature vectors of different data types to the range of [0, 1] to eliminate the dimensional influence between different feature vectors.

[0034] S2: Initially set the number of user actions t=1.

[0035] S3: Calculate the probability of the user selecting each scenic spot in the tth action:

[0036]

[0037] in, represents the probability that the user chooses to go to attraction i during the tth action; represents the optimal probability of the user's t-1th action, , It represents the user's preference for scenic spot i calculated based on scenic spot data and user preference behavior data.

[0038] S4: Extract the maximum value from the probability of selecting each scenic spot when the user takes the tth action as the optimal probability when the user takes the tth action , the scenic spot corresponding to the optimal probability is taken as the optimal scenic spot that the user chooses to reach when taking the tth action;

[0039]

[0040] in, Indicates finding the maximum value.

[0041] S5: Determine whether the number of user actions t is equal to the maximum number of actions N. If so, proceed to S6; otherwise, increment t by 1 and return to S3.

[0042] S6: Construct a route plan for the user's scenic spot tour in the scenic area based on the optimal scenic spot selected to be reached during N actions.

[0043] In this embodiment, the user's preference for scenic spot i is calculated based on the scenic spot data and the user's preference behavior data. The calculation formula is:

[0044]

[0045] in, , , , , , and They are all weight coefficients, and their sizes are set by technicians in this field according to their needs; Indicates the matching degree between the user's interest tag and the tag of scenic spot i, where matching is 1 and mismatch is 0; Indicates the matching degree between the scenic spot that the user wants to visit and the opening status of scenic spot i, where matching is 1 and mismatch is 0; Indicates the matching degree between the user's planned tour time period and the opening hours of scenic spot i, with matching being 1 and mismatching being 0; Indicates the degree of match between the time period when the user plans to dine and whether there is a restaurant at scenic spot i, with a match value of 1 and a mismatch value of 0; represents the travel time from attraction k to attraction i, and k represents the optimal attraction chosen by the user at the t-1th action; It represents the flow of people at scenic spot i. If the flow of people is greater than the flow threshold, it is 1; if it is less than the flow threshold, it is 0. Indicates that the user plans to watch a performance at attraction i, where 1 is the value if the user plans to watch a performance and 0 is the value if the user does not plan to watch a performance.

[0046] It should be noted that, since scenic spot tours are often single-line, that is, each scenic spot is only visited in turn, the scenic spot range selected in the t-th action is set in this embodiment to be the remaining scenic spots other than the optimal scenic spot selected to reach before the t-th action.

[0047] Furthermore, the method of this embodiment is not only used to start recommendations based on the starting point of the scenic spot, but can also be used for dynamic recommendations based on the user's sightseeing situation. At this time, by recording the scenic spots that the user has visited and removing them from the scenic spot set, a set of unvisited scenic spots is obtained, and then the scenic spots for selection in the calculation process are set as the scenic spots in the unvisited scenic spot set.

[0048] Furthermore, when the scenic spot data and the user preference behavior data change during the user's tour, the scenic spot tour route planning is performed again using the method of this embodiment.

[0049] The embodiments of the present invention can provide personalized tour route recommendations for each tourist, making their tour experience richer and more comfortable. By using dynamic planning and real-time information, the tour route can be adjusted in real time to avoid crowded scenic spots and peak hours, so that tourists can visit the scenic area more effectively and save time and energy. By real-time monitoring of information such as the flow of people in the scenic area and the opening of scenic spots, scenic area managers can better understand the flow of tourists, reasonably arrange scenic area resources and manpower, and improve scenic area management efficiency and service quality. Personalized recommendations and optimized route planning will make tourists more satisfied, improve their experience and satisfaction with the scenic area, thereby increasing the return rate of tourists and the word-of-mouth communication effect.

[0050] Embodiment 2:

[0051] The present invention also provides a terminal device for recommending scenic spot tour routes, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the above-mentioned method embodiment of the first embodiment of the present invention when executing the computer program.

[0052] Further, as an executable solution, the scenic spot tour route recommendation terminal device can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The scenic spot tour route recommendation terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the composition structure of the above-mentioned scenic spot tour route recommendation terminal device is only an example of the scenic spot tour route recommendation terminal device, and does not constitute a limitation on the scenic spot tour route recommendation terminal device. It may include more or less components than the above, or a combination of certain components, or different components. For example, the scenic spot tour route recommendation terminal device may also include input and output devices, network access devices, buses, etc., and the embodiments of the present invention do not limit this.

[0053] Further, as an executable solution, the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the scenic spot tour route recommendation terminal device, and uses various interfaces and lines to connect various parts of the entire scenic spot tour route recommendation terminal device.

[0054] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the scenic spot tour route recommendation terminal device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required for a function; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0055] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method in the embodiment of the present invention are implemented.

[0056] If the module / unit integrated in the scenic area tour route recommendation terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory) and software distribution medium, etc.

[0057] Although the present invention has been specifically shown and described in conjunction with the preferred embodiments, it should be understood by those skilled in the art that various changes may be made to the present invention in form and details without departing from the spirit and scope of the present invention as defined by the appended claims, all of which are within the scope of protection of the present invention.

Claims

1. A method for recommending scenic spot tour routes, characterized in that: The following steps are involved: S1: Collect scenic spot data and user preference behavior data; S2: Initially set the number of user actions t=1; S3: Calculate the probability of the user selecting each scenic spot in the tth action: in, represents the probability that the user chooses to go to attraction i during the tth action; represents the optimal probability of the user's t-1th action, , represents the user's preference for scenic spot i calculated based on scenic spot data and user preference behavior data; S4: Extract the maximum value from the probability of selecting each scenic spot when the user takes the tth action as the optimal probability when the user takes the tth action , the scenic spot corresponding to the optimal probability is taken as the optimal scenic spot that the user chooses to reach when taking the tth action; in, It means to find the maximum value; S5: Determine whether the number of user actions t is equal to the maximum number of actions N. If so, proceed to S6; otherwise, increment t by 1 and return to S3; S6: Construct a route plan for the user's scenic spot tour in the scenic area based on the optimal scenic spot selected to be reached during N actions.

2. The method for recommending scenic spot tour routes according to claim 1, characterized in that: The user's preference for scenic spot i is calculated based on scenic spot data and user preference behavior data The calculation formula is: in, , , , , , and are weight coefficients, Indicates the matching degree between the user's interest tag and the tag of scenic spot i, Indicates the matching degree between the scenic spots that the user wants to visit and the opening status of scenic spot i, Indicates the matching degree between the user's planned tour time period and the opening hours of scenic spot i, Indicates the degree of match between the time period when the user plans to dine and whether there is a restaurant at scenic spot i. represents the travel time from attraction k to attraction i, k represents the optimal attraction chosen by the user for the t-1th action, represents the flow of people at scenic spot i, Indicates that the user plans to watch a performance at attraction i.

3. The method for recommending scenic spot tour routes according to claim 1, characterized in that: Scenic spot data include: the label of each scenic spot, the opening status of each scenic spot, the opening hours of each scenic spot, whether each scenic spot has a restaurant, the time required to travel between two scenic spots, and the flow of people at the scenic spots.

4. The method for recommending scenic spot tour routes according to claim 1, characterized in that: User preference behavior data includes: user's interest tags, attractions the user wants to visit, the time period the user plans to browse, the time period the user plans to dine, and whether the user plans to watch performances at each attraction.

5. The method for recommending scenic spot tour routes according to claim 1, characterized in that: Step S1 also includes preprocessing, feature extraction and data normalization of the acquired scenic spot data and user preference behavior data; the preprocessing includes the processing of incomplete data and abnormal data.

6. A terminal device for recommending scenic spot tour routes, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 5 when executing the computer program.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 5 are implemented.

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