Tourism data management system and method based on multi-source data
By analyzing multi-source data, the location of public service facilities in scenic areas was optimized, which solved the problems of resource waste and service quality decline caused by manual site selection, and improved the service quality and tourist experience of scenic areas.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- YANGZHOU ZIZAI ISLAND ECO-TOURISM INVESTMENT DEV CO LTD
- Filing Date
- 2026-01-15
- Publication Date
- 2026-06-05
AI Technical Summary
In existing technologies, the placement of public service facilities in scenic areas relies on manual selection, leading to resource waste, reduced service quality, and negatively impacting the visitor experience.
By analyzing multi-source data, we can obtain a map of the scenic area and tourist routes. Combining tourist information and attraction appeal, we can determine differentiated configuration standards for public service facilities and optimize the location of facilities.
This has enabled the precise deployment of public service facilities, improving the quality of services and the visitor experience in the scenic area.
Smart Images
Figure CN122155148A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data management technology, specifically to a tourism data management system and method based on multi-source data. Background Technology
[0002] The essence of smart tourism is to promote the application of intelligent technology in the tourism industry, improve the management of scenic area tourism services, and optimize the modernization of tourism resources. With the rapid development of the tourism industry in recent years, various tourism service facilities are also constantly being improved. When traveling, people pay more attention to the natural and cultural environment of scenic spots. The cultural environment is mainly reflected in the services of scenic spots. In scenic spots with well-developed infrastructure, there are basically mobile public service facilities. At present, the placement of public service facilities mostly depends on manual selection. However, due to the wide range of scenic spots, the scattered distribution of attractions, and the strong subjectivity of manual site selection, it is easy to cause unreasonable placement of public service facilities. This will not only waste resources, but also directly reduce the quality of scenic area services and seriously affect the tourist experience. Summary of the Invention
[0003] The purpose of this invention is to provide a tourism data management system and method based on multi-source data to solve the problems raised in the prior art.
[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution: The tourism data management method based on multi-source data includes the following steps: Obtain a map of the scenic area and mark the areas of attractions; Obtain tourist routes and mark them on a map. Based on the duration and area of each attraction's visit, determine the attraction's attractiveness to tourists. Because the attractions within a scenic area have different appeals to different groups of people, their attractiveness to specific tourist groups varies significantly. This leads to differences in the depth of visit to each attraction for different groups. At the same time, there are also significant differences in the frequency and type of public service facilities used by different groups. Therefore, by combining basic tourist information with the functional positioning of the attractions, we can determine the differentiated configuration standards for public service facilities and the appropriate locations for their placement.
[0005] Historical usage records of public service facilities are retrieved, and corresponding user facial images and usage times are extracted. Based on the facial images of tourists who have completed identity verification, tagged users are obtained. Based on the basic information of the tagged users and the attraction's appeal to tourists, the degree of favoritism of the scenic area towards public service facilities is obtained. Since the scenic area contains not only tourists, but also staff, maintenance personnel, security personnel, etc., in order to ensure the reliability of the calculation data, it is necessary to extract tourists, i.e., mark users. Establish a planar coordinate system for the scenic area, set a spatial window and a window sliding step size, extract several feature points within the scenic area, and obtain the feature values of each feature point based on the scenic area's preference for public service facilities. Obtain the number of public service facilities currently planned to be deployed in the scenic area, divide the scenic area into several sub-areas, with the number of facilities being the same as the number of sub-areas, optimize and iterate the sub-areas based on the feature values of each feature point to obtain the final target area, obtain the target location corresponding to the target area, and deploy the public service facilities at the target location.
[0006] Furthermore, the degree of preference the scenic area places on public service facilities was obtained, including: The basic information of the marked users includes several indicators, and each indicator includes several elements. Based on the basic information of the marked users who have used public service facilities in the past, the probability of each element in each indicator is obtained. Obtain basic information about tourists within the scenic spot, and based on the elements corresponding to each tourist's various indicators and the probabilities corresponding to each element within each indicator, obtain the indicator matrix corresponding to the scenic spot. Based on the indicator matrix, the total number of visitors to the attraction, and the attraction's attractiveness to each type of visitor, the target value for the attraction is obtained as follows: Where M is the total number of tourists, and P is... m N represents the attraction of the attraction to the m-th tourist, N is the number of indicators, and W is the number of indicators. n It is the weight of the nth indicator. It represents the probability of the m-th tourist in relation to the element corresponding to the n-th indicator; The target value for each scenic spot is obtained and normalized. The normalized target value is used as the degree of bias of each scenic spot towards public service facilities. The purpose of normalization is to map the value to the range of 0 to 1, which can be obtained by using the existing sigmoid function. The normalization function is an existing technology and will not be described in detail here.
[0007] Furthermore, based on the duration and area visited by tourists at each attraction, the attraction's attractiveness to tourists is determined, including: Obtain the playable areas and obstructing components within the attraction. Obstructing components are physical obstacles and structures that block the view of tourists and divide the playable areas. Obtain the tourist's play route and, based on the areas passed through during the play, obtain the total exploration area of the tourist during the visit to the attraction. The duration of tourists' visits to attractions is obtained to determine the attraction's attractiveness to tourists. Where R0 is the area of the intersection between the total exploration area and the playable area, R1 is the area of the playable area of the attraction, e is a natural constant, and D is the duration of the visitor's stay at the attraction.
[0008] Formula y=1-e -x Let y be a function of x, where y takes values from 0 to 1 when x is greater than 0, and y increases as x increases. In this scheme, the greater the time tourists spend at the attraction D and the larger the area R0 of the intersecting region, the stronger the tourists' desire to visit the attraction. Therefore, the attraction should be more attractive to tourists. So, in this scheme, it is reasonable and reliable to design the attraction P in this way, and the value of P is in the range of 0 to 1.
[0009] Furthermore, based on the areas traversed during the visit, the total exploration area of the tourist during their visit to the attraction is obtained, including: Set the maximum viewing angle and maximum viewing distance for tourists, extract the tourist's forward direction and location at time T, and delineate an area that covers the maximum viewing angle, extends to the maximum viewing distance, and is not obstructed by any obstructing components, with the location as the origin and the forward direction as the central axis. This area serves as the tourist's exploration area at time T. By combining several times, the total exploration area of the tourist during their visit to the attraction is obtained.
[0010] Furthermore, the target location corresponding to the target area is obtained, including: Add the feature values of all feature points in the sub-region to obtain the target value of the sub-region. Calculate the target value of each sub-region and the variance between the target values. If the variance is less than the preset variance threshold, each sub-region is taken as the target region. Extract several location points from the target region. The location with the smallest sum of distances to all location points in the area where public service facilities are allowed to be placed is taken as the target location of the target region. If the variance is not less than the variance threshold, the area of each sub-region is iterated according to the rule that the smaller the target value of the sub-region, the larger the iterated area of the sub-region. The iterated sub-regions are obtained, the target value of each sub-region is calculated, the variance between the target values is calculated and compared with the variance threshold, until the variance is less than the variance threshold, the iteration stops and the final target region is obtained.
[0011] Furthermore, the tagged users are obtained, including: Visitors must undergo identity verification upon entering and leaving the scenic area. Images of visitors who have completed identity verification and entered the scenic area but have not yet left are obtained. Object detection algorithms are used to capture the visitors' facial images and record them into the visitor face database. If a user's facial image matches a facial image in the visitor face database, the corresponding user will be marked as a user.
[0012] Furthermore, based on the scenic area's preference for public service facilities, the characteristic values of each feature point are obtained, including: Based on the spatial window and the window sliding step size, slide the window at equal intervals to extract several feature points within the scenic area; If feature point q is within the scenic area, the degree of preference of the corresponding scenic area for public service facilities will be used as the feature value of feature point q. If feature point q is not within the scenic area, the feature value of feature point q is obtained based on the degree of preference of each scenic spot for public service facilities.
[0013] Furthermore, based on the degree of preference of each scenic spot for public service facilities, the eigenvalues of the feature point q are obtained, including: Obtain the nearest distance between feature point q and each scenic spot, and set the feature weights of each scenic spot according to the rule that the smaller the nearest distance, the greater the weight; based on the bias degree and feature weight of each scenic spot, obtain the feature value of feature point q.
[0014] Since the number of facilities is the same as the number of target areas, the relationship between the target value and the area for the allocation of public service facilities is as follows: when the target value is relatively small, it indicates that the supply and demand for public service facilities in the area is weak; when the target value is relatively large, it indicates that the supply and demand for public service facilities in the area is strong. Therefore, for this scheme, when the target value deviation between each sub-area is small, that is, the variance is small, it means that the supply and demand for public service facilities in each sub-area is relatively balanced, which indicates that the division of sub-areas is reasonable and can be used as the final target area. However, when the target value deviation between each target area is large, that is, the variance is large, it indicates that the division of sub-areas is unreasonable. In this case, the sub-areas and their target values should be obtained again according to the rule that the smaller the target value of the sub-area, the larger the iterative area of the sub-area. This iterative cycle should be repeated until the final target area is obtained.
[0015] A tourism data management system based on multi-source data includes a memory and a processor, as well as a computer program stored in the memory and running on the processor. The processor is coupled to the memory, and when executing the computer program, the processor implements the aforementioned tourism data management method based on multi-source data. Since this tourism data management method based on multi-source data has already been described in detail above, it will not be repeated here.
[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention provides a tourism data management system and method based on multi-source data, including: obtaining a plan view of the scenic area, marking scenic spot areas, obtaining tourist routes, marking them on the plan view, and obtaining the attraction of scenic spots to tourists; retrieving usage records of public service facilities, obtaining marked users, obtaining basic information of marked users, and obtaining the degree of preference of the scenic area for public service facilities; extracting several feature points within the scenic area and obtaining the feature values of each feature point; obtaining the final target area, obtaining the target location corresponding to the target area, and deploying public service facilities at the target location. This invention, by analyzing scenic spot visit information, tourist information, and public service facility usage records within the scenic area, obtains the target location for the deployment of public service facilities, which aligns with the actual situation of the scenic area and public service facilities, achieving precise adaptation of facility deployment to the actual situation of the scenic area, helping to improve the service quality of the scenic area and enhance the tourist experience. Attached Figure Description
[0017] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a flowchart illustrating the tourism data management method based on multi-source data according to the present invention. Detailed Implementation
[0019] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.
[0020] Example: Figure 1 As shown, this invention provides a technical solution for tourism data management based on multi-source data, including the following steps: It should be noted that tourist attractions typically include several scenic spots. A scenic area is an independent tourist area with complete tour functions, clearly defined management boundaries, and supporting services, such as forest parks, ancient town resorts, and theme parks. Scenic spots are the core units within a scenic area that have tour value, such as viewing platforms, lakeside pavilions, and amusement areas. Public service facilities are the hardware facilities within a scenic area that provide tour assistance, emergency support, and rest and supplies, such as vending machines, mobile stalls, and emergency medical points. Because the attractiveness of different scenic spots varies according to different tourist preferences, their appeal to specific tourist groups is significantly differentiated, leading to differences in the depth of experience at each scenic spot among different groups. Simultaneously, the frequency and type of use of public service facilities also show significant differences among different groups. Therefore, by combining basic tourist information with the functional positioning of scenic spots, differentiated configuration standards for public service facilities can be determined, and appropriate locations for their placement can be identified.
[0021] Obtain a map of the scenic area and mark the areas of attractions; Identify the playable areas and obstructions within the attraction. Obstructions are physical barriers and structures that block the view of visitors and divide the playable areas; in this case, obstructions within the attraction include walls, trees, large sculptures, etc.
[0022] The tourist route is obtained and marked on a map. Explicit authorization from the tourists is required before collecting real-time route data during their visit to the scenic area. This data is anonymized. Route data collection can be achieved by deploying portable positioning terminals on tourists, such as Bluetooth beacons, GPS wristbands, or authorized positioning functions based on the tourists' mobile devices.
[0023] Set the maximum viewing angle and maximum viewing distance for tourists, extract the tourist's forward direction and location at time T, and delineate an area that covers the maximum viewing angle, extends to the maximum viewing distance, and is not obstructed by any obstructing components, with the location as the origin and the forward direction as the central axis. This area serves as the tourist's exploration area at time T. By combining several times, the total exploration area of the tourist during their visit to the attraction is obtained. In this embodiment, the maximum viewing angle is the angle of the tourist's field of vision coverage in the horizontal direction, and the maximum viewing distance is the farthest distance at which the tourist can identify the target in front. In this embodiment, the maximum viewing angle is 90° and the maximum viewing distance is 5 meters. Then, the tourist's exploration area at time T is: taking the tourist's location at time T as the origin and the forward direction as the central axis, a fan-shaped space with a horizontal angle of 45° on both sides and a radial length of 5 meters is defined. After removing the areas in the fan-shaped space that are blocked by obstructing components and cannot form an effective viewing range, the remaining space is the exploration area.
[0024] The duration of tourists' visits to attractions is obtained to determine the attraction's attractiveness to tourists. Where R0 is the area of the intersection between the total exploration area and the playable area, R1 is the area of the playable area of the attraction, e is a natural constant, and D is the duration of the visitor's stay at the attraction.
[0025] Formula y=1-e -x Let y be a function of x, where y takes values from 0 to 1 when x is greater than 0, and y increases as x increases. In this scheme, the greater the time tourists spend at the attraction D and the larger the area R0 of the intersecting region, the stronger the tourists' desire to visit the attraction. Therefore, the attraction should be more attractive to tourists. So, in this scheme, it is reasonable and reliable to design the attraction P in this way, and the value of P is in the range of 0 to 1.
[0026] Since the scenic area contains not only tourists but also staff, maintenance personnel, security personnel, and others, it is necessary to extract tourists, i.e., label users, to ensure the reliability of the calculation data. This is done by comparing tourists' faces, as detailed below: Surveillance equipment is deployed around public service facilities to retrieve historical usage records of these facilities, extract corresponding user facial images and usage times, and require tourists to undergo identity verification when entering and leaving the scenic area. Images of tourists who have completed identity verification and entered the scenic area but have not yet left are obtained, and object detection algorithms are used to capture tourists' facial images and record them in the tourist face database. If a user's facial image matches a facial image in the tourist facial database, the corresponding user is designated as a labeled user, and the tourist information that matches the image in the tourist facial database is used as the basic information of the labeled user. The basic information of the labeled user includes several indicators, each of which includes several elements. Based on the basic information of the labeled users who have historically used public service facilities, the probability of each element within each indicator is obtained. Facial image matching can be achieved using existing algorithms such as FaceNet and ArcFace, which will not be elaborated on here.
[0027] In this embodiment, the indicators include gender and age group. The gender indicator has two elements: male and female. The age group indicator has five elements: children (0-12 years old), adolescents (13-17 years old), young adults (18-35 years old), middle-aged (36-59 years old), and elderly (60 years old and above). Here's an example of how to obtain the probabilities corresponding to each element within each indicator: Assume there are 100 marked users who have historically used public service facilities, of whom 40 are male and 60 are female. For the gender indicator, the probability corresponding to the male element is 0.4, and the probability corresponding to the female element is 0.6. If, among the 100 marked users, there are 10 children, 30 teenagers, 30 young adults, 20 middle-aged individuals, and 10 elderly individuals, then for the age group indicator, the probability corresponding to the child element is 0.1, the probability corresponding to the teenager element is 0.3, the probability corresponding to the young adult element is 0.3, the probability corresponding to the middle-aged element is 0.2, and the probability corresponding to the elderly element is 0.1.
[0028] Obtain basic information about tourists within the scenic spot, and based on the elements corresponding to each tourist's various indicators and the probabilities corresponding to each element within each indicator, obtain the indicator matrix corresponding to the scenic spot. For example, if there are four tourists at a tourist attraction, Y1, Y2, Y3, and Y4, where tourist Y1 is a male in his adolescence, tourist Y2 is a female in her youth, tourist Y3 is a female in her middle age, and tourist Y4 is a male in his childhood, then the corresponding indicator matrix for the tourist attraction would be: ; Based on the indicator matrix, the total number of visitors to the attraction, and the attraction's attractiveness to each type of visitor, the target value for the attraction is obtained as follows: Where M is the total number of tourists, and P is... m N represents the attraction of the attraction to the m-th tourist, N is the number of indicators, and W is the number of indicators. n It is the weight of the nth indicator. It is the probability of the m-th tourist in terms of the element corresponding to the n-th indicator. For example, for tourist Y1, tourist Y1 is male and in his adolescence, then his probability in terms of the element corresponding to the gender indicator is 0.4, and his probability in terms of the element corresponding to the age group indicator is 0.3. And so on, we can get the probability of each tourist in terms of the element corresponding to each indicator. In this embodiment, M=4, N=2, and the attractiveness of tourists Y1, Y2, Y3, and Y4 are assumed to be P1=0.6, P2=0.8, P3=0.9, and P4=0.5, respectively. The gender weight is set to W1=0.6, and the age group weight is set to W2=0.4. Based on the indicator matrix, the total number of tourists, and the attractiveness of each tourist, the target value of the attraction is obtained as: [0.6(0.6×0.4+0.4×0.3)+0.8(0.6×0.6+0.4×0.3)+0.9(0.6×0.6+0.4×0.2)+0.5(0.6×0.4+0.4×0.1)] / 4=(0.216+0.384+0.396+0.14) / 4=0.284.
[0029] The target value for each scenic spot is obtained and normalized. The normalized target value is used as the degree of bias of each scenic spot towards public service facilities. The purpose of normalization is to map the value to the range of 0 to 1, which can be obtained by using the existing sigmoid function. The normalization function is an existing technology and will not be described in detail here.
[0030] A planar coordinate system for the scenic area is established, and a spatial window and a sliding step size are set. Based on the spatial window and the sliding step size, the window is slid at equal intervals to extract several feature points within the scenic area. In this embodiment, the size of the spatial window is set to 5m×5m. Using this window size as the sliding step size, the scenic area is divided into grids using the equal-interval sliding window method. After the division is completed, the geometric center point of each grid is extracted as a feature point, and finally a set of several feature points covering the scenic area is obtained.
[0031] If feature point q is within the scenic area, the degree of preference of the corresponding scenic area for public service facilities will be used as the feature value of feature point q. If feature point q is not within the scenic area, obtain the nearest distance between feature point q and each scenic spot. Assign feature weights to each scenic spot according to the rule that the smaller the nearest distance, the greater the weight. Based on the bias and feature weight of each scenic spot, obtain the feature value of feature point q. Calculate the product of the bias and corresponding feature weight of all scenic spots, and sum all the product results to obtain the feature value of feature point q.
[0032] Obtain the number of public service facilities currently planned to be placed in the scenic area, divide the scenic area into several sub-areas, and the number of facilities is the same as the number of sub-areas; add the feature values of all feature points in the sub-area to obtain the target value of the sub-area, calculate the target value of each sub-area, calculate the variance between the target values, if the variance is less than the preset variance threshold, take each sub-area as the target area, extract several location points from the target area, and take the location with the smallest sum of distances to all location points in the area where public service facilities are allowed to be placed as the target location of the target area; If the variance is not less than the variance threshold, the area of each sub-region is iterated according to the rule that the smaller the target value of the sub-region, the larger the iterated area of the sub-region. The iterated sub-regions are obtained, the target value of each sub-region is calculated, the variance between the target values is calculated and compared with the variance threshold, until the variance is less than the variance threshold, the iteration stops, the final target area is obtained, and the public service facilities are deployed at the target location.
[0033] Since the number of facilities is the same as the number of target areas, the relationship between the target value and the area for the allocation of public service facilities is as follows: when the target value is relatively small, it indicates that the supply and demand for public service facilities in the area is weak; when the target value is relatively large, it indicates that the supply and demand for public service facilities in the area is strong. Therefore, for this scheme, when the target value deviation between each sub-area is small, that is, the variance is small, it means that the supply and demand for public service facilities in each sub-area is relatively balanced, which indicates that the division of sub-areas is reasonable and can be used as the final target area. However, when the target value deviation between each target area is large, that is, the variance is large, it indicates that the division of sub-areas is unreasonable. In this case, the sub-areas and their target values should be obtained again according to the rule that the smaller the target value of the sub-area, the larger the iterative area of the sub-area. This iterative cycle should be repeated until the final target area is obtained.
[0034] This embodiment also provides a tourism data management system based on multi-source data, including a memory and a processor, as well as a computer program stored in the memory and running on the processor. The processor is coupled to the memory, and the processor implements the aforementioned tourism data management method based on multi-source data when executing the computer program. Since this tourism data management method based on multi-source data has already been described in detail above, it will not be repeated here.
[0035] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0036] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
[0037] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A tourism data management method based on multi-source data, characterized in that, Includes the following steps: Obtain a map of the scenic area and mark the areas of attractions; Obtain tourist routes and mark them on a map. Based on the duration and area of each attraction's visit, determine the attraction's attractiveness to tourists. Historical usage records of public service facilities are retrieved, and corresponding user facial images and usage times are extracted. Based on the facial images of tourists who have completed identity verification, tagged users are obtained. Based on the basic information of the tagged users and the attraction's appeal to tourists, the degree of favoritism of the scenic area towards public service facilities is obtained. Establish a planar coordinate system for the scenic area, set a spatial window and a window sliding step size, extract several feature points within the scenic area, and obtain the feature values of each feature point based on the scenic area's preference for public service facilities. The plan is to obtain the number of public service facilities to be deployed in the scenic area, divide the scenic area into several sub-areas, and optimize the sub-areas based on the feature values of each feature point to obtain the final target area. The target location corresponding to the target area is then obtained, and the public service facilities are deployed at the target location.
2. The tourism data management method based on multi-source data according to claim 1, characterized in that, The degree of preference the scenic area places on public service facilities includes: The basic information of the marked users includes several indicators, and each indicator includes several elements. Based on the basic information of the marked users who have used public service facilities in the past, the probability of each element in each indicator is obtained. Obtain basic information about tourists within the scenic spot, and based on the elements corresponding to each tourist's various indicators and the probabilities corresponding to each element within each indicator, obtain the indicator matrix corresponding to the scenic spot. Based on the indicator matrix, the total number of visitors to the attraction, and the attraction's attractiveness to each type of visitor, the target value for the attraction is obtained as follows: Where M is the total number of tourists, and P is... m N represents the attraction of the attraction to the m-th tourist, N is the number of indicators, and W is the number of indicators. n It is the weight of the nth indicator. It represents the probability of the m-th tourist in relation to the element corresponding to the n-th indicator; The target value for each scenic spot is obtained and normalized. The normalized target value is used as the degree of preference of each scenic spot for public service facilities.
3. The tourism data management method based on multi-source data according to claim 1, characterized in that, The attractiveness of each attraction to tourists is determined based on the duration and area visited, including: The system acquires the playable areas and obstructing components within the attraction, where the obstructing components are physical obstacles and structures that block the visitor's view and divide the playable areas; it also acquires the visitor's route and, based on the areas traversed during the visit, obtains the total exploration area of the visitor during their visit to the attraction. The duration of tourists' visits to attractions is obtained to determine the attraction's attractiveness to tourists. Where R0 is the area of the intersection between the total exploration area and the playable area, R1 is the area of the playable area of the attraction, e is a natural constant, and D is the duration of the visitor's stay at the attraction.
4. The tourism data management method based on multi-source data according to claim 3, characterized in that, Based on the areas visited during the tour, the total area explored by the tourist during their visit to the attraction is obtained, including: Set the maximum viewing angle and maximum viewing distance for tourists, extract the tourist's forward direction and location at time T, and delineate an area that covers the maximum viewing angle, extends to the maximum viewing distance, and is not obstructed by any obstructing components, using the location as the origin and the forward direction as the central axis. This area serves as the tourist's exploration area at time T. By combining several times, the total exploration area of the tourist during their visit to the scenic spot is obtained.
5. The tourism data management method based on multi-source data according to claim 1, characterized in that, Obtain the target location corresponding to the target area, including: The feature values of all feature points in the sub-region are added together to obtain the target value of the sub-region. The target value of each sub-region is calculated, and the variance between the target values is calculated. If the variance is less than a preset variance threshold, each sub-region is taken as the target region. Several location points are extracted from the target region. The location with the smallest sum of distances to all location points in the area where public service facilities are allowed to be placed is taken as the target location of the target region. If the variance is not less than the variance threshold, the area of each sub-region is iterated according to the rule that the smaller the target value of the sub-region, the larger the iterated area of the sub-region. The iterated sub-regions are obtained, the target value of each sub-region is calculated, the variance between the target values is calculated and compared with the variance threshold, until the variance is less than the variance threshold, the iteration stops and the final target region is obtained.
6. The tourism data management method based on multi-source data according to claim 1, characterized in that, The tagged users obtained include: Visitors must undergo identity verification upon entering and leaving the scenic area. Images of visitors who have completed identity verification and entered the scenic area but have not yet left are obtained. Object detection algorithms are used to capture the visitors' facial images and record them into the visitor face database. If a user's facial image matches a facial image in the visitor facial database, the corresponding user will be designated as the marked user, and the visitor information that matched in the visitor facial database will be used as the basic information of the marked user.
7. The tourism data management method based on multi-source data according to claim 1, characterized in that, Based on the scenic area's preference for public service facilities, the characteristic values of each characteristic point are obtained, including: Based on the spatial window and the window sliding step size, slide the window at equal intervals to extract several feature points within the scenic area; If feature point q is within the scenic area, the degree of preference of the corresponding scenic spot for public service facilities will be used as the feature value of feature point q. If feature point q is not within the scenic area, the feature value of feature point q is obtained based on the degree of preference of each scenic spot for public service facilities.
8. The tourism data management method based on multi-source data according to claim 7, characterized in that, Based on the degree of preference of each scenic spot for public service facilities, the feature value of the feature point q is obtained, including: Obtain the nearest distance between feature point q and each scenic spot, and set the feature weight of each scenic spot according to the rule that the smaller the nearest distance, the greater the weight; based on the bias degree and feature weight of each scenic spot, obtain the feature value of the feature point q.
9. A tourism data management system based on multi-source data, characterized in that, It includes a memory and a processor, as well as a computer program stored in the memory and running on the processor, wherein the processor is coupled to the memory, and the processor implements the tourism data management method based on multi-source data as described in any one of claims 1-8 when executing the computer program.