AI accompanying service method and device applied to scenic spot tourism

Abstract

The invention relates to the technical field of tourism information service, and discloses an AI accompanying service method and device applied to scenic spot tourism, and the method comprises the steps: obtaining scenic spot traffic information and user position information; calculating the commuting time of each road from the current starting point to the recommended scenic spot according to the scenic spot traffic information and the user position information; selecting the road with the shortest time as a recommended commuting route according to the commuting time; acquiring people number distribution information around the scenic spot, and calculating a people number distribution thermodynamic diagram according to the people number distribution information; according to the number of people distribution thermodynamic diagram, carrying out optimal playing route analysis to obtain a recommended playing route; and sending the people number distribution thermodynamic diagram, the recommended commuting route and the recommended playing route to a display device, and visually displaying to a user. The method can recommend the playing route according to the flow of people, and improves the use and playing experience of the user.

Description

Technical Field

[0001] The present invention relates to the technical field of tourism information services, and in particular to an AI accompanying service method and device for scenic spot tourism. Background Art

[0002] At present, with the improvement of living standards and the improvement of transportation facilities, more and more people choose to travel as a way of entertainment during holidays. In order to meet the growing demand for tourism, the tourism industry is also constantly developing and innovating. From traditional group tours to current free travel and customized tours, tourism products are becoming more and more diversified, with more emphasis on personalized services and in-depth experience. At the same time, with the development of Internet technology, the emergence of online booking platforms has made travel more convenient. People can easily find destination information, book air tickets and hotels, and even plan every detail of the itinerary in advance through the Internet.

[0003] In one prior art, a method and system for determining the location in a scenic spot based on a smartphone uses a smart terminal device to collect images and sounds, and analyzes and processes the collected images or sounds to identify the scenic spot at the current location. Once the user's location is determined, the system automatically pushes information such as the historical background, cultural stories, and practical suggestions of the relevant scenic spots based on their interests and current situation. Navigation services are provided to help tourists plan the best route to the next destination.

[0004] During the peak tourist season, there are many tourists in various scenic spots. Users often learn about the flow of people when they are about to arrive at the scenic spot. If there are too many people, it will cause inconvenience and negative impact on consumers. The route recommendation scheme in the existing technology lacks the function of reminding users of the real-time flow of people in advance, resulting in poor user experience. Summary of the invention

[0005] The present invention provides an AI accompanying service method, device, electronic device and storage medium for scenic spot tourism, so as to recommend tourist routes according to the flow of people and improve the user's use and travel experience.

[0006] In the first aspect, in order to solve the above technical problems, the present invention provides an AI accompanying service method for scenic spot tourism, comprising:

[0007] Obtain scenic spot traffic information and user location information;

[0008] Calculate the commuting time of each road from the current starting point to the recommended scenic spot according to the scenic spot traffic information and the user location information;

[0009] Selecting the shortest commuting route as a recommended commuting route based on the commuting time;

[0010] Obtaining the population distribution information around the scenic spot, and calculating the population distribution heat map according to the population distribution information;

[0011] Analyze the optimal travel route according to the population distribution heat map to obtain a recommended travel route;

[0012] The population distribution heat map, the recommended commuting route and the recommended sightseeing route are sent to a display device so as to be displayed to the user in a visual manner.

[0013] In an optional implementation, the calculating the commuting time of each road from the current starting point to the recommended scenic spot according to the scenic spot traffic information and the user location information includes:

[0014] The scenic spot traffic information includes the smooth travel time of each road, the waiting time at the traffic light intersection, and the degree of traffic congestion;

[0015] Calculate the commuting time from your starting point to the recommended attractions using the following formula:

[0016]

[0017] Among them, T total is the commuting time of the route, T i is the commuting time of section i on the commuting route, t i is the smooth travel time of road section i, w i is the sum of the waiting time at all traffic lights on section i, c i is the traffic congestion coefficient.

[0018] In an optional implementation, the acquiring of the population distribution information around the scenic spot and calculating the population distribution heat map according to the population distribution information includes:

[0019] Divide the scenic spot map into several grids according to preset widths;

[0020] The number of people in each of the grids is summed according to the number distribution information to obtain the number of people in each grid;

[0021] Calculate the population distribution density according to the number of people in the grid;

[0022] Mapping the population distribution density to a color value through a mapping function of a heat map, and constructing a population distribution heat map based on all color values;

[0023] The number distribution information includes: information collection time, personnel number, and personnel's longitude and latitude;

[0024] The population distribution density is calculated by the following formula:

[0025]

[0026] Where D is the population distribution density, is the sign of partial derivative, x is the number of people in the grid, S is the area of ​​the grid;

[0027] The mapping function of the population distribution heat map is calculated using the following formula:

[0028] f(D)=(R,G,B)

[0029] Among them, f(D) is the mapping function, (R,G,B) is a three-dimensional vector, R, G, B represent the color component values ​​of red, green, and blue respectively, f(0) = (0,0,0), f(100) = (255,0,0).

[0030] In an optional implementation, performing optimal tour route analysis according to the number distribution heat map to obtain a recommended tour route includes:

[0031] The crowdedness of the tour route is calculated by the following formula:

[0032] Cr=∫th(l)dl

[0033]

[0034] Among them, Cr is the congestion degree, D(l) is the population distribution density of section l on the tour route, and D threshold is the population density threshold, th(l) is the congestion judgment function of section l on the tour route;

[0035] When the congestion degree is greater than a preset congestion degree threshold, the play route corresponding to the congestion degree is used as a congested route;

[0036] Sorting all the play routes whose congestion degree is less than a preset congestion degree threshold according to the congestion degree;

[0037] The tour route with the least congestion is used as the recommended tour route;

[0038] When the recommended tour route is a crowded route, a reminder message including the crowded tourist spot is fed back to the user.

[0039] In an optional implementation, after sending the population distribution heat map, the recommended commuting route, and the recommended travel route to a display device for display to the user, the method further includes:

[0040] Obtaining user feedback information, the user feedback information includes: reselecting recommended attractions and reselecting routes;

[0041] When the user chooses to reselect an attraction, the recommended attractions are pushed to the user again;

[0042] When the user chooses to reselect a route, the recommended tour route is used as the first route;

[0043] An optimal travel route analysis is performed based on the population distribution heat map, and a second route with a lower priority than the first route is selected as a recommended travel route.

[0044] In an optional implementation, before calculating the commuting time of each road from the current starting point to the recommended scenic spot according to the scenic spot traffic information and the user location information, the method further includes:

[0045] Get user's historical gaming information;

[0046] According to the user location information, all scenic spots within a preset range are sorted by commuting time;

[0047] According to the user's historical travel information, filter out the unvisited attractions that the user has not visited;

[0048] The scenic spot with the shortest commuting time is selected as the recommended scenic spot among the unvisited scenic spots.

[0049] In an optional implementation, after sending the population distribution heat map, the recommended commuting route, and the recommended sightseeing route to a display device for display to a user, the method further includes:

[0050] Obtain user voice consultation instructions;

[0051] Generate a reply statement according to the voice consultation instruction combined with a pre-stored tourist attraction resource database;

[0052] The answer sentence is outputted by voice.

[0053] In a second aspect, the present invention provides an AI accompanying service device for scenic spot tourism, comprising:

[0054] Data acquisition module, used to obtain scenic spot traffic information and user location information;

[0055] A commuting time module, used to calculate the commuting time of each road from the current starting point to the recommended scenic spot according to the scenic spot traffic information and the user location information;

[0056] A commuting route module, used for selecting a road with the shortest commuting time as a recommended commuting route according to the commuting time;

[0057] A heat map module is used to obtain the population distribution information around the scenic spot and calculate the population distribution heat map according to the population distribution information;

[0058] A travel route module, used to analyze the optimal travel route according to the number distribution heat map, and obtain a recommended travel route;

[0059] The result display module is used to send the population distribution heat map, the recommended commuting route and the recommended sightseeing route to a display device for display to the user.

[0060] In the third aspect, the present invention also provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the above-mentioned AI accompanying service methods for scenic spot tourism.

[0061] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned AI accompanying service methods for scenic spot tourism.

[0062] Compared with the prior art, the present invention has the following beneficial effects: the present invention discloses an AI accompanying service method and device for scenic spot tourism, the method comprising obtaining scenic spot traffic information and user location information; calculating the commuting time of each road from the current starting point to the recommended scenic spot according to the scenic spot traffic information and the user location information; selecting the road with the shortest time as the recommended commuting route according to the commuting time; obtaining the population distribution information around the scenic spot, and calculating the population distribution heat map according to the population distribution information; performing the optimal tour route analysis according to the population distribution heat map to obtain the recommended tour route; sending the population distribution heat map, the recommended commuting route and the recommended tour route to the display device for display to the user. The present invention accurately calculates the commuting time of the route from the starting point to the recommended scenic spot by comprehensively considering the smooth driving time of each road, the waiting time at the traffic light intersection and the degree of traffic congestion. Compared with traditional technologies, this method can provide more accurate travel time predictions and facilitate reasonable itinerary arrangements. Furthermore, the present invention calculates the population distribution density and generates a population distribution heat map, as well as analyzes the optimal tour route. Compared with traditional technologies, this method can more accurately identify and predict crowded areas in scenic spots. By calculating the population distribution density of each road section and mapping it to a color-coded heat map, tourists can intuitively see which areas are crowded. In addition, by calculating the congestion of the tour route and setting the congestion threshold, the system can automatically determine and recommend the tour route with the lowest congestion, thereby helping tourists avoid peak areas and optimize the tour experience. When the recommended route is congested, the system will also feedback the congestion information of the scenic spot to the user, so that tourists can adjust their itinerary in time and reduce the inconvenience caused by waiting and congestion.

[0063] At the same time, the present invention further optimizes the service experience through user feedback information on the basis of providing a visualized heat map of the number of people distributed, recommended commuting routes and recommended tourist routes. When the user chooses to reselect an attraction, the system will re-push the attractions that have not been visited based on the user's historical travel information and location information, and select the scenic spot with the shortest commuting time as the new recommended attraction. If the user chooses to reselect the route, the system will use the original recommended tourist route as the first route, and analyze the second route with a lower priority as an alternative recommended tourist route based on the heat map of the number of people distributed as to meet the user's needs for different routes. In addition, the method also supports users to obtain information through voice consultation instructions. The system will generate an answer statement in combination with the pre-stored tourist resource database of attractions, and output it through voice, which can optimize the decision in combination with user feedback.

[0064] In summary, this method obtains the traffic information of scenic spots and the location information of users, calculates and recommends the shortest commuting route, and generates a heat map based on the distribution information of the number of people around the scenic spots to analyze and provide the optimal travel route. Compared with traditional technologies, this method can recommend travel routes based on the flow of people, improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a flowchart of an AI accompanying service method for scenic spot tourism provided by the first embodiment of the present invention;

[0066] Figure 2 It is a structural schematic diagram of an AI accompanying service device for scenic spot tourism provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] At present, with the improvement of living standards and the improvement of transportation facilities, more and more people choose to travel as a way of entertainment during holidays. In order to meet the growing demand for tourism, the tourism industry is also constantly developing and innovating. From traditional group tours to current free travel and customized tours, tourism products are becoming more and more diversified, with more emphasis on personalized services and in-depth experience. At the same time, with the development of Internet technology, the emergence of online booking platforms has made travel more convenient. People can easily find destination information, book air tickets and hotels, and even plan every detail of the itinerary in advance through the Internet.

[0069] In one prior art, a method and system for determining the location in a scenic spot based on a smartphone uses a smart terminal device to collect images and sounds, and analyzes and processes the collected images or sounds to identify the scenic spot at the current location. Once the user's location is determined, the system automatically pushes information such as the historical background, cultural stories, and practical suggestions of the relevant scenic spots based on their interests and current situation. Navigation services are provided to help tourists plan the best route to the next destination.

[0070] During the peak tourist season, there are many tourists in various scenic spots. Users often learn about the flow of people when they are about to arrive at the scenic spot. If there are too many people, it will cause inconvenience and negative impact on consumers. The route recommendation scheme in the existing technology lacks the function of reminding users of the real-time flow of people in advance, resulting in poor user experience.

[0071] For the above problems, refer to Figure 1 The first embodiment of the present invention provides an AI accompanying service method for scenic spot tourism, comprising the following steps:

[0072] S11, obtaining scenic spot traffic information and user location information;

[0073] S12, calculating the commuting time of each road from the current starting point to the recommended scenic spot according to the scenic spot traffic information and the user location information;

[0074] S13, selecting a road with the shortest commuting time as a recommended commuting route according to the commuting time;

[0075] S14, obtaining the population distribution information around the scenic spot, and calculating the population distribution heat map according to the population distribution information;

[0076] S15, analyzing the optimal travel route according to the heat map of the number of people distribution to obtain a recommended travel route;

[0077] S16, sending the population distribution heat map, the recommended commuting route and the recommended sightseeing route to a display device for display to the user.

[0078] In step S11, scenic spot traffic information and user location information are obtained.

[0079] In one implementation, the map software is called through the API interface to obtain the user's location information and the entire map information for subsequent location analysis. Through the data of the big data platform, real-time traffic conditions, including road length and traffic light time, can be obtained for subsequent commuting time calculation.

[0080] In step S12, the commuting time of each road from the current departure point to the recommended scenic spot is calculated based on the scenic spot traffic information and the user location information.

[0081] In one embodiment, the scenic spot traffic information includes the smooth travel time of each road, the waiting time at the traffic light intersection, and the degree of traffic congestion;

[0082] Calculate the commuting time from your starting point to the recommended attractions using the following formula:

[0083]

[0084] Among them, T total is the commuting time of the route, T i is the commuting time of section i on the commuting route, t i is the smooth travel time of road section i, w i is the sum of the waiting time at all traffic lights on section i, c i is the traffic congestion coefficient.

[0085] It is worth noting that the smooth driving time refers to the time required for a vehicle to pass through the road section under ideal conditions, that is, without any external interference (such as traffic congestion, waiting at traffic lights). The waiting time at a traffic light intersection refers to the total time a vehicle needs to wait for a traffic light when passing through the road section. The traffic congestion coefficient is a key parameter used to quantify the impact of traffic congestion on commuting time. The traffic congestion coefficient reflects the degree to which the vehicle's speed is reduced relative to the smooth speed under traffic congestion. If the traffic congestion coefficient is 0, it means there is no congestion; if the traffic congestion coefficient is greater than 0, it means there is congestion. The traffic congestion coefficient collects real-time traffic flow data through sensors or cameras installed on the road, and analyzes vehicle density and speed. And it is calculated in combination with the historical traffic congestion time. In the present invention, the traffic congestion coefficient is predicted based on information such as time, road section length, historical traffic, and real-time traffic through a linear regression model. The model training process includes: historical traffic comes from the public historical traffic data database, and a linear regression model is established through feature extraction, including time, traffic flow, etc.; the maximum likelihood estimation is used to estimate the model parameters to complete the model training. Based on the established and verified model, the future traffic congestion coefficient is predicted.

[0086] In another implementation method, the travel time index method is used to calculate the traffic congestion coefficient. First, the free flow speed is calculated, which refers to the maximum speed that a vehicle can reach on a specific road section without traffic congestion. Then the actual driving speed is measured by the traffic monitoring system, which refers to the actual driving speed of the vehicle at a specific time and on the road section. The traffic congestion coefficient refers to the ratio of the driving time under congestion conditions to the driving time under free flow conditions.

[0087] In step S13, a road with the shortest commuting time is selected as a recommended commuting route according to the commuting time.

[0088] It is worth noting that the present invention will first obtain multiple commuting route options according to the shortest path algorithm, and then calculate the specific commuting time through the commuting time algorithm. This method uses the route with the shortest commuting time as the recommended commuting route.

[0089] In one embodiment, before calculating the commuting time of each road from the current starting point to the recommended scenic spot according to the scenic spot traffic information and the user location information, it also includes: obtaining the user's historical travel information; sorting the commuting time of all scenic spots within a preset range according to the user's location information; filtering out the unvisited scenic spots that the user has not visited according to the user's historical travel information; and selecting the scenic spot with the shortest commuting time as the recommended scenic spot among the unvisited scenic spots. The historical travel records stored in the database are queried through the user's account information. These records include the scenic spots that the user has visited, the date of visit, the length of stay, etc. The commuting time of all scenic spots within the preset range is queried using the user's real-time location information. This can be achieved through the map service API to obtain the estimated travel time from the user's current location to each scenic spot. The user's historical travel information is compared with the list of scenic spots within the preset range to filter out the scenic spots that the user has not visited. This step is to provide a new travel experience and avoid repeating the recommendation of scenic spots that the user has visited. Among the filtered unvisited scenic spots, the scenic spot with the shortest commuting time is selected as the recommended scenic spot based on the previously calculated commuting time.

[0090] In step S14, the population distribution information around the scenic spot is obtained, and a population distribution heat map is calculated based on the population distribution information.

[0091] Divide the scenic spot map into several grids according to preset widths;

[0092] The number of people in each of the grids is summed according to the number distribution information to obtain the number of people in each grid;

[0093] Calculate the population distribution density according to the number of people in the grid;

[0094] Mapping the population distribution density to a color value through a mapping function of a heat map, and constructing a population distribution heat map based on all color values;

[0095] The number distribution information includes: information collection time, personnel number, and personnel's longitude and latitude;

[0096] The population distribution density is calculated by the following formula:

[0097]

[0098] Where D is the population distribution density, is the sign of partial derivative, x is the number of people in the grid, S is the area of ​​the grid;

[0099] The mapping function of the population distribution heat map is calculated using the following formula:

[0100] f(D)=(R,G,B)

[0101] Among them, f(D) is the mapping function, (R,G,B) is a three-dimensional vector, R, G, B represent the color component values ​​of red, green, and blue respectively, f(0) = (0,0,0), f(100) = (255,0,0).

[0102] It is worth noting that the preset width is set to 3 meters, so in practice, the number of people in a rectangular space of 9 square meters is counted. The preset width can be set according to demand, and this method does not limit this. The population distribution information includes: information collection time, personnel number, and the longitude and latitude of the personnel. For personnel whose longitude and latitude are distributed in the divided grid, they are determined to be personnel in the grid, and the number of people in the grid is calculated, which is the number of people in the grid.

[0103] It is worth noting that the population distribution density is defined as the number of people per unit area. The partial derivative of the area with respect to the number of people is calculated. A heat map is a statistical chart that displays data by coloring color blocks. It maps two continuous data to the x and y axes respectively, and the third continuous data to the color. In the population distribution heat map, each data point or area is assigned a color value, which reflects the density of the number of people at that location or the size of the value. The heat map uses the depth of color to indicate the strength of the data. The "hotter" color (such as red) indicates the more people, and the "colder" color (such as blue or light blue) indicates the fewer people. This color change makes the dense and sparse areas of the data clear at a glance. The mapping function is to map the population distribution density to a color value. The present invention does not limit the specific function of the mapping function, but only the minimum and maximum values. Including when the population density is 0, it is represented by black; when the population density reaches the maximum value of 100, it is represented by red.

[0104] In one implementation, linear interpolation may be used to calculate the color value. The linear interpolation formula may be expressed as:

[0105]

[0106] This is a gradient from blue to red to indicate increasing population density.

[0107] In step S15, an optimal travel route analysis is performed based on the population distribution heat map to obtain a recommended travel route.

[0108] In one implementation, the crowdedness of the tour route is calculated by the following formula:

[0109] Cr=∫th(l)dl

[0110]

[0111] Among them, Cr is the congestion degree, D(l) is the population distribution density of section l on the tour route, and D threshold is the population density threshold, th(l) is the congestion judgment function of section l on the tour route;

[0112] When the degree of congestion is greater than a preset congestion threshold, the tour route corresponding to the degree of congestion is used as a congested route; all tour routes with a degree of congestion less than the preset congestion threshold are sorted according to the degree of congestion; the tour route with the smallest degree of congestion is used as a recommended tour route; when the recommended tour route is a congested route, a reminder message including congestion of the scenic spot is fed back to the user.

[0113] It is worth noting that the congestion is calculated by the congestion judgment function of each section on the integral play route. The congestion indicates the degree of congestion on a play route, which is the sum of the lengths of all sections that are greater than the preset population distribution density threshold. Integration is a mathematical operation that is used to accumulate the contribution of each section on the route to obtain the total congestion of the entire route. The congestion judgment function is defined as: if the population distribution density of a section is less than or equal to the preset population distribution density threshold, the section is not considered to be congested, and the congestion judgment function value is 0; if the population distribution density of a section is greater than the preset population distribution density threshold, the section is considered to be congested, and the congestion judgment function value is 1. The congestion judgment function is used to calculate the congestion. For all play routes with a congestion less than the preset congestion threshold, the algorithm will sort them according to the size of the congestion. In this way, relatively less crowded routes can be identified. The preset congestion threshold is a standard used to determine whether a route is too crowded. If the calculated congestion is greater than this preset value, the route is considered to be congested. Among the sorted routes, the route with the smallest congestion is selected as the recommended play route. Such a route is considered optimal because it provides the best visitor experience and avoids overcrowding. If the crowding of the recommended tour route exceeds the preset crowding threshold, the system will provide feedback to the user about the congestion of the attraction, prompting the user to consider other routes or change the itinerary plan.

[0114] In step S16, the population distribution heat map, the recommended commuting route and the recommended sightseeing route are sent to a display device to be displayed to the user.

[0115] In one implementation, the data is integrated into the map using the API of a map service provider. These services provide a rich set of APIs that can display heat maps, mark routes, and the like. The calculated population density data is superimposed on the map in the form of a heat map, and the depth of color can indicate the number of people. The calculated recommended commuting routes are marked on the map with lines of different colors or thicknesses to distinguish them from ordinary routes for easy user identification. Similar to commuting routes, the recommended travel routes are marked on the map in a visual manner. Additional instructions or icons can be added, such as attraction markers, route instructions, and the like. An interactive user interface is provided that allows users to zoom in, out, drag the map, and click on routes and attractions to obtain more information.

[0116] In one embodiment, after sending the population distribution heat map, the recommended commuting route and the recommended tour route to a display device for display to the user, the method further includes: obtaining user feedback information, the user feedback information includes: reselecting recommended attractions, reselecting routes; when the user chooses to reselect attractions, re-pushing recommended attractions to the user; when the user chooses to reselect routes, using the recommended tour route as the first route; performing optimal tour route analysis based on the population distribution heat map, and selecting a second route with a lower priority than the first route as the recommended tour route. The method provides an interface for users to feedback their needs or preferences. User feedback information includes dissatisfaction with the recommended attractions or routes, and the user wishes to reselect. If the user chooses to reselect the recommended attractions, the system will re-push new recommended attractions based on the user's historical tour information, current location and real-time population distribution data. The present invention pushes through the attractions ranked lower in the recommended degree in the recommended attractions list. After determining the first route, the system will select the second route with a lower priority than the first route as an alternative recommended tour route. This provides an additional option for users, especially when the first route is too crowded or does not meet user preferences. Based on user selections and system recalculations, the visualization is updated to include new recommended attractions and routes, ensuring that users always have the latest information to make decisions.

[0117] In one embodiment, after sending the population distribution heat map, the recommended commuting route and the recommended travel route to a display device for display to the user, it also includes: obtaining a user voice consultation instruction; generating an answer statement according to the voice consultation instruction combined with a pre-stored scenic spot tourism resource database; and outputting the answer statement by voice. For the user's voice consultation instruction, the meaning of the sentence is understood by a natural language processing algorithm. For example, a user asks: "Where is the most famous snack at this scenic spot?" The algorithm analyzes "food" and "location", and then retrieves the relevant answer from the scenic spot tourism resource database, "This scenic spot has apples, located on the commercial street." The text is converted into speech through speech synthesis technology and output to the user in the form of speech. The scenic spot tourism resource database is a cloud-based scenic spot tourism resource database, and all data sources come from the government tourism department, travel sharing software, and the official database of the scenic spot.

[0118] It is worth noting that before the natural language processing algorithm, there is also a language translation system. For foreign language sentences input by voice, the translation system translates the sentences into the language of the information stored in the database, which is convenient for subsequent analysis. Furthermore, when outputting, the answer sentences are also translated into the language of input, which is beneficial to the experience of foreign language users.

[0119] In summary, the present invention discloses an AI accompanying service method for scenic spot tourism, which provides intelligent tourism auxiliary services for tourists by comprehensively using traffic information processing, location services, population distribution analysis and route optimization technology. The method first actively collects the traffic information of the scenic spot and the real-time location information of the user through the data acquisition module, which includes but is not limited to the smooth driving time of the road, the waiting time at the traffic light intersection and the degree of traffic congestion. Based on these data, the commuting time module uses a specific algorithm to calculate the commuting time of each road from the user's current location to the recommended scenic spot, and selects the road with the shortest time as the recommended commuting route. Further, the method analyzes the population distribution information around the scenic spot through the heat map module, and calculates the population distribution heat map to reflect the density of people in the scenic spot. The calculation of the population distribution density adopts the concept of partial derivative, and defines the population distribution density as the number of people per unit area, while the mapping function of the population distribution heat map maps the population distribution density to the color value for intuitive display. In terms of optimal tour route analysis, the method automatically determines and recommends the tour route with the lowest degree of congestion by calculating the congestion degree of the tour route and setting the congestion threshold, so as to help tourists avoid peak areas and optimize the tour experience. The method also includes a result display module, which displays the heat map of the number of people distribution, the recommended commuting route and the recommended tour route to the user in a visual way, so that the user can intuitively understand the flow of people in the tourist routes and attractions. On the user interface, the user can receive the recommendation of the tourist route based on real-time data and can adjust it according to personal preferences. In addition, the method also takes into account the user's feedback information, allowing the user to reselect the recommended attractions or routes. The system will update the recommendation according to the user's choice and real-time data to provide more personalized services. In terms of technical implementation, the method of the present invention adopts a linear regression model when calculating the commuting time, and predicts the traffic congestion coefficient according to information such as time, section length, historical traffic, and real-time traffic, thereby improving the accuracy of the commuting time prediction. At the same time, the method also adopts the driving time index method to calculate the traffic congestion coefficient, and evaluates the degree of congestion by comparing the free flow speed and the actual driving speed. In the generation of the heat map of the number of people, the present invention uses a gradient color from blue to red to indicate the increase in the density of the number of people, so that the color change of the heat map intuitively reflects the density of the flow of people. Furthermore, the AI ​​accompanying service method of the present invention also has a voice consultation function. Users can obtain information through voice consultation instructions. The system generates answer statements in combination with the pre-stored scenic spot tourism resource database and outputs them through voice, which greatly improves the user experience. Through natural language processing technology, the present invention can understand the user's voice instructions, retrieve relevant information from the database, and then convert text into voice output through speech synthesis technology.

[0120] In summary, the AI ​​accompanying service method for scenic spot tourism of the present invention provides tourists with a new intelligent tourism experience by integrating advanced data processing technology and user interaction interface. This method can recommend tourist routes according to the flow of people and improve the user's use and travel experience.

[0121] Reference Figure 2 The second embodiment of the present invention provides an AI accompanying service device for scenic spot tourism, including:

[0122] Data acquisition module, used to obtain scenic spot traffic information and user location information;

[0123] A commuting time module, used to calculate the commuting time of each road from the current starting point to the recommended scenic spot according to the scenic spot traffic information and the user location information;

[0124] A commuting route module, used for selecting a road with the shortest commuting time as a recommended commuting route according to the commuting time;

[0125] A heat map module is used to obtain the population distribution information around the scenic spot and calculate the population distribution heat map according to the population distribution information;

[0126] A travel route module, used to analyze the optimal travel route according to the population distribution heat map to obtain a recommended travel route;

[0127] The result display module is used to send the population distribution heat map, the recommended commuting route and the recommended sightseeing route to a display device for display to the user.

[0128] Preferably, the data acquisition module is used to:

[0129] Get scenic spot traffic information and user location information.

[0130] Preferably, the commuting time module is used to:

[0131] Calculating the commuting time of each road from the current starting point to the recommended scenic spot according to the scenic spot traffic information and the user location information, including:

[0132] The scenic spot traffic information includes the smooth travel time of each road, the waiting time at the traffic light intersection, and the degree of traffic congestion;

[0133] Calculate the commuting time from your starting point to the recommended attractions using the following formula:

[0134]

[0135] Among them, T total is the commuting time of the route, T iis the commuting time of section i on the commuting route, t i is the smooth travel time of road section i, w i is the sum of the waiting time at all traffic lights on section i, c i is the traffic congestion coefficient.

[0136] Preferably, the commuting route module is used to:

[0137] A road with the shortest commuting time is selected as a recommended commuting route based on the commuting time.

[0138] Preferably, the heat map module is used to:

[0139] Obtaining the population distribution information around the scenic spot, and calculating the population distribution heat map according to the population distribution information, including:

[0140] Divide the scenic spot map into several grids according to preset widths;

[0141] According to the population distribution information, the number of people in each grid is summed to obtain the number of people in each grid; and the population distribution density is calculated according to the number of people in the grid;

[0142] Mapping the population distribution density to a color value through a mapping function of a heat map, and constructing a population distribution heat map based on all color values;

[0143] The number distribution information includes: information collection time, personnel number, and personnel's longitude and latitude;

[0144] The population distribution density is calculated by the following formula:

[0145]

[0146] Where D is the population distribution density, is the sign of partial derivative, x is the number of people in the grid, S is the area of ​​the grid;

[0147] The mapping function of the population distribution heat map is calculated using the following formula:

[0148] f(D))=(R,G,B)

[0149] Among them, f(D) is the mapping function, (R,G,B) is a three-dimensional vector, R, G, B represent the color component values ​​of red, green, and blue respectively, f(0) = (0,0,0), f(100) = (255,0,0).

[0150] Preferably, the travel route module is used to:

[0151] The optimal tour route is analyzed according to the population distribution heat map to obtain a recommended tour route, including:

[0152] The crowdedness of the tour route is calculated by the following formula:

[0153] Cr=∫th(l)dl

[0154]

[0155] Among them, Cr is the congestion degree, D(l) is the population distribution density of section l on the tour route, and D threshold is the population density threshold, th(l) is the congestion judgment function of section l on the tour route;

[0156] When the congestion degree is greater than a preset congestion degree threshold, the play route corresponding to the congestion degree is used as a congested route;

[0157] Sorting all the play routes whose congestion degree is less than a preset congestion degree threshold according to the congestion degree;

[0158] The tour route with the least congestion is used as the recommended tour route;

[0159] When the recommended tour route is a crowded route, a reminder message including the crowded tourist spot is fed back to the user.

[0160] Preferably, the result display module is used to:

[0161] The population distribution heat map, the recommended commuting route and the recommended sightseeing route are sent to a display device to be displayed to the user.

[0162] Preferably, after sending the population distribution heat map, the recommended commuting route and the recommended sightseeing route to a display device for display to the user, the method further comprises:

[0163] Obtaining user feedback information, the user feedback information includes: reselecting recommended attractions and reselecting routes;

[0164] When the user chooses to reselect an attraction, the recommended attractions are pushed to the user again;

[0165] When the user chooses to reselect a route, the recommended tour route is used as the first route;

[0166] An optimal travel route analysis is performed based on the population distribution heat map, and a second route with a lower priority than the first route is selected as a recommended travel route.

[0167] Preferably, before calculating the commuting time of each road from the current starting point to the recommended scenic spot according to the scenic spot traffic information and the user location information, the method further includes:

[0168] Get user's historical gaming information;

[0169] According to the user location information, all scenic spots within a preset range are sorted by commuting time;

[0170] According to the user's historical travel information, filter out the unvisited attractions that the user has not visited;

[0171] The scenic spot with the shortest commuting time is selected as the recommended scenic spot among the unvisited scenic spots.

[0172] Preferably, after sending the population distribution heat map, the recommended commuting route and the recommended sightseeing route to a display device for displaying to a user, the method further includes:

[0173] Obtain user voice consultation instructions;

[0174] Generate a reply statement according to the voice consultation instruction combined with a pre-stored tourist attraction resource database;

[0175] The answer sentence is outputted by voice.

[0176] It should be noted that the AI ​​accompanying service device for scenic spot tourism provided in an embodiment of the present invention is used to execute all the process steps of the AI ​​accompanying service method for scenic spot tourism in the above-mentioned embodiment. The working principles and beneficial effects of the two correspond one to one, and therefore will not be repeated here.

[0177] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a data acquisition program. When the processor executes the computer program, the steps in the above-mentioned embodiments of the AI ​​accompanying service method for scenic spot tourism are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the data acquisition module.

[0178] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0179] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0180] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0181] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic 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, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), 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.

[0182] Wherein, if the module / unit integrated in the electronic 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 such an 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, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, 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 capable of carrying 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), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0183] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0184] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. An AI accompanying service method for scenic spot tourism, characterized in that: include: Obtain scenic spot traffic information and user location information; Calculate the commuting time of each road from the current starting point to the recommended scenic spot according to the scenic spot traffic information and the user location information; Selecting the shortest commuting route as a recommended commuting route based on the commuting time; Obtaining the population distribution information around the scenic spot, and calculating the population distribution heat map according to the population distribution information; Analyze the optimal travel route according to the population distribution heat map to obtain a recommended travel route; The population distribution heat map, the recommended commuting route and the recommended sightseeing route are sent to a display device to be displayed to the user.

2. The AI ​​accompanying service method for scenic spot tourism according to claim 1 is characterized in that: The calculating, based on the scenic spot traffic information and the user location information, the commuting time of each road from the current starting point to the recommended scenic spot includes: The scenic spot traffic information includes the smooth travel time of each road, the waiting time at the traffic light intersection, and the degree of traffic congestion; Calculate the commuting time from your starting point to the recommended attractions using the following formula: Among them, T total is the commuting time of the route, T i is the commuting time of section i on the commuting route, t i is the smooth travel time of road section i, w i is the sum of the waiting time at all traffic lights on section i, c i is the traffic congestion coefficient.

3. The AI ​​accompanying service method for scenic spot tourism according to claim 1 is characterized in that: The obtaining of the population distribution information around the scenic spot and calculating the population distribution heat map according to the population distribution information includes: Divide the scenic spot map into several grids according to preset widths; The number of people in each of the grids is summed according to the number distribution information to obtain the number of people in each grid; Calculate the population distribution density according to the number of people in the grid; Mapping the population distribution density to a color value through a mapping function of a heat map, and constructing a population distribution heat map based on all color values; The number distribution information includes: information collection time, personnel number, and personnel's longitude and latitude; The population distribution density is calculated by the following formula: Where D is the population distribution density, is the sign of partial derivative, x is the number of people in the grid, S is the area of ​​the grid; The mapping function of the population distribution heat map is calculated using the following formula: f(D)=(R,G,B) Among them, f(D) is the mapping function, (R,G,B) is a three-dimensional vector, R, G, B represent the color component values ​​of red, green, and blue respectively, f(0) = (0,0,0), f(100) = (255,0,0).

4. The AI ​​accompanying service method for scenic spot tourism according to claim 1 is characterized in that: The optimal tour route analysis is performed according to the number distribution heat map to obtain a recommended tour route, including: The crowdedness of the tour route is calculated by the following formula: Cr=∫th(l)dl Among them, Cr is the congestion degree, D(l) is the population distribution density of section l on the tour route, and D threshold is the population density threshold, th(l) is the congestion judgment function of section l on the tour route; When the congestion degree is greater than a preset congestion degree threshold, the play route corresponding to the congestion degree is used as a congested route; Sorting all the play routes whose congestion degree is less than a preset congestion degree threshold according to the congestion degree; The tour route with the least congestion is used as the recommended tour route; When the recommended tour route is a crowded route, a reminder message including the crowded tourist spot is fed back to the user.

5. The AI ​​accompanying service method for scenic spot tourism according to claim 1 is characterized in that: After sending the population distribution heat map, the recommended commuting route, and the recommended sightseeing route to a display device for display to a user, the method further includes: Obtaining user feedback information, the user feedback information includes: reselecting recommended attractions and reselecting routes; When the user chooses to reselect an attraction, the recommended attractions are pushed to the user again; When the user chooses to reselect a route, the recommended tour route is used as the first route; An optimal travel route analysis is performed based on the population distribution heat map, and a second route with a lower priority than the first route is selected as a recommended travel route.

6. The AI ​​accompanying service method for scenic spot tourism according to claim 1 is characterized in that: Before calculating the commuting time of each road from the current starting point to the recommended scenic spot according to the scenic spot traffic information and the user location information, the method further includes: Get user historical gaming information; According to the user location information, all scenic spots within a preset range are sorted by commuting time; According to the user's historical travel information, filter out the unvisited attractions that the user has not visited; The scenic spot with the shortest commuting time is selected as the recommended scenic spot among the unvisited scenic spots.

7. The AI ​​accompanying service method for scenic spot tourism according to claim 1 is characterized in that: After sending the population distribution heat map, the recommended commuting route and the recommended sightseeing route to a display device for display to a user, the method further includes: Obtain user voice consultation instructions; Generate a reply statement according to the voice consultation instruction combined with a pre-stored tourist attraction resource database; The answer sentence is outputted by voice.

8. An AI accompanying service device for scenic spot tourism, characterized in that: include: Data acquisition module, used to obtain scenic spot traffic information and user location information; A commuting time module, used to calculate the commuting time of each road from the current starting point to the recommended scenic spot according to the scenic spot traffic information and the user location information; A commuting route module, used for selecting a road with the shortest commuting time as a recommended commuting route according to the commuting time; A heat map module is used to obtain the population distribution information around the scenic spot and calculate the population distribution heat map according to the population distribution information; A travel route module, used to analyze the optimal travel route according to the number distribution heat map, and obtain a recommended travel route; The result display module is used to send the population distribution heat map, the recommended commuting route and the recommended sightseeing route to a display device for display to the user.

9. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the AI ​​accompanying service method for scenic spot tourism as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the AI ​​accompanying service method for scenic spot tourism as described in any one of claims 1 to 7.

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