A category-based regional travel route sharing device

Abstract

This invention discloses a device for sharing regional travel routes by category, including a mobile terminal and a travel route sharing page displayed on the mobile terminal. The sharing page has a category directory display area, a travel route display area, and a map display area. The category directory display area displays different travel category names. The travel route display area displays recommended travel routes corresponding to the selected travel category name. The map display area displays a map of the travel region, highlighting the recommended travel routes with selected options on the displayed map. This makes it easier for people to choose and manage their travel routes, better meeting their travel needs.

Description

Technical Field

[0001] This invention relates to the field of regional tourism route planning, and more specifically to a regional tourism route sharing device categorized by type. Background Technology

[0002] In recent years, with the development of my country's national economy and the improvement of people's living standards, the public's demand for tourism is increasing, and their requirements for tourism quality are also rising. However, currently, when people have tourism needs, the tour routes recommended by travel companies usually rely on tour guides in the tourism industry to make recommendations based on their own experience. This approach tends to be subjective and fails to scientifically meet the diverse needs of different tourists.

[0003] Therefore, how to provide tourists with a better travel experience and improve the quality of tourism has become a problem that tourism practitioners need to consider. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the technical problem to be solved by the present invention is: how to provide a category-based regional tourism route sharing device that can better meet people's different tourism needs and improve tourists' tourism experience.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: A device for sharing regional travel routes by category includes a mobile terminal and a travel route sharing page displayed on the mobile terminal. The travel route sharing page has a category directory display area, a travel route display area, and a map display area. The category directory display area displays different travel category names, each with a corresponding checkbox. The travel route display area displays recommended travel routes corresponding to the checked travel category names, with each recommended travel route having a corresponding selection option. The map display area displays a map of the travel region, highlighting recommended travel routes with selected options on the displayed map.

[0006] In this way, tourists can use their mobile devices to select corresponding travel routes based on their desired travel type while traveling in the region, and can intuitively view the travel route information on a map. Therefore, it makes it easier for people to choose and manage their travel routes, better meeting their travel needs.

[0007] Furthermore, the tourism category names displayed in the category directory display area include one-day tours, two-day tours, three-day tours, and multi-day tours categorized by time, and / or family tours, team building tours, and senior tours categorized by travel style, and / or study tours, ice and snow tours, rural tours, and cultural tours categorized by theme.

[0008] This way, it includes various types of tour routes, which can better meet the travel needs of different tourist groups.

[0009] Furthermore, each tourism category name corresponds to more than one recommended tour route. This allows tourists to better choose according to their preferences, thus better meeting their travel needs.

[0010] Furthermore, in the recommended tourist routes highlighted in the map display area, the geographical locations of each tourist stop are marked with dark symbols, and the names of the tourist stops are displayed next to their geographical locations. The recommended tourist stops are also connected by lines. Additionally, each tourist stop in the recommended tourist routes displayed in the map display area also has a hidden information box. This hidden information box is hyperlinked to the tourist stop's symbol or name and can pop up when clicked, displaying information about the corresponding tourist stop.

[0011] This not only allows for a more intuitive display of the selected recommended travel routes on the map, but also provides travelers with better access to information about each tourist stop along the route for comparison and selection. In practice, the information provided can include details about the unique features of each tourist stop and information on transportation options.

[0012] Furthermore, the recommended travel routes corresponding to each tourism category name are generated according to a travel route generation method. This method is based on the background data of a photo-sharing mobile software and includes the following steps: a) obtaining the names of each tourist site within the tourist destination area; b) establishing category tag information corresponding to each tourist site; c) obtaining the background data of the photo-sharing mobile software, tracking the movement path trajectory of tourists, and compiling a travel route database between each tourist site; d) pre-designing several types of recommended travel route names based on different tourism purposes, forming tag information according to the tourism purpose in each type of recommended travel route, and matching the corresponding travel route in the database obtained in step c for recommendation.

[0013] This is because mobile phones are now ubiquitous, and tourists like to take photos and share them with comments about places and attractions they are interested in. This method uses the photos and comments posted and shared by tourists on their mobile photo-sharing apps to identify corresponding tourist hotspots and calculate suitable travel routes for recommendation. Therefore, it avoids the subjective influence of manual recommendations, making it more scientific, effective, and better suited to the travel needs of the general public.

[0014] Furthermore, the background data of the photo-sharing mobile software includes photo information and text comment information uploaded by tourists within a certain time range in the tourist destination area through the photo-sharing mobile software, as well as the geographical location information when the information was uploaded.

[0015] In this way, photo and geolocation information can be used to create tourist site names, and text reviews can be used to categorize tourist sites with tags, so as to better generate tourist route recommendations.

[0016] Furthermore, the certain time range includes a period of time preceding the current date (one or several months prior), and a period of time before and after the current date within the past few years (fifteen days or one to two months prior and after).

[0017] This is because the public's interest in various tourist destinations within a region may be partly based on stable factors inherent to the destination itself, and partly based on unstable factors related to current events and trends. By acquiring data within a timeframe that includes the month preceding the current date and the current period over the past few years, the resulting selection of tourist destinations and routes can better balance these two aspects and better meet people's travel needs.

[0018] Furthermore, step a specifically includes: 1) Divide the tourist destination area into multiple continuous spatial grids according to the map, and calculate the number of information points for uploading photos in each spatial grid to obtain the corresponding information point density (the ratio of the number of information points to the area of ​​the region). 2) Randomly select a photo and compare the information point density of the spatial grid containing the photo with the density threshold (the density threshold can be adjusted based on experience and actual calculation results combined with planning needs; the lower the density threshold, the more tourist sites can be obtained. If the tourist resources are abundant, the density threshold can be lowered, and vice versa). When the information point density is less than the density threshold, it is determined that the current grid does not fall within the site area range, and the photo information uploaded in the spatial grid is deleted from the total photo information database of the tourist destination area. When the information point density is greater than the density threshold, it is determined that the current grid falls within a site area range, and the information point density of the adjacent grids around the current grid is compared with the density threshold. If the information point density of the adjacent grid is greater than the density threshold, the adjacent grid is merged into the same site area range. This process is gradually carried outward until the information point density of all adjacent grids is no longer greater than the density threshold, thus obtaining an independent site area range. All uploaded photos and corresponding information points within the site area range are regarded as a set, and the photo information uploaded within the formed site area range is removed from the total photo information of the tourist destination area. 3) Continue to repeat step 2) in the remaining total photo information database until all photos have been processed, resulting in multiple different site area ranges, each with a corresponding set of photos and a set of information points; 4) Obtain the names and geographical locations of existing attractions within the tourist destination area. Compare the geographical location information of the existing attractions with the generated area ranges of each station. If a station area contains attractions, use the attraction name as the station name corresponding to that station area. If a station area does not contain attractions, use the geographical location name or landmark name of the grid with the highest information point density in that station area as the station name.

[0019] The generated and recommended tourist site names are not simply based on tourist attractions, but rather on actual tourist popularity and tourist interests. Attractions that tourists have no interest in are excluded from the recommendations, while places that are not tourist attractions but are of interest to tourists are included. This approach to recommending tourist sites better aligns with people's actual travel interests and needs, improving the quality of the travel experience and better meeting tourist requirements.

[0020] Furthermore, the geographical location information of existing attraction names is compared with the generated area range of each station. If a station area range contains more than one attraction name, the density threshold in step 2) is reduced and the process is repeated until each station area range contains at most one attraction name. Alternatively, compare the information point density of the spatial grids containing the multiple attraction names, and use the attraction name in the spatial grid with the higher information point density as the site name corresponding to the site area.

[0021] This allows for processing situations where a single site area contains multiple existing attractions. The first processing method yields a relatively larger number of tourist site names, while the second method yields a relatively smaller number of tourist site names but with higher popularity.

[0022] Furthermore, when dividing the spatial grid, it is ensured that each spatial grid contains at most one existing attraction name. This avoids the difficulty in calculating the site name if a single spatial grid contains two existing attractions.

[0023] Furthermore, the number of spatial grids is between 10 and 100 times the number of tourist sites expected to be planned within the tourist destination area.

[0024] If the number of spatial grids is too small, it will be difficult to perform effective calculations; if it is too large, it will lead to a decrease in computational efficiency.

[0025] Based on the information disclosed so far, it is clear that step a, implemented using background data from photo-sharing mobile apps, also discloses a method for generating tourist sites based on data statistics from these apps. This method, after obtaining the names of tourist sites within a destination area using the scheme in step a (and its further optimized versions), then ranks the tourist site names from highest to lowest according to the information point density of the corresponding area, forming a tourist site popularity recommendation ranking list, which is then published and recommended to tourists. This method of recommending tourist sites better aligns with tourist popularity and actual travel demand, thus better meeting tourists' actual travel needs.

[0026] Furthermore, as one implementation of step b, it specifically includes the following steps: b1. Based on the various themed travel recommendation routes to be generated, derive the tag keyword library corresponding to each themed travel recommendation route (for example, for family-themed travel recommendation routes, tags such as child, daughter, son, kid, swing, slide, etc. can be used); b2. Collect all text review information uploaded by tourists within the area of ​​each travel site to form the text review information library corresponding to that travel site, and then match it with the tag keyword library of each themed travel recommendation route to count the total number of times all words in the tag keyword library of each themed route appear in the text review information library of each travel site; b3. Sort the total number of times each themed category appears in each travel site from high to low, and select the travel sites with the highest ranking as travel sites with the category tag of that themed category.

[0027] This tagging method is based on full-text reviews of tourist sites and can be implemented entirely by computer calculations, making it highly reliable.

[0028] As another implementation method for step b, it specifically includes the following steps: b1 Within the area of ​​each tourist site, decompose all or part of the text review information uploaded by tourists and extract individual nouns to form a review information noun database; b2 Count the number of times each noun appears repeatedly in the noun database corresponding to each tourist site and sort them from high to low according to the number of repetitions; b3 Identify each noun one by one according to the sorting. When a noun associated with a certain theme type appears first or the proportion of nouns associated with a certain theme type exceeds a set threshold, that theme type is used as the theme classification tag of the tourist site.

[0029] This labeling method offers good reliability while significantly improving computational efficiency.

[0030] Furthermore, in step b1, text reviews uploaded by tourists within the grid area representing the highest information point destination within the tourist site area can be selected for processing. This can significantly improve the efficiency of processing and judgment.

[0031] Furthermore, in step b3, identification can be done manually based on experience; or unique identification keywords can be generated in advance for various types of themed tourist routes, and identification can be carried out by matching these keywords. Identification based on keywords can be entirely computer-driven, improving processing efficiency; while manual identification based on experience can avoid misjudgments caused by inaccurate identification keywords.

[0032] Further, step c specifically includes the following steps: c1. Based on the tourist's registration information on the photo-sharing mobile app, associate the photo information with the tourist; c2. Based on the photo information uploaded by the tourist, extract the photo shooting time information, and generate the tourist's travel route according to the order in which the uploaded photos were taken; c3. Decompose each tourist's travel route into combinations of tourist sites with different numbers of sites, where each tourist site in each combination is connected in the order of the travel route; c4. Merge all the tourist site combinations decomposed from the tourist's travel route into sets of tourist site combinations with different numbers of sites, and arrange the combinations in each set of tourist site combinations with different numbers of sites in descending order of the frequency of repetition; simultaneously, based on the photo shooting time information, calculate the average of the time difference between the photos taken in all combinations to obtain the travel time for each combination.

[0033] The various combinations obtained through this statistical analysis serve as the basis for subsequent travel route recommendations. Compared to conventional travel generation methods that only consider the attributes of individual tourist attractions, this approach further considers the correlation between tourist sites. Tourist sites with the most interconnected relationships are grouped together for statistical analysis and recommendations. Therefore, the final travel route recommendations greatly improve the quality of the routes and can better meet the actual travel needs of tourists.

[0034] Further, in step d, the recommended travel route categories include categories based on travel dates (specific category names may include one-day tours, two-day tours, three-day tours, and / or multi-day tours), and / or categories based on travel methods (specific category names may include family tours, team building tours, and / or senior tours), and / or categories based on themes (specific category names may include study tours, ice and snow tours, rural tours, and / or cultural tours).

[0035] This allows for the recommendation of various types of travel routes, thus improving the quality of tourism.

[0036] Furthermore, when the recommended travel route category is divided by travel date, the recommended travel route of this category is generated according to the following steps: d1 In the set of travel station combinations with two number of stations, the two station combinations with the highest frequency of occurrence are taken as the starting two stations. Then, the two station combinations with the highest frequency of occurrence starting from the second station are found. If the second station is not included in the recommended route, it is determined as the next station. This process is repeated to form a station chain; d2 Starting from the first travel station in the station chain, the ending travel station is calculated according to the number of travel days and used as the first recommended route for each category name (one-day tour, two-day tour, three-day tour and / or multi-day tour) distinguished by date.

[0037] Furthermore, when the recommended travel route category is divided by travel date, the recommended travel route of this category generates a second recommended route according to the following steps: d1 In the set of travel station combinations with three stations, the three station combinations with the highest frequency of occurrence are taken as the starting three stations. Then, the three station combinations with the highest frequency of occurrence starting from the third station are identified, and the last two stations in these combinations are not included in the recommended route. This process is repeated to form a station chain; d2 Starting from the first travel station in this station chain, the ending travel station is calculated according to the number of travel days and used as the second recommended route for each category name (one-day tour, two-day tour, three-day tour, and / or multi-day tour) distinguished by date.

[0038] This process can be repeated to generate a third recommended route, a fourth recommended route, and so on.

[0039] In this way, the generated recommended routes reflect both the current popularity of tourist sites and the interconnectivity between them. This interconnectivity can be attributed to factors such as convenient transportation, proximity, and the connection between attractions. Therefore, such recommended routes can better improve the quality of tourism.

[0040] Furthermore, based on the content disclosed herein, this application also discloses a method for generating travel routes based on date classification. In the above scheme, step b is deleted, and the method consists only of steps a, c, and the aforementioned step d. This method can generate and recommend travel routes based on dates, enabling the recommended routes to better reflect current travel hotspots and take into account the actual correlation between travel destinations, thereby improving the travel experience for tourists.

[0041] Furthermore, when the recommended travel route category is divided by travel method (specific category names may include family travel, team building travel, and / or senior travel) and by theme (specific category names may include study tours, ice and snow tours, rural tours, and / or cultural tours), the recommended travel route for this category is generated as the first recommended route according to the following steps: d1 After obtaining the travel site combination sets with different numbers of sites in step c4, each travel site combination set retains only the travel site combination sets with the same classification label information and belonging category, forming the travel site combination set of the belonging category; in the travel site combination sets of the belonging category with two number of sites, the two site combinations with the highest frequency of occurrence are taken as the starting two sites, and then the two site combinations with the highest frequency of occurrence starting from the second site are found, and if the second site is not included in the recommended route, it is determined as the next site, and so on, repeating to form a site chain; d2 Starting from the first travel site in the site chain, the ending travel site is calculated according to the number of travel days and is taken as the first recommended route of the belonging category.

[0042] Furthermore, when the recommended travel route category is divided into categories based on travel style and categories based on theme, the recommended travel route for this category generates a second recommended route according to the following steps: d1 In the set of travel station combinations of the category with three station counts, the three station combinations with the highest frequency of occurrence are taken as the starting three stations. Then, the three station combinations with the highest frequency of occurrence starting from the third station are identified, and the last two stations in these combinations are not included in the recommended route. This process is repeated to form a station chain; d2 Starting from the first travel station in this station chain, the ending travel station is calculated based on the number of travel days and used as the second recommended route for the category.

[0043] This process can be repeated to generate corresponding third recommended routes, fourth recommended routes, and so on.

[0044] This approach effectively generates recommended travel routes for specific categories. The generated routes reflect both the current popularity of tourist sites and the interconnectivity between them, incorporating factors such as convenient transportation, proximity, and the connection between attractions. Therefore, this type of route recommendation can significantly improve the quality of the travel experience.

[0045] In summary, this method is derived from the actual travel routes of the general public, avoiding the influence of subjective factors in manual recommendations based on experience, and is more in line with the needs of the general public. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the travel route sharing page of the present invention. Detailed Implementation

[0047] The present invention will now be described in further detail with reference to specific embodiments.

[0048] Implementation: A category-based regional travel route sharing device includes a mobile terminal and a travel route sharing page displayed on the mobile terminal. See [link to implementation details]. Figure 1 The travel route sharing page has a category directory display area, a travel route display area, and a map display area. The category directory display area displays different travel category names, and each travel category name has a corresponding checkbox. The travel route display area is used to display recommended travel routes corresponding to the checked travel category names. Each recommended travel route has a corresponding selection option. The map display area is used to display a map of the travel area, and the recommended travel routes with clicked selection options are highlighted in the displayed map.

[0049] In this way, tourists can use their mobile devices to select corresponding travel routes based on their desired travel type while traveling in the region, and can intuitively view the travel route information on a map. Therefore, it makes it easier for people to choose and manage their travel routes, better meeting their travel needs.

[0050] The tourism category names displayed in the category directory display area include one-day tours, two-day tours, three-day tours and multi-day tours classified by time, and / or family tours, team building tours and senior tours classified by travel method, and / or study tours, ice and snow tours, rural tours and cultural tours classified by theme.

[0051] This way, it includes various types of tour routes, which can better meet the travel needs of different tourist groups.

[0052] Each tourism category name corresponds to more than one recommended tour route. This allows tourists to better choose according to their preferences and thus better meet their travel needs.

[0053] In the recommended tourist routes highlighted on the map, the geographical locations of each tourist stop are marked with dark symbols, and the names of the tourist stops are displayed next to their geographical locations. Furthermore, the recommended tourist stops are connected by lines along the recommended routes. Additionally, each tourist stop in the recommended tourist routes displayed on the map also has a hidden information box. This hidden information box is hyperlinked to the tourist stop's symbol or name and can be clicked to pop up, displaying information about the corresponding tourist stop.

[0054] This not only allows for a more intuitive display of the selected recommended travel routes on the map, but also provides travelers with better access to information about each tourist stop along the route for comparison and selection. In practice, the information provided can include details about the unique features of each tourist stop and information on transportation options.

[0055] In specific implementation, the recommended travel routes corresponding to the above-mentioned tourism category names are generated according to a travel route generation method. This method is based on the background data of photo-sharing mobile software and includes the following steps: a) obtaining the names of each tourist station within the tourist destination area; b) establishing classification tag information corresponding to each tourist station; c) obtaining the background data of the photo-sharing mobile software, tracking the movement path trajectory of tourists, and compiling a travel route database between each tourist station; d) pre-designing several types of recommended travel route names based on different tourism purposes, forming tag information according to the tourism purpose in each type of recommended travel route, and matching the corresponding travel route in the database obtained in step c for recommendation.

[0056] This is because mobile phones are now ubiquitous, and tourists like to take photos and share them with comments about places and attractions they are interested in. This method uses the photos and comments posted and shared by tourists on their mobile photo-sharing apps to identify corresponding tourist hotspots and calculate suitable travel routes for recommendation. Therefore, it avoids the subjective influence of manual recommendations, making it more scientific, effective, and better suited to the travel needs of the general public.

[0057] The background data of the photo-sharing mobile software includes photos and text comments uploaded by tourists within a certain time range in the tourist destination area through the photo-sharing mobile software, as well as the geographical location information at the time of uploading.

[0058] In this way, photo and geolocation information can be used to create tourist site names, and text reviews can be used to categorize tourist sites with tags, so as to better generate tourist route recommendations.

[0059] The specified time range includes a period of time preceding the current date (one or several months prior), and a period of time before and after the current date within the past few years (fifteen days or one to two months prior and after).

[0060] This is because the public's interest in various tourist destinations within a region may be partly based on stable factors inherent to the destination itself, and partly based on unstable factors related to current events and trends. By acquiring data within a timeframe that includes the month preceding the current date and the current period over the past few years, the resulting selection of tourist destinations and routes can better balance these two aspects and better meet people's travel needs.

[0061] Step a specifically includes: 1) Divide the tourist destination area into multiple continuous spatial grids according to the map, and calculate the number of information points for uploading photos in each spatial grid to obtain the corresponding information point density (the ratio of the number of information points to the area of ​​the region). 2) Randomly select a photo and compare the information point density of the spatial grid containing the photo with the density threshold (the density threshold can be adjusted based on experience and actual calculation results combined with planning needs; the lower the density threshold, the more tourist sites can be obtained. If the tourist resources are abundant, the density threshold can be lowered, and vice versa). When the information point density is less than the density threshold, it is determined that the current grid does not fall within the site area range, and the photo information uploaded in the spatial grid is deleted from the total photo information database of the tourist destination area. When the information point density is greater than the density threshold, it is determined that the current grid falls within a site area range, and the information point density of the adjacent grids around the current grid is compared with the density threshold. If the information point density of the adjacent grid is greater than the density threshold, the adjacent grid is merged into the same site area range. This process is gradually carried outward until the information point density of all adjacent grids is no longer greater than the density threshold, thus obtaining an independent site area range. All uploaded photos and corresponding information points within the site area range are regarded as a set, and the photo information uploaded within the formed site area range is removed from the total photo information of the tourist destination area. 3) Continue to repeat step 2) in the remaining total photo information database until all photos have been processed, resulting in multiple different site area ranges, each with a corresponding set of photos and a set of information points; 4) Obtain the names and geographical locations of existing attractions within the tourist destination area. Compare the geographical location information of the existing attractions with the generated area ranges of each station. If a station area contains attractions, use the attraction name as the station name corresponding to that station area. If a station area does not contain attractions, use the geographical location name or landmark name of the grid with the highest information point density in that station area as the station name.

[0062] The generated and recommended tourist site names are not simply based on tourist attractions, but rather on actual tourist popularity and tourist interests. Attractions that tourists have no interest in are excluded from the recommendations, while places that are not tourist attractions but are of interest to tourists are included. This approach to recommending tourist sites better aligns with people's actual travel interests and needs, improving the quality of the travel experience and better meeting tourist requirements.

[0063] In this process, the geographical location information of existing scenic spot names is compared with the generated area range of each station. If a station area range contains more than one scenic spot name, the density threshold in step 2) is reduced and the process is repeated until each station area range contains at most one scenic spot name. Alternatively, compare the information point density of the spatial grids containing the multiple attraction names, and use the attraction name in the spatial grid with the higher information point density as the site name corresponding to the site area.

[0064] This allows for processing situations where a single site area contains multiple existing attractions. The first processing method yields a relatively larger number of tourist site names, while the second method yields a relatively smaller number of tourist site names but with higher popularity.

[0065] Specifically, when dividing the spatial grid, it is ensured that each spatial grid contains at most one existing attraction name. This avoids the difficulty in calculating the site name if a single spatial grid contains two existing attractions.

[0066] The number of spatial grids is between 10 and 100 times the number of tourist sites expected to be planned within the tourist destination area.

[0067] If the number of spatial grids is too small, it will be difficult to perform effective calculations; if it is too large, it will lead to a decrease in computational efficiency.

[0068] Based on the information disclosed so far, it is clear that step a, implemented using background data from photo-sharing mobile apps, also discloses a method for generating tourist sites based on data statistics from these apps. This method, after obtaining the names of tourist sites within a destination area using the scheme in step a (and its further optimized versions), then ranks the tourist site names from highest to lowest according to the information point density of the corresponding area, forming a tourist site popularity recommendation ranking list, which is then published and recommended to tourists. This method of recommending tourist sites better aligns with tourist popularity and actual travel demand, thus better meeting tourists' actual travel needs.

[0069] One implementation of step b includes the following steps: b1. Based on the various themed travel recommendation routes to be generated, derive the tag keyword database corresponding to each themed travel recommendation route (for example, for family-themed travel recommendation routes, tags such as "child," "daughter," "son," "kid," "swing," and "slide" can be used as tag keywords); b2. Collect all text review information uploaded by tourists within the area of ​​each travel site to form a text review information database corresponding to that travel site, and then match it with the tag keyword database of the various themed travel recommendation routes to count the total number of times all words in the tag keyword database of each themed category appear in the text review information database of each travel site; b3. Sort the total number of times each theme type appears in each travel site from high to low, and select the travel sites with the highest total number of occurrences as travel sites with the theme category tag.

[0070] This tagging method is based on full-text reviews of tourist sites and can be implemented entirely by computer calculations, making it highly reliable.

[0071] As another implementation method for step b, it specifically includes the following steps: b1 Within the area of ​​each tourist site, decompose all or part of the text review information uploaded by tourists and extract individual nouns to form a review information noun database; b2 Count the number of times each noun appears repeatedly in the noun database corresponding to each tourist site and sort them from high to low according to the number of repetitions; b3 Identify each noun one by one according to the sorting. When a noun associated with a certain theme type appears first or the proportion of nouns associated with a certain theme type exceeds a set threshold, that theme type is used as the theme classification tag of the tourist site.

[0072] This labeling method offers good reliability while significantly improving computational efficiency.

[0073] In step b1, text reviews uploaded by tourists can be selected from the grid area with the highest information point destination within the tourist site area for processing. This can improve the efficiency of processing and judgment.

[0074] In step b3, identification can be done manually based on experience; or unique identification keywords can be generated in advance for various types of themed tourist routes, and identification can be done by matching these keywords. Identification based on keywords can be fully computerized, improving processing efficiency; while manual identification based on experience can avoid misjudgments caused by inaccurate identification keywords.

[0075] Step c specifically includes the following steps: c1. Based on the tourist's registration information on the photo-sharing mobile app, associate the photo information with the tourist; c2. Extract the photo shooting time information based on the photo information uploaded by the tourist, and generate the tourist's travel route according to the order in which the uploaded photos were taken; c3. Decompose each tourist's travel route into combinations of tourist sites with different numbers of sites, where each tourist site in each combination is connected in the order of the travel route; c4. Merge all the tourist site combinations decomposed from the tourist's travel route into sets of tourist site combinations with different numbers of sites, and arrange the combinations in each set of tourist site combinations with different numbers of sites in descending order of the frequency of repetition; simultaneously, based on the photo shooting time information, calculate the average of the time differences between the photos taken in all combinations to obtain the travel time for each combination.

[0076] The various combinations obtained through this statistical analysis serve as the basis for subsequent travel route recommendations. Compared to conventional travel generation methods that only consider the attributes of individual tourist attractions, this approach further considers the correlation between tourist sites. Tourist sites with the most interconnected relationships are grouped together for statistical analysis and recommendations. Therefore, the final travel route recommendations greatly improve the quality of the routes and can better meet the actual travel needs of tourists.

[0077] In step d, the recommended travel route categories include categories based on travel dates (specific category names may include one-day tours, two-day tours, three-day tours, and / or multi-day tours), and / or categories based on travel methods (specific category names may include family tours, team building tours, and / or senior tours), and / or categories based on themes (specific category names may include study tours, ice and snow tours, rural tours, and / or cultural tours).

[0078] This allows for the recommendation of various types of travel routes, thus improving the quality of tourism.

[0079] When the recommended travel route category is divided by travel date, the recommended travel route of this category is generated according to the following steps: d1 In the set of travel station combinations with two number of stations, the two station combinations with the highest frequency are taken as the starting two stations. Then, the two station combinations with the highest frequency starting from the second station are found. If the second station is not included in the recommended route, it is determined as the next station. This process is repeated to form a station chain; d2 Starting from the first travel station in the station chain, the ending travel station is calculated according to the number of travel days and used as the first recommended route for each category name (one-day tour, two-day tour, three-day tour and / or multi-day tour) distinguished by date.

[0080] When the recommended travel route category is divided by travel date, the recommended travel route of this category generates a second recommended route according to the following steps: d1 From the set of travel station combinations with three stations, the three station combinations with the highest frequency are taken as the starting three stations. Then, the three station combinations with the highest frequency starting from the third station are identified, and the last two stations in these combinations are not included in the recommended route. This process is repeated to form a station chain; d2 Starting from the first travel station in this station chain, the ending travel station is calculated according to the number of travel days and used as the second recommended route for each category name (one-day tour, two-day tour, three-day tour, and / or multi-day tour) distinguished by date.

[0081] This process can be repeated to generate a third recommended route, a fourth recommended route, and so on.

[0082] In this way, the generated recommended routes reflect both the current popularity of tourist sites and the interconnectivity between them. This interconnectivity can be attributed to factors such as convenient transportation, proximity, and the connection between attractions. Therefore, such recommended routes can better improve the quality of tourism.

[0083] Furthermore, based on the content disclosed herein, this application also discloses a method for generating travel routes based on date classification. In the above scheme, step b is deleted, and the method consists only of steps a, c, and the aforementioned step d. This method can generate and recommend travel routes based on dates, enabling the recommended routes to better reflect current travel hotspots and take into account the actual correlation between travel destinations, thereby improving the travel experience for tourists.

[0084] Specifically, when the recommended travel route category is divided by travel method (the specific category name may include family travel, team building travel, and / or senior travel) or by theme (the specific category name may include study tour, ice and snow tour, rural tour, and / or cultural tour), the recommended travel route of this category is generated as the first recommended route according to the following steps: d1 After obtaining the travel site combination set with different numbers of sites in step c4, each travel site combination set retains only the travel site combination set with the same classification label information and belonging to the category, forming the travel site combination set of the category; In the travel site combination set of the category with two numbers of sites, the two site combinations with the highest frequency of occurrence are taken as the starting two sites, and then the two site combinations with the highest frequency of occurrence starting from the second site are found, and if the second site has not entered the recommended route, it is determined as the next site, and so on, repeating to form a site chain; d2 Starting from the first travel site of the site chain, the ending travel site is calculated according to the number of travel days and is taken as the first recommended route of the category.

[0085] When the recommended travel route category is divided into categories based on travel method and categories based on theme, the recommended travel route for this category generates a second recommended route according to the following steps: d1 In the set of travel site combinations of the category with three number of sites, the three site combinations with the highest frequency of occurrence are taken as the starting three sites. Then, the three site combinations with the highest frequency of occurrence starting from the third site are identified, and the last two sites in these combinations are not included in the recommended route. This process is repeated to form a site chain; d2 Starting from the first travel site in this site chain, the ending travel site is calculated based on the number of travel days and is taken as the second recommended route for the category.

[0086] This process can be repeated to generate corresponding third recommended routes, fourth recommended routes, and so on.

[0087] This approach effectively generates recommended travel routes for specific categories. The generated routes reflect both the current popularity of tourist sites and the interconnectivity between them, incorporating factors such as convenient transportation, proximity, and the connection between attractions. Therefore, this type of route recommendation can significantly improve the quality of the travel experience.

[0088] In summary, this method is derived from the actual travel routes of the general public, avoiding the influence of subjective factors in manual recommendations based on experience, and is more in line with the needs of the general public.

Claims

1. A device for sharing a travel route by category and area, comprising a mobile terminal, and further comprising a travel route sharing page displayed based on the mobile terminal, characterized in that, The travel route sharing page has a category directory display area, a travel route display area, and a map display area. The category directory display area displays different travel category names, and each travel category name has a corresponding checkbox. The travel route display area is used to display recommended travel routes corresponding to the checked travel category names. Each recommended travel route has a corresponding selection option. The map display area is used to display a map of the travel area, and the recommended travel routes with clicked selection options are highlighted in the displayed map.

2. The category-based regional travel course sharing device according to claim 1, wherein The category catalog display area shows tourism category names including one-day tours, two-day tours, three-day tours, and multi-day tours categorized by time, and / or family tours, team building tours, and senior tours categorized by travel style, and / or study tours, ice and snow tours, rural tours, and cultural tours categorized by theme. 3.The category-based regional travel course sharing device according to claim 1, characterized in that, Each tourism category name corresponds to more than one recommended travel route. 4.The category-based regional travel course sharing device according to claim 1, wherein, In the recommended tourist routes highlighted on the map, the geographical locations of each tourist stop are marked with dark symbols, and the names of the tourist stops are displayed next to their geographical locations. Furthermore, the recommended tourist stops are connected by lines along the recommended routes. Additionally, each tourist stop in the recommended tourist routes displayed on the map also has a hidden information box. This hidden information box is hyperlinked to the tourist stop's symbol or name and can be clicked to pop up, displaying information about the corresponding tourist stop. 5.The category-based regional travel course sharing device according to claim 1, wherein, Recommended travel routes corresponding to each tourism category are generated using a travel route generation method based on the backend data of a photo-sharing mobile app. This method includes the following steps: a) obtaining the names of various tourist sites within the tourist destination area; b) establishing category tag information for each tourist site; c) obtaining backend data from the photo-sharing mobile app, tracking tourist movement paths, and compiling a database of travel routes between tourist sites; d) pre-designing several categories of recommended travel route names based on different tourism purposes, forming tag information for each category of recommended travel routes according to the tourism purpose, and matching the corresponding travel routes in the database obtained in step c for recommendation. The background data of the photo-sharing mobile software includes photo information and text comment information uploaded by tourists in the tourist destination area within a certain time range through the photo-sharing mobile software, as well as the geographical location information when the information was uploaded; The specified time range includes a period of time preceding the current date and a period of time preceding and following the current date within the past few years. 6.The category-based regional travel course sharing device according to claim 5, wherein, Step a specifically includes: 1) Divide the tourist destination area into multiple continuous spatial grids according to the map, and calculate the number of information points for uploaded photos in each spatial grid to obtain the corresponding information point density; 2) Randomly select a photo and compare the information point density of the spatial grid containing the photo with the density threshold. If the information point density is less than the density threshold, it is determined that the current grid does not fall within the site area range, and the photo information uploaded in the spatial grid is deleted from the total photo information database of the tourist destination area. If the information point density is greater than the density threshold, it is determined that the current grid falls within a site area range, and the information point density of the adjacent grids around the current grid is compared with the density threshold. If the information point density of the adjacent grid is greater than the density threshold, the adjacent grid is merged into the same site area range. This process is gradually carried outward until the information point density of all adjacent grids is no longer greater than the density threshold, thus obtaining an independent site area range. All uploaded photos and corresponding information points within the site area range are regarded as a set, and the photo information uploaded within the formed site area range is removed from the total photo information of the tourist destination area. 3) Continue to repeat step 2) in the remaining total photo information database until all photos have been processed, resulting in multiple different site area ranges, each with a corresponding set of photos and a set of information points; 4) Obtain the names and geographical locations of existing attractions within the tourist destination area. Compare the geographical location information of the existing attractions with the generated area ranges of each station. If a station area contains attractions, use the attraction name as the station name corresponding to that station area. If a station area does not contain attractions, use the geographical location name or landmark name of the grid with the highest information point density in that station area as the station name. The process involves comparing the geographic location information of existing attraction names with the generated site area ranges. If a site area range contains more than one attraction name, the density threshold in step 2) is lowered and the process is repeated until each site area range contains at most one attraction name. Alternatively, the information point density of the spatial grids containing the multiple attraction names is compared, and the attraction name in the spatial grid with the higher information point density is taken as the site name corresponding to the site area range. 7.The category-based regional travel course sharing device according to claim 5, characterized in that, As one implementation of step b, it specifically includes the following steps: b1. Based on the various themed travel recommendation routes to be generated, derive the tag keyword library corresponding to each themed travel recommendation route (for example, for family-themed travel recommendation routes, tags such as child, daughter, son, kid, swing, slide, etc. can be used as tag keywords); b2. Collect all text review information uploaded by tourists within the area of ​​each travel site to form the text review information library corresponding to that travel site, and then match it with the tag keyword library of each themed travel recommendation route to count the total number of times all words in the tag keyword library of each themed category appear in the text review information library of each travel site; b3. Sort the total number of times each theme type appears in each travel site from high to low, and select the travel sites with the highest ranking as travel sites with the theme category tag. 8.The category-based regional travel course sharing device according to claim 5, wherein, As another implementation method for step b, it specifically includes the following steps: b1 Within the area of ​​each tourist site, decompose all or part of the text review information uploaded by tourists and extract individual nouns to form a review information noun database; b2 Count the number of times each noun appears repeatedly in the noun database corresponding to each tourist site and sort them from high to low according to the number of repetitions; b3 Identify each noun one by one according to the sorting. When a noun associated with a certain theme type appears first or the proportion of nouns associated with a certain theme type exceeds a set threshold, that theme type is used as the theme classification tag of the tourist site. In step b1, text comments uploaded by tourists in the grid area with the highest information point destination within the tourist site area can be selected for processing; In step b3, identification can be done manually based on experience; or unique identification keywords can be generated in advance for various types of themed tourist routes, and identification can be carried out by matching identification keywords. 9.The category-based regional travel course sharing device according to claim 5, characterized in that, Step c specifically includes the following steps: c1. Based on the registration information of tourists on the photo-sharing mobile app, associate photo information with tourists; c2. Extract the photo shooting time information based on the photo information uploaded by tourists, and generate the tourist's travel route according to the order in which the photos were taken; c3. Decompose each tourist's travel route into combinations of tourist sites with different numbers of sites, where each tourist site in each combination is connected in the order of the travel route; c4. Merge all the tourist site combinations decomposed from the tourist's travel route into sets of tourist site combinations with different numbers of sites, and arrange the combinations in each set of tourist site combinations with different numbers of sites in descending order of repetition; simultaneously, based on the photo shooting time information, calculate the average of the photo shooting time differences among all combinations to obtain the travel time for each combination. 10.The category-based regional travel course sharing device according to claim 5, wherein, In step d, the recommended travel route categories include categories divided by travel date, and / or categories divided by travel method, and / or categories divided by theme; When the recommended travel route category is divided into categories based on travel style and categories based on theme, the recommended travel route for this category is generated according to the following steps: d1 After obtaining the travel site combination sets with different numbers of sites in step c4 of step c, each travel site combination set retains only the travel site combination sets with the same classification label information and belonging category, forming the travel site combination set of the belonging category; In the travel site combination sets of the belonging category with two numbers of sites, the two site combinations with the highest frequency of occurrence are taken as the starting two sites, and then the two site combinations with the second site as the starting site with the highest frequency of occurrence are found, and if the second site has not entered the recommended route, it is determined as the next site, and so on, repeating to form a site chain; d2 starts from the first tourist site in the site chain, calculates the end tourist site based on the number of tourist days, and uses it as the first recommended route for the corresponding tourist category. When the recommended travel route category is divided into categories based on travel style and categories based on theme, the recommended travel route of this category generates a second recommended route according to the following steps: d1 In the set of travel station combinations of the category to which the number of three stations belongs, take the three station combinations with the highest frequency as the starting three stations, then find the three station combinations with the third station as the starting station with the highest frequency, and determine the latter two stations as subsequent stations if they are not included in the recommended route, and repeat in this way to form a station chain; d2 starts from the first tourist stop in the site chain, calculates the ending tourist stop based on the number of tourist days, and uses it as the second recommended route for the corresponding tourist category.

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