Tourist route personalized recommendation system based on AI matrix algorithm

Abstract

The invention belongs to the technical field of tourist route recommendation, and particularly relates to a tourist route personalized recommendation system based on an AI matrix algorithm. According to the invention, highly customized tourist route recommendation can be provided for the user, the defect that the traditional recommendation system is not accurate enough is avoided, the recommended route can be dynamically adjusted according to the latest feedback of the user, uninterested scenic spots are eliminated, the recommended route scheme is ensured to be matched with the expectation of the user to the greatest extent, and the user experience is improved. The method comprehensively considers the preference, time and individual demands of the user, enables the recommended route to be reasonable, can greatly improve the tourism experience of the user, and reduces the pressure of tourism planning.

Description

Technical Field

[0001] The present invention belongs to the technical field of tourist route recommendation, and in particular relates to a personalized tourist route recommendation system based on an AI matrix algorithm. Background Art

[0002] With the rapid development of the global tourism industry, tourists' demand for personalized travel experience is increasing. Traditional travel route recommendation systems usually rely on preset routes or simple tag recommendation methods, which cannot fully meet the personalized needs of different users. Since each user's interests, travel preferences, budgets, time constraints and other factors are different, simple recommendation methods often cannot provide highly personalized travel route planning.

[0003] In recent years, the rapid development of artificial intelligence (AI) technology has provided new breakthroughs for tourism recommendation systems, especially matrix algorithms have shown significant advantages in processing multidimensional data. By introducing AI matrix algorithms, users' multi-source information (such as historical travel records, real-time locations, hobbies, etc.) and multi-dimensional data of tourism resources (such as the popularity of scenic spots, seasonal opening information, traffic conditions, etc.) can be comprehensively analyzed to generate highly personalized travel route recommendation plans. Matrix algorithms can effectively handle complex correlations and identify potential patterns hidden in multidimensional data, so that the recommendation system can provide dynamic, real-time and accurate travel route planning according to the personalized needs of users.

[0004] Existing travel recommendation systems are often unable to cope with users' demands for preferred attractions, travel time, etc. Due to the lack of a dynamic feedback mechanism, users' actual travel choices are often different from the suggestions provided by the recommendation system. This situation not only affects users' travel experience, but also calls into question the rationality of the recommendations. Summary of the invention

[0005] The purpose of the present invention is to provide a personalized travel route recommendation system based on an AI matrix algorithm, which can perform multi-level screening of user preferences and travel information, and the generated routes are highly personalized and accurate.

[0006] The technical solution adopted by the present invention is as follows: A personalized travel route recommendation system based on AI matrix algorithm, including scenic spot preference module, first reference, second reference, recommended route and target route module; The attraction preference module is used to obtain the user's travel preference information and tourist attraction information, and obtain the tourist preference attraction information based on the travel preference information and the corresponding tourist attraction information; A first reference module is used to obtain network user travel route data and local travel route data, and summarize them into a travel route set, and obtain a first reference travel route set from the travel route set according to the travel preference scenic spot information; The second reference module is used to obtain the user's planned travel duration, and obtain the corresponding second reference travel route set from the first reference travel route set according to the user's planned travel duration; A recommended route module, used for obtaining information of must-visit tourist attractions from tourist preference tourist attraction information according to the tourist preference information of the user, and obtaining a recommended tourist route set from a second reference tourist route set according to the information of must-visit tourist attractions; The target route module is used to obtain the removed scenic spot information from the recommended tourist route set according to the user's travel preference information, and generate the target tourist route according to the removed scenic spot information and the recommended tourist route set.

[0007] In a preferred solution, the scenic spot preference module includes a travel preference acquisition unit, a tourist attraction acquisition unit, a preference matrix unit, an attraction matrix unit, a preference degree unit and a travel preference unit; A travel preference acquisition unit, used to acquire user travel preference information; A tourist attraction acquisition unit, used to acquire tourist attraction information; A preference matrix unit is used to construct a tourism preference matrix based on tourism preference information; The scenic spot matrix unit is used to construct a tourist attraction matrix corresponding to the tourist preference matrix according to the tourist attraction information; A preference unit, used to obtain a tourist attraction preference matrix according to a tourist preference matrix and a corresponding tourist attraction matrix; The tourist preference unit is used to obtain tourist attractions that meet the first preset condition according to the tourist attraction preference matrix and mark them as tourist preference tourist attraction information.

[0008] In a preferred embodiment, the preference degree unit includes a preference threshold unit and a preference judgment unit; A preference threshold unit, used to obtain a scenic spot preference threshold; A preference judgment unit, used to judge whether each element in the scenic spot preference matrix exceeds the scenic spot preference threshold; If the elements in the attraction preference matrix exceed the attraction preference threshold, the tourist attraction is determined to be in line with the user's preferences, and the tourist attraction corresponding to the elements exceeding the attraction preference threshold is marked as tourist preference attraction information.

[0009] In a preferred solution, the first reference module includes an existing route unit, a repeated scenic spot unit, a scenic spot quantity unit and a scenic spot judgment unit; The existing route unit is used to obtain the travel route data of network users and the local travel route data, and summarize them into a travel route set; A repeating attraction unit is used to obtain the same number of tourist attractions corresponding to the tourist preference attraction information in each tourist route according to the tourist route set; The attraction quantity unit is used to obtain the quantity of all preferred attractions in the tourist preferred attraction information, and obtain the attraction threshold quantity according to the quantity of preferred attractions; An attraction judgment unit, used to judge whether the same number of each tourist attraction exceeds the threshold number of attractions; If the same number of tourist attractions exceeds the threshold number of attractions, it is determined that the tourist routes corresponding to the same number of tourist attractions meet the user's travel preference requirements, and all tourist routes that meet the user's travel preference requirements are summarized as a first reference tourist route set.

[0010] In a preferred embodiment, the second reference module includes a reference duration unit, a planned duration unit, a threshold duration unit and a duration judgment unit; A reference duration unit, used to obtain the recommended duration for sightseeing at scenic spots in each reference tourist route in the first reference tourist route set and the duration of the journey to adjacent scenic spots, and summarize them into the first reference tourist route set; Planned duration unit, used to obtain the user's planned travel duration; A threshold duration unit, used to obtain a travel threshold duration according to the user's travel preference information and the user's planned travel duration; A duration determination unit, used to determine whether each first reference travel duration exceeds a travel threshold duration; If the first reference travel duration does not exceed the travel threshold duration, the travel routes corresponding to the first reference travel duration that does not exceed the travel threshold duration are aggregated into a second reference travel route set.

[0011] In a preferred solution, the recommended route module includes a must-see scenic spot unit, a second scenic spot unit and a recommended route unit; A must-visit scenic spot unit is used to obtain must-visit scenic spot information from the tourist preference scenic spot information according to the user's tourist preference information; The second scenic spot unit is used to obtain the tourist attraction information included in each tourist route in the second reference tourist route set and mark it as the second tourist attraction information; A recommended route unit, used for judging whether each second tourist attraction information completely contains the must-visit tourist attraction information; If the second tourist attraction information completely includes the must-visit tourist attraction information, the tourist routes corresponding to the second tourist attraction information completely including the must-visit tourist attraction information are summarized as a recommended tourist route set.

[0012] In a preferred embodiment, the target route module includes a recommended scenic spot unit, a removed scenic spot unit, a remaining scenic spot unit and a target route unit; The recommended scenic spot unit is used to obtain the tourist attraction information contained in each tourist route in the recommended tourist route set, and mark it as recommended route scenic spot information; A scenic spot removal unit is used to obtain the removed scenic spot information from each recommended route scenic spot information according to the user's travel preference information; The remaining scenic spot unit is used to remove the tourist attractions of the corresponding tourist route according to the removed scenic spot information to obtain the remaining tourist attraction information; The target route unit is used to generate a target tourist route according to the adjusted tourist attractions.

[0013] In a preferred embodiment, the target route unit includes a route preference unit, a remaining route unit, a remaining matrix unit, a route preference unit and a target route unit; A route preference unit, used to obtain user travel route preference information according to the user travel preference information, and obtain a travel route preference matrix according to the user travel route preference information; The remaining route unit is used to obtain the corresponding tourist route according to each remaining tourist attraction information and mark it as the remaining tourist route information; The remaining matrix unit is used to obtain the corresponding remaining travel route matrix of multiple rows and columns according to the remaining travel route information; A route preference unit, used for obtaining a route preference matrix according to the travel route preference matrix and the remaining travel route matrices; The target route unit is used to obtain a travel route that meets the second preset condition according to the route preference matrix and mark it as a target travel route.

[0014] In a preferred embodiment, it further includes an actual route unit, a deviation route unit, a preference deviation unit and a preference update unit; The actual route unit is used to obtain the user's actual travel route; A deviation route unit, used to obtain deviation route information between the user's actual travel route and the target travel route; a preference deviation unit, used for obtaining preference deviation information according to the deviation route information; The preference updating unit is used to update the user's travel preference information according to the preference deviation information.

[0015] And, a personalized travel route recommendation terminal based on AI matrix algorithm, including: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the personalized travel route recommendation system based on the AI ​​matrix algorithm.

[0016] The technical effects achieved by the present invention are: The present invention can provide users with highly customized travel route recommendations, avoiding the defects of inaccuracy in traditional recommendation systems, and can dynamically adjust the recommended routes according to the latest feedback from users, eliminating uninteresting attractions, ensuring that the recommended route plan is consistent with the user's expectations to the greatest extent possible, and comprehensively considering the user's preferences, time and individual needs, so that the recommended routes are not only reasonable, but also can greatly enhance the user's travel experience and reduce the pressure of travel planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a system module diagram provided by the present invention; Figure 2 It is a flow chart provided by the present invention. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0019] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" that appears in different places in this specification does not refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0021] Secondly, the present invention is described in detail in conjunction with schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the schematic diagrams are only examples and should not limit the scope of protection of the present invention.

[0022] Please see attached Figure 1 and Figure 2 As shown, a personalized travel route recommendation system based on an AI matrix algorithm is provided, including a scenic spot preference module, a first reference, a second reference, a recommended route and a target route module; The attraction preference module is used to obtain the user's travel preference information and tourist attraction information, and obtain the tourist preference attraction information based on the travel preference information and the corresponding tourist attraction information; A first reference module is used to obtain network user travel route data and local travel route data, and summarize them into a travel route set, and obtain a first reference travel route set from the travel route set according to the travel preference scenic spot information; The second reference module is used to obtain the user's planned travel duration, and obtain the corresponding second reference travel route set from the first reference travel route set according to the user's planned travel duration; A recommended route module, used for obtaining information of must-visit tourist attractions from tourist preference tourist attraction information according to the tourist preference information of the user, and obtaining a recommended tourist route set from a second reference tourist route set according to the information of must-visit tourist attractions; The target route module is used to obtain the removed scenic spot information from the recommended tourist route set according to the user's travel preference information, and generate the target tourist route according to the removed scenic spot information and the recommended tourist route set.

[0023] In the above, by acquiring the user's historical travel data, hobbies and other information, a user's travel preference matrix is ​​constructed. At the same time, scenic spots matching the user's preferences are extracted from the tourist attraction information to generate a travel preference scenic spot information matrix. The travel route data of other users on the Internet and the travel route data in the local database are screened and summarized to form a travel route set. According to the travel preference scenic spot information matrix, a first reference travel route set containing the user's preferred scenic spots is screened out. According to the user's planned time, a suitable second reference travel route set is further screened out from the first reference travel route set. According to the user's travel preference information, the scenic spots containing the user's "must-see" spots are further extracted from the second reference travel route set. The system generates a set of routes for "going to scenic spots", giving priority to ensuring that users can visit the scenic spots that they are most interested in, generating a set of recommended tourist routes, and removing these uninteresting scenic spots from the recommended route set according to the personalized needs of users (such as scenic spots that users want to avoid), generating the final target tourist route, and providing users with highly customized tourist route recommendations, avoiding the defects of inaccurate traditional recommendation systems, and dynamically adjusting recommended routes according to the latest feedback from users (such as removing uninteresting scenic spots), ensuring that the recommended route plan is in line with the user's expectations to the greatest extent possible, and comprehensively considering the user's preferences, time and individual needs, so that the recommended route is not only reasonable, but also can greatly enhance the user's travel experience and reduce the pressure of travel planning.

[0024] The scenic spot preference module includes a tourist preference acquisition unit, a tourist attraction acquisition unit, a preference matrix unit, an attraction matrix unit, a preference degree unit and a tourist preference unit; A travel preference acquisition unit, used to acquire user travel preference information; A tourist attraction acquisition unit, used to acquire tourist attraction information; A preference matrix unit is used to construct a tourism preference matrix based on tourism preference information; The scenic spot matrix unit is used to construct a tourist attraction matrix corresponding to the tourist preference matrix according to the tourist attraction information; A preference unit, used to obtain a tourist attraction preference matrix according to a tourist preference matrix and a corresponding tourist attraction matrix; The tourist preference unit is used to obtain tourist attractions that meet the first preset condition according to the tourist attraction preference matrix and mark them as tourist preference tourist attraction information.

[0025] It should be noted that the tourism preference matrix is ​​a matrix with one row, the tourism preference matrix includes vectors of multiple preference types, the tourist attraction matrix has a row number corresponding to the number of tourist attractions, and the tourist attraction matrix has a row number corresponding to the number of multiple preference types.

[0026] As mentioned above, the user's travel preference information is collected. This preference information can come from the user's historical travel data, interests and hobbies, frequently visited travel types (such as natural scenery, cultural relics, etc.), and the user's interactive behavior on the platform. Through these data, the user's travel preference information can be generated, and the attraction-related data is obtained from the tourist destination, covering the type, characteristics, and evaluation of the attractions. By analyzing the travel preference information, a matrix with one row is constructed, namely the travel preference matrix. Each element in the matrix represents the user's preference vector for a certain type of attraction (such as historical relics, museums, natural scenery, etc.). According to the tourist attraction information, a tourist attraction matrix is ​​constructed. The number of rows in the matrix represents the number of attractions in the tourist destination, and the number of columns corresponds to the number of vectors in the user's travel preference matrix. Each element represents the matching degree of the attraction in the corresponding preference type. For example, a scenic spot can have a high matching degree in "natural scenery" but a low matching degree in "cultural experience". The user's travel preference matrix is ​​compared with the travel preference matrix of the tourist destination. The tourist attraction matrix is ​​matched to generate an attraction preference matrix. The calculation formula of the attraction preference matrix is ​​S=U*D, where S represents the attraction preference matrix, U represents the tourist preference matrix, and D represents the tourist attraction matrix. The matrix is ​​used to calculate the degree of match between the user's preferences and each attraction, that is, the overall preference score of each attraction under multiple preference types. According to the attraction preference matrix, tourist attractions that meet the first preset condition (usually multiple attractions with high preference scores) are screened out. These screened out attractions will be marked as tourist preference attraction information and will be used as candidate attractions for subsequent recommended routes. It can deeply analyze the user's travel preferences and screen out the attractions that best suit their interests based on their personalized needs, avoiding the "wide net" method in traditional recommendation systems, improving the accuracy of recommendations, and quickly screening out qualified tourist attractions, greatly improving the calculation and recommendation efficiency, and users can obtain travel route suggestions that suit their interests more quickly.

[0027] The preference degree unit includes a preference threshold unit and a preference judgment unit; A preference threshold unit, used to obtain a scenic spot preference threshold; A preference judgment unit, used to judge whether each element in the scenic spot preference matrix exceeds the scenic spot preference threshold; If the elements in the attraction preference matrix exceed the attraction preference threshold, the tourist attraction is determined to be in line with the user's preferences, and the tourist attraction corresponding to the elements exceeding the attraction preference threshold is marked as tourist preference attraction information.

[0028] As described above, a scenic spot preference threshold is obtained, that is, a preset minimum standard, which is used to measure whether the scenic spot meets the user's travel preferences. This threshold can be determined by analyzing historical data, user personalized settings or system default values. The elements in the scenic spot preference matrix represent the user's preference for a certain scenic spot. If an element exceeds the threshold, it means that the user has a high preference for the scenic spot. By comparing each element in the scenic spot preference matrix with the scenic spot preference threshold, it is determined whether the scenic spot meets the user's travel preferences. If the value of an element is greater than or equal to the set scenic spot preference threshold, the tourist attraction corresponding to the element is marked as meeting the user's preferences. It can automatically screen out scenic spots with high preference and mark these scenic spots as tourist preference scenic spot information for subsequent route recommendations.

[0029] The first reference module includes an existing route unit, a repeated scenic spot unit, a scenic spot quantity unit and a scenic spot judgment unit; The existing route unit is used to obtain the travel route data of network users and the local travel route data, and summarize them into a travel route set; A repeating attraction unit is used to obtain the same number of tourist attractions corresponding to the tourist preference attraction information in each tourist route according to the tourist route set; The attraction quantity unit is used to obtain the quantity of all preferred attractions in the tourist preferred attraction information, and obtain the attraction threshold quantity according to the quantity of preferred attractions; An attraction judgment unit, used to judge whether the same number of each tourist attraction exceeds the threshold number of attractions; If the same number of tourist attractions exceeds the threshold number of attractions, it is determined that the tourist routes corresponding to the same number of tourist attractions meet the user's travel preference requirements, and all tourist routes that meet the user's travel preference requirements are summarized as a first reference tourist route set.

[0030] In the above, the existing travel route information is obtained from the travel route data of network users and the local travel route data, and these data are aggregated into a travel route set. This set contains multiple historical travel routes, and each route corresponds to a different combination of tourist attractions. By comparing each travel route in the travel route set with the user's travel preference attraction information, the number of attractions in each route that are the same as the user's preferred attractions is calculated, and it is determined which of the existing routes contain the user's preferred tourist attractions, and their matching degree is quantified. The number of all preferred attractions in the user's travel preference attraction information is obtained, and a threshold number of attractions is set to measure whether the number of attractions in a route is sufficient to meet the user's preference requirements. The attraction threshold can be set by the system or adjusted according to the user's personalized preferences to ensure that the selected routes With a certain number of preferred attractions, it is determined whether the number of attractions in each travel route that are the same as the user's preferred attractions exceeds the set attraction threshold number. If the number of matching attractions in a route exceeds the threshold, the route is determined to be in line with the user's travel preferences. All routes that meet the user's preferences are aggregated to generate a first reference travel route set, which can quickly screen out the routes that best match the user's preferences from a large amount of historical data, avoiding the trouble of users selecting one by one from a large number of routes, greatly improving efficiency. Through the role of the attraction number unit and the attraction judgment unit, the system not only considers the user's preference for a single attraction, but also sets the attraction threshold to ensure that the screened routes contain enough attractions that the user is interested in. This matching standard improves the accuracy of the recommended routes and ensures that the recommended routes can meet the overall needs of the user.

[0031] The second reference module includes a reference duration unit, a planned duration unit, a threshold duration unit and a duration judgment unit; A reference duration unit, used to obtain the recommended duration for sightseeing at scenic spots in each reference tourist route in the first reference tourist route set and the duration of the journey to adjacent scenic spots, and summarize them into the first reference tourist route set; Planned duration unit, used to obtain the user's planned travel duration; A threshold duration unit, used to obtain a travel threshold duration according to the user's travel preference information and the user's planned travel duration; A duration determination unit, used to determine whether each first reference travel duration exceeds a travel threshold duration; If the first reference travel duration does not exceed the travel threshold duration, the travel routes corresponding to the first reference travel duration that does not exceed the travel threshold duration are aggregated into a second reference travel route set.

[0032] In the above, the detailed time information of each travel route in the first reference travel route set is obtained, including the recommended playing time of each scenic spot and the travel time between adjacent scenic spots. These time information are summarized as the total playing time of each route, that is, the first reference travel time. In this way, an accurate time estimate can be provided for each route, which is convenient for subsequent matching with the user's planned travel time, and the user's planned travel time is obtained, that is, the total amount of time the user hopes to spend in the entire trip. This information is usually entered by the user, such as a few days' itinerary, or specific to the daily playing time. According to the user's travel preference information and planned travel time, The travel threshold duration is to ensure that the duration of the recommended route does not exceed the time arrangement set by the user, while taking into account the user's preferences for attractions. For example, by analyzing the user's preferences for certain attractions, the visit time of certain attractions can be appropriately extended or shortened, thereby dynamically adjusting the threshold duration to determine whether each first reference travel duration exceeds the travel threshold duration. If the total duration of a route does not exceed the threshold duration, the route is considered to meet the user's time requirements. These routes that meet the time requirements are aggregated to generate a second reference travel route set, that is, a candidate route set after further screening.

[0033] The recommended route module includes a must-see scenic spot unit, a second scenic spot unit and a recommended route unit; A must-visit scenic spot unit is used to obtain must-visit scenic spot information from the tourist preference scenic spot information according to the user's tourist preference information; The second scenic spot unit is used to obtain the tourist attraction information included in each tourist route in the second reference tourist route set and mark it as the second tourist attraction information; A recommended route unit, used for judging whether each second tourist attraction information completely contains the must-visit tourist attraction information; If the second tourist attraction information completely includes the must-visit tourist attraction information, the tourist routes corresponding to the second tourist attraction information completely including the must-visit tourist attraction information are summarized as a recommended tourist route set.

[0034] As described above, based on the user's travel preference information, the attractions that the user most wants to visit are screened out from the travel preference attractions information, which is called the must-visit tourist attractions information. These must-visit attractions are the most important to the user during the travel process, and the recommended routes will be ensured to include these attractions in the subsequent screening process. For example, the user may particularly want to visit a landmark attraction or a special attraction in a city. The system will mark these attractions as "must-visit" targets, and extract the attraction information contained in each travel route from the second reference travel route set, and mark it as the second travel attraction information. After the previous screening, the second reference travel route set is already in line with the user's travel For routes with time requirements, the second tourist attraction information of each route is compared with the user's must-see tourist attractions information to determine whether each route completely includes the user's must-see attractions. If the second tourist attraction information in a route includes all the must-see tourist attractions, then the route is considered to meet the user's core needs. These routes that completely include the must-see attractions are summarized as a recommended tourist route set for the user's final choice, ensuring that the recommended routes must include the attractions that the user is particularly concerned about. This solves the user's most concerned travel needs. Even among multiple candidate routes, those routes that include must-see attractions are prioritized to ensure that the user's expectations are not disappointed.

[0035] The target route module includes a recommended scenic spot unit, a removed scenic spot unit, a remaining scenic spot unit and a target route unit; The recommended scenic spot unit is used to obtain the tourist attraction information contained in each tourist route in the recommended tourist route set, and mark it as recommended route scenic spot information; A scenic spot removal unit is used to obtain the removed scenic spot information from each recommended route scenic spot information according to the user's travel preference information; The remaining scenic spot unit is used to remove the tourist attractions of the corresponding tourist route according to the removed scenic spot information to obtain the remaining tourist attraction information; The target route unit is used to generate a target tourist route according to the adjusted tourist attractions.

[0036] As described above, the specific scenic spot information contained in each route is extracted from the recommended travel route set and marked as recommended route scenic spot information. The recommended travel route set is generated on the basis of the previous module (recommended route module), and already includes the user's favorite must-see scenic spots and routes that meet the time schedule. By obtaining the scenic spot information in each recommended route, the scenic spot distribution of each route can be fully grasped. According to the user's travel preference information, the scenic spots that the user is not interested in are identified from the scenic spot information of each recommended route, and these scenic spots are marked as removed scenic spot information. For example, the user may not be interested in certain types of attractions (such as museums and shopping areas). According to these preferences, the scenic spots that do not meet the user's preferences will be automatically identified and excluded, ensuring that the recommended route is further personalized, avoiding the inclusion of scenic spots that the user does not want to visit, and improving the route. The degree of fit is determined by removing the scenic spot information, removing the corresponding scenic spots from each recommended route, leaving the part that the user is interested in, and obtaining the remaining tourist attractions information. While retaining the scenic spots that the user is interested in, the scenic spots that do not meet the preferences or needs are eliminated, making the recommended route more accurate. The remaining scenic spots fully meet the user's interests and needs, avoiding wasting time and energy on uninteresting scenic spots. According to the remaining tourist attractions information, the tourist route is adjusted and optimized to generate the final target tourist route. The target tourist route is a personalized route after multiple screening, optimization and adjustment, ensuring the maximum satisfaction of the user's travel experience. By reasonably adjusting the order of scenic spots or itinerary arrangements, the time and efficiency of the route can be further optimized, allowing users to visit the most interesting attractions in the shortest time, thereby improving the overall travel efficiency and comfort.

[0037] The target route unit includes a route preference unit, a remaining route unit, a remaining matrix unit, a route preference unit and a target route unit; A route preference unit, used to obtain user travel route preference information according to the user travel preference information, and obtain a travel route preference matrix according to the user travel route preference information; The remaining route unit is used to obtain the corresponding tourist route according to each remaining tourist attraction information and mark it as the remaining tourist route information; The remaining matrix unit is used to obtain the corresponding remaining travel route matrix of multiple rows and columns according to the remaining travel route information; A route preference unit, used for obtaining a route preference matrix according to the travel route preference matrix and the remaining travel route matrices; The target route unit is used to obtain a travel route that meets the second preset condition according to the route preference matrix and mark it as a target travel route.

[0038] It is worth mentioning that the travel route preference matrix is ​​a row matrix. Each element in the travel route preference matrix corresponds to the user's travel route preference vector. The number of rows of the remaining travel route matrix corresponds to the number of travel routes in the remaining travel route information, and the number of columns of the remaining travel route matrix corresponds to the number of multiple elements in the travel route preference matrix.

[0039] As mentioned above, the travel route preference information is extracted according to the user's travel preference information and converted into a travel route preference matrix. This preference matrix is ​​usually a matrix with one row, and each element corresponds to the user's preference for a certain route or a certain travel experience. For example, the user may prefer natural scenic spots, historical sites or shopping areas. These preferences are quantified as vector values ​​in the matrix. The travel route preference matrix provides an accurate reference for subsequent route selection. By comparing the various characteristics of the remaining routes, the best route that meets the user's needs can be effectively identified. According to each remaining tourist attraction information, the corresponding tourist route is extracted, and generated and marked as the remaining tourist route information. These routes are the results of the previous screening after removing the attraction information, and the attractions that the user is interested in are retained. On this basis, all the remaining routes that may be suitable for the user are obtained to ensure that there is sufficient data support for subsequent screening and optimization. A multi-row and multi-column remaining tourist route matrix is ​​generated according to the remaining tourist route information. The number of rows in the matrix corresponds to the number of remaining tourist routes, and the number of columns corresponds to the number of multiple elements in the tourist route preference matrix. Each row represents a remaining tourist route, and each column represents the elements (preferences) in the user's tourist route preference matrix. The features corresponding to the good vectors are obtained by matching the features of each route with the user's preferences, so that the routes that meet the user's needs can be further screened out. The user's travel route preference matrix is ​​calculated with the generated remaining travel route matrix to output a route preference matrix. The calculation formula of the route preference matrix is ​​K=P*Y, where K represents the route preference matrix, P represents the travel route preference matrix, and D represents the remaining travel route matrix. Through calculation, the scores of each remaining route in each preference dimension can be obtained to measure the degree of matching with the user's preferences. For example, if the attractions of a remaining route are exactly the same as the type that the user is most interested in, the corresponding preference will be higher. According to the route preference matrix, it is judged whether each remaining route meets the second preset condition (for example, the matching degree exceeds a certain threshold or the maximum value of 1, 2 or 3 is selected from the calculation results and recommended to the user, and the user makes further selection according to his own needs). The travel routes that meet the conditions will be marked as target travel routes and finally recommended to the user. The target travel route is generated by accurately matching the comprehensive user preference matrix and the remaining route matrix, ensuring that the final recommended route is the most suitable for the user's needs.

[0040] It also includes an actual route unit, a deviation route unit, a preference deviation unit, and a preference update unit; The actual route unit is used to obtain the user's actual travel route; A deviation route unit, used to obtain deviation route information between the user's actual travel route and the target travel route; a preference deviation unit, used for obtaining preference deviation information according to the deviation route information; The preference updating unit is used to update the user's travel preference information according to the preference deviation information.

[0041] As mentioned above, the travel routes chosen by users in actual travel are obtained. These data can be obtained through users' route software positioning, travel records, social media sharing, feedback from travel applications, etc. By collecting actual route information, users' choices and behaviors can be understood in real time, providing a data basis for subsequent deviation analysis, and obtaining deviation route information between users' actual travel routes and previously generated target travel routes. The sources of deviation may include changes in user preferences, route adjustments caused by actual conditions (such as weather, closure of attractions, etc.), or users' impromptu choices during travel. By comparing the differences between users' actual choices and the target route, a route information containing all deviations is generated. According to the deviation route information or the user's preference deviation information, by comparing the attractions actually selected by the user with the attractions in the target travel route, the user's preference changes for certain types of attractions during the travel process can be identified. For example, if the user frequently chooses historical sites in the actual travel, and the target route is mainly concentrated in natural attractions, this deviation will be recorded, reflecting that the user may have a stronger preference for historical attractions. The preference deviation information is used to update the user's travel preference information. Through the obtained deviation, the user's travel preference model can be automatically adjusted to ensure that it reflects the latest interests and preferences, and can continuously self-learn to provide more personalized and adaptive recommendations.

[0042] And, a personalized travel route recommendation terminal based on AI matrix algorithm, including: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the personalized travel route recommendation system based on the AI ​​matrix algorithm.

[0043] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. A personalized travel route recommendation system based on AI matrix algorithm, characterized in that: Including attraction preference module, first reference, second reference, recommended route and target route module; The attraction preference module is used to obtain the user's travel preference information and tourist attraction information, and obtain the tourist preference attraction information based on the travel preference information and the corresponding tourist attraction information; A first reference module is used to obtain network user travel route data and local travel route data, and summarize them into a travel route set, and obtain a first reference travel route set from the travel route set according to the travel preference scenic spot information; The second reference module is used to obtain the user's planned travel duration, and obtain the corresponding second reference travel route set from the first reference travel route set according to the user's planned travel duration; A recommended route module, used for obtaining information of must-visit tourist attractions from tourist preference tourist attraction information according to the tourist preference information of the user, and obtaining a recommended tourist route set from a second reference tourist route set according to the information of must-visit tourist attractions; The target route module is used to obtain the removed scenic spot information from the recommended tourist route set according to the user's travel preference information, and generate the target tourist route according to the removed scenic spot information and the recommended tourist route set.

2. The personalized travel route recommendation system based on AI matrix algorithm according to claim 1 is characterized in that: The scenic spot preference module includes a tourist preference acquisition unit, a tourist attraction acquisition unit, a preference matrix unit, an attraction matrix unit, a preference degree unit and a tourist preference unit; A travel preference acquisition unit, used to acquire user travel preference information; A tourist attraction acquisition unit, used to acquire tourist attraction information; A preference matrix unit is used to construct a tourism preference matrix based on tourism preference information; The scenic spot matrix unit is used to construct a tourist attraction matrix corresponding to the tourist preference matrix according to the tourist attraction information; A preference unit, used to obtain a tourist attraction preference matrix according to a tourist preference matrix and a corresponding tourist attraction matrix; The tourist preference unit is used to obtain tourist attractions that meet the first preset condition according to the tourist attraction preference matrix and mark them as tourist preference tourist attraction information.

3. The personalized travel route recommendation system based on AI matrix algorithm according to claim 1 is characterized in that: The preference degree unit includes a preference threshold unit and a preference judgment unit; A preference threshold unit, used to obtain a scenic spot preference threshold; A preference judgment unit, used to judge whether each element in the scenic spot preference matrix exceeds the scenic spot preference threshold; If the elements in the attraction preference matrix exceed the attraction preference threshold, the tourist attraction is determined to be in line with the user's preferences, and the tourist attraction corresponding to the elements exceeding the attraction preference threshold is marked as tourist preference attraction information.

4. The personalized travel route recommendation system based on AI matrix algorithm according to claim 1 is characterized in that: The first reference module includes an existing route unit, a repeated scenic spot unit, a scenic spot quantity unit and a scenic spot judgment unit; The existing route unit is used to obtain the travel route data of network users and the local travel route data, and summarize them into a travel route set; A repeating attraction unit is used to obtain the same number of tourist attractions corresponding to the tourist preference attraction information in each tourist route according to the tourist route set; The attraction quantity unit is used to obtain the quantity of all preferred attractions in the tourist preferred attraction information, and obtain the attraction threshold quantity according to the quantity of preferred attractions; An attraction judgment unit, used to judge whether the same number of each tourist attraction exceeds the threshold number of attractions; If the same number of tourist attractions exceeds the threshold number of attractions, it is determined that the tourist routes corresponding to the same number of tourist attractions meet the user's travel preference requirements, and all tourist routes that meet the user's travel preference requirements are summarized as a first reference tourist route set.

5. The personalized travel route recommendation system based on AI matrix algorithm according to claim 1 is characterized in that: The second reference module includes a reference duration unit, a planned duration unit, a threshold duration unit and a duration judgment unit; A reference duration unit, used to obtain the recommended duration for sightseeing at scenic spots in each reference tourist route in the first reference tourist route set and the duration of the journey to adjacent scenic spots, and summarize them into the first reference tourist route set; Planned duration unit, used to obtain the user's planned travel duration; A threshold duration unit, used to obtain a travel threshold duration according to the user's travel preference information and the user's planned travel duration; A duration determination unit, used to determine whether each first reference travel duration exceeds a travel threshold duration; If the first reference travel duration does not exceed the travel threshold duration, the travel routes corresponding to the first reference travel duration that does not exceed the travel threshold duration are aggregated into a second reference travel route set.

6. The personalized travel route recommendation system based on AI matrix algorithm according to claim 1 is characterized in that: The recommended route module includes a must-see scenic spot unit, a second scenic spot unit and a recommended route unit; A must-visit scenic spot unit is used to obtain must-visit scenic spot information from the tourist preference scenic spot information according to the user's tourist preference information; The second scenic spot unit is used to obtain the tourist attraction information included in each tourist route in the second reference tourist route set and mark it as the second tourist attraction information; A recommended route unit, used for judging whether each second tourist attraction information completely contains the must-visit tourist attraction information; If the second tourist attraction information completely includes the must-visit tourist attraction information, the tourist routes corresponding to the second tourist attraction information completely including the must-visit tourist attraction information are summarized as a recommended tourist route set.

7. The personalized travel route recommendation system based on AI matrix algorithm according to claim 1 is characterized in that: The target route module includes a recommended scenic spot unit, a removed scenic spot unit, a remaining scenic spot unit and a target route unit; The recommended scenic spot unit is used to obtain the tourist attraction information contained in each tourist route in the recommended tourist route set, and mark it as recommended route scenic spot information; A scenic spot removal unit is used to obtain the removed scenic spot information from each recommended route scenic spot information according to the user's travel preference information; The remaining scenic spot unit is used to remove the tourist attractions of the corresponding tourist route according to the removed scenic spot information to obtain the remaining tourist attraction information; The target route unit is used to generate a target tourist route according to the adjusted tourist attractions.

8. The personalized travel route recommendation system based on AI matrix algorithm according to claim 7 is characterized in that: The target route unit includes a route preference unit, a remaining route unit, a remaining matrix unit, a route preference unit and a target route unit; A route preference unit, used to obtain user travel route preference information according to the user travel preference information, and obtain a travel route preference matrix according to the user travel route preference information; The remaining route unit is used to obtain the corresponding tourist route according to each remaining tourist attraction information and mark it as the remaining tourist route information; The remaining matrix unit is used to obtain the corresponding remaining travel route matrix of multiple rows and columns according to the remaining travel route information; A route preference unit, used for obtaining a route preference matrix according to the travel route preference matrix and the remaining travel route matrices; The target route unit is used to obtain a travel route that meets the second preset condition according to the route preference matrix and mark it as a target travel route.

9. The personalized travel route recommendation system based on AI matrix algorithm according to claim 1 is characterized in that: It also includes an actual route unit, a deviation route unit, a preference deviation unit, and a preference update unit; The actual route unit is used to obtain the user's actual travel route; A deviation route unit, used to obtain deviation route information between the user's actual travel route and the target travel route; a preference deviation unit, used for obtaining preference deviation information according to the deviation route information; The preference updating unit is used to update the user's travel preference information according to the preference deviation information.

10. A personalized travel route recommendation terminal based on AI matrix algorithm, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the personalized travel route recommendation system based on the AI ​​matrix algorithm as described in any one of claims 1 to 9.

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