A personalized travel route recommendation system based on the AI matrix algorithm

Abstract

The present invention belongs to the technical field of tourist route recommendation, and particularly relates to a personalized tourist route recommendation system based on an AI matrix algorithm. This invention can provide users with highly customized tourist route recommendations, avoid the inaccuracies in traditional recommendation systems, dynamically adjust the recommended routes according to the latest feedback from users, eliminate uninteresting scenic spots, ensure that the recommended route plan maximally matches the user's expectations, comprehensively consider the user's preferences, time, and individual needs, making the recommended route not only reasonable but also greatly enhancing the user's tourism experience and reducing the pressure of tourism planning.

Description

Technical Field

[0001] The present invention belongs to the technical field of tourist route recommendation, and particularly 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' personalized demands for tourism experiences are increasing day by day. Traditional tourist route recommendation systems usually rely on preset routes or simple tag recommendation methods, and cannot fully meet the personalized demands of different users. Due to the different factors of each user, such as interests, tourism preferences, budgets, time limitations, etc., simple recommendation methods often cannot provide highly personalized tourist route planning.

[0003] In recent years, the rapid development of artificial intelligence (AI) technology has provided a new breakthrough for tourist recommendation systems. Especially, matrix algorithms show significant advantages in processing multi-dimensional data. By introducing the AI matrix algorithm, multi-source information of users (such as historical tourism records, real-time locations, interests and hobbies, etc.) can be comprehensively analyzed with multi-dimensional data of tourism resources (such as the popularity of scenic spots, seasonal opening information, traffic conditions, etc.), and then a highly personalized tourist route recommendation plan can be generated. Matrix algorithms can effectively process complex correlations and identify potential patterns hidden in multi-dimensional data, enabling the recommendation system to provide dynamic, real-time and accurate tourist route planning according to users' personalized demands.

[0004] Existing tourist recommendation systems often appear unable to cope when dealing with users' demands such as favorite scenic spots and tourism time. Due to the lack of a dynamic feedback mechanism, the choices of users during actual tourism often deviate from the suggestions provided by the recommendation system. This situation not only affects users' tourism experiences but also questions the rationality of the recommendations. Summary of the Invention

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

[0006] The technical solution adopted by the present invention is specifically as follows:

[0007] A personalized tourist route recommendation system based on an AI matrix algorithm includes a scenic spot preference module, a first reference, a second reference, a recommended route, and a target route module;

[0008] The scenic spot preference module is used to obtain users' tourism preference information and scenic spot information of tourist destinations, and obtain preferred scenic spot information according to the tourism preference information and the corresponding scenic spot information of tourist destinations;

[0009] The first reference module is used to obtain the travel route data of network users and local travel route data, aggregate them into a travel route set, and obtain the first reference travel route set from the travel route set according to the travel preference scenic spot information;

[0010] The second reference module is used to obtain the planned travel duration of the user, and obtain the corresponding second reference travel route set from the first reference travel route set according to the planned travel duration of the user;

[0011] The recommended route module is used to obtain the must-visit scenic spot information from the travel preference scenic spot information according to the user's travel preference information, and obtain the recommended travel route set from the second reference travel route set according to the must-visit scenic spot information;

[0012] The target route module is used to obtain the removed scenic spot information from the recommended travel route set according to the user's travel preference information, and generate the target travel route according to the removed scenic spot information and the recommended travel route set.

[0013] In a preferred solution, the scenic spot preference module includes a travel preference acquisition unit, a travel destination scenic spot acquisition unit, a preference matrix unit, a scenic spot matrix unit, a preference degree unit, and a travel preference unit;

[0014] The travel preference acquisition unit is used to obtain the user's travel preference information;

[0015] The travel destination scenic spot acquisition unit is used to obtain the scenic spot information of the travel destination;

[0016] The preference matrix unit is used to construct a travel preference matrix according to the travel preference information;

[0017] The scenic spot matrix unit is used to construct a travel destination scenic spot matrix corresponding to the travel preference matrix according to the scenic spot information of the travel destination;

[0018] The preference degree unit is used to obtain the scenic spot preference degree matrix according to the travel preference matrix and the corresponding travel destination scenic spot matrix;

[0019] The travel preference unit is used to obtain the travel scenic spots that meet the first preset condition according to the scenic spot preference degree matrix, and mark them as travel preference scenic spot information.

[0020] In a preferred solution, the preference degree unit includes a preference threshold unit and a preference judgment unit;

[0021] The preference threshold unit is used to obtain the scenic spot preference threshold;

[0022] The preference judgment unit is used to judge whether each element in the scenic spot preference degree matrix exceeds the scenic spot preference threshold;

[0023] If an element in the scenic spot preference matrix exceeds the scenic spot preference threshold, it is determined that the tourist scenic spot meets the user's preferences, and the tourist scenic spots corresponding to the elements exceeding the scenic spot preference threshold are marked as tourist preference scenic spot information.

[0024] In a preferred solution, the first reference module includes an existing route unit, a duplicate scenic spot unit, a scenic spot quantity unit, and a scenic spot judgment unit;

[0025] The existing route unit is used to obtain network user travel route data and local travel route data, and summarize them into a travel route set;

[0026] The duplicate scenic spot unit is used to obtain the same quantity of tourist scenic spots corresponding to the tourist preference scenic spot information within each travel route according to the travel route set;

[0027] The scenic spot quantity unit is used to obtain all the preferred scenic spot quantities within the tourist preference scenic spot information, and obtain the scenic spot threshold quantity according to the preferred scenic spot quantity;

[0028] The scenic spot judgment unit is used to judge whether the same quantity of each tourist scenic spot exceeds the scenic spot threshold quantity;

[0029] If the same quantity of tourist scenic spots exceeds the scenic spot threshold quantity, it is determined that the travel route corresponding to the same quantity of tourist scenic spots meets the user's travel preference requirements, and all the travel routes corresponding to the user's travel preference requirements are summarized into a first reference travel route set.

[0030] In a preferred solution, the second reference module includes a reference duration unit, a planned duration unit, a threshold duration unit, and a duration judgment unit;

[0031] The reference duration unit is used to obtain the recommended play duration of the scenic spots within each reference travel route in the first reference travel route set and the travel duration between adjacent scenic spots, and summarize them into a first reference travel duration;

[0032] The planned duration unit is used to obtain the user's planned travel duration;

[0033] The threshold duration unit is used to obtain the travel threshold duration according to the user's travel preference information and the user's planned travel duration;

[0034] The duration judgment unit is used to judge whether each first reference travel duration exceeds the travel threshold duration;

[0035] 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 summarized into a second reference travel route set.

[0036] In a preferred solution, the recommended route module includes a must-visit scenic spot unit, a second scenic spot unit, and a recommended route unit;

[0037] A must-visit attraction unit, configured to obtain must-visit attraction information from the travel preference attraction information according to the user's travel preference information;

[0038] A second attraction unit, configured to obtain the attraction information included in each travel route in the second reference travel route set and label it as second travel attraction information;

[0039] A recommended route unit, configured to determine whether each second travel attraction information completely includes the must-visit attraction information;

[0040] If the second travel attraction information completely includes the must-visit attraction information, then summarize the travel routes corresponding to the second travel attraction information that completely includes the must-visit attraction information into a recommended travel route set.

[0041] In a preferred solution, the target route module includes a recommended attraction unit, a removed attraction unit, a remaining attraction unit, and a target route unit;

[0042] A recommended attraction unit, configured to obtain the attraction information included in each travel route in the recommended travel route set and label it as recommended route attraction information;

[0043] A removed attraction unit, configured to obtain removed attraction information from each recommended route attraction information according to the user's travel preference information;

[0044] A remaining attraction unit, configured to remove the attractions of the corresponding travel route according to the removed attraction information to obtain remaining travel attraction information;

[0045] A target route unit, configured to generate a target travel route according to the adjusted travel attractions.

[0046] In a preferred solution, the target route unit includes a route preference unit, a remaining route unit, a remaining matrix unit, a route preference degree unit, and a target route unit;

[0047] A route preference unit, configured to obtain the user's travel route preference information according to the user's travel preference information and obtain a travel route preference matrix according to the user's travel route preference information;

[0048] A remaining route unit, configured to obtain the corresponding travel route according to each remaining travel attraction information and label it as remaining travel route information;

[0049] A remaining matrix unit, configured to obtain a remaining travel route matrix with multiple rows and multiple columns corresponding to the remaining travel route information;

[0050] A route preference degree unit, configured to obtain a route preference degree matrix according to the travel route preference matrix and the remaining travel route matrix;

[0051] A target route unit, configured to obtain a travel route that meets the second preset condition according to a route preference matrix and mark it as a target travel route.

[0052] In a preferred solution, it further includes an actual route unit, a deviation route unit, a preference deviation unit, and a preference update unit;

[0053] The actual route unit is configured to obtain the actual travel route of the user;

[0054] The deviation route unit is configured to obtain deviation route information between the actual travel route of the user and the target travel route;

[0055] The preference deviation unit is configured to obtain preference deviation information according to the deviation route information;

[0056] The preference update unit is configured to update the travel preference information of the user according to the preference deviation information.

[0057] And, a travel route personalized recommendation terminal based on an AI matrix algorithm, including:

[0058] One or more processors;

[0059] A storage device storing one or more programs thereon;

[0060] When the one or more programs are executed by the one or more processors, the one or more processors implement the travel route personalized recommendation system based on the AI matrix algorithm.

[0061] The technical effects achieved by the present invention are: [[ID=�3]]

[0062] The present invention can provide highly customized travel route recommendations for users, avoid the inaccuracies in traditional recommendation systems, can dynamically adjust the recommended routes according to the latest feedback of users, eliminate uninteresting scenic spots, ensure that the recommended route plan maximally matches the expectations of users, comprehensively considers the preferences, time, and personality needs of users, makes the recommended route not only reasonable, but also greatly improves the travel experience of users and reduces the pressure of travel planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a system module diagram provided by the present invention; a

[0064] Figure 2 It is a flowchart provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be given with reference to the accompanying drawings of the specification.

[0066] In the following description, many specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0067] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The phrase "in a preferred embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or selectively exclusive embodiment with other embodiments.

[0068] Thirdly, the present invention is described in detail in conjunction with schematic diagrams. When detailing the embodiments of the present invention, for the sake of convenience of explanation, the schematic diagrams are only examples and should not limit the scope of protection of the present invention here.

[0069] Please refer to the 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;

[0070] The scenic spot preference module is used to obtain user travel preference information and scenic spot information of the travel destination, and obtain travel preference scenic spot information according to the travel preference information and the corresponding scenic spot information of the travel destination;

[0071] The 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;

[0072] The second reference module is used to obtain the planned travel duration of the user, and obtain the corresponding second reference travel route set from the first reference travel route set according to the planned travel duration of the user;

[0073] The recommended route module is used to obtain travel essential scenic spot information from the travel preference scenic spot information according to the user travel preference information, and obtain a recommended travel route set from the second reference travel route set according to the travel essential scenic spot information;

[0074] The target route module is used to obtain removed scenic spot information from the recommended travel route set according to the user travel preference information, and generate a target travel route according to the removed scenic spot information and the recommended travel route set.

[0075] As described above, by obtaining information such as the user's historical travel data and hobbies, a travel preference matrix of the user is constructed. At the same time, scenic spots matching the user's preferences are extracted from the scenic spot information of the travel destination to generate a travel preference scenic spot information matrix. The travel route data of other users on the network 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, the first reference travel route set containing the user's preferred scenic spots is screened out. According to the time planned by the user, 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 route set containing the user's "must-visit scenic spots" is further extracted from the second reference travel route set, ensuring that the user can visit the most interesting scenic spots first, generating a recommended travel route set. According to the user's personalized needs (such as the scenic spots the user wants to avoid), these uninteresting scenic spots are excluded from the recommended route set to generate the final target travel route, which can provide highly customized travel route recommendations for users, avoid the inaccuracies in traditional recommendation systems, and can dynamically adjust the recommended routes according to the user's latest feedback (such as excluding uninteresting scenic spots), ensuring that the recommended route plan maximally matches the user's expectations. Considering the user's preferences, time, and personalized needs comprehensively, the recommended route is not only reasonable but also greatly enhances the user's travel experience and reduces the pressure of travel planning.

[0076] The scenic spot preference module includes a travel preference acquisition unit, a travel destination scenic spot acquisition unit, a preference matrix unit, a scenic spot matrix unit, a preference degree unit, and a travel preference unit;

[0077] The travel preference acquisition unit is used to acquire the user's travel preference information;

[0078] The travel destination scenic spot acquisition unit is used to acquire the scenic spot information of the travel destination;

[0079] The preference matrix unit is used to construct a travel preference matrix according to the travel preference information;

[0080] The scenic spot matrix unit is used to construct a travel destination scenic spot matrix corresponding to the travel preference matrix according to the scenic spot information of the travel destination;

[0081] The preference degree unit is used to obtain a scenic spot preference degree matrix according to the travel preference matrix and the corresponding travel destination scenic spot matrix;

[0082] The travel preference unit is used to obtain travel scenic spots meeting the first preset condition according to the scenic spot preference degree matrix and mark them as travel preference scenic spot information.

[0083] It should be noted that the travel preference matrix is a matrix with one row. The travel preference matrix includes vectors of multiple preference types. The travel destination scenic spot matrix has the number of rows corresponding to the number of scenic spots at the travel destination, and the number of rows of the travel destination scenic spot matrix corresponds to the number of multiple preference types.

[0084] As described above, collect the travel preference information of users. These preference information can come from the users' historical travel data, hobbies, frequently visited travel types (such as natural scenery, cultural sites, etc.), and the interaction behaviors of users on the platform, etc. Through these data, the travel preference information of users can be generated. Obtain the scenic spot-related data from the travel destination, covering the types, features, evaluations, etc. of the scenic spots. By analyzing the travel preference information, construct a matrix with one row, that is, the travel preference matrix. Each element in the matrix represents the preference degree vector of the user for a certain type of scenic spot (such as historical sites, museums, natural scenery, etc.). According to the travel destination scenic spot information, construct a travel destination scenic spot matrix. The number of rows of the matrix represents the number of scenic spots at the travel 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 scenic spot in the corresponding preference type. For example, a scenic spot may have a high matching degree in "natural scenery" and a low matching degree in "cultural experience". Match the user's travel preference matrix with the travel destination scenic spot matrix to generate a scenic spot preference degree matrix. The calculation formula of the scenic spot preference degree matrix is S = U * D, where S represents the scenic spot preference degree matrix, U represents the travel preference matrix, and D represents the travel destination scenic spot matrix. Through matrix calculation, the matching degree between the user's preferences and each scenic spot is obtained, that is, the overall preference degree score of each scenic spot under multiple preference types. According to the scenic spot preference degree matrix, screen out the travel scenic spots that meet the first preset condition (usually multiple scenic spots with higher preference degree scores). These screened scenic spots will be marked as travel preference scenic spot information and will be used as candidate scenic spots for subsequent recommended routes. It can deeply analyze the travel preferences of users and screen out the scenic spots that best meet their interests according to the personalized needs of users, avoiding the "extensive casting of the net" method in traditional recommendation systems, improving the accuracy of recommendations, quickly screening out the scenic spots that meet the conditions, greatly improving the calculation and recommendation efficiency, and enabling users to obtain travel route suggestions that meet their interests more quickly.

[0085] The preference degree unit includes a preference threshold unit and a preference judgment unit;

[0086] The preference threshold unit is used to obtain the scenic spot preference threshold;

[0087] The preference judgment unit is used to judge whether each element in the scenic spot preference degree matrix exceeds the scenic spot preference threshold;

[0088] If an element in the scenic spot preference matrix exceeds the scenic spot preference threshold, it is determined that the tourist scenic spot meets the user's preferences, and the tourist scenic spots corresponding to the elements exceeding the scenic spot preference threshold are marked as tourist preference scenic spot information.

[0089] As mentioned above, a scenic spot preference threshold is obtained, which is a preset minimum standard used to measure whether a scenic spot meets the user's travel preferences. This threshold can be determined by analyzing historical data, user personal settings, or system default values. The elements in the scenic spot preference matrix represent the user's preference degree for a certain scenic spot. If an element exceeds this threshold, it means that the user has a high preference for this scenic spot. By comparing each element in the scenic spot preference matrix with the scenic spot preference threshold, it is judged 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 scenic spot corresponding to this element is marked as meeting the user's preferences, which can automatically screen out scenic spots with high preference degrees and mark these scenic spots as tourist preference scenic spot information for subsequent route recommendations.

[0090] The first reference module includes an existing route unit, a duplicate scenic spot unit, a scenic spot quantity unit, and a scenic spot judgment unit;

[0091] The existing route unit is used to obtain network user travel route data and local travel route data and summarize them into a travel route set;

[0092] The duplicate scenic spot unit is used to obtain the quantity of the same tourist scenic spots corresponding to the tourist preference scenic spot information within each travel route according to the travel route set;

[0093] The scenic spot quantity unit is used to obtain the quantity of all preference scenic spots in the tourist preference scenic spot information and obtain the scenic spot threshold quantity according to the preference scenic spot quantity;

[0094] The scenic spot judgment unit is used to judge whether the quantity of each same tourist scenic spot exceeds the scenic spot threshold quantity;

[0095] If the quantity of the same tourist scenic spots exceeds the scenic spot threshold quantity, it is determined that the travel route corresponding to the quantity of the same tourist scenic spots meets the user's travel preference requirements, and all the travel routes corresponding to meeting the user's travel preference requirements are summarized into a first reference travel route set.

[0096] As described above, existing travel route information is obtained from the travel route data of network users and 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 preferred tourist attraction information, the number of attractions in each route that are the same as the user's preferred attractions is calculated to determine which of the existing routes contain the tourist attractions preferred by the user and quantify their matching degree. The total number of preferred attractions in the user's travel preference attraction information is obtained, and at the same time, 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 have a certain number of preferred attractions. It is judged whether the number of attractions in each travel route that are the same as the user's preferred attractions exceeds the set threshold number of attractions. If the number of matching attractions in a certain route exceeds the threshold, then this route is determined to meet the user's travel preferences. All the routes that meet the user's preference requirements 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 the user selecting one by one from a vast number of routes and greatly improving the efficiency. Through the role of the attraction quantity unit and the attraction judgment unit, the system not only considers the user's individual attraction preferences but also ensures that the selected routes contain enough attractions of interest to the user by setting an attraction threshold. This matching criterion improves the accuracy of the recommended routes and ensures that the recommended routes can meet the overall needs of the user.

[0097] The second reference module includes a reference duration unit, a planned duration unit, a threshold duration unit, and a duration judgment unit;

[0098] The reference duration unit is used to obtain the recommended playing duration of attractions within each reference travel route in the first reference travel route set and the travel duration between adjacent attractions, and aggregate them into the first reference travel duration;

[0099] The planned duration unit is used to obtain the user's planned travel duration;

[0100] The threshold duration unit is used to obtain the travel threshold duration according to the user's travel preference information and the user's planned travel duration;

[0101] The duration judgment unit is used to judge whether each first reference travel duration exceeds the travel threshold duration;

[0102] If the first reference travel duration does not exceed the travel threshold duration, then the travel routes corresponding to the first reference travel durations that do not exceed the travel threshold duration are aggregated into a second reference travel route set.

[0103] As described above, detailed time information for each travel route in the first reference travel route set is obtained, including the recommended play duration for each scenic spot and the travel duration between adjacent scenic spots. These time information are summarized into the total play time for each route, that is, the first reference travel duration. In this way, an accurate time estimate can be provided for each route, facilitating subsequent matching with the planned duration of the user. The planned travel duration of the user is obtained, that is, the total time the user hopes to spend during the entire itinerary. This information is usually input by the user himself, such as the itinerary arrangement for several days, or the play time for each day specifically. According to the user's travel preference information and the planned travel duration, a travel threshold duration is calculated. The travel threshold duration is to ensure that the duration of the recommended route does not exceed the time arrangement set by the user. At the same time, considering the user's preference for scenic spots, for example, by analyzing the user's preference for certain scenic spots, the play time of certain scenic spots can be appropriately extended or shortened, thereby dynamically adjusting the threshold duration. It is judged whether each first reference travel duration exceeds the travel threshold duration. If the total duration of a certain route does not exceed the threshold duration, then this route is considered to meet the user's time requirements. These routes that meet the duration requirements are summarized to generate a second reference travel route set, that is, a further screened candidate route set.

[0104] The recommended route module includes a must-visit scenic spot unit, a second scenic spot unit, and a recommended route unit;

[0105] The must-visit scenic spot unit is used to obtain the must-visit scenic spot information from the travel preference scenic spot information according to the user's travel preference information;

[0106] The second scenic spot unit is used to obtain the scenic spot information included in each travel route in the second reference travel route set and mark it as the second travel scenic spot information;

[0107] The recommended route unit is used to judge whether each second travel scenic spot information completely includes the must-visit scenic spot information;

[0108] If the second travel scenic spot information completely includes the must-visit scenic spot information, then the travel routes corresponding to the second travel scenic spot information that completely includes the must-visit scenic spot information are summarized into the recommended travel route set.

[0109] As described above, according to the user's travel preference information, the most desired scenic spots are selected from the travel preference scenic spot information, which are called must-visit scenic spot information for travel. These must-visit scenic spots are the most important ones for the user during the travel process. In the subsequent screening process, it is ensured that the recommended routes can include these scenic spots. For example, if the user particularly wants to visit a landmark scenic spot or a special scenic spot in a certain city, the system will mark these scenic spots as "must-visit" targets. From the second reference travel route set, the scenic spot information included in each travel route is extracted and marked as the second travel scenic spot information. The second reference travel route set has already been a route that meets the user's travel duration requirements after the previous screening. The second travel scenic spot information of each route is compared with the user's must-visit scenic spot information for travel to determine whether each route completely includes the user's must-visit scenic spots. If the second travel scenic spot information in a certain route includes all the must-visit scenic spots for travel, then this route is considered to meet the core needs of the user. The routes that completely include the must-visit scenic spots are summarized into a recommended travel route set for the user to finally select, ensuring that the recommended routes must include the scenic spots that the user particularly cares about. This solves the travel needs that the user cares most about. Even among multiple candidate routes, those routes that include the must-visit scenic spots are preferentially screened out to ensure that the user's expectations are not disappointed.

[0110] 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;

[0111] The recommended scenic spot unit is used to obtain the scenic spot information included in each travel route in the recommended travel route set and mark it as the recommended route scenic spot information;

[0112] The removed scenic spot 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;

[0113] The remaining scenic spot unit is used to remove the scenic spots of the corresponding travel route according to the removed scenic spot information to obtain the remaining travel scenic spot information;

[0114] The target route unit is used to generate a target travel route according to the adjusted travel scenic spots.

[0115] As mentioned 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 scenic spots (such as museums and shopping areas). Based on 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 and removing the corresponding scenic spots from each recommended route, leaving the part that the user is interested in and obtaining the remaining tourist attraction 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 scenic spots that are not of interest. The tourist route is adjusted and optimized according to the remaining tourist attraction information 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 scenic spots in the shortest time, thereby improving the overall travel efficiency and comfort.

[0116] 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;

[0117] A route preference unit, configured to obtain user travel route preference information based on the user travel preference information, and obtain a travel route preference matrix based on the user travel route preference information;

[0118] The remaining route unit is used to obtain the corresponding tourist route according to the information of each remaining tourist attraction and mark it as the remaining tourist route information;

[0119] The remaining matrix unit is used to obtain a corresponding remaining travel route matrix of multiple rows and columns according to the remaining travel route information;

[0120] A route preference unit, configured to obtain a route preference matrix based on the travel route preference matrix and the remaining travel route matrices;

[0121] 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.

[0122] 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 in the remaining travel route matrix corresponds to the number of travel routes in the remaining travel route information, and the number of columns in the remaining travel route matrix corresponds to the number of elements in the travel route preference matrix.

[0123] As described above, the travel route preference information is extracted based on the user's travel preference information and converted into a travel route preference matrix. This preference matrix is usually a row matrix, and each element corresponds to the user's preference degree for a certain route or a certain travel experience. For example, the user may prefer natural scenic spots, historical sites or shopping areas, and 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 characteristics of the remaining routes, the best route that meets the user's needs can be effectively identified. The corresponding travel routes are extracted based on each remaining travel attraction information and generated and marked as the remaining travel route information. These routes are the results after filtering out the attraction information, retaining the attractions that the user is interested in. On this basis, all possible remaining routes suitable for the user are obtained to ensure that there is sufficient data support for subsequent screening and optimization. A remaining travel route matrix with multiple rows and columns is generated based on the remaining travel route information. The number of rows in the matrix corresponds to the number of remaining travel routes, and the number of columns corresponds to the number of elements in the travel route preference matrix. Each row represents a remaining travel route, and each column represents the characteristics corresponding to each element (preference vector) in the user's travel route preference matrix. By matching the characteristics of each route with the user's preferences, the routes that meet the user's needs can be further screened. The user's travel route preference matrix is calculated with the generated remaining travel route matrix, and a route preference degree matrix is output. The calculation formula of the route preference degree matrix is K = P * Y, where K represents the route preference degree matrix, P represents the travel route preference matrix, and D represents the remaining travel route matrix. Through calculation, the score of each remaining route in each preference dimension can be obtained to measure the degree of its matching with the user's preferences. For example, if the attractions of a certain remaining route are exactly the same as the types that the user is most interested in, its corresponding preference degree will be higher. It is judged whether each remaining route meets the second preset condition according to the route preference degree matrix (for example, the matching degree exceeds a certain threshold or 1, 2 or 3 values with the largest values are selected from the calculation results and recommended to the user, and the user makes further selections according to their 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 routes are generated through the precise matching of the comprehensive user preference matrix and the remaining route matrix, ensuring that the finally recommended routes are the most suitable for the user's needs.

[0124] It also includes an actual route unit, a deviation route unit, a preference deviation unit and a preference update unit;

[0125] An actual route unit for obtaining the actual travel route of the user;

[0126] A deviation route unit for obtaining deviation route information between the actual travel route of the user and the target travel route;

[0127] A preference deviation unit for obtaining preference deviation information based on the deviation route information;

[0128] A preference update unit for updating the user's travel preference information based on the preference deviation information.

[0129] As described above, obtain the travel route selected by the user during the actual trip. These data can be obtained through various methods such as the location of the user's route software, travel records, social media sharing, feedback from travel applications, etc. By collecting actual route information, it is possible to understand the user's choices and behaviors in real time, providing a data basis for subsequent deviation analysis. Obtain the deviation route information between the user's actual travel route and the previously generated target travel route. The sources of deviation may include changes in user preferences, route adjustments due to actual situations (such as weather, attraction closures, etc.), or the user's impromptu choices during the trip. Compare the differences between the user's actual choices and the target route to generate a route information containing all deviations. Based on the deviation route information, obtain the user's preference deviation information. By comparing the attractions actually selected by the user with the attractions in the target travel route, it is possible to identify the tendency changes of the user for certain types of attractions during the travel process. For example, if the user frequently selects historical sites during the actual trip while the target route mainly focuses on natural attractions, this deviation will be recorded, reflecting that the user may have a stronger preference for historical attractions. Use the preference deviation information to update the user's travel preference information. By obtaining the deviation, the user's travel preference model can be automatically adjusted to ensure that it reflects the latest interests and preferences, and can continuously learn by itself, providing more personalized and adaptable recommendations.

[0130] And, a personalized travel route recommendation terminal based on the AI matrix algorithm, including:

[0131] One or more processors;

[0132] A storage device on which one or more programs are stored;

[0133] 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.

[0134] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention. The structures, devices, and operation methods not specifically described and explained in the present invention, unless otherwise specifically stated and limited, are implemented according to the conventional means in the art.

Claims

1. A personalized travel route recommendation system based on AI matrix algorithm, characterized by: 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 online 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 based on the tourist preference attraction information; The second reference module is used to obtain the user's planned travel duration, and obtain a corresponding second reference travel route set from the first reference travel route set according to the user's planned travel duration; A route recommendation module is used to obtain information about must-see tourist attractions from the preferred tourist attraction information according to the user's travel preference information, and obtain a recommended tourist route set from the second reference tourist route set according to the information about must-see tourist attractions; A target route module is used to obtain and remove scenic spot information from a set of recommended tourist routes based on user travel preference information, and generate a target tourist route based on the removed scenic spot information and the set of recommended tourist routes; 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 included in each tourist route in the recommended tourist route set and mark it as recommended route attraction 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; A target route unit, used for generating a target tourist route according to the adjusted tourist attractions; 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, configured to obtain user travel route preference information based on the user travel preference information, and obtain a travel route preference matrix based on the user travel route preference information; The remaining route unit is used to obtain the corresponding tourist route according to the information of each remaining tourist attraction and mark it as the remaining tourist route information; The remaining matrix unit is used to obtain a corresponding remaining travel route matrix of multiple rows and columns according to the remaining travel route information; A route preference unit, configured to obtain a route preference matrix based on 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.

2. The personalized travel route recommendation system based on AI matrix algorithm according to claim 1 is characterized in that: The attraction preference module includes a tourism preference acquisition unit, a tourist attraction acquisition unit, a preference matrix unit, an attraction matrix unit, a preference degree unit and a tourism 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, 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 based on the tourist attraction information; A preference unit is used to obtain a tourist attraction preference matrix based on 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 attraction preference matrix and mark them as tourist preference 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 is used to judge whether each element in the attraction preference matrix exceeds the attraction 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 element exceeding the attraction preference threshold is marked as the 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 network user travel route data and local travel route data and aggregate them into a travel route set; Repeating attraction units, 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 preference attraction information and obtain the attraction threshold quantity based on the quantity of preferred attractions; An attraction judgment unit, used to judge whether the number of identical attractions of each tourist attraction exceeds a threshold number of attractions; If the number of identical tourist attractions exceeds the threshold number of attractions, the tourist routes corresponding to the identical number of tourist attractions are determined to meet the user's travel preference requirements, and all tourist routes corresponding to 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 is used to obtain the recommended duration for visiting attractions in each reference tourist route in the first reference tourist route set and the duration of the journey to adjacent attractions, and summarize them into the first reference tourist route duration; Planned duration unit, used to obtain the user's planned travel duration; A threshold duration unit is used to obtain a travel threshold duration based on the user's travel preference information and the user's planned travel duration; a duration determination unit, configured 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-see scenic spot unit is used to obtain must-see scenic spot information from tourist preference scenic spot information according to user travel preference information; The second tourist attraction unit is used to obtain tourist attraction information included in each tourist route in the second reference tourist route set and mark it as second tourist attraction information; The route recommendation unit is used to determine whether each second tourist attraction information completely contains the information of the must-see tourist attractions; If the second tourist attraction information completely includes the must-see tourist attraction information, the tourist routes corresponding to the second tourist attraction information completely including the must-see tourist attraction information are aggregated into 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: It also includes an actual route unit, a deviation route unit, a preference deviation unit, and a preference update unit; Actual route unit, used to obtain the user's actual travel route; A deviation route unit is used to obtain the deviation route information between the user's actual travel route and the target travel route; a preference deviation unit, configured to obtain preference deviation information based on the deviation route information; The preference updating unit is used to update the user's travel preference information according to the preference deviation information.

8. A personalized travel route recommendation terminal based on AI matrix algorithm, characterized by: 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 7.

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