A tourism route planning method and system based on artificial intelligence

Abstract

The application discloses a kind of tourism route planning method and system based on artificial intelligence, comprising the following steps: step one: multiple modal input data are collected and multiple modal data set is constructed;Step two: multiple modal data set is input to Perceiver IO model and is encoded;Step three: original city environment atlas is constructed;Step four: based on intent-environment two-way remapping network, generate individualized city environment atlas;Step five: construct tourism path set;Step six: based on improved cuckoo search algorithm, the tourism path set is optimized, and the heterogenous reconfiguration egg exchange mechanism is introduced to realize leap evolution update;Step seven: when meeting preset evolution termination condition, end evolution update, generate optimized tourism path set, and select the highest fitness as the current recommended path.The application fuses multiple modal modeling and improved cuckoo search algorithm, and realizes the efficient planning of individualized tourism path.

Description

Technical Field

[0001] This invention relates to the field of tourism recommendation technology, and in particular to a tourism route planning method and system based on artificial intelligence. Background Technology

[0002] With the rapid growth in demand for smart travel services and personalized travel recommendations, how to automatically plan high-quality travel routes based on user interests and urban spatial characteristics has become an important research direction in the fields of artificial intelligence and smart cities. Existing travel route planning methods mostly rely on preset attraction levels, historical check-in popularity, or user ratings for route construction, but in practical applications, they generally have the following problems:

[0003] The lack of deep semantic understanding of multimodal user preference data (such as text, images, and behavioral records) makes it difficult to accurately depict individualized tourism intentions; the construction of urban environmental maps is based solely on static traffic or attraction distribution, failing to dynamically adjust according to user interests, resulting in low matching degree between generated paths and actual user preferences; most optimization algorithms adopt traditional genetic algorithms or ant colony algorithms, with small granularity and slow convergence in the path update process, making it easy to get trapped in local optima; path replacement is only based on single points or edges, lacking a structural-level reconstruction mechanism, which limits the leapfrog evolution capability of tourism paths and makes it difficult to effectively explore diverse and high-quality path spaces.

[0004] Therefore, how to provide a tourism route planning method and system based on artificial intelligence is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] One objective of this invention is to propose an artificial intelligence-based tourism route planning method and system. This invention accurately analyzes user intent by constructing a Perceiver IO model, generates personalized urban environment maps by combining intent-environment bidirectional remapping networks, and improves the diversity of path evolution and global search capabilities by introducing an improved cuckoo search algorithm with a heterogeneous reconstruction egg exchange mechanism. Finally, it selects the tourism route with the highest fitness as the current recommended route. It has comprehensive advantages such as high personal matching degree, strong evolution update efficiency, and high recommendation result quality, effectively overcoming the problems of single path and optimization getting stuck in local optima in traditional methods. It is applicable to various application scenarios such as intelligent tourism services.

[0006] According to an embodiment of the present invention, a tourism route planning method based on artificial intelligence includes the following steps:

[0007] Step 1: Collect multimodal input data from users and construct a multimodal dataset;

[0008] Step 2: Input the multimodal dataset into the Perceiver IO model for encoding to obtain the user intent representation vector;

[0009] Step 3: Construct the original urban environment map, which consists of interest nodes, reachable path edges, and initial edge weights;

[0010] Step 4: Input the user intent representation vector into the intent-environment bidirectional remapping network, calculate the importance weights of interest nodes and personalized edge weights, adjust the original urban environment map, and generate a personalized urban environment map.

[0011] Step 5: Based on the personalized urban environment map, generate multiple tourist routes according to the set time budget constraints and maximum number of nodes constraints. The tourist routes are composed of interest nodes arranged in the order of visit, and construct a set of tourist routes.

[0012] Step Six: Optimize the set of tourist routes based on the improved cuckoo search algorithm. The improvement of the improved cuckoo search algorithm is that it introduces a heterogeneous reconstruction egg exchange mechanism. In each round of tourist route replacement, the tourist route is divided into several functional segments to achieve leapfrog evolution and update.

[0013] Step 7: When the preset evolution termination condition is met, end the evolution update, generate an optimized set of travel routes, and select the travel route with the highest fitness as the current recommended route.

[0014] Optionally, the multimodal input data specifically includes text information, image information, user history behavior records, and interest preference information.

[0015] Optionally, step two specifically involves:

[0016] The text information is segmented into words, and words are converted into text token embedding vectors through word vector encoding.

[0017] The image information is divided into multiple image blocks, and each image block is flattened and convolutionally encoded to obtain the image token embedding vector.

[0018] The user's historical behavior records and interest preference information are structured and each behavior record is represented as a multi-field data structure including behavior timestamp, interest tag, access object type and interaction operation type. The structured fields are mapped to a vector representation of a set dimension through a multilayer perceptron coding network to obtain the behavior token embedding vector.

[0019] The text token embedding vector, image token embedding vector, and behavior token embedding vector are concatenated to form a unified token sequence, and modality type encoding and location information encoding are added to each token to form the model input tensor;

[0020] The model input tensor is input into the cross-attention encoding module of the Perceiver IO model. Cross-modal associations are established among all input tokens through the attention mechanism. Multimodal token information is integrated using global attention weighting to generate latent representations.

[0021] The latent representation is input into a stacked self-attention module for multi-layer representation updates. The updated latent representation is then mapped to an output token sequence through a reverse cross-attention module. The output token sequence is then pooled to obtain the user intent representation vector.

[0022] Optionally, step three specifically includes:

[0023] Based on the city's geospatial information data, we screen geographical entities with tourism, leisure, culture and entertainment functions, construct a set of interest nodes, and extract structured attributes such as geographical coordinates, category, opening time and popularity level for each interest node.

[0024] Based on road network data, pedestrian access data, and public transportation route maps, access paths in the city are extracted and analyzed to identify traffic paths connecting different interest nodes and obtain access path edges. The access path edges include the identifiers of the starting and ending interest nodes, path length, estimated travel time, and traffic mode type attribute information.

[0025] The path length and estimated travel time between interest nodes on the reachable path are weighted and calculated to generate the initial edge weights.

[0026] Based on the interest nodes, access path edges, and initial edge weights, an original urban environment graph is constructed to represent the connection relationships between interest nodes in the city.

[0027] Optionally, the intent-environment bidirectional remapping network specifically includes an interest node enhancement module, an edge weight adjustment module, and a graph fusion module;

[0028] The user intent representation vector is input into the interest node enhancement module, and the cosine similarity between the user intent representation vector and the attribute vector of each interest node in the original urban environment map is calculated to generate the importance weight of the interest node.

[0029] Based on the importance weight of interest nodes, the set of interest nodes in the original urban environment map is filtered out, and interest nodes whose importance weight is less than the preset importance weight threshold are removed. Also, based on the connection relationship between interest nodes, associated access path edges are removed simultaneously.

[0030] The edge weight adjustment module extracts the importance weights of the starting interest nodes at both ends of each access path edge, and performs a weighted fusion based on the importance weights of the starting interest nodes at both ends and the initial edge weight to calculate the personalized edge weight of each access path edge.

[0031] The initial edge weight of each reachable path edge is replaced with a personalized edge weight, and reachable path edges with personalized edge weights greater than a preset edge weight threshold are subjected to connection pruning operations.

[0032] A personalized urban environment map is generated based on the retained interest nodes, the updated access path edges, and the personalized edge weights.

[0033] Optionally, step five specifically includes:

[0034] Based on the set of interest nodes and access path edge information in the personalized urban environment map, the importance weights of the interest nodes are sorted in descending order, and the top N interest nodes are selected to construct the initial candidate node set.

[0035] Each interest node is selected sequentially from the set of initial candidate nodes as the starting point of the path, the travel path is initialized, and time budget constraints and maximum number of nodes constraints are set for the construction of the travel path.

[0036] Using the last interest node in the current travel path as the reference node (when the travel path just begins to expand, the starting candidate node is the starting point of the path and also the last interest node), traverse the adjacent interest nodes that are connected by accessible path edges, sort them based on the personalized edge weights of the accessible path edges, and select the interest node with the smallest personalized edge weight that does not appear in the current travel path as the next expansion node.

[0037] The cumulative travel time is updated in real time after each node expansion. When the cumulative travel time exceeds the preset time budget, or the number of visited nodes exceeds the preset maximum number of nodes, the path expansion process is terminated.

[0038] Complete the tourism path construction process for all initial candidate nodes in sequence, generate multiple tourism paths that meet the time budget constraint and the maximum number of nodes constraint, and construct a tourism path set.

[0039] Optionally, step six specifically includes:

[0040] The population is initialized as a set of travel routes, and the fitness of each travel route is calculated as follows:

[0041] The importance weights of all nodes of interest in the travel route are summed and multiplied by a preset first weighting factor to obtain the first calculated value; the personalized edge weights of all accessible path edges in the travel route are summed and multiplied by a preset second weighting factor to obtain the second calculated value; the fitness of the travel route is obtained by subtracting the second calculated value from the first calculated value.

[0042] In each round of tourism route evolution and update, the tourism route with the lowest fitness value is selected from the current tourism route set as the target route, and a tourism route is randomly selected from the top 20% of tourism routes with fitness values ​​from high to low as the parent route.

[0043] Based on the category attribute of the interest nodes, consecutive interest nodes of the same category in the parent path are divided into a functional segment to construct a sequence of functional segments of the tourism path;

[0044] The heterogeneous reconstruction egg exchange mechanism of the improved cuckoo search algorithm specifically includes: randomly selecting one or more functional segments from the functional segment sequence and replacing functional segments with the same category attributes in the target path;

[0045] If no functional segment of the same category exists in the target path, the selected functional segment is inserted into the target path in the original access order to generate a candidate path.

[0046] The candidate paths are checked for connectivity integrity. If there are two adjacent interest nodes that are not connected, the shortest path connecting the two adjacent interest nodes is found in the personalized urban environment map, and an intermediate interest node is inserted to connect the paths.

[0047] The fitness of the candidate paths after connection integrity verification is recalculated. If the fitness of the candidate path is higher than that of the original target path, the target path is replaced by the candidate path and it enters the next generation of the population. Otherwise, the original target path remains unchanged.

[0048] Optionally, dividing consecutive interest nodes of the same category in the parent path into a functional segment specifically involves:

[0049] The parent path is scanned according to the category attributes of the interest nodes. Two or more interest nodes that are visited consecutively and have the same category attributes are divided into a functional segment. The position index of the start and end interest nodes in the parent path is recorded for each functional segment. This is used by the heterogeneous reconstruction egg exchange mechanism to find matching segments and determine the insertion position in the target path.

[0050] Optionally, the preset evolution termination condition includes at least one of the following:

[0051] Scenario 1: The current evolutionary generation has reached the preset maximum generation threshold;

[0052] Scenario 2: The maximum fitness improvement in the set of tourism routes across several consecutive generations is less than the set convergence threshold;

[0053] Scenario 3: The fitness standard deviation of all travel routes in the current population is less than the set stability threshold;

[0054] During the tourism route optimization process, the preset evolution termination conditions are continuously monitored. If the preset evolution termination conditions are met (i.e., any one or more of the above situations occur), the evolution process is immediately terminated, the final generation is output as the optimized tourism route set, and the tourism route with the highest fitness is selected as the current recommended route.

[0055] According to an embodiment of the present invention, a tourism route planning system based on artificial intelligence includes the following modules:

[0056] The multimodal data acquisition module is used to collect users' multimodal input data and construct multimodal datasets;

[0057] The user intent modeling module is used to input the multimodal dataset into the Perceiver IO model for encoding and to generate user intent representation vectors.

[0058] The urban environment map construction module is used to construct the original urban environment map, which consists of interest nodes, reachable path edges, and initial edge weights.

[0059] The map personalization adjustment module is used to input the user intent representation vector into the intent-environment bidirectional remapping network, calculate the importance weight of interest nodes and the personalized edge weight of access paths, adjust the original urban environment map, and generate a personalized urban environment map.

[0060] The initial path generation module is used to generate multiple tourist paths based on a personalized urban environment map, according to the set time budget constraints and maximum node number constraints, and to construct a set of tourist paths.

[0061] The path optimization module is used to optimize the set of tourist paths based on the improved rhododendron search algorithm. The improved rhododendron search algorithm includes a heterogeneous reconstruction egg exchange mechanism, which divides the parent path into functional segments and performs structural-level reconstruction in each round of evolution to achieve leapfrog evolution updates, while performing connection integrity verification and fitness calculation.

[0062] An evolution control module is used to set and detect preset evolution termination conditions. If the preset evolution termination conditions are met, the evolution process is stopped.

[0063] The route recommendation module is used to select the most adaptive tourist route from the final optimized tourist route set after the evolution process is completed, and output it as the current recommended route.

[0064] The beneficial effects of this invention are:

[0065] This invention addresses the problems of ambiguous user intent modeling, low path matching accuracy, and local optima in existing tourism route planning by introducing the Perceiver IO model and the intent-environment bidirectional remapping network. It adopts a unified multimodal data fusion and personalized urban map reconstruction mechanism, combined with an improved cuckoo search algorithm based on a heterogeneous reconstruction egg exchange mechanism, to achieve leapfrog evolution optimization of tourism routes. In the user intent modeling stage, a cross-attention mechanism is used to deeply fuse and encode text, image, and behavioral data, outputting a high-dimensional unified user intent representation vector that effectively captures multimodal preference features. In the environment modeling stage, the weights of interest nodes and access paths in the urban environment map are dynamically adjusted based on user intent, achieving map reconstruction that is highly coupled with individual preferences. In the path optimization stage, the parent path is divided into functional segments and a heterogeneous reconstruction egg exchange mechanism is introduced to enhance the path evolution span and diversity. The accessibility and superiority of the path are ensured through connection integrity verification and fitness evaluation. Ultimately, the system can efficiently generate personalized tourism recommendation paths with reasonable structure, high preference matching, and strong convergence in complex urban traffic environments based on user interests, significantly improving the intelligence and personalization level of path planning. Attached Figure Description

[0066] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0067] Figure 1 This is an overall flowchart of a tourism route planning method based on artificial intelligence proposed in this invention;

[0068] Figure 2 This is a schematic diagram of the structure of an artificial intelligence-based tourism route planning system proposed in this invention. Detailed Implementation

[0069] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0070] refer to Figure 1 An artificial intelligence-based tourism route planning method includes the following steps:

[0071] Step 1: Collect multimodal input data from users and construct a multimodal dataset;

[0072] Step 2: Input the multimodal dataset into the Perceiver IO model for encoding to obtain the user intent representation vector;

[0073] Step 3: Construct the original urban environment map, which consists of interest nodes, reachable path edges, and initial edge weights;

[0074] Step 4: Input the user intent representation vector into the intent-environment bidirectional remapping network, calculate the importance weights of interest nodes and personalized edge weights, adjust the original urban environment map, and generate a personalized urban environment map.

[0075] Step 5: Based on the personalized urban environment map, generate multiple tourist routes according to the set time budget constraints and maximum number of nodes constraints. The tourist routes are composed of interest nodes arranged in the order of visit, and construct a set of tourist routes.

[0076] Step Six: Optimize the set of tourist routes based on the improved cuckoo search algorithm. The improvement of the improved cuckoo search algorithm is that it introduces a heterogeneous reconstruction egg exchange mechanism. In each round of tourist route replacement, the tourist route is divided into several functional segments to achieve leapfrog evolution and update.

[0077] Step 7: When the preset evolution termination condition is met, end the evolution update, generate an optimized set of travel routes, and select the travel route with the highest fitness as the current recommended route.

[0078] In this embodiment, the multimodal input data specifically includes text information, image information, user history behavior records, and interest preference information.

[0079] In this embodiment, step two specifically includes:

[0080] The text information is segmented into words, and words are converted into text token embedding vectors through word vector encoding.

[0081] The image information is divided into multiple image blocks, and each image block is flattened and convolutionally encoded to obtain the image token embedding vector.

[0082] The user's historical behavior records and interest preference information are structured and each behavior record is represented as a multi-field data structure including behavior timestamp, interest tag, access object type and interaction operation type. The structured fields are mapped to a vector representation of a set dimension through a multilayer perceptron coding network to obtain the behavior token embedding vector.

[0083] The text token embedding vector, image token embedding vector, and behavior token embedding vector are concatenated to form a unified token sequence, and modality type encoding and location information encoding are added to each token to form the model input tensor;

[0084] The model input tensor is input into the cross-attention encoding module of the Perceiver IO model. Cross-modal associations are established among all input tokens through the attention mechanism. Multimodal token information is integrated using global attention weighting to generate latent representations.

[0085] The latent representation is input into a stacked self-attention module for multi-layer representation updates. The updated latent representation is mapped to an output token sequence through a reverse cross-attention module. The output token sequence is then pooled to obtain the user intent representation vector.

[0086] The Perceiver IO model in this invention is a general neural network structure suitable for multimodal information processing. It encodes multimodal input data from text, images, user behavior records, and interest preference information into a unified user intent representation vector. The Perceiver IO model mainly consists of three parts: a cross-attention module, a self-attention module, and a reverse cross-attention module. First, the encoded multimodal token sequence is input to the cross-attention module. This module, based on the attention mechanism in Transformer, establishes cross-modal associations among all input tokens, achieving semantic fusion between different modalities. This module does not directly perform self-attention calculation on all tokens; instead, it aggregates the embedded information of all tokens into a compressed representation through the attention mechanism, improving processing efficiency and semantic abstraction capabilities.

[0087] After generating the initial fused latent representation, it is input into a stacked self-attention module for multi-layer representation updates. This module adopts a standard multi-head self-attention structure, containing multiple layers of Transformer encoder units. Each layer models the semantic dependencies within the representation through a self-attention mechanism, and combines a feedforward network to complete feature transformation and normalization processing, thereby progressively improving the abstract expressive power of the representation.

[0088] The updated latent representation is further fed into the inverse cross-attention module. This module is structurally similar to the cross-attention module but operates in the opposite direction. Its core principle is to construct an attention mechanism using the token embeddings from the input phase as queries and the latent representations as keys and values. By calculating the attention distribution of each input token to each unit in the latent representation, the most relevant semantic information is extracted, achieving the reconstruction from abstract representations to specific modal semantics. Finally, pooling operations are used to aggregate the output token sequence into vectors, resulting in a fixed-dimensional user intent representation vector.

[0089] In this embodiment, step three specifically includes:

[0090] Based on the city's geospatial information data, we screen geographical entities with tourism, leisure, culture and entertainment functions, construct a set of interest nodes, and extract structured attributes such as geographical coordinates, category, opening time and popularity level for each interest node.

[0091] Based on road network data, pedestrian access data, and public transportation route maps, access paths in the city are extracted and analyzed to identify traffic paths connecting different interest nodes and obtain access path edges. The access path edges include the identifiers of the starting and ending interest nodes, path length, estimated travel time, and traffic mode type attribute information.

[0092] The path length and estimated travel time between interest nodes on the reachable path are weighted and calculated to generate the initial edge weights.

[0093] Based on the interest nodes, access path edges, and initial edge weights, an original urban environment graph is constructed to represent the connection relationships between interest nodes in the city.

[0094] In this embodiment, the intention-environment bidirectional remapping network specifically includes an interest node enhancement module, an edge weight adjustment module, and a graph fusion module;

[0095] The user intent representation vector is input into the interest node enhancement module, and the cosine similarity between the user intent representation vector and the attribute vector of each interest node in the original urban environment map is calculated to generate the importance weight of the interest node.

[0096] Based on the importance weight of interest nodes, the set of interest nodes in the original urban environment map is filtered out, and interest nodes whose importance weight is less than the preset importance weight threshold are removed. Also, based on the connection relationship between interest nodes, associated access path edges are removed simultaneously.

[0097] The edge weight adjustment module extracts the importance weights of the starting interest nodes at both ends of each access path edge, and performs a weighted fusion based on the importance weights of the starting interest nodes at both ends and the initial edge weight to calculate the personalized edge weight of each access path edge.

[0098] ;

[0099] in, Represents personalized edge weights. Represents the original edge weights. Representing interest nodes Importance weight, Representing interest nodes Importance weight, and The value represents the weighting coefficient, where the personalized edge weight represents the travel cost. The smaller the value, the more the access path matches the user's interests and preferences, and the stronger the user's willingness to travel.

[0100] The initial edge weight of each reachable path edge is replaced with a personalized edge weight, and reachable path edges with personalized edge weights greater than a preset edge weight threshold are subjected to connection pruning operations.

[0101] A personalized urban environment map is generated based on the retained interest nodes, the updated access path edges, and the personalized edge weights.

[0102] In this embodiment, step five specifically includes:

[0103] Based on the set of interest nodes and access path edge information in the personalized urban environment map, the importance weights of the interest nodes are sorted in descending order, and the top N interest nodes are selected to construct the initial candidate node set.

[0104] Each interest node is selected sequentially from the set of initial candidate nodes as the starting point of the path, the travel path is initialized, and time budget constraints and maximum number of nodes constraints are set for the construction of the travel path.

[0105] Using the last interest node in the current travel path as the reference node (when the travel path just begins to expand, the starting candidate node is the starting point of the path and also the last interest node), traverse the adjacent interest nodes that are connected by accessible path edges, sort them based on the personalized edge weights of the accessible path edges, and select the interest node with the smallest personalized edge weight that does not appear in the current travel path as the next expansion node.

[0106] The cumulative travel time is updated in real time after each node expansion. When the cumulative travel time exceeds the preset time budget, or the number of visited nodes exceeds the preset maximum number of nodes, the path expansion process is terminated.

[0107] Complete the tourism path construction process for all initial candidate nodes in sequence, generate multiple tourism paths that meet the time budget constraint and the maximum number of nodes constraint, and construct a tourism path set.

[0108] In this embodiment, step six specifically includes:

[0109] The population is initialized as a set of travel routes, and the fitness of each travel route is calculated as follows:

[0110] The importance weights of all nodes of interest in the travel route are summed and multiplied by a preset first weighting factor to obtain the first calculated value; the personalized edge weights of all accessible path edges in the travel route are summed and multiplied by a preset second weighting factor to obtain the second calculated value; the fitness of the travel route is obtained by subtracting the second calculated value from the first calculated value.

[0111] In each round of tourism route evolution and update, the tourism route with the lowest fitness value is selected from the current tourism route set as the target route, and a tourism route is randomly selected from the top 20% of tourism routes with fitness values ​​from high to low as the parent route.

[0112] Based on the category attribute of the interest nodes, consecutive interest nodes of the same category in the parent path are divided into a functional segment to construct a sequence of functional segments of the tourism path;

[0113] The heterogeneous reconstruction egg exchange mechanism of the improved cuckoo search algorithm specifically includes: randomly selecting one or more functional segments from the functional segment sequence and replacing functional segments with the same category attributes in the target path;

[0114] If no functional segment of the same category exists in the target path, the selected functional segment is inserted into the target path in the original access order to generate a candidate path.

[0115] The candidate paths are checked for connectivity integrity. If there are two adjacent interest nodes that are not connected, the shortest path connecting the two adjacent interest nodes is found in the personalized urban environment map, and an intermediate interest node is inserted to connect the paths.

[0116] The fitness of the candidate paths after connection integrity verification is recalculated. If the fitness of the candidate path is higher than that of the original target path, the target path is replaced by the candidate path and enters the next generation of the population; otherwise, the original target path is kept unchanged.

[0117] In the AI-based tourism route planning method of this invention, an improved cuckoo search algorithm is proposed to address the limitations of existing cuckoo search algorithms in optimizing complex path combinations. This algorithm introduces a heterogeneous reconstruction egg exchange mechanism to achieve leapfrog evolution updates, thereby significantly improving the diversity of path generation and personalized matching effects.

[0118] The core of the improved Cuckoo Search algorithm lies in dividing the travel route into multiple functional segments. Each functional segment consists of interest nodes with consistent category attributes and continuous visits from the parent path, representing a certain type of travel preference scenario (such as cultural tours, food tours, and nature tours). After the division, the start and end position indices of each functional segment in the parent path are recorded as a structural reference. In each iteration, the path with the lowest fitness is selected from the current travel route set as the target path, and a parent path is randomly selected from the top 20% of paths in terms of fitness. At the same time, a heterogeneous reconstruction egg exchange mechanism is introduced to randomly select one or more functional segments from the functional segment sequence of the parent path to replace the functional segments with consistent category attributes in the target path. If there are no functional segments of the same category in the target path, the selected functional segments are inserted into the target path in the original access order.

[0119] The key to achieving leapfrog evolutionary updates lies in the fact that selected functional segments do not need to maintain their original structural positions or orders. Instead, by matching categories, they are directly replaced with segments of the same category in the target path, or new functional segments are inserted sequentially at appropriate positions in the target path. This breaks through the limitations of traditional path point fine-tuning and achieves structural-level leap mutations. This segment-level reconstruction operation gives path evolution a greater capacity for structural mutations, avoids getting trapped in local optima, and improves global exploration efficiency.

[0120] To ensure the feasibility of the reconstructed path, this invention checks the connectivity integrity of adjacent interest nodes in the new path to see if there is a connection. If there is no connection, the shortest reachable path is automatically found in the personalized urban environment map, and necessary intermediate nodes are inserted to complete the path. The fitness of the candidate paths is recalculated, and those with better fitness are included in the next generation of the population, gradually approaching the optimal recommended path.

[0121] In this embodiment, dividing consecutive interest nodes of the same category in the parent path into a functional segment specifically means:

[0122] The parent path is scanned according to the category attributes of the interest nodes. Two or more interest nodes that are visited consecutively and have the same category attributes are divided into a functional segment. The position index of the start and end interest nodes in the parent path is recorded for each functional segment. This is used by the heterogeneous reconstruction egg exchange mechanism to find matching segments and determine the insertion position in the target path.

[0123] In this embodiment, the preset evolution termination condition includes at least one of the following:

[0124] Scenario 1: The current evolutionary generation has reached the preset maximum generation threshold;

[0125] Scenario 2: The maximum fitness improvement in the set of tourism routes across several consecutive generations is less than the set convergence threshold;

[0126] Scenario 3: The fitness standard deviation of all travel routes in the current population is less than the set stability threshold;

[0127] During the tourism route optimization process, the preset evolution termination conditions are continuously monitored. If the preset evolution termination conditions are met (i.e., any one or more of the above situations occur), the evolution process is immediately terminated, the final generation is output as the optimized tourism route set, and the tourism route with the highest fitness is selected as the current recommended route.

[0128] refer to Figure 2 An artificial intelligence-based tourism route planning system includes the following modules:

[0129] The multimodal data acquisition module is used to collect users' multimodal input data and construct multimodal datasets;

[0130] The user intent modeling module is used to input the multimodal dataset into the Perceiver IO model for encoding and to generate user intent representation vectors.

[0131] The urban environment map construction module is used to construct the original urban environment map, which consists of interest nodes, reachable path edges, and initial edge weights.

[0132] The map personalization adjustment module is used to input the user intent representation vector into the intent-environment bidirectional remapping network, calculate the importance weight of interest nodes and the personalized edge weight of access paths, adjust the original urban environment map, and generate a personalized urban environment map.

[0133] The initial path generation module is used to generate multiple tourist paths based on a personalized urban environment map, according to the set time budget constraints and maximum node number constraints, and to construct a set of tourist paths.

[0134] The path optimization module is used to optimize the set of tourist paths based on the improved rhododendron search algorithm. The improved rhododendron search algorithm includes a heterogeneous reconstruction egg exchange mechanism, which divides the parent path into functional segments and performs structural-level reconstruction in each round of evolution to achieve leapfrog evolution updates, while performing connection integrity verification and fitness calculation.

[0135] An evolution control module is used to set and detect preset evolution termination conditions. If the preset evolution termination conditions are met, the evolution process is stopped.

[0136] The route recommendation module is used to select the most adaptive tourist route from the final optimized tourist route set after the evolution process is completed, and output it as the current recommended route.

[0137] Example 1:

[0138] To verify the feasibility of this invention in practice, it was applied to a tourism route planning platform. A simulated user environment was constructed and system modules were deployed. The planning effect of personalized tourism routes for users was tested and compared in detail.

[0139] In this embodiment, the platform first collects a set of multimodal input data from test users, including interest keywords, uploaded images, historical collection records, and click behavior logs. After unified modeling using the Perceiver IO model to obtain the user intent vector, it is input into the intent-environment bidirectional remapping network to adjust the importance weights of each interest node and the personalized edge weights of the access paths in the original urban environment map, thereby generating a personalized urban environment map that is highly correlated with the user's intent.

[0140] The system generates an initial set of travel routes based on the set time budget and the maximum number of visits to interest nodes. Each route consists of interest nodes in the order of visits and satisfies connectivity and travel cost constraints. Then the system enters the route optimization stage, the core of which is the improved cuckoo search algorithm based on the heterogeneous reconstruction egg exchange mechanism proposed in this invention.

[0141] Traditional path optimization methods mostly employ fixed-granularity node replacement or whole-path fine-tuning strategies, which suffer from problems such as local convergence, narrow search space, and lack of structural leaps. To address these issues, this invention proposes for the first time to divide the path into semantically continuous functional segments, each composed of consecutive interest nodes of the same category, and then perform structural-level leap-based reconstruction based on these segments.

[0142] In each round of evolution, the system selects the tourism path with the lowest fitness from the current path set as the target path, and randomly selects a parent path from the top 20% of paths in terms of fitness. After performing a category scan on the parent path, a sequence of functional segments is constructed, and 1 to 3 functional segments are randomly selected to execute a heterogeneous reconstruction egg exchange mechanism: if a functional segment of the same category exists in the target path, it is directly replaced; otherwise, the selected functional segment is inserted into the target path to form a new candidate path.

[0143] This mechanism enables leapfrog reorganization of paths at the structural level, overcoming the limitations of traditional algorithms that rely on point-by-point replacement and local fine-tuning, and significantly improving population diversity and convergence efficiency. After candidate paths are constructed, the system also checks path connectivity. If disconnected node pairs exist, the system uses a personalized urban environment map to find the shortest reachable path and inserts intermediate nodes to ensure path integrity.

[0144] Finally, the candidate paths are re-evaluated and compared with the original paths: if the fitness is better, the candidate path is replaced and introduced into the next generation of the population; otherwise, the original path is kept unchanged. The system executes this process in each round of evolution until the final preset evolution termination condition is met.

[0145] In the experimental comparison, we selected the traditional genetic algorithm (GA), the standard cuckoo search algorithm (CSA), and the improved cuckoo search algorithm (ICSA) of this invention for path optimization and compared their performance in different aspects.

[0146] Table 1. Comparison of the performance of three optimization algorithms in tourism route planning tasks.

[0147]

[0148] As can be clearly seen from the data in Table 1 above, the improved Cuckoo Search Algorithm (ICSA algorithm) proposed in this invention performs excellently in several key performance indicators, outperforming the compared GA algorithm and the standard CSA algorithm. In terms of average path fitness, the ICSA algorithm reaches 91.4, significantly higher than the GA algorithm's 83.2 and the standard CSA algorithm's 86.7, indicating that this method can generate higher-quality personalized travel routes that are more closely matched to user interests and path constraints.

[0149] The number of convergence algebras is an important indicator of the efficiency of optimization algorithms. ICSA only requires 18 generations to reach the convergence criterion, while the GA algorithm requires 35 generations and the standard CSA algorithm requires 28 generations. This clearly demonstrates that the ICSA algorithm significantly improves the evolutionary efficiency of the search process through functional segment partitioning and heterogeneous reconstruction exchange mechanisms. In terms of optimization time, the ICSA algorithm maintains a time of 3.9 seconds, which is slightly higher than CSA's 3.6 seconds, but much lower than GA's 4.1 seconds. Combined with other indicators, it can be seen that a good balance is achieved between time consumption and performance.

[0150] In terms of point-of-interest (POI) coverage, the ICSA algorithm achieved 88.2% coverage, a significant improvement compared to GA's 76.5% and CSA's 81.4%. This means the route better covers the areas of interest to users, enhancing the richness of the route's content and the travel value. Regarding user satisfaction ratings, the ICSA algorithm also achieved a high score of 9.0, significantly outperforming GA's 7.2 and the standard CSA's 8.1, further validating that the improved evolutionary mechanism brings a significant increase in user satisfaction in actual user experience.

[0151] Overall, the method of this invention, by introducing leapfrog evolution and structure-level reconstruction operations, not only significantly outperforms traditional algorithms in path optimization quality, but also demonstrates comprehensive performance improvements in optimization efficiency, interest point matching degree, and user satisfaction, verifying its feasibility and advancement in intelligent tourism path planning tasks.

[0152] Furthermore, to further demonstrate the leapfrog evolutionary capability of the heterogeneous reconstructed egg exchange mechanism, we recorded the path diversity and fitness fluctuations of each generation of the population. Compared to other algorithms, this invention can quickly differentiate fitness levels in early iterations, indicating that it possesses strong global exploration and population differentiation capabilities.

[0153] Table 2. Statistics on path fitness and population diversity during optimization.

[0154]

[0155] As can be seen from the data listed in Table 2 above, the improved Cuckoo Search Algorithm (ICSA) exhibits stronger fitness convergence ability and population path diversity maintenance ability during evolutionary path optimization. Firstly, the fitness fluctuation value of the ICSA algorithm rapidly decreases from 4.5 in generation 4 to 0.9 in generation 16, a significantly larger decrease than that of the GA algorithm (from 1.4 to 0.2) and the standard CSA algorithm (from 2.1 to 0.3). This indicates that during evolution, ICSA can more effectively compress the fluctuation space of invalid path solutions and accelerate convergence to a better solution. Especially after generation 8, ICSA still maintains a high fitness change amplitude, indicating that its optimization potential remains strong in the mid-to-late stages.

[0156] Furthermore, regarding the path diversity index, although the fitness fluctuations of the ICSA algorithm gradually converged during the evolution process, its path diversity did not decay rapidly, remaining at 0.65 from 0.83 in the 4th generation to 0.65 in the 16th generation. This result reflects that by introducing a heterogeneous reconstruction egg exchange mechanism, the ICSA algorithm can maintain path diversity while steadily promoting the overall population to evolve towards a better region, effectively avoiding premature convergence.

[0157] This embodiment improves the traditional rhododendron search algorithm by introducing a heterogeneous reconstruction egg exchange mechanism, significantly enhancing its optimization capabilities in personalized tourism route planning tasks. This mechanism divides the parent path into functional segments based on the category of interest nodes, achieving a leapfrog evolutionary update based on category matching during path replacement. This overcomes the slow convergence and local optima traps caused by local replacements in traditional evolutionary strategies. The improved algorithm balances the overall diversity of path structure and the trend of fitness improvement in each iteration, effectively enhancing the global search capability and local optimization accuracy of the population. Simultaneously, it integrates personalized urban environmental maps to achieve path connection repair, ensuring the structural integrity and smoothness of the generated paths. This not only improves the adaptability and coverage of tourism routes but also considers user preferences and urban accessibility, providing strong technical support for achieving more intelligent and personalized tourism services.

[0158] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1.A method for planning a travel route based on artificial intelligence, characterized by, The method comprises the following steps: Step 1: collecting multi-modal input data of a user, and constructing a multi-modal data set; Step 2: inputting the multi-modal data set into a Perceiver IO model for encoding to obtain a user intention representation vector; Step 3: constructing an original urban environment graph, which is composed of interest nodes, accessible path edges and initial edge weights; Step 4: inputting the user intention representation vector into an intention-environment bidirectional remapping network to calculate interest node importance weights and personalized edge weights, adjusting the original urban environment graph to generate a personalized urban environment graph; Step 5: based on the personalized urban environment graph, generating a plurality of tourist paths according to a set time budget constraint and a maximum node number constraint, wherein the tourist paths are composed of interest nodes arranged in an access order, and a tourist path set is constructed; Step 6: optimizing the tourist path set based on an improved cuckoo search algorithm, wherein the improvement of the improved cuckoo search algorithm is that a heterogeneous restructuring egg exchange mechanism is introduced, and in each round of tourist path replacement, the tourist path is divided into a plurality of functional segments to realize jump evolution update; Step 7: when the preset evolution termination condition is met, ending the evolution update, generating an optimized tourist path set, and selecting a tourist path with the highest fitness as the current recommended path; The step 6 is specifically: initializing a population as the tourist path set, and calculating the fitness of each tourist path, wherein the calculation method is: summing all interest node importance weights in the tourist path, multiplying by a preset first weighting factor to obtain a first calculation value; summing all personalized edge weights of the accessible path edges in the tourist path, multiplying by a preset second weighting factor to obtain a second calculation value; subtracting the second calculation value from the first calculation value to obtain the fitness of the tourist path; in each round of tourist path evolution update, selecting a tourist path with the lowest fitness value from the current tourist path set as a target path, and randomly selecting a tourist path from the top 20% of tourist paths in terms of fitness value as a parent path; according to the category attribute of the interest nodes, dividing the interest nodes with the same category in the parent path into a functional segment to construct a functional segment sequence of the tourist path; performing a heterogeneous restructuring egg exchange mechanism of the improved cuckoo search algorithm, wherein the heterogeneous restructuring egg exchange mechanism specifically comprises: randomly selecting one or more functional segments from the functional segment sequence, and replacing the functional segments with the same category attribute in the target path; if there is no functional segment with the same category in the target path, inserting the selected functional segment into the target path in the original access order to generate a candidate path; performing a connection integrity check on the candidate path, if there are two adjacent interest nodes that are not connected, finding the shortest path connecting the two adjacent interest nodes in the personalized urban environment graph, and inserting an intermediate interest node to realize path connection; Re-calculate the fitness of the candidate path that passes the connection integrity check, if the fitness of the candidate path is higher than the fitness of the original target path, replace the target path with the candidate path into the next generation population, otherwise keep the original target path unchanged. 2.The method of claim 1, wherein, The multi-modal input data specifically includes text information, image information, user historical behavior records and interest preference information. 3.The method of claim 1, wherein, The step two specifically is: The text information is segmented and processed, and the words are converted into text token embedding vectors through word vector encoding method; The image information is divided into multiple image blocks, each image block is flattened and convolutionally encoded to obtain an image token embedding vector; The user historical behavior records and interest preference information are structured, each behavior record is represented as a multi-field data structure including behavior timestamp, interest label, access object type and interaction operation type, and the structured fields are mapped to a vector representation of a set dimension through a multi-layer perception encoding network to obtain a behavior token embedding vector; The text token embedding vector, image token embedding vector and behavior token embedding vector are spliced to form a unified token sequence, and a modal type code and a position information code are added to each token to form a model input tensor; The model input tensor is input into the cross-attention encoding module of the Perceiver IO model, the cross-modal association relationship between all input tokens is established through the attention mechanism, and the multi-modal token information is integrated by using global attention weighting to generate a latent representation; The latent representation is input into the stacked self-attention module for multi-layer representation updating, and the updated latent representation is mapped to an output token sequence through the reverse cross-attention module, and the output token sequence is pooled to obtain a user intent representation vector. 4.The method of claim 1, wherein, The step three specifically is: Based on the geographic spatial information data of the city, the geographic location entities with tourism, leisure, culture and entertainment functions are screened, the interest node set is constructed, and the geographic coordinates, belonging category, opening time and heat level structured attributes of each interest node are extracted; Based on the road network data, walking channel data and public transportation line map, the accessible paths in the city are extracted and analyzed, the traffic paths connecting different interest nodes are identified, and the accessible path edges are obtained, including the start and end interest node identifiers, path length, expected travel time and traffic mode type attribute information; The path length and expected travel time between the interest nodes of the accessible path edge are weighted calculated to generate an initial edge weight; Based on the interest nodes, accessible path edges and initial edge weights, an original city environment graph is constructed to represent the connection relationship between the interest nodes in the city. 5.The method of claim 1, wherein, The intent-environment bidirectional remapping network specifically includes an interest node enhancement module, an edge weight adjustment module and a graph fusion module. inputting the user intention representation vector into the interest node enhancement module, calculating cosine similarity between the user intention representation vector and attribute vectors of each interest node in the original urban environment graph, and generating an interest node importance weight; screening the set of interest nodes in the original urban environment graph according to the interest node importance weight, removing interest nodes with an importance weight less than a preset importance weight threshold, and synchronously removing associated accessibility path edges according to the connection relationship between interest nodes; the edge weight adjustment module extracts the importance weights of the starting interest nodes at both ends of each accessibility path edge, and obtains the personalized edge weight of each accessibility path edge by weighted fusion based on the importance weights of the starting interest nodes at both ends and the initial edge weight; replacing the initial edge weight of each accessibility path edge with the personalized edge weight, and performing a connection pruning operation on the accessibility path edge with a personalized edge weight greater than a preset edge weight threshold; based on the reserved interest nodes and the updated accessibility path edges, and the personalized edge weight, generating a personalized urban environment graph. 6.The method of claim 1, wherein, The step five is specifically: based on the set of interest nodes and the accessibility path edge information in the personalized urban environment graph, sorting the interest node importance weight in descending order, and selecting the top N interest nodes to construct a starting candidate node set; selecting each interest node from the starting candidate node set as a path starting point in turn, initializing a tourist path, and setting a time budget constraint and a maximum node number constraint for the tourist path construction; taking the last interest node in the current tourist path as a reference node, traversing adjacent interest nodes connected by accessibility path edges, sorting based on the personalized edge weight of the accessibility path edge, and selecting an interest node with the smallest personalized edge weight and not appearing in the current tourist path as the next expansion node; updating the cumulative travel time in real time after each node expansion, and terminating the path expansion process when the cumulative travel time exceeds a preset time budget or the number of visited nodes exceeds a preset maximum node number; sequentially completing the tourist path construction process of all starting candidate nodes, generating a plurality of tourist paths that meet the time budget constraint and the maximum node number constraint, and constructing a tourist path set. 7.The method of claim 1, wherein, The continuous interest nodes with the same category in the parent path are divided into a functional segment, specifically: scanning the parent path according to the category attribute of the interest node, dividing two or more interest nodes with consistent category attributes and continuous access into a functional segment, and recording the position index of the start and end interest nodes in the parent path for each functional segment, which is used for the heterogenous reconstruction egg switch mechanism to find a matching segment and determine an insertion position in the target path. 8.The method of claim 1, wherein, The preset evolution termination condition includes at least one of the following cases: Case one: the current evolution generation reaches a preset maximum generation threshold; Case two: the maximum fitness improvement amplitude in the continuous several generations of the tourist path set is less than a set convergence threshold; Case three: the standard deviation of the fitness of all tourist paths in the current population is less than a set stability threshold. The preset evolution termination condition is continuously detected in the tourism path optimization process. If the preset evolution termination condition is met, the evolution process is immediately terminated, the final generation is output as the optimized tourism path set, and the tourism path with the highest fitness is selected from the set as the current recommended path. 9.A tourism route planning system based on artificial intelligence, performing a tourism route planning method based on artificial intelligence according to any one of claims 1 to 8, characterized in that, The method comprises the following modules: A multi-modal data acquisition module is configured to acquire multi-modal input data of a user and construct a multi-modal data set. A user intention modeling module is configured to input the multi-modal data set into a Perceiver IO model for encoding to generate a user intention representation vector. A city environment graph construction module is configured to construct an original city environment graph, which is composed of interest nodes, accessible path edges, and initial edge weights. A graph personalization adjustment module is configured to input the user intention representation vector into an intention-environment bidirectional remapping network to calculate interest node importance weights and personalized edge weights of accessible paths, and adjust the original city environment graph to generate a personalized city environment graph. An initial path generation module is configured to generate a plurality of tourism paths based on the personalized city environment graph and according to a set time budget constraint and a maximum node number constraint to construct a tourism path set. A path optimization module is configured to optimize the tourism path set based on an improved cuckoo search algorithm, which comprises a heterogeneous reconstruction egg exchange mechanism. The heterogeneous reconstruction egg exchange mechanism divides the parent path into functional segments and performs structural level reconstruction in each evolution round to realize jump evolution update, while performing connection integrity verification and fitness calculation. Specifically, The population is initialized as the tourism path set, and the fitness of each tourism path is calculated. The calculation method is as follows: The importance weights of all interest nodes in the tourism path are summed and multiplied by a preset first weighting factor to obtain a first calculation value. The personalized edge weights of all accessible path edges in the tourism path are summed and multiplied by a preset second weighting factor to obtain a second calculation value. The fitness of the tourism path is obtained by subtracting the second calculation value from the first calculation value. In each round of tourism path evolution update, the tourism path with the lowest fitness value is selected from the current tourism path set as the target path, and a tourism path ranked in the top 20% in terms of fitness value is randomly selected as the parent path. According to the category attribute of the interest nodes, the interest nodes with the same category in the parent path are divided into a functional segment to construct a functional segment sequence of the tourism path. The heterogeneous reconstruction egg exchange mechanism of the improved cuckoo search algorithm is executed. The heterogeneous reconstruction egg exchange mechanism specifically comprises: one or more functional segments are randomly selected from the functional segment sequence, and the functional segments with consistent category attributes in the target path are replaced. If there is no functional segment with the same category in the target path, the selected functional segment is inserted into the target path in the original access order to generate a candidate path. The connection integrity of the candidate path is verified. If there are two adjacent interest nodes that are not connected, the shortest path connecting the two adjacent interest nodes is found in the personalized city environment graph, and an intermediate interest node is inserted to realize path connection. recomputing fitness of the candidate path that passes the connection integrity check, and if the fitness of the candidate path is higher than that of the original target path, replacing the target path with the candidate path into the next generation population, otherwise keeping the original target path unchanged; an evolution control module, configured to set and detect a preset evolution termination condition, and stop the evolution process if the preset evolution termination condition is met; a path recommendation module, configured to select a travel path with the highest fitness from the set of travel paths optimized finally as a current recommended path and output the current recommended path after the evolution process ends.

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