Tourist route recommendation system based on prospective information

Abstract

The invention relates to a tourist route recommendation system based on prospective information, and the system comprises a scenic spot future passenger flow prediction module which is used for predicting the scenic spot future passenger flow through an LSTM model based on the scenic spot historical passenger flow; the personalized route design module is used for constructing a heuristic method by using an ant colony algorithm based on deterministic information including tourist preference, time budget and scenic spot opening time and prospective information based on future scenic spot congestion degree prediction so as to generate and optimize a tourist route and output an optimal tourist route; the future scenic spot crowding degree is determined by the ratio of the predicted future scenic spot passenger flow volume to the bearing capacity of the scenic spot. The prediction module can predict the future passenger flow volume of the scenic spot, further predict the future crowdedness degree of the scenic spot and provide prospective information for subsequent route design, and the personalized route design module can design and optimize routes according to the requirements of tourists and reality constraints and output the optimal tourist route, so that the travel experience and satisfaction of the tourists are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of tourist routes, and more particularly to a tourist route recommendation system based on forward-looking information. Background Art

[0002] The modern tourism market is undergoing major changes. On the one hand, the market structure is shifting from the mass market to the free travel market. On the other hand, the behavior of free travel tourists has changed a lot. Tourists are increasingly inclined to choose personalized travel routes instead of standard travel packages. Therefore, travel recommendation systems have received increasing attention. However, due to various potential emergencies, such as traffic congestion, weather changes, etc., the travel routes designed by the recommendation system may become suboptimal or even impractical during the actual travel process. Therefore, how to avoid various potential uncertain events in advance when designing routes has become a common concern of many tourism practitioners and researchers.

[0003] Existing research or solutions have the following shortcomings: (1) Most studies only focus on the design of travel routes in deterministic environments, ignoring the negative impact of various uncertain factors on established travel routes; (2) Existing studies mainly use probability theory or fuzzy optimization methods to deal with uncertainty, but the former relies on a huge amount of data, and the latter will lose a lot of useful information within the feasible domain; (3) Existing studies mainly focus on the uncertainty of transfer time, the research problems are relatively fixed, and the system is not very scalable. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a travel route recommendation system based on forward-looking information, which can infer various uncertain events that may occur during the journey according to relevant historical data, so as to avoid these events in advance when designing the route, and improve the travel experience and satisfaction of tourists; at the same time, the route design comprehensively considers the personalized requirements of tourists and various realistic constraints, and has high practicality.

[0005] The technical solution of the present invention is as follows:

[0006] On the one hand, a travel route recommendation system based on forward-looking information includes:

[0007] The module for predicting the future passenger flow of scenic spots is used to predict the future passenger flow of scenic spots based on the historical passenger flow of scenic spots using the LSTM model;

[0008] The personalized route design module is used to generate and optimize the travel route by using an ant colony algorithm to construct a heuristic method based on deterministic information including tourist preferences, time budgets and opening hours of attractions, as well as forward-looking information based on future attraction congestion predictions, and output the best travel route; the future attraction congestion is determined by the ratio of the predicted future passenger flow of the attraction to the carrying capacity of the attraction.

[0009] Preferably, the personalized route design module specifically includes:

[0010] The initialization unit is used to encode the movement of tourists based on the spatiotemporal structure of the route and generate the initialization route design by using a double-layer variable chromosome;

[0011] The solution set construction unit is used to place all tourists at the starting point before each iteration, and the tourist code will be initialized to an empty chromosome; then, each tourist will move independently according to the deposited pheromone and the reality constraint; when all tourists reach the end node, an iteration will be completed, and Q feasible routes will be generated; wherein, each movement of the tourist is based on the transfer probability, which is calculated based on the deposited pheromone, the potential utility value of the next attraction, and the transfer time between attractions;

[0012] The local search unit is used to select the best route S in the current iteration from Q feasible routes after each iteration. cbest ; for S cbest Perform a local search to check whether there is a neighborhood solution with a better utility value. If so, replace S with this neighborhood solution. cbest ; The global optimal solution S so far gbest Will be S gbest and S cbest It is replaced by the one with better utility value;

[0013] The pheromone updating unit is used to update the pheromone after the local search and enter the next round of iteration; the units in the personalized route design module are repeatedly executed until the set number of iterations is reached, and finally the best travel route is output.

[0014] Preferably, in the initialization unit, the upper layer of the double layers represents the selection and visiting order of scenic spots, and the lower layer of the double layers represents the visiting duration of the corresponding scenic spots.

[0015] Preferably, in the solution set construction unit, from the scenic spot v i Move to attraction v j The transition probability p k The calculation method of (i,j) is as follows:

[0016]

[0017] Among them, τ(i,j) represents the scenic spot v i and attractions j The pheromone concentration between, i, j ∈ [1, N], N represents the number of scenic spots; allowed l represents the set of unvisited attractions; u j (t) means the tourist arrives at the scenic spot v at time t j The utility value obtained; t(v i ,v j ) indicates scenic spot v i and attractions j The transfer time between .

[0018] Preferably, tourists from scenic spot v at time t j The utility value u obtained j (t), expressed as follows:

[0019] u j (t) = MS j (t)·p j ·sc j (t)

[0020] Among them, MS j (t) is a marginal decreasing function; p j is a value between 0 and 1; sc j (t) is a nonlinear function of the congestion level of attraction j at time t.

[0021] Preferably, in the pheromone updating unit, the updating rule adopted is as follows:

[0022]

[0023] Among them, τ g (i, j) represents the pheromone of route (i, j) at the g-th iteration; ρ represents the rate of increase used to control the pheromone; i, j∈[1, N], N represents the number of scenic spots; represents the added value of pheromones brought by tourist l to route (i, j) at the g-th iteration; represents the pheromone increase value brought by the global best tourist to route (i, j) at the g-th iteration; The total utility value U obtained by tourist l from the route at the gth iteration l and the number of paths in the route P l decided.

[0024] Preferably, the total utility value U l , which is expressed as follows:

[0025]

[0026] Where D represents the budgeted number of travel days; It represents the utility value obtained by tourists in the mth stage on the kth day. Each time a tourist visits a scenic spot, it is recorded as a stage. Assume that the number of times a tourist visits a scenic spot during the kth day of travel is The whole travel process can be divided into i, j∈[1, N], N represents the number of scenic spots; represents the starting time of the mth stage of the kth day, which is determined by the maximum value of the arrival time of the tourists and the opening time of the scenic spot; It means the end of the visit time; is a marginal decreasing function; p i is a value between 0 and 1; Indicates whether to visit attraction i in the mth stage on the kth day. If yes, then otherwise It is a nonlinear function of the crowdedness of attraction i at time t on the kth day.

[0027] Preferably, the nonlinear function of the crowdedness of attraction i at time t on day k is It is expressed as follows:

[0028]

[0029] Among them, cap iα Indicates the crowding threshold that affects the tourist experience; cap iβ It indicates the cut-off point where crowding leads to a decrease in satisfaction; γ indicates the sensitivity of tourists to crowding, and the smaller the value, the more sensitive tourists are to crowding; represents the crowdedness of attraction i at time t on day k, which is determined by the number of tourists to the attraction in the future and the carrying capacity of the attractions i The ratio is determined by .

[0030] Preferably, the future tourist flow prediction module of the scenic spot includes:

[0031] A data preprocessing unit, used to preprocess the collected historical passenger flow data, including data standardization and division into training sets and test sets;

[0032] The LSTM training unit is used to train the LSTM model based on the processing results of the data preprocessing unit, and optimize the batch size, learning rate, and number of hidden layer units during the model training process;

[0033] The prediction generation unit is used to predict the future passenger flow of the scenic spot based on the trained LSTM model.

[0034] The beneficial effects of the present invention are as follows:

[0035] The present invention provides a forward-looking tourist route recommendation system, which takes into account the tourist route design problem under the influence of uncertain events and solves the problem with the help of prediction technology. Compared with the existing tourist route recommendation system, the system proposed by the present invention can effectively avoid the negative impact of various uncertain events (such as traffic congestion, weather changes, etc.) on the tourist itinerary, thereby improving the travel experience and satisfaction of tourists. At the algorithm design level, LSTM is introduced to generate predictions for uncertain events. The model can effectively balance prediction accuracy and efficiency; based on the ant colony algorithm, variable neighborhood search and differential evolution algorithm are integrated to optimize the tourist route in time and space, and the optimization efficiency is improved by improving the search structure; it has high scalability and provides a reference for solving the negative impact of other common uncertain events in the travel process.

[0036] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments, but the travel route recommendation system based on forward-looking information of the present invention is not limited to the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a system framework diagram of an embodiment of the present invention;

[0038] Figure 2 1 is a structural diagram of an LSTM unit in an embodiment of the present invention;

[0039] Figure 3 is an operation flow chart of a personalized route design module according to an embodiment of the present invention;

[0040] Figure 4 is a schematic diagram of route coding according to an embodiment of the present invention;

[0041] Figure 5 Schematic diagram of the exchange and insertion operations of an embodiment of the present invention; wherein (a) is an itinerary map for exchanging any two days, (b) is a sequence diagram for exchanging any two scenic spots in the itinerary of the same day, (c) is an itinerary map for exchanging any two scenic spots in any two days, (d) is an itinerary map for inserting an unvisited scenic spot map at any position in the itinerary, and (e) is a route map after inserting an unvisited scenic spot on the basis of (d). DETAILED DESCRIPTION

[0042] The present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0043] like Figure 1 As shown, the present invention provides a travel route recommendation system based on forward-looking information, comprising:

[0044] The future passenger flow prediction module 1 of the scenic spot is used to predict the future passenger flow of the scenic spot based on the historical passenger flow of the scenic spot using the LSTM model;

[0045] The personalized route design module 2 is used to generate and optimize the travel route by using an ant colony algorithm based on deterministic information including tourist preferences, time budgets and opening hours of scenic spots, as well as forward-looking information based on future scenic spot congestion predictions, and output the best travel route; the future scenic spot congestion is determined by the ratio of the predicted future passenger flow of the scenic spot to the carrying capacity of the scenic spot.

[0046] The future tourist flow prediction module 1 of the scenic spot includes:

[0047] A data preprocessing unit 10 is used to preprocess the collected historical passenger flow data, including data standardization and division into a training set and a test set;

[0048] The LSTM training unit 11 is used to perform LSTM model training based on the processing result of the data preprocessing unit 10, and optimize the batch size, learning rate and number of hidden layer units during the model training process;

[0049] The prediction generation unit 12 is used to predict the future passenger flow of the scenic spot based on the trained LSTM model.

[0050] The personalized route design module 2 specifically includes:

[0051] An initialization unit 20 is used to encode the movement of tourists based on the spatiotemporal structure of the route and generate an initialization route design by using a double-layer variable chromosome;

[0052] The solution set construction unit 21 is used to place all tourists at the starting point before each iteration, and the codes of the tourists will be initialized to empty chromosomes; then, each tourist will move independently according to the deposited pheromone and the reality constraints; when all tourists arrive at the end node, one iteration will be completed, and Q feasible routes will be generated at this time; wherein, each movement of the tourists is based on the transfer probability, and the transfer probability is calculated based on the deposited pheromone, the potential utility value of the next attraction, and the transfer time between attractions;

[0053] The local search unit 22 is used to select the best route S in the current iteration from the Q feasible routes after each iteration. cbest ; for S cbest Perform a local search to check whether there is a neighborhood solution with a higher utility value. If so, replace S with this neighborhood solution. cbest ; The global optimal solution S so far gbest Will be S gbest and S cbest It is replaced by the one with higher utility value;

[0054] The pheromone updating unit 23 is used to update the pheromone after the local search and to carry out the next round of iteration; the units in the personalized route design module 2 are repeatedly executed until the set number of iterations is reached, and finally the best travel route is output.

[0055] From the above, the travel route recommendation system based on forward-looking information of the present invention can infer various uncertain events that may occur during the journey according to relevant historical data, so as to avoid these events in advance when designing the itinerary, thereby improving the travel experience and satisfaction of tourists. The present invention comprehensively considers the personalized requirements of tourists and various practical constraints, so the designed route has high practicality. The system will be described in detail below in combination with crowd avoidance during the travel process.

[0056] The route design problem is first described using a mathematical model as follows. Equation (1) represents the objective function of route design, which is to enable tourists to obtain the highest overall utility value U in a D-day journey, where It represents the utility value obtained by tourists in the mth stage on the kth day. Each time a tourist visits a scenic spot, it is recorded as a stage. Assume that the number of times a tourist visits a scenic spot during the kth day of travel is The whole travel process can be divided into N represents the number of scenic spots.

[0057]

[0058] Tourists are in the mth stage on the kth day The utility value obtained and tourists’ preference for the attraction (p i ), the visit time in this stage Marginal subjective perception and tourists’ satisfaction with crowding Therefore, in The utility value obtained in the stage can be expressed as formula (2):

[0059]

[0060] In the formula, represents the starting time of the mth stage of the kth day, which is determined by the maximum value of the arrival time of the tourists and the opening time of the scenic spot; It means the end of the visit time; is a marginal decreasing function; p i is a value between 0 and 1; Indicates whether to visit attraction i in the mth stage on the kth day. If yes, then otherwise

[0061] is the crowdedness of attraction i at time t on day k The nonlinear function is defined as shown in formula (3):

[0062]

[0063] Among them, cap iα Refers to the crowding threshold that affects the visitor experience; cap iβ It refers to the cut-off point where crowding leads to a decrease in satisfaction; γ represents the sensitivity of tourists to crowding, and the smaller the value, the more sensitive tourists are to crowding; represents the crowdedness of scenic spot i at time t on day k, which is determined by the number of tourists at that time. and the carrying capacity of the scenic spot i ), the future passenger flow of the scenic spot (the number of tourists in the future of the scenic spot) is an uncertain variable (i.e., currently unknown information), and the present invention adopts the future passenger flow prediction module 1 of the scenic spot to process the variable.

[0064] The following equations (4)-(10) are the realistic constraints for route design. Equation (4) indicates that tourists start from a starting point (V S ) and reaches an end point (V) on the last day (D) F ); Equations (5) and (6) indicate that except for the last day, the rest point (V R ) is also the starting point of the second day’s journey; Formula (7) indicates that the visiting time of the scenic spot should be within its opening time window, and Respectively represent attractions v i Opening and closing times, V A is a set of scenic spots; Formula (8) indicates that only one scenic spot can be visited in each stage; Formulas (9) and (10) are used to ensure the connectivity of the route, t(Λ j ,Λ j+1 ) represents the transition time between two stages, represents the arrival time of the m+1th stage on the kth day, It indicates whether to visit attraction j immediately after visiting attraction i on day k. Indicates yes, Indicates no.

[0065]

[0066] Among them, v i is the starting point set (V S ) in a node. j is the set of end points (V F ) in a node.

[0067] Indicates the first stage of the first day to visit the scenic spot i (i.e. v i , v i It is also a point in the starting point set), which means that the point visited in the first stage of the first day is a point in the starting point set. Indicates the Dth day and the Mth D Stage (M D Stage D is the last stage of the day) to visit scenic spots j (i.e. v j , v j is another point in the set of end points). Indicates the Mth day on the kth day k Whether the node i is visited in stage i. Indicates the Mth day on the kth day k stage, It means the first stage on the k+1th day.

[0068] In addition to the above-mentioned realistic constraints that must be met, the present invention also provides some personalized options to meet tourists' specific needs and preferences, as defined in equations (11)-(14). Specifically, tourist destinations often include many attractions, and tourists often book some must-visit attractions and some must-not-visit attractions. If the designed itinerary does not include must-visit attractions or involves must-not-visit attractions, the tourists' travel experience will be significantly affected. To this end, equations (11) and (12) constrain this problem, where S V Indicates a collection of must-see attractions, S A represents the set of attractions that must be avoided. Formula (13) constrains the time spent on daily travel to be within the time budget of tourists. represents the waiting time of the mth stage on the kth day, which is caused by the arrival time of tourists earlier than the opening time of the scenic spot; τ k represents the starting time of the k-th day. Finally, for multi-day tours, some tourists may not want to change resting places (hotels) every day during the trip. Therefore, formula (14) constrains tourists' willingness to change hotels.

[0069]

[0070] In order to solve the above problems, the present invention proposes a tourist route recommendation system including a future tourist flow prediction module 1 and a personalized route design module 2. The future tourist flow prediction module 1 of the tourist attraction is used to predict the future based on historical data (such as predicting the future crowdedness of tourist attractions), so as to provide forward-looking information for subsequent route design. The personalized route design module 2 is used to design and optimize the route according to tourist needs and actual constraints. Considering that tourist route design is an NP-hard problem, it is difficult to obtain its exact solution through a polynomial algorithm in a limited time, especially in the context of urban tourism. The spatiotemporal correlation between attractions and the variability of the urban environment further complicate the difficulty of solving the problem.

[0071] For the future visitor flow prediction module 1 of the scenic spot, the present invention uses a long short-term memory neural network (LSTM) to generate future predictions. The operating principle of LSTM is as follows Figure 2 As shown, its ability comes from its unique "gating" mechanism, mainly the input gate (i t ), forget gate (f t ) and the output gate (o t ). Assume that the historical passenger flow sequence of the scenic spot is X = [x1, x2,…, x t ], then its propagation in the LSTM unit mainly includes the following four steps. First, using f t Determine the state of the previous unit (c t-1 ) should be removed. t will decide what new information will be kept in the current cell state (c t ), in this step, a new candidate variable is generated Furthermore, with the help of c t-1 and Generate c t Finally, t Will determine what information can be output.

[0072] LSTM is a deep learning model with good prediction accuracy and computational efficiency. In addition, it can extract time dependencies well and effectively avoid problems such as gradient vanishing and gradient exploding. Generating predictions with the help of LSTM requires processing through three units: data preprocessing unit 10, which preprocesses the collected historical passenger flow data, including data standardization and dividing the training set and test set; LSTM training unit 11, in which important parameters such as batch size, learning rate, and number of hidden layer units need to be optimized; prediction generation unit 12.

[0073] The present invention provides forward-looking information for route design with the help of a future passenger flow prediction module 1 for scenic spots. Specifically, the module takes the historical passenger flow data of each scenic spot as input, and outputs the prediction of the future passenger flow of these scenic spots. The passenger flow prediction is forward-looking information (i.e., it is unknown at the moment). Bringing this information into the subsequent route design can help tourists avoid the more crowded visiting time periods of the scenic spots in advance, thereby improving the travel experience.

[0074] For the personalized route design module 2, the present invention constructs a heuristic method based on the ant colony algorithm to generate and optimize the travel route. The personalized route design module 2 includes four parts: an initialization unit 20, a solution set construction unit 21, a local search unit 22, and a pheromone update unit 23. The initialization unit 20 will preliminarily set some important parameters of the ant colony algorithm, and use a double-layer variable chromosome to directly encode the initial solution; the solution set construction unit 21 will calculate the transfer probability of the ant colony based on the deposited pheromones and realistic constraints, thereby constructing a feasible route set; the local search unit 22 integrates variable neighborhood search and differential evolution algorithms to optimize the spatiotemporal structure of the route; the pheromone update unit 23 updates the deposited pheromones according to the best route in the ant colony. The operation flow chart of the personalized route design module 2 is shown below. Figure 3 shown.

[0075] (1) Initialization unit

[0076] In this unit, it is first necessary to preliminarily set the important parameters of the ant colony algorithm, including the number of iterations (G), population size (Q), pheromone importance (α), heuristic factor importance (β) and pheromone volatility (ρ). In the context of tourist route design, the coding rules of the ant colony algorithm can regard each ant as a tourist, and the tourist's movement route is the tourist route. Taking into account the time (visiting time) and space (attraction selection and visiting order) structure of the route, the present invention uses a double-layer, variable chromosome to encode the movement of tourists. Figure 4 The figure shows the structure of the route code, where the upper layer is the selection and visiting order of the attractions, and the lower layer is the visiting time of the corresponding attractions. Therefore, the schematic route code indicates that the tourist visited attractions v4, v7 and v1 consecutively on the first day, and the visiting time at these attractions was 35, 30 and 60 minutes respectively.

[0077] (2) Solution set construction unit

[0078] Before each iteration, all tourists will be placed at the starting point and their codes will be initialized to empty chromosomes. Subsequently, each tourist moves independently according to the deposited pheromones and real constraints. When all tourists reach the end node, an iteration is completed and Q feasible routes are generated. Each movement of tourists is mainly based on the transition probability, which is calculated based on the deposited pheromones, the potential utility value of the next attraction, and the transfer time between attractions. For example, for tourist l, it moves from attraction v to attraction v. i Move to attraction v j The probability p k The calculation method of (i, j) is shown in equations (11) and (12). i and attractions j The pheromone concentration between l is a collection of unvisited attractions; j (t) means the tourist arrives at the scenic spot v at time t j The utility value obtained; t(v i ,v j ) indicates scenic spot v i and attractions j The transfer time between .

[0079]

[0080]

[0081] Tourists from scenic spot v at time t j The utility value u obtained j (t), expressed as follows:

[0082] u j (t) = MS j (t)·p j ·sc j (t) (13)

[0083] Among them, MS j (t) is a marginal decreasing function; p j is a value between 0 and 1; sc j (t) is a nonlinear function of the congestion level of attraction j at time t.

[0084] (3) Local Search Unit

[0085] In this unit, the present invention introduces a local search process to further enhance the optimization capability of the ant colony algorithm. Specifically, after each iteration, the best route S in the current iteration is selected from Q feasible routes. cbest . Then, S cbestPerform a local search to check if there is a better neighborhood solution. If so, replace S with that solution. cbest Finally, the global optimal solution (S gbest ) will be S gbest and S cbest The above local search is mainly based on variable neighborhood search and differential evolution algorithm. Among them, variable neighborhood search seeks a better solution by changing the neighborhood structure of the system, which is mainly used to optimize the spatial structure of the route. Considering the problem context and the quality and efficiency of the solution, the present invention designs two neighborhood structures: exchange operation and insertion operation. The structure is shown in the figure. Figure 5 shown. Figure 5 (a) To exchange any two days of itinerary, Figure 5 (b) To exchange the order of any two attractions in the same day’s itinerary, Figure 5 (c) To exchange any two attractions in any two-day itinerary, Figure 5 (d) To insert an unvisited attraction at any point in the itinerary, Figure 5 (e) Figure 5 (d) is a route after inserting an unvisited attraction. All newly constructed routes need to meet the real constraints before they can be saved. After completing the spatial structure optimization, the differential evolution algorithm will be used to optimize the temporal structure of the route. The differential evolution algorithm uses operations such as mutation, crossover, and selection to make the initial solution converge to the global optimal solution.

[0086] (4) Pheromone update unit

[0087] After the local search, the pheromone will be updated. The existing technology mainly adopts two update strategies: ranking-based ant system or elite ant system. The update rule adopted by the present invention is defined as formula (14), where τ g (i, j) represents the pheromone of route (i, j) at the g-th iteration; represents the added value of pheromone brought by tourist l (globally optimal tourist) to route (i, j) at the g-th iteration. As shown in formula (15), is the total utility value (U l ) and the number of paths in the route (P l ). ρ is used to control the rate of increase of pheromones, thereby reducing the probability of local optimality. In addition, by integrating the global optimal tourists, this method can effectively accelerate the aggregation speed.

[0088]

[0089] In summary, the personalized route design module 2 is responsible for generating route recommendations based on various information and realistic constraints. Specifically, the input of the personalized route design module 2 is deterministic information such as tourist preferences, time budget, scenic spot opening hours, and forward-looking information such as future scenic spot crowding prediction. Based on this information, the personalized route design module 2 first generates an initialization route design, then performs a series of complex optimizations on the spatiotemporal structure of the initialization route, and finally outputs the best travel route.

[0090] The above embodiments are only used to illustrate the present invention, and are not used to limit the present invention. As long as they are based on the technical essence of the present invention, changes and modifications to the above embodiments will fall within the scope of the claims of the present invention.

Claims

1. A travel route recommendation system based on forward-looking information, characterized in that: include: The module for predicting the future passenger flow of scenic spots is used to predict the future passenger flow of scenic spots based on the historical passenger flow of scenic spots using the LSTM model; The personalized route design module is used to generate and optimize the travel route by using an ant colony algorithm to construct a heuristic method based on deterministic information including tourist preferences, time budgets and opening hours of attractions, as well as forward-looking information based on future attraction congestion predictions, and output the best travel route; the future attraction congestion is determined by the ratio of the predicted future passenger flow of the attraction to the carrying capacity of the attraction.

2. The travel route recommendation system based on forward-looking information according to claim 1, characterized in that: The personalized route design module specifically includes: The initialization unit is used to encode the movement of tourists based on the spatiotemporal structure of the route and generate the initialization route design by using a double-layer variable chromosome; The solution set construction unit is used to place all tourists at the starting point before each iteration, and the tourist code will be initialized to an empty chromosome; then, each tourist will move independently according to the deposited pheromone and the reality constraint; when all tourists reach the end node, an iteration will be completed, and Q feasible routes will be generated; wherein, each movement of the tourist is based on the transfer probability, which is calculated based on the deposited pheromone, the potential utility value of the next attraction, and the transfer time between attractions; The local search unit is used to select the best route S in the current iteration from Q feasible routes after each iteration. cbest ; for S cbest Perform a local search to check whether there is a neighborhood solution with a better utility value. If so, replace S with this neighborhood solution. cbest ; The global optimal solution S so far gbest Will be S gbest and S cbest It is replaced by the one with better utility value; The pheromone updating unit is used to update the pheromone after the local search and enter the next round of iteration; the units in the personalized route design module are repeatedly executed until the set number of iterations is reached, and finally the best travel route is output.

3. The travel route recommendation system based on forward-looking information according to claim 2, characterized in that: In the initialization unit, the upper layer of the double layer represents the selection and visiting order of scenic spots, and the lower layer of the double layer represents the visiting duration of the corresponding scenic spots.

4. The travel route recommendation system based on forward-looking information according to claim 2, characterized in that: In the solution set construction unit, from the scenic spot v i Move to attraction v j The transition probability p k The calculation method of (i,j) is as follows: Among them, τ(i,j) represents the scenic spot v i and attractions j The pheromone concentration between, i, j ∈ [1, N], N represents the number of scenic spots; allowed l represents the set of unvisited attractions; u j (t) means the tourists arrive at the scenic spot v at time t j The utility value obtained; t(v i ,v j ) indicates scenic spot v i and attractions j The transfer time between .

5. The travel route recommendation system based on forward-looking information according to claim 4, characterized in that: Tourists from scenic spot v at time t j The utility value u obtained j (t), expressed as follows: u j (t)=MS j (t)·p j ·sc j (t) Among them, MS j (t) is a marginal decreasing function; p j is a value between 0 and 1; sc j (t) is a nonlinear function of the congestion level of attraction j at time t.

6. The travel route recommendation system based on forward-looking information according to claim 2, characterized in that: In the pheromone updating unit, the updating rule adopted is as follows: Among them, τ g (i, j) represents the pheromone of route (i, j) at the g-th iteration; ρ represents the rate of increase used to control the pheromone; i, j∈[1, N], N represents the number of scenic spots; represents the added value of pheromones brought by tourist l to route (i, j) at the g-th iteration; represents the pheromone increase value brought by the global best tourist to route (i, j) at the g-th iteration; The total utility value U obtained by tourist l from the route at the gth iteration l and the number of paths in the route P l decided.

7. The travel route recommendation system based on forward-looking information according to claim 6, characterized in that: Total utility value U l , which is expressed as follows: Where D represents the budgeted number of travel days; It represents the utility value obtained by tourists in the mth stage on the kth day. Each time a tourist visits a scenic spot, it is recorded as a stage. Assume that the number of times a tourist visits a scenic spot during the kth day of travel is The whole travel process can be divided into i, j∈[1, N], N represents the number of scenic spots; represents the starting time of the mth stage of the kth day, which is determined by the maximum value of the arrival time of the tourists and the opening time of the scenic spot; It means the end of the visit time; is a marginal decreasing function; p i is a value between 0 and 1; Indicates whether to visit attraction i in the mth stage on the kth day. If yes, then otherwise It is a nonlinear function of the crowdedness of attraction i at time t on the kth day.

8. The travel route recommendation system based on forward-looking information according to claim 7, characterized in that: The nonlinear function of the crowdedness of attraction i at time t on day k It is expressed as follows: Among them, cap iα Indicates the crowding threshold that affects the tourist experience; cap iβ It indicates the cut-off point where crowding leads to a decrease in satisfaction; γ indicates the sensitivity of tourists to crowding, and the smaller the value, the more sensitive tourists are to crowding; represents the crowdedness of attraction i at time t on day k, which is determined by the number of tourists to the attraction in the future and the carrying capacity of the attractions i The ratio is determined by .

9. The travel route recommendation system based on forward-looking information according to claim 1, characterized in that: The future tourist flow prediction module of the scenic spot includes: A data preprocessing unit, used to preprocess the collected historical passenger flow data, including data standardization and division into training sets and test sets; The LSTM training unit is used to train the LSTM model based on the processing results of the data preprocessing unit, and optimize the batch size, learning rate, and number of hidden layer units during the model training process; The prediction generation unit is used to predict the future passenger flow of the scenic spot based on the trained LSTM model.

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