Hotel reservation method and device, electronic equipment and storage medium

Abstract

The invention provides a hotel booking method and device, electronic equipment and a storage medium, and the method comprises the steps: booking a first booked hotel based on first scene data before playing; during playing, booking a second booked hotel according to a temporary accommodation demand text, the emotion data and the second scene data; and after check-in, adjusting the first reservation hotel to a third reservation hotel. According to the method, the demand change condition of the tourist in the playing process is considered, a more flexible reservation mode is formulated for the tourist, the scene data and the emotion data are considered during reservation, more accurate hotel matching is performed for the tourist, and the playing experience is improved.

Description

Technical Field

[0001] The present application relates to the technical field of hotel reservation, and in particular to a method, device, electronic device and storage medium for hotel reservation. Background Art

[0002] With the gradual improvement of living standards, travel has become an important part of daily leisure activities. To achieve the best travel experience, itinerary planning is generally necessary before traveling. Itinerary planning generally involves planning travel routes and accommodation hotels.

[0003] In the prior art, when making hotel reservations, the reservation is made according to the visiting time and travel time of each scenic spot in the tourist route. After the reservation, the reservation is generally made according to the hotel reservation, which has poor flexibility and limits the travel experience. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a hotel reservation method, device, electronic device and storage medium to overcome the problems in the prior art.

[0005] In a first aspect, an embodiment of the present application provides a method for hotel reservation, the method comprising:

[0006] Before a tourist travels along a preset travel route, a plurality of first scenic spots where accommodation is required after the tourist travels are determined in the preset travel route based on the tourist's personal characteristics, and first scene data of an accommodation period of the first scenic spots is predicted based on first historical scene data of the first scenic spots;

[0007] According to the initial accommodation demand text of the tourist before the visit and the first scenario data, matching a first reserved hotel for the first scenic spot from a first set of hotels; wherein the first set of hotels is all hotels obtained from different platforms within a first preset range of the location of the first scenic spot;

[0008] When the tourist visits various scenic spots along the preset tourist route and a temporary accommodation demand text of the tourist is received, the tourist's current emotion data and second scenario data affecting accommodation at the tourist's current scenic spot are obtained;

[0009] Matching a second reservation hotel for the current attraction from a second set of hotels based on the temporary accommodation demand text, the emotion data, and the second scenario data; wherein the second set of hotels is selected from all hotels acquired from different platforms within a second preset range of the current attraction;

[0010] After the tourist checks into the second reserved hotel, adjusting the first attraction following the current attraction in the preset travel route according to the personal characteristics of the tourist to obtain an adjusted second attraction;

[0011] According to the target accommodation demand text and the second scenario data, a third reservation hotel is matched for the second scenic spot in the second overall hotel; the second overall hotel is all hotels obtained from different platforms within a third preset range of the second scenic spot location.

[0012] In some technical solutions of the present application, the first historical scene data includes first sub-data of a first change frequency and second sub-data of a second change frequency; wherein the first change frequency is greater than the second change frequency;

[0013] The predicting of obtaining the first scene data of the first scenic spot's check-in period based on the first historical scene data of the first scenic spot includes:

[0014] Predicting the first sub-data using a first number of prediction methods to obtain a first prediction result;

[0015] Predicting the second sub-data using a second number prediction method to obtain a second prediction result; wherein the first number is greater than the second number;

[0016] The first prediction result and the second prediction result are fused to obtain the first scene data.

[0017] In some technical solutions of the present application, the above method matches the first preset range in the following manner:

[0018] determining a first initial range according to the historical distances between the historical attractions and the historical accommodation hotels during the tourists' historical travels;

[0019] Based on the first scene data, the first initial range is adjusted to obtain an adjusted first preset range.

[0020] In some technical solutions of the present application, the first initial range is adjusted based on the first scene data to obtain the adjusted first preset range, including:

[0021] Dividing the first initial range into a plurality of first sub-areas according to the distances from the first scenic spot;

[0022] performing feature coding processing on the first scene data to obtain a first scene feature vector corresponding to the first scene data;

[0023] Calculating a risk score for each of the first sub-areas based on the first scene feature vector;

[0024] The first initial range is adjusted according to the risk scores of the first sub-regions to obtain an adjusted first preset range.

[0025] In some technical solutions of the present application, the above method determines the second preset range in the following manner:

[0026] determining a second initial range according to the historical distances between the historical attractions and the historical accommodation hotels during the tourists' historical travels;

[0027] Assessing the current mobility of the tourist to obtain a degree of mobility limitation;

[0028] The second initial range is adjusted according to the movement limitation to obtain an adjusted second preset range.

[0029] In some technical solutions of the present application, matching a second reserved hotel for the current scenic spot from a second overall hotel based on the temporary accommodation demand text, the emotion data, and the second scenario data includes:

[0030] Mapping the temporary accommodation demand text, the emotion data, and the second scenario data onto a three-dimensional decision space as a decision axis of the three-dimensional decision space;

[0031] Determining decision parameters corresponding to each of the decision axes based on the temporary accommodation demand text, the emotion data, and the second scenario data;

[0032] Based on the temporary accommodation demand text, the emotion data, the second scenario data and corresponding decision parameters, a second reservation hotel is matched for the current scenic spot from a second overall hotel.

[0033] In some technical solutions of the present application, the above method determines the target accommodation demand text in the following manner:

[0034] determining whether the tourist has submitted a new accommodation demand text; if the new accommodation demand text exists, fusing the new accommodation demand text with the initial accommodation demand text to obtain the target accommodation demand text;

[0035] If the newly added accommodation demand text does not exist, the initial accommodation demand text is used as the target accommodation demand text.

[0036] In a second aspect, an embodiment of the present application provides a hotel reservation device, the device comprising:

[0037] a prediction module for determining, before a tourist travels along a preset tourist route, a plurality of first scenic spots in the preset tourist route where the tourist needs to stay overnight based on the tourist's personal characteristics, and predicting first scene data for an accommodation period of the first scenic spots based on first historical scene data of the first scenic spots;

[0038] a first matching module configured to match a first reserved hotel for the first scenic spot from a first set of hotels based on the tourist's initial accommodation demand text before the visit and the first scenario data; wherein the first set of hotels is comprised of all hotels acquired from different platforms within a first preset range of the location of the first scenic spot;

[0039] an acquisition module for acquiring the tourist's current emotion data and second scenario data affecting accommodation at the tourist's current attraction upon receiving a temporary accommodation demand text from the tourist during the tourist's visit to various attractions along a preset tourist route;

[0040] a second matching module for matching a second reservation hotel for the current scenic spot from a second set of hotels based on the temporary accommodation demand text, the emotion data, and the second scenario data; wherein the second set of hotels is selected from all hotels obtained from different platforms within a second preset range of the current scenic spot;

[0041] an adjustment module, configured to adjust a first scenic spot following the current scenic spot in the preset travel route according to the personal characteristics of the tourist after the tourist checks into the second reserved hotel, to obtain an adjusted second scenic spot;

[0042] The third matching module is used for the prediction module, which is used to match the third reservation hotel for the second scenic spot in the second overall hotel based on the target accommodation demand text and the second scenario data; the second overall hotel is all hotels obtained from different platforms within a third preset range of the second scenic spot location.

[0043] In a third aspect, an embodiment of the present application provides an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned hotel reservation method when executing the computer program.

[0044] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned hotel reservation method are executed.

[0045] The technical solutions provided by the embodiments of the present application may have the following beneficial effects:

[0046] The method of the present application includes, before a tourist plays along a preset tourist route, determining, according to the personal characteristics of the tourist, a plurality of first scenic spots in the preset tourist route where accommodation is required after the tour, and predicting first scenario data of an accommodation period for the first scenic spot based on first historical scenario data of the first scenic spot; matching a first reserved hotel for the first scenic spot in a first overall hotel according to the initial accommodation demand text of the tourist before the tour and the first scenario data; wherein the first overall hotel is all hotels obtained from different platforms within a first preset range of the location of the first scenic spot; and obtaining the current emotional data and The second scenario data of the current scenic spot where the tourist is located affecting accommodation; according to the temporary accommodation demand text, the emotional data and the second scenario data, matching the second reserved hotel for the current scenic spot from the second overall hotel; wherein, the second overall hotel is all hotels obtained from different platforms within a second preset range of the current scenic spot; after the tourist checks into the second reserved hotel, adjusting the first scenic spot after the current scenic spot in the preset travel route according to the personal characteristics of the tourist to obtain an adjusted second scenic spot; according to the target accommodation demand text and the second scenario data, matching the third reserved hotel for the second scenic spot in the second overall hotel; the second overall hotel is all hotels obtained from different platforms within a third preset range of the location of the second scenic spot.

[0047] This application takes into account the changing needs of tourists during their travels, develops a more flexible booking method for tourists, considers scene data and emotional data when booking, and matches hotels for tourists more accurately, thereby improving the travel experience.

[0048] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0050] Figure 1 A schematic diagram showing a flow chart of a hotel reservation method provided in an embodiment of the present application is shown;

[0051] Figure 2 A schematic diagram of a first initial range provided in an embodiment of the present application is shown;

[0052] Figure 3 A schematic diagram of a hotel reservation device provided in an embodiment of the present application is shown;

[0053] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of illustration and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowcharts can be implemented out of sequence, and steps without logical context can be reversed or implemented simultaneously. In addition, those skilled in the art, under the guidance of the contents of this application, can add one or more other operations to the flowchart, or remove one or more operations from the flowchart.

[0055] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present application.

[0056] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0057] With the gradual improvement of living standards, travel has become an important part of daily leisure activities. To achieve the best travel experience, itinerary planning is generally necessary before traveling. Itinerary planning generally involves planning travel routes and accommodation hotels.

[0058] In the prior art, when making hotel reservations, the reservation is made according to the visiting time and travel time of each scenic spot in the tourist route. After the reservation, the reservation is generally made according to the hotel reservation, which has poor flexibility and limits the travel experience.

[0059] Based on this, embodiments of the present application provide a hotel reservation method, device, electronic device, and storage medium, which are described below through embodiments.

[0060] Figure 1 A flowchart of a hotel reservation method provided in an embodiment of the present application is shown, wherein the method includes steps S101-S106; specifically:

[0061] S101. Before a tourist travels along a preset travel route, a plurality of first scenic spots where a tourist needs to stay after the travel are determined in the preset travel route based on the tourist's personal characteristics, and first scene data of a check-in period for the first scenic spots is predicted based on first historical scene data of the first scenic spots.

[0062] S102: Matching a first reserved hotel for the first scenic spot from a first set of hotels based on the tourist's initial accommodation demand text before the visit and the first scenario data; wherein the first set of hotels is all hotels obtained from different platforms within a first preset range of the location of the first scenic spot;

[0063] S103, when the tourist receives a temporary accommodation request text from the tourist while the tourist is visiting various scenic spots along the preset travel route, obtaining the tourist's current emotion data and second scenario data affecting accommodation at the tourist's current scenic spot;

[0064] S1044. Match a second reservation hotel for the current attraction from a second set of hotels based on the temporary accommodation demand text, the emotion data, and the second scenario data; wherein the second set of hotels is selected from all hotels obtained from different platforms within a second preset range of the current attraction;

[0065] S105, after the tourist checks into the second reserved hotel, adjusting the first scenic spot following the current scenic spot in the preset travel route according to the personal characteristics of the tourist to obtain an adjusted second scenic spot;

[0066] S106. Match a third reservation hotel for the second scenic spot in a second overall hotel according to the target accommodation demand text and the second scenario data; the second overall hotel is all hotels obtained from different platforms within a third preset range of the second scenic spot location.

[0067] This application takes into account the changing needs of tourists during their travels, develops a more flexible booking method for tourists, considers scene data and emotional data when booking, and matches hotels for tourists more accurately, thereby improving the travel experience.

[0068] The following describes some embodiments of the present application in detail. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0069] The present application provides a hotel reservation method that operates on a hotel reservation service provider. The hotel reservation service provider may be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, an in-vehicle device, a wearable device, or the like. The method may be implemented by a processor invoking computer-readable instructions stored in a memory. Alternatively, the method may be executed by a server. The hotel reservation service provider is connected to a tourist service provider, which is also connected to a hotel service provider. The preset travel route in the present application embodiment is developed by the hotel reservation service provider. For example, the hotel reservation service provider obtains travel requirements from the tourist service provider and performs semantic analysis on the travel requirements to customize the route. It should be noted that the preset travel route in the present application embodiment includes multiple target attractions and multiple first attractions. The target attractions are attractions that are only visited, and the first attractions are attractions that require hotel accommodation after the visit. When determining the first attraction, the present application embodiment is based on the personal characteristics of the tourist. Specifically, the relationship between historical travel time periods and historical accommodation time periods is determined by analyzing the tourist's historical travel history. Then, according to the visiting time and travel time of each target attraction in the preset route, the first attraction is determined from the target attractions.

[0070] When booking a hotel at the first attraction, existing technology only considers the tourist's initial needs and hotel availability. For example, if a tourist requests a three-star hotel, existing technology typically selects the three-star hotel closest to the first attraction as the tourist's accommodation. This approach, which only considers straight-line distance, can affect the hotel's occupancy rate during actual check-in, potentially due to traffic congestion, emergencies, or sudden increases in visitor traffic. This can even prevent tourists from checking in, negatively impacting their travel experience.

[0071] Before a tourist follows a preset itinerary, this embodiment of the present application considers not only the distance between the first hotel and the first attraction when determining the first hotel reservation, but also comprehensively considers the first scenario data for the first hotel reservation. The distance between the first hotel and the first attraction is primarily based on the historical distances between historical attractions and hotels during the tourist's past travels. The historical distances between historical attractions and hotels are calculated to obtain an average value for these distances. Furthermore, a function for the tourist's own physical aging is established, and the average value is further adjusted based on this function to obtain an adjusted first initial range.

[0072] After obtaining the first initial range, the embodiment of the present application further considers first scenario data. The first initial range is adjusted based on the first scenario data to obtain an adjusted first preset range. The first scenario data herein includes meteorological data, traffic data, emergency data, etc. The first scenario data herein is predicted based on the first historical scenario data of the first scenic spot.

[0073] When predicting the first historical scene data, since the change frequencies of different data in the scene data are different, the impact brought by the scene data also has an impact. Specifically, the embodiment of the present application divides the first historical scene data into sub-data of different frequencies: first sub-data of the first change frequency and second sub-data of the second change frequency. For example, meteorological data and traffic data are divided into first sub-data, and emergencies are divided into second sub-data. When predicting the first historical scene data of different frequencies, in order to ensure the accuracy of the prediction, the present application uses a different number of prediction methods for the first historical scene data of different frequencies. For example, the first sub-data is predicted using the first number of prediction methods to obtain a first prediction result; the second sub-data is predicted using the second number of prediction methods to obtain a second prediction result; wherein, the first number is greater than the second number; the first prediction result and the second prediction result are fused to obtain the first scene data. That is, for data with a relatively fast changing frequency, such as weather data and traffic data, embodiments of the present application use a plurality of prediction methods to perform predictions, and then collate the prediction results of each prediction method to obtain a first prediction result. For data with a relatively slow changing frequency, such as emergencies, embodiments of the present application use a fewer prediction methods to perform predictions, and then collate the prediction results of each prediction method to obtain a second prediction result. For example, the first sub-data may be predicted using five prediction methods, and the second sub-data may be predicted using two prediction methods.

[0074] When predicting the second sub-data, due to the low frequency and uncertainty of emergencies, there is a situation where the second prediction result is empty. In response to this situation, the embodiment of the present application considers a buffered data, that is, when the second prediction result is less than a preset event data threshold, the buffered data is used as the second prediction result. The buffered data here can be determined based on the historical second sub-data, or based on the travel habits of tourists. For example, the second prediction result is expressed as 2 minutes for the journey time to the first booked hotel, which is less than the preset 5 minutes. In this case, 5 minutes is used as the second prediction result.

[0075] After obtaining the first scenario data, the embodiment of the present application needs to adjust the first initial range according to the first scenario data to obtain the first preset range. When adjusting the first initial range, the embodiment of the present application divides the first initial range into multiple first sub-areas. When dividing, the division is based on the journey time. The journey here is the fastest journey time to reach the area. For example, it takes twenty minutes to reach by subway and fifteen minutes to drive, so the journey time to this area is fifteen minutes. Figure 2 As shown, the square direction is the first scenic spot, and the first initial range is divided into area A, area B, area C, and area D according to the travel time. After the division, the embodiment of the present application adjusts the first initial range by eliminating each first sub-area.

[0076] Specifically, the embodiment of the present application performs feature encoding processing on the first scene data to obtain a first scene feature vector corresponding to the first scene data; based on the first scene feature vector, the risk score of each first sub-area is calculated; according to the risk score of each first sub-area, the first initial range is adjusted to obtain an adjusted first preset range.

[0077] For example, the first scene feature vector is obtained by the following method: weather = [35.6, 22.4, 8.3, 2] # rainfall / temperature / wind speed / warning level; traffic = [8.2, 3, 0.91] # congestion index / number of accidents / road network traffic rate; events = [0.9, 5000, 1] # event severity / impact radius / type encoding. Generate feature vectors through encoder:

[0078] scene_vector=scene_encoder(torch.tensor([weather, traffic, events]))

[0079] Output: tensor([0.92,-0.15,...,0.78], dtype=torch.float32) Backpropagation through significance reveals key dimensions: Calculate the contribution of each input feature to the output:

[0080] saliency=torch.autograd.grad(scene_vector.sum(),encoder.parameters()).

[0081] After obtaining the first scenario feature vector, each sub-score is calculated based on the first scenario feature vector and the hotel feature vectors in each first sub-region. These sub-scores are then integrated to obtain a risk score for the first sub-region. First sub-regions with risk scores below a preset risk threshold are then eliminated to obtain a first preset range.

[0082] After determining the first preset range, the first hotel is matched to the tourist's initial accommodation requirement text from the first set of hotels within the first preset range. The first set of hotels here refers to all hotels obtained from different platforms within the first preset range. These different platforms include both online and offline platforms. Online platforms can be obtained through interface calls or crawling, while offline platforms can be obtained through direct communication with the customization specialist.

[0083] After the first reserved hotel is determined for the tourist in the above manner, the tourist may still need temporary accommodation while traveling along the preset travel route. When a tourist needs a historical accommodation, he or she can send a temporary accommodation request text to the customization expert at any time. At this time, the embodiment of the present application needs to match the tourist with a second reserved hotel. When determining the second reserved hotel, since the tourist's request is a temporary one, in order to ensure the travel experience, the embodiment of the present application not only considers the second scenario data, but also pays attention to the tourist's emotional data. The emotional data and the second scenario data here are both obtained in real time, and the specific acquisition method is not limited here.

[0084] When determining the second reserved hotel, the embodiment of the present application needs to first determine the second preset range. When determining the second preset range, first determine the second initial range based on the historical distance between the historical attractions and the historical accommodation hotels during the tourist's historical travel. It should be noted that the second initial range at this time is determined based on the normal emotions of the tourist, taking into account that the tourist's accommodation demand text is temporarily proposed and there are emotional changes. After determining the second initial range, the embodiment of the present application also needs to adjust the second initial range. During the specific adjustment, the embodiment of the present application first reduces the second initial range based on the current behavioral ability of the tourist, and then considers the second scene data to eliminate the second sub-area of the reduced second initial range to obtain the second preset range. The process of eliminating the second sub-area here is the same as the process of eliminating the first sub-area, and will not be repeated here.

[0085] After determining the second preset range, the embodiment of the present application matches a second reserved hotel for the current scenic spot from the second overall hotel based on the temporary accommodation demand text, the emotional data, and the second scenario data. Specifically, the temporary accommodation demand text, the emotional data, and the second scenario data are mapped onto a three-dimensional decision space, serving as decision axes of the three-dimensional decision space; based on the temporary accommodation demand text, the emotional data, and the second scenario data, decision parameters corresponding to each of the decision axes are determined; and based on the temporary accommodation demand text, the emotional data, the second scenario data, and the corresponding decision parameters, a second reserved hotel is matched for the current scenic spot from the second overall hotel.

[0086] For example, the temporary accommodation demand text, the emotional data and the second scene data features are standardized and then aligned across modalities, after which the coordinates are generated: X = cosine_similarity (temporary accommodation demand text, hotel feature vector), matching dimension; Y = sigmoid (second scene data vector, hotel scene response vector), scene adaptability; Z = 1-normalized_euclidean (emotion vector, hotel emotional portrait), emotional fit.

[0087] Construct the scoring function of the three:

[0088] Score=e -λ(1-X) ×(αY+βZ)

[0089] Exponential decay coefficient λ: λ = 0.3 + 0.1·σ (eurgency)

[0090] σ(·): Sigmoid function:

[0091] eurgency∈[0,1]: The urgency value output by sentiment analysis.

[0092] Scene weight α and emotion weight β:

[0093] β=1-α

[0094] Scene confidence (∈scene is the LSTM-Autoencoder reconstruction error).

[0095] Emotional stability (Var(Ehist)) is the variance of user historical emotions.

[0096] λ∈[0.2,0.5]: exponential decay coefficient, increasing with user urgency; α+β=1: dynamic weighting of scenario and emotion. For every 0.1 increase in λ urgency, λ increases by 0.05. When the α / β scenario confidence is greater than 0.7, α=0.6; when the emotion fluctuation is greater than 0.3, β=0.5.

[0097] The second reserved hotel is matched from the second overall hotel pool using the aforementioned method. After the second reserved hotel is determined, the tourist decides whether to check in. After the tourist checks into the second reserved hotel, the first attraction after the current attraction in the pre-set itinerary (in chronological order) needs to be adjusted due to time constraints. Specifically, the second attraction where the tourist needs to stay after the tour is re-determined, and then the third reserved hotel is matched to the second attraction.

[0098] When determining the third hotel reservation, the user may need to check in in advance, which may result in an additional accommodation request. Besides the parts that are identical to the first reservation, the user is also required to determine whether the user has submitted an additional accommodation request. If so, the additional accommodation request is combined with the initial accommodation request to generate the target accommodation request. If not, the initial accommodation request is used as the target accommodation request.

[0099] Figure 3 A schematic diagram of the structure of a hotel reservation device provided in an embodiment of the present application is shown, wherein the device includes:

[0100] a prediction module for determining, before a tourist travels along a preset tourist route, a plurality of first scenic spots in the preset tourist route where the tourist needs to stay overnight based on the tourist's personal characteristics, and predicting first scene data for an accommodation period of the first scenic spots based on first historical scene data of the first scenic spots;

[0101] a first matching module configured to match a first reserved hotel for the first scenic spot from a first set of hotels based on the tourist's initial accommodation demand text before the visit and the first scenario data; wherein the first set of hotels is comprised of all hotels acquired from different platforms within a first preset range of the location of the first scenic spot;

[0102] an acquisition module for acquiring the tourist's current emotion data and second scenario data affecting accommodation at the tourist's current attraction upon receiving a temporary accommodation demand text from the tourist during the tourist's visit to various attractions along a preset tourist route;

[0103] a second matching module for matching a second reservation hotel for the current scenic spot from a second set of hotels based on the temporary accommodation demand text, the emotion data, and the second scenario data; wherein the second set of hotels is selected from all hotels obtained from different platforms within a second preset range of the current scenic spot;

[0104] an adjustment module, configured to adjust a first scenic spot following the current scenic spot in the preset travel route according to the personal characteristics of the tourist after the tourist checks into the second reserved hotel, to obtain an adjusted second scenic spot;

[0105] The third matching module is used for the prediction module, which is used to match the third reservation hotel for the second scenic spot in the second overall hotel based on the target accommodation demand text and the second scenario data; the second overall hotel is all hotels obtained from different platforms within a third preset range of the second scenic spot location.

[0106] The first historical scene data includes first sub-data of a first change frequency and second sub-data of a second change frequency; wherein the first change frequency is greater than the second change frequency;

[0107] The predicting of obtaining the first scene data of the first scenic spot's check-in period based on the first historical scene data of the first scenic spot includes:

[0108] Predicting the first sub-data using a first number of prediction methods to obtain a first prediction result;

[0109] Predicting the second sub-data using a second number prediction method to obtain a second prediction result; wherein the first number is greater than the second number;

[0110] The first prediction result and the second prediction result are fused to obtain the first scene data.

[0111] Match the first preset range by:

[0112] determining a first initial range according to the historical distances between the historical attractions and the historical accommodation hotels during the tourists' historical travels;

[0113] Based on the first scene data, the first initial range is adjusted to obtain an adjusted first preset range.

[0114] Adjusting the first initial range based on the first scene data to obtain an adjusted first preset range includes:

[0115] Dividing the first initial range into a plurality of first sub-areas according to the travel time to the first scenic spot;

[0116] performing feature coding processing on the first scene data to obtain a first scene feature vector corresponding to the first scene data;

[0117] Calculating a risk score for each of the first sub-areas based on the first scene feature vector;

[0118] The first initial range is adjusted according to the risk scores of the first sub-regions to obtain an adjusted first preset range.

[0119] The second preset range is determined by:

[0120] determining a second initial range according to the historical distances between the historical attractions and the historical accommodation hotels during the tourists' historical travels;

[0121] Assessing the current mobility of the tourist to obtain a degree of mobility limitation;

[0122] The second initial range is adjusted according to the movement restriction and the second scene data to obtain an adjusted second preset range.

[0123] The step of matching a second reserved hotel for the current scenic spot from a second set of hotels based on the temporary accommodation demand text, the emotion data, and the second scenario data includes:

[0124] Mapping the temporary accommodation demand text, the emotion data, and the second scenario data onto a three-dimensional decision space as a decision axis of the three-dimensional decision space;

[0125] Determining decision parameters corresponding to each of the decision axes based on the temporary accommodation demand text, the emotion data, and the second scenario data;

[0126] Based on the temporary accommodation demand text, the emotion data, the second scenario data and corresponding decision parameters, a second reservation hotel is matched for the current scenic spot from a second overall hotel.

[0127] The target accommodation requirement text is determined by:

[0128] determining whether the tourist has submitted a new accommodation demand text; if the new accommodation demand text exists, fusing the new accommodation demand text with the initial accommodation demand text to obtain the target accommodation demand text;

[0129] If the newly added accommodation demand text does not exist, the initial accommodation demand text is used as the target accommodation demand text.

[0130] like Figure 4As shown, an embodiment of the present application provides an electronic device for executing the hotel reservation method in the present application, the device including a memory, a processor, a bus, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the hotel reservation method when executing the computer program.

[0131] Specifically, the above-mentioned memory and processor may be general-purpose memory and processor, which are not specifically limited here. When the processor runs the computer program stored in the memory, the above-mentioned hotel reservation method can be executed.

[0132] Corresponding to the hotel reservation method in the present application, an embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, the steps of the above-mentioned hotel reservation method are executed.

[0133] Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk, etc. When the computer program on the storage medium is run, the above-mentioned hotel reservation method can be executed.

[0134] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. The system embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, indirect coupling or communication connection of the system or unit, which can be electrical, mechanical or other forms.

[0135] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0136] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0137] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0138] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0139] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The scope of protection of the present application is not limited thereto. Although the present application has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed in the present application, or perform equivalent replacements for some of the technical features thereof. However, these modifications, changes, or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application. They should all be included in the scope of protection of the present application. Therefore, the scope of protection of the present application shall be subject to the scope of protection of the claims.

Claims

1. A hotel reservation method, characterized in that: The method comprises: Before a tourist travels along a preset travel route, a plurality of first scenic spots where accommodation is required after the tourist travels are determined in the preset travel route based on the tourist's personal characteristics, and first scene data of an accommodation period of the first scenic spots is predicted based on first historical scene data of the first scenic spots; According to the initial accommodation demand text of the tourist before the visit and the first scenario data, matching a first reserved hotel for the first scenic spot from a first set of hotels; wherein the first set of hotels is all hotels obtained from different platforms within a first preset range of the location of the first scenic spot; When the tourist visits various scenic spots along the preset tourist route and a temporary accommodation demand text of the tourist is received, the tourist's current emotion data and second scenario data affecting accommodation at the tourist's current scenic spot are obtained; Matching a second reservation hotel for the current attraction from a second set of hotels based on the temporary accommodation demand text, the emotion data, and the second scenario data; wherein the second set of hotels is selected from all hotels acquired from different platforms within a second preset range of the current attraction; After the tourist checks into the second reserved hotel, adjusting the first attraction following the current attraction in the preset travel route according to the personal characteristics of the tourist to obtain an adjusted second attraction; According to the target accommodation demand text and the second scenario data, a third reservation hotel is matched for the second scenic spot in the second overall hotel; the second overall hotel is all hotels obtained from different platforms within a third preset range of the second scenic spot location.

2. The method according to claim 1, characterized in that The first historical scene data includes first sub-data of a first change frequency and second sub-data of a second change frequency; wherein the first change frequency is greater than the second change frequency; The predicting of obtaining the first scene data of the first scenic spot's check-in period based on the first historical scene data of the first scenic spot includes: Predicting the first sub-data using a first number of prediction methods to obtain a first prediction result; Predicting the second sub-data using a second number prediction method to obtain a second prediction result; wherein the first number is greater than the second number; The first prediction result and the second prediction result are fused to obtain the first scene data.

3. The method according to claim 1, characterized in that The method matches the first preset range in the following manner: determining a first initial range according to the historical distances between the historical attractions and the historical accommodation hotels during the tourists' historical travels; Based on the first scene data, the first initial range is adjusted to obtain an adjusted first preset range.

4. The method according to claim 3, characterized in that The adjusting the first initial range based on the first scene data to obtain an adjusted first preset range includes: Dividing the first initial range into a plurality of first sub-areas according to the travel time to the first scenic spot; performing feature coding processing on the first scene data to obtain a first scene feature vector corresponding to the first scene data; Calculating a risk score for each of the first sub-areas based on the first scene feature vector; The first initial range is adjusted according to the risk scores of the first sub-regions to obtain an adjusted first preset range.

5. The method according to claim 1, wherein The method determines the second preset range by: determining a second initial range according to the historical distances between the historical attractions and the historical accommodation hotels during the tourists' historical travels; Assessing the current mobility of the tourist to obtain a degree of mobility limitation; The second initial range is adjusted according to the movement restriction and the second scene data to obtain an adjusted second preset range.

6. The method according to claim 1, wherein The step of matching a second reserved hotel for the current scenic spot from a second set of hotels based on the temporary accommodation demand text, the emotion data, and the second scenario data includes: Mapping the temporary accommodation demand text, the emotion data, and the second scenario data onto a three-dimensional decision space as a decision axis of the three-dimensional decision space; Determining decision parameters corresponding to each of the decision axes based on the temporary accommodation demand text, the emotion data, and the second scenario data; Based on the temporary accommodation demand text, the emotion data, the second scenario data and corresponding decision parameters, a second reservation hotel is matched for the current scenic spot from a second overall hotel.

7. The method according to claim 1, characterized in that The method determines the target accommodation requirement text in the following manner: determining whether the tourist has submitted a new accommodation demand text; if the new accommodation demand text exists, fusing the new accommodation demand text with the initial accommodation demand text to obtain the target accommodation demand text; If the newly added accommodation demand text does not exist, the initial accommodation demand text is used as the target accommodation demand text.

8. A hotel reservation device, characterized in that: The device comprises: a prediction module for determining, before a tourist travels along a preset tourist route, a plurality of first scenic spots in the preset tourist route where the tourist needs to stay overnight based on the tourist's personal characteristics, and predicting first scene data for an accommodation period of the first scenic spots based on first historical scene data of the first scenic spots; a first matching module configured to match a first reserved hotel for the first scenic spot from a first set of hotels based on the tourist's initial accommodation demand text before the visit and the first scenario data; wherein the first set of hotels is comprised of all hotels acquired from different platforms within a first preset range of the location of the first scenic spot; an acquisition module for acquiring the tourist's current emotion data and second scenario data affecting accommodation at the tourist's current attraction upon receiving a temporary accommodation demand text from the tourist during the tourist's visit to various attractions along a preset tourist route; a second matching module for matching a second reservation hotel for the current scenic spot from a second set of hotels based on the temporary accommodation demand text, the emotion data, and the second scenario data; wherein the second set of hotels is selected from all hotels obtained from different platforms within a second preset range of the current scenic spot; an adjustment module, configured to adjust a first scenic spot following the current scenic spot in the preset travel route according to the personal characteristics of the tourist after the tourist checks into the second reserved hotel, to obtain an adjusted second scenic spot; The third matching module is used for the prediction module, which is used to match the third reservation hotel for the second scenic spot in the second overall hotel based on the target accommodation demand text and the second scenario data; the second overall hotel is all hotels obtained from different platforms within a third preset range of the second scenic spot location.

9. An electronic device, characterized in that: include: A processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate via the bus. When the machine-readable instructions are executed by the processor, the steps of the hotel reservation method according to any one of claims 1 to 7 are performed.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, executes the steps of the hotel reservation method according to any one of claims 1 to 7.

Images