A method and device for recommending scenic spot tour routes

Abstract

The present invention provides a method and device for recommending scenic spot tour routes, which relates to the field of data processing technology and can be used in the financial field or other technical fields. The method includes: determining the real-time congestion level of each sub-scenic area of ​​the scenic spot, obtaining historical tourist tour data of the sub-scenic area corresponding to the real-time congestion level; determining the estimated tour time required for tourists to visit the sub-scenic area based on the historical tourist tour data; determining the estimated travel time of tourists to each sub-scenic area based on the current location of the tourists, and determining the tour route recommendation weight parameter based on the estimated tour time and the estimated travel time; according to the real-time monitoring results of the monitoring data of the sub-scenic area, adaptively adjusting the route recommendation weight parameter, and determining the target tour route recommended to the tourists based on the adaptively adjusted tour route recommendation weight parameter. The device executes the above method. The method and device provided by the embodiment of the present invention enable tourism resources to be fully utilized.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and device for recommending scenic spot tour routes. Background Art

[0002] With the gradual increase in the number of travel enthusiasts, in order to better serve tourists, each scenic spot recommends sub-scenic spot tour routes to tourists, thereby guiding tourists to improve their travel experience.

[0003] However, the existing tour route recommendations only serve as a guide, that is, they instruct tourists which route to take to go to a certain sub-attraction. They are unable to provide tourists with more optimized tour routes based on richer and more detailed tour information. Tourism resources cannot be fully utilized, and tourists have a poor travel experience. Summary of the invention

[0004] In view of the problems in the prior art, an embodiment of the present invention provides a method and device for recommending scenic spot tour routes, which can at least partially solve the problems in the prior art.

[0005] On the one hand, the present invention provides a method for recommending a scenic spot tour route, comprising:

[0006] Determine the real-time crowding degree of each sub-scenic area of ​​the scenic spot, and obtain historical tourist visitor data of the sub-scenic area corresponding to the real-time crowding degree;

[0007] Determine the estimated time required for tourists to visit the sub-attraction area based on the historical tourist visit data;

[0008] Determine the estimated travel time of the tourist to each sub-attraction area according to the current location of the tourist, and determine the recommended weight parameter of the tour route according to the estimated tour time and the estimated travel time;

[0009] According to the real-time monitoring results of the monitoring data of the sub-scenic area, the route recommendation weight parameter is adaptively adjusted, and the target sightseeing route recommended to the tourist is determined according to the sightseeing route recommendation weight parameter after the adaptive adjustment.

[0010] The historical tourist tour data includes the browsing time of all tourists browsing the sub-attraction area within a preset statistical period; accordingly, determining the estimated browsing time required for tourists to visit the sub-attraction area based on the historical tourist tour data includes:

[0011] According to the browsing time of all tourists browsing the sub-attraction area within a preset statistical period, the average browsing time of tourists browsing the sub-attraction area is calculated, and the average browsing time is determined as the estimated sightseeing time.

[0012] The step of determining a weight parameter for recommending a tour route according to the estimated tour time and the estimated travel time includes:

[0013] The sum of the estimated sightseeing time and the estimated travel time is determined as the weight parameter for the sightseeing route recommendation.

[0014] Wherein, the monitoring data includes the real-time congestion degree; accordingly, the route recommendation weight parameter is adaptively adjusted according to the real-time monitoring result of the monitoring data of the sub-scenic spot area, including:

[0015] If it is determined that the congestion index value corresponding to the real-time congestion level is greater than a first preset congestion index threshold, increasing the route recommendation weight parameter;

[0016] If it is determined that the congestion index value corresponding to the real-time congestion level is less than a second preset congestion index threshold, the route recommendation weight parameter is reduced.

[0017] Wherein, the monitoring data includes the current time; accordingly, the route recommendation weight parameter is adaptively adjusted according to the real-time monitoring result of the monitoring data of the sub-scenic area, including:

[0018] If it is determined that the time difference between the current time and the end time of the sub-scenic spot tour is greater than a first preset time difference threshold, the route recommendation weight parameter is reduced;

[0019] If it is determined that the time difference between the current time and the end time of the sub-scenic spot tour is less than a second preset time difference threshold, the route recommendation weight parameter is increased.

[0020] The step of determining the real-time congestion level of each sub-attraction area of ​​the scenic spot includes:

[0021] Get the number of people per unit area in each sub-attraction area;

[0022] The real-time congestion degree is determined according to the numerical range of the number of people per unit area and a preset corresponding relationship; wherein the preset corresponding relationship includes a corresponding relationship between a preset numerical range and a preset real-time congestion degree.

[0023] The step of obtaining the number of people per unit area of ​​each sub-attraction area includes:

[0024] An image recognition algorithm is used to perform portrait recognition in each sub-scenic area, and the number of people per unit area is obtained according to the portrait recognition result.

[0025] In one aspect, the present invention provides a device for recommending scenic spot tour routes, comprising:

[0026] The first determining unit is used to determine the real-time crowding degree of each sub-scenic area of ​​the scenic spot, and obtain historical tourist visiting data of the sub-scenic area corresponding to the real-time crowding degree;

[0027] A second determining unit is used to determine the estimated visiting time required for tourists to visit the sub-attraction area according to the historical tourist visiting data;

[0028] A third determining unit is used to determine the estimated travel time of the tourist to each sub-attraction area according to the current location of the tourist, and determine a recommended weight parameter of the tour route according to the estimated tour time and the estimated travel time;

[0029] The fourth determination unit is used to adaptively adjust the route recommendation weight parameter according to the real-time monitoring result of the monitoring data of the sub-scenic area, and determine the target sightseeing route recommended to the tourist according to the adaptively adjusted sightseeing route recommendation weight parameter.

[0030] In another aspect, an embodiment of the present invention provides an electronic device, including: a processor, a memory, and a bus, wherein:

[0031] The processor and the memory communicate with each other via the bus;

[0032] The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the following method:

[0033] Determine the real-time crowding degree of each sub-scenic area of ​​the scenic spot, and obtain historical tourist visitor data of the sub-scenic area corresponding to the real-time crowding degree;

[0034] Determine the estimated time required for tourists to visit the sub-attraction area based on the historical tourist visit data;

[0035] Determine the estimated travel time of the tourist to each sub-attraction area according to the current location of the tourist, and determine the recommended weight parameter of the tour route according to the estimated tour time and the estimated travel time;

[0036] According to the real-time monitoring results of the monitoring data of the sub-scenic area, the route recommendation weight parameter is adaptively adjusted, and the target sightseeing route recommended to the tourist is determined according to the sightseeing route recommendation weight parameter after the adaptive adjustment.

[0037] An embodiment of the present invention provides a non-transitory computer-readable storage medium, including:

[0038] The non-transitory computer-readable storage medium stores computer instructions, which cause the computer to execute the following method:

[0039] Determine the real-time crowding degree of each sub-scenic area of ​​the scenic spot, and obtain historical tourist visitor data of the sub-scenic area corresponding to the real-time crowding degree;

[0040] Determine the estimated time required for tourists to visit the sub-attraction area based on the historical tourist visit data;

[0041] Determine the estimated travel time of the tourist to each sub-attraction area according to the current location of the tourist, and determine the recommended weight parameter of the tour route according to the estimated tour time and the estimated travel time;

[0042] According to the real-time monitoring results of the monitoring data of the sub-scenic area, the route recommendation weight parameter is adaptively adjusted, and the target sightseeing route recommended to the tourist is determined according to the sightseeing route recommendation weight parameter after the adaptive adjustment.

[0043] The method and device for recommending scenic spot tour routes provided by the embodiments of the present invention determine the real-time congestion level of each sub-scenic spot area of ​​the scenic spot, obtain historical tourist tour data of the sub-scenic spot area corresponding to the real-time congestion level; determine the estimated tour time required for tourists to visit the sub-scenic spot area based on the historical tourist tour data; determine the estimated travel time of the tourists to each sub-scenic spot area based on the current location of the tourists, and determine the tour route recommendation weight parameter based on the estimated tour time and the estimated travel time; according to the real-time monitoring results of the monitoring data of the sub-scenic spot area, adaptively adjust the route recommendation weight parameter, and determine the target tour route recommended to the tourists based on the adaptively adjusted tour route recommendation weight parameter, so that tourism resources are fully utilized and a better tourism experience is brought to the tourists. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0045] Figure 1 It is a flowchart of a method for recommending scenic spot tour routes provided by one embodiment of the present invention.

[0046] Figure 2 It is a flowchart of a method for recommending scenic spot tour routes provided by another embodiment of the present invention.

[0047] Figure 3 It is a flowchart of a method for recommending scenic spot tour routes provided by another embodiment of the present invention.

[0048] Figure 4 It is a structural schematic diagram of a scenic spot tour route recommendation device provided by an embodiment of the present invention.

[0049] Figure 5 A schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention. It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other arbitrarily.

[0051] Figure 1 FIG. 1 is a flow chart of a method for recommending scenic spot tour routes provided by an embodiment of the present invention. Figure 1 As shown, the method for recommending scenic spot tour routes provided by the embodiment of the present invention includes:

[0052] Step S1: determining the real-time crowding degree of each sub-scenic spot area of ​​the scenic spot, and obtaining historical tourist visitor data of the sub-scenic spot area corresponding to the real-time crowding degree.

[0053] Step S2: Determine the estimated time required for tourists to visit the sub-attraction area based on the historical tourist visit data.

[0054] Step S3: determining the estimated travel time of the tourist to each sub-attraction area according to the current location of the tourist, and determining a recommended weight parameter of the tour route according to the estimated tour time and the estimated travel time.

[0055] Step S4: Adaptively adjust the route recommendation weight parameter according to the real-time monitoring result of the monitoring data of the sub-scenic area, and determine the target tour route recommended to the tourist according to the adaptively adjusted tour route recommendation weight parameter.

[0056] In the above step S1, the device determines the real-time crowding degree of each sub-attraction area of ​​the scenic spot, and obtains the historical tourist visit data of the sub-attraction area corresponding to the real-time crowding degree. The device can be a computer device that executes the method, for example, it can include a server.

[0057] It should be noted that scenic spots usually include some sub-attractions. For example, the sub-attractions of West Lake include Three Pools Mirroring the Moon and Su Causeway Spring Dawn.

[0058] The real-time congestion level of each sub-attraction area can vary greatly. Referring to the above example, the Three Pools Mirroring the Moon may have fewer tourists and be uncrowded; while the Su Causeway Spring Dawn may have many tourists and be very crowded.

[0059] The real-time congestion level may include very congested, generally congested, and not congested, without specific limitation.

[0060] Referring to the above example, the historical tourist travel data of Su Causeway Chunxiao under very crowded conditions was obtained. The very crowded situation was affected by many factors, such as conventional influencing factors such as the peak tourist season, holidays, and noon time every day, as well as unconventional influencing factors such as people's enthusiasm for travel after the end of the epidemic.

[0061] The historical tourist visit data includes the browsing time of all tourists browsing the sub-attraction area within a preset statistical period. The preset statistical period can be set independently according to actual conditions, and can be selected as the most recent year or month, etc.

[0062] Referring to the above example, the historical tourist visit data may be the browsing time of all tourists who visited Su Causeway Spring Dawn under very crowded conditions within one month. The browsing time of all tourists may be understood as the sum of the browsing time of each tourist.

[0063] Determining the real-time congestion level of each sub-attraction area of ​​the scenic spot includes:

[0064] Get the number of people per unit area in each sub-attraction area; the number of people per unit area can reflect the density of crowd flow.

[0065] The real-time crowding degree is determined according to the numerical interval of the number of people per unit area and the preset corresponding relationship; wherein the preset corresponding relationship includes the corresponding relationship between the preset numerical interval and the preset real-time crowding degree. If the preset numerical interval is 100-200 people, the corresponding preset real-time crowding degree is not crowded; if the preset numerical interval is 200-500 people, the corresponding preset real-time crowding degree is generally crowded; if the preset numerical interval is 500-800 people, the corresponding preset real-time crowding degree is very crowded.

[0066] If the number of people per unit area is 650, the numerical range is 500-800 people, and the real-time congestion level is determined to be very crowded.

[0067] The method of obtaining the number of people per unit area of ​​each sub-attraction area includes:

[0068] Use an image recognition algorithm to perform portrait recognition in each sub-attraction area, and obtain the number of people per unit area based on the portrait recognition result. The image recognition algorithm can be selected as U-Net. Figure 2 As shown, the description is as follows:

[0069] The real-time flow of people can be captured by cameras in various parts of the scenic area, and the image recognition algorithm U-Net can be used to identify the portraits in each area. The model is first pre-trained using the MS-COCO dataset, and then the pre-trained weights are obtained. Transfer learning training for identifying human images is performed on the open source training set. Finally, the real-time images taken by the camera are tested to count the number of pedestrians in each period, and the real-time congestion level of each area can be displayed in the scenic area cloud (it can also be displayed on physical LED screens in various parts of the scenic area at the same time).

[0070] The image recognition algorithm adopted in the embodiment of the present invention is the U-Net algorithm which is widely used in the field of image recognition and is a convolutional neural network commonly used for two-dimensional image segmentation. First, the U-Net algorithm is trained. The training set is obtained from the existing open source training set, and relevant pictures containing characters are selected (all pictures from different shooting angles are selected). In order to improve the initial performance of the training, further training is performed on the MS-COCO dataset using pre-trained training weights. On the images in the training set, rotation, translation, and elastic deformation operations are performed to effectively augment the image, so that the trained weights are more robust.

[0071] In order to capture the flow of people in each area of ​​the scenic area, the cameras in each area of ​​the scenic area are set to capture a video from the monitoring screen every minute. Figure 1 The captured images are injected into the trained U-Net model for verification. The U-Net model will identify the outline and number of humans in these images. The number of humans is then summarized and compared with the maximum carrying capacity of each area in the scenic area to determine the degree of congestion in each sub-area. Visitors are prompted on the scenic area cloud applet on WeChat and on the LED screens in each area of ​​the scenic area.

[0072] In the above step S2, the device determines the estimated time required for tourists to visit the sub-attraction area according to the historical tourist tour data. The method of determining the estimated time required for tourists to visit the sub-attraction area according to the historical tourist tour data includes:

[0073] According to the browsing time of all tourists browsing the sub-attraction area during the preset statistical period, the average browsing time of tourists browsing the sub-attraction area is calculated, and the average browsing time is determined as the estimated visiting time. Referring to the above description, the browsing time of all tourists can be understood as the sum of the browsing time of each tourist, and the sum of the browsing time of each tourist is divided by the total number of tourists to obtain the average browsing time of tourists browsing the sub-attraction area.

[0074] In the above step S3, the device determines the estimated travel time of the tourist to each sub-attraction area according to the current location of the tourist, and determines the recommended weight parameter of the tour route according to the estimated tour time and the estimated travel time. Referring to the above example, from the current location of the tourist to the Three Pools Mirroring the Moon corresponds to one estimated travel time; from the current location of the tourist to Su Causeway Spring Dawn corresponds to another estimated travel time.

[0075] The determining of the weight parameter for recommending a tour route according to the estimated tour time and the estimated travel time comprises:

[0076] The sum of the estimated sightseeing time and the estimated travel time is determined as the weight parameter for the sightseeing route recommendation. Figure 3 As shown, the description is as follows:

[0077] First, the real-time location of the tourist is authorized to be obtained through the mobile phone positioning system. Then the tourist's location A and the locations of all sub-attractions in the scenic area (assuming there are three, set as B, C, and D) are added to a set. According to the real-time congestion level obtained above, combined with the tourist visit time of each sub-attraction under the real-time congestion level, the estimated visit time of each sub-attraction (b, c, d) is estimated in minutes. Then, based on the distance between points A, B, C, and D, the estimated travel time between each point is estimated (the travel time from A to B is set to ab, and so on), which is estimated in minutes.

[0078] The estimated travel time and the estimated sightseeing time between each point are added together to determine the recommended weight parameter for the sightseeing route between the two points, which is simply referred to as the weight below.

[0079] For example, the weight from location A to location B is the sum of the travel time between A and B and the estimated visiting time of attraction B, which is (ab+b). From this, the route weights qAB between A, B, C, and D are calculated.

[0080] According to the route weights between points A, B, C, and D, the global shortest path is calculated by Dijkstra algorithm. If the tourist is currently at point A, the route weights qAB, qAC, and qAD from point A to the other three points are calculated first. If it is not possible to reach directly from point A, the route weight is set to infinity.

[0081] Assuming that qAB is the smallest among the three, then find the point with the shortest route weight between A and B except for point A and point B. Assuming it is C, calculate the latest qAB, qAC, qAD that can be reached through points A, B, and C. Then compare the latest qAB, qAC, qAD with the previous qAB, qAC, qAD. If the new q is smaller, update it. And so on, finally the shortest distance from A to all other points can be calculated, and these shortest distances can be connected to obtain the target tour route recommended to tourists. The above determination of the target tour route does not involve adaptive adjustment of the tour route recommendation weight parameters.

[0082] In the above step S4, the device adaptively adjusts the route recommendation weight parameter according to the real-time monitoring result of the monitoring data of the sub-scenic area, and determines the target tour route recommended to the tourist according to the adaptively adjusted tour route recommendation weight parameter. Referring to the above description, the tour route corresponding to the tour route recommendation weight parameter with the smallest value is selected as the target tour route recommended to the tourist.

[0083] The monitoring data includes the real-time congestion level; accordingly, the route recommendation weight parameter is adaptively adjusted according to the real-time monitoring result of the monitoring data of the sub-scenic area, including:

[0084] If it is determined that the congestion index value corresponding to the real-time congestion level is greater than the first preset congestion index threshold, the route recommendation weight parameter is increased; the first preset congestion index threshold can be independently set according to actual conditions, and the increase amplitude of the increase processing can be changed in the same proportion according to the numerical amplitude of the congestion index value greater than the first preset congestion index threshold.

[0085] If it is determined that the congestion index value corresponding to the real-time congestion level is less than the second preset congestion index threshold, the route recommendation weight parameter is reduced. The second preset congestion index threshold can be set independently according to actual conditions, and the reduction range of the reduction process can be changed in the same proportion according to the range of the congestion index value less than the second preset congestion index threshold. The second preset congestion index threshold is less than the first preset congestion index threshold.

[0086] The monitoring data includes the current time; accordingly, the route recommendation weight parameter is adaptively adjusted according to the real-time monitoring result of the monitoring data of the sub-scenic area, including:

[0087] If it is determined that the time difference between the current time and the end time of the sub-scenic spot tour is greater than the first preset time difference threshold, the route recommendation weight parameter is reduced; the first preset time difference threshold can be set independently according to actual conditions, and the reduction amplitude of the reduction processing can be changed in the same proportion according to the numerical amplitude of the time difference greater than the first preset time difference threshold.

[0088] If it is determined that the time difference between the current time and the end time of the sub-scenic spot tour is less than the second preset time difference threshold, the route recommendation weight parameter is increased. The second preset time difference threshold can be set independently according to actual conditions, and the increase range of the increase process can be changed in the same proportion according to the numerical range of the time difference less than the second preset time difference threshold. The second preset time difference threshold is less than the first preset time difference threshold.

[0089] The embodiment of the present invention recommends target tour routes to tourists, which helps to guide tourists to less crowded sub-scenic spots with less travel time. In addition, the scheme can periodically determine the real-time congestion level, has strong real-time performance, and is more helpful to timely and adaptively adjust the target tour route and optimize the above-mentioned diversion strategy, so that tourism resources are fully utilized, business applications are optimized, and tourists are given a better travel experience.

[0090] The method for recommending scenic spot tour routes provided by the embodiment of the present invention determines the real-time congestion level of each sub-scenic spot area of ​​the scenic spot, obtains historical tourist tour data of the sub-scenic spot area corresponding to the real-time congestion level; determines the estimated tour time required for tourists to visit the sub-scenic spot area based on the historical tourist tour data; determines the estimated travel time of the tourists to each sub-scenic spot area based on the current location of the tourists, and determines the tour route recommendation weight parameter based on the estimated tour time and the estimated travel time; according to the real-time monitoring results of the monitoring data of the sub-scenic spot area, the route recommendation weight parameter is adaptively adjusted, and according to the adaptively adjusted tour route recommendation weight parameter, a target tour route recommended to the tourists is determined, so that tourism resources are fully utilized and a better tourism experience is brought to the tourists.

[0091] Furthermore, the historical tourist tour data includes the browsing time of all tourists browsing the sub-attraction area within a preset statistical period; accordingly, determining the estimated browsing time required for tourists to visit the sub-attraction area based on the historical tourist tour data includes:

[0092] According to the browsing time of all tourists browsing the sub-attraction area in the preset statistical period, the average browsing time of tourists browsing the sub-attraction area is calculated, and the average browsing time is determined as the estimated visiting time. Please refer to the above description and no further details will be given.

[0093] The scenic spot tour route recommendation method provided by the embodiment of the present invention facilitates the calculation of the estimated tour time, thereby helping to quickly obtain the target tour route recommended to tourists.

[0094] Further, determining a weight parameter for recommending a tour route according to the estimated tour time and the estimated travel time includes:

[0095] The sum of the estimated sightseeing time and the estimated travel time is determined as the weight parameter for the recommended sightseeing route.

[0096] The scenic spot tour route recommendation method provided by the embodiment of the present invention facilitates the calculation of tour route recommendation weight parameters, thereby helping to quickly obtain a target tour route recommended to tourists.

[0097] Further, the monitoring data includes the real-time congestion level; accordingly, the route recommendation weight parameter is adaptively adjusted according to the real-time monitoring result of the monitoring data of the sub-scenic area, including:

[0098] If it is determined that the congestion index value corresponding to the real-time congestion level is greater than the first preset congestion index threshold, the route recommendation weight parameter is increased; please refer to the above description and will not be repeated here.

[0099] If it is determined that the congestion index value corresponding to the real-time congestion level is less than the second preset congestion index threshold, the route recommendation weight parameter is reduced.

[0100] The scenic spot tour route recommendation method provided by the embodiment of the present invention adaptively adjusts the route recommendation weight parameters based on the real-time congestion level, and further optimizes the target tour route recommended to tourists.

[0101] Further, the monitoring data includes the current time; accordingly, the route recommendation weight parameter is adaptively adjusted according to the real-time monitoring result of the monitoring data of the sub-scenic area, including:

[0102] If it is determined that the time difference between the current time and the end time of the sub-scenic spot tour is greater than the first preset time difference threshold, the route recommendation weight parameter is reduced; please refer to the above description and will not be repeated here.

[0103] If it is determined that the time difference between the current time and the end time of the sub-scenic spot tour is less than the second preset time difference threshold, the route recommendation weight parameter is increased. Please refer to the above description and no further details will be given.

[0104] The scenic spot tour route recommendation method provided by the embodiment of the present invention adaptively adjusts the route recommendation weight parameter based on the time difference between the current time and the end time of the sub-scenic spot tour, and further optimizes the target tour route recommended to tourists.

[0105] Furthermore, the determining of the real-time congestion level of each sub-attraction area of ​​the scenic spot includes:

[0106] Get the number of people per unit area of ​​each sub-attraction area; refer to the above description and no longer repeat it.

[0107] The real-time congestion degree is determined according to the numerical interval of the number of people per unit area and the preset corresponding relationship, wherein the preset corresponding relationship includes the corresponding relationship between the preset numerical interval and the preset real-time congestion degree. Please refer to the above description and will not repeat it again.

[0108] The scenic spot tour route recommendation method provided by the embodiment of the present invention facilitates the determination of the real-time congestion level, thereby helping to quickly obtain a target tour route recommended to tourists.

[0109] Furthermore, obtaining the number of people per unit area of ​​each sub-attraction area includes:

[0110] The image recognition algorithm is used to perform human portrait recognition in each sub-scenic area, and the number of people per unit area is obtained according to the human portrait recognition result. Please refer to the above description and no further details will be given.

[0111] The scenic spot tour route recommendation method provided by the embodiment of the present invention further facilitates the determination of the real-time congestion level, thereby helping to quickly obtain the target tour route recommended to tourists.

[0112] It should be noted that the scenic spot tour route recommendation method provided in the embodiment of the present invention can be used in the financial field, and can also be used in any technical field other than the financial field. The embodiment of the present invention does not limit the application field of the scenic spot tour route recommendation method.

[0113] Figure 4 FIG. 1 is a schematic diagram of the structure of a device for recommending scenic spot tour routes provided by an embodiment of the present invention. Figure 4 As shown, the scenic spot tour route recommendation device provided by the embodiment of the present invention includes a first determination unit 401, a second determination unit 402, a third determination unit 403 and a fourth determination unit 404, wherein:

[0114] The first determination unit 401 is used to determine the real-time congestion level of each sub-scenic area of ​​the scenic area, and obtain historical tourist tour data of the sub-scenic area corresponding to the real-time congestion level; the second determination unit 402 is used to determine the estimated tour time required for tourists to visit the sub-scenic area based on the historical tourist tour data; the third determination unit 403 is used to determine the estimated travel time of the tourists to each sub-scenic area according to the current location of the tourists, and determine the weight parameter for recommended tour routes according to the estimated tour time and the estimated travel time; the fourth determination unit 404 is used to adaptively adjust the route recommendation weight parameter according to the real-time monitoring result of the monitoring data of the sub-scenic area, and determine the target tour route recommended to the tourist according to the adaptively adjusted tour route recommendation weight parameter.

[0115] Specifically, the first determination unit 401 in the device is used to determine the real-time congestion level of each sub-scenic area in the scenic area, and obtain historical tourist tour data of the sub-scenic area corresponding to the real-time congestion level; the second determination unit 402 is used to determine the estimated tour time required for tourists to visit the sub-scenic area based on the historical tourist tour data; the third determination unit 403 is used to determine the estimated travel time of the tourists to each sub-scenic area according to the current location of the tourists, and determine the weight parameter for recommended tour routes according to the estimated tour time and the estimated travel time; the fourth determination unit 404 is used to adaptively adjust the route recommendation weight parameter according to the real-time monitoring results of the monitoring data of the sub-scenic area, and determine the target tour route recommended to the tourist based on the adaptively adjusted tour route recommendation weight parameter.

[0116] The scenic spot tour route recommendation device provided by the embodiment of the present invention determines the real-time congestion level of each sub-scenic spot area of ​​the scenic spot, obtains historical tourist tour data of the sub-scenic spot area corresponding to the real-time congestion level; determines the estimated tour time required for tourists to visit the sub-scenic spot area based on the historical tourist tour data; determines the estimated travel time of the tourists to each sub-scenic spot area based on the current location of the tourists, and determines the tour route recommendation weight parameter based on the estimated tour time and the estimated travel time; according to the real-time monitoring result of the monitoring data of the sub-scenic spot area, the route recommendation weight parameter is adaptively adjusted, and according to the adaptively adjusted tour route recommendation weight parameter, a target tour route recommended to the tourists is determined, so that tourism resources are fully utilized and a better tourism experience is brought to the tourists.

[0117] Further, the second determining unit 402 is specifically configured to:

[0118] According to the browsing time of all tourists browsing the sub-attraction area within a preset statistical period, the average browsing time of tourists browsing the sub-attraction area is calculated, and the average browsing time is determined as the estimated sightseeing time.

[0119] The scenic spot tour route recommendation device provided by the embodiment of the present invention facilitates the calculation of the estimated tour time, thereby helping to quickly obtain the target tour route recommended to tourists.

[0120] Further, the third determining unit 403 is specifically configured to:

[0121] The sum of the estimated sightseeing time and the estimated travel time is determined as the weight parameter for the sightseeing route recommendation.

[0122] The scenic spot tour route recommendation device provided by the embodiment of the present invention facilitates the calculation of tour route recommendation weight parameters, thereby helping to quickly obtain the target tour route recommended to tourists.

[0123] Further, the monitoring data includes the real-time congestion level; accordingly, the fourth determining unit 404 is specifically configured to:

[0124] If it is determined that the congestion index value corresponding to the real-time congestion level is greater than a first preset congestion index threshold, increasing the route recommendation weight parameter;

[0125] If it is determined that the congestion index value corresponding to the real-time congestion level is less than a second preset congestion index threshold, the route recommendation weight parameter is reduced.

[0126] The scenic spot tour route recommendation device provided by the embodiment of the present invention adaptively adjusts the route recommendation weight parameters based on the real-time congestion level, and further optimizes the target tour route recommended to tourists.

[0127] Further, the monitoring data includes the current time; accordingly, the fourth determining unit 404 is specifically configured to:

[0128] If it is determined that the time difference between the current time and the end time of the sub-scenic spot tour is greater than a first preset time difference threshold, the route recommendation weight parameter is reduced;

[0129] If it is determined that the time difference between the current time and the end time of the sub-scenic spot tour is less than a second preset time difference threshold, the route recommendation weight parameter is increased.

[0130] The scenic spot tour route recommendation device provided by the embodiment of the present invention adaptively adjusts the route recommendation weight parameter based on the time difference between the current time and the end time of the sub-scenic spot tour, and further optimizes the target tour route recommended to tourists.

[0131] Further, the first determining unit 401 is specifically configured to:

[0132] Get the number of people per unit area in each sub-attraction area;

[0133] The real-time congestion degree is determined according to the numerical range of the number of people per unit area and a preset corresponding relationship; wherein the preset corresponding relationship includes a corresponding relationship between a preset numerical range and a preset real-time congestion degree.

[0134] The scenic spot tour route recommendation device provided by the embodiment of the present invention facilitates the determination of the real-time congestion level, thereby helping to quickly obtain a target tour route recommended to tourists.

[0135] Furthermore, the first determining unit 401 is further specifically configured to:

[0136] An image recognition algorithm is used to perform portrait recognition in each sub-scenic area, and the number of people per unit area is obtained according to the portrait recognition result.

[0137] The scenic spot tour route recommendation device provided by the embodiment of the present invention further facilitates the determination of the real-time congestion level, thereby helping to quickly obtain the target tour route recommended to tourists.

[0138] The embodiment of the scenic spot tour route recommendation device provided by the embodiment of the present invention can be specifically used to execute the processing flow of the above-mentioned method embodiments. Its functions are not repeated here, and reference can be made to the detailed description of the above-mentioned method embodiments.

[0139] Figure 5 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention, such as Figure 5 As shown, the electronic device includes: a processor (processor) 501, a memory (memory) 502 and a bus 503;

[0140] The processor 501 and the memory 502 communicate with each other via a bus 503;

[0141] The processor 501 is used to call the program instructions in the memory 502 to execute the methods provided by the above method embodiments, for example, including:

[0142] Determine the real-time crowding degree of each sub-scenic area of ​​the scenic spot, and obtain historical tourist visitor data of the sub-scenic area corresponding to the real-time crowding degree;

[0143] Determine the estimated time required for tourists to visit the sub-attraction area based on the historical tourist visit data;

[0144] Determine the estimated travel time of the tourist to each sub-attraction area according to the current location of the tourist, and determine the recommended weight parameter of the tour route according to the estimated tour time and the estimated travel time;

[0145] According to the real-time monitoring results of the monitoring data of the sub-scenic area, the route recommendation weight parameter is adaptively adjusted, and the target sightseeing route recommended to the tourist is determined according to the sightseeing route recommendation weight parameter after the adaptive adjustment.

[0146] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can perform the methods provided by the above method embodiments, for example, including:

[0147] Determine the real-time crowding degree of each sub-scenic area of ​​the scenic spot, and obtain historical tourist visitor data of the sub-scenic area corresponding to the real-time crowding degree;

[0148] Determine the estimated time required for tourists to visit the sub-attraction area based on the historical tourist visit data;

[0149] Determine the estimated travel time of the tourist to each sub-attraction area according to the current location of the tourist, and determine the recommended weight parameter of the tour route according to the estimated tour time and the estimated travel time;

[0150] According to the real-time monitoring results of the monitoring data of the sub-scenic area, the route recommendation weight parameter is adaptively adjusted, and the target sightseeing route recommended to the tourist is determined according to the sightseeing route recommendation weight parameter after the adaptive adjustment.

[0151] This embodiment provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program enables the computer to execute the methods provided by the above method embodiments, for example, including:

[0152] Determine the real-time crowding degree of each sub-scenic area of ​​the scenic spot, and obtain historical tourist visitor data of the sub-scenic area corresponding to the real-time crowding degree;

[0153] Determine the estimated time required for tourists to visit the sub-attraction area based on the historical tourist visit data;

[0154] Determine the estimated travel time of the tourist to each sub-attraction area according to the current location of the tourist, and determine the recommended weight parameter of the tour route according to the estimated tour time and the estimated travel time;

[0155] According to the real-time monitoring results of the monitoring data of the sub-scenic area, the route recommendation weight parameter is adaptively adjusted, and the target sightseeing route recommended to the tourist is determined according to the sightseeing route recommendation weight parameter after the adaptive adjustment.

[0156] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0157] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0158] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0159] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0160] In the description of this specification, the description with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0161] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for recommending scenic spot tour routes, characterized in that: include: Determine the real-time crowding degree of each sub-scenic spot area of ​​the scenic spot, and obtain historical tourist visitor data of the sub-scenic spot area corresponding to the real-time crowding degree; Determine the estimated time required for tourists to visit the sub-attraction area based on the historical tourist visit data; Determine the estimated travel time of the tourist to each sub-attraction area according to the current location of the tourist, and determine the recommended weight parameter of the tour route according to the estimated tour time and the estimated travel time; According to the real-time monitoring results of the monitoring data of the sub-scenic area, the route recommendation weight parameter is adaptively adjusted, and the target sightseeing route recommended to the tourist is determined according to the sightseeing route recommendation weight parameter after the adaptive adjustment.

2. The method for recommending scenic spot tour routes according to claim 1, characterized in that: The historical tourist visit data includes the visit time of all tourists visiting the sub-attraction area within a preset statistical period; accordingly, determining the estimated visit time required for tourists to visit the sub-attraction area based on the historical tourist visit data includes: According to the visiting time of all tourists visiting the sub-attraction area within a preset statistical period, the average visiting time of tourists visiting the sub-attraction area is calculated, and the average visiting time is determined as the estimated visiting time.

3. The method for recommending scenic spot tour routes according to claim 1, characterized in that: The determining of the weight parameter for recommending a tour route according to the estimated tour time and the estimated travel time comprises: The sum of the estimated sightseeing time and the estimated travel time is determined as the weight parameter for the sightseeing route recommendation.

4. The method for recommending scenic spot tour routes according to claim 1, characterized in that: The monitoring data includes the real-time congestion level; accordingly, the route recommendation weight parameter is adaptively adjusted according to the real-time monitoring result of the monitoring data of the sub-scenic area, including: If it is determined that the congestion index value corresponding to the real-time congestion level is greater than a first preset congestion index threshold, increasing the route recommendation weight parameter; If it is determined that the congestion index value corresponding to the real-time congestion level is less than a second preset congestion index threshold, the route recommendation weight parameter is reduced.

5. The method for recommending scenic spot tour routes according to claim 1, characterized in that: The monitoring data includes the current time; accordingly, the route recommendation weight parameter is adaptively adjusted according to the real-time monitoring result of the monitoring data of the sub-scenic area, including: If it is determined that the time difference between the current time and the end time of the sub-scenic spot tour is greater than a first preset time difference threshold, the route recommendation weight parameter is reduced; If it is determined that the time difference between the current time and the end time of the sub-scenic spot tour is less than a second preset time difference threshold, the route recommendation weight parameter is increased.

6. The method for recommending scenic spot tour routes according to any one of claims 1 to 5, characterized in that: Determining the real-time congestion level of each sub-attraction area of ​​the scenic spot includes: Get the number of people per unit area in each sub-attraction area; The real-time congestion degree is determined according to the numerical range of the number of people per unit area and a preset corresponding relationship; wherein the preset corresponding relationship includes a corresponding relationship between a preset numerical range and a preset real-time congestion degree.

7. The method for recommending scenic spot tour routes according to claim 6, characterized in that: The method of obtaining the number of people per unit area of ​​each sub-attraction area includes: An image recognition algorithm is used to perform portrait recognition in each sub-scenic area, and the number of people per unit area is obtained according to the portrait recognition result.

8. A device for recommending scenic spot tour routes, characterized in that: include: The first determining unit is used to determine the real-time crowding degree of each sub-scenic area of ​​the scenic spot, and obtain historical tourist visiting data of the sub-scenic area corresponding to the real-time crowding degree; A second determining unit is used to determine the estimated visiting time required for tourists to visit the sub-attraction area according to the historical tourist visiting data; A third determining unit is used to determine the estimated travel time of the tourist to each sub-attraction area according to the current location of the tourist, and determine a recommended weight parameter of the tour route according to the estimated tour time and the estimated travel time; The fourth determination unit is used to adaptively adjust the route recommendation weight parameter according to the real-time monitoring result of the monitoring data of the sub-scenic area, and determine the target sightseeing route recommended to the tourist according to the adaptively adjusted sightseeing route recommendation weight parameter.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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