Automatic recommendation method for target scenic spot and related equipment

Abstract

The invention provides a target scenic spot automatic recommendation method, the target scenic spots are a plurality of scenic spots in a target scenic spot, and the automatic recommendation method comprises the following steps: obtaining basic identification information of a to-be-played scenic spot in the target scenic spot; determining a plurality of target tourist attractions according to the basic identification information of the tourist attractions to be played; obtaining a current to-be-watched scene experience influence factor corresponding to each target scenic spot; determining a current to-be-viewed experience coefficient corresponding to each target scenic spot according to the current to-be-viewed experience influence factor corresponding to each target scenic spot; generating a viewing sequence of a plurality of target tourist attractions according to the plurality of to-be-viewed experience coefficients; and recommending the viewing sequence of the plurality of target tourist attractions to the user. According to the invention, the intelligent technology is effectively utilized to intelligently recommend the playing arrangement of the tourists, and the playing experience feeling of the tourists in the scenic spot is improved.

Description

Technical Field

[0001] The present invention relates to the field of smart tourism technology, and in particular to an automatic recommendation method for target tourist attractions and related equipment. Background Art

[0002] Smart tourism is also known as intelligent tourism. "Smart tourism" is a brand-new proposition. It is an application of technologies such as the Internet of Things, cloud computing, next-generation communication networks, high-performance information processing, and intelligent data mining in tourism experience and industrial development, so that tourism physical resources and information resources are highly systematically integrated and deeply developed and activated, and serve the public, enterprises, and other new forms of tourism facing the future. It is based on integrated communication and information technology, centered on the interactive experience of tourists, guaranteed by integrated industry information management, and characterized by stimulating industrial innovation and promoting industrial structure upgrading. Smart tourism is to use new technologies such as mobile cloud computing and the Internet, with the help of portable terminal Internet devices, to actively perceive tourism-related information, and to arrange and adjust travel plans in a timely manner. Simply put, it is real-time interaction between tourists and the Internet, allowing travel arrangements to enter the intelligent era.

[0003] In the prior art, when tourists visit scenic spots, they often lack intelligent guidance and recommendations, which makes the tourists' travel experience not good, especially during holidays when there is a peak in traffic. How to use intelligent technology to make intelligent recommendations on tourists' travel arrangements has been a technical problem that has been urgently needed to be solved in this field. Summary of the invention

[0004] The present invention provides an automatic recommendation method for target tourist attractions and related equipment to solve at least one of the above technical problems.

[0005] A first aspect of the present invention provides an automatic recommendation method for target tourist attractions, wherein the target tourist attractions are multiple tourist attractions within a target tourist scenic area, each tourist attraction is equipped with an independent online ticket sales and ticket verification system, and the automatic recommendation method includes obtaining basic identification information of tourist attractions to be visited within the target tourist scenic area; determining a number of target tourist attractions based on the basic identification information of the tourist attractions to be visited; obtaining a current to-be-visited experience influencing factor corresponding to each of the target tourist attractions; determining a current to-be-visited experience coefficient corresponding to each of the target tourist attractions based on the current to-be-visited experience influencing factor corresponding to each of the target tourist attractions; generating a viewing order of a number of target tourist attractions based on a number of to-be-visited experience coefficients; and recommending the viewing order of the number of target tourist attractions to a user.

[0006] Optionally, the factors influencing the experience of the sights to be viewed include information on the number of remaining sightseeing tourists, and obtaining the factors influencing the experience of the sights to be viewed corresponding to each of the target tourist attractions includes: obtaining the current position coordinate information of the current position of the user; obtaining the path navigation information corresponding to each of the target tourist attractions at the current moment of the user according to the current position coordinate information, and the path navigation information includes walking time information t 1 ; Obtain the current T in each of the target tourist attractions according to the ticket verification system 1 The number of current sightseeing tourists at the time of arrival and the entry time of each current sightseeing tourist into the target tourist attraction are calculated; the exit time of each current sightseeing tourist in the corresponding target tourist attraction is calculated based on the historical average duration of a single sightseeing tourist in the entire journey of each target tourist attraction and the entry time of each current sightseeing tourist in the target tourist attraction; the T of the user arriving at each target tourist attraction is calculated based on the exit time of each current sightseeing tourist in the corresponding target tourist attraction. 2 The remaining number of tourists at the time T 2 Moment and T 1 The time between the moments is t 1 The generating of the viewing order of several target tourist attractions according to the several viewing experience coefficients specifically includes: recommending the target tourist attractions with the smallest number of remaining viewing tourists to the user.

[0007] Optionally, the waiting-to-view experience influencing factor also includes information on the number of tourists waiting to enter the park to view the scenery, and the step of obtaining the current waiting-to-view experience influencing factor corresponding to each of the target tourist attractions further includes: obtaining the current waiting-to-view experience influencing factor according to the ticketing system in the T 2 The number of tickets sold for each of the target tourist attractions at the time; obtaining the ticket verification system at the time T 2 The number of verified scenic tickets for each of the target tourist attractions at the moment; the number of tourists waiting to enter the park based on the verified number of scenic tickets and the number of sold scenic tickets; the number of remaining tourists at each of the target tourist attractions and the number of tourists waiting to enter the park at each of the target tourist attractions are calculated at the T 2 The estimated total number of tourists at each of the target tourist attractions at a given moment; generating a viewing order of a plurality of target tourist attractions based on a plurality of the viewing experience coefficients specifically includes: recommending the target tourist attraction with the smallest estimated total number of tourists to the user.

[0008] Optionally, the target tourist attraction includes a target tourist attraction A, a target tourist attraction B, a target tourist attraction C, and a target tourist attraction D, and the estimated total number of tourists at the target tourist attractions is sorted from small to large as target tourist attraction A, target tourist attraction B, target tourist attraction C, and target tourist attraction D. The automatic recommendation method further includes: when the user leaves the target tourist attraction A, 3 At this moment, get user T 3 The current location coordinate information of the location at the moment; based on the user T 3 The current position coordinate information of the user T at the time 3 The path navigation information corresponding to the target tourist attraction B at the moment, the path navigation information includes walking time information t 1B ; Based on user T 3 The current position coordinate information of the user T at the time 3 The path navigation information corresponding to the target tourist attraction C at the time, the path navigation information includes walking time information t 1C ; Based on user T 3 The current position coordinate information of the user T at the time 3 The path navigation information corresponding to the target tourist attraction D at the moment, the path navigation information includes walking time information t 1D According to the ticket verification system, the target tourist attractions B, C and D are obtained at the current T 3 The current number of tourists at the time of sightseeing, and the entry time of each current tourist into the target tourist attraction; the exit time of each current tourist in the corresponding target tourist attraction is calculated based on the historical average duration of the entire journey of the target tourist attraction B, the target tourist attraction C, and the target tourist attraction D, and the entry time of each current tourist into the target tourist attraction; the exit time of the target tourist attraction B at T is calculated based on the exit time of each current tourist in the target tourist attraction B. 41 The remaining number of tourists at the time P 4B , the T 41 Moment and T 3 The time between the moments is t 1B Calculate the time of departure of the target tourist attraction C in T according to the departure time of each current tourist in the target tourist attraction C in the corresponding target tourist attraction C. 42 The remaining number of tourists at the time P 4C , the T 42 Moment and T 3 The time between the moments is t 1CCalculate the time of departure of each current tourist in the target tourist attraction D in T according to the departure time of each current tourist in the target tourist attraction D in the corresponding target tourist attraction D. 43 The remaining number of tourists at the time P 4D , the T 43 Moment and T 3 The time between the moments is t 1D The generating of the viewing sequence of several target tourist attractions according to the several viewing experience coefficients specifically includes: the remaining viewing tourist number information P 4B , the remaining number of tourists viewing information P 4C , the remaining number of tourists viewing information P 4D The smallest one is recommended to the user.

[0009] Optionally, the generating of the viewing sequence of a plurality of target tourist attractions according to the plurality of viewing experience coefficients specifically includes: generating a viewing sequence of a plurality of target tourist attractions according to the formula S 基础 =M*the number of remaining tourists + N*the walking time information to calculate the basic waiting viewing experience coefficient S of the target tourist attraction B, the target tourist attraction C and the target tourist attraction D respectively 基础 , when the remaining number of tourists viewing the scenery is the remaining number of tourists viewing the scenery information P 4B When the walking time information is t 1B , when the remaining number of tourists viewing the scenery is the remaining number of tourists viewing the scenery information P 4C When the walking time information is t 1C , when the remaining number of tourists viewing the scenery is the remaining number of tourists viewing the scenery information P 4D When the walking time information is t 1D , said M+N=1; said generating a viewing order of a plurality of target tourist attractions according to a plurality of said to-be-viewed experience coefficients specifically comprises: recommending the target tourist attractions B, target tourist attractions C and target tourist attractions D having the smallest basic to-be-viewed experience coefficients to the user.

[0010] Optionally, the generating of the viewing sequence of several target tourist attractions according to the several viewing experience coefficients specifically includes: obtaining the viewing experience coefficients of the T 41 The number of tickets sold for the target tourist attraction B at time T 42 The information on the number of tickets sold for the target tourist attraction C at the time T 43 The number of tickets sold for the target tourist attraction D at the time; and obtaining the ticket verification system at the time T 41 At the moment, at T 42 Moment and T 43The number of verified scenic spot tickets of the target tourist attractions B, the target tourist attractions C and the target tourist attractions D at their respective corresponding times; and the number of verified scenic spot tickets and the number of sold scenic spot tickets are calculated based on the information on the number of verified scenic spot tickets and the information on the number of sold scenic spot tickets. 41 At the moment, at T 42 Moment and T 43 According to the remaining number of tourists of the target tourist attraction B, the target tourist attraction C and the target tourist attraction D and the corresponding number of tourists waiting to enter the park, the information is calculated at the time T 41 At the moment, at T 42 Moment and T 43 The estimated total number of tourists at the target tourist attraction B at the time P 4B总 , the estimated total number of tourists at the target tourist attraction C 4C总 , the estimated total number of tourists at the target tourist attraction D 4D总 The generating of the viewing sequence of several target tourist attractions according to the several viewing experience coefficients specifically includes: The generating of the viewing sequence of several target tourist attractions according to the several viewing experience coefficients specifically includes: According to the formula S 标准 =M*estimated total number of tourists + N*walking time information, respectively calculate the standard waiting viewing experience coefficients of the target tourist attractions B, C and D. When the estimated total number of tourists is the estimated total number of tourists information P 4B总 When the walking time information is t 1B , when the estimated total number of tourists is the estimated total number of tourists information P 4C总 When the walking time information is t 1C , when the estimated total number of tourists is the estimated total number of tourists information P 4D总 When the walking time information is t 1D ; The generating of the viewing order of several target tourist attractions based on several of the waiting viewing experience coefficients specifically includes: recommending the target tourist attractions B, target tourist attractions C and target tourist attractions D with the smallest standard waiting viewing experience coefficients to the user.

[0011] Optionally, the obtaining of basic identification information of the tourist attractions to be visited in the target tourist scenic area specifically includes: obtaining information E of the number of comments on the tourist attractions to be visited in the target tourist scenic area. 1 ; Obtain the number of favorable comments on the tourist attractions to be visited in the target tourist attraction E 2 ; Obtain the ticket price information of a single tourist attraction to be visited in the target tourist attraction F单 ; Obtain the total ticket price information of all tourist attractions to be visited in the target tourist attraction F 总 The method of determining a number of target tourist attractions based on the basic identification information of the tourist attractions to be visited specifically includes: calculating the recommendation factor Q of each tourist attraction to be visited according to the following formula: Q = (E 2 / E 1 )-(F 单 / F 总 ); Sort the recommendation factor Q of each tourist attraction to be visited from large to small, and select the top four tourist attractions to be visited as target tourist attractions.

[0012] In a second aspect, the present invention further provides an automatic recommendation device for target tourist attractions, wherein the target tourist attractions are multiple tourist attractions within a target tourist scenic area, each tourist attraction is equipped with an independent online ticket sales and ticket verification system, and the automatic recommendation device includes: a basic identification information acquisition module, configured to acquire the basic identification information of the tourist attractions to be visited within the target tourist scenic area; a target tourist attractions determination module, configured to determine a number of target tourist attractions based on the basic identification information of the tourist attractions to be visited; a current to-be-viewed experience influence factor acquisition module, configured to acquire the current to-be-viewed experience influence factor corresponding to each of the target tourist attractions; a current to-be-viewed experience coefficient determination module, configured to determine the current to-be-viewed experience coefficient corresponding to each of the target tourist attractions based on the current to-be-viewed experience influence factor corresponding to each of the target tourist attractions; a viewing order generation module, configured to generate a viewing order of a number of target tourist attractions based on a number of the to-be-viewed experience coefficients; and a recommendation module, configured to recommend the viewing order of the number of target tourist attractions to a user.

[0013] In a third aspect, the present invention further provides an electronic device, comprising: a memory, a processor, wherein the processor is configured to implement any of the above-described methods when executing a computer management program stored in the memory.

[0014] In a fourth aspect, the present invention further provides a computer-readable storage medium on which a computer management program is stored, and when the computer management program is executed by a processor, it implements any of the methods described above.

[0015] To sum up, it can be seen that in the embodiment provided by the present invention, by obtaining the basic identification information of the tourist attractions to be visited in the target tourist scenic area, and then determining a number of target tourist attractions based on the basic identification information of the tourist attractions to be visited, and obtaining the current viewing experience influencing factor corresponding to each target tourist attraction, and then determining the current viewing experience coefficient corresponding to each target tourist attraction based on the current viewing experience influencing factor corresponding to each target tourist attraction, finally generating a viewing order of a number of target tourist attractions based on a number of viewing experience coefficients, and recommending a viewing order of a number of target tourist attractions to users, so that users can view and play according to the recommended viewing order, effectively using intelligent technology to make intelligent recommendations on tourists' play arrangements, and improving the play experience of tourists in the scenic area. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a flow chart of a method for automatically recommending target tourist attractions provided by an embodiment of the present invention;

[0017] Figure 2 A schematic diagram of an implementation scenario provided for an embodiment of the present invention;

[0018] Figure 3 A schematic diagram of an electronic device according to an embodiment of the present invention;

[0019] Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0021] In the following description, specific embodiments of the present invention will be described with reference to steps and symbols performed by one or more computers, unless otherwise stated. Therefore, these steps and operations will be mentioned several times as being performed by a computer, and computer execution referred to herein includes operations by a computer processing unit of electronic signals representing data in a structured form. This operation converts the data or maintains it at a location in the computer's memory system, which can be reconfigured or otherwise change the operation of the computer in a manner familiar to testers in the field. The data structure maintained by the data is a physical location in the memory, which has specific characteristics defined by the data format. However, the principles of the present invention are described in the above text, which does not represent a limitation, and testers in the field will understand that the various steps and operations described below can also be implemented in hardware.

[0022] The principles of the present invention operate with many other general or special purpose computing, communication environments or configurations. Examples of well-known computing systems, environments and configurations suitable for use with the present invention may include, but are not limited to, handheld phones, personal computers, servers, multiprocessor systems, microcomputer-based systems, mainframe computers, and distributed computing environments, including any of the above systems or devices.

[0023] The terms "first", "second", and "third" in the present invention are used to distinguish different objects rather than to describe a specific order. In addition, the terms "include", "have", and any variations thereof are intended to cover non-exclusive inclusions.

[0024] "Smart tourism" is a brand-new proposition. It is a new form of tourism that uses technologies such as the Internet of Things, cloud computing, next-generation communication networks, high-performance information processing, and intelligent data mining in tourism experience and industrial development, so that tourism physical resources and information resources are highly systematically integrated and deeply developed and activated, and serve the public, enterprises, etc. It is based on integrated communication and information technology, centered on tourist interactive experience, guaranteed by integrated industry information management, and characterized by stimulating industrial innovation and promoting industrial structure upgrading. Smart tourism is to use new technologies such as mobile cloud computing and the Internet, with the help of portable terminal Internet devices, to actively perceive tourism-related information, and to arrange and adjust travel plans in a timely manner. Simply put, it is real-time interaction between tourists and the Internet, allowing travel arrangements to enter the smart era.

[0025] The following describes the method for automatically recommending target tourist attractions provided by the present invention from the perspective of an automatic recommendation device for target tourist attractions. The automatic recommendation device for target tourist attractions can be a terminal device (the terminal device includes but is not limited to a mobile phone, a computer, a tablet, an electronic touch screen in a scenic area, etc.), or a server, or a service unit in a server, without specific limitation.

[0026] See also Figure 1-2 , Figure 1 A schematic flow chart of a method for automatically recommending target tourist attractions provided by an embodiment of the present invention, wherein the method for automatically recommending target tourist attractions comprises:

[0027] 101. Obtain basic identification information of tourist attractions to be visited in the target tourist scenic area;

[0028] Specifically, the target tourist attractions are multiple tourist attractions in the target tourist scenic area, such as target tourist attraction A, target tourist attraction B, target tourist attraction C, target tourist attraction D, target tourist attraction E, target tourist attraction F, etc., which can be named and sorted in order according to the number of tourist attractions; each tourist attraction is equipped with an independent online ticket sales and ticket verification system, the online ticket sales are used for ticket sales in the corresponding tourist attractions, and the ticket verification system is used for ticket verification in the corresponding tourist attractions; the basic identification information of the tourist attractions to be visited in the target tourist scenic area is obtained specifically including: obtaining the number of comments information E of the tourist attractions to be visited in the target tourist scenic area 1 ; Obtain the number of favorable comments on the tourist attractions to be visited in the target tourist attraction E 2 ; Obtain the ticket price information of a single tourist attraction to be visited in the target tourist attraction F 单 ; Obtain the total ticket price information of all tourist attractions to be visited in the target tourist attraction F 总 Among them, the number of reviews information E 1 The number of comments made by historical users on the tourist attractions they visited. For example, if 10,000 historical users visited the tourist attraction, the number of comments can be 10,000, 9,000, or 8,000, etc. The number of comments can be displayed in real time according to the historical user comment records; the number of positive reviews information E 2 is the review score of the tourist attractions visited by historical users, such as 5 points for good reviews, 4 points for good, 3 points for average, 2 points for not recommended, and 1 point for difference. At this time, the number of good reviews is E 2 is the number of reviews with a score of 5, or the sum of reviews with a score of 5 and 4; the total ticket price information F 总 is the total ticket price of all the tourist attractions to be visited. For example, the ticket price of the target tourist attraction A is X1, the ticket price of the target tourist attraction B is X2, the ticket price of the target tourist attraction C is X3, the ticket price of the target tourist attraction D is X4, the ticket price of the target tourist attraction E is X5, and the ticket price of the target tourist attraction F is X6. Then F 总 =X1+X2+X3+X4+X5+X6.

[0029] 102. Determine a number of target tourist attractions based on the basic identification information of the tourist attractions to be visited;

[0030] In this embodiment, the determining of a number of target tourist attractions according to the basic identification information of the tourist attractions to be visited specifically includes: calculating a recommendation factor Q of each tourist attraction to be visited according to the following formula:

[0031] Q=(E 2 / E 1 )-(F 单 / F 总 );

[0032] Specifically, through the number of positive reviews information E 2 Divide by the number of reviews information E 1 , which can reflect the number of positive reviews E 2 The proportion of the total number of reviews in the information is used to characterize the recommendation index of the tourist attractions to be visited from the perspective of user intuitive experience. The number of positive reviews information E 2 Divide by the number of reviews E 1 The larger the ratio is, the higher the recommendation index of the user's intuitive feeling is. On the other hand, the ticket price information of a single attraction F 单 Divide by the total fare information F 总 , which can reflect the proportion of a single attraction ticket price in the total ticket price, and thus characterize the recommendation index of the tourist attractions to be visited from the dimension of cost performance. The ticket price information of a single attraction F 单 Divide by the total fare information F 总 The lower the price / performance ratio, the higher the recommendation index. 2 / E 1 )-(F 单 / F 总 ) are subtracted from each other to finally obtain the recommendation factor Q of each tourist attraction to be visited. Those skilled in the art can obviously understand that the larger the Q is, the higher the overall recommendation index is. Then, the recommendation factor Q of each tourist attraction to be visited is sorted from large to small, and the top four (or top three or top two, which can be determined according to the actual travel needs of the user) tourist attractions to be visited are selected as target tourist attractions.

[0033] 103. Obtaining the current viewing experience influencing factor corresponding to each of the target tourist attractions;

[0034] 104. Determine the current sightseeing experience coefficient corresponding to each of the target tourist attractions according to the current sightseeing experience impact factor corresponding to each of the target tourist attractions;

[0035] 105. Generating a viewing sequence of a plurality of target tourist attractions according to a plurality of the viewing experience coefficients;

[0036] 106. Recommending a viewing order of the plurality of target tourist attractions to the user.

[0037] In this embodiment, the influencing factor of the sightseeing experience may include the number of remaining sightseeing tourists. When the influencing factor of the sightseeing experience is only the number of remaining sightseeing tourists, the obtaining of the current influencing factor of the sightseeing experience corresponding to each of the target tourist attractions includes: obtaining the current position coordinate information of the user's current location; obtaining the path navigation information of each of the target tourist attractions corresponding to the user at the current time according to the current position coordinate information, and the path navigation information includes the walking time information t 1 ; Obtain the current T in each of the target tourist attractions according to the ticket verification system 1 The number of current sightseeing tourists at the time of arrival and the entry time of each current sightseeing tourist into the target tourist attraction are calculated; the exit time of each current sightseeing tourist in the corresponding target tourist attraction is calculated based on the historical average duration of a single sightseeing tourist in the entire journey of each target tourist attraction and the entry time of each current sightseeing tourist in the target tourist attraction; the T of the user arriving at each target tourist attraction is calculated based on the exit time of each current sightseeing tourist in the corresponding target tourist attraction. 2 The remaining number of tourists at the time T 2 Moment and T 1 The time between the moments is t 1 The generating of the viewing order of several target tourist attractions according to the several viewing experience coefficients specifically includes: recommending the target tourist attractions with the smallest number of remaining viewing tourists to the user.

[0038] Specifically, the information on the number of remaining sightseeing tourists can be used to reflect the current flow of people in the scenic spot. When the flow of people is less, the viewing experience is better. At this time, the information on the number of remaining sightseeing tourists is used as an influencing factor of the viewing experience. In order to accurately know the number of remaining sightseeing tourists when the user arrives at the corresponding scenic spot to be visited, the current position coordinate information of the user's current location is first obtained; and the path navigation information corresponding to each target tourist attraction at the current time of the user is obtained based on the current position coordinate information. The path navigation information includes walking time information t 1 ; To determine the current T 1 time, and the walking time information t 1 After T 2The time when the user arrives at each target tourist attraction is determined based on the user's historical average duration of a single tourist visitor, the entry time, and the exit time. 2 The remaining number of tourists at the time of the visit is the information. It should be noted that the historical average duration of a single tourist visitor can be used to estimate the average duration of each target tourist attraction based on the historical duration of the visit. At the same time, if the distance between the user's current location and each target tourist attraction is different, the path navigation information (walking duration information t 1 ), and the walking time information t of reaching each target tourist attraction 1 After T 2 The time is also different. Finally, the target tourist attraction with the smallest number of remaining tourists is recommended to the user. As mentioned above, the smallest number of remaining tourists also represents the smallest current flow of people. At this time, the user passes by the walking time information t 1 After arriving at the attraction, T 2 The experience of starting the game at any time is also the best.

[0039] Of course, in addition to T 2 In addition to the remaining sightseeing tourists in the target tourist attraction at the moment, there may also be tourists waiting to enter the park who have purchased tickets but have not yet entered the park. At this time, in order to make more accurate recommendations for users, the waiting-to-see experience influencing factor may also include information on the number of tourists waiting to enter the park. At this time, obtaining the current waiting-to-see experience influencing factor corresponding to each of the target tourist attractions also includes: obtaining the waiting-to-see experience influencing factor at the T according to the ticketing system 2 The number of tickets sold for each of the target tourist attractions at the time; obtaining the ticket verification system at the time T 2 The number of verified scenic tickets for each of the target tourist attractions at the moment; the number of tourists waiting to enter the park based on the verified number of scenic tickets and the number of sold scenic tickets; the number of remaining tourists at each of the target tourist attractions and the number of tourists waiting to enter the park at each of the target tourist attractions are calculated at the T 2 The estimated total number of tourists at each of the target tourist attractions at a given moment; generating a viewing order of a plurality of target tourist attractions based on a plurality of the viewing experience coefficients specifically includes: recommending the target tourist attraction with the smallest estimated total number of tourists to the user.

[0040] It is understandable that through the ticketing system and ticket verification system, T 2The number of tourists who have not yet entered the target tourist attraction at that moment is calculated, and combined with the information on the number of remaining tourists to obtain the estimated total number of tourists, and the target tourist attraction with the smallest estimated total number of tourists is recommended to the user, achieving the beneficial effect of making more accurate recommendations to users.

[0041] As a specific recommendation method of this embodiment, it is assumed that the target tourist scenic area includes target tourist attractions A, target tourist attractions B, target tourist attractions C and target tourist attractions D as the recommendation factor Q of the tourist attractions to be visited = (E 2 / E 1 )-(F 单 / F 总 ) calculates the top four target tourist attractions, at which time the estimated total number of tourists of the target tourist attractions is ranked from small to large as target tourist attraction A, target tourist attraction B, target tourist attraction C and target tourist attraction D. The first attraction visited by the user is target tourist attraction A. After visiting target tourist attraction A, in order to continue to make automatic recommendations to the user, the automatic recommendation method further includes: when the user leaves the target tourist attraction A, 3 At this moment, get user T 3 The current location coordinate information of the location at the moment; based on the user T 3 The current position coordinate information of the user T at the time 3 The path navigation information corresponding to the target tourist attraction B at the moment, the path navigation information includes walking time information t 1B ; Based on user T 3 The current position coordinate information of the user T at the time 3 The path navigation information corresponding to the target tourist attraction C at the time, the path navigation information includes walking time information t 1C ; Based on user T 3 The current position coordinate information of the user T at the time 3 The path navigation information corresponding to the target tourist attraction D at the moment, the path navigation information includes walking time information t 1D According to the ticket verification system, the target tourist attractions B, C and D are obtained at the current T 3The current number of tourists at the time of sightseeing, and the entry time of each current tourist into the target tourist attraction; the exit time of each current tourist in the corresponding target tourist attraction is calculated based on the historical average duration of the entire journey of the target tourist attraction B, the target tourist attraction C, and the target tourist attraction D, and the entry time of each current tourist into the target tourist attraction; the exit time of the target tourist attraction B at T is calculated based on the exit time of each current tourist in the target tourist attraction B. 41 The remaining number of tourists at the time P 4B , the T 41 Moment and T 3 The time between the moments is t 1B Calculate the time of departure of the target tourist attraction C in T according to the departure time of each current tourist in the target tourist attraction C in the corresponding target tourist attraction C. 42 The remaining number of tourists at the time P 4C , the T 42 Moment and T 3 The time between the moments is t 1C Calculate the time of departure of each current tourist in the target tourist attraction D in T according to the departure time of each current tourist in the target tourist attraction D in the corresponding target tourist attraction D. 43 The remaining number of tourists at the time P 4D , the T 43 Moment and T 3 The time between the moments is t 1D The generating of the viewing sequence of several target tourist attractions according to the several viewing experience coefficients specifically includes: the remaining viewing tourist number information P 4B , the remaining number of tourists viewing information P 4C , the remaining number of tourists viewing information P 4D The smallest one is recommended to the user.

[0042] Specifically, after the target tourist attraction A is visited, the remaining number of tourists can be used to reflect the current flow of people in the target tourist attractions B, C, and D. The less the flow of people, the better the viewing experience. At this time, the remaining number of tourists is used as the influencing factor of the viewing experience. In order to accurately know the remaining number of tourists when the user arrives at the corresponding tourist attraction, the T at the time of leaving the park can be used. 3 At this moment, get user T 3 The current location coordinate information of the location at the moment; and based on the user T 3 The current location coordinate information of the user T is obtained3 The path navigation information corresponding to the target tourist attraction B at that moment (walking time information t 1B ), and according to user T 3 The current location coordinate information of the user T is obtained 3 The path navigation information corresponding to the target tourist attraction C at any time (walking time information t 1C ), and according to user T 3 The current position coordinate information of the user T at the time 3 The path navigation information corresponding to the target tourist attraction D at any time (walking time information t 1D ), in order to determine the current T 3 time, and the walking time information t relative to the target tourist attraction B 1B After T 41 time, relative to the target tourist attraction C, the walking time information t 1C After T 42 time, relative to the target tourist attraction D, the walking time information t 1D After T 43 time; and according to the historical average duration of the entire journey of individual visitors to the target tourist attractions B, C and D, and the entry time of each current visitor into the target tourist attractions, the exit time of each current visitor in the corresponding target tourist attractions is calculated; according to the exit time of each current visitor in the target tourist attraction B in the corresponding target tourist attraction B, the exit time of the target tourist attraction B at T is calculated. 41 The remaining number of tourists at the time P 4B Calculate the time of departure of the target tourist attraction C in T according to the departure time of each current tourist in the target tourist attraction C in the corresponding target tourist attraction C. 42 The remaining number of tourists at the time P 4C Calculate the time of departure of each current tourist in the target tourist attraction D in T according to the departure time of each current tourist in the target tourist attraction D in the corresponding target tourist attraction D. 43 The remaining number of tourists at the time P 4D Finally, the target tourist attraction with the smallest number of remaining tourists is recommended to the user. As mentioned above, the smallest number of remaining tourists also represents the smallest current flow of people. At this time, the user passes by the walking time information t 1 After arriving at the attraction, T 2 The experience of starting the game at any time is also the best.

[0043] Furthermore, in order to comprehensively consider the number of remaining sightseeing tourists and the walking time information, so as to facilitate the user to select the best sightseeing sequence within the optimal time-consuming scheme, the sightseeing sequence of several target tourist attractions is generated according to several sightseeing experience coefficients, specifically including: according to formula S 基础 =M*the number of remaining tourists + N*the walking time information to calculate the basic waiting viewing experience coefficient S of the target tourist attraction B, the target tourist attraction C and the target tourist attraction D respectively 基础 , when the remaining number of tourists viewing the scenery is the remaining number of tourists viewing the scenery information P 4B When the walking time information is t 1B , when the remaining number of tourists viewing the scenery is the remaining number of tourists viewing the scenery information P 4C When the walking time information is t 1C , when the remaining number of tourists viewing the scenery is the remaining number of tourists viewing the scenery information P 4D When the walking time information is t 1D , said M+N=1.

[0044] As a preferred solution, M = 0.7, N = 0.3, with the minimum flow of people as the priority factor, and 70% as the calculation factor for S 基础 The remaining 30% is used as the coefficient for calculating S 基础 The coefficient of the walking time information in the formula is the smaller the number of remaining sightseeing tourists is and the shorter the walking time information is, the higher the S is. 基础 The smallest experience coefficient of the scene to be viewed is recommended to the user as the optimal recommendation option, that is, the viewing order of several target tourist attractions generated based on several experience coefficients of the scene to be viewed specifically includes: recommending the target tourist attractions B, target tourist attractions C and target tourist attractions D with the smallest basic experience coefficients of the scene to be viewed to the user.

[0045] Of course, in addition to T 41 Time / T 42 Time / T 43 At this time, in addition to the remaining sightseeing tourists in the target tourist attraction B / target tourist attraction C / target tourist attraction D, there may also be tourists waiting to enter the park who have purchased tickets but have not yet started to enter the park. At this time, in order to make more accurate recommendations for users, at this time, the viewing sequence of several target tourist attractions generated according to several waiting viewing experience coefficients specifically includes: obtaining the viewing experience coefficients at the T according to the ticketing system respectively 41 The number of tickets sold for the target tourist attraction B at time T 42 The information on the number of tickets sold for the target tourist attraction C at the time T 43The number of tickets sold for the target tourist attraction D at the time; and obtaining the ticket verification system at the time T 41 At the moment, at T 42 Moment and T 43 The number of verified scenic spot tickets of the target tourist attractions B, the target tourist attractions C and the target tourist attractions D at their respective corresponding times; and the number of verified scenic spot tickets and the number of sold scenic spot tickets are calculated based on the information on the number of verified scenic spot tickets and the information on the number of sold scenic spot tickets. 41 At the moment, at T 42 Moment and T 43 According to the remaining number of tourists of the target tourist attraction B, the target tourist attraction C and the target tourist attraction D and the corresponding number of tourists waiting to enter the park, the information is calculated at the time T 41 At the moment, at T 42 Moment and T 43 The estimated total number of tourists at the target tourist attraction B at the time P 4B总 , the estimated total number of tourists at the target tourist attraction C 4C总 , the estimated total number of tourists at the target tourist attraction D 4D总 The generating of the viewing sequence of several target tourist attractions according to the several viewing experience coefficients specifically includes: The generating of the viewing sequence of several target tourist attractions according to the several viewing experience coefficients specifically includes: According to the formula S 标准 =M*estimated total number of tourists + N*walking time information, respectively calculate the standard waiting viewing experience coefficients of the target tourist attractions B, C and D. When the estimated total number of tourists is the estimated total number of tourists information P 4B总 When the walking time information is t 1B , when the estimated total number of tourists is the estimated total number of tourists information P 4C总 When the walking time information is t 1C , when the estimated total number of tourists is the estimated total number of tourists information P 4D总 When the walking time information is t 1D Similarly, as a preferred solution, M = 0.7, N = 0.3, with the minimum flow of people as the priority factor, and 70% as the calculation factor for S 标准 The coefficient of the estimated total number of tourists in the formula is used as the remaining 30% to calculate S 标准 The coefficient of the walking time information in the formula is the smaller the estimated total number of tourists and the shorter the walking time information. 标准The smallest experience coefficient of the scene to be viewed is recommended to the user as the optimal recommendation option, that is, the viewing order of several target tourist attractions generated based on several experience coefficients of the scene to be viewed specifically includes: recommending the target tourist attractions B, target tourist attractions C and target tourist attractions D with the smallest standard experience coefficients of the scene to be viewed to the user.

[0046] In summary, it can be seen that in the embodiment provided by the present invention, the automatic recommendation method obtains the basic identification information of the tourist attractions to be visited in the target tourist scenic area, and then determines a number of target tourist attractions based on the basic identification information of the tourist attractions to be visited, and obtains the current viewing experience influence factor corresponding to each target tourist attraction, and then determines the current viewing experience coefficient corresponding to each target tourist attraction based on the current viewing experience influence factor corresponding to each target tourist attraction, and finally generates a viewing order of a number of target tourist attractions based on the number of viewing experience coefficients, and recommends a viewing order of a number of target tourist attractions to the user, so that the user can view and play according to the recommended viewing order, effectively using intelligent technology to make intelligent recommendations on tourists' travel arrangements, thereby improving the travel experience of tourists in the scenic area.

[0047] The above describes the embodiment of the present invention from the perspective of the automatic recommendation method for target tourist attractions. The following describes the embodiment of the present invention from the perspective of the automatic recommendation device for target tourist attractions.

[0048] A schematic diagram of the virtual structure of an apparatus for an automatic recommendation method based on target tourist attractions in an embodiment of the present invention, the apparatus comprising: a basic identification information acquisition module, configured to acquire basic identification information of tourist attractions to be visited within the target tourist scenic area; a target tourist attractions determination module, configured to determine a number of target tourist attractions based on the basic identification information of the tourist attractions to be visited; a current to-be-viewed experience influence factor acquisition module, configured to acquire the current to-be-viewed experience influence factor corresponding to each of the target tourist attractions; a current to-be-viewed experience coefficient determination module, configured to determine the current to-be-viewed experience coefficient corresponding to each of the target tourist attractions based on the current to-be-viewed experience influence factor corresponding to each of the target tourist attractions; a viewing sequence generation module, configured to generate a viewing sequence of a number of target tourist attractions based on a number of the to-be-viewed experience coefficients; and a recommendation module, configured to recommend the viewing sequence of the number of target tourist attractions to a user.

[0049] In the embodiment provided by the present invention, the automatic recommendation device obtains basic identification information of the tourist attractions to be visited in the target tourist scenic area, then determines a number of target tourist attractions based on the basic identification information of the tourist attractions to be visited, and obtains the current viewing experience impact factor corresponding to each target tourist attraction, then determines the current viewing experience coefficient corresponding to each target tourist attraction based on the current viewing experience impact factor corresponding to each target tourist attraction, and finally generates a viewing order of a number of target tourist attractions based on the number of viewing experience coefficients, and recommends a viewing order of a number of target tourist attractions to the user, so that the user can view and play according to the recommended viewing order, effectively using intelligent technology to make intelligent recommendations on tourists' play arrangements, thereby improving the play experience of tourists in the scenic area.

[0050] The automatic recommendation device in the embodiment of the present invention is described above from the perspective of modular functional entities. The automatic recommendation device in the embodiment of the present invention is described in detail below from the perspective of hardware processing. Figure 3 As shown, an embodiment of the present invention provides an electronic device, including a memory 710, a processor 720, and a computer program 711 stored in the memory 710 and executable on the processor 720. When the processor 720 executes the computer program 711, the following steps are implemented:

[0051] Obtaining basic identification information of tourist attractions to be visited within the target tourist scenic area;

[0052] Determining a number of target tourist attractions based on the basic identification information of the tourist attractions to be visited;

[0053] Obtaining the current viewing experience impact factor corresponding to each of the target tourist attractions;

[0054] Determining the current sightseeing experience coefficient corresponding to each of the target tourist attractions according to the current sightseeing experience impact factor corresponding to each of the target tourist attractions;

[0055] Generating a viewing sequence of a plurality of target tourist attractions according to a plurality of the viewing experience coefficients;

[0056] The viewing order of the target tourist attractions is recommended to the user.

[0057] In the specific implementation process, when the processor 720 executes the computer program 711, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0058] Since the electronic device introduced in this embodiment is a device used to implement an automatic recommendation device in the embodiment of the present invention, based on the method introduced in the embodiment of the present invention, technical personnel in this field can understand the specific implementation method of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present invention is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present invention falls within the scope of protection of the present invention.

[0059] See also Figure 4 , Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention.

[0060] like Figure 4 As shown, the embodiment of the present invention further provides a computer-readable storage medium 800, on which a computer program 811 is stored. When the computer program 811 is executed by a processor, the following steps are implemented:

[0061] Obtaining basic identification information of tourist attractions to be visited within the target tourist scenic area;

[0062] Determining a number of target tourist attractions based on the basic identification information of the tourist attractions to be visited;

[0063] Obtaining the current viewing experience impact factor corresponding to each of the target tourist attractions;

[0064] Determining the current sightseeing experience coefficient corresponding to each of the target tourist attractions according to the current sightseeing experience impact factor corresponding to each of the target tourist attractions;

[0065] Generating a viewing sequence of a plurality of target tourist attractions according to a plurality of the viewing experience coefficients;

[0066] The viewing order of the target tourist attractions is recommended to the user.

[0067] In the specific implementation process, the computer program 811 is executed by the processor to achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0068] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0069] 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.

[0070] 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 computer, or other programmable data processing device to generate 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.

[0071] 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.

[0072] 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.

[0073] The embodiment of the present invention also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device executes the following Figure 1 The process in the corresponding embodiment.

[0074] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a server, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server, or data center. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integrations. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0075] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0076] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There are 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 an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0077] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] In addition, each functional unit in each embodiment of the present invention 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. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0079] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-On l yMemory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0080] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for automatically recommending target tourist attractions, wherein the target tourist attractions are multiple tourist attractions within a target tourist scenic area, each tourist attraction is equipped with an independent online ticketing system and ticket verification system, characterized in that: The automatic recommendation method comprises: Obtaining basic identification information of tourist attractions to be visited within the target tourist scenic area; Determining a number of target tourist attractions based on the basic identification information of the tourist attractions to be visited; Obtaining the current viewing experience impact factor corresponding to each of the target tourist attractions; Determining the current sightseeing experience coefficient corresponding to each of the target tourist attractions according to the current sightseeing experience impact factor corresponding to each of the target tourist attractions; Generating a viewing sequence of a plurality of target tourist attractions according to a plurality of the viewing experience coefficients; The viewing order of the target tourist attractions is recommended to the user.

2. The automatic recommendation method according to claim 1, characterized in that: The influencing factor of the sightseeing experience to be viewed includes the information of the number of remaining sightseeing tourists, and the step of obtaining the influencing factor of the current sightseeing experience to be viewed corresponding to each of the target tourist attractions includes: Get the current location coordinate information of the user's current location; Acquire the path navigation information of each of the target tourist attractions corresponding to the user at the current moment according to the current position coordinate information, wherein the path navigation information includes walking time information t1; According to the ticket verification system, the number of current sightseeing tourists at the current time T1 in each of the target tourist attractions and the entry time of each current sightseeing tourist into the target tourist attractions are obtained; According to the historical average duration of each single tourist at each target tourist attraction and the entry time of each current tourist into the target tourist attraction, the exit time of each current tourist at the corresponding target tourist attraction is estimated; Calculate the number of remaining sightseeing tourists at time T2 when the user arrives at each of the target tourist attractions according to the departure time of each current sightseeing tourist in the corresponding target tourist attraction, where the time between time T2 and time T1 is t1; The generating of the viewing sequence of a plurality of target tourist attractions according to the plurality of the to-be-viewed experience coefficients specifically includes: recommending the target tourist attraction having the smallest information on the number of remaining viewing tourists to the user.

3. The automatic recommendation method according to claim 2, characterized in that: The factors influencing the sightseeing experience to be viewed also include the number of tourists to enter the park for sightseeing, and the steps of obtaining the current factors influencing the sightseeing experience to be viewed corresponding to each of the target tourist attractions also include: Obtaining information on the number of tickets sold for each of the target tourist attractions at time T2 according to the ticketing system; Obtaining information on the number of verified tickets for each of the target tourist attractions at time T2 according to the ticket verification system; Calculate the number of tourists waiting to enter the park for sightseeing according to the verified number of scenic spot tickets and the sold number of scenic spot tickets; Calculate the estimated total number of tourists for each target tourist attraction at the time T2 based on the remaining number of tourists for each target tourist attraction and the number of tourists waiting to enter the park for each target tourist attraction; The generating of the viewing sequence of a plurality of target tourist attractions according to the plurality of the to-be-viewed experience coefficients specifically includes: recommending the target tourist attraction having the smallest estimated total number of tourists to the user.

4. The automatic recommendation method according to claim 3, characterized in that: The target tourist scenic spot includes a target tourist attraction A, a target tourist attraction B, a target tourist attraction C and a target tourist attraction D, and the estimated total number of tourists of the target tourist attractions is sorted from small to large as target tourist attraction A, target tourist attraction B, target tourist attraction C and target tourist attraction D, and the automatic recommendation method further includes: At time T3 when the user leaves the target tourist attraction A, obtaining the current location coordinate information of the user at time T3; According to the current position coordinate information of the user at time T3, the path navigation information corresponding to the target tourist attraction B at time T3 is obtained, and the path navigation information includes the walking time information t 1B ; Obtain the path navigation information corresponding to the target tourist attraction C at the time T3 of the user according to the current position coordinate information of the user at the time T3, and the path navigation information includes the walking time information t 1C ; Obtain the path navigation information corresponding to the target tourist attraction D at the time T3 of the user according to the current position coordinate information of the user at the time T3, and the path navigation information includes the walking time information t 1D ; According to the ticket verification system, respectively obtain the current number of tourists visiting the target tourist attraction B, the target tourist attraction C and the target tourist attraction D at the current time T3, and the entry time of each current tourist visiting the target tourist attraction; According to the historical average duration of each single tourist at the target tourist attraction B, the target tourist attraction C and the target tourist attraction D, and the entry time of each current tourist into the target tourist attraction, the exit time of each current tourist at the corresponding target tourist attraction is calculated; The time of departure of each current tourist in the target tourist attraction B in T is calculated according to the departure time of each current tourist in the target tourist attraction B in the corresponding target tourist attraction B. 41 The remaining number of tourists at the time P 4B , the T 41 The time between the moment and the T3 moment is t 1B Calculate the time of departure of the target tourist attraction C in T according to the departure time of each current tourist in the target tourist attraction C in the corresponding target tourist attraction C. 42 The remaining number of tourists at the time P 4C , the T 42 The time between the moment and the T3 moment is t 1C Calculate the time of departure of each current tourist in the target tourist attraction D in T according to the departure time of each current tourist in the target tourist attraction D in the corresponding target tourist attraction D. 43 The remaining number of tourists at the time P 4D , the T 43 The time between the moment and the T3 moment is t 1D ; The generating of the viewing sequence of several target tourist attractions according to the several viewing experience coefficients specifically includes: 4B , the remaining number of tourists viewing information P 4C , the remaining number of tourists viewing information P 4D The smallest one is recommended to the user.

5. The automatic recommendation method according to claim 4, characterized in that: The generating of the viewing sequence of a plurality of target tourist attractions according to the plurality of viewing experience coefficients specifically includes: According to formula S 基础 =M*the number of remaining tourists + N*the walking time information to calculate the basic waiting viewing experience coefficient S of the target tourist attraction B, the target tourist attraction C and the target tourist attraction D respectively 基础 , when the remaining number of tourists viewing the scenery is the remaining number of tourists viewing the scenery information P 4B When the walking time information is t 1B , when the remaining number of tourists viewing the scenery is the remaining number of tourists viewing the scenery information P 4C When the walking time information is t 1C , when the remaining number of tourists viewing the scenery is the remaining number of tourists viewing the scenery information P 4D When the walking time information is t 1D , said M+N=1; The generating of the viewing order of several target tourist attractions based on the several experience coefficients of the sights to be viewed specifically includes: recommending the target tourist attractions B, C and D having the smallest basic experience coefficients of the sights to be viewed to the user.

6. The automatic recommendation method according to claim 5, characterized in that: The generating of the viewing sequence of a plurality of target tourist attractions according to the plurality of viewing experience coefficients specifically includes: According to the ticketing system, obtain 41 The number of tickets sold for the target tourist attraction B at time T 42 The information on the number of tickets sold for the target tourist attraction C at the time T 43 Information on the number of tickets sold for the target tourist attraction D at the moment; According to the ticket checking system, obtain 41 At the moment, at T 42 Moment and T 43 The number of verified tickets for the target tourist attraction B, the target tourist attraction C, and the target tourist attraction D at the respective corresponding moments; The target tourist attraction B, the target tourist attraction C and the target tourist attraction D are calculated based on the verified ticket quantity information and the sold ticket quantity information in their respective corresponding T 41 At the moment, at T 42 Moment and T 43 Information on the number of tourists waiting to enter the park at any given moment; According to the remaining number of tourists of the target tourist attraction B, the target tourist attraction C and the target tourist attraction D and the corresponding number of tourists waiting to enter the park, the number of tourists in the T 41 At the moment, at T 42 Moment and T 43 The estimated total number of tourists at the target tourist attraction B at the time P 4B总 , the estimated total number of tourists at the target tourist attraction C 4C总 , the estimated total number of tourists at the target tourist attraction D 4D总 ; The generating of the viewing sequence of the target tourist attractions according to the plurality of the viewing experience coefficients specifically includes: The generating of the viewing sequence of the target tourist attractions according to the plurality of the viewing experience coefficients specifically includes: According to formula S 标准 =M*estimated total number of tourists + N*walking time information, respectively calculate the standard waiting viewing experience coefficients of the target tourist attractions B, C and D. When the estimated total number of tourists is the estimated total number of tourists information P 4B总 When the walking time information is t 1B , when the estimated total number of tourists is the estimated total number of tourists information P 4C总 When the walking time information is t 1C , when the estimated total number of tourists is the estimated total number of tourists information P 4D总 When the walking time information is t 1D ; The generating of the viewing order of several target tourist attractions according to the several experience coefficients of the sights to be viewed specifically includes: recommending the target tourist attractions B, C and D having the smallest standard experience coefficients of the sights to be viewed to the user.

7. The automatic recommendation method according to any one of claims 1 to 6, characterized in that: The obtaining of basic identification information of the tourist attractions to be visited in the target tourist scenic area specifically includes: obtaining information E1 on the number of comments on the tourist attractions to be visited in the target tourist scenic area; obtaining information E2 on the number of favorable comments on the tourist attractions to be visited in the target tourist scenic area; obtaining information F on the ticket price of a single tourist attraction to be visited in the target tourist scenic area. 单 ; Obtain the total ticket price information of all tourist attractions to be visited in the target tourist attraction F 总 ; The determining of a number of target tourist attractions according to the basic identification information of the tourist attractions to be visited specifically includes: calculating a recommendation factor Q of each tourist attraction to be visited according to the following formula: Q=(E2 / E1)-(F 单 / F 总 ); Sort the recommendation factor Q of each tourist attraction to be visited from large to small, and select the top four tourist attractions to be visited as target tourist attractions.

8. An automatic recommendation device for target tourist attractions, wherein the target tourist attractions are multiple tourist attractions within a target tourist scenic area, each tourist attraction is equipped with an independent online ticket sales and ticket verification system, characterized in that: The automatic recommendation device comprises: A basic identification information acquisition module is configured to acquire basic identification information of tourist attractions to be visited in the target tourist scenic area; A target tourist attraction determination module is configured to determine a number of target tourist attractions according to the basic identification information of the tourist attractions to be visited; A current sightseeing experience influencing factor acquisition module is configured to acquire the current sightseeing experience influencing factor corresponding to each of the target tourist attractions; The current sightseeing experience coefficient determination module is configured to determine the current sightseeing experience coefficient corresponding to each of the target tourist attractions according to the current sightseeing experience influence factor corresponding to each of the target tourist attractions; A viewing sequence generating module is configured to generate a viewing sequence of a plurality of target tourist attractions according to a plurality of viewing experience coefficients; The recommendation module is configured to recommend the viewing order of the plurality of target tourist attractions to the user.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the processor is configured to implement the method as claimed in any one of claims 1 to 7 when executing a computer management program stored in the memory.

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

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