CN115936154A

CN115936154A - Recommendation reservation method, device, equipment and storage medium based on artificial intelligence

Info

Publication number: CN115936154A
Application number: CN202211443336.6A
Authority: CN
Inventors: 黄文涛; 陈海江
Original assignee: Zhejiang Lishi Technology Co Ltd
Current assignee: Zhejiang Lishi Technology Co Ltd
Priority date: 2023-01-05
Filing date: 2023-01-05
Publication date: 2023-04-07

Abstract

The application belongs to the field of recommendation reservation and relates to a recommendation reservation method, a recommendation reservation device, computer equipment and a storage medium based on artificial intelligence, wherein the method comprises the steps that an APP client side obtains ticket purchasing information of a user; the APP client side obtains the timely item queuing time and item position information; acquiring the real-time position of a user, calling a route from the user to the position of the item in the queue, and calculating the time from the user to the position of the item in the queue and the expected queuing time of the corresponding playing item; judging whether the playing items are optimal or not at the current time point according to the route time and the expected queuing time, if the expected queuing time does not exceed the optimal route time threshold, recommending the user to go, and if not, executing the next step; and acquiring nearby play items according to the real-time position of the user, and if the whole play time of the recommended item is less than the predicted queuing time of the item in the queue, sending a push message by the APP client, and recommending that the user go to the recommended play item. The queuing time cost is reduced, and the consumption capacity of the scenic spot playing items is improved.

Description

Recommendation reservation method, device, equipment and storage medium based on artificial intelligence

Technical Field

The present application relates to the field of recommendation reservation technologies, and in particular, to a recommendation reservation method and apparatus based on artificial intelligence, a computer device, and a storage medium.

Background

Along with the rapid development of the information era, the intelligent scenic spot construction greatly improves the service quality of tourists, and the artificial intelligence technology also enables the scenic spot tourists to experience different convenience. Of course, the new techniques have not been able to completely replace manual work, such as: the plan and arrangement of play, the tedious ticket buying and checking and the like need to be artificially controlled. Particularly, when the user plays scenic spots in scenic spots with many playing items and large passenger flow, the user needs to queue at the entrance in advance after buying tickets, the time wasted in queuing accounts for a large amount of time, and the situation of 'waiting for 1 hour for 5 minutes' can occur; the queuing condition and the related distance of the nearby project cannot be known in real time, and the playing plan cannot be flexibly and reasonably adjusted according to different conditions, that is, the queuing condition and the related distance of the nearby project cannot be timely acquired, and the playing plan cannot be reasonably arranged.

Disclosure of Invention

The embodiment of the application aims to provide a recommendation reservation method, a recommendation reservation device, computer equipment and a storage medium based on artificial intelligence, so as to solve the problems that the queuing condition and the related distance of nearby projects cannot be obtained in time and a play plan cannot be reasonably arranged when the existing projects are played.

In order to solve the above technical problem, on one hand, the present application provides a recommendation reservation method based on artificial intelligence, which adopts the following technical scheme, including the following steps:

the APP client side obtains ticket purchasing information of a user, wherein the ticket purchasing information comprises a user name, playing time and online queuing time;

the APP client side obtains the timely item queuing time and item position information;

acquiring the real-time position of a user, calling a route from the user to the position of the item in the queue, calculating the time from the user to the position of the item in the queue, and calculating the expected queue time of the corresponding play item;

judging whether the playing items are optimal or not at the current time point according to the route time and the expected queuing time, if the expected queuing time does not exceed the optimal route time threshold, recommending that the user immediately go, and if not, executing the next step;

and acquiring nearby play items according to the real-time position of the user, and if the whole play time of the recommended item is smaller than the predicted queue time of the item in the queue, sending a push message by the APP client side, and recommending the user to the recommended play item.

Further, the recommending the overall playing time of the item comprises: route time, play time, and queue time.

Further, the optimal route time threshold is 10-60 minutes.

On the other hand, the application also provides another recommendation reservation method based on artificial intelligence, which adopts the following technical scheme and comprises the following steps:

the APP server side obtains the user position authority and obtains the user real-time position at a certain frequency;

the APP server side obtains user play project information, wherein the user play project information comprises predicted queuing time and play project positions;

judging whether the current time point goes to the playing item is optimal or not according to the route time and the expected queuing time of the playing item, if the expected queuing time does not exceed the optimal route time threshold, recommending that the user goes to the playing item immediately, and if not, executing the next step;

and obtaining nearby playing items according to the real-time position of the user, and if the whole playing time of the recommended item is less than the predicted queuing time of the item in the queue, sending a push message to the APP client by the APP server, and recommending the user to go to the recommended playing item.

the APP server obtains face information reported by the monitoring equipment;

the APP server compares the face information in a face library, and acquires user information corresponding to the face information according to a comparison result;

acquiring user playing item information according to the user information, wherein the playing item information comprises predicted queuing time and item positions;

judging whether the current time point is optimal for the play item according to the position of the monitoring equipment, the travel route time of the play item and the predicted queuing time, recommending the user to the play item if the predicted queuing time does not exceed the optimal route time threshold, and executing the next step if not;

and obtaining nearby play items according to the real-time position of the user, and if the whole play time of the recommended item is less than the expected queuing time of the play item, pushing a message to the APP client by the APP server, and recommending that the user go to the recommended play item.

In order to solve the above technical problem, the present application further provides a recommendation reservation apparatus based on artificial intelligence, which adopts the following technical scheme, including:

the system comprises a first acquisition module, a first management module and a second acquisition module, wherein the first acquisition module is used for an APP client to acquire ticket purchasing information of a user, and the ticket purchasing information comprises a user name, playing time and online queuing time;

the second acquisition module is used for the APP client to acquire timely project queuing time and project position information;

the first calculation module is used for acquiring the real-time position of the user, calling out a route from the user to the position of the item in the queue, calculating the time from the user to the position of the item in the queue and calculating the expected queue time of the corresponding playing item;

the first judgment module is used for judging whether the playing item is optimal when the current time point goes to the playing item according to the route time and the expected queuing time, if the expected queuing time does not exceed the optimal route time threshold, the user is recommended to go immediately, and if not, the next step is executed;

and the first recommending module is used for acquiring nearby playing items according to the real-time position of the user, and if the overall playing time of the recommended item is less than the predicted queuing time of the item in the queue, the APP client sends a push message to recommend the user to go to the recommended playing item.

In order to solve the above technical problem, the present application further provides another recommendation reservation apparatus based on artificial intelligence, which adopts the following technical solution, including:

the third acquisition module is used for the APP server side to acquire the user position authority and acquire the real-time position of the user at a certain frequency;

the fourth acquisition module is used for the APP server side to acquire user playing project information, wherein the user playing project information comprises estimated queuing time and playing project positions;

the judging module is used for judging whether the current time point goes to the playing item is optimal or not according to the route time and the expected queuing time of the playing item, if the expected queuing time does not exceed the optimal route time threshold, the user is recommended to go immediately, and if not, the next step is executed;

and the second recommendation module is used for acquiring nearby playing items according to the real-time position of the user, and if the whole playing time of the recommended items is less than the predicted queuing time of the items in the queue, the APP server sends a push message to the APP client, and the recommended user goes to the recommended playing items.

a fifth obtaining module, configured to obtain, by the APP server, the face information reported by the monitoring device;

a sixth obtaining module, configured to compare, by the APP server, the face information in a face library, and obtain, according to a comparison result, user information corresponding to the face information;

a seventh obtaining module, configured to obtain information of a user playing item according to the user information, where the information of the playing item includes predicted queuing time and item position;

the second calculation module is used for judging whether the current time point is optimal for the playing item or not according to the position of the monitoring equipment, the route-going time of the playing item and the estimated queuing time, if the estimated queuing time does not exceed the optimal route time threshold, the user is recommended to go to the playing item, and if not, the next step is executed;

and the third recommending module is used for acquiring nearby playing items according to the real-time position of the user, and if the whole playing time of the recommended item is less than the expected queuing time of the playing item, the APP server pushes a message to the APP client, and the recommended user goes to the recommended playing item.

In order to solve the above technical problem, the present application further provides a computer device, which adopts the following technical solution, and includes a memory and a processor, where the memory stores computer readable instructions, and the processor implements the steps of the above artificial intelligence based recommended reservation method when executing the computer readable instructions.

In order to solve the above technical problem, the present application further provides a computer-readable storage medium, which adopts the following technical solution, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions, when executed by a processor, implement the steps of the above artificial intelligence-based recommendation reservation method.

Compared with the prior art, the application mainly has the following beneficial effects:

through supervisory equipment and APP customer end combination, realize that scenic spot sight spot recreation project is according to real-time position and the individualized recommendation message of propelling movement of plan, help the better plan arrangement of visitor to play the project, reduced the time cost of lining up, the audio-visual condition of lining up of understanding each recreation project, great improvement the consumption ability of scenic spot recreation project.

Drawings

In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow diagram of one embodiment of an artificial intelligence based referral appointment method of the subject application;

FIG. 3 is a flow diagram of an application scenario in FIG. 2;

FIG. 4 is a flow diagram of another embodiment of an artificial intelligence based referral appointment method of the present application;

FIG. 5 is a flow diagram of an application scenario of FIG. 4;

FIG. 6 is a flow diagram of yet another embodiment of an artificial intelligence based referral appointment method of the present application;

FIG. 7 is a flow diagram of an application scenario of FIG. 6;

FIG. 8 is a schematic block diagram of an embodiment of an artificial intelligence based referral appointment apparatus of the present application;

FIG. 9 is a schematic block diagram of another embodiment of an artificial intelligence based referral appointment apparatus of the present application;

FIG. 10 is a schematic structural diagram of yet another embodiment of an artificial intelligence based recommendation reservation apparatus of the present application;

FIG. 11 is a block diagram of one embodiment of a computer device of the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the foregoing drawings are used for distinguishing between different objects and not for describing a particular sequential order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, the system architecture 100 may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may interact with the server 105 via the network 104 using the first terminal device 101, the second terminal device 102 or the third terminal device 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like, may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103.

The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, motion Picture Experts compression standard Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, motion Picture Experts compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.

The server 105 may be a server that provides various services, such as a background server that provides support for pages displayed on the first terminal apparatus 101, the second terminal apparatus 102, and the third terminal apparatus 103.

It should be noted that the artificial intelligence based recommendation reservation method provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, an artificial intelligence based recommendation reservation apparatus is generally disposed in the server/terminal device.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

Example one

With continued reference to FIG. 2, a flow diagram of one embodiment of an artificial intelligence based recommendation reservation method of the present application is shown. The recommendation reservation method based on artificial intelligence comprises the following steps:

step S201, the APP client side obtains ticket purchasing information of the user, wherein the ticket purchasing information comprises a user name, playing time and online queuing time.

In this embodiment, the electronic device (for example, the server/terminal device shown in fig. 1) on which the artificial intelligence based recommendation reservation method operates may receive the artificial intelligence based recommendation reservation request through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G/5G connection, a WiFi connection, a bluetooth connection, a wimax x connection, a Zigbee connection, an UWB (ultra wideband) connection, and other wireless connection means now known or developed in the future.

The APP customer end is equipped with the APP, and through the APP, the user can carry out online ticket booking through modes such as believe a little, payment treasured, bank card, and the ticket booking information includes user name, play time and carries out online time of lining up.

Step S202, the APP client side obtains the timely item queuing time and item position information.

In the embodiment, the APP timely updates the real-time queuing condition of the current play items and displays the specific position of each play item. A plurality of playing project sites in the scenic spot are interconnected through a network, and the playing project sites are timely used for on-line queuing, waiting time and the like to feed back to a playing project server in the scenic spot at a certain frequency. And the scenic spot playing project server uniformly feeds back to the APP client.

Step S203, acquiring the real-time position of the user, calling out the route of the user to the position of the item in the queue, calculating the time of the user to the position of the item in the queue, and calculating the expected queue time of the corresponding playing item.

In this embodiment, the APP client obtains the real-time location of the user through GPS positioning.

And step S204, judging whether the playing item is optimal or not at the current time point according to the route time and the expected queuing time, recommending the user to go to the playing item immediately if the expected queuing time does not exceed the optimal route time threshold, and executing the next step if not.

In some optional implementations of the present embodiment, the optimal route time threshold may be set to 10-60 minutes, depending on the play time. For example, the optimal route time threshold may be set to 60 minutes for the time of fifone and eleven golden week, and 10 minutes for the time of weekdays from monday to friday.

And S205, acquiring nearby play items according to the real-time position of the user, and if the whole play time of the recommended item is smaller than the predicted queue time of the item in the queue, sending a push message by the APP client, and recommending the user to the recommended play item.

In particular, recommending the overall play time of the item may include: route time, play time, and queue time.

Fig. 3 is a flow diagram of an application scenario in fig. 2. As shown in fig. 3, a recommendation reservation method based on artificial intelligence includes the steps of:

(1) The tourists swipe faces through the APP client to purchase tickets, select item playing time and queue online;

(2) After ticket buying is successful, the APP client synchronously calls back and obtains item queuing time and item positions;

(3) Calculating routes and time to the positions of the items in the queue according to the real-time positions of the tourists, and comparing the expected queue time of the corresponding playing items;

(4) Judging whether the playing items are optimal or not at the current time point according to the route time and the expected queuing time, and if the expected queuing time exceeds the route time by 15 minutes, not recommending to go to the playing items immediately;

(5) Currently, if there is no play item needing to go to immediately, an intelligent recommendation mechanism is triggered: and acquiring nearby play items according to the real-time position of the visitor, and if the whole play time (route time, play time and queue time) of the recommended item is less than the predicted queue time of the item in the queue, pushing APP client information to recommend the visitor to go.

In fig. 3, the APP client provides information purchase query functions such as queuing and tickets for the playing items of scenic spots, and intelligently recommends the playing items according to the recorded faces of the tourists and the acquired real-time positions. The system server, processing system service logic, includes but is not limited to: information inquiry of lines, play items, queue and the like; carrying out ticket purchasing and checking logic processing; calculating a line; and intelligently recommending game item calculation. And the play item queue records the queue information queue of each play item. And the map route service is used for displaying and calculating map route information of scenic spots in the scenic region.

According to the embodiment, the positions of the tourists are obtained and reported through the APP client, queuing reminding of the tourists for playing items in the team and intelligent recommendation of the nearby playing items are achieved, personalized recommendation information can be pushed according to the real-time positions of the users and the timely queuing conditions of the playing items, the tourists are helped to plan the playing items better, the time cost wasted in queuing is reduced, the queuing conditions of the playing items are intuitively known, and the consumption capacity of the playing items in scenic spots is greatly improved.

The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with computer readable instructions, which can be stored in a computer readable storage medium, and when executed, the processes of the embodiments of the methods described above can be included. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless otherwise indicated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of execution is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

Example two

With continued reference to FIG. 3, a flow diagram of another embodiment of the artificial intelligence based referral appointment method of the subject application is shown. The recommendation reservation method based on artificial intelligence comprises the following steps:

step S301, the APP server side obtains the user position authority, and obtains the user real-time position at a certain frequency.

In this embodiment, the user real-time location may be obtained at 30 seconds per polling.

Step S302, the APP server side obtains user play item information, wherein the user play item information comprises predicted queuing time and play item positions.

Step S303, judging whether the current time point is optimal for the play item according to the route time and the expected queuing time of the play item, if the expected queuing time does not exceed the optimal route time threshold, recommending the user to go to immediately, otherwise executing the next step.

And step S304, acquiring nearby playing items according to the real-time position of the user, and if the whole playing time of the recommended item is less than the predicted queuing time of the item in the queue, sending a push message to the APP client by the APP server, and recommending that the user go to the recommended playing item.

Fig. 5 is a flow diagram of an application scenario in fig. 4. As shown in fig. 5, a recommendation reservation method based on artificial intelligence includes the steps of:

(1) The tourist gives the APP background to obtain position authority, and the APP polls for 30 seconds/time to obtain a real-time position and reports the real-time position to the system;

(2) After receiving the real-time position, the system server acquires the information of the playing items in the queue of the tourists, such as predicted queuing time, project positions and the like;

(3) Judging whether the playing item is optimal when going to the current time point according to the route time and the expected queuing time of the items in the queue, if the expected queuing time exceeds the route time by 15 minutes, not recommending to go to the current time point immediately;

(4) Currently, if there is no play item needing to go to immediately, an intelligent recommendation mechanism is triggered: and acquiring nearby play items according to the real-time position of the visitor, and if the whole play time (route time, play time and queue time) of the recommended item is less than the predicted queue time of the item in the queue, pushing APP client information to recommend the visitor to go.

In fig. 5, the APP client provides information purchase query functions such as queuing, entrance ticket and the like for the playing items of scenic spots, and intelligently recommends the playing items according to the recorded faces of the tourists and the acquired real-time positions. The system server, processing system service logic, includes but is not limited to: information inquiry of lines, play items, queue and the like; carrying out ticket purchasing and checking logic processing; calculating a line; and intelligently recommending the game item calculation. And the play item queue records the queue information queue of each play item. And the map route service is used for displaying and calculating map route information of scenic spots in the scenic region.

According to the embodiment, the user position authority is obtained through the APP server side, the real-time position of the user is obtained at a certain frequency, queuing reminding of the game items of the tourists in the queue and intelligent recommendation of the game items nearby are achieved, personalized pushing of recommendation information can be achieved according to the real-time position of the user and the timely queuing condition of the game items, the tourists are helped to plan the game items better, time cost wasted in queuing is reduced, the queuing condition of each game item is intuitively known, and consumption capacity of the game items in scenic spots is greatly improved.

EXAMPLE III

With continued reference to FIG. 6, a flow diagram of yet another embodiment of the artificial intelligence based referral appointment method of the present application is shown. The recommendation reservation method based on artificial intelligence comprises the following steps:

step S401, the APP server obtains the face information reported by the monitoring equipment.

The gate ticket checking equipment is used for checking tickets by identifying and comparing the faces of the tourists; providing information inquiry such as item queue.

And S402, the APP server compares the face information in a face library according to the face information, and acquires user information corresponding to the face information according to a comparison result.

Face library: after the tourist arrives at the scenic spot on the same day, the tourist can record the face and sign in, and the face information is stored in the face library.

Step S403, obtaining user playing item information according to the user information, wherein the playing item information comprises the predicted queuing time and the item position.

Step S404, judging whether the travel item at the current time point is optimal or not according to the position of the monitoring equipment, the travel route time of the play item and the expected queuing time, if the expected queuing time does not exceed the optimal route time threshold, recommending the user to travel to the play item, otherwise, executing the next step.

Step S405, obtaining nearby playing items according to the real-time position of the user, and if the whole playing time of the recommended item is smaller than the expected queuing time of the playing item, pushing a message to the APP client by the APP server, and recommending that the user go to the recommended playing item.

Fig. 7 is a flow diagram of an application scenario in fig. 6. As shown in fig. 7, a recommendation reservation method based on artificial intelligence includes the steps of:

(1) When the tourist fixes the card-punching point or passes through the monitoring equipment, the monitoring equipment captures the face and reports the face to a system server;

(2) The system server receives the face information, compares the face information with a face library and acquires the visitor information;

(3) Acquiring playing item information in the queue according to the identity of the tourist, wherein the playing item information comprises predicted queuing time, item positions and the like;

(4) Judging whether the playing item is optimal or not at the current time point according to the monitoring point position, the route going time of the item in the queue and the expected queuing time, and if the expected queuing time exceeds the route time by 5 minutes, not recommending to go immediately;

(5) Currently, if there is no play item needing to go to immediately, an intelligent recommendation mechanism is triggered: and acquiring nearby playing items according to the real-time position of the visitor, and if the overall playing time (route time, playing time and queuing time) of the recommended items is less than the expected queuing time of the items in the queue, pushing APP client information to recommend the visitor to go.

In fig. 7, the intelligent monitoring device captures the faces of the visitors at fixed points (e.g., card points), compares the captured faces with the face library of the visitors on the same day, and intelligently recommends playing items according to the positions and queuing information of the visitors after comparison. The system server, which processes the system service logic, includes but is not limited to: information inquiry of lines, play items, queue and the like; carrying out ticket purchasing and checking logic processing; calculating a line; and intelligently recommending game item calculation. And the play item queue records the queue information queue of each play item. And the map route service is used for displaying and calculating map route information of scenic spots in the scenic region.

According to the embodiment, the face information reported by the monitoring equipment is obtained through the APP server, comparison is carried out in the face library, the user information corresponding to the face information is obtained according to the comparison result, the user playing item information of the user is obtained according to the user information, the playing item information comprises predicted queuing time, item positions and the like, queuing reminding of playing items of tourists in a team and intelligent recommendation of nearby playing items are achieved, personalized pushing of recommendation information can be achieved according to the real-time positions of the users and the timely queuing conditions of the playing items, the tourists are helped to plan the playing items better, time cost wasted in queuing is reduced, the queuing conditions of the playing items are known visually, and consumption capacity of the playing items in scenic spots is greatly improved.

Example four

With further reference to fig. 8, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an artificial intelligence based recommendation reservation apparatus, which corresponds to the embodiment of the method shown in fig. 2, and which is specifically applicable to various electronic devices.

As shown in fig. 8, the artificial intelligence based recommendation reservation apparatus 500 according to the present embodiment includes: a first obtaining module 501, a second obtaining module 502, a first calculating module 503, a first judging module 504 and a first recommending module 505. Wherein:

the first obtaining module 501 is configured to obtain ticket purchasing information of a user by an APP client, where the ticket purchasing information includes a user name, playing time, and online queuing time;

a second obtaining module 502, configured to obtain, by the APP client, the in-time item queuing time and item location information;

the first calculation module 503 is configured to obtain a real-time location of the user, call out a route from the user to the location of the item in the queue, calculate time from the user to the location of the item in the queue, and calculate expected queue time of the corresponding play item;

the first judging module 504 is configured to judge whether the play item is optimal at the current time point according to the route time and the expected queuing time, and recommend the user to go to the current time point immediately if the expected queuing time does not exceed the optimal route time threshold, otherwise execute the next step;

the first recommendation module 505 is configured to obtain nearby play items according to the real-time location of the user, and if the overall play time of the recommended item is smaller than the expected queuing time of the item in the queue, the APP client sends a push message to recommend the user to go to the recommended play item.

In this embodiment, the APP client provides information purchase inquiry functions such as queuing and admission tickets for the playing items of scenic spot scenic spots, and intelligently recommends the playing items according to the recorded faces of the tourists and the acquired real-time positions. And the play item queue records the queue information queue of each play item.

EXAMPLE five

With further reference to fig. 9, as an implementation of the method shown in fig. 3, the present application provides another embodiment of an artificial intelligence based recommendation reservation apparatus, which corresponds to the method embodiment shown in fig. 3, and which can be applied in various electronic devices.

As shown in fig. 9, the artificial intelligence based recommendation reservation apparatus 600 according to the present embodiment includes: a third obtaining module 601, a fourth obtaining module 602, a judging module 603 and a second recommending module 604. Wherein:

a third obtaining module 601, configured to obtain, by the APP server, a user location authority, and obtain a real-time location of the user at a certain frequency;

a fourth obtaining module 602, configured to obtain, by an APP server, user play item information, where the user play item information includes predicted queuing time and a play item position;

the judging module 603 is configured to judge whether the current time point is optimal for the play item according to the route time and the expected queuing time of the play item, and if the expected queuing time does not exceed the optimal route time threshold, recommend the user to go immediately, otherwise execute the next step;

and a second recommending module 604, configured to obtain nearby play items according to the real-time location of the user, and if the total play time of the recommended item is less than the expected queuing time of the item in the queue, send a push message to the APP client by the APP server, so that the recommended user goes to the recommended play item.

In this embodiment, the APP client provides information purchase inquiry functions such as queuing, admission tickets, etc. for the playing items of scenic spots, and intelligently recommends the playing items according to the recorded faces of the tourists and the acquired real-time positions. And the play item queue records the queue information queue of each play item.

EXAMPLE six

With further reference to fig. 10, as an implementation of the method shown in fig. 4, the present application provides another embodiment of an artificial intelligence based recommendation reservation apparatus, which corresponds to the method shown in fig. 4 and can be applied to various electronic devices.

As shown in fig. 10, the artificial intelligence based recommendation reservation apparatus 700 according to this embodiment includes: a fifth obtaining module 701, a sixth obtaining module 702, a seventh obtaining module 703, a second calculating module 704 and a third recommending module 705. Wherein:

a fifth obtaining module 701, configured to obtain, by the APP server, the face information reported by the monitoring device;

a sixth obtaining module 702, configured to compare, by the APP server, the face information in a face library, and obtain, according to a comparison result, user information corresponding to the face information;

a seventh obtaining module 703, configured to obtain information of a play item of the user according to the user information, where the information of the play item includes predicted queuing time and an item position;

the second calculation module 704 is used for judging whether the travel item at the current time point is optimal or not according to the position of the monitoring equipment, the travel route time of the travel item and the predicted queuing time, if the predicted queuing time does not exceed the optimal route time threshold, the user is recommended to travel to the travel item, and if not, the next step is executed;

and a third recommending module 705, configured to obtain nearby play items according to the real-time location of the user, and if the overall play time of the recommended item is less than the expected queuing time of the play item, push, by the APP server, a message to the APP client, and recommend the user to go to the recommended play item.

In this embodiment, the intelligent monitoring device captures faces of the tourists at fixed points (e.g., card points), compares the captured faces with the face library of the tourists on the same day, and intelligently recommends playing items according to the positions of the tourists and the queuing information after comparison. The system server, processing system service logic, includes but is not limited to: information inquiry of lines, play items, queue and the like; ticket buying and checking logic processing; calculating a line; and intelligently recommending the game item calculation. And the play item queue records the queue information queue of each play item. And the map route service is used for displaying and calculating map route information of scenic spots in the scenic region.

EXAMPLE seven

In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 11, fig. 11 is a block diagram of a basic structure of a computer device according to the embodiment.

The computer device 8 comprises a memory 81, a processor 82, a network interface 83 communicatively connected to each other via a system bus. It is noted that only a computer device 8 having a component memory 81, a processor 82 and a network interface 83 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.

The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.

The memory 81 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the storage 81 may be an internal storage unit of the computer device 8, such as a hard disk or a memory of the computer device 8. In other embodiments, the memory 81 may also be an external storage device of the computer device 8, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 8. Of course, the memory 81 may also comprise both an internal storage unit of the computer device 8 and an external storage device thereof. In this embodiment, the memory 81 is generally used for storing an operating system installed in the computer device 8 and various types of application software, such as computer readable instructions of an artificial intelligence based recommended reservation method. Further, the memory 81 may also be used to temporarily store various types of data that have been output or are to be output.

The processor 82 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 82 is typically used to control the overall operation of the computer device 8. In this embodiment, the processor 82 is configured to execute computer readable instructions stored in the memory 81 or process data, such as computer readable instructions for executing the artificial intelligence based recommended reservation method. The recommendation reservation method based on artificial intelligence comprises the following steps:

acquiring the real-time position of a user, calling out a route from the user to the position of the item in the queue, calculating the time from the user to the position of the item in the queue, and calculating the predicted queue time of the corresponding play item;

and acquiring nearby play items according to the real-time position of the user, and if the whole play time of the recommended item is less than the predicted queuing time of the item in the queue, sending a push message by the APP client, and recommending that the user go to the recommended play item.

Or, the recommendation reservation method based on artificial intelligence comprises the following steps:

Still alternatively, the artificial intelligence based recommendation reservation method comprises the steps of:

the APP server obtains face information reported by the monitoring equipment;

The network interface 83 may comprise a wireless network interface or a wired network interface, and the network interface 83 is generally used for establishing communication connections between the computer device 8 and other electronic devices.

According to the embodiment, the positions of the tourists are obtained and reported through the APP client, so that queuing reminding of the tourists for playing items in a team and intelligent recommendation of nearby playing items are realized; the monitoring equipment captures the face information and the position of the tourist and reports the face information and the position to the system, so that queuing reminding of the tourist playing items in the team and intelligent recommendation of the nearby playing items are realized; according to the online queuing mechanism of ticket buying and reservation information on the line of the tourists, the problem that the tourists waste the queuing time of the items is solved, and the scenic spot items are played more reasonably and sufficiently.

Example eight

The present application further provides another embodiment, which is a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the artificial intelligence based recommended reservation method as described above; the recommendation reservation method based on artificial intelligence comprises the following steps:

judging whether the playing items are optimal or not at the current time point according to the route time and the expected queuing time, if the expected queuing time does not exceed the optimal route time threshold, recommending the user to go to the next step, and if not, executing the next step;

the APP server side obtains user playing item information, wherein the user playing item information comprises predicted queuing time and playing item positions;

Still alternatively, the aforementioned method for recommending a reservation based on artificial intelligence includes:

the APP server obtains face information reported by the monitoring equipment;

and acquiring nearby play items according to the real-time position of the user, and if the whole play time of the recommended item is smaller than the expected queuing time of the play item, pushing a message to the APP client by the APP server, and recommending the user to the recommended play item.

According to the embodiment, the positions of the tourists are obtained and reported through the APP client, so that queuing reminding of the tourists playing items in the team and intelligent recommendation of the nearby playing items are realized; the monitoring equipment captures the face information and the position of the tourist and reports the face information and the position to the system, so that queuing reminding of the tourist playing items in the team and intelligent recommendation of the nearby playing items are realized; according to the online queuing mechanism of ticket buying and reservation information on the line of the tourists, the problem that the tourists waste the queuing time of the items is solved, and the scenic spot items are played more reasonably and sufficiently.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.

It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.

Claims

1. A recommendation reservation method based on artificial intelligence is characterized by comprising the following steps:

2. A recommendation reservation method based on artificial intelligence is characterized by comprising the following steps:

3. A recommendation reservation method based on artificial intelligence is characterized by comprising the following steps:

the APP server obtains face information reported by the monitoring equipment;

4. The artificial intelligence based recommendation reservation method of claim 1, wherein the recommendation item overall play time comprises:

route time, play time, and queue time.

5. The artificial intelligence based recommended reservation method of claim 1, wherein the optimal route time threshold is 10-60 minutes.

6. An artificial intelligence based recommendation reservation apparatus, comprising:

the first judgment module is used for judging whether the playing items are optimal when the user goes to the current time point according to the route time and the expected queuing time, if the expected queuing time does not exceed the optimal route time threshold, the user is recommended to go to immediately, and if not, the next step is executed;

7. An artificial intelligence-based recommendation reservation apparatus, comprising:

the fourth acquisition module is used for the APP server side to acquire user playing item information, wherein the user playing item information comprises predicted queuing time and playing item positions;

and the second recommendation module is used for acquiring nearby play items according to the real-time position of the user, and if the overall play time of the recommended items is less than the predicted queue time of the items in the queue, the APP server sends a push message to the APP client side, so that the user is recommended to go to recommend the play items.

8. An artificial intelligence-based recommendation reservation apparatus, comprising:

a seventh obtaining module, configured to obtain information of a play item of the user according to the user information, where the information of the play item includes an expected queuing time and an item position;

the second calculation module is used for judging whether the travel item at the current time point is optimal or not according to the position of the monitoring equipment, the travel route time of the travel item and the predicted queuing time, if the predicted queuing time does not exceed the optimal route time threshold, the user is recommended to travel to the travel item, and if not, the next step is executed;

and the third recommendation module is used for acquiring nearby play items according to the real-time position of the user, and if the overall play time of the recommended items is less than the expected queuing time of the play items, the APP server pushes a message to the APP client, and the recommended user goes to the recommended play items.

9. A computer device comprising a memory having computer readable instructions stored therein and a processor that when executed performs the steps of the artificial intelligence based recommended reservation method of any of claims 1 to 3.

10. A computer readable storage medium having computer readable instructions stored thereon which, when executed by a processor, implement the steps of the artificial intelligence based recommendation reservation method of any of claims 1 to 3.