TWM546561U - Tourist attraction recommendation system with selectivity - Google Patents

Description

Selective tourist attraction recommendation system

This creation provides a tourist attraction recommendation system, especially a selective tourist attraction recommendation system, which compares, compares and analyzes user behavior pattern rules through a large data device, and integrates it into a rule database to feed back to the mobile device. And it is interpreted as a walking module, a locomotive module or a driving module by a mobile device for appropriately searching for a radius, and searching for the target location of the big data device by GPS to achieve the function of adapting the user's needs.

According to the conventional tourist attraction recommendation system, the program system searches for a circle with a radius of one kilometer. When the user wants to know a larger range of recommended attractions, the user needs to be located adjacent to the recommended attraction. It is inconvenient to search.

In view of this, the creator has been engaged in the manufacturing development and design experience of related products for many years. After the detailed design and careful evaluation of the above objectives, the creator will have a practical and practical creation.

That is to say, the main purpose of the present invention is to provide a selective tourist attraction recommendation system, and the problem to be solved is that it is necessary to display the recommended attraction for about one kilometer before the search for the recommended attraction, resulting in the user. Knowing the gap in the range of information on attractions, it is less humane, so this problem needs to be improved.

The technical features of the problem solving include at least: a large data device, comprising a receiving unit receiving a huge amount of data of the plurality of mobile devices in the cloud, and paralleling the plurality of servers via the cloud, wherein the servers are respectively used for statistics and comparison. The user's behavior pattern rules are analyzed and analyzed, and are integrated into a rule database by a terminal and fed back to each mobile device by the cloud; when each mobile device is interpreted as a walking module, the mobile device uses the rule Retrieving the database, finding one of the walking modules to properly search for the radius target, and using one of the mobile devices to search for and navigate the target location recommended by the big data device; when each mobile device is interpreted as a riding bicycle In the module, the mobile device retrieves the appropriate search radius target of the rider module by feedback from the rule database, and selects and navigates the recommended data of the big data device by using the GPS search of the mobile device. Target location; when each mobile device is interpreted as a driving module, the mobile device uses the feedback of the rule database to find out the driving module When the search radius of the subject matter, and to the search for the mobile device's GPS to navigate and click on the large data device recommended destination.

The user can select the walking module of the mobile device, the riding bicycle module or the driving module according to requirements.

Among them, the server uses the cloud to detect user trends, determine routes, avoid low-rated attractions, or measure immediate traffic conditions.

Wherein, the appropriate search radius of the walking module is within a radius of 15 km, which is a distance allowed by the walking family.

Wherein, the appropriate search radius target of the rider locomotive system is within a radius of 30 kilometers, which is the allowable distance of the locomotive family.

Wherein the appropriate search radius of the driving module is within 50 km It is the circumferential range of the radius and is the distance allowed by the car family.

The embodiments, structures, and features of the present invention are described in detail with reference to the preferred embodiments of the present invention.

10‧‧‧ big data device

12‧‧‧ Receiving unit

14‧‧‧Server

16‧‧‧ Terminal

18‧‧‧ rule database

20‧‧‧ mobile devices

22‧‧‧Walking module

24‧‧‧ riding locomotive module

26‧‧‧ Driving Module

30‧‧‧Appropriate search for radius targets

40‧‧‧GPS search

Figure 1 is a three-dimensional implementation of the creation.

Figure 2 is a flow chart of the operation of the big data device of this creation.

Figure 3 is a block diagram of the flow of this creation.

Please refer to FIGS. 1~3, which are preferred embodiments of the present block diagram, but these embodiments are for illustrative purposes only, and are not limited by the structure in the patent application, and include at least one: The data device 10 includes a receiving unit 12 for receiving a huge amount of data of the plurality of mobile devices 20 in the cloud, and paralleling the plurality of servers 14 via the cloud, each of the servers 14 for respectively calculating, comparing, and analyzing the behavior pattern rules of the user. And being integrated into a rule database 18 by a terminal device 16 and fed back to each mobile device 20 by the cloud; when each mobile device 20 is interpreted as a walking module 22, the mobile device 20 is controlled by the rule database 18 Retrieving, finding one of the walking modules 22 to properly search for the radius target 30, and using one of the mobile devices 20 GPS search 40 to click and navigate the target location recommended by the big data device 10; when each mobile device 20 When the locomotive module 24 is interpreted as a rider, the mobile device 20 retrieves the appropriate search half of the rider module 24 by feedback from the rule database 18. The target object 30, and the GPS search 40 of the mobile device 20 selects and navigates the target location recommended by the big data device 10; when each mobile device 20 interprets the drive module 26 as a driving module 26, the mobile device 20 The feedback of the rule database 18 is used to find the appropriate search radius target 30 of the driving module 26, and the GPS search 40 of the mobile device 20 selects and navigates the target location recommended by the big data device 10. .

The user can select the walking module 22 of the mobile device 20, the riding bicycle module 24 or the driving module 26 according to requirements.

The server 14 senses user trends, determines routes, avoids low-rated spots, or measures immediate traffic conditions by the cloud.

The appropriate search radius element 30 of the walking module 22 is within a radius of 15 kilometers, which is a distance allowed by the walking family.

The appropriate search radius element 30 of the rider module 24 is within a radius of 30 kilometers, which is a distance allowed by the locomotive family.

The appropriately searched radius target 30 of the driving module 26 is a circumferential range with a radius of 50 kilometers, which is a distance allowed by the automobile family.

With the above structure and composition design, the use situation of the present creation is explained as follows: when the user wants to find a recommended attraction, the mobile device 20 interprets the behavior mode of the user and is fed back by the big data device 10. The data of the behavior mode, and by the GPS search 40 of the mobile device 20, the evaluated scenic spots are displayed on the mobile device 20, and the user can click and navigate to the attraction.

Thereby, the present application feeds back the data by the big data device 10 to facilitate The user does not need to waste a lot of time to find the scenic spots, and can also make changes according to the user's habits, thereby making the creation practical and progressive.

By summarizing the above description, the design of the above structure can effectively overcome the lack of the recommended tourist attraction recommendation system, and further has the above-mentioned numerous advantages and practical value, so this creation is an innovative new creation, and the same The same or similar product creation or public use has not been seen in the technical field. Therefore, this creation has met the requirements of the novelty related to "novelty" and "progressiveness", and is applied according to law.

10‧‧‧ big data device

12‧‧‧ Receiving unit

14‧‧‧Server

16‧‧‧ Terminal

18‧‧‧ rule database

20‧‧‧ mobile devices

22‧‧‧Walking module

24‧‧‧ riding locomotive module

26‧‧‧ Driving Module

30‧‧‧Appropriate search for radius targets

40‧‧‧GPS search

Claims (6)

A selective tourist attraction recommendation system includes at least: a large data device, comprising a receiving unit receiving a huge amount of data of the plurality of mobile devices in the cloud, and paralleling the plurality of servers via the cloud, wherein the servers are respectively used for statistics Comparing and analyzing the behavior pattern rules of the user, and integrating them into a rule database by a terminal and feeding back to the mobile devices by the cloud; when each mobile device is interpreted as a walking module, the mobile device is Retrieving the rule database, finding one of the walking modules to properly search for the radius target, and using one of the mobile devices to search for and navigate the target location recommended by the big data device; when each mobile device interprets as When riding the locomotive module, the mobile device retrieves the appropriate search radius target of the rider module by feedback from the rule database, and selects and navigates the big data device by using the GPS search of the mobile device. Recommended target location; when each mobile device is interpreted as a driving module, the mobile device finds the driving by feedback from the rule database The group of subject matter appropriate search radius, and the search to the mobile device's GPS to navigate and click on the large data device recommended destination. The selective tourist attraction recommendation system described in claim 1, wherein the user can select the walking module, the riding bicycle module or the driving module of the mobile device according to requirements. The selective tourist attraction recommendation system described in claim 1, wherein the server senses user trends, determines routes, avoids low evaluation spots, or measures immediate traffic conditions by using the cloud. For example, the selective tourist attraction recommendation system described in claim 1 is characterized in that the appropriate search radius of the walking module is within a radius of 15 kilometers. A selective tourist attraction recommendation system as described in claim 1 of the patent application, wherein The appropriate search radius of the rider module is within a radius of 30 kilometers. The selective tourist attraction recommendation system of claim 1, wherein the appropriate search radius of the driving module is within a radius of 50 km.