WO2013005912A1

WO2013005912A1 - Navigation system using cloud computing technology, method for managing same, and computer-readable recording medium for recording a program for the system and method

Info

Publication number: WO2013005912A1
Application number: PCT/KR2012/002850
Authority: WO
Inventors: 김철민; 권재현
Original assignee: 가온미디어 주식회사
Priority date: 2011-07-05
Filing date: 2012-04-14
Publication date: 2013-01-10
Also published as: KR20130005035A

Abstract

The present invention relates to a navigation technology that provides an optimum route for a vehicle using a cloud computing technology. More particularly, the present invention relates to a navigation technology that uses the cloud computing technology having only a minimum of input/output means in a navigation terminal of the vehicle, and performs most information collection and calculation in a central server, such that the navigation terminal is connected to a navigation network installed in a plurality of vehicles, information from the plurality of vehicles is collected by the central server, and an optimized route can be provided to a driver of the vehicle in consideration of not only current traffic, but also of traffic in future time periods derived from each vehicle moving along suggested routes. According to the present invention, an optimized route can be provided to the driver of the vehicle by taking into consideration not only the current traffic conditions, but also the traffic conditions in future time periods, and the structure of the navigation terminal installed in the vehicle can be minimized. Further, according to the present invention, since load balancing of traffic is enabled, all roadways can be efficiently used.

Description

Navigation system of cloud computing method and its operation method, and computer readable recording medium recording program therefor

The present invention relates to a navigation technology that provides an optimal path of a vehicle by cloud computing. In particular, the present invention uses a cloud computing technology that has a minimum input and output means in the navigation terminal of the vehicle and processes most of the information gathering and processing in a central server to connect the navigation mounted on different cars to the network and to the central server. The present invention relates to a navigation technology that collects information on a large number of vehicles and provides an optimal route to a vehicle driver in consideration of current traffic as well as future time-phased traffic when an individual vehicle moves along a given route.

Recently, vehicle drivers can use a navigation terminal mounted on a car to search for a route to a destination they want to go to, and also obtain information about a time required for the destination. In addition, since the navigation terminal has a large amount of map data, it is possible to provide surrounding maps and road information of the region regardless of the region.

As such, vehicle navigation is no longer an additional component for motorists, but a necessity. Currently released navigation terminals detect the current location of the vehicle using GPS and match it with a large amount of map data to display the location of the vehicle along with a map on the screen. In addition, by implementing the map data in 3D, it has a function to more three-dimensionally determine the position and the moving direction of the vehicle.

As such, in order to store a large amount of map data and search a route through it, a high-end computing function must be provided in the navigation. In addition, since the map data changes from time to time, the vehicle user must regularly update the map data and the program of the navigation.

In other words, the higher the information processing performance of the navigation, the greater the cost burden for the purchase and repair of the navigation terminal, and no matter how high the performance of the navigation terminal, there is a limit in predicting the traffic conditions of roads made by many vehicles. Has a problem.

Recently, as next-generation 4G mobile communication networks such as Wibro, Wifi, and LTE, which are capable of high-capacity high-speed data transmission, are commercialized, a foundation for implementing cloud computing technology using these communication resources is expected to be laid in the near future. However, a vehicle navigation terminal technology that provides an optimal path in consideration of not only current traffic but also future time-phase traffic by using such cloud computing technology has not been actualized yet.

An object of the present invention can provide an optimal route to the vehicle driver in consideration of the current traffic conditions as well as future traffic conditions in the future time zone, minimize the configuration of the navigation terminal mounted on the vehicle, and load balancing of road traffic This makes it possible to provide cloud computing navigation technology that allows the road to be used efficiently throughout.

According to another aspect of the present invention, there is provided a cloud computing navigation system including: a computer-readable recording medium that records an operation program of a navigation terminal for receiving vehicle route information including a starting point and a destination from a user and transmitting the same to a cloud central server; A cloud database storing traffic information including a vehicle route database; Receives vehicle route information from the navigation terminal, generates a vehicle route database and stores it in the cloud database, calculates an estimated position for each vehicle from the vehicle route database, and estimates traffic time for each position based on the estimated position for each time zone And a cloud central server configured to calculate a recommendation route of the vehicle based on the estimated traffic information according to time slots and transmit the recommended route of the vehicle to the navigation terminal of the vehicle through the cloud network.

In addition, in the cloud computing type navigation system of the present invention, the cloud central server may include: an interface module configured to receive vehicle route information from a plurality of navigation terminals through a cloud network, and transmit an internally calculated recommendation route to a corresponding navigation terminal; A vehicle route database generation module which collects vehicle route information from a plurality of navigation terminals, generates a vehicle route database, and stores the vehicle route database in a cloud database; Estimated location for each vehicle based on the origin and destination of each vehicle included in the vehicle route database, and estimated traffic information for each location based on the estimated location for each time zone to store traffic in the cloud database module; It is preferably configured to include; optimal path calculation module for calculating the recommended route of the vehicle based on the estimated traffic information for each time zone.

In addition, in the cloud computing type navigation system, the navigation terminal transmits the current location information and waypoint information of the vehicle to the cloud central server, and the traffic prediction module adds time to each vehicle based on the current location information and waypoint information. It is preferable to calculate the estimated location for each unit, and to calculate the estimated traffic information for each time slot for each location and store the data in a cloud database.

In addition, the cloud central server in a cloud computing navigation system further comprises a vehicle information management module for extracting and managing road preference information for each vehicle by repeatedly comparing the difference with the recommended route by tracking the vehicle's location information in real time. When calculating the recommendation route, the optimum route calculation module receives road preference information for the vehicle from the vehicle information management module and calculates the recommendation route in consideration of the road preference.

In addition, the traffic prediction module in a cloud computing navigation system further comprises a traffic simulation module for simulating each vehicle registered in the vehicle path database to virtually move over time to calculate the estimated traffic information for each time zone for each location. It is preferred to be configured.

In addition, in the cloud computing type navigation system, the optimum route calculation module simulates the movement of a specific vehicle according to a combination of various routes over time based on the estimated traffic information according to time slots calculated through the traffic simulation module to recommend the vehicle. It is preferably configured to further include; an optimal path simulation module for calculating a path.

According to an aspect of the present invention, there is provided a cloud computing method of operating a navigation, comprising: (A) receiving vehicle route information including a starting point and a destination through a navigation terminal; (B) the navigation terminal transmits the vehicle route information to the cloud central server; (C) the cloud central server collecting vehicle route information from the plurality of navigation terminals to generate a vehicle route database; (D) the cloud central server calculating an estimated position for each vehicle based on a departure point and a destination included in the vehicle route database; (E) the cloud central server calculating the estimated traffic information according to time slots for each location based on the estimated time slots; (F) calculating, by the cloud central server, a recommended route of the vehicle based on the estimated traffic information for each time zone; (G) the cloud central server transmits the recommended route to the navigation terminal of the vehicle through the cloud network.

In addition, the cloud computing method of the navigation operation method of the present invention (B) step further comprises the step of the navigation terminal to transmit the current location information and waypoint information of the vehicle to the cloud central server, (D) step cloud central server The method may further include calculating an estimated position for each vehicle based on the current location information and waypoint information.

In addition, in the cloud computing type navigation operation method of the present invention, step (E) simulates the cloud central server to virtually move each vehicle registered in the vehicle route database over time, thereby calculating the estimated traffic information for each time slot. It is preferably configured to further comprise a step.

In addition, in the cloud computing type navigation operation method of the present invention, step (F) is based on the estimated traffic information according to time slots calculated through the simulation of step (E), and the movement of a specific vehicle is performed on a combination of various paths according to time. The method may further include the step of simulating accordingly and calculating a recommended route of the vehicle from the results.

According to the present invention, it is possible to provide an optimal route to the vehicle driver in consideration of not only the current traffic situation but also future traffic conditions according to the time zone, thereby significantly improving the reliability of providing the shortest time route.

In addition, according to the present invention, since the load balancing of traffic is possible, there is an effect of effectively using the road as a whole. In particular, individual vehicles can recommend routes in a way that optimizes overall load balancing even if they take relatively longer time, thereby maximizing the efficiency of the entire roadway.

In addition, according to the present invention, since most of the processing is performed in a central server connected to a cloud network, the configuration of a navigation terminal mounted on a vehicle can be provided at a low cost, and further, a smartphone, a smart pad, a set top box for a vehicle, etc. Even if only the software is installed, it is possible to provide a high-performance navigation service.

1 is a block diagram showing the overall configuration of a cloud computing navigation system;

2 is a block diagram showing in more detail the configuration of the cloud central server 300 in FIG.

3 is an example of calculating the estimated position of each time zone in the cloud central server 300,

4 is an example of calculating the estimated traffic information for each time zone in the cloud central server 300,

Figure 5 is a flow chart showing the overall operation of the navigation operation method cloud computing method.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram showing the overall configuration of a cloud computing navigation system.

The navigation terminal 100 is a device that can be mounted on a vehicle such as a car and informs driving information through a screen, and is preferably configured with only a minimum input means and a display in accordance with the purpose of implementing cloud computing. That is, the navigation terminal 100 is provided with input means for allowing the driver to input vehicle route information such as a starting point and a destination, and a display means for providing a road guidance to the driver through a map screen.

When the vehicle route information is input from the driver, it is transmitted to the cloud central server 300 through the cloud network 200. The vehicle route information may include the current location information of the vehicle using GPS, the waypoint information separately input by the driver, in addition to the information about the starting point and the destination input by the driver.

On the other hand, the navigation terminal 100 constituting the present invention may be implemented in various forms. For example, it may be implemented as a navigation-only terminal mounted in a vehicle, or may be implemented by installing a related application on a smartphone (eg, an iPhone) or a smart pad (eg, an iPad). In addition, the navigation terminal 100 may be implemented by mounting the relevant software on a vehicle AV system or various set-top boxes (eg, satellite set-top boxes, IPTV set-top boxes, etc.). In addition, the navigation terminal 100 according to the present invention may be implemented by accessing a web page providing a vehicle route providing service according to the present invention through a laptop computer or the like through the Internet.

The cloud network 200 is a wired or wireless medium that connects the plurality of navigation terminals 100 and the cloud central server 300. In this case, in order to enable cloud computing, since the navigation terminal 100 has only a minimum input / output means and most of the processing is performed in the cloud central server 300, the cloud network 200 may transmit a large amount of data. It should be implemented as possible communication network.

Therefore, the cloud network 200 may be implemented to include WiBro (Wibro), wireless LAN (Wifi, WiMax), a next-generation 4G mobile communication network (for example, LTE, etc.) capable of high-capacity high-speed data transmission.

The cloud central server 300 collects information from a number of navigation terminals 100 registered in a cloud computing service, processes cloud computing, and transmits the processing result to each navigation terminal 100.

First, when the cloud central server 300 receives vehicle route information from the plurality of navigation terminals 100 through the cloud network 200, the cloud central server 300 generates a vehicle route database therefrom. The generated vehicle route database is stored in the cloud database 400.

The cloud central server 300 uses the vehicle route database stored in the cloud database 400 to estimate the time-based estimated position for each time period, which roughly predicts which position at which time each vehicle will be reached when it moves along a given route. Calculate per vehicle. This may be calculated in consideration of the current road traffic situation by referring to the accumulated traffic information, or may be variously implemented in other ways.

In addition, by calculating the estimated traffic information for each location based on the calculated estimated time slots for each location, not only current traffic but also future traffic according to time slots can be estimated. For example, it is possible to calculate the traffic volume of a specific road point in a specific time zone through the predicted location of each vehicle time zone, thereby predicting future traffic for each location on the map.

In addition, by utilizing the high-performance cloud computing resources of the cloud central server 300 to simulate the movement of a number of vehicles by moving the virtual traffic can be predicted for each time zone in the future. In addition, it is possible to estimate future time-phased traffic in various ways. 3 and 4 show examples of the estimated time slots and the estimated time slots.

The cloud central server 300 calculates the shortest time path for each vehicle based on the estimated traffic information for each time zone calculated through this process. The shortest route can be selected to minimize the amount of traffic calculated at each time zone for each location of the route through which the vehicle will pass, and each vehicle registered in the service can be simulated through numerous calculations according to various combinations of routes over time. You can also choose an optimized path.

That is, since the cloud central server 300 holds the route information of a number of vehicles, it is possible to provide the optimal route suitable for each vehicle in real time by comprehensively analyzing it at once. In addition, this also has the side effect of enabling load balancing of road traffic.

The cloud central server 300 transmits the shortest time path or the optimized path to the navigation terminal 100 of the vehicle through the cloud network 200. Then, the navigation terminal 100 displays the result received from the cloud central server 300 on the screen, whereby the driver can use the optimal path calculated in consideration of future traffic.

The cloud database 400 stores various traffic information including a vehicle route database. The cloud database 400 must be capable of high-capacity and high-speed input / output to be suitable for performing cloud computing, and is preferably implemented to be sufficiently backed up in case of an accident.

FIG. 2 is a block diagram illustrating an internal configuration of the cloud central server 300 and related components in FIG. 1 in more detail.

In the present invention, the navigation terminal 100 is preferably provided with a GPS module 110, the cloud central server 300, the interface module 310, vehicle route database generation module 320, traffic prediction module 330, The optimum path calculation module 340 and the vehicle information management module 350 are provided.

It looks at the detailed configuration of the cloud central server 300.

The interface module 310 receives vehicle route information from the plurality of navigation terminals 100 through the cloud network 200 and provides the vehicle route database generation module 320 to the vehicle route database generation module 320, and the shortest time route from the optimal route calculation module 340. By receiving the transmission to the corresponding navigation terminal 100 serves as an interface between the server and the network for cloud computing.

When the vehicle route database generation module 320 receives the vehicle route information through the interface module 310, the vehicle route database generation module 320 accumulates the vehicle route information by accumulating the vehicle route information. The vehicle route information includes information such as a starting point and a destination of the driving vehicle. In addition, the vehicle route information may include current location information, waypoint information, preferred route information, priority, etc. of the driving vehicle.

The vehicle route database generation module 320 collects a lot of vehicle route information transmitted from the plurality of navigation terminals 100 connected to the cloud network 200, and then generates a vehicle route database. The generated vehicle route database may be collectively managed by the server and may be stored in the cloud database 400 for the purpose of later statistical processing.

The traffic prediction module 330 reads the vehicle route database from the cloud database 400 and calculates future changes in road traffic on the basis of the starting point and the destination of each vehicle included in the vehicle route database. Preferably, each vehicle calculates an estimated location for each time zone, and future changes in road traffic may be derived by synthesizing the estimated location for each individual vehicle over time, or in a more theoretical way, for example statistical techniques or iterations. It can be expected by reflecting the simulation result. In addition, when the current position information and waypoint information of the driving vehicle can be read from the vehicle route database, the estimated position for each vehicle time zone is calculated based on the additional reference.

At this time, the estimated position of each vehicle by time is not merely adding up the average time required for each section as provided by the existing navigation service, but by utilizing the advantages of cloud computing, the number of vehicles included in the vehicle route database. It is calculated by considering each vehicle's destination, current road situation, and various fixed and measured variables.

Thereafter, the calculated estimated time zones of each vehicle are stored in the cloud database 400.

In addition, the traffic prediction module 330 calculates estimated traffic information according to time slots to know how much traffic will be in future for each location based on the estimated time slots calculated above. . At this time, the point at which the estimated traffic information for each time zone is calculated is implemented to be calculated for each discontinuous or continuous position on the map. For example, after 2 hours, it may be implemented by quantifying how much traffic is congested at Pangyo Junction.

Thereafter, the calculated estimated traffic information for each location for each time zone is stored in the cloud database 400.

In addition, the latter may be sequentially calculated from the former, or may be implemented so that a balanced value is calculated by linking the two information. have. That is, the location information of the vehicle and the traffic information of each location may feed back each other or organically combine the information, and simulate the virtual movement of a large number of vehicles registered in the vehicle route database over time through many operations. It is desirable to realize the traffic situation more accurately by making the most of the cloud computing resources.

The optimal route calculation module 340 calculates an optimized route for each individual vehicle based on the estimated traffic information according to time slots calculated by the traffic prediction module 330. That is, the shortest time path to reach the destination can be obtained for each individual vehicle. This recommends relatively less clogged roads for individual vehicles, resulting in a load balancing of the entire road.

Optionally, it may be implemented to more aggressively provide a slightly delayed route for each vehicle so that the entire road is well balanced. That is, the route takes longer than the optimal route from the perspective of the individual vehicle, but can provide the route with the best result in terms of overall load balancing. Route recommendation in this way maximizes the use of social resources, maximizes the overall energy use efficiency, and further prevents road congestion by refraining from recommending routes to the area in advance, for example in the case of road construction. By additionally assisting the in and out of the construction vehicle, the additional effect of smoothly completing the construction can be achieved.

In this case, as in the case of calculating the estimated traffic information according to the time zone, the optimized route may be calculated by organically combining or feeding back various types of information. It is also possible to obtain an optimized route by simulating a combination of various routes to calculate the shortest route of the vehicle.

The optimal path calculation module 340 provides the calculated route to the interface module 310, and the interface module 310 provides the optimized route through the cloud network 200 to each corresponding navigation terminal 100. ), The optimal path providing service of the cloud central server 300 is achieved.

The vehicle information management module 350 manages information on the navigation terminal 100 connected to the cloud network 200. Since the navigation terminal 100 is provided with the GPS module 110, the cloud central server 300 can grasp the current location information of the navigation terminal 100 in real time through the cloud network 200, and the information obtained through the navigation network 100. Can manage.

After recommending a route, the cloud central server 300 may grasp the route in which the vehicle actually travels in real time or even after the fact. Through this, the cloud driver may determine the propensity of the vehicle driver. In particular, the vehicle driver may obtain information on a road that he or she particularly prefers. If a situation in which a particular road is repeated without following the recommended route is repeated, the driver may determine that the road is particularly preferred. Such a road preference of the driver is stored in the vehicle information management module 350, it is preferable to consider first when generating the recommendation route in the optimal path calculation module 340 in the future. For example, after deriving a recommendation route primarily in the optimum route calculation module 340, and if there is a road that the driver prefers within a certain distance from the recommendation route, the recommendation route is designated as a waypoint by way of the designated route. You can think of how to rewrite it. Alternatively, it is possible to implement a recommendation route selection algorithm by assigning an additional point to the preferred road.

In addition, the vehicle information management module 350 may recognize the unique identifier (GPS ID) of the GPS module 110 embedded in the navigation terminal 100 and manage the update in real time. Through this, when the vehicle is stolen, the vehicle is lost, or theft of the navigation terminal 100 occurs, the cloud central server 300 may track the position of the GPS module 110.

3 is an exemplary view of calculating the estimated position for each time zone in the cloud central server 300.

Since the cloud central server 300 has route information for each vehicle registered in the vehicle route database, each vehicle in consideration of the number of vehicles, the destination of each vehicle, the current road situation, and various fixed and measured variables You can estimate the timephased location of. Referring to FIG. 3, vehicle 1 is currently passing through Suwon. In view of the route and related traffic conditions presented to vehicle 1, it can be expected to pass Gimcheon in the next three hours. As the number of terminals receiving services according to the present invention increases, the number of vehicles approaching a specific region can be counted after several hours, so that future traffic conditions can be predicted relatively accurately.

4 is an exemplary view of calculating the estimated traffic information for each time zone in the cloud central server 300.

If the congestion level is set to 1 ~ 10, Pangyo Junction is currently 9, so the traffic is very heavy and crowded, but after 2 hours, it can be predicted that the general traffic volume will be reached.

However, in the case of the eye component origin, the current value is 2, and the traffic situation is very smooth, but after 2 hours, the traffic volume increases, and it can be seen that the traffic volume is almost the same as the Pangyo junction.

The embodiment shown in FIG. 4 corresponds to future traffic information represented by quantifying congestion by discrete time for each discrete location, but is further divided by further subdividing a space unit and a time unit using high-performance cloud computing resources. Can be calculated. In addition, as shown, instead of the simplified data, it is possible to construct expected traffic information in a form suitable for obtaining an optimal path by organically combining the received information and the result in real time.

5 is a flowchart illustrating the overall operation of the cloud computing method navigation operation method.

First, a vehicle user (driver) operates the cloud computing dedicated navigation terminal 100 mounted on the vehicle to input vehicle route information such as a departure point, a destination, a waypoint, and the like (S100).

Then, the navigation terminal 100 transmits the input vehicle path information to the cloud central server 300 through the cloud network 200 (S110). In addition, the navigation terminal 100 may recognize the current location information of the vehicle using the GPS module 110 and transmit additional information to the cloud central server 300. Optionally, the current location information of the vehicle may be determined by using the GPS or other information communication technology directly from the cloud central server 300.

Next, the cloud central server 300 collects vehicle route information from the plurality of navigation terminals 100 to generate a vehicle route database (S120). The vehicle route database generated as described above is stored in the cloud database 400.

Next, the cloud central server 300 calculates an estimated position for each vehicle in consideration of traffic conditions based on a departure point and a destination included in the vehicle route database (S130). Alternatively, the estimated position of each vehicle may be calculated based on current location information and waypoint information of the driving vehicle.

In other words, it predicts at what time and at what position each individual vehicle will travel as it follows a given route. This may be calculated in consideration of the current road traffic situation by referring to the accumulated traffic information, or may be variously implemented in other ways.

Next, the cloud central server 300 provides the estimated traffic information according to time slots, which can know how much traffic will be required for each location based on the estimated time slots calculated above, and thus the traffic will be desired. It calculates (S140). At this time, the point at which the estimated traffic information for each time slot is calculated may be implemented to be calculated for each discontinuous or continuous position on the map, thereby making it possible to predict not only the current traffic but also future time-based traffic.

For example, it is possible to calculate the traffic volume of a specific road point in a specific time zone through the predicted location of each vehicle time zone, thereby predicting future traffic for each location on the map. In addition, by utilizing the high-performance cloud computing resources of the cloud central server 300, it is possible to calculate the estimated traffic information for each location by time by virtually simulating and moving a number of vehicles registered in the vehicle route database over time. . In addition, it is possible to estimate future time-phased traffic in various ways.

Next, the cloud central server 300 calculates the recommended route of the individual vehicle based on the estimated traffic information for each time zone (S150). A preferred embodiment of the recommended route is the shortest route. The shortest route may select a route so that the amount of traffic calculated at each time zone is minimized for each specific position of the route through which the vehicle will pass, and time movement of a specific vehicle based on the estimated traffic information at each time zone calculated through the simulation of the previous process. Depending on the combination of the various paths can be simulated to calculate the shortest time path of the vehicle.

That is, since the cloud central server 300 has route information of a number of vehicles utilizing the navigation service according to the present invention, it can be analyzed at once to provide an optimal route suitable for each vehicle in real time. This also enables load balancing of traffic.

As mentioned above, the estimated position of each vehicle in each time zone, the estimated traffic information of each position in each time zone, the optimum route of a specific vehicle, and the like are not merely calculated in sequence, but are balanced by linking the respective information. It can also be implemented to be calculated. That is, it is desirable to implement the information to predict future traffic conditions more accurately by maximizing the resources of cloud computing, such as feeding back each information or organically combining the information, and applying virtual simulation as described above. Do.

Next, the cloud central server 300 transmits the recommendation route calculated through the above process to the navigation terminal 100 of the vehicle through the cloud network 200 (S160). Then, the navigation terminal 100 displays the result of the transmission on the screen so that the driver can know the optimal route calculated in consideration of future traffic.

Next, the cloud central server 300 continuously tracks the location of the vehicle in real time and continuously updates information about the vehicle (S170). The driver may not drive the vehicle along the recommended route and takes into account abnormal situations such as vehicle theft or navigation theft.

Finally, the cloud central server 300 extracts and manages information for each vehicle (S180). It is desirable for the driver of the vehicle to check whether there is a road that he or she particularly prefers, and if there is a preferred road, it is desirable to consider this preferred road first for future route recommendations. For example, after deriving a recommendation route primarily in the optimum route calculation module 340, and if there is a road that the driver prefers within a certain distance from the recommendation route, the recommendation route is designated as a waypoint by way of the designated route. You can think of how to rewrite it. Alternatively, it is possible to implement a recommendation route selection algorithm by assigning an additional point to the preferred road. In addition, by real-time management of the current location for each GPS ID, it effectively responds to the theft of vehicle theft or navigation theft.

Claims

A computer-readable recording medium having recorded thereon an operation program of the navigation terminal 100 which receives vehicle route information including a starting point and a destination from a user and transmits the vehicle route information to a central server through a cloud network;

A cloud database 400 in which traffic information including a vehicle route database is stored;

Receives vehicle route information from the navigation terminal, generates a vehicle route database and stores it in a cloud database, calculates an estimated position for each vehicle from a vehicle route database, and estimates traffic for each position based on the estimated position for each time slot. A cloud central server 300 that calculates information, calculates a recommended route of the vehicle based on the estimated traffic information according to time zones, and transmits the recommended route to the navigation terminal of the vehicle through the cloud network;

Cloud computing navigation system configured to include.
The method according to claim 1,

The cloud central server 300,

An interface module 310 for receiving vehicle route information from a plurality of navigation terminals through a cloud network and transmitting an internally calculated recommendation route to a corresponding navigation terminal;

A vehicle route database generation module 320 for collecting vehicle route information from a plurality of navigation terminals to generate a vehicle route database and storing the vehicle route database in a cloud database;

Traffic estimated at each time zone based on the starting point and destination of each vehicle included in the vehicle route database, and estimated traffic information at each time slot based on the estimated time slots, and stored in the cloud database. Prediction module 330;

An optimum route calculation module 340 for calculating a recommendation route of the corresponding vehicle based on the estimated traffic information for each time slot;

Cloud computing method navigation system characterized in that it comprises a.
The method according to claim 2,

The navigation terminal 100 transmits the current location information and waypoint information of the vehicle obtained from the GPS module 110 to the cloud central server 300 through the cloud network,

The traffic prediction module 330 calculates an estimated position for each time zone based on the current location information and waypoint information, and calculates estimated traffic information for each time slot for each location and stores it in the cloud database 400. Cloud computing navigation system.
The method according to claim 3,

The cloud central server 300 is configured to further include a vehicle information management module 350 for extracting and managing road preference information for each vehicle by repeatedly tracking the location information of the vehicle in real time and comparing the difference with the recommended route. ,

The optimum route calculation module 340 receives road preference information for the vehicle from the vehicle information management module 350 when calculating the recommendation route and considers the preferred route according to the road preference. Cloud computing method navigation system characterized in that the calculation.
The method according to any one of claims 1 to 4,

The traffic prediction module 330 may include: a traffic simulation module 331 for simulating virtually moving each vehicle registered in the vehicle route database according to a time flow to calculate estimated traffic information for each time zone;

The cloud computing method of the navigation system further comprises.
The method according to claim 5,

The optimal path calculation module 340 simulates the movement of a specific vehicle based on a combination of various paths over time based on the estimated traffic information according to time slots calculated by the traffic simulation module 331, and recommends a route of the corresponding vehicle. An optimal path simulation module 341 that calculates a value;

The cloud computing system navigation system further comprises a.
(A) receiving vehicle route information including a starting point and a destination through a navigation terminal;

(B) the navigation terminal transmits the vehicle route information to the cloud central server;

(C) the cloud central server collecting vehicle route information from the plurality of navigation terminals to generate a vehicle route database;

(D) the cloud central server calculating an estimated position for each vehicle based on a departure point and a destination included in the vehicle route database;

(E) the cloud central server calculating the estimated traffic information according to time slots for each location based on the estimated time slots;

(F) calculating, by the cloud central server, a recommended route of the vehicle based on the estimated traffic information for each time zone;

(G) the cloud central server transmitting the recommended route to the navigation terminal of the vehicle through the cloud network;

Cloud computing method navigation operation method comprising a.
The method according to claim 7,

The step (B) further includes the navigation terminal transmitting the current location information and waypoint information of the vehicle to the cloud central server,

The step (D) is a cloud computing method of the cloud computing method, characterized in that the cloud central server further comprises the step of calculating the estimated position for each time zone based on the additional location information and waypoint information.
The method according to claim 8,

Step (E),

Calculating, by the cloud central server, virtually moving each vehicle registered in the vehicle route database according to a time flow, and calculating estimated traffic information according to time slots for each location;

Cloud computing method navigation operation method characterized in that it further comprises.
The method according to claim 9,

Step (F) is

Calculating a recommendation route of the vehicle by simulating a movement of a specific vehicle according to a combination of various routes according to time flow based on the estimated traffic information for each time zone calculated through the simulation of the step (E);

Cloud computing method navigation operation method characterized in that it further comprises.
A computer-readable recording medium having recorded thereon a navigation operation program for performing a cloud computing type navigation operation method according to any one of claims 7 to 10.