KR102456136B1 - Method, apparatus and computer program for finding possible destinations from a plurality of departure nodes - Google Patents

Abstract

The present invention provides a method in which a computing device acquires a possible destination by using a cost lower than or equal to a critical cost from N (N is a natural number) departure nodes on a map database including nodes and links. The method comprises: a step of generating an instance for searching the N departure nodes for a route; a step of generating a visit count list in which the number of instances visiting nodes is recorded; a step of recording visit information of instances for nodes in the visit count list, and searching for a route until each instance reaches a maximum search range; and a step of acquiring nodes visited by all of N instances based on the visit count list, and determining the acquired nodes as a destination.

Description

Method, apparatus and computer program for finding possible destinations from a plurality of departure nodes

The present disclosure relates to a method for finding connected destinations in a plurality of departure nodes. Specifically, it relates to a method for a computing device to obtain an reachable destination using a cost less than or equal to a threshold cost from each of N (N is a natural number) departure nodes on a map database including nodes and links.

The biggest concern when making an appointment is the location. Since users depart from different locations by different means of transportation, it is difficult to find an optimal meeting place in consideration of time. To solve this problem, map data is being utilized.

Map data consists of nodes and links. In addition, the link is given information about traffic conditions. Research on a technology for finding an optimal meeting place at each of a plurality of starting locations in consideration of information on traffic conditions is continuing.

Registered Patent No. 1538042 provides a method for searching a route.

The present disclosure has been devised in response to the above-mentioned background art, and a computing device can arrive from each of N (N is a natural number) starting node on a map database including nodes and links using a cost less than or equal to a threshold cost. We want to provide a way to obtain a destination.

In order to solve the above problem, there is provided a method in which a computing device acquires an reachable destination from each of N (N is a natural number) departure nodes on a map database including nodes and links using a cost less than or equal to a threshold cost. can be The method includes: generating an instance for searching a path for each of N departure nodes; generating a Visit Count List in which the number of instances visited to a node is recorded; recording visit information of instances for a node in the visit count list, and searching a path until each instance reaches a maximum search range; and based on the visited count list, obtaining nodes visited by all N instances, and determining the obtained nodes as destinations; , and the cost is a value calculated by weight information given to the link between the node and the node, and the maximum search range may be a range of nodes in which the cost used by the instance to visit the node is less than or equal to the threshold cost.

Alternatively, the method may include: obtaining target number information; Further comprising, based on the visit count list, obtaining the nodes visited by the N instances, and determining the obtained nodes as destinations includes: the number of nodes visited by the N instances is the target number information determining whether the number is less than the number indicated by ? increasing a threshold cost and a maximum search range when the determined number of destinations is less than the number indicated by information on the number of destinations; recording the visit information of the instances for the node in the visit count list, and based on the increased maximum search range, searching a path until each instance reaches the increased maximum search range; and based on the visited count list, obtaining nodes visited by N instances, and determining the obtained nodes as destinations; may further include.

Alternatively, the computing device, navigating the route until each instance reaches the increased maximum search range, searches the route based on the maintained computational information, wherein the computational information includes: Visit flag data indicating whether a node has been reached; arrival information data indicating which path the instance has taken to reach the node; and Queue state data holding information about the node and the set of costs to reach the node; may include at least one of

Alternatively, the computing device monitors in real time whether the number of nodes visited by all N instances is equal to or greater than the number indicated by the target number information, and when the number of nodes visited by the N instances is equal to or greater than the number indicated by the target number information , it can end navigating the route.

Alternatively, the step of increasing the threshold cost and the maximum search range may increase the degree of the critical cost increasing for at least one of the instances differently from the degree of the critical cost increasing for the other instances. have.

Alternatively, the computing device may provide a user with an interface for selecting place group information, and a node visited by the instance may be determined based on information input in response to the provided interface.

Alternatively, the computing device may obtain information on the traffic condition, and the weight information may be determined based on the obtained information on the traffic condition.

As another embodiment for solving the above problems, a computer program stored in a computer-readable storage medium may be provided. The computer program includes instructions for causing one or more processors to obtain an reachable destination using a cost less than or equal to a threshold cost from each of N (N is a natural number) departure nodes on a map database including nodes and links, , the instructions may include: generating an instance for searching a path for each of the N departure nodes; generating a Visit Count List in which the number of instances visited to a node is recorded; recording visit information of instances for a node in the visit count list, and searching a path until each instance reaches a maximum search range; and based on the visited count list, obtaining nodes visited by all N instances, and determining the obtained nodes as destinations; , and the cost is a value calculated by weight information given to the link between the node and the node, and the maximum search range may be a range of nodes in which the cost used by the instance to visit the node is less than or equal to the threshold cost.

According to an embodiment of the present disclosure, an arriveable destination may be obtained from each of the N departure nodes using a cost less than or equal to a threshold cost.

1 is a diagram for explaining a map database according to an embodiment of the present disclosure.
2A to 2C are schematic diagrams for explaining a method of acquiring a destination according to an embodiment of the present disclosure;
3 is a diagram for explaining the computing device 100 according to an embodiment of the present disclosure.
4 is a diagram for explaining a method of acquiring a destination according to an embodiment of the present disclosure.
5 is a diagram for explaining in more detail a method of obtaining nodes visited by all N instances and determining the obtained nodes as destinations based on a visit count list, according to an embodiment of the present disclosure;
6 is a schematic and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the present disclosure. However, it is apparent that these embodiments may be practiced without these specific descriptions.

The terms “component,” “module,” “system,” and the like, as used herein, refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or execution of software. For example, a component can be, but is not limited to being, a process running on a processor, a processor, an object, a thread of execution, a program, and/or a computer. For example, both an application running on a computing device and the computing device may be a component. One or more components may reside within a processor and/or thread of execution. A component may be localized within one computer. A component may be distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored therein. Components may communicate via a network such as the Internet with another system, for example via a signal having one or more data packets (eg, data and/or signals from one component interacting with another component in a local system, distributed system, etc.) may communicate via local and/or remote processes depending on the data being transmitted).

In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless otherwise specified or clear from context, "X employs A or B" is intended to mean one of the natural implicit substitutions. That is, X employs A; X employs B; or when X employs both A and B, "X employs A or B" may apply to either of these cases. It should also be understood that the term “and/or” as used herein refers to and includes all possible combinations of one or more of the listed related items.

Also, the terms "comprises" and/or "comprising" should be understood to mean that the feature and/or element in question is present. However, it should be understood that the terms "comprises" and/or "comprising" do not exclude the presence or addition of one or more other features, elements and/or groups thereof. Also, unless otherwise specified or unless it is clear from context to refer to a singular form, the singular in the specification and claims should generally be construed to mean “one or more”.

In addition, the term “at least one of A or B” should be interpreted to mean “includes only A”, “includes only B”, and “combined with the configuration of A and B”.

Those skilled in the art will further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented in electronic hardware, computer software, or combinations of both. It should be recognized that they can be implemented with To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

Descriptions of the presented embodiments are provided to enable those skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art of the present disclosure. The generic principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present invention is not limited to the embodiments presented herein. The invention is to be construed in its widest scope consistent with the principles and novel features presented herein.

1 is a diagram for explaining a map database according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the map database may be composed of nodes and links.

Here, the node may represent a place where a change in speed occurs when the vehicle travels on a road. Specifically, the node may represent an intersection, an overpass, a starting and ending point of a road, an underpass, a starting and ending point of a tunnel, an administrative boundary, and the like, but is not limited thereto.

In addition, the link may mean a node that is a speed change occurrence point and a line connecting the nodes, and may represent a real road. Specifically, the link may represent a road, a bridge, an overpass, an underpass, a tunnel, and the like, but is not limited thereto.

According to an embodiment of the present disclosure, weight information may be assigned to a link. The weight information indicates a cost used in the link, and the computing device 100 may assign the weight information to the link in advance. Also, the computing device 100 may obtain information on the traffic condition and determine weight information based on the obtained information on the traffic condition. More specifically, the computing device 100 may assign a high weight value to a link matching a road having high traffic congestion. In this case, the computing device 100 may update the weight value in a predetermined time unit (eg, 5 minutes, etc.). As another embodiment, the computing device 100 may update a weight value according to a traffic situation in response to a user input.

According to an embodiment of the present disclosure, a cost for a path from node to node may be calculated. For example, referring to FIG. 1 , a path from a node a1 to a node a3 must go through a link 1 and a link 2 . If the weight value of link 1 is (Wo) and the weight value of link 2 is (W1), the cost used in the path from node (a1) to node (a3) is equal to the weight value (Wo) given to link 1 It may be the sum of the weight values W1 given to link2. In addition, according to another embodiment, the cost used in the path from the node a1 to the node a3 is calculated by calculating the weight value Wo given to the link 1 and the weight value W1 given to the link 2 in advance. It may be an output value output after input to, but is not limited thereto.

2A to 2C are schematic diagrams for explaining a method of acquiring a destination according to an embodiment of the present disclosure;

Referring to FIG. 2A according to an embodiment of the present disclosure, the computing device 100 may acquire a plurality of starting nodes. For example, when a plurality of users use the computing device 100 to set an appointment, the computing device 100 may obtain the current locations of the plurality of users as departure nodes. Also, the computing device 100 may provide an interface through which the user can select a location, and determine departure nodes based on location information input by the user in response to the provided interface.

Referring to FIG. 2B according to an embodiment of the present disclosure, the computing device 100 may search for a destination by performing a predetermined algorithm for each of the departure nodes within the maximum search range. Also, the computing device 100 may record a node commonly visited by an instance of each departure node while searching for a destination.

Also, when there are no destinations of a predetermined number within the maximum search range, the computing device 100 may increase the maximum search range and then continue the destination search. In this case, resources of the computing device 100 may be reduced by maintaining the existing search process data.

Referring to FIG. 2C according to an embodiment of the present disclosure, the computing device 100 may end the search action when a predetermined number of destinations are obtained. By searching for a destination (eg, a meeting place) through these processes, the computing device 100 may provide an optimal meeting place to a plurality of users.

Hereinafter, a method of acquiring a destination according to an embodiment of the present disclosure will be described in more detail.

3 is a diagram for explaining the computing device 100 according to an embodiment of the present disclosure.

In an embodiment of the present disclosure, the computing device 100 may include other components for performing the computing environment of the computing device 100 , and only some of the disclosed components may configure the computing device 100 .

The computing device 100 may include a processor 110 , a memory 130 , and a network unit 150 .

The processor 110 may include one or more cores, and a central processing unit (CPU) of a computing device, a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU). unit) and the like, and may include a processor for data analysis and deep learning. The processor 110 may read a computer program stored in the memory 130 to perform data processing for machine learning according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor 110 may perform an operation for learning the neural network. The processor 110 for learning of the neural network, such as processing input data for learning in deep learning (DL), extracting features from the input data, calculating an error, updating the weight of the neural network using backpropagation calculations can be performed. At least one of a CPU, a GPGPU, and a TPU of the processor 110 may process learning of a network function. For example, the CPU and the GPGPU can process learning of a network function and data classification using the network function. Also, in an embodiment of the present disclosure, learning of a network function and data classification using the network function may be processed by using the processors of a plurality of computing devices together. In addition, the computer program executed in the computing device according to an embodiment of the present disclosure may be a CPU, GPGPU or TPU executable program.

According to an embodiment of the present disclosure, the memory 130 may store any type of information generated or determined by the processor 110 and any type of information received by the network unit 150 .

According to an embodiment of the present disclosure, the memory 130 is a flash memory type, a hard disk type, a multimedia card micro type, and a card type memory (eg, For example, SD or XD memory), Random Access Memory (RAM), Static Random Access Memory (SRAM), Read-Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Programmable Read (PROM) -Only Memory), a magnetic memory, a magnetic disk, and an optical disk may include at least one type of storage medium. The computing device 100 may operate in relation to a web storage that performs a storage function of the memory 130 on the Internet. The description of the above-described memory is only an example, and the present disclosure is not limited thereto.

The network unit 150 according to an embodiment of the present disclosure includes a Public Switched Telephone Network (PSTN), x Digital Subscriber Line (xDSL), Rate Adaptive DSL (RADSL), Multi Rate DSL (MDSL), VDSL ( A variety of wired communication systems such as Very High Speed DSL), Universal Asymmetric DSL (UADSL), High Bit Rate DSL (HDSL), and Local Area Network (LAN) can be used.

In addition, the network unit 150 presented herein is CDMA (Code Division Multi Access), TDMA (Time Division Multi Access), FDMA (Frequency Division Multi Access), OFDMA (Orthogonal Frequency Division Multi Access), SC-FDMA ( A variety of wireless communication systems may be used, such as Single Carrier-FDMA) and other systems.

In the present disclosure, the network unit 150 may be configured regardless of its communication mode, such as wired and wireless, and may be configured with various communication networks such as a personal area network (PAN) and a wide area network (WAN). can In addition, the network may be a well-known World Wide Web (WWW), and may use a wireless transmission technology used for short-range communication such as Infrared Data Association (IrDA) or Bluetooth.

The computing device 100 of the present disclosure may obtain an reachable destination from each of N departure nodes on a map database including nodes and links using a cost less than or equal to a threshold cost. Hereinafter, it will be described in detail with reference to FIGS. 4 and 5 .

4 is a diagram for explaining a method of acquiring a destination according to an embodiment of the present disclosure.

In step S410 , the computing device 100 may generate an instance for searching a path for each of the N departure nodes.

According to an embodiment of the present disclosure, the computing device 100 may generate an instance for searching a path for each of the N departure nodes. An instance may mean an object in an object-oriented program, but is not limited thereto.

For example, when the number of departure nodes is N, the computing device 100 searches for an instance for searching for a destination with respect to a first departure node, an instance for searching for a destination with respect to a second departure node, and searching for a destination for a third departure node. instance, … ., an instance searching for a destination with respect to the N-1th departure node and an instance searching for a destination with respect to the Nth starting node may be generated.

In operation S420 , the computing device 100 may generate a Visit Count List in which the number of instances visited to a node is recorded.

According to an embodiment of the present disclosure, the Visit Count List may retain information on the number of instances that have visited a node. For example, when N instances are generated for N departure nodes, the Visit Count List may retain information on how many instances of the N instances are visited for the node.

In step S430 , the computing device 100 records the visit information of the instance for the node in the visit count list, and searches the path until each instance reaches the maximum search range.

According to an embodiment of the present disclosure, the computing device 100 may have a threshold cost. The threshold cost is an upper bound on the predetermined cost. The computing device 100 may hold a threshold cost in the form of a specific value, and may hold a threshold cost in the form of time, but is not limited thereto.

Also, the maximum search range means a range of nodes in which the cost used by the instance to visit the node is less than or equal to the threshold cost.

For example, if the threshold cost is 10, the maximum search range means nodes that can arrive at a cost less than 10 from the starting node. For another example, if the threshold cost is 10 seconds, the maximum search range means nodes that can arrive from the starting node by consuming less than 10 seconds.

According to an embodiment of the present disclosure, the computing device 100 may record visit information of an instance for a node in a visit count list, and search a path until each instance reaches the maximum search range.

Specifically, if the threshold cost is 10, the computing device 100 may sequentially visit nodes that can arrive at a cost of less than 10 from the departure node. For example, if there is a first node that can be reached by consuming a cost of 1, a second node that can be reached by spending a cost of 5, and a third node that can be reached by spending a cost of 7, the instance is the first node and the first node. We will visit node 2 and node 3 sequentially. In this case, when the instance visits the first node, the second node, and the third node, the computing device 100 may record information that the instance has visited the node in the visit count list. In this case, if a plurality of instances are generated, the computing device 100 may record all information on nodes visited by the plurality of instances in the visit count list.

In operation S440 , the computing device 100 may obtain nodes visited by all N instances based on the visit count list, and determine the obtained nodes as destinations.

According to an embodiment of the present disclosure, the visit count list may retain information on how many instances have visited a specific node.

Based on the information recorded in the visit count list, the computing device 100 may acquire nodes visited by all N instances generated for the N departure nodes, and may determine the obtained nodes as destinations.

5 is a diagram for explaining in more detail a method of obtaining nodes visited by all N instances and determining the obtained nodes as destinations based on a visit count list, according to an embodiment of the present disclosure;

According to an embodiment of the present disclosure, the computing device 100 may acquire target number information. The target number information is information on the number of destinations to be found, and the computing device 100 may acquire target number information based on information input from a user, and may retain target number information in advance, limited thereto. doesn't happen

In step S441, the computing device 100 may determine whether the number of nodes visited by the N instances is less than the number indicated by the target number information.

For example, when the target number information indicates 3 and there are two nodes visited by all N instances, the computing device 100 may determine that the number of nodes visited by the N instances is less than the target number information. .

In step S443, when the determined number of destinations is less than the number indicated by information on the number of destinations, the computing device 100 may increase the threshold cost and the maximum search range.

For example, if the target number information indicates 3, the threshold cost is 10, and there are 2 nodes visited by all N instances in the maximum search range, the computing device 100 increases the threshold cost to 20 and The maximum search range can be increased according to the critical cost.

In addition, if the target number information indicates 3, the threshold cost is 10 seconds, and there are 2 nodes visited by all N instances in the maximum search range based on it, the computing device 100 increases the threshold cost to 20 seconds and increases the threshold Depending on the cost, the maximum search range can be increased. In this case, the degree of the increased threshold cost may be predetermined, may be selected by the user, but is not limited thereto.

According to another embodiment of the present disclosure, the degree of the increased threshold cost may be determined differently for each instance.

Specifically, the degree of the critical cost increased for at least one of the instances may be different from the degree of the critical cost increased for other instances.

For example, if a first user, a second user, and a third user meet and would like to have a meeting point relatively close to the first user, the computing device 100 may be at an increased threshold cost of matching the first user. By setting the degree (the degree of the threshold cost increased for the instance for the starting node of the first user) to be smaller than the degree of the threshold cost increased for other instances, the destination can be obtained close to the first user. .

In this case, the computing device 100 may provide an interface for selecting at least one user who wants a relatively nearby place, and based on the selection information input in response to the provided interface, it is possible to determine the degree of increase in the threshold cost. have.

Specifically, the computing device 100 may select a user based on selection information input in response to an interface provided to the user (hereinafter, at least one user may be selected, hereinafter referred to as A). The computing device 100 may obtain a destination close to the selected users by setting the degree of the critical cost increased for the instance created for A to be smaller than the degree of the critical cost increased for other instances. .

In step S445, the computing device 100 records the visit information of the instance for the node in the visit count list, and based on the increased maximum search range, the path until each instance reaches the increased maximum search range. can explore.

According to an embodiment of the present disclosure, the computing device 100 records visit information of instances for a node in a visit count list, and searches a path until each instance reaches an increased maximum search range. .

In this case, the computing device 100 may maintain existing calculation information. Computational information includes visit flag data indicating whether the instance reached which node during navigation, arrival information data indicating which path the instance took to reach the node, and the node and cost to reach the node. It may include at least one of queue state data (Queue state date) holding information about the set of , but is not limited thereto.

According to an embodiment of the present disclosure, the computing device 100 may increase the efficiency of an algorithm for finding a destination by expanding the maximum search range while maintaining the existing calculation information.

In step S447 , the computing device 100 may obtain nodes visited by the N instances based on the visit count list, and determine the obtained nodes as destinations.

According to an embodiment of the present disclosure, the visit count list may retain information on how many instances have visited a specific node.

Based on the information recorded in the visit count list, the computing device 100 may acquire nodes visited by all N instances generated for the N departure nodes, and may determine the obtained nodes as destinations.

According to another embodiment of the present disclosure, it does not acquire more than the number of nodes indicated by the target number information (here, nodes refer to nodes visited by N instances) despite searching based on the increased maximum search range. If not, the threshold cost and the maximum search range may be increased again according to step S443, and a route may be searched according to step S445 based on the increased maximum search range again. In this case, the computing device 100 may repeat steps S443 and S445 until it acquires more than the number of nodes indicated by the target number information (here, nodes refer to nodes visited by N instances). have.

According to another embodiment of the present disclosure, the computing device 100 monitors in real time whether the number of nodes visited by all N instances is equal to or greater than the number indicated by the target number information, and the number of nodes visited by the N instances is the target number. When the number information is equal to or greater than the number indicated, searching for a route may be terminated. More specifically, if the computing device 100 determines that the number of nodes visited by all N instances is greater than or equal to the number indicated by the target number information, even before the search range of the instance reaches the maximum search range, , and the obtained nodes may be determined as destinations.

Also, the computing device 100 may provide the determined destinations to the user.

According to another embodiment of the present disclosure, when providing the method according to FIGS. 4 and 5 , the computing device 100 provides an interface for selecting place group information to the user, and information input in response to the provided interface Based on , it is possible to determine a node visited by the instance.

Specifically, the computing device 100 may provide the user with an interface to select places such as cafes, restaurants, entertainment facilities, and movie theaters, and may acquire selection information selected by the user in response to the provided interface, and obtain Based on one selection information, the computing device 100 may determine nodes to be searched for.

More specifically, assuming that the user has selected a cafe, the computing device 100 may perform the method of acquiring the destination according to the above description by targeting only nodes matching the cafe.

6 is a schematic and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Although the present disclosure has been described above generally as being capable of being implemented by a computing device, those skilled in the art will appreciate that the present disclosure is a combination of hardware and software and/or in combination with computer-executable instructions and/or other program modules that may be executed on one or more computers. It will be appreciated that it can be implemented as a combination.

Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, those skilled in the art will appreciate that the methods of the present disclosure can be applied to single-processor or multiprocessor computer systems, minicomputers, mainframe computers as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like. It will be appreciated that each of these may be implemented in other computer system configurations, including those capable of operating in connection with one or more associated devices.

The described embodiments of the present disclosure may also be practiced in distributed computing environments where certain 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 memory storage devices.

Computers typically include a variety of computer-readable media. Any medium accessible by a computer can be a computer readable medium, and such computer readable media includes volatile and nonvolatile media, transitory and non-transitory media, removable and non-transitory media. including removable media. By way of example, and not limitation, computer-readable media may include computer-readable storage media and computer-readable transmission media. Computer-readable storage media includes volatile and non-volatile media, temporary and non-transitory media, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. includes media. A computer-readable storage medium may be RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage device, magnetic cassette, magnetic tape, magnetic disk storage device, or other magnetic storage device. device, or any other medium that can be accessed by a computer and used to store the desired information.

Computer readable transmission media typically embodies computer readable instructions, data structures, program modules or other data, etc. in a modulated data signal such as a carrier wave or other transport mechanism, and Includes any information delivery medium. The term modulated data signal means a signal in which one or more of the characteristics of the signal is set or changed so as to encode information in the signal. By way of example, and not limitation, computer-readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

An exemplary environment 1100 implementing various aspects of the disclosure is shown including a computer 1102 , the computer 1102 including a processing unit 1104 , a system memory 1106 , and a system bus 1108 . do. A system bus 1108 couples system components, including but not limited to system memory 1106 , to the processing device 1104 . The processing device 1104 may be any of a variety of commercially available processors. Dual processor and other multiprocessor architectures may also be used as processing unit 1104 .

The system bus 1108 may be any of several types of bus structures that may be further interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. System memory 1106 includes read only memory (ROM) 1110 and random access memory (RAM) 1112 . A basic input/output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, EEPROM, etc., which BIOS is the basic input/output system (BIOS) that helps transfer information between components within computer 1102, such as during startup. contains routines. RAM 1112 may also include high-speed RAM, such as static RAM, for caching data.

The computer 1102 may also be configured with an internal hard disk drive (HDD) 1114 (eg, EIDE, SATA) - this internal hard disk drive 1114 may also be configured for external use within a suitable chassis (not shown). Yes-, magnetic floppy disk drive (FDD) 1116 (eg, for reading from or writing to removable diskette 1118), and optical disk drive 1120 (eg, CD-ROM) for reading from, or writing to, disk 1122, or other high capacity optical media such as DVD. The hard disk drive 1114 , the magnetic disk drive 1116 , and the optical disk drive 1120 are connected to the system bus 1108 by the hard disk drive interface 1124 , the magnetic disk drive interface 1126 , and the optical drive interface 1128 , respectively. ) can be connected to The interface 1124 for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

These drives and their associated computer readable media provide non-volatile storage of data, data structures, computer executable instructions, and the like. For computer 1102, drives and media correspond to storing any data in a suitable digital format. Although the description of computer readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art will use zip drives, magnetic cassettes, flash memory cards, cartridges, etc. It will be appreciated that other tangible computer-readable media such as etc. may also be used in the exemplary operating environment and any such media may include computer-executable instructions for performing the methods of the present disclosure.

A number of program modules may be stored in the drive and RAM 1112 , including an operating system 1130 , one or more application programs 1132 , other program modules 1134 , and program data 1136 . All or portions of the operating system, applications, modules, and/or data may also be cached in RAM 1112 . It will be appreciated that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

A user may enter commands and information into the computer 1102 via one or more wired/wireless input devices, for example, a pointing device such as a keyboard 1138 and a mouse 1140 . Other input devices (not shown) may include a microphone, IR remote control, joystick, game pad, stylus pen, touch screen, and the like. Although these and other input devices are often connected to the processing unit 1104 through an input device interface 1142 that is connected to the system bus 1108, parallel ports, IEEE 1394 serial ports, game ports, USB ports, IR interfaces, It may be connected by other interfaces, etc.

A monitor 1144 or other type of display device is also coupled to the system bus 1108 via an interface, such as a video adapter 1146 . In addition to the monitor 1144, the computer typically includes other peripheral output devices (not shown), such as speakers, printers, and the like.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148 via wired and/or wireless communications. Remote computer(s) 1148 may be workstations, computing device computers, routers, personal computers, portable computers, microprocessor-based entertainment devices, peer devices, or other common network nodes, and are typically connected to computer 1102 . Although it includes many or all of the components described for it, only memory storage device 1150 is shown for simplicity. The logical connections shown include wired/wireless connections to a local area network (LAN) 1152 and/or a larger network, eg, a wide area network (WAN) 1154 . Such LAN and WAN networking environments are common in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a worldwide computer network, for example, the Internet.

When used in a LAN networking environment, the computer 1102 is connected to the local network 1152 through a wired and/or wireless communication network interface or adapter 1156 . Adapter 1156 may facilitate wired or wireless communication to LAN 1152 , which also includes a wireless access point installed therein for communicating with wireless adapter 1156 . When used in a WAN networking environment, the computer 1102 may include a modem 1158, be connected to a communication computing device on the WAN 1154, or establish communications over the WAN 1154, such as over the Internet. have other means. A modem 1158 , which may be internal or external and a wired or wireless device, is coupled to the system bus 1108 via a serial port interface 1142 . In a networked environment, program modules described for computer 1102 or portions thereof may be stored in remote memory/storage device 1150 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communication link between the computers may be used.

Computer 1102 may be associated with any wireless device or object that is deployed and operates in wireless communication, for example, printers, scanners, desktop and/or portable computers, portable data assistants (PDAs), communication satellites, wireless detectable tags. It operates to communicate with any device or place, and phone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network or may simply be an ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) makes it possible to connect to the Internet, etc. without a wire. Wi-Fi is a wireless technology such as cell phones that allows these devices, eg, computers, to transmit and receive data indoors and outdoors, ie anywhere within range of a base station. Wi-Fi networks use a radio technology called IEEE 802.11 (a, b, g, etc) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks may operate in unlicensed 2.4 and 5 GHz radio bands, for example, at 11 Mbps (802.11a) or 54 Mbps (802.11b) data rates, or in products that include both bands (dual band). .

One of ordinary skill in the art of this disclosure will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, instructions, information, signals, bits, symbols, and chips that may be referenced in the above description are voltages, currents, electromagnetic waves, magnetic fields or particles, optical field particles or particles, or any combination thereof.

A person of ordinary skill in the art of the present disclosure will recognize that the various illustrative logical blocks, modules, processors, means, circuits and algorithm steps described in connection with the embodiments disclosed herein include electronic hardware, (convenience For this purpose, it will be understood that it may be implemented by various forms of program or design code (referred to herein as software) or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. A person skilled in the art of the present disclosure may implement the described functionality in various ways for each specific application, but such implementation decisions should not be interpreted as a departure from the scope of the present disclosure.

The various embodiments presented herein may be implemented as methods, apparatus, or articles of manufacture using standard programming and/or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include magnetic storage devices (eg, hard disks, floppy disks, magnetic strips, etc.), optical disks (eg, CDs, DVDs, etc.), smart cards, and flash drives. memory devices (eg, EEPROMs, cards, sticks, key drives, etc.). Also, various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is understood that the specific order or hierarchy of steps in the presented processes is an example of exemplary approaches. Based on design priorities, it is to be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of the present disclosure. The appended method claims present elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not intended to be limited to the embodiments presented herein, but is to be construed in the widest scope consistent with the principles and novel features presented herein.

Claims (8)

A method for a computing device to obtain an reachable destination using a cost less than or equal to a threshold cost from each of N (N is a natural number) departure nodes on a map database including nodes and links, the method comprising:
generating an instance for searching a path for each of the N departure nodes;
generating a Visit Count List in which the number of instances visited to a node is recorded;
recording visit information of instances for a node in the visit count list, and searching a path until each instance reaches a maximum search range; and
obtaining nodes visited by all N instances based on the visited count list, and determining the obtained nodes as destinations;
including,
The cost is a value calculated by the weight information given to the link between the node and the node, and the maximum search range is the range of nodes for which the cost used by the instance to visit the node is less than or equal to the threshold cost.
A method for a computing device to obtain an reachable destination from each of a plurality of departure nodes using a cost less than or equal to a threshold cost. The method of claim 1,
obtaining target number information;
further comprising,
Based on the visited count list, obtaining nodes visited by N instances, and determining the obtained nodes as destinations includes:
determining whether the number of nodes visited by the N instances is less than the number indicated by the target number information;
increasing a threshold cost and a maximum search range when the determined number of destinations is less than the number indicated by information on the number of destinations;
recording the visit information of the instances for the node in the visit count list, and based on the increased maximum search range, searching a path until each instance reaches the increased maximum search range; and
obtaining nodes visited by N instances based on the visited count list, and determining the obtained nodes as destinations;
further comprising,
A method for a computing device to obtain an reachable destination from each of a plurality of departure nodes using a cost less than or equal to a threshold cost. The computing device of claim 2 , further comprising:
The step of searching the path until each instance reaches the increased maximum search range includes: searching the path based on the maintained calculation information;
Calculation information is:
Visit flag data indicating whether the instance has reached which node during navigation;
arrival information data indicating which path the instance has taken to reach the node; and
Queue state data holding information about the node and the set of costs to reach the node;
comprising at least one of
A method for a computing device to obtain an reachable destination from each of a plurality of departure nodes using a cost less than or equal to a threshold cost. 3. The method of claim 2, wherein the computing device comprises:
Monitoring whether the number of nodes visited by all N instances in real time is greater than or equal to the number indicated by the target number information,
If the number of nodes visited by the N instances is equal to or greater than the number indicated by the target number information, terminating the path search,
A method for a computing device to obtain an reachable destination from each of a plurality of departure nodes using a cost less than or equal to a threshold cost. 3. The method of claim 2,
The steps of increasing the critical cost and the maximum search range are:
wherein the degree of the critical cost increasing for at least one of the instances increases differently from the degree of the critical cost increasing for the other instances;
A method for a computing device to obtain an reachable destination from each of a plurality of departure nodes using a cost less than or equal to a threshold cost. The method of claim 1,
The computing device provides an interface for selecting place group information to a user, and a node to which an instance visits is determined based on information input in response to the provided interface;
A method for a computing device to obtain an reachable destination from each of a plurality of departure nodes using a cost less than or equal to a threshold cost. The method of claim 1,
The computing device obtains information on traffic conditions, and the weight information is determined based on the obtained information on traffic conditions,
A method for a computing device to obtain an reachable destination from each of a plurality of departure nodes using a cost less than or equal to a threshold cost. A computer program stored on a computer-readable storage medium, wherein the computer program is configured to cause one or more processors to use a cost less than or equal to a threshold cost from each of N (N is a natural number) starting nodes on a map database including nodes and links. instructions to obtain an reachable destination, the instructions comprising:
generating an instance for searching a path for each of the N departure nodes;
generating a Visit Count List in which the number of instances visited to a node is recorded;
recording visit information of instances for a node in the visit count list, and searching a path until each instance reaches a maximum search range; and
obtaining nodes visited by all N instances based on the visited count list, and determining the obtained nodes as destinations;
including,
The cost is a value calculated by the weight information given to the link between the node and the node, and the maximum search range is the range of nodes for which the cost used by the instance to visit the node is less than or equal to the threshold cost.
A computer program stored on a computer-readable storage medium.

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