JP2023048846A - Management device and processing method - Google Patents

Abstract

To cause a mobile body suitable for processing job data to process the job data.SOLUTION: In a grid computing process, a control unit 505 causes a mobile body 10 that has specifications corresponding to conditions required for processing of job data among a plurality of available mobile bodies 10 to process the job data. The conditions required for the processing of the job data include at least one of responsiveness, reliability, and execution frequency. The specifications of the mobile body 10 include at least one of communication performance, communication stability, and calculation completion certainty indicating certainty of being able to complete calculation of the job data.SELECTED DRAWING: Figure 15

Description

The technology disclosed herein relates to grid computing.

Patent Literature 1 discloses a distributed processing system composed of a base station and an in-vehicle terminal that can be connected to each other by wireless communication. The base station has resource information storage means and processing allocation means. The resource information storage means stores resource information, which is information relating to computing resources possessed by the vehicle-mounted terminal functioning as a computing node. The process allocation means specifies an on-vehicle terminal to execute the requested process based on the resource information, and causes the specified on-vehicle terminal to execute the process. The vehicle-mounted terminal has resource information registration means and calculation means. The resource information registration means transmits the resource information of the own terminal to the base station and registers the own terminal as a calculation node in the distributed processing system. The calculation means executes processing assigned by the base station.

JP 2007-87273 A

Patent Literature 1 does not discuss how to allocate requested processing (job data) to an in-vehicle terminal (an example of a mobile object).

The technology disclosed herein has been made in view of this point, and its purpose is to allow a moving body suitable for processing job data to process the job data.

The technology disclosed herein relates to a management device that manages grid computing processing that allows a plurality of available mobile units among a plurality of mobile units each having an arithmetic unit to process job data. In the grid computing process, the management device causes a mobile object having specifications according to the conditions required for processing the job data to process the job data, out of the plurality of available mobile objects. Prepare. The conditions required for processing the job data include at least one of responsiveness, reliability, and execution frequency. The specifications of the mobile unit include at least one of communication performance, communication stability, and calculation completion accuracy indicating the likelihood that calculation of the job data can be completed.

With the above configuration, job data can be processed by a mobile object suitable for processing the job data.

In the management device, at least one of the communication performance, the communication stability, and the calculation completion accuracy of the mobile unit processing the job data increases as the response required in processing the job data increases. may

With the above configuration, job data can be processed by a moving object suitable for the responsiveness required in processing the job data.

In the management device, the higher the reliability required in processing the job data, the higher the calculation completion accuracy of the moving body that processes the job data.

With the above configuration, job data can be processed by a moving object suitable for the reliability required in processing the job data.

In the management device, at least one of the communication performance and the communication stability of the mobile unit that processes the job data may increase as the execution frequency required for processing the job data increases.

In the above configuration, job data can be processed by a moving object suitable for the execution frequency required for processing the job data.

In the management device described above, the specifications of the moving object may include computation performance, which is the performance of the computation device.

With the above configuration, job data can be processed by a moving object having computing performance suitable for processing the job data.

In the management device, the conditions required for processing the job data may include a processing scale, which is a scale of processing of the job data. The computing performance of the mobile object that processes job data may be increased.

With the above configuration, job data can be processed by a moving object having computing performance suitable for the processing scale required for processing the job data.

In the management device, the conditions required for processing the job data may include processing time, which is the time required for processing the job data. The computing performance of the mobile object that processes the job data may be increased.

With the above configuration, the job data can be processed by a moving object having computing performance suitable for the processing time required for processing the job data.

The technology disclosed herein relates to a processing method for causing a plurality of available mobile bodies among a plurality of mobile bodies each having an arithmetic device to process job data transmitted from a management device. In this processing method, the management device transmits the job data to one of the plurality of usable mobile bodies that has specifications according to conditions required in processing the job data, and to process the job data. The conditions required for processing the job data include at least one of responsiveness, reliability, and execution frequency, and the specifications of the mobile device include communication performance, communication stability, and completion of calculation of the job data. including at least one computation completion probability that indicates the likelihood that

In the above method, job data can be processed by a mobile unit suitable for processing the job data.

According to the technology disclosed herein, job data can be processed by a mobile object suitable for processing the job data.

1 is a schematic diagram illustrating the configuration of a system of an embodiment; FIG. 1 is a conceptual diagram for explaining grid computing; FIG. 1 is a block diagram illustrating the configuration of a vehicle; FIG. 3 is a block diagram illustrating the configuration of a user terminal; FIG. It is a block diagram which illustrates the structure of a client server. 4 is a block diagram illustrating the configuration of a management server; FIG. 8 is a flowchart illustrating job reception processing; 4 is a schematic diagram illustrating an image of a job reception screen; FIG. 6 is a flowchart illustrating position prediction processing; 9 is a flowchart illustrating capacity prediction processing; 9 is a flowchart illustrating communication prediction processing; 9 is a flowchart illustrating probability prediction processing; 6 is a flowchart illustrating matching processing; 4 is a flowchart illustrating grid computing processing; FIG. 5 is a diagram illustrating a correspondence table showing a correspondence relationship between vehicle processing levels and job data processing loads; 9 is a flowchart illustrating a modification of matching processing;

Hereinafter, embodiments will be described in detail with reference to the drawings. The same reference numerals are given to the same or corresponding parts in the drawings, and the description thereof will not be repeated.

(embodiment)
FIG. 1 illustrates the configuration of system 1 of the embodiment. This system 1 includes a plurality of vehicles 10, a plurality of user terminals 20, a client server 30, and a management server 50. These components can communicate with each other via a communication network 5 (communication line). These components also communicate with each other to send and receive various information and data as needed. A computing device 105 is mounted on each of the plurality of vehicles 10 . Note that the system 1 may be provided with a plurality of client servers 30 .

[Grid computing]
As shown in FIG. 2, in the system 1 of the embodiment, a grid computing (distributed processing system) is configured by vehicles 10 selected from a plurality of vehicles 10, and the vehicles 10 available from among the plurality of vehicles 10 A grid computing process is performed to process the job data in (specifically, the arithmetic unit 105 mounted on the vehicle 10).

Note that when the vehicle 10 requires the computing power of the computing device 105, the computing device 105 is brought into operation, and the computing power of the computing device 105 is used. For example, when the vehicle 10 is running, the computing device 105 needs a computing power for controlling the running of the vehicle 10, and the computing device 105 is in operation.

On the other hand, when the computing power of the computing device 105 becomes unnecessary in the vehicle 10, the computing device 105 is put into a stopped state, and the computing power of the computing device 105 is no longer used. For example, when the vehicle 10 stops and the power source of the vehicle 10 is turned off, the calculation capacity of the arithmetic device 105 becomes unnecessary, and the arithmetic device 105 is in a stopped state.

Here, when the computing power of the computing device 105 is unnecessary in the vehicle 10, by providing the computing power of the computing device 105 to the grid computing process, the computing power of the computing device 105 can be effectively used. becomes. For example, it may be desirable to provide the computing power of computing device 105 to grid computing operations while vehicle 10 is parked.

〔vehicle〕
Vehicle 10 is owned by a user. A user drives the vehicle 10 . In this example, vehicle 10 is a four-wheeled motor vehicle. A battery (not shown) is mounted on the vehicle 10 . The power of the battery is supplied to onboard equipment such as the arithmetic unit 105 . Examples of such vehicles 10 include electric vehicles and plug-in hybrid vehicles. In addition, the vehicle 10 can perform communication using vehicle-to-vehicle network communication (V2N) and communication using vehicle-to-vehicle communication (V2V).

As shown in FIG. 3 , vehicle 10 includes actuator 11 , sensor 12 , input section 101 , output section 102 , communication section 103 , storage section 104 , and arithmetic device 105 .

The actuator 11 includes a drive system actuator, a steering system actuator, a braking system actuator, and the like. Examples of drive system actuators include engines, transmissions, and motors. An example of a braking system actuator is a brake. Steering is an example of a steering system actuator.

The sensor 12 acquires various information used for controlling the vehicle 10 . Examples of the sensor 12 include an exterior camera that captures images of the exterior of the vehicle, an interior camera that captures images of the interior of the vehicle, a radar that detects objects outside the vehicle, a vehicle speed sensor, an acceleration sensor, a yaw rate sensor, an accelerator opening sensor, a steering sensor, a brake oil pressure sensor, and the like. is mentioned.

The input unit 101 inputs information and data. Examples of the input unit 101 include an operation unit that inputs information according to an operation by being operated, a camera that inputs an image representing information, a microphone that inputs sound representing information, and the like. Examples of the operation unit include operation buttons and touch sensors of a car navigation system. Information and data input to the input unit 101 are sent to the arithmetic unit 105 .

The output unit 102 outputs information and data. Examples of the output unit 102 include a display unit that outputs an image representing information, a speaker that outputs sound representing information, and the like. An example of the display unit is a display of a car navigation device. An example of a speaker is a speaker of a car navigation system.

The communication unit 103 transmits and receives information and data. Information and data received by the communication unit 103 are sent to the arithmetic unit 105 .

The storage unit 104 stores information and data.

Arithmetic device 105 controls each part of vehicle 10 . In this example, the computing device 105 controls the actuator 11 according to various information obtained by the sensor 12 . Arithmetic device 105 communicates with external devices (components of system 1, etc.) via communication unit 103 . Arithmetic device 105 appropriately updates the information and data stored in storage unit 104 based on the information and data input to input unit 101 and the information and data received via communication unit 103 .

The computing device 105 has a processor, memory, and the like. Examples of processors include CPUs (Central Processing Units) and GPUs (Graphics Processing Units). The memory stores programs for operating the processor, information and data indicating processing results of the processor, and the like. Various functions of the arithmetic unit 105 are realized by the processor (computer) executing the programs stored in the memory.

Note that the number of processors installed in the arithmetic unit 105 may be one or plural. Also, the processor mounted on the arithmetic unit 105 may be either one of the CPU and the GPU, or may be both the CPU and the GPU. In this example, computing device 105 has both a CPU and a GPU. For example, the arithmetic unit 105 is configured by one or more ECUs (Electronic Control Units).

In this example, the storage unit 104 stores basic vehicle information D11, arithmetic device information D12, vehicle state information D13, function usage information D14, vehicle usage information D15, position management information D16, and operation management information D17. , communication management information D18 and result management information D19.

<Vehicle basic information>
The vehicle basic information D11 is basic information about the vehicle 10 . For example, the vehicle basic information D11 includes a vehicle ID set for the vehicle 10, a user ID set for the user who owns the vehicle, and vehicle performance information indicating vehicle performance. The vehicle ID is an example of vehicle identification information that identifies the vehicle 10 . A user ID is an example of user identification information that identifies a user.

<Computing device information>
The computing device information D<b>12 is information about the computing device 105 . For example, the arithmetic device information D12 includes the arithmetic device ID set in the arithmetic device 105, arithmetic device performance information indicating the performance of the arithmetic device 105, and the like. The arithmetic device ID is an example of arithmetic device identification information that identifies the arithmetic device 105 . The performance of the arithmetic device 105 indicated by the arithmetic device performance information includes the computing power of the computing device 105 (specifically, the maximum computing power), the ratio of the CPU and GPU in the computing device 105, the communication performance of the computing device 105, the computing device 105 distributed processing performance. The calculation capacity of the arithmetic device 105 is the amount of data that the arithmetic device 105 can calculate per unit time.

<Vehicle status information>
The vehicle state information D13 indicates the state of the vehicle 10. FIG. For example, the vehicle state information D13 includes vehicle position information, vehicle communication information, vehicle power supply information, vehicle remaining battery level information, vehicle charging information, and the like.

The vehicle position information indicates the position (latitude and longitude) of the vehicle 10 . For example, vehicle position information can be acquired by GPS (Global Positioning System). The vehicle communication information indicates the communication state of vehicle 10 . The vehicle power supply information indicates the state of the power supply of the vehicle 10 . For example, the vehicle power information indicates whether the ignition power is on/off, the accessory power is on/off, and the like.

The vehicle battery remaining amount information indicates the remaining amount of a battery (not shown) mounted on the vehicle 10 . The vehicle charging information indicates whether or not the vehicle 10 is being charged in a charging facility (not shown) capable of charging the battery of the vehicle 10 .

Arithmetic device 105 monitors the state of vehicle 10 and appropriately (for example, periodically) updates vehicle state information D13 based on the monitoring results.

<Function usage information>
The function usage information D14 indicates usage history (past usage) and usage schedule (future usage) of various functions of the vehicle 10 . In other words, the function usage information D14 indicates, for each function of the vehicle 10, at what time the function was used (or whether it is scheduled to be used). For example, the function usage information D14 indicates, for each function of the vehicle 10, whether or not the function is used and the time, in association with each other. An example of the function of the vehicle 10 is OTA (Over The Air).

The computing device 105 updates the function usage information D14 as appropriate. For example, upon inputting information relating to the usage history or usage schedule of various functions of the vehicle 10, the computing device 105 updates the function usage information D14 based on the information.

<Vehicle usage information>
The vehicle usage information D15 indicates the usage history (past usage status) and usage schedule (future usage status) of the vehicle 10 . In other words, the vehicle usage information D15 indicates at what time the vehicle 10 was used (or scheduled to be used). For example, the vehicle usage information D15 indicates whether or not the vehicle 10 is used in association with the time.

The usage status of the vehicle 10 indicated in the vehicle usage information D15 is the usage status for purposes other than the grid computing processing. An example of such another purpose of use is running of the vehicle 10 . For example, whether or not the vehicle 10 is used for driving can be determined based on the on/off history and schedule of the power source (specifically, the ignition power source) of the vehicle 10 .

The computing device 105 appropriately updates the vehicle usage information D15. For example, upon inputting information relating to vehicle usage history or usage schedule, the arithmetic unit 105 updates the vehicle usage information D15 based on the information.

<Location management information>
Position management information D16 indicates the past position and future position of vehicle 10 . In other words, the position management information D16 indicates where the vehicle 10 was at what time (or where it is scheduled to be). For example, the position management information D16 indicates the position of the vehicle 10 and the time in association with each other.

Note that the position management information D16 may include scene information indicating the scene of the vehicle 10 . Examples of scenes of the vehicle 10 include a scene in which the vehicle 10 is traveling in an urban area, a scene in which the vehicle 10 is traveling on a highway, and a scene in which the vehicle 10 is stopped. For example, the arithmetic unit 105 recognizes the external environment of the vehicle 10 based on the output of the sensor 12, estimates the scene of the vehicle 10 based on the result of the recognition, and stores the estimated scene of the vehicle 10 as the position management information. Register for D16. The position management information D16 may indicate the "position of the vehicle 10", the "scene of the vehicle 10", and the "time" in association with each other.

The computing device 105 appropriately (for example, periodically) updates the position management information D16. Updating the position management information D16 will be described later in detail.

<Operation management information>
The operation management information D17 indicates the operation history (past utilization rate of computing capacity) and the operation schedule (future utilization rate of computing capacity) of the arithmetic device 105 mounted on the vehicle 10 . In other words, the operation management information D17 indicates what the utilization rate of the computing capacity of the vehicle 10 was (or is scheduled to be) at what time. For example, the operation management information D17 indicates the usage rate of the computational capacity of the arithmetic unit 105 and the time in association with each other.

The operation status (utilization rate of computing capacity) of the arithmetic unit 105 of the vehicle 10 indicated in the operation management information D17 is the operation status for purposes other than grid computing processing.

The computing device 105 appropriately (for example, periodically) updates the operation management information D17. Updating the operation management information D17 will be described later in detail.

<Communication management information>
Communication management information D18 indicates the communication history (past communication state) and communication schedule (future communication state) of vehicle 10 . In other words, the communication management information D18 indicates what state the communication state of the vehicle 10 was in at what time (or what state it is scheduled to be in). Specifically, communication management information D18 indicates, for each device with which vehicle 10 communicates, the communication state (past communication state and future communication state) between that device and vehicle 10 . For example, the communication management information D18 indicates, for each device with which the vehicle 10 communicates, the "communication state between the device and the vehicle 10", the "position of the vehicle 10", and the "time" in association with each other.

In this example, the communication management information D18 includes the communication status between the "vehicle 10" and the "management server 50 that communicates with the vehicle 10 using inter-vehicle network communication (V2N)", and the "vehicle 10 (self vehicle)” and “another vehicle 10 (another vehicle) that communicates with the vehicle 10 (own vehicle) using vehicle-to-vehicle communication (V2V)”. The state of communication between the vehicle 10 and the management server 50 includes the state of communication between the "vehicle 10" and a "relay device (not shown) that relays communication between the vehicle 10 and the management server 50" and the state of communication with the relay device. and the communication state between the management server 50 and the management server 50 . Examples of repeaters include base stations of the communication network 5 and communication devices installed in houses and facilities.

The communication state includes information such as communication quality, communication band, and available communication time. The communication quality information includes latency, throughput, packet loss, error rate, number of communication interruptions, and the like. For example, the arithmetic unit 105 acquires these pieces of information by transmitting/receiving a test signal to/from a device with which the vehicle 10 communicates. The information about communication quality may include latency, throughput, packet loss, error rate, part of the number of communication interruptions, all of them, or other correlated information. Information may be included.

Further, the communication state includes communication stability indicating the degree of communication stability between the "vehicle 10" and the "equipment with which the vehicle 10 communicates". Specifically, arithmetic unit 105 derives communication stability based on at least one of communication quality, communication band, and available communication time. In other words, when at least one of communication quality, communication band, and available communication time changes, communication stability changes.

In this example, arithmetic unit 105 derives communication stability based on communication quality, communication band, and available communication time. The higher the communication quality, the higher the communication stability. The wider the communication band, the higher the communication stability. The longer the available communication time, the higher the communication stability.

The computing device 105 appropriately (for example, periodically) updates the communication management information D18. The updating of the communication management information D18 will be described later in detail.

<Performance management information>
The track record management information D19 indicates the calculation completion track record in the grid computing process of the vehicle 10. FIG. For example, the performance management information D19 indicates the ratio of "the number of grid computing processes in which the vehicle 10 was able to complete the calculation of job data" to "the number of grid computing processes in which the vehicle 10 was used". .

When the grid computing process using the vehicle 10 is completed, the arithmetic unit 105 stores the result management information based on the calculation completion record (whether the calculation was completed) of the vehicle 10 in the grid computing process. Update D19.

[User terminal]
A user terminal 20 is owned by a user. A user operates the user terminal 20 to use various functions. Also, the user can carry the user terminal 20 around. Examples of such user terminals 20 include smartphones, tablets, notebook personal computers, and the like.

As shown in FIG. 4 , the user terminal 20 includes an input section 201 , an output section 202 , a communication section 203 , a storage section 204 and a control section 205 .

The input unit 201 inputs information and data. Examples of the input unit 201 include an operation unit that inputs information corresponding to an operation by being operated, a camera that inputs an image representing information, a microphone that inputs sound representing information, and the like. Examples of the operation unit include operation buttons and touch sensors. Information input to the input unit 101 is sent to the arithmetic unit 105 .

The output unit 202 outputs information and data. Examples of the output unit 202 include a display unit that outputs an image representing information, a speaker that outputs sound representing information, and the like.

The communication unit 203 transmits and receives information and data. Information and data received by the communication unit 303 are sent to the control unit 205 .

The storage unit 204 stores information and data.

The control unit 205 controls each unit of the user terminal 20 . The control unit 205 communicates with external devices (components of the system 1, etc.) via the communication unit 203 . The control unit 205 appropriately updates the information and data stored in the storage unit 204 based on the information and data input to the input unit 201 and the information and data received via the communication unit 203 .

The control unit 205 has a processor, memory, and the like. The memory stores programs for operating the processor, information and data indicating processing results of the processor, and the like. Various functions of the control unit 205 are realized by the processor (computer) executing the programs stored in the memory.

In this example, the storage unit 204 stores terminal information D21, terminal state information D22, and schedule information D23.

<Device information>
The terminal information D<b>21 is information about the user terminal 20 . For example, the terminal information D21 includes a user terminal ID set in the user terminal 20, user terminal performance information indicating the performance of the user terminal 20, and the like. The user terminal ID is an example of user terminal identification information that identifies the user terminal 20 .

<Terminal status information>
The terminal state information D22 is information indicating the state of the user terminal 20. FIG. The terminal state information D22 includes user terminal position information indicating the position of the user terminal 20, user terminal communication state information indicating the communication state of the user terminal 20, and the like.

The control unit 205 monitors the state of the user terminal 20 and appropriately (for example, periodically) updates the terminal state information D22 based on the monitoring results.

<Schedule information>
The schedule information D23 indicates the action history (past action) and action schedule (future action) of the user who owns the user terminal 20 . In other words, the schedule information D23 indicates where the user was at what time. For example, the schedule information D23 indicates the user's position and time in association with each other. Note that the schedule information D23 can be obtained by the schedule function installed in the user terminal 20. FIG. Specifically, the user uses the schedule function to input his or her own action history and action schedule into the user terminal 20, thereby obtaining schedule information D23 indicating the user's action history and action schedule.

The schedule information D23 may also include information indicating that the user's action is "action involving the use of the vehicle 10". For example, the schedule information D23 may indicate "user's position", "whether or not the vehicle 10 is used", and "time" in association with each other.

The control unit 205 updates the schedule information D23 stored in the storage unit 204 as appropriate. For example, when information about user behavior is input to the input unit 101, the control unit 205 updates the schedule information D23 based on the information.

[Client server]
The client server 30 is owned by the client. The client requests calculation of job data. Examples of such clients include companies, research institutes, and educational institutions.

As shown in FIG. 5 , the client server 30 includes an input section 301 , an output section 302 , a communication section 303 , a storage section 304 and a control section 305 .

The input unit 301 inputs information and data. Examples of the input unit 301 include an operation unit that inputs information according to an operation when operated, a camera that inputs an image representing information, a microphone that inputs sound representing information, and the like. Examples of operation units include operation buttons, touch sensors, keyboards, and mice. Information and data input to the input unit 301 are sent to the control unit 305 .

The output unit 302 outputs information and data. Examples of the output unit 302 include a display unit that outputs an image representing information, a speaker that outputs sound representing information, and the like.

The communication unit 303 transmits and receives information and data. Information and data received by the communication unit 303 are sent to the control unit 305 .

The storage unit 304 stores information and data.

The control unit 305 controls each unit of the client server 30 . The control unit 305 communicates with external devices (components of the system 1, etc.) via the communication unit 303 . The control unit 305 appropriately updates the information and data stored in the storage unit 304 based on the information and data input to the input unit 301 and the information and data received via the communication unit 303 .

The control unit 305 has a processor, memory, and the like. The memory stores programs for operating the processor, information and data indicating processing results of the processor, and the like. Various functions of the control unit 305 are realized by the processor (computer) executing the programs stored in the memory.

In this example, the storage unit 304 stores client information D31 and job data D1.

<Client information>
The client information D31 is information about the client. The client information D31 includes the client ID set to the client, the client server ID set to the client server 30 owned by the client, the name of the person in charge, the address, the telephone number, and the like. A client ID is an example of client identification information that identifies a client. The client server ID is an example of client server identification information that identifies the client server 30 .

<Job data>
The job data D1 is data corresponding to a job, and is data processed for execution of the job.

Note that the job data D1 can be classified by calculation type. Examples of the calculation type include a CPU-based calculation type, a GPU-based calculation type, and the like. The CPU-based calculation type job data D1 tends to require complex calculations with many conditional branches, such as simulation calculations. GPU-based calculation type job data D1 tends to require a huge amount of simple calculations such as image processing and machine learning.

Also, the job data D1 can be classified according to processing conditions. Examples of processing conditions include processing conditions that require constant communication, processing conditions that do not require constant communication, and the like. The job data D1, which has a processing condition that requires constant communication, requires that the vehicle 10 can always communicate in grid computing processing. In the job data D1 with the processing condition that does not require constant communication, it is not required that the vehicle 10 is always communicable in the grid computing process.

<Job information>
Note that the storage unit 304 may store job information about jobs. The job information includes job name information indicating the name of the job, job content information describing the content of the job, job data information regarding job data corresponding to the job, job delivery date information indicating the delivery date of the job, and the like. The job data information indicates the calculation type of job data, processing conditions, required calculation capacity, and the like.

[Management server]
The management server 50 manages the operation of the system 1 in which grid computing is configured. The management server 50 is owned by an operator who operates the system 1 . The management server 50 is an example of a management device that manages grid computing processing.

As shown in FIG. 6 , the management server 50 includes an input section 501 , an output section 502 , a communication section 503 , a storage section 504 and a control section 505 . The configuration of the input unit 501, the output unit 502, the communication unit 503, the storage unit 504, and the control unit 505 of the management server 50 is the same as that of the input unit 301, output unit 302, communication unit 303, storage unit 304, and control unit 305 of the client server 30. is the same as the configuration of

In this example, the storage unit 504 includes a user table D51, a vehicle table D52, a client table D53, a job table D54, a position prediction table D55, a capability prediction table D56, a communication prediction table D57, and an accuracy table D60. , matching table D58, job data D1, and calculation result data D2 are stored.

<User table>
The user table D51 is a table for managing users. The user table D51 contains, for each user, a user ID set to the user, a vehicle ID set to the vehicle 10 owned by the user, and a computing device ID set to the computing device 105 owned by the user. , a user terminal ID set in the user terminal 20 owned by the user, and the like are registered.

The control unit 505 appropriately updates the user table D51.

For example, when a new user joins the system 1, the control unit 505 updates the user table D51 by registering information related to the new user in the user table D51. Specifically, the control unit 505 newly sets a user ID for the new user, and sets the "user ID" set for the new user and the "user ID" set for the vehicle 10 owned by the user. vehicle ID", the "computing device ID" set in the computing device 105 mounted on the vehicle 10, and the "user terminal ID" set in the user terminal 20 owned by the new user, Register in the user table D51.

By communication between the vehicle 10 owned by the new user and the management server 50, it is possible to obtain the "vehicle ID" and the "computing device ID" related to the new user. Also, through communication between the user terminal 20 owned by the new user and the management server 50, it is possible to obtain the "user terminal ID" related to the new user.

<Vehicle table>
The vehicle table D52 is a table for managing the vehicle 10. FIG. In this example, the vehicle table D52 includes, for each vehicle 10, basic vehicle information D11, arithmetic device information D12, vehicle state information D13, function usage information D14, vehicle usage information D15, position management information D16, operation Management information D17, communication management information D18, performance management information D19, etc. are registered.

The control unit 505 appropriately updates the vehicle table D52.

For example, when a new vehicle 10 joins the system 1, the control unit 505 updates the vehicle table D52 by registering information related to the new vehicle 10 in the vehicle table D52. Specifically, the control unit 505 controls vehicle basic information D11, arithmetic device information D12, vehicle state information D13, function usage information D14, vehicle usage information D15, position management information D16, operation management information D17, It is registered in the vehicle table D52 in association with the communication management information D18.

By communication between the new vehicle 10 and the management server 50, information related to the new vehicle 10 (in this example, basic vehicle information D11, arithmetic device information D12, vehicle state information D13, function usage information D14, vehicle usage Information D15, position management information D16, operation management information D17, communication management information D18, performance management information D19) can be obtained. Also, by referring to the user table D51, it is possible to obtain the "user ID" associated with the new vehicle 10. FIG.

Further, the control unit 505 communicates with each vehicle 10 appropriately (for example, periodically) to obtain information on the vehicle 10 (specifically, vehicle state information D13, function usage information D14, vehicle usage information D15). , position management information D16, operation management information D17, communication management information D18, performance management information D19), and updates the vehicle table D52 based on the acquired information.

<Client table>
The client table D53 is a table for managing clients. In the client table D53, for each client, a client ID set to the client, a client server ID set to the client server 30 owned by the client, the name of the person in charge of the client, an address, a telephone number, etc. are registered. be.

The control unit 505 appropriately updates the client table D53.

For example, when a new client joins the system 1, the control unit 505 updates the client table D53 by registering information related to the new client in the client table D53. Specifically, the control unit 505 newly sets a client ID for the new client, and the "client ID" set for the new client and the "client ID" set for the client server 30 owned by the new client. The "client server ID" obtained and the "person in charge", "address" and "telephone number" of the new client are associated and registered in the client table D53.

By communication between the client server 30 and the management server 50, it is possible to obtain the "client server ID", "person in charge", "address" and "telephone number" of the new client.

<Job table>
The job table D54 is a table for managing jobs requested by clients. In the job table D54, for each job, the reception number set for the job, the client ID set for the client who requested the job, the name and contents of the job, and the like are registered. In addition, the job table D54 contains, for each job, calculation type and processing conditions of job data corresponding to the job, required calculation capacity which is the calculation capacity required for calculation of the job data, and delivery date set for the job. etc. are registered.

<Position prediction table>
The position prediction table D<b>55 is a table for managing prediction results of the position of the vehicle 10 . In this example, the position prediction table D55 registers, for each vehicle 10, the vehicle ID set for the vehicle 10, the position prediction information D5 regarding the vehicle 10, and the like. The position prediction information D5 indicates a prediction result of temporal changes in the position of the vehicle 10 . For example, the position prediction information D5 indicates the predicted value of the position of the vehicle 10 and the time in association with each other. Processing for predicting the temporal change in the position of the vehicle 10 (position prediction processing) will be described later in detail.

<Capability prediction table>
The capacity prediction table D56 is a table for managing prediction results of the computational capacity of the arithmetic device 105 of the vehicle 10 (computational capacity available for grid computing processing). In this example, for each vehicle 10, the vehicle ID set for the vehicle 10, the performance prediction information D6 regarding the vehicle 10, and the like are registered in the performance prediction table D56. The capacity prediction information D6 indicates a prediction result of temporal change in computational capacity available for grid computing processing of the arithmetic unit 105 of the vehicle 10 . For example, the capacity prediction information D6 indicates the predicted value of the available computing capacity of the arithmetic unit 105 of the vehicle 10 and the time in association with each other. A process (capacity prediction process) for predicting a temporal change in the computational capacity that can be used in the grid computing process of the arithmetic unit 105 of the vehicle 10 will be described later in detail.

<Communication prediction table>
The communication prediction table D<b>57 is a table for managing prediction results of the communication state of the vehicle 10 . In this example, the communication prediction table D57 registers, for each vehicle 10, the vehicle ID set for the vehicle 10, the communication prediction information D7 related to the vehicle 10, and the like. The communication prediction information D7 indicates a prediction result of a temporal change in the communication state of the vehicle 10. FIG. For example, the communication prediction table D57 shows predicted values of the communication state of the vehicle 10 and times in association with each other. Processing for predicting temporal changes in the communication state of the vehicle 10 (communication prediction processing) will be described later in detail.

<Accuracy table>
The accuracy table D60 is a table for managing calculation completion accuracy of the vehicle 10 . The calculation completion accuracy indicates the probability that the vehicle 10 can complete the calculation of the job data D1 in the grid computing process. In this example, for each vehicle 10, the vehicle ID set for the vehicle 10, the accuracy information D8 regarding the vehicle 10, and the like are registered in the accuracy table D60. The accuracy information D8 indicates the calculation completion accuracy of the vehicle 10 . Processing for deriving the calculation completion accuracy of the vehicle 10 (accuracy derivation processing) will be described later in detail.

<Matching table>
The matching table D58 is a table for managing results of matching processing, which will be described later. In the matching table D58, for each job, the reception number set for the job, the job data corresponding to the job, the vehicle ID set for the vehicle 10 assigned to the job data by the matching process, etc. are registered. be done.

<Job data>
The job data D1 stored in the storage unit 504 is the job data D1 accepted by the job acceptance process described later.

<Calculation result data>
The calculation result data D2 stored in the storage unit 504 indicates the calculation result of the job data D1 by grid computing processing described later.

[Update location management information]
Next, updating of the position management information D16 will be described. Arithmetic device 105 monitors the position of vehicle 10, and updates the past position of vehicle 10 indicated in position management information D16 based on the results of the monitoring. Further, when acquiring information that can be used for estimating the position of the vehicle 10, the arithmetic unit 105 updates the position of the vehicle 10 indicated in the position management information D16 based on the information.

Examples of information that can be used to estimate the position of the vehicle 10 include car navigation information indicating the travel history and travel schedule of the vehicle 10, vehicle usage information D15 stored in the storage unit 104 of the vehicle 10, and storage of the user terminal 20. Schedule information D23 stored in unit 204 and the like are included.

<Updating location management information based on car navigation information>
In this example, arithmetic unit 105 estimates the position (past position and future position) of vehicle 10 based on the travel history and travel schedule of vehicle 10 indicated in the car navigation information input to input unit 101. do. Arithmetic device 105 then updates position management information D16 stored in storage unit 104 based on the estimated position of vehicle 10 .

<Updating location management information based on vehicle usage information>
Also, in this example, the computing device 105 accesses the storage unit 104 and acquires the vehicle usage information D15 stored in the storage unit 104 . Arithmetic device 105 estimates the travel situation (travel history and travel schedule) of vehicle 10 based on the usage status (usage history and travel schedule) of vehicle 10 indicated in vehicle usage information D15, and calculates the result of the estimation. Based on this, the position of the vehicle 10 (past position and future position) is estimated. Arithmetic device 105 then updates position management information D16 stored in storage unit 104 based on the estimated position of vehicle 10 .

<Updating location management information based on schedule information>
Further, in this example, the computing device 105 sends the schedule information D23 stored in the storage unit 204 of the user terminal 20 to the user terminal 20 owned by the user who owns the vehicle 10 in which the computing device 105 is mounted. request access to. The control unit 205 of the user terminal 20 permits access to the schedule information D23 in response to the request.

Next, the arithmetic unit 105 accesses the storage unit 204 of the user terminal 20, and selects the vehicle from among the user's actions (past actions and future actions) indicated in the schedule information D23 stored in the storage unit 204. Detect behaviors involving the use of 10. For example, the computing device 105 detects the action history of the user using the vehicle 10 from the action history, which is the past actions of the user indicated in the schedule information D23. Further, the arithmetic device 105 detects an action plan in which the user is planning to use the vehicle 10 from the action plans, which are future actions, among the actions of the user indicated in the schedule information D23.

Next, the arithmetic unit 105 estimates the driving situation (driving history and driving schedule) of the vehicle 10 based on the detected user behavior (action history and driving schedule), and based on the result of the estimation, Estimate the position (past position and future position) of the vehicle 10 . Arithmetic device 105 then updates position management information D16 stored in storage unit 104 based on the estimated position of vehicle 10 .

[Update operation management information]
Next, updating of the operation management information D17 will be described. Arithmetic device 105 monitors the operating rate (utilization rate of computing capacity) of that computing device 105, and updates the past operating rate of computing device 105 indicated in operation management information D17 based on the monitoring result. . Further, when acquiring information that can be used to estimate the operating rate of the computing device 105, the computing device 105 updates the operating rate of the computing device 105 indicated in the operation management information D17 based on the information.

Examples of information that can be used to estimate the operating rate of the arithmetic unit 105 include car navigation information indicating the travel history and travel schedule of the vehicle 10, function usage information D14 stored in the storage unit 104 of the vehicle 10, and vehicle usage information. D15, location management information D16, schedule information D23 stored in the storage unit 204 of the user terminal 20, and the like.

<Updating operation management information based on car navigation information>
In this example, the arithmetic device 105 calculates the operating rate of the vehicle 10 (past operating rate and future operating rate ). For example, "the operating rate of the arithmetic device 105 during the period when the vehicle 10 is stopped" and "the operating rate of the arithmetic device 105 during the period when the vehicle 10 is scheduled to stop" are estimated to be zero. Then, the computing device 105 updates the operation management information D17 stored in the storage unit 104 based on the estimated operating rate of the computing device 105 .

<Updating operation management information based on function usage information>
Also, in this example, the computing device 105 accesses the storage unit 104 and acquires the function usage information D14 stored in the storage unit 104 . Arithmetic device 105 detects the usage status of functions that involve the use of arithmetic device 105 from the usage statuses (history of usage and scheduled usage) of various functions indicated in function usage information D14. The computing device 105 estimates the operating rate (past operating rate and future operating rate) of the computing device 105 based on the detected usage status of the function. Then, the computing device 105 updates the operation management information D17 stored in the storage unit 104 based on the estimated operating rate of the computing device 105 .

<Updating operation management information based on vehicle usage information>
Also, in this example, the computing device 105 accesses the storage unit 104 and acquires the vehicle usage information D15 stored in the storage unit 104 . Arithmetic device 105 estimates the travel situation (travel history and travel schedule) of vehicle 10 based on the usage status (usage history and travel schedule) of vehicle 10 indicated in vehicle usage information D15, and calculates the result of the estimation. Based on this, the operating rate (past operating rate and future operating rate) of the arithmetic unit 105 is estimated. Then, the computing device 105 updates the operation management information D17 stored in the storage unit 104 based on the estimated operating rate of the computing device 105 .

<Updating operation management information based on location management information>
Also, in this example, the computing device 105 accesses the storage unit 104 and acquires the position management information D16 stored in the storage unit 104 . Next, the arithmetic unit 105 estimates the travel situation (travel history and travel schedule) of the vehicle 10 based on the position (past position and future position) of the vehicle 10 indicated in the position management information D16, and Based on the result of the estimation, the operating rate (past operating rate and future operating rate) of the arithmetic unit 105 is estimated. Then, the computing device 105 updates the operation management information D17 stored in the storage unit 104 based on the estimated operating rate of the computing device 105 .

<Updating location management information based on schedule information>
Further, in this example, similarly to "updating the position management information based on the schedule information", the computing device 105 is stored in the storage unit of the user terminal 20 owned by the user who owns the vehicle 10 in which the computing device 105 is mounted. 204 to detect an action involving the use of the vehicle 10 from the user's actions (action history and action schedule) indicated in the schedule information D23 stored in the storage unit 204. FIG.

Next, the arithmetic unit 105 estimates the driving situation (driving history and driving schedule) of the vehicle 10 based on the detected user behavior (action history and driving schedule), and based on the result of the estimation, The operating rate (past operating rate and future operating rate) of the arithmetic unit 105 is estimated. Then, the computing device 105 updates the operation management information D17 stored in the storage unit 104 based on the estimated operating rate of the computing device 105 .

[Update communication management information]
Next, updating of the communication management information D18 will be described. Arithmetic device 105 monitors the communication state of vehicle 10 in which arithmetic device 105 is mounted, and updates the past communication state of vehicle 10 indicated in communication management information D18 based on the monitoring results. Further, when acquiring information that can be used for estimating the communication state of vehicle 10, arithmetic unit 105 updates the communication state of vehicle 10 indicated in communication management information D18 based on the information.

Examples of information that can be used for estimating the communication state of the vehicle 10 include car navigation information indicating the travel history and travel schedule of the vehicle 10, function usage information D14 stored in the storage unit 104 of the vehicle 10, and vehicle usage information D15. , location management information D16, schedule information D23 stored in the storage unit 204 of the user terminal 20, and the like.

<Update communication management information based on car navigation information>
In this example, arithmetic unit 105 calculates the communication state (past communication state and future communication state) of vehicle 10 based on the travel history and travel schedule of vehicle 10 indicated in the car navigation information input to input unit 101 ). For example, "the communication state of the vehicle 10 during the period when the vehicle 10 was stopped at a position with a relatively good communication environment" and "during the period when the vehicle 10 is scheduled to stop at a position with a relatively good communication environment "Communication state of vehicle 10" is estimated to be "relatively good communication state". Arithmetic device 105 then updates communication management information D18 stored in storage unit 104 based on the estimated communication state of vehicle 10 .

<Update communication management information based on function usage information>
Also, in this example, the computing device 105 accesses the storage unit 104 and acquires the function usage information D14 stored in the storage unit 104 . Arithmetic unit 105 detects the usage status of functions that involve the use of communication from the usage statuses (usage history and usage schedule) of various functions indicated in function usage information D14. Arithmetic device 105 estimates the communication state (past communication state and future communication state) of vehicle 10 based on the detected usage status of the function. Arithmetic device 105 then updates communication management information D18 stored in storage unit 104 based on the estimated communication state of vehicle 10 .

<Update communication management information based on vehicle usage information>
Also, in this example, the computing device 105 accesses the storage unit 104 and acquires the vehicle usage information D15 stored in the storage unit 104 . Arithmetic device 105 estimates the travel situation (travel history and travel schedule) of vehicle 10 based on the usage status (usage history and travel schedule) of vehicle 10 indicated in vehicle usage information D15, and calculates the result of the estimation. Based on this, the communication state of the vehicle 10 (past communication state and future communication state) is estimated. Arithmetic device 105 then updates communication management information D18 stored in storage unit 104 based on the estimated communication state of vehicle 10 .

<Update communication management information based on location management information>
Also, in this example, the computing device 105 accesses the storage unit 104 and acquires the position management information D16 stored in the storage unit 104 . Next, the arithmetic unit 105 estimates the travel situation (travel history and travel schedule) of the vehicle 10 based on the position (past position and future position) of the vehicle 10 indicated in the position management information D16, and Based on the result of the estimation, the communication state (past communication state and future communication state) of the vehicle 10 is estimated. Arithmetic device 105 then updates communication management information D18 stored in storage unit 104 based on the estimated communication state of vehicle 10 .

<Update communication management information based on schedule information>
Further, in this example, similarly to "updating the position management information based on the schedule information", the computing device 105 is stored in the storage unit of the user terminal 20 owned by the user who owns the vehicle 10 in which the computing device 105 is mounted. 204 to detect an action involving the use of the vehicle 10 from the user's actions (action history and action schedule) indicated in the schedule information D23 stored in the storage unit 204. FIG.

Next, the arithmetic unit 105 estimates the driving situation (driving history and driving schedule) of the vehicle 10 based on the detected user behavior (action history and driving schedule), and based on the result of the estimation, The communication state (past communication state and future communication state) of the vehicle 10 is estimated. Arithmetic device 105 then updates communication management information D18 stored in storage unit 104 based on the estimated communication state of vehicle 10 .

[Processing by Control Unit (Management Method)]
The control unit 505 performs job acceptance processing, position prediction processing, capacity prediction processing, communication prediction processing, probability derivation processing, matching processing, and grid computing processing. These processes are examples of management methods for managing grid computing processes.

[Job reception processing]
Next, job reception processing will be described with reference to FIG. In the job reception process, job data D1 for which calculation is requested by the client is received. For example, the control unit 505 performs the following processing every time a client requests calculation of the job data D1.

<Step S11>
First, the management server 50 receives a job request from a client. Specifically, the client server 30 transmits a job request application to the management server 50 in response to an operation by the person in charge of the client. The control unit 505 of the management server 50 performs the following processing in response to the application.

The control unit 505 requests the client server 30 to send information necessary for accepting a job (specifically, client information about the client requesting the job and job information about the job). In this example, the control unit 505 transmits image data of the job reception screen to the client server 30 . The control unit 305 of the client server 30 reproduces the image of the job reception screen from the image data, and causes the output unit 302 (display unit) to output (display) the image.

As shown in FIG. 8, the job reception screen is a screen for inputting information necessary for job reception. The job reception screen includes a client name entry field R101 for entering the client name, a person in charge entry field R102 for entering the name of the person in charge of the client, an address entry field R104 for entering the address of the client, and a job name entry field R104 for entering the name of the job. A name input field R111, a job content input field R112 for inputting an explanation about the contents of the job, a calculation type input field R113 for inputting the calculation type of the job data corresponding to the job, and a processing condition input field R114 for inputting the job data processing conditions. , a required computing capacity entry field R115 for entering the required computing capacity for job data, a delivery date entry field R116 for entering the delivery date of the job, and a registration button B100.

The person in charge of the client operates the input unit 301 (operation unit) of the client server 30 to input necessary information on the job reception screen. As a result, the client information about the client requesting the job and the job information about the job are input. When the input of these information is completed, the person in charge of the client operates the input unit 301 (operation unit) of the client server 30 and presses the registration button B100 on the job reception screen. When the registration button B<b>100 is pressed, the control unit 305 of the client server 30 transmits the information (client information and job information) input on the job reception screen to the management server 50 . The control unit 505 of the management server 50 receives client information and job information.

Next, the control unit 505 requests the client server 30 to transmit job data D1 corresponding to the job. The control unit 305 of the client server 30 transmits job data D1 corresponding to the job to the management server 50 in response to the request. The control unit 505 of the management server 50 receives the job data D1.

<Step S12>
Next, the control unit 505 of the management server 50 analyzes the job data D1 received in step S11. Specifically, the control unit 505 analyzes the calculation type, processing conditions, required calculation capacity, and the like of the job data D1. Then, the control unit 505 corrects the job information received in step S11 based on the analysis result of the job data D1.

Note that if the job information received in step S11 is sufficiently reliable, the process of step S12 may be omitted.

<Step S13>
Next, the control unit 505 of the management server 50 associates the client information received in step S11 with the job information corrected as necessary in step S12 (or the job information received in step S11), Register in the job table D54. Further, the control unit 505 stores the job data D1 received in step S11 in the storage unit 504. FIG.

[Position prediction processing]
Next, position prediction processing will be described with reference to FIG. In the position prediction process, the control unit 505 predicts a temporal change in the position of the vehicle 10 based on the position management information D16 of the vehicle 10 registered in the vehicle table D52. For example, when the position management information D16 of the vehicle 10 registered in the vehicle table D52 is updated, the control unit 505 performs the following processing for the vehicle 10. FIG.

<Step S21>
First, the control unit 505 acquires the position management information D16 of the vehicle 10 registered in the vehicle table D52. Note that, similarly to "updating the position management information", the control unit 505 updates the position management information D16 of the vehicle 10 registered in the vehicle table D52 based on the information that can be used for estimating the position of the vehicle 10. , the updated position management information D16 may be obtained.

<Step S22>
Next, the control unit 505 predicts a temporal change in the position of the vehicle 10 based on the "position management information D16" of the vehicle 10 acquired in step S21.

Specifically, the control unit 505 predicts the tendency (pattern) of change in the position of the vehicle 10 from the position of the vehicle indicated in the position management information D16. The prediction of the tendency of changes in the position of the vehicle 10 may be realized by machine learning. Then, the control unit 505 predicts a temporal change in the position of the vehicle 10 (at what position the vehicle 10 is at what time) based on the trend of change in the position of the vehicle 10 .

<Step S23>
Next, the control unit 505 registers (overwrites) the position prediction information D5 indicating "temporal change in the position of the vehicle 10" predicted in step S22 in the position prediction table D55. Thereby, the position prediction table D55 is updated. If the future position (estimated value) of vehicle 10 indicated in position management information D16 is sufficiently reliable, the future position of vehicle 10 may be registered in position prediction information D5.

[Capacity prediction processing]
Next, the capacity prediction processing will be described with reference to FIG. In the capacity prediction process, the control unit 505 predicts the computational capacity of the arithmetic unit 105 of the vehicle 10 that can be used in the grid computing process based on the operation management information D17 of the vehicle 10 registered in the vehicle table D52. For example, when the operation management information D17 of the vehicle 10 registered in the vehicle table D52 is updated, the control unit 505 performs the following processing for the vehicle 10.

<Step S31>
First, the control unit 505 acquires the arithmetic device information D12 and the operation management information D17 of the vehicle 10 registered in the vehicle table D52. Note that, in the same manner as "updating the operation management information", the control unit 505 updates the operation management information of the vehicle 10 registered in the vehicle table D52 based on the information that can be used for estimating the utilization rate of the computing capacity of the vehicle 10. D17 may be updated and the updated operation management information D17 may be acquired.

<Step S32>
Next, based on the "computing device information D12" and the "operation management information D17" of the vehicle 10 acquired in step S31, the control unit 505 determines whether the computing device 105 of the vehicle 10 can be used in grid computing processing. Predict changes in computing power over time.

Specifically, the control unit 505 predicts the tendency (pattern) of change in the utilization rate of the computing capacity of the computing device 105 of the vehicle 10 from the operation status of the computing device 105 of the vehicle 10 indicated in the operation management information D17. do. Prediction of the trend of change in the utilization rate of the computational capacity of the arithmetic unit 105 may be realized by machine learning. Then, based on the trend of change in the utilization rate of the computational capacity of the arithmetic device 105 of the vehicle 10, the control unit 505 determines the period during which the computational capacity of the arithmetic device 105 of the vehicle 10 has a surplus (the utilization rate of the computational capacity is 100%). %) is predicted, and that period is defined as "a period during which the computational capacity of the arithmetic unit 105 of the vehicle 10 can be used for grid computing processing". For example, the control unit 505 uses "70%" of the computing capacity of the computing device 105 of the vehicle 10 during the period in which the usage rate of the computing capacity of the computing device 105 of the vehicle 10 is "30%" in the grid computing process. period during which it is possible to

<Step S33>
Next, the control unit 505 registers (overwrites) in the capacity prediction table D56 the capacity prediction information D6 indicating "temporal change in computational capacity available for grid computing processing of the arithmetic unit 105" predicted in step S32. do. Thereby, the capacity prediction table D56 is updated.

[Communication prediction processing]
Next, with reference to FIG. 11, communication prediction processing will be described. In the communication prediction process, the control unit 505 predicts temporal changes in the communication state of the vehicle 10 based on the communication management information D18 of the vehicle 10 registered in the vehicle table D52. For example, when the communication management information D18 of the vehicle 10 registered in the vehicle table D52 is updated, the control unit 505 performs the following processing for the vehicle 10.

<Step S41>
First, the control unit 505 acquires the communication management information D18 of the vehicle 10 registered in the vehicle table D52. Note that, similarly to the "update of communication management information", the control unit 505 updates the communication management information D18 of the vehicle 10 registered in the vehicle table D52 based on information that can be used for estimating the communication state of the vehicle 10. and the updated communication management information D18 may be acquired.

<Step S42>
Next, the control unit 505 predicts temporal changes in the communication state of the vehicle 10 based on the communication management information D18 of the vehicle 10 acquired in step S41.

Specifically, control unit 505 predicts a tendency (pattern) of change in the communication state of vehicle 10 from the communication state of the vehicle indicated in communication management information D18. Prediction of the tendency of changes in the communication state of the vehicle 10 may be realized by machine learning. Then, the control unit 505 predicts temporal changes in the communication state of the vehicle 10 (what time and what state the communication state of the vehicle 10 is in) based on the tendency of changes in the communication state of the vehicle 10. .

<Step S43>
Next, the control unit 505 registers (overwrites) the communication prediction information D7 indicating "change in the communication state of the vehicle 10 over time" predicted in step S42 in the communication prediction table D57. Thereby, the communication prediction table D57 is updated. If the future communication state (estimated value) of vehicle 10 indicated in communication management information D18 is sufficiently reliable, the future communication state of vehicle 10 may be registered in communication prediction information D7.

[Accuracy derivation process]
Next, the probability derivation process will be described with reference to FIG. The control unit 505 appropriately (for example, periodically) performs the following processes.

<Step S45>
First, the control unit 505 acquires, for each vehicle 10 , information that can be used for deriving the calculation completion accuracy of the vehicle 10 . Examples of information that can be used to derive the calculation completion accuracy of the vehicle 10 include the remaining battery level of the vehicle 10, the power supply state, the calculation completion record, the communication state, the available time of the arithmetic device 105, and the calculation capacity of the arithmetic device 105. Availability rate, etc.

In this example, the remaining battery level of the vehicle 10 is indicated in vehicle battery remaining amount information included in the vehicle state information D13 regarding the vehicle 10 registered in the vehicle table D52. The power supply state of the vehicle 10 is indicated in the vehicle charging information included in the vehicle state information D13 regarding the vehicle 10 registered in the vehicle table D52. The calculation completion record of the vehicle 10 (ratio of "the number of grid computing processes in which the vehicle 10 was able to complete the job data calculation" to the "number of grid computing processes in which the vehicle 10 was used") is It is shown in the performance management information D19 regarding the vehicle 10 registered in the vehicle table D52. The communication state of the vehicle 10 is shown in the communication prediction information D6 regarding the vehicle 10 registered in the communication prediction table D57.

The available time of the computing device 105 of the vehicle 10 (the time during which the computational capacity of the computing device 105 can be continuously provided to the grid computing process) is the capacity prediction information D6 about the vehicle 10 registered in the capacity prediction table D55. is derived based on the temporal change (predicted value) of the available computing power of the arithmetic unit 105 shown in . Specifically, the time during which the available computational capacity of the computational device 105 indicated in the performance prediction information D6 continues to be greater than zero is the "usable time of the computational device 105".

The utilization rate of the computational capacity of the arithmetic unit 105 of the vehicle 10 (ratio of computational capacity that can be used in the grid computing process) is shown in the capacity prediction information D6 regarding the vehicle 10 registered in the capacity prediction table D55. It is derived based on the temporal change (predicted value) of the available computing power of the arithmetic unit 105 .

<Step S46>
Next, the control unit 505 derives the calculation completion accuracy of the vehicle 10 for each vehicle 10 based on the "information usable for deriving the calculation completion accuracy of the vehicle 10" acquired in step S46. For example, the control unit 505 controls the vehicle 10 based on at least one of the remaining battery level of the vehicle 10, the power supply state, the calculation completion record, the available time of the arithmetic device 105, and the availability rate of the calculation capacity of the arithmetic device 105. Derive the calculation completion probability.

For example, as the remaining battery charge of the vehicle 10 increases, the calculation completion accuracy of the vehicle 10 increases. The calculation completion accuracy of the vehicle 10 when the power supply state of the vehicle 10 is "power feeding (charging in the charging facility)" is calculated when the power feeding state of the vehicle 10 is "non-power feeding (not charging in the charging facility)". state)” is higher than the calculation completion accuracy of the vehicle 10. The calculation completion accuracy of the vehicle 10 increases as the calculation completion record of the vehicle 10 increases. As the communication state of the vehicle 10 becomes better, the calculation completion accuracy of the vehicle 10 becomes higher. The longer the available time of the arithmetic unit 105 of the vehicle 10 is, the higher the calculation completion accuracy of the vehicle 10 is. The higher the availability of the computational capacity of the computing device 105 of the vehicle 10, the higher the computation completion accuracy of the vehicle 10 becomes.

In addition, the goodness of the communication state of the vehicle 10 changes according to the communication quality of the vehicle 10, the communication band, and the communication available time. For example, the higher the communication quality of the vehicle 10 is, the better the communication state of the vehicle 10 is. The wider the communication band of the vehicle 10 is, the better the communication state of the vehicle 10 is. The communication state of the vehicle 10 becomes better as the communication available time of the vehicle 10 becomes longer.

<Step S47>
Next, for each vehicle 10, the control unit 505 registers (overwrites) the accuracy information D8 indicating the "calculation completion accuracy of the vehicle 10" derived in step S46 in the accuracy table D60. The accuracy table D60 is thereby updated.

[Matching process]
Next, referring to FIG. 13, matching processing will be described. The matching process is a process of allocating the vehicle 10 that can be used in the grid computing process among the plurality of vehicles 10 to the job data D1 received in the reception process. For example, the control unit 505 performs the following process after completing the job reception process.

<Step S51>
First, the control unit 505 selects a job to be subjected to matching processing from the jobs registered in the job table D54. Then, the control unit 505 selects the job data D1 corresponding to the job to be subjected to matching processing from the job data D1 stored in the storage unit 504 .

<Step S52>
Next, the control unit 505 predicts the "prediction result of the temporal change in computational capacity available in the grid computing process of each of the plurality of vehicles 10 registered in the capacity prediction table D56" and the "prediction result registered in the communication prediction table D57." Based on the prediction result of the temporal change in the communication state of each of the plurality of vehicles 10, the job data D1 selected from among the plurality of vehicles 10 in step S51 can be used in the grid computing process. A vehicle 10 is selected.

Specifically, the control unit 505 determines a scheduled calculation period during which the grid computing process for the job data D1 is executed, and provides communication and computing capacity from among the plurality of vehicles 10 during the scheduled calculation period. A vehicle 10 capable of detecting the vehicle 10 is detected. Then, the control unit 505 sets the scheduled calculation period so that the "total computing power provided for the grid computing process" is greater than or equal to the "computing power required for calculating the job data D1 in the grid computing process". A vehicle 10 to be assigned to job data D1 is selected from vehicles 10 that can provide computing power in .

<Step S53>
Next, the control unit 505 assigns the vehicle 10 selected in step S52 to the job data D1 selected in step S51. Then, the control unit 505 registers matching result information indicating which vehicle 10 is assigned to which job data D1 in the matching table D58. For example, the control unit 505 associates the reception number set in the job data D1 (job) with the vehicle ID set in the vehicle 10 assigned to the job data D1, and registers them in the matching table D58.

[Grid computing processing]
Next, grid computing processing will be described with reference to FIG. In grid computing processing, a plurality of available vehicles 10 out of a plurality of vehicles 10 are caused to process job data D1. For example, the control unit 505 performs the following processes after completing the matching process. Note that the grid computing process is an example of a processing method for causing a plurality of available vehicles 10 out of the plurality of vehicles 10 to process the job data D<b>1 transmitted by the management server 50 .

<Step S61>
First, the control unit 505 refers to the matching table D58 and distributes the job data D1 to be subjected to the grid computing process to the vehicle 10 assigned to the job data D1 in the matching process. Specifically, control unit 505 transmits part of job data D1 to each of vehicles 10 assigned to job data D1. Thereby, the job data D1 is processed in parallel by the vehicles 10 assigned to the job data D1.

<Step S62>
Next, when the calculation of the partial job data (a part of the job data D1) transmitted to the vehicle 10 is completed, each of the vehicles 10 receives the partial calculation result data (a part of the calculation result data D2) obtained by the calculation. part) to the management server 50 . The control unit 505 of the management server 50 receives the partial calculation result data transmitted from the vehicle 10 and stores the partial calculation result data in the storage unit 504 .

<Step S63>
The control unit 505 determines whether all the vehicles 10 to which the job data D1 has been distributed in step S61 have completed the calculation. If all the vehicles 10 have completed the calculation, the process of step S64 is performed, and if not, the process of step S62 is performed.

<Step S64>
When all of the arithmetic devices 105 complete the calculation, the control unit 505 combines the partial calculation result data stored in the storage unit 504 to obtain calculation result data corresponding to the job data D1 to be subjected to grid computing processing. D2 (calculation result data D2 indicating the result of calculation of job data D1) is generated. Then, the control unit 505 transmits the calculation result data D2 corresponding to the job data D1 to be subjected to grid computing processing to the client server 30 of the client that requested the calculation of the job data D1.

<Step S65>
Next, users who have provided the computing power of the arithmetic unit 105 of the vehicle 10 to the grid computing process are rewarded by the operator of the system 1 . Examples of rewards given to the user include points that can be used in the system 1, virtual currency, discount benefits for products, and the like. For example, the control unit 505 of the management server 50 performs processing for rewarding the user who has provided the computing power of the arithmetic unit 105 of the vehicle 10 to the grid computing processing. Examples of processing for giving rewards include processing for associating the “user ID” set for the user with “points” (or virtual currency) that can be used in the system 1 and registering them in the user table D51; For example, a process of transmitting information indicating a discount benefit of a product to the owned user terminal 20, or the like. Information indicating the reward may be registered for each job in the job table D54.

Also, a reward may be provided by the client to the user who has provided the computing power of the computing unit 105 of the vehicle 10 to the grid computing process. For example, the control unit 305 of the client server 30 may perform processing for rewarding the user who has provided the computing power of the computing device 105 of the vehicle 10 to grid computing processing.

[Characteristics of grid computing processing]
Next, features of grid computing processing will be described. In grid computing processing, the control unit 505 performs the following processing.

In addition, below, the multiple vehicles 10 that can be used in the grid computing process are described as "multiple usable vehicles 10". In this example, the “vehicles 10 that can be used for grid computing processing” are among the plurality of vehicles 10 that have less than 100% utilization of the computational capacity of the arithmetic unit 105 during the period in which the grid computing processing is performed. and the vehicle 10 capable of communicating with the management server 50 .

In the grid computing process, the control unit 505 assigns the job data D1 (partial job data in detail) to the vehicle 10 having specifications according to the conditions required in the processing of the job data D1 among a plurality of available vehicles 10. to process.

[Required conditions for job data processing]
The conditions required for processing the job data D1 include at least one of responsiveness, reliability, and execution frequency. In this example, the conditions required for processing job data D1 include responsiveness, reliability, execution frequency, processing scale, and processing time. For example, these conditions can be obtained by analyzing the job data D1.

<responsiveness>
Responsiveness indicates the speed of response of the output of the calculation result data D2 to the input of the job data D1. Specifically, the responses from "transmission of partial job data by management server 50 (transmission to vehicle)" to "reception of partial calculation result data by management server 50 (partial calculation result data transmitted from vehicle 10)" indicates the speed of The higher the responsiveness, the shorter the time (response time) required from the input of the job data D1 to the output of the calculation result data D2.

<reliability>
Reliability indicates the likelihood that calculation result data D2 representing the calculation result of job data D1 can be obtained. Specifically, it indicates the probability that “partial calculation result data indicating the calculation result of partial job data” can be obtained from each of the plurality of vehicles 10 . The higher the reliability, the higher the probability that the calculation result data D2 can be obtained.

<Execution frequency>
The execution frequency is the frequency of execution of the processing of job data D1. Examples of execution frequency include the frequency of data storage, the frequency of data search, the frequency of data addition, the frequency of data deletion, and the frequency of data optimization.

<Processing scale>
The processing scale is the scale of processing of the job data D1. Examples of processing scale include program size included in data, size of data, scale of functions included in data, number of functions included in data, number of data loops, calculation precision required for data, and data processing. and the like.

For example, the smaller the size of the program included in the job data D1, the smaller the processing scale of the job data D1. As the size of the job data D1 becomes smaller, the processing scale of the job data D1 becomes smaller. The smaller the scale of the functions included in the job data D1, the smaller the processing scale of the job data D1. The smaller the number of functions included in the job data D1, the smaller the processing scale of the job data D1.

The smaller the number of loops of the job data D1, the smaller the processing scale of the job data D1. The lower the calculation accuracy required for the job data D1, the smaller the processing scale of the job data D1. As the range of processing of the job data D1 becomes narrower, the scale of processing of the job data D1 becomes smaller.

<processing time>
The processing time is the time required to process the job data D1. For example, the processing time is the time estimated to be required from the start to the end of processing the job data D1.

[Vehicle specifications]
The specifications of the vehicle 10 include at least one of communication performance, communication stability, and calculation completion accuracy. In this example, the specifications of the vehicle 10 include communication performance, communication stability, calculation completion accuracy, and arithmetic performance. The communication stability of the vehicle 10 is the communication stability between the vehicle 10 and the management server 50 . Arithmetic performance is the performance of the arithmetic device 105 of the vehicle 10 .

The communication performance and arithmetic performance of the vehicle 10 are shown in the arithmetic device performance information included in the arithmetic device information D12 registered in the vehicle table D52. The communication stability of vehicle 10 is indicated in communication prediction information D7 registered in communication prediction table D57. The calculation completion accuracy of the vehicle 10 is shown in accuracy information D8 registered in the accuracy table D60.

[Relationship between job data processing conditions and vehicle specifications]
In this example, the higher the "response" required in processing the job data D1, the more at least one of the "communication performance", "communication stability", and "computation completion accuracy" of the vehicle 10 that processes the job data D1. one gets higher.

Further, the higher the "reliability" required in processing the job data D1, the higher the "calculation completion accuracy" of the vehicle 10 that processes the job data D1.

At least one of the "communication performance" and "communication stability" of the vehicle 10 processing the job data D1 increases as the "execution frequency" required for processing the job data D1 increases.

Further, the larger the "processing scale" required for processing the job data D1, the higher the "computing performance" of the vehicle 10 that processes the job data D1.

Further, the shorter the "processing time" required for processing the job data D1, the higher the "computing performance" of the vehicle 10 that processes the job data D1.

[Specific example of relationship between job data processing conditions and vehicle specifications]
For example, the relationship between the conditions required for processing the job data D1 and the specifications of the vehicle 10 is as shown in FIG.

In the example of FIG. 15, the processes of the job data D1 are classified, and the correspondence relationship between the conditions required for the processing of the job data D1 and the specifications of the vehicle 10 is shown for each classification of the processes. Processing classifications include real-time processing, batch processing, database processing, pure storage processing, and routing processing.

Real-time processing is processing that immediately performs processing when a processing request occurs and returns processing results in a short response. Immediate execution and speed of response are important in real-time processing, so it is desirable to have the vehicle 10 with high communication performance, communication stability, and calculation completion accuracy perform the processing.

Batch processing is processing in which a plurality of processes are collectively executed sequentially. In batch processing, there is a range in how the processing can be grouped (processing volume), so it is desirable to allow the vehicle 10 having the necessary computing performance to perform the processing.

Database-based processing is processing performed on hierarchical or organized data groups, and processing such as data storage, update, and search is performed. In database processing, there is a wide range of update frequency and update data volume, and reliability is also required. 10 is desirable.

A pure storage process is a low-level data processing storage process that is highly non-hierarchical and without advanced search capabilities. In pure storage processing, there is a range in update frequency and update data volume, so it is desirable to have the vehicle 10, which has high communication performance and communication stability, and has computational performance as required, perform processing.

The routing system processing is, for example, route calculation processing for packets and the like. Since the routing system processing requires immediate computation, it is desirable to allow the vehicle 10 with high communication performance, communication stability, and calculation completion accuracy to perform the processing.

In the example of FIG. 15, the levels of "communication performance", "communication stability", "computation completion accuracy", and "calculation performance" of the vehicle 10 are three levels of "high", "medium", and "low". are categorized. The processing time is classified into two stages, "short" and "long," the execution frequency is classified into two stages, "high" and "low," and the processing scale is classified into two stages, "large" and "small." be.

Also, the example of FIG. 15 shows that when job data D1 is classified as "real-time processing" or "routing-related processing", the required "response" is higher than other processing. there is Also, the example of FIG. 15 indicates that the required "reliability" is higher when the job data D1 is classified as "database processing" than other processes.

In the first row example (No. 1) of the correspondence table shown in FIG. It indicates that the level of each of "communication performance", "communication stability", "computation completion accuracy", and "calculation performance" of the vehicle 10 that processes the job data D1 is desirably "high".

In the third row example (No.3) of the correspondence table shown in FIG. When the processing time for processing data D1 is "short", the levels of "communication performance", "communication stability", and "calculation performance" of vehicle 10 that processes job data D1 are "high". This indicates that it is desirable that the level of "calculation completion accuracy" of the vehicle 10 is "medium".

In the example (No.7) in the seventh row of the correspondence table shown in FIG. When the processing scale in the processing of the job data D1 is "large", each of the "communication performance", "communication stability", "computation completion accuracy", and "computation performance" of the vehicle 10 that processes the job data D1. It indicates that a level of "high" is desirable.

Comparing the first row example (No.1) and the second row example (No.2) shown in FIG. larger than the "processing scale" in the example of The "computing performance" in the example on the first line is higher than the "computing performance" in the example on the second line. Thus, the example of FIG. 15 shows that the larger the processing scale of the job data D1, the higher the computing performance of the vehicle 10 that processes the job data D1.

Comparing the third row example (No.3) and the fifth row example (No.5) shown in FIG. shorter than the "processing time" in the example. The "computing performance" in the example on the third line is higher than the "computing performance" in the example on the fifth line. Thus, the example of FIG. 15 shows that the shorter the processing time of the job data D1, the higher the computing performance of the vehicle 10 that processes the job data D1.

[Details of matching process]
In the matching process, the control unit 505 of the management server 50 acquires information indicating conditions required for processing the job data D1. For example, the control unit 505 analyzes the job data D1 to acquire the conditions required for processing the job data D1.

Next, the control unit 505 refers to various types of information, selects a vehicle 10 having specifications according to the conditions required in the processing of the job data D1 from among a plurality of available vehicles 10, and selects the vehicle 10 as "job data D1". "candidates for the vehicle 10 to be processed".

Specifically, the higher the "responsiveness" required in the processing of the job data D1, the higher the "communication performance", the "communication stability", and the "calculation completion accuracy" of the vehicle 10 selected as the candidate. one is higher. Further, the higher the "reliability" required in the processing of the job data D1, the higher the "calculation completion accuracy" of the vehicle 10 selected as the candidate. At least one of the "communication performance" and "communication stability" of the vehicle 10 selected as the candidate increases as the "execution frequency" required in the processing of the job data D1 increases. Further, the larger the "processing scale" required for processing the job data D1, the higher the "computing performance" of the vehicle 10 selected as the candidate. Further, the shorter the "processing time" required for processing the job data, the higher the "computing performance" of the vehicle 10 selected as the candidate.

For example, the control unit 505 refers to the information table showing the correspondence relationship shown in FIG. Vehicles 10 having the detected specifications are selected as "candidates of vehicles 10 for which job data D1 is to be processed".

Next, the control unit 505 refers to the capacity prediction table D56, and the "total computing power provided for the grid computing process" is changed to the "computing power required for processing the job data D1 in the grid computing process". As described above, "the vehicle 10 for which the job data is to be processed" is determined from among the candidates for the vehicle 10 for which the job data D1 is to be processed.

Next, the control unit 505 divides the job data D1 into a plurality of partial job data. Then, the control unit 505 refers to the capacity prediction table D56 and determines, for each of the plurality of partial job data, the vehicle 10 to process the partial job data. After completing the allocation of the vehicles 10 to the partial job data, the control unit 505 generates assignment information indicating the correspondence between the vehicles 10 and the partial job data (which vehicle 10 is to process which partial job data).

Next, the control unit 505 registers the assigned information in the matching table D56 in association with the job data D1. As a result, the responsible information is registered in the matching table D58 for each job data D1.

[Details of grid computing processing]
In the grid computing process, the control unit 505 of the management server 50 divides the job data D1 to be processed in the grid computing process into a plurality of partial job data. Next, the control unit 505 refers to the information in charge registered in the matching table D58, and sets each of the plurality of partial job data to the vehicle 10 (conditions required in the processing of the job data D1) that is to process the partial job data. to a vehicle 10) having specifications according to the

Each of the vehicles 10 calculates the partial job data transmitted from the control unit 505 . Thereby, in each of the plurality of vehicles 10, partial calculation result data indicating the calculation result of the partial job data is obtained. Then, each of the plurality of vehicles 10 transmits partial calculation result data to control unit 505 .

The control unit 505 of the management server 50 receives partial calculation result data transmitted from each of the plurality of vehicles 10 . Then, the control unit 505 generates calculation result data D2 indicating the calculation result of the job data D1 by integrating a plurality of partial calculation result data.

[Effect of Embodiment]
As described above, in the embodiment, in the grid computing process, the control unit 505 selects the job data D1 from among the plurality of available vehicles 10 and has specifications according to the conditions required in the processing of the job data D1. Let the vehicle 10 process. With such a configuration, the job data D1 can be processed by the vehicle 10 suitable for processing the job data D1.

Further, in the embodiment, the higher the responsiveness required in processing the job data D1, the higher at least one of the communication performance, communication stability, and calculation completion accuracy of the vehicle 10 that processes the job data D1. With such a configuration, the job data D1 can be processed by the vehicle 10 suitable for the responsiveness required in the processing of the job data D1.

Further, in the embodiment, the higher the reliability required in processing the job data D1, the higher the calculation completion accuracy of the vehicle 10 that processes the job data D1. With such a configuration, the job data D1 can be processed by the vehicle 10 suitable for the reliability required in the processing of the job data D1.

Further, in the embodiment, at least one of communication performance and communication stability of the vehicle 10 that processes the job data D1 increases as the execution frequency required for processing the job data D1 increases. With such a configuration, the job data D1 can be processed by the vehicle 10 suitable for the execution frequency required for processing the job data D1.

Further, in the embodiment, the specifications of the vehicle 10 include the computing performance of the vehicle 10 . With such a configuration, the job data D1 can be processed by the vehicle 10 having computing performance suitable for processing the job data D1.

Further, in the embodiment, the larger the processing scale required for processing the job data D1, the higher the computing performance of the vehicle 10 that processes the job data D1. With such a configuration, the job data D1 can be processed by the vehicle 10 having the computing performance suitable for the processing scale required for processing the job data D1.

Further, in the embodiment, the shorter the processing time required for processing the job data D1, the higher the computing performance of the vehicle 10 that processes the job data D1. With such a configuration, the job data D1 can be processed by the vehicle 10 having the computing performance suitable for the processing time required for processing the job data D1.

(Modified example of matching processing)
In the above description, instead of the matching process shown in FIG. 13, the matching process shown in FIG. 16 may be performed. In this modification, the control unit 505 of the management server 50 appropriately (for example, periodically) performs the following processes.

In the following description, a combination of a plurality of vehicles 10 that are capable of communicating and capable of providing computing power to grid computing processes during the same period of time will be referred to as a "grid group."

<Step S55>
First, the control unit 505 predicts the “prediction result of temporal change in computational capacity available in grid computing processing of each of the plurality of vehicles 10 registered in the capacity prediction table D56” and the “prediction result registered in the communication prediction table D57”. A plurality of grid groups are prepared based on the prediction result of the temporal change in the communication state of each of the plurality of vehicles 10.

Each of the plurality of grid groups differs in at least one of "period during which computing power can be provided to grid computing processing" and "total computing power that can be provided to grid computing processing".

<Step S56>
Next, the control unit 505 selects a job to be subjected to matching processing from among the jobs registered in the job table D54. Then, the control unit 505 selects the job data D1 corresponding to the job to be subjected to matching processing from the job data D1 stored in the storage unit 504 .

<Step S57>
Next, the control unit 505 selects a grid group to be used in grid computing processing for the job data D1 selected in step S56 from among the plurality of grid groups.

Specifically, the control unit 505 determines a scheduled calculation period during which the grid computing process for job data D1 is executed, and selects a grid group that can be used during the scheduled calculation period from among a plurality of grid groups. Then, the control unit 505 sets the scheduled calculation period so that the "total computing power provided for the grid computing process" is greater than or equal to the "computing power required for calculating the job data D1 in the grid computing process". A grid group to be assigned to the job data D1 is selected from among the available grid groups in .

Next, the control unit 505 assigns the grid selected from among the plurality of grid groups to the job data D1 selected in step S56. Then, the control unit 505 registers matching result information indicating which vehicle 10 is assigned to which job data D1 in the matching table D58.

(Other embodiments)
In the above description, the case where the management server 50 and the vehicle 10 directly communicate with each other without passing through another vehicle 10 was taken as an example, but the communication is not limited to this. For example, the management server 50 and the vehicle 10 may communicate indirectly via another vehicle 10 .

Further, in the above description, a correspondence table (lookup table) is given as an example of correspondence information indicating the correspondence relationship between the "processing level of the vehicle 10" and the "processing load of job data to be processed by the vehicle 10". but not limited to this. For example, the correspondence information may be a learning model obtained by machine learning. When the "processing level of the vehicle 10" is input to this learning model, the "processing load of job data to be processed by the vehicle 10" is output from this learning model.

Also, in the above description, the case where the control unit 505 of the management server 50 performs the position prediction process was taken as an example, but the present invention is not limited to this. For example, the position prediction process may be performed by the computing device 105 of the vehicle 10 . In this case, the computing device 105 may transmit the position prediction information D5 obtained by the position prediction process to the management server 50. FIG. The control unit 505 of the management server 50 may update the position prediction table D55 by registering (overwriting) the position prediction information D5 transmitted from the vehicle 10 in the position prediction table D55. The same applies to the operation prediction process and the communication prediction process.

Also, in the above description, the case where the control units 505 are integrated into the single management server 50 is taken as an example, but the present invention is not limited to this. For example, the control unit 505 may be distributed among a plurality of management servers 50 (not shown) that communicate with each other via the communication network 5 .

Moreover, in the above description, the storage unit 504 may be configured by a single storage device, or may be configured by a plurality of storage devices. A plurality of storage devices may be integrated into a single management server 50 or distributed among a plurality of management servers 50 (not shown) communicating with each other via the communication network 5 .

Also, in the above description, the control section 505 may be configured by a single control unit, or may be configured by a plurality of control units. A plurality of control units may be integrated into a single management server 50, or may be distributed among a plurality of management servers 50 (not shown) that communicate with each other via the communication network 5. FIG.

Further, in the above description, the arithmetic device 105 may be composed of a single arithmetic unit, or may be composed of a plurality of arithmetic units.

Also, in the above description, the vehicle 10 (specifically, a four-wheeled motor vehicle) has been cited as an example of a mobile object on which the arithmetic device 105 is mounted, but the mobile object is not limited to this. Arithmetic device 105 may be mounted on a moving object other than vehicle 10 . Examples of such mobile objects include transportation machines, personal digital assistants, and the like. Examples of transportation machines include motorcycles, rail vehicles, ships, aircraft, drones, and the like. A vehicle is an example of a transportation machine. Examples of portable information terminals include notebook personal computers, tablets, smartphones, and the like.

Further, in the above description, not only the computing power of the computing device 105 mounted on the vehicle 10 but also the computing power of another computing device (not shown) may be supplied to the grid computing process. Such other computing device may be a stationary computing device (for example, a desktop personal computer).

Also, the above embodiments may be combined as appropriate. The above embodiments are essentially preferred examples, and are not intended to limit the scope of the technology, its applications, or its uses disclosed herein.

As described above, the technology disclosed herein is useful as a grid computing technology.

1 system 10 vehicle 105 arithmetic device 20 user terminal 30 client server 50 management server 501 input unit 502 output unit 503 communication unit 504 storage unit 505 control unit D1 job data D2 calculation result data

Claims (8)

A management device for managing grid computing processing that causes a plurality of available mobile units among a plurality of mobile units each having an arithmetic unit to process job data,
a control unit that, in the grid computing process, causes a mobile object having specifications according to conditions required for processing the job data, among the plurality of available mobile objects, to process the job data;
the conditions required for processing the job data include at least one of responsiveness, reliability, and execution frequency;
The management device, wherein the specifications of the mobile unit include at least one of communication performance, communication stability, and calculation completion accuracy indicating the likelihood that calculation of the job data can be completed. The management device of claim 1,
management characterized in that at least one of the communication performance, the communication stability, and the calculation completion accuracy of the mobile unit processing the job data increases as the responsiveness required in processing the job data increases. Device. In the management device according to claim 1 or 2,
The management apparatus, wherein the calculation completion accuracy of the moving body that processes the job data increases as the reliability required in processing the job data increases. In the management device according to any one of claims 1 to 3,
A management device, wherein at least one of the communication performance and the communication stability of the mobile unit that processes the job data increases as the execution frequency required in processing the job data increases. In the management device according to any one of claims 1 to 4,
A management device, wherein the specifications of the moving object include computational performance, which is the performance of the computational device. In the management device of claim 5,
the conditions required for processing the job data include a processing scale, which is the scale of processing of the job data;
A management device, wherein the computational performance of the moving object for processing the job data increases as the scale of processing required for processing the job data increases. In the management device according to claim 5 or 6,
The conditions required for processing the job data include processing time, which is the time required for processing the job data. A management device characterized in that the computing performance of is enhanced. A processing method for causing a plurality of available mobile bodies among a plurality of mobile bodies each having a computing device to process job data transmitted from a management device,
transmitting the job data by the management device to one of the plurality of usable mobile bodies that has specifications according to conditions required in processing the job data;
processing the job data by the mobile body;
the conditions required for processing the job data include at least one of responsiveness, reliability, and execution frequency;
The processing method, wherein the specifications of the mobile device include at least one of communication performance, communication stability, and calculation completion accuracy indicating the likelihood that calculation of the job data can be completed.

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