Program update device, program update method, and program update program

Abstract

This program updating device comprises: a prediction unit that predicts a non-operational period in which a power conversion device of a moving body is non-operational, such prediction being on the basis of prediction data for predicting a non-operational period during which the power conversion device of the moving body is non-operational; and an execution unit that executes installation or activation of a power conversion device program that controls the power conversion device of the moving body during the predicted non-operational period.

Description

[Technical Field]

[0001] The present disclosure relates to a program update device, a program update method, and a program update program.

[0002] This application is based on and claims the benefit of priority from patent application serial number 2022-158840, filed September 30, 2022, the entire contents of which are incorporated herein by reference. [Background technology]

[0003] Japanese Patent Application Laid-Open Publication No. 2022-22833 discloses a program update device for controlling a vehicle, the program update device including: a storage device that stores multiple types of vehicle control software used for multiple types of control of the vehicle; a vehicle control execution unit that controls the vehicle using the vehicle control software; a rewrite processing unit that performs a rewrite process for the vehicle control software to be rewritten among the multiple types of vehicle control software when there is a request to rewrite the vehicle control software; and a state determination unit that determines whether the control processing of the vehicle using the vehicle control software to be rewritten will be executed in conjunction with the rewrite process, and the vehicle control execution unit, when it determines that the control processing will be executed in conjunction with the rewrite process, changes the control state of the vehicle prior to the rewrite process so that the control processing will not be executed in conjunction with the rewrite process. Summary of the Invention [Problem to be solved by the invention]

[0004] For example, if a program for a power conversion device is installed or activated while a power conversion device mounted on a mobile object is in operation, the mobile object may perform unexpected operations.

[0005] An object of the present disclosure is to provide a program update device, a program update method, and a program update program that can prevent a moving body from performing unexpected operations. [Means for solving the problem]

[0006] A program update device according to a first aspect of the present disclosure includes a prediction unit that predicts a non-operating period based on prediction data for predicting a non-operating period during which a power conversion device of a mobile body will be non-operating, and an execution unit that installs or activates a program for a power conversion device that controls the power conversion device of the mobile body during the predicted non-operating period.

[0007] A program update method according to a second aspect includes a process in which at least one processor predicts a non-operating period during which a power conversion device of a mobile body will be non-operating based on prediction data for predicting the non-operating period, and performs installation or activation of a power conversion device program that controls the power conversion device of the mobile body during the predicted non-operating period.

[0008] A program update program according to a third aspect causes at least one processor to execute processing including predicting a non-operating period during which a power conversion device of a mobile body will be non-operating based on prediction data for predicting the non-operating period, and installing or activating a program for a power conversion device that controls the power conversion device of the mobile body during the predicted non-operating period. [Effects of the Invention]

[0009] According to the present disclosure, an effect is achieved in that it is possible to prevent a moving body from making unexpected movements. [Brief explanation of the drawings]

[0010] The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which: [Figure 1] FIG. 1 is a diagram showing the configuration of a vehicle control system. [Figure 2] FIG. 2 is a diagram showing the hardware configuration of the server. [Figure 3] Figure 3 is a functional block diagram of the server. [Figure 4] FIG. 4 is a diagram illustrating an example of driving history data; [Figure 5] FIG. 5 is a diagram illustrating an example of travel schedule data; [Figure 6] FIG. 6 is a diagram showing an example of a charging schedule; [Figure 7] FIG. 7 is a diagram showing an example of a reservation schedule. [Figure 8] FIG. 8 is a flowchart of the program update process. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the technology of the present disclosure will be described in detail with reference to the drawings.

[0012] 1, a vehicle control system 10 of this embodiment includes a server 12 and a vehicle 14. The server 12 and the vehicle 14 are connected via a network 16.

[0013] The vehicle 14 includes a communication ECU (Electronic Control Unit) 18, an inverter ECU (Electronic Control Unit) 20, an inverter 22, a memory unit 24, a drive battery 26, a charge control unit 28, a rapid charge port 30, a normal charge port 32, and a motor 34. The vehicle 14 is an example of a mobile body according to the present disclosure. The inverter 22 is an example of a power conversion device according to the present disclosure.

[0014] The inverter 22 includes a switching circuit 36 ​​and an inverter power supply 38. The switching circuit 36 ​​includes a plurality of switching elements (not shown). The switching elements may be, for example, insulated gate bipolar transistors (IGBTs), but are not limited to these. The on / off of each switching element is controlled by instructions from the inverter ECU 20.

[0015] The inverter power supply 38 is turned on and off under the control of the communication ECU 18, and supplies power for driving the gates of the switching elements of the switching circuit 36.

[0016] The switching circuit 36 ​​is supplied with power from the drive battery 26 to drive the motor 34, and the switching circuit 36 ​​is controlled to switch so that a drive voltage for driving the motor 34 is output to the motor 34.

[0017] The storage unit 24 is configured by, for example, a nonvolatile memory, and stores an inverter program 24A executed by the inverter ECU 20. The inverter program 24A is an example of a power conversion device program.

[0018] The communication ECU 18 downloads the inverter program 24A to be executed by the inverter ECU 20 from the server 12 via the network 16, and installs or activates the downloaded inverter program 24A. Here, installing the inverter program 24A refers to storing the inverter program 24A in the storage unit 24 and making it executable. Activating the inverter program 24A refers to enabling a function realized by executing the inverter program 24A. Hereinafter, installing or activating the inverter program 24A may be referred to as updating the inverter program 24A.

[0019] The inverter ECU 24 controls the switching circuit 36 ​​of the inverter 22 by executing an installed or activated inverter program 24A.

[0020] The drive battery 26 is a power source that supplies a DC voltage for driving the motor 34, and charging is controlled by a charging control unit .

[0021] The charging control unit 28 charges the drive battery 26 based on power supplied from an external power source (not shown) to the quick charge port 30 or the normal charge port 32. Specifically, the charging control unit 28 converts an AC voltage such as AC 200 V supplied to the normal charge port 32 into DC to charge the drive battery 26. The charging control unit 28 also charges the drive battery 26 with the high voltage DC supplied to the quick charge port 30.

[0022] 2 is a block diagram showing the hardware configuration of the server 12. As shown in FIG.

[0023] 2, the controller 40 includes a central processing unit (CPU) 40A, a read-only memory (ROM) 40B, a random access memory (RAM) 40C, and an input / output interface (I / O) 40D. The CPU 40A, ROM 40B, RAM 40C, and I / O 40D are connected to each other via a bus 40E. The bus 40E includes a control bus, an address bus, and a data bus. A communication unit 41 and a storage unit 42 are connected to the I / O 40D.

[0024] The communication unit 41 is an interface for performing data communication with the communication ECU 18 of the vehicle 14 and the like.

[0025] The storage unit 42 is configured, for example, with a non-volatile memory. As shown in Fig. 2, the storage unit 42 stores a program update program 44, prediction data 46, and the like. The prediction data 46 includes, for example, multiple types of prediction data such as driving history data 48, driving schedule data 50, charging schedule 52, and reservation schedule 54. The prediction data 46 is updated sequentially.

[0026] The CPU 40A is an example of a processor. The term "processor" used here refers to a processor in a broad sense, and includes a general-purpose processor (e.g., a CPU) and a dedicated processor (e.g., a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a programmable logic device, etc.).

[0027] The program update program 44 may be stored in a non-volatile, non-transitory recording medium or distributed via a network and installed on the server 12 as needed.

[0028] Examples of non-volatile non-transitory recording media include CD-ROMs (Compact Disc Read Only Memory), magneto-optical disks, HDDs (Hard Disk Drives), DVD-ROMs (Digital Versatile Disc Read Only Memory), flash memory, and memory cards.

[0029] Fig. 3 is a block diagram showing the functional configuration of the CPU 40A of the server 12. As shown in Fig. 3, the CPU 40A functionally includes a prediction unit 56 and an execution unit 58. The CPU 40A functions as each of the functional units by reading and executing the program update program 44 stored in the storage unit 42.

[0030] The prediction unit 56 predicts the non-operating period of the inverter 22 based on the prediction data 46 for predicting the non-operating period during which the inverter 22 of the vehicle 14 will be non-operating.

[0031] The execution unit 58 executes installation or activation of the inverter program 24A that controls the inverter 22 of the vehicle 14 during the non-operation period of the vehicle 14 predicted by the prediction unit 56.

[0032] 2, the prediction data 46 includes past driving history data 48 of the vehicle 14 as an example of driving-related data related to the driving of the vehicle 14. Note that the driving-related data is an example of movement-related data according to the present disclosure. Also, the driving history data is an example of movement history data according to the present disclosure.

[0033] 4, the driving history data 48 includes, for example, data related to the driving history of the vehicle 14, such as the vehicle ID, date and time, vehicle position (latitude and longitude), vehicle speed, and shift position. The server 12 sequentially collects driving history data from each vehicle 14 and stores it in the storage unit 42 as driving history data 48.

[0034] The prediction unit 56 can predict the non-operating period of the inverter 22 based on the driving history data 48 shown in Fig. 4. For example, it can be predicted that the inverter 22 is not operating when the vehicle speed is 0 km / h or the shift position is P (parking), and therefore predicts the period when the vehicle speed is 0 km / h or the shift position is P. Specifically, based on the driving history data 48, a period when the vehicle 14 is stopped is derived using, for example, a statistical method, and the derived period is predicted as the non-operating period of the inverter 22. Note that a prediction model that uses the driving history data shown in Fig. 4 as input and outputs the non-operating period of the inverter 22 may be trained by machine learning, and the trained prediction model may be used to predict the non-operating period of the inverter 22.

[0035] 2, the prediction data 46 includes travel schedule data 50 of the vehicle 14 as an example of travel-related data. The travel schedule data 50 is used to predict the non-operating period of the inverter 22, for example, when the vehicle 14 is an autonomous vehicle or a vehicle that travels according to travel instructions from an external device, such as a connected car. The travel schedule data 50 includes data related to the travel schedule, such as a vehicle ID, a scheduled travel start date and time, a scheduled travel end date and time, and a destination, as shown in FIG. 5. The travel schedule data is an example of travel schedule data related to the present disclosure.

[0036] Based on the driving schedule data 50, the prediction unit 56 can predict the period during which the vehicle 14 is not scheduled to drive, i.e., the period excluding the scheduled driving period from the driving start date and time to the scheduled driving end date and time, as the non-operating period of the inverter 22.

[0037] The prediction data 46 also includes, as an example of schedule-related data, a charging schedule 52 for the vehicle 14. As shown in Fig. 6, the charging schedule 52 includes data related to charging, such as a vehicle ID, a charging start date and time, and a charging end date and time.

[0038] The prediction unit 56 can predict, based on the charging schedule 52, the period during which the vehicle 14 is charged, that is, the charging period from the charging start date and time to the charging end date and time, as the non-operating period of the vehicle 14.

[0039] The prediction data 46 also includes, as an example of schedule-related data, a reservation schedule 54 for the rental of the vehicle 14. The reservation schedule 54 is used to predict the non-operating period of the inverter 22, for example, when the vehicle 14 is a rental vehicle such as a rental car or a car sharing vehicle. As shown in FIG. 7 , the reservation schedule 54 includes data related to the rental, such as a vehicle ID, a start date and time of use, and an end date and time of use.

[0040] Based on the reservation schedule 54, the prediction unit 56 can predict the period when the vehicle 14 is not reserved, i.e., the period other than the rental period from the start date and time of use to the end date and time of use, as the non-operating period of the vehicle 14.

[0041] The prediction unit 56 may predict the non-operating period of the inverter 22 based on multiple types of prediction data included in the prediction data 46. When multiple non-operating periods are predicted based on multiple types of prediction data, the period obtained by the logical product of the multiple non-operating periods may be determined as the execution period for installing or activating the inverter program 24A. When there is no period obtained by the logical product of multiple non-operating periods, the non-operating period with the most overlapping periods among the multiple non-operating periods may be determined as the execution period for installing or activating the inverter program 24A.

[0042] Furthermore, when predicting a plurality of non-operating periods, the prediction unit 56 may determine an execution period for installing or activating the inverter program 24A from among the plurality of non-operating periods based on the time required for installing or activating the inverter program 24A. For example, among the plurality of non-operating periods, the shortest non-operating period that is longer than the time required for installing or activating the inverter program 24A may be determined as the execution period for installing or activating the inverter program 24A.

[0043] In addition, in order to prevent the non-operating period from ending before the installation or activation of the inverter program 24A is completed as much as possible, the longest non-operating period among the multiple non-operating periods may be determined as the execution period for the installation or activation of the inverter program 24A.

[0044] The execution period may also be determined from among multiple non-operating periods based on the update content of the inverter program 24A to be installed or activated. For example, if the update content affects the driving function, the shortest non-operating period among the multiple non-operating periods may be determined as the execution period. Alternatively, if the update content does not affect the driving function, the longest non-operating period among the multiple non-operating periods may be determined as the execution period.

[0045] Furthermore, when the prediction unit 56 predicts multiple non-operating periods, the prediction unit 56 may allow the user of the vehicle 14 to select an execution period for installing or activating the inverter program 24A from the multiple non-operating periods. For example, the predicted multiple non-operating periods may be transmitted to the vehicle 14 and displayed on a display of the vehicle 14, or transmitted to a mobile device such as a smartphone owned by the user and displayed on a display of the mobile device, thereby allowing the user to select an execution period for installing or activating the inverter program 24A from the multiple non-operating periods.

[0046] Furthermore, when predicting multiple non-operating periods, the prediction unit 56 may determine an execution period for installing or activating the inverter program 24A from among the multiple non-operating periods, based on a priority period previously set by the user of the vehicle 14. For example, the priority period previously set by the user of the vehicle 14 may be stored in advance in the storage unit 24, and a period in which the multiple predicted non-operating periods overlap with the priority period previously set by the user may be determined as the execution period for installing or activating the inverter program 24A.

[0047] Next, a flowchart of the program update process executed by the CPU 40A of the server 12 will be described with reference to Fig. 8. The process of Fig. 8 is repeatedly executed.

[0048] In step S100, the CPU 40A determines whether an update of the inverter program 24A is prepared, that is, whether installation or activation of the inverter program 24A is prepared. If an update of the inverter program 24A is prepared, the CPU 40A proceeds to step S101, and if an update of the inverter program 24A is not prepared, the CPU 40A ends this routine.

[0049] In step S101, the CPU 40A predicts a non-operating period of the inverter 22. That is, as described above, the CPU 40A predicts a non-operating period of the inverter 22 based on at least one of the prediction data included in the prediction data 46, namely, the driving history data 48, the driving schedule data 50, the charging schedule 52, and the reservation schedule 54.

[0050] In step S102, the CPU 40A determines an execution period for installing or activating the inverter program 24A based on the non-operating period of the inverter 22 predicted in step S101. For example, if there is one non-operating period predicted in step S101, that non-operating period is determined as the execution period for installing or activating the inverter program 24A. On the other hand, if there are multiple non-operating periods predicted in step S101, the execution period is determined from the multiple non-operating periods using the determination method described above.

[0051] In step S103, the CPU 40A determines whether the execution period for installing or activating the inverter program 24A determined in step S102 has arrived. If the execution period has arrived, the process proceeds to step S104, and if the execution period has not arrived, the process waits until the execution period arrives.

[0052] In step S104, the CPU 40A determines whether the inverter 22 is not operating. For example, the CPU 40A determines whether the inverter 22 is not operating by inquiring of the vehicle 14 whether the inverter 22 is not operating. If the inverter 22 is not operating, the CPU 40A proceeds to step S105. On the other hand, if the inverter 22 is operating, the CPU 40A ends this routine.

[0053] In step S105, the CPU 40A installs or activates the inverter program 24A. Specifically, when installing the inverter program 24A, the CPU 40A transmits the inverter program 24A to be installed to the vehicle 14 and instructs the vehicle 14 to install it. When activating the inverter program 24A after the inverter program 24A has already been installed, the CPU 40A instructs the vehicle 14 to activate the inverter program 24A. This causes the communication ECU 18 of the vehicle 14 to install or activate the inverter program 24A.

[0054] As described above, in this embodiment, the non-operating period is predicted based on the prediction data 46 for predicting the non-operating period during which the inverter 22 of the vehicle 14 will be non-operating, and the inverter program 24A that controls the inverter 22 is installed or activated during the predicted non-operating period. This makes it possible to prevent the vehicle from performing unexpected operations.

[0055] The present disclosure is not limited to the above-described embodiments, and various modifications and applications are possible within the scope of the gist of the present disclosure.

[0056] For example, as a modified example of predicting the non-operating period of the inverter 22, the non-operating period of the inverter 22 may be predicted based on behavior prediction data of the user of the vehicle 14. Specifically, the non-operating period of the inverter 22 may be predicted by predicting the period during which the vehicle 14 will not be used based on behavior prediction data that is the result of analyzing big data based on user attributes, such as gender, age, and family structure.

[0057] In addition, if the distance between vehicle 14 and the user using vehicle 14 is greater than or equal to a predetermined threshold, i.e., if the user is located in a location where vehicle 14 is not likely to be used, inverter 22 may be predicted to be non-operating, and inverter program 24A may be updated.

[0058] In the above embodiment, the server 12 executes the process shown in Fig. 8, but the communication ECU 18 of the vehicle 14 may execute the process shown in Fig. 8. In this case, the communication ECU 18 serves as the program update device of the present disclosure.

[0059] In addition, the configuration of the vehicle control system 10 described in the above embodiment (see Figure 1) is one example, and it goes without saying that unnecessary parts may be deleted or new parts may be added within the scope of the gist of the technology of the present disclosure.

[0060] Furthermore, the processing flow of the program update program 44 described in the above embodiment (see FIG. 8) is also an example, and it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be changed within the scope of the gist of the technology of the present disclosure.

[0061] Furthermore, in the above embodiment, the mobile body is described as a vehicle 14, but this is not limiting. The mobile body may be, for example, air mobility such as an airplane, or a ship.

[0062] Furthermore, in the above embodiment, the power conversion device is described as inverter 22 for driving vehicle 14, but this is not limiting. For example, the power conversion device may be an inverter for an air conditioner, an inverter for electric power steering, or a DC-DC converter. Furthermore, the DC-DC converter may be a step-down DC-DC converter, a step-up DC-DC converter, a step-up / step-down DC-DC converter, or a charging DC-DC converter.

[0063] The controller and methods described herein may be implemented by a special-purpose computer having a processor programmed to perform one or more functions embodied in a computer program. Alternatively, the apparatus and methods described herein may be implemented by a special-purpose computer having a processor configured with dedicated hardware logic circuitry. Alternatively, the apparatus and methods described herein may be implemented by one or more special-purpose computers configured by a combination of a processor executing a computer program and one or more hardware logic circuits. Furthermore, the computer program may be stored as instructions executed by a computer on a computer-readable non-transitory storage medium.

[0064] <Additional Notes> (Appendix 1) a prediction unit that predicts a non-operating period based on prediction data for predicting a non-operating period during which a power electronics device of the mobile body is not operating; an execution unit that executes installation or activation of a power electronics device program that controls a power electronics device of the mobile body during the predicted non-operating period; A program update device comprising: (Appendix 2) the prediction data includes movement-related data related to movement of the moving object, The prediction unit predicts the non-operating period based on the movement-related data. 2. The program update device of claim 1. (Appendix 3) the movement-related data includes past movement history data of the moving object; The prediction unit predicts the non-operating period based on the movement history data. 3. The program update device according to claim 2. (Appendix 4) the movement-related data includes movement schedule data of the moving object; The prediction unit predicts, based on the movement schedule data, a period during which the moving object is not scheduled to move as the non-operating period. 4. The program update device according to claim 2 or 3. (Appendix 5) the prediction data includes schedule-related data related to a schedule of the mobile object; The prediction unit predicts the non-operating period based on the schedule-related data. 5. A program update device according to any one of appendices 1 to 4. (Appendix 6) the schedule-related data includes a charging schedule for the mobile object; The prediction unit predicts a period during which charging of the mobile body is performed as the non-operating period based on the charging schedule. 6. The program update device according to claim 5. (Appendix 7) the vehicle is a rental vehicle, the schedule-related data includes a reservation schedule for the rental; The prediction unit predicts a period during which the mobile object is not reserved as the non-operating period based on the reservation schedule. 7. The program update device according to claim 5 or 6. (Appendix 8) the prediction data includes a plurality of types of prediction data, The prediction unit predicts the non-operating period based on a plurality of types of the prediction data. 8. A program update device according to any one of appendices 1 to 7. (Appendix 9) When a plurality of non-operating periods are predicted, the prediction unit determines an execution period for executing installation or activation of the power conversion device program from among the plurality of non-operating periods based on a time required for installation or activation of the power conversion device program. 9. A program update device according to any one of appendices 1 to 8. (Appendix 10) When the prediction unit predicts a plurality of non-operating periods, the prediction unit allows a user of the mobile body to select an execution period for executing installation or activation of the power conversion device program from the plurality of non-operating periods. 9. A program update device according to any one of appendices 1 to 8. (Appendix 11) When the prediction unit predicts a plurality of the non-operating periods, the prediction unit determines an execution period for executing installation or activation of the power conversion device program from among the plurality of non-operating periods based on a priority period preset by a user of the mobile body. 9. A program update device according to any one of appendices 1 to 8. (Appendix 12) At least one processor predicting a non-operating period in which a power electronics device of the mobile body is not operating based on prediction data for predicting the non-operating period; During the predicted non-operating period, a program for a power conversion device that controls a power conversion device of the mobile body is installed or activated. A program update method that performs a process including: (Appendix 13) At least one processor has predicting a non-operating period in which a power electronics device of the mobile body is not operating based on prediction data for predicting the non-operating period; During the predicted non-operating period, a program for a power conversion device that controls a power conversion device of the mobile body is installed or activated. A program update program that performs a process including:

Claims

1. a prediction unit (56) that predicts a non-operating period based on prediction data (48) for predicting a non-operating period during which a power conversion device (22) of a mobile object (14) will be non-operating; an execution unit (58) that executes installation or activation of a power conversion device program (24A) that controls a power conversion device of the mobile body during the predicted non-operating period; A program update device (12) comprising:

2. the prediction data includes movement-related data related to movement of the moving object, The prediction unit predicts the non-operating period based on the movement-related data.

2. The program update device according to claim 1.

3. The movement-related data includes past movement history data (48) of the moving object; The prediction unit predicts the non-operating period based on the movement history data.

3. The program update device according to claim 2.

4. The movement-related data includes movement schedule data (50) of the moving object, The prediction unit predicts, based on the movement schedule data, a period during which the moving object is not scheduled to move as the non-operating period.

3. The program update device according to claim 2.

5. the prediction data includes schedule-related data related to a schedule of the mobile object; The prediction unit predicts the non-operating period based on the schedule-related data.

2. The program update device according to claim 1.

6. The schedule-related data includes a charging schedule (52) for the mobile unit; The prediction unit predicts a period during which charging of the mobile body is performed as the non-operating period based on the charging schedule.

6. The program updating device according to claim 5.

7. the vehicle is a rental vehicle, the schedule-related data includes a reservation schedule (54) for the rental; The prediction unit predicts a period during which the mobile object is not reserved as the non-operating period based on the reservation schedule.

6. The program updating device according to claim 5.

8. the prediction data includes a plurality of types of prediction data, The prediction unit predicts the non-operating period based on a plurality of types of the prediction data.

2. The program update device according to claim 1.

9. When a plurality of non-operating periods are predicted, the prediction unit determines an execution period for executing installation or activation of the power conversion device program from among the plurality of non-operating periods based on a time required for installation or activation of the power conversion device program.

2. The program update device according to claim 1.

10. When the prediction unit predicts a plurality of the non-operating periods, the prediction unit allows a user of the mobile body to select an execution period in which to install or activate the power conversion device program from the plurality of the non-operating periods.

2. The program update device according to claim 1.

11. When the prediction unit predicts a plurality of non-operating periods, the prediction unit determines an execution period for executing installation or activation of the power conversion device program from among the plurality of non-operating periods based on a priority period preset by a user of the mobile body.

2. The program update device according to claim 1.

12. At least one processor predicting a non-operating period in which a power electronics device of the mobile body is not operating based on prediction data for predicting the non-operating period; During the predicted non-operating period, a program for a power conversion device that controls a power conversion device of the mobile body is installed or activated. A program update method that performs a process including:

13. At least one processor predicting a non-operating period in which a power electronics device of the mobile body is not operating based on prediction data for predicting the non-operating period; During the predicted non-operating period, a program for a power conversion device that controls a power conversion device of the mobile body is installed or activated. A program update program that performs a process including:

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