Carbon dioxide emission estimation system, electric power consumption estimation system, carbon dioxide emission estimation program, and electric power consumption estimation program

Abstract

To provide a carbon dioxide emission amount estimation system and a carbon dioxide emission amount estimation program capable of estimating an emission amount of carbon dioxide by an image processing device using a machine learning model, and a power consumption amount estimation system and a power consumption amount estimation program capable of estimating power consumption by the image processing device using the machine learning model.SOLUTION: The carbon dioxide emission amount estimation system identifies a job execution emission amount of carbon dioxide emission amounts by an image processing apparatus due to execution of a job in the image processing apparatus by using a machine learning model for identifying the job execution emission amount (S164), identifies a fixed emission amount of carbon dioxide emission amounts fixedly emitted by the image processing apparatus according to setting in the image processing apparatus without using the machine learning model (S165), and identifies an emission amount by using the job execution emission amount and the fixed emission amount (S166).SELECTED DRAWING: Figure 14

Description

[Technical Field]

[0001] The present invention relates to a carbon dioxide emission amount estimation system and a carbon dioxide emission amount estimation program for estimating the amount of carbon dioxide emitted by an image processing device, and a power consumption amount estimation system and a power consumption amount estimation program for estimating the amount of power consumed by an image processing device. [Background technology]

[0002] BACKGROUND ART Conventionally, carbon dioxide emission amount estimation systems that estimate the amount of carbon dioxide emitted by an image processing device are known (see, for example, Patent Documents 1 and 2). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2006-021414 [Patent Document 2] Japanese Patent Application Laid-Open No. 2009-058749 Summary of the Invention [Problem to be solved by the invention]

[0004] However, conventional carbon dioxide emission amount estimation systems have a problem in that they cannot estimate the amount of carbon dioxide emission by an image processing device using a machine learning model.

[0005] Therefore, the present invention aims to provide a carbon dioxide emission estimation system and a carbon dioxide emission estimation program that can estimate the amount of carbon dioxide emitted by an image processing device using a machine learning model, and a power consumption estimation system and a power consumption estimation program that can estimate the amount of power consumed by an image processing device using a machine learning model. [Means for solving the problem]

[0006] The carbon dioxide emission estimation system of the present invention includes a carbon dioxide emission estimation unit that estimates the amount of carbon dioxide emitted by an image processing device, and the carbon dioxide emission estimation unit identifies, among the emissions, job execution emissions that are due to the execution of a job on the image processing device using a machine learning model for identifying the job execution emissions, and the carbon dioxide emission estimation unit identifies, among the emissions, fixed emissions that are emitted by the image processing device in a fixed manner according to the settings on the image processing device using data for identifying the fixed emissions without using the machine learning model, and the carbon dioxide emission estimation unit identifies the emissions based on the job execution emissions and the fixed emissions.

[0007] With this configuration, the carbon dioxide emission estimation system of the present invention uses a machine learning model to identify the job execution emissions due to job execution on the image processing device, identifies the fixed emissions that are emitted by the image processing device in a fixed manner according to the settings on the image processing device using data without using a machine learning model, and identifies the carbon dioxide emission amount by the image processing device from the job execution emissions and the fixed emissions, so that the carbon dioxide emission amount by the image processing device can be estimated using a machine learning model.

[0008] The power consumption estimation system of the present invention includes a power consumption estimation unit that estimates the amount of power consumed by an image processing device, and the power consumption estimation unit identifies, from the amount of power consumed, a job execution amount that is consumed by executing a job in the image processing device using a machine learning model for identifying the job execution amount, and the power consumption estimation unit identifies, from the amount of power consumed, a fixed amount that is consumed by the image processing device in a fixed manner according to settings in the image processing device using data for identifying the fixed amount without using the machine learning model, and the power consumption estimation unit identifies the amount of power consumed based on the job execution amount and the fixed amount.

[0009] With this configuration, the power consumption estimation system of the present invention uses a machine learning model to identify the job execution consumption due to job execution in the image processing device, identifies the fixed consumption that is consumed by the image processing device in a fixed manner according to the settings in the image processing device using data without using a machine learning model, and identifies the power consumption by the image processing device from the job execution consumption and the fixed consumption, so that the power consumption by the image processing device can be estimated using the machine learning model.

[0010] The carbon dioxide emission estimation program of the present invention causes a computer to implement a carbon dioxide emission estimation unit that estimates the amount of carbon dioxide emitted by an image processing device, and the carbon dioxide emission estimation unit identifies, among the emissions, job execution emissions that are due to the execution of a job on the image processing device using a machine learning model for identifying the job execution emissions, and the carbon dioxide emission estimation unit identifies, among the emissions, fixed emissions that are emitted by the image processing device in accordance with settings on the image processing device using data for identifying the fixed emissions without using the machine learning model, and is characterized in that the carbon dioxide emission estimation unit identifies the emissions based on the job execution emissions and the fixed emissions.

[0011] With this configuration, a computer executing the carbon dioxide emission estimation program of the present invention uses a machine learning model to identify job execution emissions due to job execution in an image processing device, identifies fixed emissions emitted by the image processing device in accordance with the settings in the image processing device using data without using a machine learning model, and identifies the carbon dioxide emission amount by the image processing device from the job execution emissions and the fixed emissions, so that the carbon dioxide emission amount by the image processing device can be estimated using a machine learning model.

[0012] The power consumption estimation program of the present invention causes a computer to implement a power consumption estimation unit that estimates the amount of power consumed by an image processing device, and the power consumption estimation unit identifies, from the amount of power consumed, a job execution amount that is consumed by executing a job in the image processing device using a machine learning model for identifying the job execution amount, and the power consumption estimation unit identifies, from the amount of power consumed, a fixed amount that is consumed by the image processing device in a fixed manner according to settings in the image processing device using data for identifying the fixed amount without using the machine learning model, and the power consumption estimation unit identifies the amount of power consumed based on the job execution amount and the fixed amount.

[0013] With this configuration, a computer executing the power consumption estimation program of the present invention uses a machine learning model to identify job execution consumption due to job execution in an image processing device, identifies fixed consumption that is consumed by the image processing device in a fixed manner according to the settings in the image processing device using data without using a machine learning model, and identifies the power consumption by the image processing device from the job execution consumption and the fixed consumption, so that the power consumption by the image processing device can be estimated using a machine learning model. [Effects of the Invention]

[0014] The carbon dioxide emission estimation system and carbon dioxide emission estimation program of the present invention can estimate the amount of carbon dioxide emission by an image processing device using a machine learning model. Also, the power consumption estimation system and power consumption estimation program of the present invention can estimate the amount of power consumption by an image processing device using a machine learning model. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a block diagram of an example of a system according to a first embodiment of the present invention. [Figure 2]2 is a block diagram of an example of the image processing device shown in FIG. 1 when configured as an MFP. [Figure 3] FIG. 2 is a block diagram of an example of the device management system shown in FIG. 1 when configured with one computer. [Figure 4] FIG. 4 is a diagram illustrating an example of device management information illustrated in FIG. [Figure 5] FIG. 4 is a diagram showing an example of counter data management information shown in FIG. 3. [Figure 6] 4 is a diagram showing an example of setting status management information shown in FIG. 3. FIG. [Figure 7] FIG. 2 is a block diagram of an example of the carbon dioxide emission estimation system shown in FIG. 1 when configured with one computer. [Figure 8] FIG. 8 is a diagram showing an example of carbon dioxide emission history information shown in FIG. [Figure 9] FIG. 8 is a diagram showing an example of data storage location information shown in FIG. 7. [Figure 10] FIG. 8 is a diagram showing an example of default fixed discharge amount information shown in FIG. 7. [Figure 11] 8 is a flowchart of a method for generating the carbon dioxide emission estimation model shown in FIG. 7. [Figure 12] 1 when the device management system collects information about the image processing apparatus. FIG. [Figure 13] 2 is a sequence diagram of the operation of the carbon dioxide emission amount estimation system shown in FIG. 1 when collecting information from an image processing device. FIG. [Figure 14] 8 is a flowchart of the operation of the carbon dioxide emission amount estimation system shown in FIG. 7 when estimating the amount of carbon dioxide emission of the image processing device. [Figure 15] 15 is a flowchart of the job execution discharge amount specifying process shown in FIG. 14. [Figure 16] 15 is a flowchart of the fixed discharge amount specification process shown in FIG. 14. [Figure 17] 2 is a block diagram of an example of a power consumption estimation system included in the system shown in FIG. 1. FIG. [Figure 18] FIG. 10 is a block diagram of an example of a system according to a second embodiment of the present invention. [Figure 19] FIG. 19 is a block diagram of an example of the image processing device shown in FIG. 18 when configured as an MFP. [Figure 20] FIG. 20 is a diagram showing an example of carbon dioxide emission history information shown in FIG. [Figure 21] FIG. 20 is a diagram showing an example of data storage location information shown in FIG. DETAILED DESCRIPTION OF THE INVENTION

[0016] Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0017] (First embodiment) First, the configuration of a system according to a first embodiment of the present invention will be described.

[0018] FIG. 1 is a block diagram of an example of a system 10 according to the present embodiment.

[0019] 1, the system 10 includes an image processing device 20 that processes images. The system 10 may also include at least one other image processing device having a similar configuration to the image processing device 20. The image processing device may be an image forming device such as a dedicated printer or an MFP (Multifunction Peripheral), or may be a dedicated scanner.

[0020] The system 10 includes a device management system 30 that manages image processing devices. The device management system 30 may be configured with a single computer such as a PC (Personal Computer), or may be configured with multiple computers. The device management system 30 may be configured on the same LAN (Local Area Network) as the image processing devices, or may be configured on the cloud.

[0021] The system 10 includes a carbon dioxide emission estimation system 40 that estimates the amount of carbon dioxide emitted by an image processing device. The carbon dioxide emission estimation system 40 may be configured with a single computer such as a PC, or may be configured with multiple computers. The carbon dioxide emission estimation system 40 may be configured on the same LAN as the image processing device, or may be configured on the cloud.

[0022] The system 10 includes a data storage system 50 that stores data (hereinafter referred to as "fixed emission amount data") indicating the amount of carbon dioxide emitted by the image processing device in a fixed manner according to the settings of the image processing device (hereinafter referred to as "fixed emission amount"). The system 10 may also include at least one other data storage system having a configuration similar to that of the data storage system 50. The data storage system may be configured with a single computer such as a PC, or may be configured with multiple computers. The data storage system may be configured on the same LAN as the image processing device, or may be configured on the cloud. The data storage system may be at least one of the image processing device, the device management system 30, and the carbon dioxide emission amount estimation system 40. The fixed emission amount data may be, for example, a document file such as a manual for the image processing device that describes the fixed emission amount, or a file other than a document file.

[0023] FIG. 2 is a block diagram of an example of the image processing device 20 when configured as an MFP.

[0024] As shown in Figure 2, the image processing device 20 is a computer that includes an operation unit 21, which is an operation device such as a button through which various operations are input; a display unit 22, which is a display device such as an LCD (Liquid Crystal Display) that displays various information; a printer 23, which is a printing device that prints images on a recording medium such as paper; a scanner 24, which is a reading device that reads images from a document; a communication unit 25, which is a communication device that communicates with an external device via a network such as a LAN or the Internet, or directly via a wired or wireless connection without using a network; a fax communication unit 26, which is a fax device that communicates with an external facsimile device (not shown) via a communication line such as a public telephone line; a memory unit 27, which is a non-volatile memory device such as a semiconductor memory or an HDD (Hard Disk Drive) that stores various information; and a control unit 28 that controls the entire image processing device 20.

[0025] The printer 23 includes, for example, a photosensitive drum 23a for attaching toner to a recording medium, and a drum heater 23b for evaporating moisture attached to the surface of the photosensitive drum 23a.

[0026] The storage unit 27 can store a device information transmission program 27a for transmitting information about the image processing device 20. The device information transmission program 27a may be installed in the image processing device 20 during the manufacturing stage of the image processing device 20, or may be additionally installed in the image processing device 20 from an external storage medium such as a USB (Universal Serial Bus) memory, or may be additionally installed in the image processing device 20 from a network.

[0027] The storage unit 27 can store counter data 27b that changes according to the operation of the image processing device 20. The counter data 27b may include, for example, the number of monochrome prints performed by the printer 23, the number of color prints performed by the printer 23, the number of monochrome scans performed by the scanner 24, and the number of color scans performed by the scanner 24.

[0028] The storage unit 27 can store setting status information 27c that indicates the setting status of a setting that always discharges a fixed amount of carbon dioxide when the setting is ON (hereinafter referred to as a "fixed emission amount setting"). The fixed emission amount setting may include, for example, an ON / OFF setting of the drum heater 23b and an ON / OFF setting of the sleep mode of the image processing device 20.

[0029] Control unit 28 includes, for example, a CPU (Central Processing Unit), a ROM (Read Only Memory) that stores programs and various data, and a RAM (Random Access Memory) that serves as a memory used as a work area for the CPU of control unit 28. The CPU of control unit 28 executes programs stored in storage unit 27 or the ROM of control unit 28.

[0030] The control unit 28 executes the device information transmission program 27a to realize a device information transmission unit 28a that transmits information about the image processing device 20.

[0031] FIG. 3 is a block diagram of an example of a device management system 30 configured by one computer.

[0032] As shown in FIG. 3, the device management system 30 includes an operation unit 31, which is an operation device such as a keyboard or mouse through which various operations are input; a display unit 32, which is a display device such as an LCD that displays various information; a communication unit 33, which is a communication device that communicates with external devices via a network such as a LAN or the Internet, or directly via a wired or wireless connection without using a network; a memory unit 34, which is a non-volatile memory device such as a semiconductor memory or HDD that stores various information; and a control unit 35 that controls the entire device management system 30.

[0033] The storage unit 34 can store a device management program 34a for managing the image processing device. The device management program 34a may be installed in the device management system 30 during the manufacturing stage of the device management system 30, or may be additionally installed in the device management system 30 from an external storage medium such as a USB memory, or may be additionally installed in the device management system 30 from a network.

[0034] The storage unit 34 can store device management information 34b for managing information such as the model of the image processing device acquired from the image processing device.

[0035] FIG. 4 is a diagram showing an example of the device management information 34b.

[0036] As shown in Fig. 4, the device management information 34b includes, for each image processing device, a device ID as identification information for the image processing device and the model of the image processing device. The device management information 34b shown in Fig. 4 is drawn with some information omitted.

[0037] As shown in FIG. 3, the storage unit 34 can store counter data management information 34c for managing counter data acquired from the image processing device.

[0038] FIG. 5 is a diagram showing an example of the counter data management information 34c.

[0039] As shown in Fig. 5, the counter data management information 34c includes the device ID of the image processing device and the counter data of the image processing device for each image processing device. Some information is omitted from the counter data management information 34c shown in Fig. 5.

[0040] As shown in FIG. 3, the storage unit 34 can store setting status management information 34d for managing setting status information acquired from the image processing device.

[0041] FIG. 6 is a diagram showing an example of the setting status management information 34d.

[0042] As shown in Fig. 6, the setting status management information 34d includes, for each image processing device, the device ID of the image processing device and the setting status information of the image processing device. The setting status information 34d shown in Fig. 6 is drawn with some information omitted.

[0043] 3 includes, for example, a CPU, a ROM that stores programs and various data, and a RAM that serves as a memory used as a work area for the CPU of the control unit 35. The CPU of the control unit 35 executes programs stored in the storage unit 34 or the ROM of the control unit 35.

[0044] The control unit 35 executes the device management program 34a to implement a device management unit 35a that manages the image processing device.

[0045] FIG. 7 is a block diagram of an example of a carbon dioxide emission estimation system 40 configured by a single computer.

[0046] As shown in Figure 7, carbon dioxide emission estimation system 40 includes an operation unit 41, which is an operation device such as a keyboard or mouse through which various operations are input; a display unit 42, which is a display device such as an LCD that displays various information; a communication unit 43, which is a communication device that communicates with external devices via a network such as a LAN or the Internet, or directly via a wired or wireless connection without using a network; a memory unit 44, which is a non-volatile memory device such as a semiconductor memory or HDD that stores various information; and a control unit 45 that controls the entire carbon dioxide emission estimation system 40.

[0047] The storage unit 44 can store a carbon dioxide emission amount estimation program 44a for estimating the amount of carbon dioxide emitted by the image processing device. The carbon dioxide emission amount estimation program 44a may be installed in the carbon dioxide emission amount estimation system 40 during the manufacturing stage of the carbon dioxide emission amount estimation system 40, or may be additionally installed in the carbon dioxide emission amount estimation system 40 from an external storage medium such as a USB memory, or may be additionally installed in the carbon dioxide emission amount estimation system 40 from a network.

[0048] The storage unit 44 can store a carbon dioxide emission estimation model 44b as a machine learning model in which the counter data is an explanatory variable and the amount of carbon dioxide emitted (hereinafter referred to as "job execution emission") due to the execution of a job that causes a change in the counter data (hereinafter referred to as "counter change causing job") among the operations of the image processing device is an objective variable. The counter change causing job includes, for example, a monochrome printing job, a color printing job, a monochrome scanning job, and a color scanning job.

[0049] The storage unit 44 can store device management information 44c that manages information such as the model of the image processing device, etc. The configuration of the device management information 44c is similar to the configuration of the device management information 34b (see FIG. 4).

[0050] The storage unit 44 can store counter data management information 44d for managing counter data of the image processing device. The configuration of the counter data management information 44d is similar to the configuration of the counter data management information 34c (see FIG. 5).

[0051] The storage unit 44 can store setting status management information 44e of the image processing device. The configuration of the setting status management information 44e is similar to the configuration of the setting status management information 34d (see FIG. 6).

[0052] The storage unit 44 can store carbon dioxide emission history information 44f that indicates the history of the amount of carbon dioxide emitted by the image processing device.

[0053] FIG. 8 is a diagram showing an example of the carbon dioxide emission history information 44f.

[0054] As shown in Fig. 8, the carbon dioxide emission history information 44f includes, for each history, the date and time when the emission amount of the subject of the history was calculated, the device ID of the image processing device of the subject of the history, the emission amount of the subject of the history, the counter data at the time when the counter data was used to calculate the emission amount of the subject of the history, and the setting status information at the time when the counter data was used to calculate the emission amount of the subject of the history. The carbon dioxide emission history information 44f shown in Fig. 8 is drawn with some information omitted.

[0055] As shown in FIG. 7, the storage unit 44 can store data storage location information 44g that indicates the location where the fixed discharge amount data is stored.

[0056] FIG. 9 is a diagram showing an example of the data storage location information 44g.

[0057] 9, the data storage location information 44g includes, for each combination of fixed discharge amount setting and model, the fixed discharge amount setting that is the target of the fixed discharge amount data, the model of the image processing device that is the target of the fixed discharge amount data, and the location where the fixed discharge amount data is stored. The data storage location information 44g shown in FIG. 9 is drawn with some information omitted.

[0058] As shown in FIG. 7, the storage unit 44 can store default fixed discharge amount information 44h that indicates a default fixed discharge amount per unit time.

[0059] FIG. 10 is a diagram showing an example of the default fixed discharge amount information 44h.

[0060] As shown in Fig. 10, the default fixed discharge amount information 44h includes a fixed discharge amount setting and a fixed discharge amount per unit time for each fixed discharge amount setting. The default fixed discharge amount information 44h shown in Fig. 10 is drawn with some information omitted.

[0061] 7 includes, for example, a CPU, a ROM that stores programs and various data, and a RAM that serves as a memory used as a work area for the CPU of the control unit 45. The CPU of the control unit 45 executes programs stored in the storage unit 44 or the ROM of the control unit 45.

[0062] The control unit 45 executes the carbon dioxide emission amount estimation program 44a to realize a carbon dioxide emission amount estimation unit 45a that estimates the amount of carbon dioxide emitted by the image processing device. The carbon dioxide emission amount estimation unit 45a can estimate the amount of carbon dioxide emitted by job execution in the image processing device, for example, by using the carbon dioxide emission amount estimation model 44b.

[0063] Next, a method for generating the carbon dioxide emission amount estimation model 44b will be described.

[0064] FIG. 11 is a flowchart of a method for generating the carbon dioxide emission estimation model 44b.

[0065] 11, a person who generates a carbon dioxide emission estimation model (hereinafter referred to as "model generator") prepares training data in which the counter data of an image processing device in which all fixed emission settings are OFF is used as an explanatory variable, and the job execution emission amount of this image processing device is used as a target variable, i.e., correct answer data (S101). Here, the image processing device in the correct answer data may be only an image processing device of the same model as image processing device 20, or may include an image processing device of a different model from image processing device 20.

[0066] When the step of S101 is completed, the model generator generates a carbon dioxide emission estimation model (S102) using the training data prepared in S101. Therefore, the carbon dioxide emission estimation model generated in S102 estimates the job execution emission amount in the image processing device when all fixed emission settings are OFF.

[0067] When the step of S102 is completed, the model generator stores the carbon dioxide emission amount estimation model generated in S102 in the carbon dioxide emission amount estimation system 40 as the carbon dioxide emission amount estimation model 44b (S103), and ends the operation shown in FIG. 11.

[0068] The operation of the system 10 will now be described.

[0069] First, the operation of the system 10 when the device management system 30 collects information about the image processing device 20 will be described.

[0070] FIG. 12 is a sequence diagram of the operation of the system 10 when the device management system 30 collects information about the image processing device 20. In FIG.

[0071] As shown in FIG. 12, the device management unit 35a of the device management system 30 requests information about the image processing device 20 from the image processing device 20 at specific timing, such as periodically (S121).

[0072] When the device information sending unit 28a of the image processing device 20 receives the request in S121, it sends the device ID of the image processing device 20, the model of the image processing device 20, counter data that is the same as the counter data 27b of the image processing device 20, and setting status information that is the same as the setting status information 27c of the image processing device 20 to the device management system 30 (S122).

[0073] When the device management unit 35a of the device management system 30 receives the information transmitted in S122, it stores the received information in the device management information 34b, the counter data management information 34c, and the setting status management information 34d (S123). Specifically, the device management unit 35a associates the model transmitted in S122 with the device ID transmitted in S122 and stores the associated model in the device management information 34b. The device management unit 35a associates the counter data transmitted in S122 with the device ID transmitted in S122 and stores the associated model in the counter data management information 34c. The device management unit 35a associates the setting status information transmitted in S122 with the device ID transmitted in S122 and stores the associated model in the setting status management information 34d.

[0074] Next, the operation of the carbon dioxide emission estimation system 10 when the system 40 collects information from the image processing device 20 will be described.

[0075] FIG. 13 is a sequence diagram of the operation of the carbon dioxide emission estimation system 40 when the system 10 collects information from the image processing device 20. In FIG.

[0076] The carbon dioxide emission amount estimation unit 45a of the carbon dioxide emission amount estimation system 40 requests information about the image processing device 20 from the device management system 30 at specific times, such as periodically, as shown in Fig. 13 (S141). The request in S141 includes the device ID of the image processing device 20.

[0077] When the device management unit 35a of the device management system 30 receives the request in S141, it transmits the device ID of the image processing device 20, the model of the image processing device 20, the counter data of the image processing device 20, and the setting status information of the image processing device 20 to the carbon dioxide emission estimation system 40 (S142). In S142, the device management unit 35a identifies the model of the image processing device 20 based on the device ID included in the request in S141 and the device management information 34b. In S142, the device management unit 35a identifies the counter data of the image processing device 20 based on the device ID included in the request in S141 and the counter data management information 34c. In S142, the device management unit 35a identifies the setting status information of the image processing device 20 based on the device ID included in the request in S141 and the setting status management information 34d.

[0078] When the carbon dioxide emission estimation unit 45a of the carbon dioxide emission estimation system 40 receives the information transmitted in S142, it stores the received information in the device management information 44c, the counter data management information 44d, and the setting status management information 44e (S143). Specifically, the carbon dioxide emission estimation unit 45a associates the model transmitted in S142 with the device ID transmitted in S142 and stores it in the device management information 44c. The carbon dioxide emission estimation unit 45a associates the counter data transmitted in S142 with the device ID transmitted in S142 and stores it in the counter data management information 44d. The carbon dioxide emission estimation unit 45a associates the setting status information transmitted in S142 with the device ID transmitted in S142 and stores it in the setting status management information 44e.

[0079] Next, the operation of the carbon dioxide emission amount estimation system 40 when estimating the carbon dioxide emission amount of the image processing device 20 will be described.

[0080] FIG. 14 is a flowchart of the operation of the carbon dioxide emission amount estimation system 40 when estimating the amount of carbon dioxide emission of the image processing device 20.

[0081] The carbon dioxide emission amount estimation unit 45a of the carbon dioxide emission amount estimation system 40 executes the operation shown in FIG. 14 at specific timings, such as periodic timings.

[0082] As shown in FIG. 14, the carbon dioxide emission amount estimation unit 45a identifies the current date and time (S161).

[0083] When the process of S161 is completed, the carbon dioxide emission amount estimation unit 45a identifies the latest history associated with the device ID of the image processing device 20 in the carbon dioxide emission history information 44f (S162).

[0084] The carbon dioxide emission amount estimation unit 45a calculates the elapsed time from the date and time in the latest history identified in S162 to the current date and time by subtracting the date and time in the latest history identified in S162 from the current date and time identified in S161 (S163).

[0085] When the process of S163 is completed, the carbon dioxide emission amount estimation unit 45a executes a job execution emission amount specification process (see FIG. 15) for specifying the emission amount by job execution in the image processing device 20 (S164).

[0086] FIG. 15 is a flowchart of the job execution discharge amount specifying process shown in FIG.

[0087] As shown in FIG. 15, the carbon dioxide emission estimation unit 45a calculates the job execution emission amount based on the current counter data by inputting the counter data of the image processing device 20 indicated in the counter data management information 44d into the carbon dioxide emission estimation model 44b (S181).

[0088] When the processing of S181 is completed, the carbon dioxide emission estimation unit 45a calculates the job execution emission amount based on the previous counter data by inputting the counter data shown in the history identified in S162 into the carbon dioxide emission estimation model 44b (S182).

[0089] When the processing of S182 is completed, the carbon dioxide emission amount estimation unit 45a calculates the job execution emission amount for the elapsed time calculated in S163 by subtracting the emission amount calculated in S182 from the emission amount calculated in S181 (S183), and terminates the job execution emission amount determination processing shown in Figure 15.

[0090] As shown in FIG. 14, when the job execution emission amount determination process of S164 is completed, the carbon dioxide emission amount estimation unit 45a executes a fixed emission amount determination process (see FIG. 16) to determine a fixed emission amount based on the setting status of the fixed emission amount setting (S165).

[0091] FIG. 16 is a flowchart of the fixed discharge amount specification process shown in FIG.

[0092] As shown in FIG. 16, the carbon dioxide emission amount estimation unit 45a determines whether or not any fixed emission amount setting of the image processing device 20 is ON based on the setting status management information 44e (S201).

[0093] If the carbon dioxide emission amount estimation unit 45a determines in S201 that there is no fixed emission amount setting that is ON for the image processing device 20, it specifies the fixed emission amount for the entire image processing device 20 as zero (S202).

[0094] When the carbon dioxide emission estimation unit 45a determines in S201 that there is a fixed emission setting that is ON in the image processing device 20, it targets only one fixed emission setting that has not yet been targeted in this fixed emission amount identification process among the fixed emission settings that it determined to be ON in S201 (S203).

[0095] When the processing of S203 is completed, the carbon dioxide emission amount estimation unit 45a attempts to acquire fixed emission amount data from the location associated in the data storage location information 44g with the fixed emission amount setting targeted in S203 and the model associated in the device management information 44c with the device ID of the image processing device 20 (S204).

[0096] When the process of S204 is completed, the carbon dioxide emission amount estimation unit 45a determines whether or not the acquisition of the fixed emission amount data in S204 was successful (S205).

[0097] When the carbon dioxide emission amount estimation unit 45a determines in S205 that the fixed emission amount data has been successfully acquired, it attempts to extract the fixed emission amount per unit time from the fixed emission amount data acquired in S204 (S206).

[0098] When the process of S206 is completed, the carbon dioxide emission amount estimation unit 45a determines whether or not the extraction of the fixed emission amount per unit time in S206 was successful (S207).

[0099] When the carbon dioxide emission estimation unit 45a determines in S207 that the extraction of the fixed emission amount per unit time has been successful, it identifies the product of the fixed emission amount per unit time extracted in S206 and the elapsed time calculated in S163 as the fixed emission amount for the current target fixed emission setting (S208).

[0100] If the carbon dioxide emission estimation unit 45a determines in S205 that the acquisition of fixed emission amount data was not successful, or determines in S207 that the extraction of the fixed emission amount per unit time was not successful, it identifies the product of the default fixed emission amount per unit time, which is associated with the current target fixed emission amount setting in the default fixed emission amount information 44h, and the elapsed time calculated in S163, as the fixed emission amount for the current target fixed emission amount setting (S209).

[0101] When the processing of S208 or S209 is completed, the carbon dioxide emission amount estimation unit 45a determines whether or not there are any fixed emission amount settings that have not yet been targeted in the current fixed emission amount identification process among the fixed emission amount settings that were determined to be ON in S201 (S210).

[0102] If the carbon dioxide emission estimation unit 45a determines in S210 that there is a fixed emission amount setting that has not yet been targeted in the current fixed emission amount identification process among the fixed emission amount settings that have been determined to be ON in S201, the carbon dioxide emission amount estimation unit 45a executes the process of S203.

[0103] If the carbon dioxide emission estimation unit 45a determines in S210 that there are no fixed emission settings that have not yet been targeted in the current fixed emission amount identification process among the fixed emission amount settings that have been determined to be ON in S201, it determines the sum of all fixed emission amounts identified in the current fixed emission amount identification process as the fixed emission amount for the entire image processing device 20 (S211).

[0104] When the process of S202 or S211 is completed, the carbon dioxide emission amount estimation unit 45a ends the fixed emission amount specification process shown in FIG.

[0105] As shown in Figure 14, when the fixed emission amount determination process of S165 is completed, the carbon dioxide emission estimation unit 45a determines the sum of the job execution emission amount determined in S164 and the fixed emission amount determined in S165 as the carbon dioxide emission amount by the image processing device 20 for the elapsed time calculated in S163 (S166), and terminates the operation shown in Figure 14.

[0106] As described above, the carbon dioxide emission estimation system 40 determines the job execution emissions due to job execution in the image processing device using the carbon dioxide emission estimation model 44b as a machine learning model (S164), determines the fixed emissions that are emitted by the image processing device in a fixed manner according to the settings in the image processing device using the fixed emission data without using the carbon dioxide emission estimation model 44b (S165), and determines the carbon dioxide emissions by the image processing device from the job execution emissions and the fixed emission amounts (S166), so that the carbon dioxide emissions by the image processing device can be estimated using the machine learning model.

[0107] When the explanatory variables of the carbon dioxide emission estimation model 44b include the setting status of the fixed emission amount setting, and the target variable of the carbon dioxide emission estimation model 44b is the carbon dioxide emission amount including the fixed emission amount, the carbon dioxide emission estimation system 40 can estimate the carbon dioxide emission amount including the fixed emission amount using the carbon dioxide emission estimation model 44b without using the fixed emission amount data. However, because the carbon dioxide emission estimation system 40 identifies the fixed emission amount using the fixed emission amount data without using the carbon dioxide emission estimation model 44b, the number of explanatory variables of the carbon dioxide emission estimation model 44b can be reduced. Therefore, the carbon dioxide emission estimation system 40 can shorten the calculation time when generating the carbon dioxide emission estimation model 44b and can improve the accuracy of the emission amount estimation by the carbon dioxide emission estimation model 44b.

[0108] Even the amount of carbon dioxide emitted corresponding to the same fixed emission setting may differ depending on the model of image processing device. For example, there will be a difference in the amount of carbon dioxide emitted by the drum heater when the drum heater is turned on between a model equipped with a photosensitive drum whose maximum printable size is A3 size and a model equipped with a photosensitive drum whose maximum printable size is A4 size. Because the carbon dioxide emission estimation system 40 determines the fixed emission amount using the fixed emission data without using the carbon dioxide emission estimation model 44b, there is no need to use different carbon dioxide emission estimation models for each model of image processing device.

[0109] In this embodiment, the system 10 has the device management system 30 acquire information about the image processing device 20 from the image processing device 20 by requesting the information from the image processing device 20, but the device management system 30 may also acquire information about the image processing device 20 from the image processing device 20 by the image processing device 20 sending the information to the device management system 30 without receiving a request from the device management system 30.

[0110] In this embodiment of the system 10, the carbon dioxide emission estimation system 40 requests information about the image processing device 20 from the device management system 30, thereby causing the carbon dioxide emission estimation system 40 to obtain information about the image processing device 20 from the device management system 30. However, the carbon dioxide emission estimation system 40 may also obtain information about the image processing device 20 from the device management system 30 by transmitting the information about the image processing device 20 to the carbon dioxide emission estimation system 40 without receiving a request from the device management system 30.

[0111] In the present embodiment, the carbon dioxide emission amount estimation system 40 of the system 10 acquires information about the image processing device 20 from the device management system 30, but the carbon dioxide emission amount estimation system 40 may also acquire information about the image processing device 20 directly from the image processing device 20. In the system 10, the carbon dioxide emission amount estimation system 40 may acquire information about the image processing device 20 from the image processing device 20 by requesting the information about the image processing device 20 from the image processing device 20, or the carbon dioxide emission amount estimation system 40 may acquire information about the image processing device 20 from the image processing device 20 by the image processing device 20 transmitting the information about the image processing device 20 to the carbon dioxide emission amount estimation system 40 without receiving a request from the carbon dioxide emission amount estimation system 40.

[0112] The above describes a system 10 equipped with a carbon dioxide emission estimation system 40 that estimates the amount of carbon dioxide emitted by an image processing device. Here, the amount of carbon dioxide emitted by an image processing device is proportional to the amount of power consumed by the image processing device. Therefore, the above-described system 10 can also be configured as a system equipped with a power consumption estimation system that estimates the amount of power consumed by the image processing device.

[0113] FIG. 17 is a block diagram of an example of a power consumption estimation system 60 that may be included in the system 10.

[0114] The power consumption estimation system 60 shown in FIG. 17 includes a power consumption estimation program 64a for estimating the amount of power consumed by the image processing device, a power consumption estimation model 64b as a machine learning model in which counter data is an explanatory variable and the amount of power consumed by executing a job causing a change in the counter (hereinafter referred to as "job execution consumption") is an objective variable, power consumption history information 64f showing the history of power consumption of the image processing device, and a fixed amount of power consumed by the image processing device according to settings in the image processing device (hereinafter referred to as "fixed consumption"). 7), and default fixed consumption amount information 64h (see FIG. 7). The carbon dioxide emission amount estimation program 44a (see FIG. 7), carbon dioxide emission estimation model 44b (see FIG. 7), carbon dioxide emission history information 44f (see FIG. 7), data storage location information 44g (see FIG. 7), and default fixed emission amount information 44h (see FIG. 7) are replaced with data storage location information 64g indicating the location where data indicating the amount of consumption per unit time (hereinafter referred to as "fixed consumption amount data") is stored, and default fixed consumption amount information 64h indicating the default fixed consumption amount per unit time. This is the same as the configuration of the carbon dioxide emission amount estimation system 40 shown in FIG. 7. The power consumption history information 64f includes, for each history, the date and time when the consumption of the target of the history was calculated, the device ID of the image processing device of the target of the history, the consumption of the target of the history, the counter data used to calculate the consumption of the target of the history, and setting status information used to calculate the consumption of the target of the history. The data storage location information 64g includes, for each combination of fixed consumption setting and model, a setting for the fixed consumption data that always consumes a fixed amount of power when the setting is ON (hereinafter referred to as the "fixed consumption setting"), the model of the image processing device for which the fixed consumption data is targeted, and the location where the fixed consumption data is stored. The default fixed consumption information 64h includes, for each fixed consumption setting, the fixed consumption per unit time. The control unit 45 shown in FIG. 17 executes a power consumption estimation program 64a to implement a power consumption estimation unit 65a that estimates the amount of power consumed by the image processing device.

[0115] The difference between the carbon dioxide emission estimation system 40 shown in Fig. 7 and the power consumption estimation system 60 shown in Fig. 17 is that the carbon dioxide emission estimation system 40 shown in Fig. 7 executes processing related to the amount of carbon dioxide emission by the image processing device, while the power consumption estimation system 60 shown in Fig. 17 executes processing related to the amount of power consumed by the image processing device. Therefore, the power consumption estimation system 60 shown in Fig. 17 can achieve the same effects as the carbon dioxide emission estimation system 40 shown in Fig. 7. For example, the power consumption estimation system 60 shown in Fig. 17 specifies the job execution consumption due to job execution in the image processing device using a power consumption estimation model 64b as a machine learning model, specifies fixed consumption that is fixedly discharged by the image processing device according to the settings of the image processing device using fixed consumption data without using the power consumption estimation model 64b, and specifies the amount of power consumed by the image processing device from the job execution consumption and the fixed consumption. Therefore, the power consumption by the image processing device can be estimated using the machine learning model.

[0116] (Second embodiment) First, the configuration of a system according to the second embodiment of the present invention will be described.

[0117] Among the components of the system according to this embodiment, those components that are similar to those of the system 10 according to the first embodiment (see FIG. 1) are given the same reference numerals as those of the system 10, and detailed explanations thereof will be omitted.

[0118] FIG. 18 is a block diagram of an example of a system 310 according to this embodiment.

[0119] 18, the configuration of system 310 is the same as that of system 10, except that system 10 does not include device management system 30 (see FIG. 1) and carbon dioxide emission estimation system 40 (see FIG. 1), and instead includes image processing device 320 that processes images instead of image processing devices including image processing device 20 (see FIG. 1). Image processing device 320 may be an image forming device such as a dedicated printer or MFP, or may be a dedicated scanner.

[0120] FIG. 19 is a block diagram of an example of the image processing device 320 when configured as an MFP.

[0121] As shown in FIG. 19 , the configuration of the image processing device 320 is the same as that of the image processing device 20, except that the image processing device 20 includes a carbon dioxide emission estimation model 327d as a machine learning model in which counter data is an explanatory variable and job execution emission amount is a target variable, carbon dioxide emission history information 327e indicating the history of carbon dioxide emission amounts of the image processing device 320, data storage location information 327f indicating the location where fixed emission data is stored, and default fixed emission information 327g indicating the default fixed emission amount per unit time, and the image processing device 20 includes a carbon dioxide emission estimation program 327a for estimating the amount of carbon dioxide emission emitted by the image processing device 320 instead of the device information transmission program 27a (see FIG. 2 ). The control unit 28 executes the carbon dioxide emission estimation program 327a to realize a carbon dioxide emission estimation unit 328a that estimates the amount of carbon dioxide emission emitted by the image processing device 320. The image processing device 320 constitutes a carbon dioxide emission estimation system that estimates the amount of carbon dioxide emission by the image processing device 320 itself.

[0122] FIG. 20 is a diagram showing an example of the carbon dioxide emission history information 327d.

[0123] As shown in Figure 20, the carbon dioxide emission history information 327d includes, for each history, the date and time when the emission amount of the subject of the history was calculated, the emission amount of the subject of the history, the counter data at the time when it was used to calculate the emission amount of the subject of the history, and the setting status information at the time when it was used to calculate the emission amount of the subject of the history. The carbon dioxide emission history information 327d shown in Figure 20 is drawn with some information omitted.

[0124] FIG. 21 is a diagram showing an example of the data storage location information 327f.

[0125] As shown in Fig. 21, the data storage location information 327f includes, for each fixed discharge amount setting, the fixed discharge amount setting for which the fixed discharge amount data is to be stored and the location where the fixed discharge amount data is to be stored. The data storage location information 327f shown in Fig. 21 is drawn with some information omitted.

[0126] The configuration of the default fixed discharge amount information 327g is similar to the configuration of the default fixed discharge amount information 44h (see FIG. 10).

[0127] Next, the operation of the image processing device 320 when estimating its own carbon dioxide emission amount will be described.

[0128] When estimating the amount of carbon dioxide emission of the image processing device 320 itself, the carbon dioxide emission amount estimation unit 328a executes the same operations as those shown in FIGS. 14 to 16. However, in S162, the carbon dioxide emission amount estimation unit 328a identifies the latest history in the carbon dioxide emission history information 327d. In S181, the carbon dioxide emission amount estimation unit 328a inputs the same counter data as the counter data 27b into the carbon dioxide emission estimation model 327d to calculate the amount of emission due to job execution based on the current counter data. In S201, the carbon dioxide emission amount estimation unit 328a determines, based on the setting status information 27c, whether any fixed emission setting of the image processing device 320 is ON. In S204, the carbon dioxide emission amount estimation unit 328a attempts to acquire fixed emission data from a location associated in the data storage location information 327f with the fixed emission setting selected in S203. In addition, in S209, the carbon dioxide emission amount estimation unit 328a identifies the product of the default fixed emission amount per unit time, which is associated with the current target fixed emission setting in the default fixed emission amount information 327g, and the elapsed time calculated in S163, as the fixed emission amount for the current target fixed emission amount setting.

[0129] The above describes an image processing device 320 that estimates the amount of carbon dioxide emitted by the image processing device 320 itself. Here, the amount of carbon dioxide emitted by the image processing device is proportional to the amount of power consumed by the image processing device. Therefore, the image processing device 320 described above can also be configured as a power consumption estimation system that estimates the amount of power consumed by the image processing device 320 itself. [Explanation of symbols]

[0130] 20 Image processing device 40 Carbon dioxide emissions estimation system (computer) 44a Carbon dioxide emissions estimation program 44b Carbon dioxide emissions estimation model (machine learning model) 45a Carbon dioxide emissions estimation section 60 Power consumption estimation system (computer) 64a Power consumption estimation program 64b Power consumption prediction model (machine learning model) 65a Power consumption estimation section 320 Image processing device (carbon dioxide emission estimation system, power consumption estimation system, computer) 327a Carbon dioxide emissions estimation program 327d Carbon dioxide emissions estimation model (machine learning model) 328a Carbon dioxide emissions estimation section

Claims

1. a carbon dioxide emission estimation unit that estimates the amount of carbon dioxide emitted by the image processing device; the carbon dioxide emission amount estimation unit identifies a job execution emission amount resulting from job execution in the image processing device, among the emission amounts, by using a machine learning model for identifying the job execution emission amount; the carbon dioxide emission estimation unit identifies, among the emissions, fixed emissions that are fixedly emitted by the image processing device in accordance with settings in the image processing device, by using data for identifying the fixed emissions, without using the machine learning model; The carbon dioxide emission amount estimation system is characterized in that the carbon dioxide emission amount estimation unit specifies the emission amount based on the job execution emission amount and the fixed emission amount.

2. a power consumption estimation unit that estimates the amount of power consumed by the image processing device; the power consumption estimation unit identifies a job execution consumption amount resulting from execution of a job in the image processing device, among the consumption amounts, by using a machine learning model for identifying the job execution consumption amount; the power consumption estimation unit identifies a fixed consumption amount, which is a fixed consumption amount consumed by the image processing device in accordance with a setting in the image processing device, from the consumption amount by using data for identifying the fixed consumption amount without using the machine learning model; The power consumption estimation system is characterized in that the power consumption estimation unit specifies the consumption based on the job execution consumption and the fixed consumption.

3. A carbon dioxide emission estimation unit that estimates the amount of carbon dioxide emitted by the image processing device is implemented in a computer; the carbon dioxide emission amount estimation unit identifies a job execution emission amount resulting from job execution in the image processing device, among the emission amounts, by using a machine learning model for identifying the job execution emission amount; the carbon dioxide emission estimation unit identifies, among the emissions, fixed emissions that are fixedly emitted by the image processing device in accordance with settings in the image processing device, by using data for identifying the fixed emissions, without using the machine learning model; The carbon dioxide emission amount estimation unit specifies the emission amount based on the job execution emission amount and the fixed emission amount.

4. A power consumption estimation unit that estimates the amount of power consumed by the image processing device is implemented in a computer, the power consumption estimation unit identifies a job execution consumption amount resulting from execution of a job in the image processing device, among the consumption amounts, by using a machine learning model for identifying the job execution consumption amount; the power consumption estimation unit identifies a fixed consumption amount, which is a fixed consumption amount consumed by the image processing device in accordance with a setting in the image processing device, from the consumption amount by using data for identifying the fixed consumption amount without using the machine learning model; The power consumption estimation program is characterized in that the power consumption estimation unit specifies the consumption based on the job execution consumption and the fixed consumption.

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