Information processing device, image forming system, and program

Abstract

To enable implementation of an appropriate measure to prolong the life of an image formation device while reflecting its actual usage.SOLUTION: In a case of processing information indicating an image information device that performs image formation on media or a state of a unit mounted on the image information device, an information processing device collects device state information indicating a state of the image formation device, life information of the image formation device or the unit after the image formation device has entered a life-prolongation mode where its performance is made lower than that in a normal mode, and control parameters of the image formation device or the unit in the life-prolongation mode as learning information. Then, a learning model for extending the life is generated on the basis of the collected learning information.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an information processing apparatus, an image forming system, and a program, and more particularly to an information processing apparatus, an image forming system, and a program for taking appropriate measures to extend the life of equipment such as an image forming apparatus. [Background technology]

[0002] Conventionally, when operating equipment such as image forming devices, life extension processes and parameter adjustment processes are performed to change the control depending on the state of the equipment in order to extend the machine's lifespan and to avoid downtime until maintenance is performed in the event of a malfunction or abnormality. In this case, the diagnosis of the fault condition and the prediction of the abnormality are performed using the status data collected from the device. The diagnosis and prediction of the abnormality using this status data can be performed either inside the device or remotely by the server that manages the device.

[0003] When developing these devices, appropriate life extension and adjustment processes are generally prepared in advance. For example, when the total number of printed sheets in an image forming device exceeds a specified number, the paper feed speed may be slowed down to avoid problems due to deterioration and to extend the device's lifespan.

[0004] Patent Document 1 describes a technology for determining whether an image forming device has failed and implementing life-extending measures. In this technology, the image forming device transmits the detection results of a sensor within the image forming device to a maintenance server, and the maintenance server determines whether there has been a failure and notifies the image forming device of life-extending measures. Examples of life-extending measures include slowing down the printing speed, reducing the print density, and increasing the margins. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2012-242763 Summary of the Invention [Problem to be solved by the invention]

[0006] As described in Patent Document 1, the content of the conventional life extension process is selected by the user or a worker who performs maintenance and inspection from a plurality of processes prepared at the development stage of the equipment. However, with devices such as image forming devices, actual usage patterns vary widely, and it is difficult to cover all usage patterns in the market during the development stage. Therefore, predetermined life extension processes may not be sufficient to extend the life of the device.

[0007] In recent years, the number of control parameters for devices such as image forming devices has been increasing as they have become faster and more multifunctional. As a result, the number of parameters that can be tuned to perform life extension processing has increased, making it difficult to perform appropriate life extension processing that matches the machine state.

[0008] An object of the present invention is to provide an information processing device, an image forming system, and a program that enable appropriate life extension measures that reflect actual usage conditions. [Means for solving the problem]

[0009] The information processing device of the present invention is an information processing device that processes information indicating the status of an image forming device that forms an image on a medium or a unit attached to the image forming device, and the information processing device processes device status information indicating the status of the image forming device and information indicating whether the image forming device is in a mode other than a normal mode. Image formation with constraints on control parameters The image forming apparatus includes a collection unit that collects, as learning information, life information of the image forming device or unit after it has entered a life extension mode with reduced capacity and control parameters of the image forming device or unit in the life extension mode, and a generation unit that generates a learning model for life extension based on the learning information collected by the collection unit.

[0010] Furthermore, the image forming system of the present invention is an image forming system in which the above-mentioned information processing device is used as a server, and a learning model generated by a generation unit of the server is distributed to the image forming device. Here, the image forming device includes an acquisition unit that acquires a learning model distributed from a server, and a control unit that adjusts the control parameters of the image forming device by executing the learning model acquired by the acquisition unit.

[0011] In addition, the program of the present invention is a program that provides a computer with information indicating the status of an image forming device that forms an image on a medium or a unit attached to that image forming device, and executes a procedure to obtain instructions to extend the life of the image forming device. The program of the present invention then receives device status information indicating the status of the image forming device and a program for receiving a program for receiving a program when the image forming device is in a mode other than the normal mode. Image formation with restrictions on control parameters This is a program that causes a computer to execute a collection procedure that collects, as learning information, life information of an image forming device or unit after it has entered a life extension mode with reduced capacity and control parameters of the image forming device or unit in the life extension mode, and a learning model generation procedure that generates a learning model for life extension based on the learning information collected by the collection procedure. [Effects of the Invention]

[0012] According to the present invention, it is possible to optimally tune control parameters according to the actual state of the image forming apparatus, thereby achieving maximum life extension. Specifically, by learning a control model, it is possible to select and apply optimal control parameters that are tailored to the past usage state and individual variations of each image forming apparatus. This reduces downtime during which the image forming apparatus becomes unusable, while also reducing the number of visits by service personnel for maintenance and inspection. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a diagram showing a configuration of an image forming apparatus according to an embodiment of the present invention; [Figure 2]1 is a block diagram showing an example of the configuration of an image forming system according to an embodiment of the present invention; [Figure 3] FIG. 10 is a diagram illustrating an example of processing performed by an edge system and a server according to an embodiment of the present invention. [Figure 4] 1 is a flowchart showing a processing flow according to an embodiment of the present invention. [Figure 5] FIG. 10 is a diagram showing an example of correspondence between control parameters and device status information according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0014] An embodiment of the present invention (hereinafter referred to as "this example") will be described below with reference to the accompanying drawings.

[0015] [Configuration example of image forming device] Fig. 1 shows the configuration of an image forming apparatus G in this example. The image forming apparatus G shown in Fig. 1 is a device called a multifunction peripheral (MFP) that has a copy function of optically reading an original and forming a duplicate image on paper, and a print function of receiving print data from an external device such as a personal computer and forming and outputting the corresponding image on paper.

[0016] As shown in FIG. 1, the image forming apparatus G includes a print controller g1, a paper feed unit g2, a main body unit g3, and a post-processing device g4. The print controller g1 receives PDL (Page Description Language) data from a terminal on the network, and rasterizes the received PDL data to generate image data in bitmap format. The print controller g1 generates image data for each of the colors C (cyan), M (magenta), Y (yellow), and K (black), and outputs the data to the main unit g3.

[0017] The paper feed unit g2 is equipped with multiple large-capacity paper feed trays. The paper feed unit g2 transports paper from a paper feed tray designated by the main unit g3 to the main unit g3.

[0018] The main body unit g3 includes an operation unit 3, a display unit 4, an automatic document feeder 61, a scanner unit 6, an image forming unit 8, a paper feed tray g31, a reading unit 9, a correction unit 10, and the like. The main unit g3 forms an image on the paper using the image forming unit 8 based on image data obtained by reading the original paper D using the scanner unit 6 or image data generated by the print controller g1. The main unit g3 transports the paper on which the image has been formed to the post-processing device g4.

[0019] The post-processing device g4 performs post-processing on the sheets transported from the main unit g3 and discharges them. Examples of post-processing include stapling, punching holes, folding, and binding. Post-processing is not essential, and the post-processing device g4 performs it only when instructed to do so by the main unit g3. If no post-processing is required, the post-processing device g4 simply discharges the transported sheets.

[0020] FIG. 2 shows the configuration of the main unit g3. As shown in Figure 2, the main unit g3 is configured to include a control unit 1, a memory unit 2, an operation unit 3, a display unit 4, a communication unit 5, an automatic document transport unit 61, a scanner unit 6, an image processing device 7, an image forming unit 8, a reading unit 9, a correction unit 10, and an edge system 100.

[0021] The control unit 1 includes a CPU, a RAM, etc. The control unit 1 reads out a program stored in the storage unit 2, and controls each unit of the image forming apparatus G in accordance with the read out program. For example, the control unit 1 controls the paper feed unit g2 or the paper feed tray g31 to feed paper in accordance with the job settings. The control unit 1 also controls the image processing device 7 to correct and process image data, and controls the image forming unit 8 to form an image. If the job settings include post-processing settings, the control unit 1 instructs the post-processing device g4 to perform post-processing.

[0022] The storage unit 2 stores programs, files, etc. that can be read by the control unit 1. For example, a storage medium such as a hard disk or a ROM (Read Only Memory) is used as the storage unit 2. The storage unit 2 also stores a reference image for measuring the print position.

[0023] The operation unit 3 includes operation keys, a touch panel integrated with the display unit 4, and the like, and outputs operation signals corresponding to these operations to the control unit 1. The user can use the operation unit 3 to input instructions such as job settings and changes to processing content. The display unit 4 is configured with a display device such as an LCD (Liquid Crystal Display), and displays an operation screen and the like according to instructions from the control unit 1.

[0024] The communication unit 5 communicates with the server 200 via the network in accordance with instructions from the control unit 1. The server 200 manages the operating status of the image forming apparatus G, and also transmits control parameters to the image forming apparatus G to control the state of the image forming apparatus G. In addition, instead of sending control parameters to the image forming apparatus G, the server 200 may instruct the serviceman terminal 300 held by the serviceman who performs maintenance and inspection of the image forming apparatus G to adjust the image forming apparatus G.

[0025] Returning to the explanation of the image forming device G, the automatic document transport unit 61 is configured with a loading tray on which the document paper D is placed, a mechanism for transporting the document paper D, transport rollers, etc., and transports the document paper D to a predetermined transport path. The scanner unit 6 is configured with an optical system including a light source and a reflecting mirror, and reads an image of the original paper D transported along a predetermined transport path or the original paper D placed on the platen glass, generates image data for each color of R (red), G (green), and B (blue), and outputs it to the image processing device 7.

[0026] The image processing device 7 corrects image data input from the scanner unit 6 or the print controller g1, performs image processing on the image data, and outputs the processed image data to the image forming unit 8. As shown in FIG. 2, the image processing device 7 includes a color conversion section 71, a tone correction section 72, and a halftone processing section 73.

[0027] The color conversion unit 71 performs color conversion processing on the image data of each of the colors R, G, and B output from the scanner unit 6, and outputs image data of each of the colors C, M, Y, and K. For color correction, the color conversion unit 71 can also perform color conversion processing on the image data of each color of C, M, Y, and K output from the print controller g1, and output color-corrected image data of each color of C, M, Y, and K.

[0028] During color conversion processing in the color conversion unit 71, an LUT (Look Up Table) is used, which is a correspondence table in which the gradation values ​​of each of the colors C, M, Y, and K after color conversion are determined for the gradation values ​​of each of the colors R, G, and B. Furthermore, during color correction in the color conversion unit 71, an LUT in which the gradation values ​​of each of the colors C, M, Y, and K after color correction are determined is used.

[0029] The gradation corrector 72 corrects the gradation of the image data output from the color converter 71 or the print controller g1. When correcting the gradation in the gradation correction unit 72, an LUT is used in which correction values ​​corresponding to each gradation value are determined so that the gradation characteristics of the image match the target gradation characteristics. The gradation correction unit 72 obtains correction values ​​corresponding to the gradation value of each pixel of the image data from the gradation correction LUT, and outputs image data consisting of the correction values.

[0030] The halftone processing unit 73 performs halftone processing on the image data output from the tone correction unit 72. The halftone processing is, for example, a screen process using a dither matrix, an error diffusion process, or the like. The halftone processing unit 73 outputs the image data after halftone processing to the image forming unit 8.

[0031] The image forming unit 8 forms an image on a sheet based on the image data output from the image processing device 7. 1, the image forming unit 8 includes an exposure unit 81, a photoconductor 82, and a developing unit 83 for each of the colors C, M, Y, and K. The image forming unit 8 also includes an intermediate transfer belt 84, a secondary transfer roller 85, a fixing device 86, and an inverting mechanism 87.

[0032] The exposure unit 81 is equipped with an LD (Laser Diode) as a light-emitting element. The exposure unit 81 drives the LD based on image data and irradiates and exposes the charged photoconductor 82 with laser light. The development unit 83 supplies toner onto the photoconductor 82 using a charged development roller, and develops the electrostatic latent image formed on the photoconductor 82 by exposure.

[0033] In this way, the images formed with toner of each color on the four photosensitive drums 82 are transferred in order from each photosensitive drum 82 onto the intermediate transfer belt 84 in a superimposed state, thereby forming a color image on the intermediate transfer belt 84. The intermediate transfer belt 84 is an endless belt wound around multiple rollers, and rotates in accordance with the rotation of each roller.

[0034] The secondary transfer roller 85 transfers the color image on the intermediate transfer belt 84 onto a sheet fed from the sheet feed unit g2 or the sheet feed tray g31. The fixing device 86 applies heat and pressure to the sheet after transfer to fix the image.

[0035] When forming images on both sides of a sheet of paper, the image forming unit 8 turns the sheet over using the reversing mechanism 87, and forms an image on the other side. The reversing mechanism 87 has a transport path that turns over the sheet of paper passing through it and transports the sheet again to the transfer position by the secondary transfer roller 85.

[0036] 1, the reading unit 9 is provided below the conveying path and reads an image formed on a sheet of paper conveyed from the upstream side in the conveying direction of the reading unit 9. The sheet of paper from which the image has been read by the reading unit 9 is conveyed to the post-processing device g4. The control unit 1 measures the distance from the edge of the paper to the reference image based on the reference image read by the reading unit 9, and calculates the actual printing position of the reference image. Then, the control unit 1 adjusts the printing position so that the image will be formed in the original printing position from the next time onwards.

[0037] Furthermore, the control unit 1 can calculate not only the image position but also image deformation, magnification, density, gradation, gloss, noise, and color shift from the image read by the reading unit 9. However, among these pieces of information, some of the information such as the gloss of the image may be acquired from a detection unit (not shown) separate from the reading unit 9.

[0038] The image forming apparatus G may also include a media characteristic detection unit that detects the type and condition of the paper (media) on which the image is formed by the image forming unit 8. Examples of the condition of the paper detected by the media characteristic detection unit include the stiffness, basis weight, thickness, moisture absorption state, grain, and surface condition of the paper. Furthermore, the image forming apparatus G may be provided with an environment detection unit that detects information about the environment in which the apparatus is used, such as temperature, humidity, and dust generation.

[0039] The correction unit 10 corrects deformation of the paper after the fixing process and flattens the paper surface. Here, because paper is easily deformed by the fixing process, it is necessary to flatten the paper when reading the reference image. For this reason, the correction unit 10 is disposed between the fixing device 86 and the reading unit 9 in the paper transport direction.

[0040] 2 diagnoses the state of each part of the image forming apparatus G in this example, generates control parameters for controlling the operation of the image forming apparatus G based on the diagnosis results, and sends the control parameters to the control unit 1. The edge system 100 performs a process of obtaining the control parameters using a learning model sent and set from the server 200 or a learning model that has been set in advance. The edge system 100 is a computer that includes a program that executes a learning model, a CPU that performs calculations according to the program, RAM, etc. The computer that constitutes the edge system 100 may be configured as a separate device from the control unit 1 of the image forming apparatus G, or the control unit 1 may also function as the edge system 100.

[0041] Furthermore, the configuration of each part of the image forming apparatus G described above is an example, and other configurations for forming images on paper may also be used. For example, the image forming apparatus G may be configured to include an inkjet head and form an image on a medium such as paper by ejecting ink from the inkjet head.

[0042] [Processing performed by edge systems and servers] 3 shows the configuration of the edge system 100 and server 200 in this example in terms of the functions performed, and an overview of the processing realized by that configuration. Note that although the hardware configuration of the edge system 100 and server 200 is omitted, each is comprised of an information processing device called a computer, and is equipped with a CPU (Central Processing Unit) that performs arithmetic processing, memory, etc., and realizes the functions described below by executing the implemented programs.

[0043] The edge system 100 provided in the image forming apparatus G includes an image feature amount acquisition unit 101 that acquires, as image feature amounts, the sensing results of an image formed on paper (media) by the image forming apparatus G. The image feature amount acquired by the image feature amount acquisition unit 101 is acquired based on, for example, information read by the reading unit 9 shown in FIG.

[0044] The reading unit 9 detects, for example, at least one of the position, deformation, magnification, density, gradation, gloss, noise, and color shift of the image formed on the paper as an image feature. Then, the image feature acquisition unit 101 of the edge system 100 acquires the image feature detected by the reading unit 9. The image feature acquired by the image feature acquisition unit 101 of the edge system 100 is also transmitted to the server 200 via the network.

[0045] The edge system 100 also includes a media feature acquisition unit 102 that acquires feature quantities of the paper (media) on which an image has been formed by the image forming apparatus G. The media feature quantities acquired by the media feature acquisition unit 102 include at least one of the media's stiffness, basis weight, thickness, moisture absorption state, grain, and surface condition. The media feature quantities acquired by the edge system 100 are also transmitted to the server 200 via the network.

[0046] The edge system 100 also includes a status information acquisition unit 103 that acquires status information, which is sensing information detected by sensors arranged in various parts of the image forming apparatus G. The status information acquired by the status information acquisition unit 103 is information acquired from sensors arranged in various units within the image forming apparatus G and sensors arranged in various components within the image forming apparatus G. The status information acquired from the sensors arranged in various units and components within the image forming apparatus G includes, for example, status information relating to the wear or deterioration status of the units or components. The status information also includes information on the mode (normal mode, life extension mode, etc.) in which the image forming apparatus G is currently operating and information indicating the occurrence of a malfunction. Furthermore, if the image forming apparatus G includes an environment detection unit for detecting temperature and other factors, the status information may also include information on the operating environment. Furthermore, the state information acquired by the edge system 100 by the state information acquisition unit 103 is also transmitted to the server 200 via the network.

[0047] The image features, media features, and status information acquired by the image feature acquisition unit 101, media feature acquisition unit 102, and status information acquisition unit 103 are sent to the edge AI processing unit 110 in the edge system 100. A learning model generated by the learning model generation unit 210 in the server 200 is transmitted to and set in the edge AI processing unit 110. However, in the initial state, a learning model prepared in advance during the device development stage is set and used in the edge AI processing unit 110 in the edge system 100. The edge AI processing unit 110 then performs calculations using the set learning model based on the image features, media features, and status information acquired by the image feature acquisition unit 101, media feature acquisition unit 102, and status information acquisition unit 103, and diagnoses the status of the image forming device G. If the status information includes information about the usage environment, such as temperature, the usage environment is also taken into account when calculating the learning model.

[0048] Based on the diagnosis results, the edge AI processing unit 110 determines whether the image forming apparatus G should be operated in a normal mode or in a life extension mode in which the image forming capability is reduced compared to the normal mode. The edge system 100 then instructs the control unit 1 of the image forming apparatus G to generate control parameters that will operate the image forming apparatus G in the determined mode. The control unit 1 operates each unit within the image forming apparatus G using the instructed control parameters. Specific examples of the control parameters will be described later.

[0049] In this example, the edge system 100 is designed to ensure proper operation when the image forming device G is operating in life extension mode, as described below, but even in normal mode, the image feature acquisition unit 101, media feature acquisition unit 102, and status information acquisition unit 103 acquire image features, media features, and status information, respectively. Here, the normal mode and life extension mode set by the edge system 100 will be explained. The normal mode is a mode in which control is performed using normal control parameters and tables that are built into the device at the time of shipment. In this normal mode, the control parameters are adjusted within ranges determined at the time of design to accommodate individual differences between devices and to adjust colors according to user preferences.

[0050] On the other hand, the life extension mode is a mode that is set when a unit or part is judged to be nearing the end of its life based on the device state. In this example, in the life extension mode, the device is controlled based on the control parameters output by the learning model in the edge system 100. The learning model here outputs control parameters based on the device status information so as to maximize the time until the end of the life of the relevant unit or part.

[0051] Although there is a possibility that the performance of the image forming apparatus will be affected by entering the life extension mode, the learning model learns control parameters that will have as little effect on image quality as possible. In many cases, there are multiple control parameters, and the learning model determines which parameters should be adjusted based on device status information.

[0052] The control parameters are updated by the learning model as needed, and the optimal control parameters are adjusted each time. Here, the updating by the learning model as needed may be performed, for example, a set number of times per day, or after each job execution. In the life extension mode, the edge system 100 constantly predicts the time until the relevant unit or part reaches the end of its life. This life prediction is performed by linearly interpolating the time-series changes in information that can express the wear and deterioration status of the part, for example.

[0053] When the relevant unit or part reaches the end of its life in this life extension mode, that is, when the unit or part in the life extension state is unable to reach the standard level of image quality no matter what control parameters are adjusted, the life information predicted in the life extension mode is changed to the time when the unit or part actually reaches its end of life, and this is used to train the learning model. Furthermore, when the image forming apparatus G enters the life extension mode, a warning requesting replacement of the relevant unit or part is displayed on the operation screen of the display unit 4 of the image forming apparatus G. Normally, such a display will result in the relevant unit or part being replaced before it reaches the end of its life. Therefore, when the server 200, which will be described next, learns the lifespan, the predicted value of the lifespan information is used.

[0054] As already explained, the image features acquired by the image feature acquisition unit 101, the media features acquired by the media feature acquisition unit 102, and the state information acquired by the state information acquisition unit 103 are transmitted to the server 200 via the network. The server 200 collects the transmitted feature amounts through the case collection unit 201. The information collected by the case collection unit 201 here is information about all image forming devices G managed by the server 200. In other words, the case collection unit 201 collects case studies of a large number of image forming devices G. Then, based on each feature collected by the case collection unit 201, the server 200 associates the state when an abnormality occurs in the image forming device G with successful cases of responding to that abnormal state, and creates an abnormality response database 203.

[0055] In addition, the server 200 has an orderer information collecting unit 202 collect image feature quantities indicating printing trends and the requests of each orderer (deliverer) of the image forming device G. Then, based on the image feature quantities collected by the orderer information collecting unit 202 and the requests from the orderers, the server 200 creates a quality database 204 indicating the required quality for each orderer in the image forming device G.

[0056] The learning model generation unit 210 of the server 200 generates a learning model for managing the lifespan of each image forming apparatus G based on the information in the abnormality response database 203 and the information in the quality database 204. When generating this learning model, the learning model is generated using rewards based on the lifespan information and image quality information of the units or parts. For example, learning is performed with a high reward value for being able to maintain image quality and a high reward value for extending the lifespan of the units or parts, thereby generating an optimal learning model for lifespan management. Then, the learning model for lifespan management suitable for each image forming device G generated by the learning model generation unit 210 is transmitted via the network to the edge system 100 installed in each image forming device G.

[0057] In addition, when the learning model generation unit 210 of the server 200 determines, based on the results of applying each feature collected by the case collection unit 201 to the generated learning model, that a specific unit or part of the image forming device G needs to be replaced or that adjustment by a service technician is required, it notifies the service technician terminal 300 carried by the service technician who manages each image forming device G.

[0058] Alternatively, instead of the edge system 100 instructing the control unit 1 on the mode and control parameters, the edge system 100 or the server 200 may instruct the mode and control parameters to the serviceman terminal 300. Upon receiving this instruction, the serviceman carrying the serviceman terminal 300 goes to the location where the corresponding image forming apparatus G is installed and adjusts the image forming apparatus G to the instructed mode and control parameters.

[0059] [Specific processing flow performed by the edge system] 4 is a flowchart showing the flow of specific processes performed by the edge system 100 while communicating with the server 200. In the flowchart of FIG. 4, the processes performed by the edge system 100 are shown in the right column, and the processes performed by the server 200 are shown in the left column.

[0060] First, the edge system 100 detects an abnormality or a sign of an abnormality in the image forming apparatus G based on information acquired from the image forming apparatus G (step S11). When the edge system 100 detects an abnormality or a sign of an abnormality in the image forming apparatus G, the edge system 100 performs a learning model acquisition process to deal with the detected abnormality or sign (step S20).

[0061] In the learning model acquisition process of step S20, first, the edge system 100 requests the server 200 to call a learning model (step S21). Upon receiving this call request, the server 200 selects a learning model suitable for the current state of the image forming device G and transmits the selected learning model to the edge system 100 (step S22). The edge system 100 receives the transmitted learning model (step S23) and terminates the learning model acquisition process of step S20. This learning model acquisition process can be performed irregularly each time the image forming device G transitions to life extension mode, but instead, the edge system 100 can periodically check with the server 200 to see if there are any updates to the learning model suitable for the image forming device G, and acquire the latest learning model each time there is an update.

[0062] Next, the edge system 100 performs a mode transition process for the image forming apparatus G (step S30). In the mode transition processing of step S30, the edge AI processing unit 110 of the edge system 100 switches to control of the image forming device G using the learning model acquired in step S23 (step S31), and changes the operation of the image forming device G from normal mode to life extension mode. Then, the edge system 100 starts counting the time since the image forming apparatus G shifted to the life extension mode and the transport distance (travel distance) of the paper (media) since the shifted to the life extension mode (step S32).

[0063] Then, the image forming apparatus G starts executing the job in the life extension mode based on the control of the edge system 100 (step S40). When the job of step S40 is executed, the edge system 100 acquires the status of the image forming apparatus G and each unit as apparatus status information, and the edge system 100 records this as apparatus status information in the life extension mode (step S41).

[0064] Then, the edge AI processing unit 110 of the edge system 100 inputs the device status information into the learning model, supplies the control parameters output by the learning model to the image forming device G, and records the control parameters output by the learning model (step S42). Furthermore, the edge AI processing unit 110 uses the learning model to predict the lifespan of each unit and part of the image forming apparatus G (step S43). The processing in steps S41 to S43 is repeated for each execution of a job. However, the life prediction in step S43 may be performed every time a job is executed to some extent.

[0065] Next, the edge system 100 determines whether or not a failure has occurred in the image forming apparatus G (step S12). When it is determined in step S12 that a failure has occurred in the image forming device G (YES in step S12), the edge system 100 records the elapsed time since the device entered the life extension mode when the failure occurred and the distance traveled since the device entered the life extension mode (step S13).

[0066] Then, when it is determined in step S12 that no malfunction has occurred in the image forming device G (NO in step S12), and when it is determined that a unit or part needs to be replaced after recording the elapsed time and mileage in step S13, the edge AI processing unit 110 notifies the serviceman terminal 300 via the server 200 to replace the relevant unit or part (step S14). Thereafter, the edge system 100 transmits the device status information obtained in step S41, the lifespan information such as the elapsed time obtained in step S13, and the control parameters generated by the learning model to the server 200 (step S15).

[0067] The server 200 receives and stores the device status information, lifespan information, and control parameters transmitted from the edge system 100 (step S16). Then, the server 200 generates a new learning model by adding the stored information, stores the generated learning model (step S17), and ends the processing in the life extension mode. After the replacement of the unit or defective item reported to the serviceman terminal 300 in step S14 is completed, the image forming apparatus G may be returned from the life extension mode to the normal mode.

[0068] [Example of correspondence between equipment status and control parameters] FIG. 5 is a diagram showing, in a matrix, device status information that is input to the learning model executed by the edge system 100 and control parameters that are output by the learning model. The vertical columns in FIG. 5 show control parameters divided into the supply / conveyor system and the process system, and the horizontal columns show apparatus status information divided into image quality information, wear information, failure information, and environmental information.

[0069] That is, the control parameters in the vertical column are considered to be parameters of the paper (media) feeding and transporting system and parameters of the process system for forming images. For example, control parameters for the feeding and conveying system may include the timing at which each roller that conveys paper is turned on and off, the speed of each roller, the JAM detection threshold for detecting paper jams and other conveying problems, the amount of air sent to the paper feeding section, and the spacing between sheets of paper being conveyed (paper gap).

[0070] Further, the control parameters of the process system may include the speed of the photosensitive member for each color, the charging voltage, the toner stirring time, the transfer voltage, the charge removal voltage, the fixing temperature, and the fixing warm-up time.

[0071] Here, a specific example of control will be described. As an example, suppose that the photoconductor speed of a certain color has been changed to reduce wear because the photoconductor has entered life extension mode. In this case, the change in the photoconductor speed is thought to affect the image magnification, image density, gradation, and color misregistration marked with "O". Therefore, by executing the learning model, the photoconductor speeds of other colors, charging voltage, toner agitation time, transfer voltage, on / off timing of each roller, and each roller speed are adjusted, which minimizes the impact on image quality while extending the life of the photoconductor.

[0072] In another example, suppose that a specific roller, A, has entered life extension mode, causing the speed of roller A to be changed. In this case, the speed change of roller A affects the image position, image deformation, and image magnification marked with "O." Therefore, by executing the learning model, the speed of other rollers and the on / off timing of other rollers can be adjusted. This makes it possible to extend the life of roller A while minimizing the impact on image quality.

[0073] As another example, although not shown in Figure 5, suppose that the discharge roller enters life extension mode and cannot be moved. Even when the discharge roller enters life extension mode, the learning model is executed to switch to discharging paper from the reverse roller instead. This allows the image forming apparatus G to maintain its functionality while extending its life.

[0074] The example described here is just one example, and the learning model learns how image quality changes when a control parameter is changed in a certain way based on learning data collected from individual image forming devices, and the adjustment items shown in Figure 5 are just one example. Learning data is constantly collected in server 200, regardless of the state of the image forming apparatus. When server 200 generates a learning model, for example, by setting a reward so that image quality information is given top priority, when a restriction is placed on a specific control parameter due to the life extension mode, the learning model changes other control parameters to maintain image quality.

[0075] The lifespan information used for learning may be, for example, the remaining time until the end of use of a certain unit or part. This remaining time may be calculated, for example, using the remaining mileage or usage time for the unit life, which is the lifespan of the unit determined during the development stage. Alternatively, the remaining time may be calculated using a predicted value that can be calculated from the current wear state obtained from sensor information that can grasp the wear state of the relevant unit or part. In other words, by treating the calculated predicted value as lifespan information, even if a unit is controlled in lifespan extension mode and replaced before it reaches its lifespan, the extent to which the unit's lifespan was extended can be used as learning information.

[0076] [Variations] In the above-described embodiment, the server 200 generates a learning model, and the edge system 100 incorporated in the image forming device G executes the generated learning model. This configuration of the server 200 and the edge system 100 is just one example, and the server 200 may also generate a learning model and control the image forming device G by executing the generated learning model. Alternatively, conversely, the edge system 100 may obtain the information necessary for generating the learning model from the server 200, and the edge system 100 may also generate the learning model.

[0077] In addition, the adjustment of the control parameters of the image forming device G when executing the learning model can be performed automatically by the control of the server 200 or the edge system 100, or instructions can be sent to the serviceman terminal 300 so that the serviceman can perform the adjustment. Alternatively, the control parameters may be displayed on an operation screen of the image forming apparatus G, and the user of the image forming apparatus G may adjust the control parameters. When the operation mode of the image forming apparatus G is changed from the normal mode to the life extension mode, the user of the image forming apparatus G may be asked to consent to the change on the operation screen, and the image forming apparatus G may be set to the life extension mode if consent is given.

[0078] Furthermore, if printing is attempted using the control parameters set in the image forming apparatus G and the image is not formed properly, the control parameters may be changed in the edge system 100 and image formation may be performed again. Here, the case where the image is not formed properly corresponds to, for example, a case where an error is detected from information read by the reading unit 9. The image quality information and device status information at this time are also sent from the edge system 100 to the server 200 and are used to generate a learning model.

[0079] In addition, switching from normal mode to life extension mode can be done using a fixed threshold, for example, when the value detected by a sensor within the device exceeds the threshold, but it is also possible to prepare a separate learning model that determines whether to switch to life extension mode.

[0080] Furthermore, the server 200 and the edge system 100 may be configured as dedicated devices, or may be configured by installing a program that executes the functions shown in Fig. 3 and the processing flow shown in the flowchart of Fig. 4 in a general-purpose information processing device (computer device). In this case, the program may be installed in the information processing device via various recording media such as a memory or a disk, or may be downloaded via a network. [Explanation of symbols]

[0081] 1...control unit, 2...storage unit, 3...operation unit, 4...display unit, 5...communication unit, 6...scanner unit, 7...image processing device, 8...image forming unit, 9...reading unit, 10...correction unit, 61...automatic document feeder unit, 71...color conversion unit, 72...gradation correction unit, 73...halftone processing unit, 81...exposure unit, 82...photosensitive member, 83...developing unit, 84...intermediate transfer belt, 85...secondary transfer roller, 86...fixing device, 87...reversal mechanism, 100...edge system, 101...image feature acquisition unit, 102...media feature acquisition unit, 103...status information acquisition unit, 110...edge AI processing unit, 200...server, 201...case collection unit, 202...orderer information collection unit, 203...abnormality response database, 204...quality database, 210...learning model generation unit, 300...serviceman terminal, G...image forming apparatus, g1...print controller, g2...paper feed unit, g3...main unit, g31...paper feed tray, g4...post-processing device

Claims

1. An information processing device that processes information indicating the status of an image forming device that forms an image on a medium or a unit attached to the image forming device, a collection unit that collects, as learning information, device status information indicating the status of the image forming device, life information of the image forming device or the unit after the image forming device has entered a life extension mode in which image forming capability is reduced by imposing restrictions on control parameters compared to a normal mode, and the control parameters of the image forming device or the unit in the life extension mode; a generation unit that generates a learning model for life extension based on the learning information collected by the collection unit. Information processing device.

2. The device status information includes image quality information corresponding to the image quality of an image formed by the image forming device. The information processing device according to claim 1 .

3. Furthermore, the device status information includes at least one of wear information indicating a wear state of a part provided in the unit or the image forming device, and failure information indicating a failure state of the image forming device or the unit. The information processing device according to claim 2 .

4. The image quality information is generated based on an image that is read by a reading unit installed in the image forming apparatus and that is formed on the medium.

4. The information processing device according to claim 2 or 3.

5. The image quality information is obtained by inputting the inspection result of the image formed on the medium by the user. The information processing device according to claim 2 .

6. The image quality information includes at least one of the position, deformation, magnification, density, gradation, gloss, noise, and color shift of the image formed on the medium. The information processing device according to claim 2 .

7. The lifespan information is the time from when the image forming apparatus enters the life extension mode until a unit or a part installed in the image forming apparatus reaches a specific state. The information processing device according to any one of claims 1 to 6.

8. The specific state is a state of wear or deterioration of a unit or part attached to the image forming apparatus, which is acquired by a sensor attached to the image forming apparatus. The information processing device according to claim 7 .

9. Furthermore, the collection unit collects information about the media on which an image is formed, and adds the information about the media to learning information when the generation unit generates the learning model. The information processing device according to any one of claims 1 to 8.

10. The information about the media is information detected by a characteristic detection unit installed in the image forming apparatus. The information processing device according to claim 9 .

11. The information about the media includes at least one of stiffness, basis weight, thickness, moisture absorption state, grain, and surface state of the media. The information processing device according to claim 9 .

12. Furthermore, the collection unit collects information about the usage environment of the image forming apparatus, and adds the information about the usage environment to learning information when the generation unit generates the learning model. The information processing device according to any one of claims 1 to 11.

13. The generation unit generates a learning model using a reward based on the lifespan information and the image quality information. The information processing device according to claim 2 .

14. an information processing device including a collection unit and a generation unit as a server; the server distributes the learning model generated by the generation unit to the image forming device; the image forming apparatus, an acquisition unit that acquires a learning model distributed from the server; a control unit that adjusts control parameters of the image forming apparatus by executing the learning model acquired by the acquisition unit. The image forming system according to any one of claims 1 to 13.

15. A program for executing a procedure to provide a computer with information indicating the status of an image forming apparatus that forms an image on a medium or a unit attached to the image forming apparatus, and to obtain an instruction to extend the life of the image forming apparatus, a collection procedure for collecting, as learning information, device status information indicating the status of the image forming device, life information of the image forming device or the unit after the image forming device has entered a life extension mode in which image forming capability is reduced by imposing restrictions on control parameters compared to a normal mode, and the control parameters of the image forming device or the unit in the life extension mode; a learning model generation step of generating a learning model for life extension based on the learning information collected by the collection step, program.

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