Information processing device, system, information processing method, and program thereof

Abstract

To provide an information processing device, an image processing method and a program therefor, capable of simplifying work by a user.SOLUTION: An information processing device 30 includes an acquisition unit 33 that acquires image data and situation data, a transmission unit 36 that transmits the image data and the situation data to a server 50, a reception unit 37 that receives first data 48 generated by the server on the basis of the image data and the situation data from the server, a storage unit 35 that stores the first data received by the reception unit and second data 49, and a control unit 34. The first data is data specifying a learned model learned by machine learning, the learned model outputting cloth data indicating a feature amount of cloth when the image data and the situation data are input. The second data is data indicating a correspondence relation between the cloth data and recommendation parameters. The control unit derives the recommendation parameters from the image data and the situation data on the basis of the first data and the second data.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to an information processing device, system, The present invention relates to an information processing method and a program therefor. [Background technology]

[0002] Patent Document 1 describes a textile printer system that performs a textile printing process on fabric. The textile printer system includes, for example, an inkjet device, a pre-processing device, and a post-processing device. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2004-174943 A Summary of the Invention [Problem to be solved by the invention]

[0004] In a textile printer system, appropriate parameters for the pre-treatment device and post-treatment device vary depending on the characteristic amount of the fabric in order to obtain a predetermined image quality. For example, the moisture content of the fabric, which is a characteristic amount of the fabric, may vary depending on the environment and usage conditions of the textile printer system. Such changes in the characteristic amount of the fabric also vary depending on the composition of the fabric.

[0005] In recent years, while there are fabrics made of a single type of fiber, there are also fabrics made of multiple types of fibers, i.e., blended fabrics. With a wide variety of fabrics on the market, it is difficult to cover all the features of fabrics that change depending on the environment and usage conditions. In this case, users need to determine appropriate parameters by repeating the printing process while changing the parameters of the pre-treatment device and post-treatment device. This can make the user's work complicated. [Means for solving the problem]

[0006] The information processing device that solves the above problem is an information processing device that processes information related to a textile printing process performed by an inkjet device that draws an image by ejecting ink onto a fabric, a pre-processing device that performs pre-processing on the fabric before the image is drawn, and / or a post-processing device that performs post-processing on the fabric after the image is drawn, and includes an acquisition unit that acquires image data that is electronically converted into an image of the fabric before the textile printing process is performed, and status data that indicates usage status including environmental information of the inkjet device, the pre-processing device, and the post-processing device, a transmission unit that transmits the image data and the status data to a server, and a display unit that is generated by the server based on the image data and the status data. The system includes a receiving unit that receives first data from the server, a memory unit that stores the first data received by the receiving unit and second data different from the first data, and a control unit, wherein the first data is data that specifies a trained model trained by machine learning and that outputs fabric data indicating features of the fabric when the image data and the situation data are input, and the second data is data that indicates a correspondence between the fabric data and recommended parameters for at least one of the pre-processing device and the post-processing device, and the control unit derives the recommended parameters from the image data and the situation data based on the first data and the second data.

[0007] An information processing device that solves the above problem is an information processing device that processes information related to a textile printing process performed by an inkjet device that draws an image by ejecting ink onto a fabric, and at least one of a pre-processing device that performs pre-processing on the fabric before the image is drawn on it, and a post-processing device that performs post-processing on the fabric after the image is drawn on it, and is equipped with an acquisition unit that acquires image data that has been digitized as an image of the fabric before the textile printing process is performed, and status data that indicates the usage status including environmental information of the inkjet device, the pre-processing device, and the post-processing device, a transmission unit that transmits the image data and the status data to a server, and a receiving unit that receives from the server recommended parameters for at least one of the pre-processing device and the post-processing device derived by the server based on the image data and the status data.

[0008] An information processing method that solves the above problem is an information processing method for processing information related to a textile printing process performed by an inkjet device that draws an image by ejecting ink onto a fabric, a pre-processing device that performs pre-processing on the fabric before the image is drawn, and / or a post-processing device that performs post-processing on the fabric after the image is drawn, and includes the steps of: acquiring image data in which the fabric before the textile printing process is digitized as an image, and status data indicating usage status including environmental information of the inkjet device, the pre-processing device, and the post-processing device; transmitting the image data and the status data to a server; receiving from the server first data that is data generated by the server based on the image data and the status data, the first data specifying a trained model that is trained by machine learning and that outputs fabric data indicating features of the fabric when the image data and the status data are input; and deriving the recommended parameters from the image data and the status data based on the first data and second data indicating a correspondence between the fabric data and recommended parameters for at least one of the pre-processing device and the post-processing device.

[0009] An information processing method that solves the above problem is an information processing method for processing information related to a textile printing process performed by an inkjet device that draws an image by ejecting ink onto a fabric, and at least one of a pre-processing device that performs pre-processing on the fabric before the image is drawn, and a post-processing device that performs post-processing on the fabric after the image is drawn, and includes obtaining image data in which the fabric before the textile printing process is digitized as an image, and status data that indicates usage status including environmental information of the inkjet device, the pre-processing device, and the post-processing device, transmitting the image data and the status data to a server, and receiving from the server recommended parameters for at least one of the pre-processing device and the post-processing device derived by the server based on the image data and the status data.

[0010] The program for solving the above problem is a program for making a computer process information relating to a textile printing process performed by an inkjet device that draws an image by ejecting ink onto a fabric, a pre-processing device that performs pre-processing on the fabric before the image is drawn, and / or a post-processing device that performs post-processing on the fabric after the image is drawn, the program acquiring image data that digitizes the fabric before the textile printing process as an image, and status data that indicates the usage status including environmental information of the inkjet device, the pre-processing device, and the post-processing device, and processing the image data and the status data. receiving from the server first data which is data generated by the server based on the image data and the situation data, the first data defining the trained model which is trained by machine learning and which outputs fabric data indicating features of the fabric when the image data and the situation data are input; and deriving the recommended parameters from the image data and the situation data based on the first data and second data indicating a correspondence between the fabric data and recommended parameters for at least one of the pre-processing device and the post-processing device.

[0011] A program that solves the above problem is a program that causes a computer to process information regarding a textile printing process performed by an inkjet device that draws an image by ejecting ink onto a fabric, a pre-processing device that performs pre-processing on the fabric before the image is drawn on it, and / or a post-processing device that performs post-processing on the fabric after the image is drawn on it, and the program executes the following operations: acquiring image data in which the fabric before the textile printing process is digitized as an image, and status data that indicates the usage status including environmental information of the inkjet device, the pre-processing device, and the post-processing device; transmitting the image data and the status data to a server; and receiving from the server recommended parameters for at least one of the pre-processing device and the post-processing device derived by the server based on the image data and the status data. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a block diagram showing a textile printing system. [Diagram 2] 4 is a flowchart showing the procedure of a textile printing process. [Diagram 3] FIG. 1 is a block diagram showing an information processing apparatus according to a first embodiment. [Figure 4] 5 is a flowchart showing the operation of the information processing apparatus according to the first embodiment. [Diagram 5] 5 is a flowchart showing the operation of a server connected to the information processing apparatus of the first embodiment. [Figure 6] FIG. 4 is a diagram showing a pre-printing image and a spectral image. [Figure 7] 6 is a flowchart showing an operation different from that shown in FIG. 5 . [Figure 8] FIG. 11 is a block diagram showing an information processing apparatus according to a second embodiment. [Figure 9] 10 is a flowchart showing the operation of an information processing apparatus according to a second embodiment. [Figure 10] 10 is a flowchart showing the operation of a server connected to an information processing apparatus according to the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] First Embodiment Hereinafter, a first embodiment of the information processing device will be described with reference to the drawings. The information processing device is a device that processes information related to the textile printing process performed by the textile printing system. First, the textile printing system will be described.

[0014] 1, the textile printing system 10 is composed of, for example, a pre-treatment device 11, an inkjet device 12, and a post-treatment device 13. The textile printing system 10 may be composed of the pre-treatment device 11 and the inkjet device 12, or may be composed of the inkjet device 12 and the post-treatment device 13. In other words, the textile printing system 10 includes the inkjet device 12 and at least one of the pre-treatment device 11 and the post-treatment device 13.

[0015] The textile printing system 10 is a system that performs a textile printing process on a fabric 99. The textile printing system 10 performs the textile printing process on the fabric 99 through, for example, three processes: a pre-processing process, a drawing process, and a post-processing process. The pre-processing process is performed by a pre-processing device 11. The drawing process is performed by an inkjet device 12. The post-processing process is performed by a post-processing device 13.

[0016] The textile printing process is realized, for example, by processing the fabric 99 in the order of pre-treatment, drawing process, and post-treatment. When the textile printing system 10 is composed of a pre-treatment device 11 and an inkjet device 12, the textile printing process is realized by the pre-treatment and drawing process. When the textile printing system 10 is composed of an inkjet device 12 and a post-treatment device 13, the textile printing process is realized by the drawing process and at least one of the pre-treatment and the post-treatment.

[0017] The textile printing process may be realized by an apparatus owned by one user, or may be realized in cooperation with an apparatus owned by another user. For example, the textile printing process may be realized by a pre-processing device 11 owned by a first user, and an inkjet device 12 and a post-processing device 13 owned by a second user different from the first user. In this case, the textile printing process is realized by the first user performing pre-processing on the fabric 99, and the second user performing drawing processing and post-processing on the fabric 99.

[0018] In the textile printing system 10, the pre-treatment device 11, the inkjet device 12, and the post-treatment device 13 may or may not be linked together. That is, the pre-treatment device 11, the inkjet device 12, and the post-treatment device 13 may or may not exchange information with each other.

[0019] The pretreatment device 11 is a device that performs pretreatment on the fabric 99 before an image is drawn on the fabric 99. The pretreatment is a treatment that is performed before the drawing treatment. The pretreatment device 11 includes, for example, an application unit 14, a straightening unit 15, and a pretreatment drying unit 16.

[0020] The application unit 14 is configured to apply a pretreatment liquid to the fabric 99. The application unit 14 includes, for example, a storage tank that stores the pretreatment liquid. For example, the fabric 99 passes through the storage tank, whereby the pretreatment liquid is applied to the fabric 99. The pretreatment liquid is a liquid for fixing the ink to the fabric 99 in the drawing process. The pretreatment liquid affects the hydrophilicity of the fabric 99 with respect to the ink.

[0021] The straightening unit 15 is configured to straighten the fabric 99. The straightening unit 15 stretches the warp or weft threads constituting the fabric 99, for example, by applying a force to the fabric 99. This straightens the fabric 99. The straightening unit 15 includes, for example, a roller around which the fabric 99 is wound, pins that hold both sides of the fabric 99, clips, and the like. The straightening unit 15 is a so-called tenter. When the pretreatment liquid is applied to the fabric 99, shrinkage may occur in the fabric 99. Therefore, the straightening unit 15 straightens the fabric 99 by stretching it.

[0022] The pretreatment drying section 16 is configured to dry the fabric 99. The pretreatment drying section 16 is a drying section included in the pretreatment device 11. The pretreatment drying section 16 dries the fabric 99 to which the pretreatment liquid has been applied, for example, by heating the fabric 99. The pretreatment drying section 16 includes, for example, a heater.

[0023] Inkjet device 12 is a device that performs drawing processing on fabric 99. The drawing processing is processing for drawing an image on fabric 99. Inkjet device 12 draws an image by ejecting ink onto fabric 99. Inkjet device 12 draws, for example, a picture image such as a picture or a pattern. Inkjet device 12 includes, for example, a conveying unit 17, a head 18, and a carriage 19.

[0024] Conveyor unit 17 is configured to convey fabric 99. Conveyor unit 17 is, for example, a belt, a roller, etc. Conveyor unit 17 conveys fabric 99 intermittently, for example. The head 18 is configured to eject ink onto the fabric 99. The head 18 has nozzles 21 from which the ink is ejected. The nozzle resolution of the head 18 is, for example, 600 dpi. Therefore, the head 18 can draw an image on the fabric 99 with a resolution of 600 dpi.

[0025] The carriage 19 carries the head 18. The carriage 19 is configured to scan across the fabric 99. While the carriage 19 scans, the head 18 ejects ink onto the fabric 99, thereby drawing, i.e., printing, an image on the fabric 99. In this manner, the inkjet device 12 is a so-called serial type printer.

[0026] The carriage 19 is configured to be able to mount, for example, a container 22 that contains ink. The container 22 is, for example, an ink cartridge. When the container 22 is mounted on the carriage 19, ink is supplied from the container 22 to the head 18. The container 22 is provided with a code 23 that indicates the type of ink contained therein. The code 23 is, for example, a barcode.

[0027] The container 22 is not limited to being mounted on the carriage 19, and may be connected to the head 18 via a tube, for example. The container 22 may be, for example, a container for refilling a storage container separately provided in the inkjet device 12 with ink, that is, a so-called ink bottle.

[0028] The post-processing device 13 is a device that performs post-processing on the fabric 99 after an image has been drawn on it. The post-processing is a process that is performed after the drawing process. The post-processing device 13 includes, for example, a post-processing drying section 24, a steam section 25, and a cleaning section 26.

[0029] The post-processing drying section 24 is configured to dry the fabric 99. The post-processing drying section 24 is a drying section included in the post-processing device 13. The post-processing drying section 24 dries the fabric 99 onto which the ink has been ejected, for example, by heating the fabric 99. The post-processing drying section 24 includes, for example, a heater. The post-processing drying section 24 may be the same drying section as the pre-processing drying section 16. That is, the pre-processing device 11 and the post-processing device 13 may share a drying section.

[0030] Steam unit 25 is configured to supply high-temperature steam to fabric 99. When steam unit 25 heats fabric 99 with steam, fixation of the ink ejected onto fabric 99 is promoted.

[0031] Cleaning unit 26 is configured to clean fabric 99. Cleaning unit 26 includes, for example, a cleaning tank that stores a cleaning liquid. For example, fabric 99 is cleaned by passing fabric 99 through the cleaning tank. The cleaning liquid is, for example, water. When fabric 99 is cleaned, ink, pretreatment liquid, and the like that are not fixed to fabric 99 are removed from fabric 99.

[0032] The printing system 10 performs a printing process, for example, in accordance with the flowchart shown in Fig. 2. The printing process is started by, for example, a user. As shown in FIG. 2, the textile printing system 10 first applies a pretreatment liquid to the fabric 99 by the application unit 14 in step S11.

[0033] In step S12, the textile printing system 10 straightens the fabric 99 using the straightening unit 15. In step S13, the textile printing system 10 dries the fabric 99 by the pretreatment drying section 16.

[0034] In step S14, the textile printing system 10 prints an image on the fabric 99 by ejecting ink from the head 18 onto the fabric 99 transported by the transport unit 17. At this time, the carriage 19 is driven together with the head 18.

[0035] In step S15, the textile printing system 10 dries the fabric 99 by the post-treatment drying unit 24. At this time, the textile printing system 10 dries the fabric 99 to a degree that suppresses offset of the ink ejected onto the fabric 99. That is, in step S15, the textile printing system 10 dries the fabric 99 to a degree that the surface of the ink ejected onto the fabric 99 is dried.

[0036] In step S16, the textile printing system 10 supplies steam to the fabric 99 by the steam unit 25. In step S17, the textile printing system 10 cleans the fabric 99 using the cleaning unit 26.

[0037] In step S18, the textile printing system 10 dries the fabric 99 by the post-treatment drying section 24. In step S18, unlike step S15, the textile printing system 10 completely dries the fabric 99. As a result, the fabric 99 wetted with the cleaning liquid is dried. When the processing of step S18 is completed, the textile printing process is completed.

[0038] Next, the information processing device 30 will be described. 1, the information processing device 30 is electrically connected to the textile printing system 10. For example, the information processing device 30 is electrically connected to the pre-processing device 11, the inkjet device 12, and the post-processing device 13. Therefore, the information processing device 30 can exchange information with the pre-processing device 11, the inkjet device 12, and the post-processing device 13.

[0039] The information processing device 30 is a device that processes information related to the textile printing process. The information processing device 30 may also serve as a control device that controls the textile printing system 10. In this case, a user controls the textile printing system 10 via the information processing device 30. The information processing device 30 controls textile printing parameters related to the textile printing process of the textile printing system 10. The textile printing parameters include, for example, pre-processing parameters related to the pre-processing of the pre-processing device 11, drawing processing parameters related to the drawing processing of the inkjet device 12, post-processing parameters related to the post-processing of the post-processing device 13, and the like.

[0040] The pretreatment parameters include, for example, the amount of pretreatment liquid applied by the application unit 14, the application time of the pretreatment liquid by the application unit 14, the type of pretreatment liquid, the direction of the force applied by the straightening unit 15 to the fabric 99, the magnitude of the force applied by the straightening unit 15 to the fabric 99, the time during which the straightening unit 15 applies the force to the fabric 99, the drying time by the pretreatment drying unit 16, the drying temperature by the pretreatment drying unit 16, etc.

[0041] The drawing process parameters include, for example, the transport speed of the fabric 99 by the transport unit 17, the distance between the head 18 and the fabric 99, the movement speed of the carriage 19, the number of passes of the carriage 19, the printing mode, and the printing direction indicating unidirectional printing or bidirectional printing.

[0042] The post-treatment parameters include, for example, the drying time by the post-treatment drying section 24, the drying temperature by the post-treatment drying section 24, the temperature of the steam supplied by the steam section 25, the supply time of the steam by the steam section 25, the cleaning time by the cleaning section 26, and the temperature of the cleaning water.

[0043] The information processing device 30 is, for example, a personal computer. The information processing device 30 may be configured as a circuit including: α: one or more processors that execute various processes according to a computer program; β: one or more dedicated hardware circuits, such as application specific integrated circuits, that execute at least a part of the various processes; or γ: a combination thereof. The processor includes a CPU and memory, such as a RAM and a ROM, and the memory stores program code or instructions configured to cause the CPU to execute processes. The memory, i.e., computer-readable medium, includes any readable medium that can be accessed by a general-purpose or dedicated computer.

[0044] As shown in FIG. 3, the information processing device 30 includes, for example, an input unit 31, an output unit 32, an acquisition unit 33, a control unit 34, a storage unit 35, a transmission unit 36, and a reception unit 37. The input unit 31 is an interface through which a user inputs data to the information processing device 30. For this purpose, the input unit 31 is connected to an input device 38 such as a mouse, a keyboard, or a touch panel. The user inputs data to the information processing device 30 through the input unit 31 by operating the input device 38. The data input through the input unit 31 is stored in the storage unit 35, for example.

[0045] The user inputs, for example, client data indicating information about the client through the input unit 31. The information about the client is, for example, the client's country, the client's name, etc. The user inputs, for example, application data indicating information about the application of the printed fabric 99 through the input unit 31. The information about application is, for example, information indicating applications such as women's clothing, children's clothing, interior design, etc. The user may input other information as data to the information processing device 30 through the input unit 31, in addition to the client data and application data.

[0046] The output unit 32 is an interface for outputting data from the information processing device 30. The output unit 32 is connected to an output device 39 such as a display or a speaker. For example, a user can understand the data output from the information processing device 30 through the output unit 32 by checking the output device 39.

[0047] The acquisition unit 33 is an interface for acquiring data from the outside. The acquisition unit 33 is connected to, for example, an image capture device 41, a weighing device 42, a reading device 43, a thermo-hygrometer 44, a network 45, the textile printing system 10, and the like. The acquisition unit 33 may also be connected to, for example, a storage medium such as a USB flash drive or a memory card. The acquisition unit 33 acquires data from the connected object. The data acquired by the acquisition unit 33 is stored in, for example, the storage unit 35.

[0048] The acquisition unit 33 acquires, for example, data indicating information related to the printing process. For example, the acquisition unit 33 acquires, from the printing system 10, printing parameters set in the pre-processing device 11, the inkjet device 12, and the post-processing device 13 when the printing system 10 performs the printing process.

[0049] Image capture device 41 is a device that captures fabric 99 as an image. Image capture device 41 is, for example, a camera, a scanner, etc. Image capture device 41 captures fabric 99 as an image by photographing or scanning fabric 99. At this time, image capture device 41 generates image data in which fabric 99 has been digitized as an image. Therefore, acquisition unit 33 acquires the image data in which fabric 99 has been digitized as an image through image capture device 41.

[0050] The user appropriately captures the fabric 99 as an image by using the image capture device 41. For example, the user captures as images the fabric 99 before the textile printing process, the fabric 99 after the textile printing process, the fabric 99 after the pre-processing and before the drawing process, the fabric 99 after the drawing process and before the post-processing, etc., using the image capture device 41. The user captures the fabric 99 as an image at the timings, for example, before the textile printing process, after the textile printing process, after the pre-processing and before the drawing process, and after the drawing process and before the post-processing. Therefore, the image capture device 41 generates pre-printing image data obtained by digitizing the image of the fabric 99 before the printing process, post-printing image data obtained by digitizing the image of the fabric 99 after the printing process, pre-drawing image data obtained by digitizing the image of the fabric 99 after the pre-processing and before the drawing process, and post-drawing image data obtained by digitizing the image of the fabric 99 before the post-processing and after the drawing process. Therefore, the acquisition unit 33 can acquire the pre-printing image data, the post-printing image data, the pre-drawing image data, and the post-drawing image data as the image data obtained by digitizing the image of the fabric 99. The acquisition unit 33 in this example acquires at least the pre-printing image data among the image data.

[0051] The image data is, for example, data in which an area of ​​the fabric 99 that is 10 mm or more in length and 10 mm or more in width is digitized at a resolution of 120 dpi or more. Such a format makes it possible to obtain image data suitable for the image analysis described below. Therefore, the image capture device 41 captures the fabric 99 as an image at a resolution of 120 dpi or more in an area of ​​the fabric 99 that is 10 mm or more in length and 10 mm or more in width. The image capture device 41 captures the fabric 99 as a full-color image, but may also capture a monochrome image or a grayscale image.

[0052] Of the image data, the post-printing image data and the post-drawing image data are preferably digitized data with a resolution equal to or higher than the resolution of the image drawn on the fabric 99. In this case, the image data can be suitable for the image analysis described below.

[0053] When capturing an image of the fabric 99 using the image capture device 41, the user may capture an image of both the front side and the back side of the fabric 99. In this case, the image data includes electronic data of the front side of the fabric 99 and electronic data of the back side of the fabric 99.

[0054] When capturing an image of the fabric 99 using the image capture device 41, the user may capture an image of the fabric 99 in a stretched state and an image of the fabric 99 in an unstretched state. In this case, the image data includes electronic data of the fabric 99 in a stretched state and electronic data of the fabric 99 in an unstretched state. For example, the user captures the fabric 99 as an image while stretching the fabric 99 by hand. The user may stretch the fabric 99 in a direction along the warp threads, may stretch the fabric 99 along the weft threads, or may stretch the fabric 99 diagonally with respect to the warp threads and weft threads.

[0055] The weighing device 42 is a device that weighs the fabric 99. A user measures the weight per unit area of ​​the fabric 99 by using the weighing device 42. As a result, the acquisition unit 33 acquires, via the weighing device 42, weighing data indicating the weight per unit area of ​​the fabric 99.

[0056] The reading device 43 is, for example, a device that reads the code 23 attached to the container 22. The reading device 43 is, for example, a reader. The user causes the reading device 43 to read the code 23 attached to the container 22. The acquisition unit 33 acquires ink data corresponding to the code 23 read by the reading device 43, for example, by referring to a database stored in the storage unit 35. The ink data is data indicating the type of ink, such as reactive ink, disperse ink, acidic ink, etc. The acquisition unit 33 acquires ink data indicating the type of ink contained in the container 22 by reading the code 23 attached to the container 22 with the reading device 43.

[0057] The thermo-hygrometer 44 is a sensor that measures temperature and humidity. The thermo-hygrometer 44 measures the temperature and humidity of the environment in which the pre-processing device 11, the inkjet device 12, and the post-processing device 13 are installed. As a result, the acquisition unit 33 acquires temperature and humidity data indicating the temperature and humidity of the environment in which the textile printing system 10 is installed.

[0058] The acquisition unit 33 may acquire data through the network 45 based on data input by a user. For example, the acquisition unit 33 may acquire device data indicating information about a device from a database on the network 45 based on data indicating the model number of the device input by a user. Information about the device includes information about the specifications, settings, etc. of the device. That is, the acquisition unit 33 may acquire device data indicating device information of the pre-processing device 11. The acquisition unit 33 may acquire device data indicating device information of the inkjet device 12. The acquisition unit 33 may acquire device data indicating device information of the post-processing device 13.

[0059] The acquisition unit 33 acquires original image data, which is original data of an image to be drawn on the fabric 99 by the inkjet device 12, from, for example, a storage medium, a network 45, etc. That is, the inkjet device 12 draws an image on the fabric 99 based on this original image data. The original image data can also be said to be original data of an image drawn by the drawing process. The original image data is, for example, data provided from a client to a user.

[0060] The acquisition unit 33 may acquire other data without being limited to image data, weighing data, ink data, device data, printing parameters, original image data, etc. The acquisition unit 33 may acquire data through the network 45, or may acquire data through the input unit 31. The above-mentioned examples of the types of data acquired by the acquisition unit 33 and the means for acquiring the data are merely examples. Therefore, the acquisition unit 33 may acquire data other than the above-mentioned types, or may acquire data by means other than the above-mentioned means.

[0061] The acquisition unit 33 may acquire status data indicating usage status of the pre-processing device 11, the inkjet device 12, and the post-processing device 13. The status data is data indicating the usage status including environmental information of the pre-processing device 11, the inkjet device 12, and the post-processing device 13.

[0062] The status data is data indicating, for example, the operating time, which is the time elapsed since the pre-treatment device 11, the inkjet device 12, and the post-treatment device 13 started operating, the temperature and humidity of the environment in which the printing system 10 is installed, the water quality of the cleaning liquid used in the cleaning unit 26, etc. The status data includes temperature and humidity data as data indicating environmental information. The acquisition unit 33 acquires, as the status data, for example, operating data indicating the operating time of each device from the printing system 10. The acquisition unit 33 acquires, as the data indicating environmental information, for example, water quality data indicating the water quality of the cleaning liquid by the user inputting the water quality of the cleaning liquid through the input unit 31.

[0063] The acquisition unit 33 may acquire altitude data indicating the altitude at which the pre-processing device 11, the inkjet device 12, and the post-processing device 13 are installed, as data indicating the environmental information. The acquisition unit 33 acquires the altitude data, for example, by the user inputting the altitude at which the pre-processing device 11, the inkjet device 12, and the post-processing device 13 are installed through the input unit 31. The acquisition unit 33 may acquire the altitude data from the network 45, or may acquire the altitude data by conversion from a barometer connected to the acquisition unit 33.

[0064] The control unit 34 is, for example, the CPU described above. The control unit 34 comprehensively controls the information processing device 30. The control unit 34 controls various components by executing a program stored in the storage unit 35. The control unit 34 can also control, for example, the printing system 10. The control unit 34 controls the printing system 10 by, for example, transmitting printing parameters to the printing system 10.

[0065] The storage unit 35 is, for example, the memory described above. In addition to the program executed by the control unit 34, the storage unit 35 stores, for example, data input through the input unit 31, data output through the output unit 32, data acquired by the acquisition unit 33, etc. The storage unit 35 stores, for example, a data set 46 and derivation data 47.

[0066] The storage unit 35 stores one or more datasets 46. The dataset 46 is a set of multiple data related to one textile printing process. The dataset 46 includes data input through the input unit 31, data acquired through the acquisition unit 33, etc. That is, the storage unit 35 stores the data input through the input unit 31, data acquired by the acquisition unit 33, etc. as the dataset 46. The dataset 46 includes at least pre-printing image data and situation data. The storage unit 35 stores, for example, the dataset 46 shown in Table 1.

[0067] [Table 1] As shown in Table 1, a plurality of data are associated with the dataset 46, such as client data, status data, original image data, printing parameters, pre-printing image data, and post-printing image data. Although Table 1 lists client data, status data, original image data, printing parameters, pre-printing image data, and post-printing image data, in reality, the data input through the input unit 31 and other data acquired by the acquisition unit 33, such as the ink data, application data, weighing data, and device data described above, are also associated with the dataset 46. The dataset 46 is a set of various data and parameters associated with one printing process.

[0068] The derivation data 47 is data for deriving recommended parameters for at least one of the pre-processing device 11 and the post-processing device 13 based on the pre-printing image data and the status data. The derivation data 47 includes first data 48 and second data 49. That is, the storage unit 35 stores the first data 48 and the second data 49 that is different from the first data 48.

[0069] The recommended parameters are printing parameters recommended for obtaining a predetermined image quality. The recommended parameters may be, for example, printing parameters recommended for obtaining an image quality equivalent to that of the original image data, or printing parameters recommended for obtaining an image quality that a client evaluates as being sufficient.

[0070] The first data 48 is data that defines a trained model trained by machine learning. The first data 48 is data that defines a trained model that outputs fabric data indicating the feature amount of the fabric 99 when the pre-printing image data and the situation data are input. The fabric data is data that indicates the feature amount of the fabric 99, such as the thickness, density, and surface roughness of the thread that constitutes the fabric 99. The fabric data may be data that quantifies the feature amount of the fabric 99, or may be data that indicates the type of the fabric 99 that is roughly classified based on the feature amount of the fabric 99.

[0071] The pre-printing image data includes images of the threads that make up the fabric 99. Therefore, the pre-printing image data includes information about the feature amounts of the fabric 99. Therefore, by analyzing the pre-printing image data, the feature amounts of the fabric 99 can be obtained.

[0072] The first data 48 is generated by a server 50 electrically connected to the information processing device 30. The first data 48 is generated by the server 50 based on the pre-printing image data and the situation data. For example, the first data 48 is generated by the server 50 performing calculations based on the teacher data.

[0073] The second data 49 is data indicating a correspondence relationship between the fabric data and recommended parameters for at least one of the pre-processing device 11 and the post-processing device 13. The second data 49 is, for example, table data in which the fabric data and the recommended parameters correspond one-to-one.

[0074] The second data 49 may indicate a correspondence relationship between the fabric data and recommended parameters for the inkjet device 12. The second data 49 may indicate, for example, a correspondence relationship between the fabric data and recommended parameters for the inkjet device 12 in addition to recommended parameters for at least one of the pre-processing device 11 and the post-processing device 13.

[0075] The second data 49 is generated, for example, by experimentally determining recommended printing parameters for obtaining a predetermined image quality when performing a printing process on the fabric 99 indicated by the fabric data. That is, by associating the experimentally determined printing parameters, i.e., the recommended parameters, with the fabric data, the second data 49 is generated as table data in which the fabric data and the recommended parameters correspond one-to-one.

[0076] The transmission unit 36 ​​is an interface for transmitting data to the server 50. The transmission unit 36 ​​transmits the before-printing image data and the status data to the server 50. The transmission unit 36 ​​may transmit the after-printing image data to the server 50. The transmission unit 36 ​​may transmit the original image data to the server 50. The transmission unit 36 ​​may transmit the data set 46 to the server 50.

[0077] The receiving unit 37 is an interface for receiving data from the server 50. The receiving unit 37 receives first data 48 from the server 50. The first data 48 received by the receiving unit 37 is stored in the storage unit 35. The receiving unit 37 may receive second data 49 from the server 50, or may receive derivation data 47 from the server 50.

[0078] The information processing device 30 operates, for example, according to the flowchart shown in Fig. 4. The series of processes shown in Fig. 4 is started, for example, by a user. The series of processes shown in Fig. 4 is executed by the control unit 34.

[0079] 4, the control unit 34 first acquires the pre-printing image data and the situation data by the acquisition unit 33 in step S20. For example, step S20 is executed before the printing process is performed by the printing system 10. At this time, the control unit 34 may acquire other data. The control unit 34 is not limited to acquiring data from an external device such as the input device 38 or the image capture device 41, and may acquire data from, for example, a data set 46 stored in the storage unit 35.

[0080] In step S21, the control unit 34 causes the transmission unit 36 ​​to transmit the before-printing image data and the situation data to the server 50. The control unit 34 may transmit to the server 50 a data set 46 including the before-printing image data and the situation data.

[0081] In step S22, the control unit 34 determines whether or not to request the first data 48. At this time, the control unit 34 determines, for example, whether or not there is an instruction from the user requesting the first data 48. If there is an instruction from the user requesting the first data 48, the control unit 34 requests the first data 48 from the server 50. In this case, the control unit 34 shifts the process to step S23. If there is no instruction from the user requesting the first data 48, the control unit 34 does not request the first data 48 from the server 50. In this case, the control unit 34 shifts the process to step S24.

[0082] In step S23, the control unit 34 receives the first data 48 from the server via the receiving unit 37. Upon receiving the first data 48, the control unit 34 stores the first data 48 in the storage unit 35. That is, the first data 48 is updated.

[0083] In step S24, the control unit 34 outputs fabric data from the before-printing image data and the situation data based on the first data 48. At this time, the control unit 51 inputs the before-printing image data and the situation data acquired in step S20 into the trained model defined by the first data 48. The control unit 34 may store the fabric data in the storage unit 35 after associating it with the before-printing image data.

[0084] In step S25, the control unit 34 outputs recommended parameters from the fabric data based on the second data 49. At this time, the control unit 51 obtains recommended parameters corresponding to the fabric data by referring to the table data, which is the second data 49. That is, in steps S24 and S25, the control unit 34 derives recommended parameters for at least one of the pre-processing device 11 and the post-processing device 13 from the pre-printing image data and the situation data based on the first data 48 and the second data 49. As a result, even if the fabric 99 is unknown to the user, recommended parameters can be derived based on the feature amounts of the fabric 99.

[0085] In step S26, the control unit 34 outputs the recommended parameters through the output unit 32. When the recommended parameters are output through the output unit 32, the user can know the recommended parameters that are recommended for obtaining a predetermined image quality. This allows the user to use the output recommended parameters as an index for obtaining a predetermined image quality. The control unit 34 may reflect the derived recommended parameters in the textile printing system 10.

[0086] In step S27, the control unit 34 controls the printing system 10 to perform the printing process. In step S28, the control unit 34 acquires the post-printing image data by the acquisition unit 33. Therefore, step S28 is executed after the printing process is performed. The post-printing image data acquired in step S28 is image data obtained by digitizing the fabric 99 depicted in the before-printing image data acquired in step S20 as a post-printing image. Therefore, the post-printing image data acquired in step S28 is data related to the before-printing image data acquired in step S20. The control unit 34 may store the acquired post-printing image data in the storage unit 35 in association with the before-printing image data.

[0087] In step S29, the control unit 34 causes the transmission unit 36 ​​to transmit the after-printing image data to the server 50. At this time, the control unit 34 may transmit the original image data in addition to the after-printing image data to the server 50. The control unit 34 may transmit a data set 46 including the after-printing image data and the original image data to the server 50.

[0088] When the control unit 34 finishes the process of step S29, the control unit 34 ends the series of processes shown in FIG. As described above, the information processing method for processing information related to the printing process includes acquiring pre-printing image data and status data, transmitting the pre-printing image data and status data to the server 50, receiving from the server 50 first data 48 generated by the server 50 based on the pre-printing image data and status data, and deriving recommended parameters from the pre-printing image data and status data based on the first data 48 and second data 49 indicating the correspondence between the fabric data and the recommended parameters. This information processing method is implemented, for example, by having a computer execute a program. This program may be stored in the storage unit 35 or may be stored in a storage medium. The control unit 34 executes the above-mentioned information processing by reading out the program.

[0089] Next, the server 50 will be described. As shown in Fig. 3, the server 50 is electrically connected to the information processing device 30. Like the information processing device 30, the server 50 may be configured as a circuit including: α: one or more processors that execute various processes according to a computer program; β: one or more dedicated hardware circuits, such as application specific integrated circuits, that execute at least a part of the various processes; or γ: a combination thereof. The processor includes a CPU and memory, such as a RAM and a ROM, and the memory stores program codes or instructions that are configured to cause the CPU to execute processes. The memory, i.e., computer-readable medium, includes any readable medium that can be accessed by a general-purpose or dedicated computer.

[0090] The server 50 includes a control unit 51 and a storage unit 52. The control unit 51 is, for example, the above-mentioned CPU. The storage unit 52 is, for example, the above-mentioned memory. When the control unit 51 receives data from the information processing device 30, it stores the data in the storage unit 52. For example, the control unit 51 stores the before-printing image data and the situation data received from the information processing device 30 in the storage unit 52. The control unit 51 may also store the after-printing image data in the storage unit 52, or may store the original image data in the storage unit 52. The control unit 51 may store the data set 46 in the storage unit 52. In this way, the control unit 51 accumulates the received data in the storage unit 52. The accumulation of data in the storage unit 52 constitutes so-called big data 53.

[0091] When the control unit 51 receives the image data, the control unit 51 performs image analysis on the image data. For example, when the control unit 51 receives the before-textile-printing image data and when the control unit 51 receives the after-textile-printing image data, the control unit 51 performs image analysis on the image data.

[0092] When the control unit 51 receives the pre-printing image data, it performs image analysis on the pre-printing image data. The control unit 51 analyzes the pre-printing image data to extract features of the fabric 99 depicted in the pre-printing image data. The control unit 51 analyzes the pre-printing image data by, for example, fast Fourier transform. When the image data is full color, the control unit 51 performs fast Fourier transform for each primary color, for example. The brightness is expressed, for example, in the Lab color system.

[0093] For example, when the control unit 51 receives the before-printing image data, the control unit 51 operates according to the flowchart shown in Fig. 5. For example, when the control unit 51 receives the before-printing image data and the situation data transmitted from the information processing device 30 in step S21, the control unit 51 starts a series of processes shown in Fig. 5.

[0094] 5, in step S31, the control unit 51 performs a Fourier transform on the pre-printing image data. At this time, the control unit 51 performs a Fourier transform in two directions, the vertical direction and the horizontal direction, with the luminance as the amplitude. As a result, a spectral image is obtained for the pre-printing image data. From the spectral image, feature quantities of the fabric 99, such as the thickness, density, and surface roughness of the threads constituting the fabric 99, are extracted.

[0095] The hydrophilicity of the fabric 99 is determined by the thickness, density, surface roughness, and the like of the threads constituting the fabric 99. The hydrophilicity of the fabric 99 is also a feature of the fabric 99. That is, by performing image analysis on the pre-printing image data, the hydrophilicity of the fabric 99 is extracted as an example of the feature of the fabric 99. The hydrophilicity of the fabric 99 significantly affects the image quality of the image drawn on the fabric 99. Therefore, by understanding the feature of the fabric 99, it is possible to perform an appropriate printing process.

[0096] As shown in Fig. 6, when extracting the feature amount of the fabric 99 from the pre-printing image data, there is a resolution of the pre-printing image data suitable for image analysis. Six patterns of resolution, resolution, pre-printing image, and spectral image are shown in Fig. 6. The six patterns of pre-printing images shown in Fig. 6 are displayed with different resolutions and resolutions.

[0097] The resolution shown in Fig. 6 is the resolution of the pre-printing image shown in Fig. 6, i.e., the resolution when fabric 99 is digitized as an image. The resolution shown in Fig. 6 is the resolution of the pre-printing image shown in Fig. 6, i.e., the resolution when fabric 99 is digitized as an image. The pre-printing image shown in Fig. 6 is an image of fabric 99 with a thread thickness of about 500 μm. The spectral image shown in Fig. 6 is an image obtained by Fourier transforming the pre-printing image located immediately above that spectral image.

[0098] As can be seen from Fig. 6, if the resolution of the pre-printed image is 120 dpi or more, a clear spectrum image can be obtained. If the resolution of the pre-printed image is less than 120 dpi, for example, 60 dpi or 24 dpi, the image becomes coarse, making it difficult to extract the feature values ​​of the fabric 99.

[0099] As described above, the area of ​​the pre-printing image data is, for example, 10 mm or more in length and 10 mm or more in width. The reason for this is that if the thread thickness is 500 μm, 20 pitches of the thread can be displayed. This makes it easier to extract the feature amount of the fabric 99.

[0100] 5, in step S32, the control unit 51 corrects the feature amount of the fabric 99 based on the situation data. The control unit 51 reflects the situation data in the fabric data by using correction data such as a conversion formula, a function formula, a correspondence table, etc. stored in the storage unit 52.

[0101] The situation data includes environmental information such as temperature, humidity, and altitude at which the pre-processing device 11, the inkjet device 12, and the post-processing device 13 are installed. Such environmental information affects the characteristic amount of the fabric. For example, when the temperature and humidity increase, the amount of moisture in the air increases, and therefore the moisture content of the fabric 99 increases. When the moisture content of the fabric 99 increases, for example, the hydrophilicity of the fabric 99 decreases. For example, when the altitude increases, ink is more likely to penetrate the fabric 99. Therefore, for example, the hydrophilicity of the fabric 99 improves. As a result, bleed-through of the fabric 99 is more likely to occur. In this way, the situation data affects, for example, the hydrophilicity of the fabric 99 as a characteristic amount of the fabric 99. The memory unit 52 stores a conversion formula for converting, for example, temperature, humidity, and altitude into the moisture content of the fabric 99.

[0102] The status data includes the operation times of the pre-processing device 11, the inkjet device 12, and the post-processing device 13. As the operation times become longer, the performance of the devices deteriorates. The storage unit 52 stores, for example, a function formula that represents the relationship between the operation times and the deterioration of the device performance.

[0103] When the performance of the device is degraded, for example, the force with which the conveying unit 17 pulls the fabric 99 when the conveying unit 17 conveys the fabric 99 may be reduced, or the efficiency with which the fabric 99 is dried by the pre-processing drying unit 16 or the post-processing drying unit 24 may be reduced. For example, when the force with which the conveying unit 17 pulls the fabric 99 is reduced, this is equivalent to the inkjet device 12 handling the fabric 99 that is less stretchable than the actual fabric 99. For example, when the efficiency with which the post-processing drying unit 24 drys the fabric 99 is reduced, this is equivalent to the post-processing device 13 handling the fabric 99 that is less dryable than the actual fabric 99. In this way, the performance degradation of the device can be reflected in the feature amount of the fabric 99.

[0104] The operating time indicates the deterioration of the device's performance, and is therefore a parameter to be considered when generating second data 49 that outputs recommended parameters from the fabric data, but in this example, the deterioration of the device's performance due to the operating time is reflected in the feature amount of the fabric 99. As a result, the recommended parameters derived from the corrected feature amount of the fabric 99 are parameters that reflect the deterioration of the device's performance due to the operating time.

[0105] In step S33, the control unit 51 stores the corrected feature amount of the fabric 99 as fabric data in the storage unit 52. At this time, the control unit 51 associates this fabric data with the before-printing image data analyzed in step S31 and stores it in the storage unit 52. That is, the control unit 51 accumulates the fabric data obtained from the before-printing image data and the situation data in the storage unit 52. Therefore, the fabric data constitutes big data 53.

[0106] The control unit 51 may store the feature amounts of the fabric 99 based on the pre-printing image data and the correction values ​​based on the situation data in the storage unit 52. Even in this case, the control unit 51 can read out the fabric data indicating the feature amounts of the fabric 99 corrected based on the correction values ​​from the storage unit 52 as necessary.

[0107] When the control unit 51 finishes the process of step S33, the control unit 51 ends the series of processes shown in FIG. When the control unit 51 receives the post-printing image data, it performs image analysis on the post-printing image data. The control unit 51 quantifies the image quality of the post-printing image data by analyzing the post-printing image data. The control unit 51 analyzes the pre-printing image data by, for example, fast Fourier transform. When the image data is full color, the control unit 51 performs, for example, fast Fourier transform for each primary color.

[0108] The after-printing image data is digitized data with a resolution equal to or higher than the resolution of the image drawn on the fabric 99. In other words, for the after-printing image data, degradation of image quality is suppressed when the fabric 99 is captured as an image. Therefore, the image quality of the after-printing image data can be appropriately quantified. If the after-printing image data includes data of the fabric 99 in an enlarged state, the image quality of the enlarged after-printing image can also be quantified.

[0109] When the control unit 51 receives the after-printing image data, the control unit 51 operates, for example, according to the flowchart shown in Fig. 7. When the control unit 51 receives the after-printing image data transmitted from the information processing device 30 in step S29, for example, the control unit 51 starts a series of processes shown in Fig. 7.

[0110] 7, in step S41, the control unit 51 performs a Fourier transform on the post-printing image data. At this time, the control unit 51 performs a Fourier transform in two directions, the vertical direction and the horizontal direction, with luminance as the amplitude. As a result, a spectrum such as a power spectrum or a Wiener spectrum can be obtained for the post-printing image data.

[0111] In step S42, the control unit 51 analyzes the spectrum to quantify the image quality of the post-printing image data as image quality parameters. Image quality includes, for example, black density, gamut, strike-through, bleeding, sharpness, color, graininess, banding, and gradation. Index values ​​that serve as indexes of such image quality correlate with the spectrum obtained in step S41. For example, if banding occurs, an index value that serves as an index of banding appears in the spectrum. Similarly, for example, with respect to graininess, an index value that serves as an index of graininess appears in the spectrum.

[0112] The control unit 51 determines an index value serving as an index of banding, for example, based on the power spectrum of the post-printing image data and a predetermined correction function that corrects visibility. The control unit 51 determines an index value serving as an index of graininess, for example, based on the Wiener spectrum of the post-printing image data and a predetermined correction function that corrects visibility. The control unit 51 evaluates banding and graininess, for example, by comparing the obtained index value with its reference value. In this way, the control unit 51 quantifies the image quality of the post-printing image data as an image quality parameter. This allows the image quality of the post-printing image data to be evaluated with an objective index.

[0113] In step S43, control unit 51 stores the image quality parameters in storage unit 52. More specifically, control unit 51 stores the image quality parameters obtained in step S42 in storage unit 52 after associating them with the after-printing image data analyzed in step S41. That is, control unit 51 accumulates the image quality of the after-printing image data in storage unit 52 in a state in which the image quality is quantified as image quality parameters. Therefore, the image quality parameters constitute big data 53. When control unit 51 finishes the process of step S43, it ends the series of processes shown in FIG. 7.

[0114] When the control unit 51 receives the original image data in addition to the after-printing image data, the control unit 51 can quantify the image quality of the after-printing image data relative to the original image data. In this case, the control unit 51 first generates analysis image data from the after-printing image data and the original image data. The control unit 51 generates analysis image data, for example, by calculating the difference in luminance for each corresponding pixel in the after-printing image data and the original image data. This allows the acquisition of analysis image data that does not affect the image pattern. In other words, the image quality of the after-printing image data can be evaluated without affecting the image pattern.

[0115] The analysis image data indicates the change in image quality between the original image data and the after-printing image data. In other words, the analysis image data indicates the image quality of the after-printing image data based on the original image data. Therefore, by analyzing the analysis image data, it is possible to evaluate how much the image quality of the after-printing image data has changed compared to the original image data, that is, the degree of image quality deterioration. The control unit 51 executes a series of processes shown in FIG. 7 for this analysis image data, thereby quantifying and storing the image quality of the after-printing image data.

[0116] The control unit 51 generates first data 48 based on the pre-printing image data and the status data. When the control unit 51 receives a request instruction for the first data 48 from the information processing device 30, the control unit 51 transmits the generated first data 48 to the information processing device 30. As a result, the first data 48 stored in the storage unit 35 of the information processing device 30 is updated.

[0117] The control unit 51 generates first data from the big data 53 stored in the storage unit 52. For example, the control unit 51 generates first data 48 that defines a trained model that outputs fabric data from the before-printing image data and the situation data, based on a large amount of accumulated before-printing image data, situation data, and fabric data.

[0118] The fabric data is data obtained from the before-printing image data and the situation data. Therefore, it can be said that the control unit 51 generates the first data 48 based on the before-printing image data and the situation data.

[0119] The control unit 51 inputs the pre-printing image data, the situation data, and the fabric data as teacher data into the model, thereby making the model learn the correlation between the pre-printing image data, the situation data, and the fabric data. An example of a learning method is deep learning. By such learning, when the pre-printing image data and the situation data are input, a trained model that outputs the fabric data is obtained. Therefore, the control unit 51 can generate the first data 48 that defines the trained model.

[0120] The control unit 51 can verify the accuracy of the first data 48. For example, the control unit 51 verifies the accuracy of the first data 48 based on the post-printing image data transmitted from the information processing device 30.

[0121] The control unit 51 quantifies the image quality of the post-printing image data as an image quality parameter, and then compares the image quality parameter with the image quality parameter expected to be obtained from the recommended parameter output by the derivation data 47. That is, the control unit 51 compares the image quality as a theoretical value obtained from the derivation data 47 with the image quality as an experimental value obtained by actually performing the printing process.

[0122] If the difference in image quality between the two is equal to or greater than a predetermined value, it can be estimated that the accuracy of the fabric data output by the first data 48 is low. In this case, the control unit 51 updates the first data 48 by, for example, re-learning the trained model.

[0123] The control unit 51 can also generate the second data 49. For example, the control unit 51 generates the second data from the big data 53 stored in the storage unit 52. The control unit 51 generates the second data 49 indicating the correspondence between the fabric data and the printing parameters recommended for obtaining a predetermined image quality, i.e., the recommended parameters, based on, for example, a large amount of accumulated fabric data, printing parameters, and image quality parameters. In this case, the control unit 51 may transmit the generated second data 49 to the information processing device 30.

[0124] The control unit 51 can generate second data 49 that specifies a trained model trained by, for example, machine learning. In this case, the control unit 51 inputs a large amount of data accumulated in the storage unit 52 to the model as training data. For example, fabric data, printing parameters, and image quality parameters are used as training data. The control unit 51 may use the data set 46 as training data. This allows the model to learn the correlation between the fabric data and the printing parameters recommended for obtaining a predetermined image quality, that is, the recommended parameters. An example of a learning method is deep learning. By such learning, when the fabric data is input, a trained model that outputs the printing parameters recommended for obtaining a predetermined image quality is obtained.

[0125] The control unit 51 can generate, for example, second data 49 that defines an analytical model obtained by multivariate analysis. In this case, the control unit 51 performs multivariate analysis on a large amount of data stored in the storage unit 52. The control unit 51 performs multivariate analysis on, for example, fabric data, printing parameters, and image quality parameters. The control unit 51 may also perform multivariate analysis on the data set 46.

[0126] An example of multivariate analysis is the MT method. First, a population, i.e., a unit space, in which the image quality of the image data after printing is equal to or higher than a predetermined image quality is created from a large amount of data. Next, the Mahalanobis distance for the unit space is calculated. This makes it possible to grasp the correlation between the image quality parameters and other data. The larger the Mahalanobis distance, the lower the image quality. Next, a threshold value for the Mahalanobis distance for the unit space is determined. This makes it possible to obtain an analytical model. According to this analytical model, it is possible to find the correlation between the fabric data and the printing parameters recommended for obtaining a predetermined image quality, i.e., the recommended parameters.

[0127] The control unit 51 can also generate the second data 49 as table data in which the fabric data and the recommended parameters correspond one-to-one to each other by associating the printing parameters found by experiment, that is, the recommended parameters, with the fabric data.

[0128] Next, the operation and effects of the above-mentioned first embodiment will be described. (1-1) The information processing device 30 includes an acquisition unit 33 that acquires pre-printing image data and status data, a transmission unit 36 ​​that transmits the pre-printing image data and status data to a server 50, a reception unit 37 that receives from the server 50 first data 48 generated by the server 50 based on the pre-printing image data and status data, a storage unit 35 that stores the first data 48 and second data 49, and a control unit 34. The first data 48 is a trained model trained by machine learning, and is data that specifies the trained model that outputs fabric data indicating the feature amount of a fabric 99 when the pre-printing image data and status data are input. The second data is data that indicates the correspondence between the fabric data and the recommended parameters. The control unit 34 derives recommended parameters from the pre-printing image data and status data based on the first data 48 and the second data 49.

[0129] The pre-printing image data includes images of the threads that make up the fabric 99. Therefore, the pre-printing image data includes information that indicates the feature amounts of the fabric 99, such as the thickness, density, and surface roughness of the threads that make up the fabric 99. By analyzing the pre-printing image data, such feature amounts of the fabric 99 can be obtained.

[0130] The usage status includes environmental information such as temperature, humidity, etc. The temperature and humidity affect, for example, the moisture content of the fabric 99, which is a feature of the fabric 99. That is, there is a correlation between the status data and the fabric data.

[0131] According to the above configuration, the control unit 34 can provide the user with recommended parameters for at least one of the pre-processing device 11 and the post-processing device 13 based on the pre-textile image data and the status data by using the first data 48 and the second data 49. The user can obtain a desired image quality by setting the recommended parameters for at least one of the pre-processing device 11 and the post-processing device 13 in that device. This simplifies the user's work.

[0132] (1-2) The environmental information includes the altitude at which the inkjet device 12, the pre-processing device 11, and the post-processing device 13 are installed. The higher the altitude, the more difficult it is for liquid to penetrate into the fabric 99. Therefore, the altitude affects, for example, the hydrophilicity of the fabric, which is a feature amount of the fabric 99. Therefore, according to the above configuration, it is possible to obtain situation data suitable for obtaining recommended parameters.

[0133] <Second embodiment> The second embodiment differs from the first embodiment in that, when the information processing device 30 transmits the pre-printing image data and the status data to the server 50, it receives recommended parameters from the server 50. In the second embodiment, the differences from the first embodiment will be mainly described.

[0134] 8, in the second embodiment, the storage unit 35 of the information processing device 30 does not store the derivation data 47, while the storage unit 52 of the server 50 stores the derivation data 47. That is, the storage unit 52 stores the first data 48 and the second data 49.

[0135] The information processing device 30 operates, for example, according to the flowchart shown in Fig. 9. The series of processes shown in Fig. 9 is started, for example, by a user. The series of processes shown in Fig. 9 is executed by the control unit 34.

[0136] As shown in FIG. 9, in step S51, the control unit 34 acquires the pre-printing image data and the situation data by the acquisition unit 33, similarly to step S20. In step S52, the control unit 34 causes the transmission unit 36 ​​to transmit the before-printing image data and the status data to the server 50, similarly to step S21.

[0137] In step S53, the control unit 34 receives the recommended parameters from the server 50 via the receiving unit 37. The recommended parameters are parameters derived by the server 50 based on the pre-printing image data and situation data transmitted by the transmitting unit 36.

[0138] In step S54, the control unit 34 outputs the recommended parameters through the output unit 32, similar to step S26. When the recommended parameters are output through the output unit 32, the user can know the printing parameters recommended for obtaining a predetermined image quality. This allows the user to use the output recommended parameters as an index for obtaining a predetermined image quality. The control unit 34 may reflect the received recommended parameters in the printing system 10.

[0139] In step S55, similarly to step S27, the control unit 34 controls the printing system 10 to perform the printing process. In step S56, the control unit 34 acquires the post-printing image data by the acquisition unit 33, similarly to step S28. Therefore, step S56 is executed after the printing process is performed. The post-printing image data acquired in step S56 is image data obtained by digitizing the fabric 99 depicted in the before-printing image data acquired in step S51 as a post-printing image. In other words, the post-printing image data acquired in step S56 is data related to the before-printing image data acquired in step S51. The control unit 34 may associate the acquired post-printing image data with the before-printing image data and store them in the storage unit 35.

[0140] In step S57, similarly to step S29, the control unit 34 transmits the after-printing image data to the server 50 by the transmission unit 36. At this time, the control unit 34 may transmit the original image data in addition to the after-printing image data to the server 50. The control unit 34 may transmit to the server 50 a data set 46 including the after-printing image data and the original image data.

[0141] When the control unit 34 finishes the process of step S57, the control unit 34 ends the series of processes shown in FIG. As described above, the information processing method for processing information related to the printing process includes acquiring pre-printing image data and status data, transmitting the pre-printing image data and status data to the server 50, and receiving recommended parameters derived by the server 50 based on the pre-printing image data and status data. This information processing method is implemented, for example, by having a computer execute a program. This program may be stored in the storage unit 35 or in a storage medium. The control unit 34 executes the above-mentioned information processing by reading out the program.

[0142] When the server 50 receives the before-printing image data and the status data from the information processing device 30, the server 50 operates, for example, according to the flowchart shown in Fig. 10 in order to transmit recommended parameters to the information processing device 30. The series of processes shown in Fig. 10 are executed by the control unit 51. For example, when the control unit 51 receives the after-printing image data transmitted from the information processing device 30 in step S52, the control unit 51 starts the series of processes shown in Fig. 10.

[0143] As shown in FIG. 10, in step S61, the control unit 51 outputs fabric data from the before-printing image data and the situation data based on the first data 48, in the same manner as in step S24.

[0144] In step S62, the control unit 51 outputs recommended parameters from the fabric data based on the second data, similarly to step S25. At this time, in steps S61 and S62, the control unit 51 derives recommended parameters from the before-printing image data and situation data, based on the first data 48 and the second data 49. Therefore, it can be said that the recommended parameters are parameters derived by the server 50 based on the before-printing image data and situation data transmitted by the transmission unit 36.

[0145] In step S63, the control unit 51 transmits the recommended parameters to the information processing device 30. That is, the receiving unit 37 receives from the server 50 the recommended parameters that the server 50 has derived based on the pre-printing image data and the situation data.

[0146] When the control unit 51 finishes the process of step S63, it ends the series of processes shown in Fig. 10. In the second embodiment, the accuracy of the second data 49 can be verified by the same method as in the first embodiment.

[0147] Next, the operation and effects of the second embodiment will be described. (2-1) The information processing device 30 includes an acquisition unit 33 that acquires pre-printing image data and status data, a transmission unit 36 ​​that transmits the pre-printing image data and status data to the server 50, and a receiving unit 37 that receives from the server 50 recommended parameters derived by the server 50 based on the pre-printing image data and status data.

[0148] According to the above configuration, the information processing device 30 can provide the user with recommended parameters for at least one of the pre-processing device 11 and the post-processing device 13 based on the pre-textile image data and the status data. The user can obtain a desired image quality by setting the recommended parameters for at least one of the pre-processing device 11 and the post-processing device 13 in that device. This simplifies the user's work.

[0149] The above-described first and second embodiments can be modified as follows. The first and second embodiments and the following modifications can be combined with each other to the extent that they are not technically inconsistent.

[0150] The program for causing a computer to process information relating to textile printing processes may be distributed or sold, for example, in a state stored on a storage medium, or distributed or sold via a communication line.

[0151] A control device that controls the textile printing system 10 may be provided separately from the information processing device 30. In this case, the user controls the textile printing system 10 through the control device based on the information provided by the information processing device 30.

[0152] The data set 46 may include evaluation data indicating information regarding a client's evaluation of the image quality of the printed image. By generating the derivation data 47 from the data set 46 including the evaluation data, the client's evaluation criteria for image quality can be understood.

[0153] The image capture device 41 may be incorporated in the textile printing system 10. For example, the image capture device 41 may be controlled by the information processing device 30. The technical ideas and effects obtained from the above-described embodiment and modified examples will be described below.

[0154] (A) An information processing device is an information processing device that processes information related to a textile printing process performed by an inkjet device that draws an image by ejecting ink onto a fabric, a pre-processing device that performs pre-processing on the fabric before the image is drawn, and / or a post-processing device that performs post-processing on the fabric after the image is drawn, and the information processing device includes an acquisition unit that acquires image data that is an electronic version of an image of the fabric before the textile printing process is performed, and status data that indicates usage status including environmental information of the inkjet device, the pre-processing device, and the post-processing device, a transmission unit that transmits the image data and the status data to a server, and a first status information generated by the server based on the image data and the status data. The device comprises a receiving unit that receives data from the server, a memory unit that stores the first data received by the receiving unit and second data different from the first data, and a control unit, wherein the first data is data that specifies a trained model trained by machine learning and that outputs fabric data indicating features of the fabric when the image data and the situation data are input, and the second data is data that indicates a correspondence between the fabric data and recommended parameters for at least one of the pre-processing device and the post-processing device, and the control unit derives the recommended parameters from the image data and the situation data based on the first data and the second data.

[0155] The image data includes the threads that make up the fabric. Therefore, the image data includes information that indicates the characteristics of the fabric, such as the thickness, density, and surface roughness of the threads that make up the fabric. By analyzing the image data, such characteristics of the fabric can be obtained.

[0156] The usage status includes environmental information such as temperature, humidity, etc. The temperature and humidity affect, for example, the moisture content of the fabric, which is a feature of the fabric. In other words, there is a correlation between the usage status data and the fabric data.

[0157] According to the above configuration, the control unit can provide the user with recommended parameters for at least one of the pre-processing device and the post-processing device based on the image data and the situation data by using the first data and the second data. The user can obtain a predetermined image quality by setting the recommended parameters for at least one of the pre-processing device and the post-processing device to the device. This simplifies the user's work.

[0158] (B) An information processing device processes information related to a textile printing process performed by an inkjet device that draws an image by ejecting ink onto a fabric, and at least one of a pre-processing device that performs pre-processing on the fabric before the image is drawn on it, and a post-processing device that performs post-processing on the fabric after the image is drawn on it, and the information processing device is equipped with an acquisition unit that acquires image data that has been digitized as an image of the fabric before the textile printing process is performed, and status data that indicates usage status including environmental information of the inkjet device, the pre-processing device, and the post-processing device, a transmission unit that transmits the image data and the status data to a server, and a receiving unit that receives from the server recommended parameters for at least one of the pre-processing device and the post-processing device derived by the server based on the image data and the status data.

[0159] The image data includes the threads that make up the fabric. Therefore, the image data includes information that indicates the characteristics of the fabric, such as the thickness, density, and surface roughness of the threads that make up the fabric. By analyzing the image data, such characteristics of the fabric can be obtained.

[0160] The situation data includes environmental information such as temperature, humidity, etc. The temperature and humidity affect, for example, the moisture content of the fabric, which is a feature of the fabric. In other words, there is a correlation between the situation data and the fabric data.

[0161] According to the above configuration, the information processing device can provide the user with recommended parameters for at least one of the pre-processing device and the post-processing device based on the image data and the situation data. The user can obtain a desired image quality by setting the recommended parameters for at least one of the pre-processing device and the post-processing device in the device. This simplifies the user's work.

[0162] (C) In the information processing device, the environmental information may include altitudes at which the inkjet device, the pre-processing device, and the post-processing device are installed. The higher the altitude, the less likely liquid will penetrate into the fabric. Therefore, the altitude affects the hydrophilicity of the fabric, which is a characteristic of the fabric. Therefore, with the above configuration, the situation data can be suitable for obtaining recommended parameters.

[0163] (D) An information processing method is an information processing method for processing information relating to a textile printing process performed by an inkjet device that draws an image by ejecting ink onto a fabric, and at least one of a pre-processing device that performs pre-processing on the fabric before the image is drawn, and a post-processing device that performs post-processing on the fabric after the image is drawn, the information processing method comprising the steps of: acquiring image data in which the fabric before the textile printing process is digitized as an image, and status data that indicates usage status including environmental information of the inkjet device, the pre-processing device, and the post-processing device; The method includes transmitting the image data and the situation data to a server; receiving from the server first data which is data generated by the server based on the image data and the situation data, the first data being a trained model trained by machine learning and which specifies the trained model that outputs fabric data indicating features of the fabric when the image data and the situation data are input; and deriving the recommended parameters from the image data and the situation data based on the first data and second data indicating a correspondence between the fabric data and recommended parameters for at least one of the pre-processing device and the post-processing device.

[0164] According to this configuration, the same effects as those of the above-mentioned information processing device can be obtained. (E) An information processing method for processing information relating to a textile printing process performed by an inkjet device that draws an image by ejecting ink onto a fabric, and at least one of a pre-processing device that performs pre-processing on the fabric before the image is drawn on it, and a post-processing device that performs post-processing on the fabric after the image is drawn on it, comprising the steps of: acquiring image data in which the fabric before the textile printing process is digitized as an image, and status data indicating usage status including environmental information of the inkjet device, the pre-processing device, and the post-processing device; transmitting the image data and the status data to a server; and receiving from the server recommended parameters for at least one of the pre-processing device and the post-processing device derived by the server based on the image data and the status data.

[0165] According to this method, the same effects as those of the above-mentioned information processing device can be obtained. (F) a program for causing a computer to process information regarding a textile printing process performed by at least one of an inkjet device that draws an image by ejecting ink onto a fabric, a pre-processing device that performs pre-processing on the fabric before the image is drawn, and a post-processing device that performs post-processing on the fabric after the image is drawn, The system executes the following steps: acquiring image data obtained by digitizing the fabric before the printing process, and status data indicating usage status including environmental information of the inkjet device, the pre-processing device, and the post-processing device; transmitting the image data and the status data to a server; receiving from the server first data which is data generated by the server based on the image data and the status data, the first data being a trained model trained by machine learning and which specifies the trained model that outputs fabric data indicating features of the fabric when the image data and the status data are input; and deriving the recommended parameters from the image data and the status data based on the first data and second data indicating a correspondence between the fabric data and recommended parameters for at least one of the pre-processing device and the post-processing device.

[0166] According to this program, the same effects as those of the above-mentioned information processing device can be obtained. (G) The program causes a computer to process information related to a textile printing process performed by an inkjet device that draws an image by ejecting ink onto a fabric, and at least one of a pre-processing device that performs pre-processing on the fabric before the image is drawn on it, and a post-processing device that performs post-processing on the fabric after the image is drawn on it, and causes the program to acquire image data that is an electronic version of an image of the fabric before the textile printing process is performed, and status data that indicates usage status including environmental information of the inkjet device, the pre-processing device, and the post-processing device, transmit the image data and the status data to a server, and receive from the server recommended parameters for at least one of the pre-processing device and the post-processing device that are derived by the server based on the image data and the status data.

[0167] According to this program, the same effects as those of the above-mentioned information processing device can be obtained. [Explanation of symbols]

[0168] 10...textile printing system, 11...pre-treatment device, 12...inkjet device, 13...post-treatment device, 14...applying section, 15...correcting section, 16...pre-treatment drying section, 17...conveying section, 18...head, 19...carriage, 21...nozzle, 22...container, 23...cord, 24...post-treatment drying section, 25...steam section, 26...cleaning section, 30...information processing device, 31...input section, 32...output section, 33...acquisition section , 34...control unit, 35...memory unit, 36...transmission unit, 37...reception unit, 38...input device, 39...output device, 41...image capture device, 42...weighing device, 43...reading device, 44...thermo-hygrometer, 45...network, 46...data set, 47...derivation data, 48...first data, 49...second data, 50...server, 51...control unit, 52...memory unit, 53...big data, 99...fabric.

Claims

1. An information processing device that processes information related to a textile printing process performed by an inkjet device that forms an image on a fabric by ejecting ink, a pre-processing device that performs pre-processing on the fabric before the image is formed, and / or a post-processing device that performs post-processing on the fabric after the image is formed, an acquisition unit that acquires image data obtained by digitizing an image of the fabric before the textile printing process, and status data that indicates usage status including environmental information of the inkjet device, the pre-processing device, and the post-processing device; a transmission unit that transmits the image data and the situation data to a server; a receiving unit that receives, from the server, first data generated by the server based on the image data and the situation data; a storage unit that stores the first data received by the receiving unit and second data different from the first data; A control unit, The first data is data that specifies a trained model trained by machine learning, the trained model outputting fabric data indicating a feature amount of the fabric when the image data and the situation data are input, the second data is data indicating a correspondence relationship between the fabric data and recommended parameters for at least one of the pre-processing device and the post-processing device, The information processing device, wherein the control unit derives the recommended parameters from the image data and the situation data based on the first data and the second data.

2. A system including an information processing device and a server for processing information relating to a textile printing process performed by an inkjet device that draws an image by ejecting ink onto a fabric, and at least one of a pre-processing device that performs pre-processing on the fabric before the image is drawn, and a post-processing device that performs post-processing on the fabric after the image is drawn, The information processing device includes: an acquisition unit that acquires image data obtained by digitizing an image of the fabric before the textile printing process, and status data that indicates usage status including environmental information of the inkjet device, the pre-processing device, and the post-processing device; a transmission unit that transmits the image data and the situation data to the server; a receiving unit that receives from the server a recommended parameter for at least one of the pre-processing device and the post-processing device, the recommended parameter being derived by the server based on the image data and the situation data; The server, a server storage unit that stores first data, which is data defining a trained model trained by machine learning and which outputs fabric data indicating features of the fabric when the image data and the situation data are input, and second data, which is data indicating a correspondence relationship between the fabric data and the recommended parameters for at least one of the pre-processing device and the post-processing device; A system comprising: a server control unit that executes the following: receiving the image data and the situation data from the information processing device; deriving the recommended parameters from the image data and the situation data based on the first data and the second data; and transmitting the recommended parameters to the information processing device.

3. The information processing apparatus according to claim 1 , wherein the environmental information includes altitudes at which the inkjet device, the pre-processing device, and the post-processing device are installed.

4. An information processing method by an information processing device that processes information regarding a textile printing process performed by an inkjet device that forms an image by ejecting ink onto a fabric, a pre-processing device that performs pre-processing on the fabric before the image is formed, and / or a post-processing device that performs post-processing on the fabric after the image is formed, comprising: The information processing device, acquiring image data obtained by digitizing an image of the fabric before the textile printing process, and status data indicating usage status including environmental information of the inkjet device, the pre-processing device, and the post-processing device; transmitting the image data and the situation data to a server; receiving, from the server, first data that specifies a trained model that is generated by the server based on the image data and the situation data, the trained model being trained by machine learning and that outputs fabric data indicating a feature amount of the fabric when the image data and the situation data are input; and deriving the recommended parameters from the image data and the situation data based on second data and the first data indicating a correspondence between the fabric data and recommended parameters for at least one of the pre-processing device and the post-processing device.

5. An information processing method by an information processing device and a server, which processes information regarding a textile printing process performed by an inkjet device that draws an image by ejecting ink onto a fabric, and at least one of a pre-processing device that performs pre-processing on the fabric before the image is drawn, and a post-processing device that performs post-processing on the fabric after the image is drawn, comprising: The information processing device, acquiring image data obtained by digitizing an image of the fabric before the textile printing process, and status data indicating usage status including environmental information of the inkjet device, the pre-processing device, and the post-processing device; transmitting the image data and the situation data to the server; receiving from the server recommended parameters for at least one of the pre-processing device and the post-processing device derived by the server based on the image data and the situation data; The server, receiving the image data and the situation data from the information processing device; Deriving the recommended parameters from the image data and the situation data based on first data, which is data defining a trained model trained by machine learning and which outputs fabric data indicating features of the fabric when the image data and the situation data are input, and second data, which is data indicating a correspondence relationship between the fabric data and the recommended parameters related to at least one of the pre-processing device and the post-processing device; and transmitting the recommended parameters to the information processing device.

6. A program for causing a computer to process information relating to a textile printing process performed by at least one of an inkjet device that draws an image by ejecting ink onto a fabric, a pre-processing device that performs pre-processing on the fabric before the image is drawn, and a post-processing device that performs post-processing on the fabric after the image is drawn, the program comprising: acquiring image data obtained by digitizing an image of the fabric before the textile printing process, and status data indicating usage status including environmental information of the inkjet device, the pre-processing device, and the post-processing device; transmitting the image data and the situation data to a server; receiving, from the server, first data that specifies a trained model that is generated by the server based on the image data and the situation data, the trained model being trained by machine learning and that outputs fabric data indicating a feature amount of the fabric when the image data and the situation data are input; and deriving the recommended parameters from the image data and the situation data based on second data and the first data indicating a correspondence between the fabric data and recommended parameters for at least one of the pre-processing device and the post-processing device.

Images