Information processing apparatus, machine learning apparatus, information processing method, and machine learning method

Abstract

To provide an information processing apparatus configured to simply and accurately estimate a temperature distribution of a target object whose temperature is adjusted.SOLUTION: An information processing apparatus 3A includes: an information acquisition unit 3020A which acquires temperature adjustment conditions for adjusting a temperature of a target object; and an information generation unit 3021A which generates temperature distribution information of the target object whose temperature is adjusted according to the temperature adjustment conditions included in input data, on the basis of output data which is output from a plurality of learning models 13-1 to 13-N by inputting the input data including at least the temperature adjustment conditions acquired by the information acquisition unit 3020A to the plurality of learning models 13-1 to 13-N. The temperature distribution information generated by the information generation unit 3021A indicates a temperature of each of a plurality of target object regions obtained by dividing the target object into the plurality of target object regions.SELECTED DRAWING: Figure 9

Description

[Technical Field]

[0001] The present invention relates to an information processing device, a machine learning device, an information processing method, and a machine learning method. [Background technology]

[0002] When part of a manufacturing process includes a step of adjusting the temperature of a product (whether it is a raw material, intermediate product, or final product) by heating or cooling the product, it is known that the manufacturing quality is affected by whether the temperature distribution of the product being temperature-adjusted is within an appropriate range. For example, Patent Document 1 discloses that when a container is manufactured from a preform by blow molding, the temperature at which the preform is heated affects the manufacturing quality of the container manufactured by blow molding. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-168857 Summary of the Invention [Problem to be solved by the invention]

[0004] The temperature control conditions for adjusting the temperature of a product (object) are not only set by combining various parameters, but also because the temperature change of the product is nonlinear, it is extremely difficult to define a formula for calculating the temperature distribution of the entire product at once. Even if a formula could be defined, it would require a large amount of calculation, and its use would be limited to simulations, making it difficult to use for real-time predictions.

[0005] The present invention has been made in consideration of the above-mentioned problems, and aims to provide an information processing device, a machine learning device, an information processing method, and a machine learning method that enable easy and highly accurate prediction of the temperature distribution of an object when the temperature of the object is adjusted. [Means for solving the problem]

[0006] In order to achieve the above object, an information processing device according to one aspect of the present invention comprises: an information acquisition unit that acquires temperature adjustment conditions when adjusting the temperature of an object; an information generation unit that inputs input data including at least the temperature adjustment conditions acquired by the information acquisition unit into one or more learning models, and generates temperature distribution information of the object when the temperature of the object is adjusted in accordance with the temperature adjustment conditions included in the input data, based on output data output from the one or more learning models; The temperature distribution information is information indicating the temperature of each of a plurality of object regions when the object is divided into the object regions. [Effects of the Invention]

[0007] According to an information processing device of one aspect of the present invention, an information generating unit inputs input data including at least a temperature adjustment condition for an object into one or more learning models, and generates temperature distribution information for the object based on output data output from the one or more learning models. Thus, it is possible to easily and accurately predict the temperature distribution of the object when the temperature of the object is adjusted.

[0008] Problems, configurations, and effects other than those described above will become apparent from the detailed description of the invention that follows. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is an overall view showing an example of a manufacturing management system 1. FIG. [Figure 2] FIG. 10 is a schematic front view showing an example of a preform heating device 20d. [Figure 3] FIG. 10 is a block diagram showing an example of a preform heating device 20d. [Figure 4] FIG. 2 is a block diagram showing an example of an information processing device 3A according to the first embodiment. [Figure 5] FIG. 3 is a data structure diagram showing an example of a database 310. [Figure 6] FIG. 2 is an explanatory diagram showing a first example of division when dividing the preform 10 into a plurality of target regions. [Figure 7] FIG. 10 is an explanatory diagram showing a second example of division when dividing the preform 10 into a plurality of target regions. [Figure 8] FIG. 2 is a functional explanatory diagram showing an example of a learning function 301A according to the first embodiment. [Figure 9] FIG. 2 is a functional explanatory diagram showing an example of an information processing function 302A according to the first embodiment. [Figure 10] FIG. 9 is a hardware configuration diagram showing an example of a computer 900 that constitutes each device. [Figure 11] 10 is a flowchart showing an example of a machine learning method by a learning function 301A according to the first embodiment. [Figure 12] 10 is a flowchart showing an example of an information processing method by an information processing function 302A according to the first embodiment. [Figure 13] FIG. 10 is a block diagram showing an example of an information processing device 3B according to a second embodiment. [Figure 14] FIG. 10 is a functional explanatory diagram showing an example of a learning function 301B according to the second embodiment. [Figure 15] FIG. 10 is a functional explanatory diagram showing an example of an information processing function 302B according to the second embodiment. [Figure 16] 10 is a flowchart showing an example of a machine learning method by a learning function 301B according to the second embodiment. [Figure 17] 10 is a flowchart showing an example of an information processing method by an information processing function 302B according to the second embodiment. [Figure 18] FIG. 10 is a block diagram showing an example of an information processing device 3C according to a third embodiment. [Figure 19] FIG. 11 is a functional explanatory diagram showing an example of a learning function 301C according to the third embodiment. [Figure 20] FIG. 11 is a functional explanatory diagram showing an example of an information processing function 302C according to the third embodiment. [Figure 21] 11 is a flowchart showing an example of a machine learning method by a learning function 301C according to the third embodiment. [Figure 22] 11 is a flowchart showing an example of an information processing method by an information processing function 302C according to the third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, an embodiment for carrying out the present invention will be described with reference to the drawings. The scope necessary for the explanation to achieve the object of the present invention will be schematically shown, and the scope necessary for explaining the relevant parts of the present invention will be mainly explained, and the parts that are omitted from the explanation will be based on publicly known techniques.

[0011] (First embodiment) 1 is a schematic diagram showing an example of a manufacturing management system 1. The manufacturing management system 1 is a system that manages various types of information related to an object in a manufacturing process for manufacturing any product.

[0012] The object may have a predetermined shape and may be made of any of metal materials, organic materials such as resin, and ceramic materials such as glass. For example, the target object may be any of raw materials, intermediate products, and final products in a manufacturing process. In this case, examples of the final products include, but are not limited to, containers, bottles, cans, etc.

[0013] In this embodiment, the object is mainly a preform 10 when a hollow bottle 11 (e.g., a PET bottle) is produced as a form of a molded article by stretching the preform 10, which is a form of a molded article, in a predetermined stretching direction by blow molding. Note that the molded article is not limited to the bottle 11, and may have any shape as long as it is produced from a molded article by blow molding.

[0014] As shown in Fig. 1, the manufacturing management system 1 mainly comprises a bottle manufacturing apparatus 2, an information processing apparatus 3A, and a user terminal apparatus 4. Each of the apparatuses 2 to 4 is configured, for example, as a general-purpose or dedicated computer (see Fig. 10 described below), and is connected to a wired or wireless network 5 so that various data can be transmitted and received between them. Note that the number of the apparatuses 2 to 4 and the connection configuration of the network 5 are not limited to the example in Fig. 1 and may be changed as appropriate.

[0015] The bottle manufacturing apparatus 2 is an apparatus that uses a mold 200 and operates in accordance with adjusted manufacturing conditions to blow-mold the preform 10 into a bottle 11. The manufacturing conditions include various device setting values ​​when each part of the bottle manufacturing apparatus 2 operates.

[0016] The bottle manufacturing apparatus 2 includes an injection molding apparatus 20a that injects synthetic resin, which is a raw material, into preforms 10, a preform removal apparatus 20b that removes the preforms 10 from the injection molding apparatus 20a and transfers them to a preform transport conveyor 20c, a preform transport conveyor 20c that transports the preforms 10, a preform heating apparatus 20d that is provided midway along the preform transport conveyor 20c and heats the preforms 10, and a preform receiving apparatus 20e that receives the preforms 10 from the preform transport conveyor 20c. The apparatus is equipped with a preform supplying device 20e that receives heated preforms 10 and supplies them to a blow molding device 20f, a blow molding device 20f that clamps heated preforms 10 between molds 200 and advances a stretch rod (not shown) while supplying blow fluid into preforms 10 to mold preforms 10 into bottles 11, a molded body removal device 20g that removes bottles 11 from blow molding device 20f and transfers them to a molded body transport conveyor 20h, and a molded body transport conveyor 20h that transports molded bottles 11. The configuration of bottle manufacturing apparatus 2 is not limited to the example in FIG. 1 and may be modified as appropriate.

[0017] The information processing device 3A is a device that works in conjunction with the bottle manufacturing device 2 and the user terminal device 4 to set manufacturing conditions (specifically, various device setting values) for multiple adjustment items related to the preforms 10 and bottles 11, and record them as manufacturing history.

[0018] The information processing device 3A includes a database 310 capable of registering production condition information A indicating production conditions and adjustment item information B indicating target values ​​and adjustment results for a plurality of adjustment items in association with each other. The production condition information A and adjustment item information B registered in the database 310 are referenced by the bottle production device 2 and the user terminal device 4 and are used in the production of the bottles 11.

[0019] The user terminal device 4 is a device used by a user such as a production manager of the bottle 11. Programs such as application programs and web browsers are installed on the user terminal device 4, and the user terminal device 4 accepts various input operations and outputs various information via a display screen or voice. The user terminal device 4 is used when the user inputs instructions to the information processing device 3A, outputs the processing results of the information processing device 3A, and edits the database 310.

[0020] The user terminal device 4 transmits various information to the user for instructing the information processing device 3A, for example. It functions as a device that receives data from the information processing device 3A, predicts the temperature distribution of the object (in this embodiment, the preform 10) by the information processing device 3A, and sets the temperature adjustment conditions (in this embodiment, part of the manufacturing conditions) when adjusting the temperature of the object.

[0021] (Preform heating device 20d) Fig. 2 is a schematic front view showing an example of the preform heating device 20d. Fig. 3 is a block diagram showing an example of the preform heating device 20d. The preform heating device 20d performs a preform heating process to heat the preforms 10 sequentially transported by the preform transport conveyor 20c. The preforms 10 are held upright by, for example, a rotatable mandrel (not shown) and transported at a predetermined transport speed by the preform transport conveyor 20c.

[0022] The preform heating apparatus 20d includes, as its main components, a plurality of preform heating units 21 that heat the preforms 10, and a control unit 22 that controls each part of the preform heating apparatus 20d. As shown in Fig. 1, the plurality of preform heating units 21 are arranged at predetermined intervals along the direction in which the preforms 10 are transported by the preform transport conveyor 20c.

[0023] Each of the multiple preform heating units 21 includes multiple preform heating modules 210 that heat various portions of the preform 10, a local preform heating module (not shown) that locally heats a portion of the preform 10, and a temperature sensor 211. The multiple preform heating modules 210 are each configured with an infrared heater or the like and are arranged at multiple positions spaced apart in a predetermined arrangement direction (in the vertical direction in this embodiment). The local preform heating module is, for example, configured with an infrared heater or the like and is arranged at a position that allows local heating of the neck portion of the preform 10. In this case, the local preform heating module may also serve as the preform heating module 210. The number and arrangement of the preform heating modules 210, the local preform heating modules, and the temperature sensor are not limited to the example in FIG. 2 and may be changed as appropriate. The preform heating unit 21 may further include a heating module that is arranged inside the preform 10 and heats the inside of the preform 10. The preform heating unit 21 may further include a blower module that supplies air at a predetermined temperature to the preforms 10 .

[0024] The control unit 22 is electrically connected to the module groups and sensor groups provided in the multiple preform heating units 21, and functions as a control unit that comprehensively controls the multiple preform heating units 21. Note that Fig. 3 illustrates the preform heating module 210 and the local preform heating module as part of the module group, and the temperature sensor 211 as part of the sensor group, but does not illustrate the other module groups and sensor groups.

[0025] The control unit 22 is configured, for example, by a general-purpose or dedicated computer (see FIG. 10 described later). The control unit 22 includes, as its main components, a control unit 220, a communication unit 221, an input unit 222, an output unit 223, and a storage unit 224.

[0026] The storage unit 224 stores various programs (such as an operating system (OS) and a preform heating program 2240) and data (such as apparatus setting information 2241) used in the operation of the preform heating apparatus 20d. The apparatus setting information 2241 is information that can register various apparatus setting values ​​when the preform heating apparatus 20d executes a preform heating process, and is configured to be editable by the apparatus user via a display screen (setting interface), for example.

[0027] The control unit 220 is configured by, for example, an arithmetic processing unit (a processor such as a CPU, MPU, or GPU) and a sequencer. The control unit 220 functions as a preform heating control unit 2200 by, for example, executing a preform heating program 2240 stored in the storage unit 224.

[0028] The preform heating control section 2200 operates the modules included in each preform heating unit 21. At that time, the preform heating control section 2200 operates each preform heating module 210 in accordance with the apparatus setting values ​​registered in the apparatus setting information 2241, thereby heating each part of the preform 10.

[0029] The communication unit 221 is connected to a communication network and functions as a communication interface for transmitting and receiving various data not only with the information processing device 3A and the user terminal device 4, but also with a terminal device (not shown) used by the device user of the preform heating device 20d, and a manufacturing management device (not shown) that performs manufacturing management in the manufacturing management system 1. The input unit 222 accepts various input operations by the user of the preform heating device 20d, and the output unit 223 functions as a user interface by outputting various information to the device user via a screen display, lighting up a signal tower, and sounding a buzzer.

[0030] (Configuration of information processing device 3A) 4 is a block diagram showing an example of an information processing device 3A according to the first embodiment. The information processing device 3A includes a control unit 30 including a processor or the like, a storage unit 31 including an HDD, an SSD, a memory or the like, a communication unit 32 which is a communication interface with the network 5 and external devices, an input unit 33 including a keyboard, a mouse or the like, and a display unit 34 including a display or the like. Note that the input unit 33 and the display unit 34 may be omitted.

[0031] The storage unit 31 stores a manufacturing condition database 310, a trained model management database 311, and an information processing program 312A, as well as an operating system, other programs, data, and the like.

[0032] The control unit 30 executes an information processing program 312A stored in the storage unit 31 to implement an information management function 300, a learning function 301A, and an information processing function 302A. The control unit 30 includes a learning data acquisition unit 3010A and a machine learning unit 3011A as units that implement the learning function 301A. The control unit 30 includes an information acquisition unit 3020A, an information generation unit 3021A, and an output processing unit 3022 as units that implement the information processing function 302A.

[0033] The data configuration of each of the functions 300, 301A, and 302A and each of the databases 310 and 311 will be described below.

[0034] (Information management function 300) The control unit 30 of the information processing device 3A uses the manufacturing condition database 310 to realize the information management function 300.

[0035] Fig. 5 is a data configuration diagram showing an example of the database 310. Fig. 6 is an explanatory diagram showing a first division example when dividing the preform 10 into a plurality of object regions. Fig. 7 is an explanatory diagram showing a second division example when dividing the preform 10 into a plurality of object regions.

[0036] The manufacturing condition database 310 associates various pieces of information handled by the manufacturing management system 1. The information includes multiple records for each bottle management ID for identifying the bottle 11. The bottle management ID is information (product number, model number, etc.) for identifying the bottle 11. Each record has fields in which multiple device setting values ​​included in the manufacturing condition information A and target values ​​and measurement results for multiple adjustment items included in the adjustment item information B can be registered, for example.

[0037] The manufacturing condition information A and the adjustment item information B registered in the manufacturing condition database 310 can be referenced from the information processing device 3A and the user terminal device 4. Note that editing operations such as adding, deleting, and correcting each piece of data may be performed on the display screen of the user terminal device 4.

[0038] The manufacturing condition information A is information indicating manufacturing conditions in the manufacturing process. The manufacturing condition information A includes, for example, heating conditions that determine device settings for the preform heating device 20d and molding conditions that determine device settings for the blow molding device 20f.

[0039] The heating conditions include an overall heating setting value for increasing or decreasing the overall heating amount of the multiple preform heating modules 210, an individual heating setting value for each preform heating module 210 for increasing or decreasing the heating amount of each of the multiple preform heating modules 210, a local heating setting value for increasing or decreasing the heating amount of each local preform heating module, a blower setting value for increasing or decreasing the air volume and air pressure of the blower module, etc. The heating conditions are not limited to these, and may also include, for example, a transport setting value for increasing or decreasing the transport speed of the preform transport conveyor 20c.

[0040] The molding conditions include, for example, stretch rod setting values ​​for increasing or decreasing the stretching amount and stretching speed of the stretch rod, blow setting values ​​for increasing or decreasing the pressure, time, and flow rate of the blow fluid, and mold setting values ​​for increasing or decreasing the amount of heat applied to the mold 200.

[0041] Each device setting value may be set by specifying a numerical value, or may be set by specifying one of a plurality of levels.

[0042] The adjustment item information B is information that indicates target values ​​and measured values ​​for a plurality of adjustment items related to the preform 10 and the bottle 11. The target values ​​are values ​​that are determined during the design and inspection stages of the preform 10 and the bottle 11, and correspond to design values ​​and standard values. The target values ​​may be specific values ​​or values ​​within a specific range, such as upper and lower limits. The measured values ​​may be measured, for example, by a measuring device installed inline or by a measuring device installed offline.

[0043] The multiple adjustment items include the temperature distribution of the preform 10, the mass distribution of the bottle 11, the height of the bottle 11, the internal volume of the bottle 11, and the like.

[0044] The temperature distribution information indicating the temperature distribution of the preform 10 is, for example, information indicating the temperature of each of a plurality of target regions when the preform 10 is divided into the plurality of target regions. The division criteria and number of the target regions may be determined according to the structure of the preform heating device 20d, or may be determined according to the characteristics of the preform 10 or the bottle 11, such as the shape and dimensions. In this case, the plurality of target regions may be divided at equal intervals or at unequal intervals.

[0045] For example, as shown in Fig. 6, the plurality of target regions are divided in the arrangement direction 210a of the preform heating modules 210. In this case, the plurality of target regions are divided between the arrangement positions of any two adjacent preform heating modules 210 in the arrangement direction 210a of the preform heating modules 210. It should be noted that when dividing the two preform heating modules 210, the division may or may not be made in the middle.

[0046] 6 shows a first division example in which the preform 10 is divided into seven target regions 10-1 to 10-7. This allows regions that are heated in the same way by a specific preform heating module 210 to be treated as the same region without being divided.

[0047] 7, the plurality of target regions are obtained by dividing the preform 10 in the stretching direction 10a. In this case, the plurality of target regions (areas to be molded) are scaled versions of the plurality of molded body regions obtained when the bottle 11 is divided into the plurality of molded body regions in the stretching direction 10a of the preform 10 based on the stretching ratio when the preform 10 is stretched into the bottle 11.

[0048] 7 shows a second division example in which preform 10 is divided into four target regions 10-1 to 10-4 by scaling four molded body regions 11-1 to 11-4 of bottle 11 based on the stretching ratio (=height HP of preform 10 / height HB of bottle 11). This makes it possible to assign each target region (molded body region) of preform 10 to correspond to each molded body region when the entire bottle 11 as a molded body is divided into a plurality of molded body regions based on, for example, functional or structural division criteria.

[0049] The criteria for dividing the object region are not limited to the above examples. For example, the object region may be divided based on the position (distance) and weight from the tip of the neck portion of the preform 10, the body diameter of the preform 10, the position (distance) and weight from the tip of the neck portion of the bottle 11, the body diameter of the bottle 11, the position (distance) from the tip of the stretch rod, etc.

[0050] (Learning function 301A) 8 is a functional explanatory diagram showing an example of a learning function 301A according to the first embodiment. A learning data acquisition unit 3010A and a machine learning unit 3011A of the information processing device 3A realize the learning function 301A using a manufacturing condition database 310 (learning data storage unit) and a trained model management database 311 (trained model storage unit).

[0051] The multiple learning models 13-1 to 13-N registered in the trained model management database 311 receive input data including at least heating conditions and output output data including the temperature of a specific object region. That is, when common input data is input to each of the multiple learning models 13-1 to 13-N, for example, learning model 13-1 outputs output data including the temperature of object region 10-1, and learning model 13-N outputs output data including the temperature of object region 10-N.

[0052] The heating conditions include an overall heating setting value for the multiple preform heating modules 210, an individual heating setting value for each preform heating module 210, a local heating setting value for a local preform heating module, a blower setting value for a blower module, and the like.

[0053] In this embodiment, the multiple learning models 13-1 to 13-N are classification models, and when input data including heating conditions is input, the output data is the classification result of the temperature category when the temperature of a specific object area is classified into multiple temperature categories.

[0054] The temperature ranges correspond to the temperature labels T1 to Tp shown in FIG. 8, and each has a predetermined temperature range. For example, the temperature label T1 is 60°C or less, the temperature label T2 is 60°C to 65°C, the temperature label T3 is 65°C to 70°C, ..., the temperature label Tp is 200°C or more. As described above, each temperature section is set. The temperature sections may be separated at equal intervals or at unequal intervals.

[0055] The plurality of learning models 13-1 to 13-N each employs, for example, a neural network structure and includes an input layer 130, an intermediate layer 131, and an output layer 132. Synapses (not shown) that connect the neurons are laid between the layers, and each synapse is associated with a weight. A weight parameter group consisting of the weights of each synapse is adjusted by a machine learning algorithm such as backpropagation.

[0056] The input layer 130 has neurons whose number corresponds to the input data, and each value is input to each neuron. The output layer 132 has neurons whose number corresponds to the output data, and outputs a classification result (inference result) of the temperature of a specific object region. As the classification result of the temperature of the object regions 10-1 to 10-N, for example, a score in the range of 0 to 1 is output for each of multiple temperature labels T1 to Tp, and the temperature label with the highest score is treated as the classification result of the temperature of that object region 10-1 to 10-N.

[0057] The learning data acquisition unit 3010A acquires a plurality of sets of learning data 12-1 to 12-N, each set consisting of input data including heating conditions and output data including the temperature of a specific object region among the plurality of object regions 10-1 to 10-N. The learning data 12-1 to 12-N are data used as teacher data (training data), verification data, and test data in supervised learning. Furthermore, characteristic parameters included in the learning data 12-1 to 12-N are data used as correct answer labels in supervised learning.

[0058] For example, the learning data acquiring unit 3010A acquires the learning data 12-1 to 12-N by referring to information registered in the manufacturing condition database 310 or by receiving an input operation from the user terminal device 4. If information corresponding to the learning data 12-1 to 12-N is stored in an external system (such as a manufacturing management system), the learning data acquiring unit 3010A may acquire the learning data 12-1 to 12-N from the external system.

[0059] The machine learning unit 3011A performs machine learning of the plurality of learning models 13-1 to 13-N for each object region using the plurality of sets of learning data 12-1 to 12-N acquired by the learning data acquisition unit 3010A. Then, the machine learning unit 3011A generates the plurality of trained learning models 13-1 to 13-N by having the plurality of learning models 13-1 to 13-N learn the correlation between the input data and the output data for each object region.

[0060] When performing machine learning, the machine learning unit 3011A can employ any method, such as online learning, batch learning, mini-batch learning, etc. Furthermore, the machine learning unit 3011A may perform predetermined pre-processing on input data to be input to the learning models 13-1 to 13-N, or may perform predetermined post-processing on output data output from the learning models 13-1 to 13-N.

[0061] The trained model management database 311 stores a plurality of trained learning models 13-1 to 13-N (specifically, adjusted weight parameter groups) generated by the machine learning unit 3011A. The trained learning models 13-1 to 13-N stored in the trained model management database 311 may be provided to other systems via the network 5, a recording medium, or the like.

[0062] In this embodiment, the data configuration of the learning data 12-1 to 12-N and the learning models 13-1 to 13-N is described as being configured as shown in FIG. 8. However, for example, A plurality of data configurations may be employed that have different conditions, such as different machine learning methods, different heating conditions, different object regions, etc. In this case, the learning data acquisition unit 3010A acquires a plurality of types of learning data 12-1 to 12-N that respectively correspond to a plurality of data configurations that have different conditions, and the machine learning unit 3011A performs machine learning using each of the learning data 12-1 to 12-N, and stores trained learning models 13-1 to 13-N in the trained model management database 311.

[0063] (Information Processing Function 302A) 9 is a functional explanatory diagram showing an example of an information processing function 302A according to the first embodiment. An information acquisition unit 3020A, an information generation unit 3021A, and an output processing unit 3022 of the information processing device 3A realize the information processing function 302A using a trained model management database 311.

[0064] The information acquisition unit 3020A acquires the heating conditions when the preform heating device 20d adjusts the temperature of the preform 10. For example, the learning data acquisition unit 3010A acquires the heating conditions by referring to the heating conditions registered in the manufacturing condition database 310, accepting an input operation from the user terminal device 4, or receiving the heating conditions from the preform heating device 20d.

[0065] The information generation unit 3021A inputs input data including the heating conditions acquired by the information acquisition unit 3020A to each of the plurality of learning models 13-1 to 13-N. Then, the information generation unit 3021A generates temperature distribution information based on the temperature of each object region included in each of the plurality of output data output from the plurality of learning models 13-1 to 13-N. Note that the information generation unit 3021A may perform predetermined pre-processing on the input data input to the learning models 13-1 to 13-N, or may perform predetermined post-processing on the output data output from the learning models 13-1 to 13-N.

[0066] 9 shows the temperature distribution information generated by information generating unit 3021A, in which the temperature of object region 10-1 is a temperature (60°C to 65°C) represented by temperature label T2, and the temperature of object region 10-N is a temperature (60°C or less) represented by temperature label T1. Note that although the temperatures of object regions 10-2 to 10-N-1 are not shown in FIG. 9, these temperatures are also included in the temperature distribution information.

[0067] The multiple learning models 13-1 to 13-N used by the information generation unit 3021A are multiple learned learning models 13-1 to 13-N stored in the learned model management database 311. When multiple learning models 13-1 to 13-N with different conditions are stored in the learned model management database 311, the information generation unit 3021A may use the multiple learning models 13-1 to 13-N selectively or in parallel according to the type of data included in the input data, for example.

[0068] The output processing unit 3022 performs output processing for outputting the temperature distribution information generated by the information generating unit 3021A. For example, as the output processing, the output processing unit 3022 may transmit display information for displaying the temperature distribution information on the user terminal device 4, or may register the temperature distribution information in the manufacturing condition database 310.

[0069] 10 is a hardware configuration diagram showing an example of a computer 900 constituting each device. In the manufacturing management system 1, the control unit 22 of the preform heating device 20d, the information processing device 3A, and the user terminal device 4 are each constituted by a general-purpose or dedicated computer 900.

[0070] 10, the computer 900 includes, as its main components, a bus 910, a processor 912, a memory 914, an input device 916, an output device 917, a display device 918, a storage device 920, a communication I / F (interface) unit 922, an external device I / F unit 924, an I / O (input / output) device I / F unit 926, and a media input / output unit 928. Note that the above components may be omitted as appropriate depending on the application of the computer 900.

[0071] The processor 912 is composed of one or more arithmetic processing devices (such as a CPU (Central Processing Unit), MPU (Micro-Processing Unit), DSP (Digital Signal Processor), GPU (Graphics Processing Unit), or NPU (Neural Processing Unit)), and operates as a control unit that controls the entire computer 900. The memory 914 stores various data and programs 930, and is composed of, for example, a volatile memory (such as a DRAM or SRAM) that functions as a main memory, a non-volatile memory (ROM), a flash memory, etc.

[0072] The input device 916 is composed of, for example, a keyboard, a mouse, a numeric keypad, an electronic pen, etc., and functions as an input unit. The output device 917 is composed of, for example, a sound (audio) output device, a vibration device, etc., and functions as an output unit. The display device 918 is composed of, for example, a liquid crystal display, an organic EL display, electronic paper, a projector, etc., and functions as an output unit. The input device 916 and the display device 918 may be integrated into one device, such as a touch panel display. The storage device 920 is composed of, for example, an HDD, an SSD, etc., and functions as a storage unit. The storage device 920 stores various data necessary for executing the operating system and the program 930.

[0073] The communication I / F unit 922 is connected to a network 940 such as the Internet or an intranet (which may be the same as network 5 in FIG. 1) via a wired or wireless connection and functions as a communication unit that transmits and receives data to and from other computers in accordance with a predetermined communication protocol. The external device I / F unit 924 is connected to an external device 950 such as a camera, printer, scanner, or reader / writer via a wired or wireless connection and functions as a communication unit that transmits and receives data to and from the external device 950 in accordance with a predetermined communication protocol. The I / O device I / F unit 926 is connected to an I / O device 960 such as various sensors and actuators and functions as a communication unit that transmits and receives various signals and data, such as detection signals from sensors and control signals to actuators, to and from the I / O device 960. The media input / output unit 928 is composed of a drive device such as a DVD drive or CD drive, a memory card slot, and a USB connector, and reads and writes data from and to media (non-transitory storage media) 970 such as a DVD, CD, memory card, or USB memory.

[0074] In the computer 900 having the above configuration, the processor 912 loads a program 930 stored in the storage device 920 into the memory 914, executes the program, and controls each unit of the computer 900 via the bus 910. The program 930 may be stored in the memory 914 instead of the storage device 920. The program 930 may be recorded on the medium 970 in an installable file format or an executable file format and provided to the computer 900 via the media input / output unit 928. The program 930 may be provided to the computer 900 by being downloaded via the communication I / F unit 922 over the network 940. Furthermore, the computer 900 may implement various functions realized by the processor 912 executing the program 930 using hardware such as an FPGA (Field-Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit).

[0075] The computer 900 is an electronic device of any type, such as a desktop computer or a portable computer. The computer 900 may be a client computer, a server computer, a cloud computer, or an embedded computer such as a control panel or a controller (including a microcomputer, a programmable logic controller, or a sequencer).

[0076] (Operation of manufacturing control system 1) Below, we will explain the functions 301A and 302A realized by the information processing device 3A as the operation of the manufacturing management system 1. Note that the information processing device 3A accesses each of the databases 310 and 311 to register and refer to various types of information, but in the following explanation, the access operation will be omitted as appropriate.

[0077] (machine learning methods) FIG. 11 is a flowchart showing an example of a machine learning method by the learning function 301A according to the first embodiment.

[0078] First, in step S100, the learning data acquisition unit 3010A refers to, for example, the manufacturing condition database 310 and acquires a plurality of heating conditions registered in a plurality of records, respectively.

[0079] Next, in step S110, the learning data acquisition unit 3010A initializes a processing index i indicating the object region 10-i to be processed to "1".

[0080] Next, in step S120, the learning data acquisition unit 3010A refers to multiple pieces of temperature distribution information associated with the multiple heating conditions acquired in step S100 in the manufacturing condition database 310, and acquires the temperature of each of the target regions 10-i identified by the processing index i.

[0081] Next, in step S121, the learning data acquisition unit 3010A acquires multiple sets (the number of records) of learning data 12-i by combining, by record, input data including the heating conditions acquired in step S100 and output data including the temperature of the target region 10-i acquired in step S120.

[0082] Next, in step S130, the machine learning unit 3011A performs machine learning of the learning model 13-i using the multiple sets of learning data 12-i acquired in step S121.

[0083] Next, in step S140, the machine learning unit 3011A increments the processing index i. Then, in step S141, the machine learning unit 3011A determines whether the processing index i exceeds the number of divisions of the object region ("7" in the example of FIG. 6, "4" in the example of FIG. 7).

[0084] If it is determined in step S141 that the processing index i does not exceed the division number of the object region, the process returns to step S120 and performs the same process on the next object region. On the other hand, if it is determined that the processing index i exceeds the division number of the object region, the process proceeds to step S150.

[0085] Then, in step S150, the machine learning unit 3011A calculates the weight parameters of the plurality of trained learning models 13-1 to 13-N (adjusted weight parameters) for which machine learning was performed in step S130. The trained model management database 311 stores the trained model (group of trained parameters).

[0086] As described above, the series of steps in the machine learning method shown in FIG. 11 is completed. In the machine learning method, steps S100 to S121 correspond to a learning data acquisition process, step S130 corresponds to a machine learning process, and step S150 corresponds to a trained model storage process. Note that in the series of steps shown in FIG. 11, machine learning of the multiple learning models 13-1 to 13-N is performed serially, but the machine learning of the multiple learning models 13-1 to 13-N may be performed in parallel. In that case, for example, learning data 12-1 to 12-N corresponding to each of the object regions 10-1 to 10-N may be acquired, and the process of performing machine learning of the multiple learning models 13-1 to 13-N in parallel may be repeated multiple times. When performing machine learning in parallel, any parallel processing (computer parallel, CPU parallel, GPU parallel, etc.) may be used.

[0087] As described above, according to the information processing function 302A and information processing method of the information processing device 3A of this embodiment, the machine learning unit 3011A performs machine learning on the multiple learning models 13-1 to 13-N for each object region, thereby causing the multiple learning models 13-1 to 13-N to learn the correlation between input data including heating conditions and output data including the temperature of a specific object region for each object region. This allows the multiple learning models 13-1 to 13-N, each specialized for each object region 10-1 to 10-N, to be divided and trained, rather than having a single learning model trained on the entire preform 10. This makes it possible to generate a trained model for each object region of the preform 10 that can easily and accurately predict the temperature of the object region.

[0088] (Information processing method) FIG. 12 is a flowchart showing an example of an information processing method by the information processing function 302A according to the first embodiment.

[0089] First, in step S200, the information acquisition unit 3020A acquires the heating conditions for adjusting the temperature of the preform 10.

[0090] Next, in step S210, the information generating unit 3021A initializes a processing index j indicating the object region 10-j to be processed to "1".

[0091] Next, in step S220, the information generator 3021A inputs the input data including the heating conditions acquired in step S200 to the trained learning model 13-j corresponding to the object region 10-j identified by the processing index j. Then, the information generator 3021A temporarily stores the output data output from the learning model 13-j in the storage unit 31.

[0092] Next, in step S230, the information generation unit 3021A increments the processing index j. Then, in step S231, the information generation unit 3021A determines whether the processing index j exceeds the division number of the object region ("7" in the example of FIG. 6, "4" in the example of FIG. 7).

[0093] If it is determined in step S231 that the processing index j does not exceed the division number of the object region, the process returns to step S220 and performs the same process on the next object region. On the other hand, if it is determined that the processing index j exceeds the division number of the object region, the process proceeds to step S240.

[0094] Then, in step S240, the information generating unit 3021A The temperature distribution information is generated based on the temperature of each object region included in each of the plurality of output data by referring to the plurality of output data stored over time.

[0095] Next, in step S250, the output processing unit 3022 performs output processing for outputting the temperature distribution information generated in step S240, for example, by transmitting display information for displaying the temperature distribution information to the user terminal device 4. As a result, the user terminal device 4 displays a display screen based on the display information, thereby presenting the temperature distribution information to the user.

[0096] As described above, the series of information processing methods shown in Fig. 12 is completed. In the above information processing method, step S200 corresponds to an information acquisition step, steps S210 to S240 correspond to an information generation step, and step S250 corresponds to an output processing step. The series of information processing methods can be executed at any timing as long as the information processing device 3A can acquire the heating conditions. For example, they may be executed before the preform heating process is performed by the preform heating device 20d (advance prediction process), during the preform heating process (real-time prediction process), or after the preform heating process is performed (post-prediction process).

[0097] As described above, according to the information processing function 302A and information processing method of the information processing device 3A of this embodiment, by inputting input data including heating conditions for the preform 10 into each of multiple learning models 13-1 to 13-N, temperature distribution information for the preform 10 is generated based on the output data output from the multiple learning models 13-1 to 13-N.

[0098] That is, instead of collectively treating the entire preform 10, a plurality of learning models 13-1 to 13-N corresponding to each of the object regions 10-1 to 10-N obtained by dividing the entire preform 10 are used to individually predict the temperature of each of the object regions 10-1 to 10-N, thereby predicting the temperature distribution throughout the preform 10. As a result, the preform 10 is treated as discrete regions, and therefore more complex calculations are not required than when treating the entire preform 10 collectively as a continuous region, and therefore the temperature distribution of the preform 10 can be predicted simply and with high accuracy.

[0099] Furthermore, by using a classification model that outputs a classification result of temperature division as the plurality of learning models 13-1 to 13-N, the temperature of each of object regions 10-1 to 10-N can be predicted as a relative index.

[0100] (Second embodiment) In the first embodiment, the information processing device 3A uses a plurality of learning models 13-1 to 13-N, whereas in the second embodiment, the information processing device 3B uses a single learning model 13A, which is different from the first embodiment. The following describes the information processing device 3B according to the second embodiment, focusing on the differences from the first embodiment.

[0101] (Configuration of information processing device 3B) 13 is a block diagram showing an example of an information processing device 3B according to the second embodiment. The control unit 30 executes an information processing program 312B stored in the storage unit 31 to implement an information management function 300, a learning function 301B, and an information processing function 302B. The control unit 30 includes a learning data acquisition unit 3010B and a machine learning unit 3011B as units that implement the learning function 301B. The control unit 30 includes an information acquisition unit 3020B, an information generation unit 3021B, and an output processing unit 3022 as units that implement the information processing function 302B.

[0102] (Learning Function 301B) 14 is a functional explanatory diagram showing an example of a learning function 301B according to the second embodiment. A learning data acquisition unit 3010B and a machine learning unit 3011B of the information processing device 3B realize the learning function 301B using a manufacturing condition database 310 (learning data storage unit) and a trained model management database 311 (trained model storage unit).

[0103] A single learning model 13A registered in the learned model management database 311 receives input data including at least heating conditions and outputs output data including the temperature of a specific object region. Note that the learning model 13A employs, for example, a neural network structure, as in the first embodiment.

[0104] In this embodiment, the single learning model 13A is a classification model, and input data including heating conditions and area information indicating a specific object area among the multiple object areas 10-1 to 10-N is input, and the output data is a classification result of the temperature range when the temperature of the specific object area indicated by the area information is classified into multiple temperature ranges.

[0105] The region information is, for example, information for identifying one of the target regions 10-1 to 10-N. The region information is specified, for example, by an identifier such as alphanumeric characters assigned to each of the target regions 10-1 to 10-N in order from the neck side of the preform 10. The region information may also be specified according to the structure of the preform heating device 20d, or according to the characteristics of the preform 10 or the bottle 11. For example, the region information may include the position (distance) and weight from the tip of the neck side of the preform 10, the body diameter of the preform 10, the position (distance) and weight from the tip of the neck side of the bottle 11, the body diameter of the bottle 11, and the position (distance) from the tip of the stretch rod.

[0106] The learning data acquisition unit 3010B acquires a plurality of sets of learning data 12-1 to 12-N, each set consisting of input data including heating conditions and region information, and output data including the temperature of a specific object region indicated by the region information.

[0107] The machine learning unit 3011B performs machine learning on the single learning model 13A using the multiple sets of learning data 12-1 to 12-N acquired by the learning data acquisition unit 3010B. Then, the machine learning unit 3011B generates the trained single learning model 13A by having the single learning model 13A learn the correlation between the input data and the output data.

[0108] (Information Processing Function 302B) 15 is a functional explanatory diagram showing an example of an information processing function 302B according to the second embodiment. An information acquisition unit 3020B, an information generation unit 3021B, and an output processing unit 3022 of the information processing device 3B realize the information processing function 302B using a trained model management database 311.

[0109] The information acquisition unit 3020B acquires the heating conditions when the temperature of the preform 10 is adjusted by the preform heating device 20d.

[0110] The information generating unit 3021B inputs a plurality of pieces of input data, each of which includes region information indicating one of the plurality of object regions 10-1 to 10-N, together with the heating conditions acquired by the information acquiring unit 3020B, into the single learning model 13A. Then, the information generating unit 3021B generates temperature distribution information based on the temperature of each object region included in the plurality of output data output from the single learning model 13A.

[0111] (Operation of manufacturing control system 1) Below, we will explain the functions 301B and 302B realized by the information processing device 3B as operations of the manufacturing management system 1. Note that the information processing device 3B accesses each of the databases 310 and 311 to register and refer to various types of information, but in the following explanation, the access operations will be omitted as appropriate.

[0112] (machine learning methods) FIG. 16 is a flowchart showing an example of a machine learning method by the learning function 301B according to the second embodiment.

[0113] First, in step S300, the learning data acquisition unit 3010B refers to the manufacturing condition database 310 and acquires a plurality of heating conditions registered in a plurality of records, respectively.

[0114] Next, in step S310, the learning data acquisition unit 3010B initializes a processing index i indicating the object region 10-i to be processed to "1".

[0115] Next, in step S320, the learning data acquisition unit 3010B refers to multiple pieces of temperature distribution information associated with the multiple heating conditions acquired in step S300 in the manufacturing condition database 310, and acquires the temperatures of the target region 10-i identified by the processing index i.

[0116] Next, in step S321, the learning data acquisition unit 3010B acquires multiple sets (number of records) of learning data 12-i by combining, by record, input data including area information indicating the object area 10-i identified by the processing index i, along with the heating conditions acquired in step S300, and output data including the temperature of the object area 10-i acquired in step S320.

[0117] Next, in step S330, the machine learning unit 3011B performs machine learning of the single learning model 13A using the multiple sets of learning data 12-i acquired in step S321.

[0118] Next, in step S340, the machine learning unit 3011B increments the processing index i. Then, in step S341, the machine learning unit 3011B determines whether the processing index i exceeds the number of divisions of the object region ("7" in the example of FIG. 6, "4" in the example of FIG. 7).

[0119] If it is determined in step S341 that the processing index i does not exceed the number of divisions of the object region, the process returns to step S320 and performs the same process on the next object region. On the other hand, if it is determined that the processing index i exceeds the number of divisions of the object region, the process proceeds to step S350.

[0120] Then, in step S350, the machine learning unit 3011B stores in the trained model management database 311 the trained single training model 13A (adjusted weight parameter group) for which machine learning was performed in step S330.

[0121] As described above, the series of steps in the machine learning method shown in Fig. 16 is completed. In the machine learning method, steps S300 to S321 correspond to the learning data acquisition step, step S330 corresponds to the machine learning step, and step S350 corresponds to the trained model storage step. Note that in the series of steps shown in Fig. 16, the learning data 12-i used for machine learning of a single learning model 13A is stored as a target. Although the learning data 12-i is acquired sequentially for each object region and machine learning is performed using a single learning model 13A, the learning data 12-i may be acquired randomly instead of for each object region. In this case, for example, the learning data 12-1 to 12-N corresponding to each object region 10-1 to 10-N may be acquired randomly, and the process of performing machine learning using a single learning model 13A may be repeated multiple times.

[0122] As described above, according to the learning function 301B and machine learning method of the information processing device 3B of this embodiment, the machine learning unit 3011B performs machine learning on the single learning model 13A, causing the single learning model 13A to learn the correlation between input data including heating conditions and output data including the temperature of a specific object region. This allows the single learning model 13A to learn about the entire preform 10, while also learning specifically about each of the object regions 10-1 to 10-N. This makes it possible to generate a trained model that can easily and accurately predict the temperature of each object region of the preform 10.

[0123] (Information processing method) FIG. 17 is a flowchart showing an example of an information processing method by the information processing function 302B according to the second embodiment.

[0124] First, in step S400, the information acquisition unit 3020B acquires the heating conditions when adjusting the temperature of the preform 10 by the preform heating device 20d.

[0125] Next, in step S410, the information generating unit 3021B initializes a processing index j indicating the object region 10-j to be processed to "1".

[0126] Next, in step S420, the information generation unit 3021B inputs input data including the heating conditions acquired in step S400 and region information indicating the object region 10-j identified by the processing index j to a single learning model 13A. Then, the information generation unit 3021B temporarily stores the output data output from the learning model 13A in the storage unit 31.

[0127] Next, in step S430, the information generation unit 3021B increments the processing index j. Then, in step S431, the information generation unit 3021B determines whether the processing index j exceeds the division number of the object region ("7" in the example of FIG. 6, "4" in the example of FIG. 7).

[0128] If it is determined in step S431 that the processing index j does not exceed the division number of the object region, the process returns to step S420 and performs the same process on the next object region. On the other hand, if it is determined that the processing index j exceeds the division number of the object region, the process proceeds to step S440.

[0129] Then, in step S440, information generation unit 3021B refers to the plurality of output data temporarily stored in step S420, and generates temperature distribution information based on the temperature of each object region included in the plurality of output data.

[0130] Next, in step S450, the output processing unit 3022 performs an output process for outputting the temperature distribution information generated in step S440, and as a result, the temperature distribution information is presented to the user.

[0131] In this manner, the series of information processing methods shown in FIG. 17 is completed. In the above, step S400 corresponds to an information acquisition step, steps S410 to S440 correspond to an information generation step, and step S450 corresponds to an output processing step. Note that the series of information processing methods can be executed at any timing as long as the information processing device 3B can acquire the heating conditions.

[0132] As described above, according to the information processing function 302B and information processing method of the information processing device 3B of this embodiment, by inputting multiple input data including heating conditions and area information for the preform 10 into a single learning model 13A, temperature distribution information for the preform 10 is generated based on the output data output from the single learning model 13A.

[0133] That is, rather than treating the entire preform 10 collectively, a single learning model 13A corresponding to each of the object regions 10-1 to 10-N obtained by dividing the entire preform 10 is used to individually predict the temperature of each of the object regions 10-1 to 10-N, thereby predicting the temperature distribution throughout the preform 10. As a result, the preform 10 is treated as discrete regions, and therefore more complex calculations are not required than when treating the entire preform 10 collectively as a continuous region, and therefore the temperature distribution of the preform 10 can be predicted simply and with high accuracy.

[0134] Furthermore, by using a classification model that outputs the classification results of temperature divisions as the single learning model 13A, the temperatures of the object regions 10-1 to 10-N can be predicted as relative indices.

[0135] (Third embodiment) In the second embodiment, the information processing device 3A receives input data including heating conditions and region information and uses a single learning model 13A that outputs the temperature of a specific object region indicated by the region information, whereas in the third embodiment, the information processing device 3C receives input data including heating conditions and uses a single learning model 13B that outputs the temperature for each object region, which is different from the second embodiment. The following describes the information processing device 3C according to the third embodiment, focusing on the differences from the first embodiment.

[0136] (Configuration of information processing device 3C) 18 is a block diagram showing an example of an information processing device 3C according to the third embodiment. The control unit 30 executes an information processing program 312C stored in the storage unit 31 to implement an information management function 300, a learning function 301C, and an information processing function 302C. The control unit 30 includes a learning data acquisition unit 3010C and a machine learning unit 3011C as units that implement the learning function 301C. The control unit 30 includes an information acquisition unit 3020C, an information generation unit 3021C, and an output processing unit 3022 as units that implement the information processing function 302C.

[0137] (Learning Function 301C) 19 is a functional explanatory diagram showing an example of a learning function 301C according to the third embodiment. A learning data acquisition unit 3010C and a machine learning unit 3011C of an information processing device 3C realize the learning function 301C using a manufacturing condition database 310 (learning data storage unit) and a trained model management database 311 (trained model storage unit).

[0138] The single learning model 13B registered in the learned model management database 311 receives input data including at least heating conditions and outputs output data including the temperature for each object region. Note that the learning model 13B employs, for example, a neural network structure, as in the first embodiment.

[0139] In this embodiment, the single learning model 13B is a classification model, and when input data including heating conditions is input, the output data is the classification result of the temperature category when the temperature of each object area is classified into multiple temperature categories.

[0140] The learning data acquisition unit 3010C acquires a plurality of sets of learning data 12 each consisting of input data including heating conditions and output data including the temperature of each object region.

[0141] The machine learning unit 3011C performs machine learning on the single learning model 13B using the multiple sets of learning data 12 acquired by the learning data acquisition unit 3010C. Then, the machine learning unit 3011C generates the trained single learning model 13B by having the single learning model 13B learn the correlation between the input data and the output data.

[0142] (Information Processing Function 302C) 20 is a functional explanatory diagram showing an example of an information processing function 302C according to the third embodiment. An information acquisition unit 3020C, an information generation unit 3021C, and an output processing unit 3022 of an information processing device 3C realize the information processing function 302C using a trained model management database 311.

[0143] The information acquisition unit 3020C acquires the heating conditions when adjusting the temperature of the preform 10 by the preform heating device 20d.

[0144] The information generation unit 3021C inputs the input data including the heating conditions acquired by the information acquisition unit 3020C into the single learning model 13B. Then, the information generation unit 3021C generates temperature distribution information based on the temperature for each object region included in the output data output from the single learning model 13B.

[0145] (Operation of manufacturing control system 1) Below, we will explain the functions 301C and 302C realized by the information processing device 3C as the operation of the manufacturing control system 1. Note that the information processing device 3C accesses each of the databases 310 and 311 to register and refer to various types of information, but in the following explanation, the access operations will be omitted as appropriate.

[0146] (machine learning methods) FIG. 21 is a flowchart showing an example of a machine learning method by the learning function 301C according to the third embodiment.

[0147] First, in step S500, the learning data acquisition unit 3010C refers to the manufacturing condition database 310 and acquires a plurality of heating conditions registered in a plurality of records, respectively.

[0148] Next, in step S510, the learning data acquisition unit 3010C refers to the plurality of pieces of temperature distribution information associated with the plurality of heating conditions acquired in step S500 in the manufacturing condition database 310, and acquires the temperature for each object region.

[0149] Next, in step S511, the learning data acquisition unit 3010C acquires multiple sets (the number of records) of learning data 12 by combining, by record, input data including the heating conditions acquired in step S500 and output data including the temperature for each object region acquired in step S510.

[0150] Next, in step S520, the machine learning unit 3011C performs machine learning of the single learning model 13B using the multiple sets of learning data 12 acquired in step S511.

[0151] Then, in step S530, the machine learning unit 3011C stores in the trained model management database 311 the trained single trained model 13B (adjusted weight parameter group) for which machine learning was performed in step S520.

[0152] This completes the series of steps in the machine learning method shown in Fig. 21. In the machine learning method, steps S500 to S511 correspond to a learning data acquisition step, step S520 corresponds to a machine learning step, and step S530 corresponds to a trained model storage step.

[0153] As described above, according to the learning function 301C and machine learning method of the information processing device 3C of this embodiment, the machine learning unit 3011C performs machine learning on the single learning model 13B, causing the single learning model 13B to learn the correlation between input data including heating conditions and output data including the temperature of each object region. This allows the single learning model 13B to learn about the entire preform 10, while also learning specifically about each of the object regions 10-1 to 10-N. This makes it possible to generate a trained model that can easily and accurately predict the temperature of each object region of the preform 10.

[0154] (Information processing method) FIG. 22 is a flowchart showing an example of an information processing method by the information processing function 302C according to the third embodiment.

[0155] First, in step S600, the information acquisition unit 3020C acquires the heating conditions when adjusting the temperature of the preform 10 by the preform heating device 20d.

[0156] Next, in step S610, the information generation unit 3021C inputs the input data including the heating conditions acquired in step S600 into a single learning model 13B. Then, the information generation unit 3021C generates temperature distribution information based on the temperature for each object region included in the output data output from the learning model 13B.

[0157] Next, in step S620, the output processing unit 3022 performs an output process for outputting the temperature distribution information generated in step S610, and as a result, the temperature distribution information is presented to the user.

[0158] In this manner, the series of information processing methods shown in Fig. 22 is completed. In the above information processing method, step S600 corresponds to an information acquisition step, step S610 corresponds to an information generation step, and step S620 corresponds to an output processing step. Note that the series of information processing methods can be executed at any timing as long as the information processing device 3C can acquire the heating conditions.

[0159] As described above, according to the information processing function 302C and information processing method of the information processing device 3C of this embodiment, by inputting input data including heating conditions for the preform 10 into a single learning model 13B, temperature distribution information for the preform 10 is generated based on the output data output from the single learning model 13B.

[0160] That is, the temperature distribution in the entire preform 10 is predicted by predicting the temperature of each of the target regions 10-1 to 10-N obtained by dividing the entire preform 10, rather than the temperature of the entire preform 10 collectively. This allows the preform 10 to be treated as discrete regions. This eliminates the need for more complex calculations than when treating the entire preform 10 collectively as a continuous region, and therefore makes it possible to predict the temperature distribution of the preform 10 easily and with high accuracy.

[0161] Furthermore, by using a classification model that outputs the classification results of temperature divisions as the single learning model 13B, the temperatures of each of the object regions 10-1 to 10-N can be predicted as relative indices.

[0162] (Other embodiments) The present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the spirit and scope of the present invention, all of which are included in the technical concept of the present invention.

[0163] In the above embodiment, the information processing devices 3A-3C generate temperature distribution information from heating conditions for heating a preform 10, which is one type of object, using one or more learning models. Alternatively, the information processing devices 3A-3C may generate temperature distribution information from cooling conditions for cooling an object using a similar function. That is, the information processing devices 3A-3C may input input data including at least temperature adjustment conditions (including heating conditions and cooling conditions) for adjusting the temperature of the object into one or more learning models, and generate temperature distribution information for the object when the temperature of the object is adjusted according to the temperature adjustment conditions included in the input data based on the output data output from the one or more learning models. In this case, the information processing devices 3A-3C may acquire temperature adjustment conditions when adjusting the temperature of the object using a temperature adjustment device that includes not only a heating device such as the preform heating device 20d but also a cooling device.

[0164] In the above embodiment, the multiple functions of the information processing devices 3A to 3C are described as being realized by one device, but each function may be distributed among multiple devices (computers) and realized by multiple devices. For example, the information processing devices 3A to 3C that realize at least the learning functions 301A to 301C operate as machine learning devices, but the machine learning devices that realize the learning functions 301A to 301C may be configured as devices different from the information processing devices 3A to 3C that realize the information processing functions 302A and 302B.

[0165] In the above embodiment, the information processing devices 3A to 3C are described as having the communication unit 32 and transmitting and receiving various data to and from the user terminal device 4 via the communication unit 32. However, the information processing devices 3A to 3C may operate as standalone devices. In this case, the information processing devices 3A to 3C may not need to have the communication unit 32.

[0166] In the above embodiment, the information processing devices 3A to 3C operate according to the flowcharts shown in Figures 11, 12, 16, and 17, but some of the steps (units) may be omitted or other steps may be added. In this case, the omitted steps (units) may be executed by an external system.

[0167] In the above embodiment, a case has been described in which a neural network is used as a learning model for realizing machine learning by the learning functions 301A to 301C, but other machine learning models may also be used. Examples of other machine learning models include tree types such as decision trees and regression trees, ensemble learning such as bagging and boosting, and neural network types (deep learning) such as recurrent neural networks, convolutional neural networks, and LSTM. clustering), hierarchical clustering, non-hierarchical clustering, k-nearest neighbors, k-means Examples of such methods include clustering methods, principal component analysis, factor analysis, multivariate analysis such as logistic regression, and support vector machines.

[0168] Various aspects of the present disclosure are summarized below as appendices.

[0169] (Appendix 1) an information acquisition unit that acquires temperature adjustment conditions when adjusting the temperature of an object; an information generation unit that inputs input data including at least the temperature adjustment conditions acquired by the information acquisition unit into one or more learning models, and generates temperature distribution information of the object when the temperature of the object is adjusted in accordance with the temperature adjustment conditions included in the input data, based on output data output from the one or more learning models; The temperature distribution information is information indicating a temperature of each of a plurality of object regions when the object is divided into the plurality of object regions. Information processing device.

[0170] (Appendix 2) The plurality of learning models Machine learning is performed for each of the object regions using learning data consisting of input data including the temperature adjustment conditions and output data including the temperature of a specific object region among the plurality of object regions, a trained model in which a correlation between the input data and the output data is trained for each of the object regions, The information generation unit inputting the input data including the temperature adjustment conditions acquired by the information acquisition unit into each of the plurality of learning models, and generating the temperature distribution information based on the temperatures for each of the object regions included in each of the plurality of output data output from the plurality of learning models; 2. The information processing device according to claim 1.

[0171] (Appendix 3) The single learning model is Machine learning is performed using learning data consisting of input data including area information indicating a specific object area among the plurality of object areas together with the temperature adjustment conditions, and output data including the temperature of the specific object area indicated by the area information, a trained model that has trained a correlation between the input data and the output data; The information generation unit a plurality of pieces of input data each including region information indicating any one of the plurality of object regions together with the temperature adjustment condition acquired by the information acquisition unit are input to the single learning model, and the temperature distribution information is generated based on the temperature for each of the object regions included in the plurality of output data output from the single learning model; 2. The information processing device according to claim 1.

[0172] (Appendix 4) The single learning model is Machine learning is performed using learning data consisting of input data including the temperature adjustment conditions and output data including the temperature for each object region, a trained model that has trained a correlation between the input data and the output data; The information generation unit generating the temperature distribution information based on the temperature for each object region included in the output data output from the single learning model by inputting the input data including the temperature adjustment condition acquired by the information acquisition unit into the single learning model; 2. The information processing device according to claim 1.

[0173] (Appendix 5) The single or multiple learning models and outputting, as the output data, a classification result of the temperature range when the temperature of the object region is classified into a plurality of temperature ranges. 5. The information processing device according to claim 1, wherein the information processing device is a

[0174] (Appendix 6) The information acquisition unit acquiring the temperature adjustment conditions including device setting values ​​for each of a plurality of temperature adjustment devices when adjusting the temperature of the object using the plurality of temperature adjustment devices respectively arranged at a plurality of arrangement positions spaced apart in a predetermined arrangement direction; The information generation unit generating temperature distribution information indicating the temperature of each of the object regions when the object is divided into a plurality of object regions between the arrangement positions where any two of the temperature control devices adjacent to each other in the arrangement direction are respectively arranged; 6. The information processing device according to claim 1,

[0175] (Appendix 7) The object is A molded body when a molded body is produced by stretching the molded body in a predetermined stretching direction by blow molding, The information generation unit When the molded body is divided into a plurality of molded body regions in the stretching direction, the plurality of molded body regions are scaled based on the stretching ratio when the molded body is stretched into the molded body, and the plurality of molded body regions are used as the plurality of object regions, and temperature distribution information indicating the temperature for each of the object regions is generated. 7. The information processing device according to claim 1, [Explanation of symbols]

[0176] 1... manufacturing control system, 2... bottle manufacturing device, 3A to 3C... information processing device 4...user terminal device, 10...preform (object, molded body), 10-1 to 10-N... object area (molded body area), 11... bottle (molded body), 12, 12-1 to 12-N... training data, 13A, 13B, 13-1~13-N...Learning model, 30...control unit, 31...storage unit, 32...communication unit, 33...input unit, 34...display unit, 300...Information management functions, 301A~301C...Learning functions, 302A~302C...Information processing functions, 310... manufacturing condition database, 311... trained model management database, 312A~312C...Information Processing Programs, 3010A to 3010C... learning data acquisition unit, 3011A to 3011C... machine learning unit, 3020A to 3020C... information acquisition unit, 3021A to 3021C... information generation unit, 3022...Output processing section

Claims

1. an information acquisition unit that acquires temperature adjustment conditions when adjusting the temperature of an object; an information generation unit that inputs input data including at least the temperature adjustment conditions acquired by the information acquisition unit into one or more learning models, and generates temperature distribution information of the object when the temperature of the object is adjusted in accordance with the temperature adjustment conditions included in the input data, based on output data output from the one or more learning models; The temperature distribution information is information indicating a temperature of each of a plurality of object regions when the object is divided into the plurality of object regions. Information processing device.

2. The plurality of learning models Machine learning is performed for each of the object regions using learning data consisting of input data including the temperature adjustment conditions and output data including the temperature of a specific object region among the plurality of object regions, a trained model in which a correlation between the input data and the output data is trained for each of the object regions, The information generation unit inputting the input data including the temperature adjustment conditions acquired by the information acquisition unit into each of the plurality of learning models, and generating the temperature distribution information based on the temperatures for each of the object regions included in each of the plurality of output data output from the plurality of learning models; The information processing device according to claim 1 .

3. The single learning model is Machine learning is performed using learning data consisting of input data including area information indicating a specific object area among the plurality of object areas together with the temperature adjustment conditions, and output data including the temperature of the specific object area indicated by the area information, a trained model that has trained a correlation between the input data and the output data; The information generation unit a plurality of pieces of input data each including region information indicating any one of the plurality of object regions together with the temperature adjustment condition acquired by the information acquisition unit are input to the single learning model, and the temperature distribution information is generated based on the temperature for each of the object regions included in the plurality of output data output from the single learning model; The information processing device according to claim 1 .

4. The single learning model is Machine learning is performed using learning data consisting of input data including the temperature adjustment conditions and output data including the temperature for each object region, a trained model that has trained a correlation between the input data and the output data; The information generation unit The input data including the temperature adjustment conditions acquired by the information acquisition unit is input to the single learning model, and the temperature distribution information is generated based on the temperatures for each of the object regions included in the output data output from the single learning model. The information processing device according to claim 1 .

5. The single or multiple learning models and outputting, as the output data, a classification result of the temperature range when the temperature of the object region is classified into a plurality of temperature ranges. The information processing device according to claim 1 .

6. The information acquisition unit acquiring the temperature adjustment conditions including device setting values ​​for each of a plurality of temperature adjustment devices when adjusting the temperature of the object using the plurality of temperature adjustment devices respectively arranged at a plurality of arrangement positions spaced apart in a predetermined arrangement direction; The information generation unit generating temperature distribution information indicating a temperature for each of the object regions when the object is divided into a plurality of object regions between the arrangement positions where any two of the temperature control devices adjacent to each other in the arrangement direction are respectively arranged; The information processing device according to claim 1 .

7. The object is A molded body when a molded body is produced by stretching the molded body in a predetermined stretching direction by blow molding, The information generation unit When the molded body is divided into a plurality of molded body regions in the stretching direction, the plurality of molded body regions are scaled based on the stretching ratio when the molded body is stretched into the molded body, and the plurality of molded body regions are used as the plurality of object regions, and temperature distribution information indicating the temperature for each of the object regions is generated. The information processing device according to claim 1 .

8. a learning data acquisition unit that acquires a plurality of sets of learning data, each set consisting of input data including temperature adjustment conditions for adjusting the temperature of an object, and output data including the temperature of a specific object region among a plurality of object regions when the object is divided into the plurality of object regions; a machine learning unit that performs machine learning of a plurality of learning models for each of the object regions using the plurality of sets of learning data acquired by the learning data acquisition unit, thereby causing the plurality of learning models to learn a correlation between the input data and the output data for each of the object regions; and a learned model storage unit that stores the plurality of learned models in which the correlations have been learned by the machine learning unit. Machine learning device.

9. a learning data acquisition unit that acquires multiple sets of learning data, each set including input data including temperature adjustment conditions for adjusting the temperature of an object, region information indicating a specific object region among a plurality of object regions when the object is divided into the plurality of object regions, and output data including the temperature of the specific object region indicated by the region information; a machine learning unit that performs machine learning of a single learning model using the plurality of sets of learning data acquired by the learning data acquisition unit, thereby causing the single learning model to learn a correlation between the input data and the output data; a learned model storage unit that stores the single learned model in which the correlation is learned by the machine learning unit, Machine learning device.

10. a learning data acquisition unit that acquires a plurality of sets of learning data, each set consisting of input data including a temperature adjustment condition for adjusting the temperature of an object and output data including a temperature for each of the object regions; a machine learning unit that performs machine learning of a single learning model using the plurality of sets of learning data acquired by the learning data acquisition unit, thereby causing the single learning model to learn a correlation between the input data and the output data; a learned model storage unit that stores the single learned model in which the correlation is learned by the machine learning unit, Machine learning device.

11. 1. A computer-implemented information processing method, comprising: an information acquisition step of acquiring temperature adjustment conditions when adjusting the temperature of the object; an information generating step of inputting input data including at least the temperature adjustment conditions acquired in the information acquiring step into one or more learning models, and generating temperature distribution information of the object when the temperature of the object is adjusted in accordance with the temperature adjustment conditions included in the input data, based on output data output from the one or more learning models; The temperature distribution information is information indicating a temperature of each of a plurality of object regions when the object is divided into the plurality of object regions. Information processing methods.

12. 1. A computer-implemented machine learning method comprising: a learning data acquisition step of acquiring a plurality of sets of learning data, each set consisting of input data including temperature adjustment conditions for adjusting the temperature of an object, and output data including the temperature of a specific object region among a plurality of object regions when the object is divided into the plurality of object regions; a machine learning process of performing machine learning of a plurality of learning models for each of the object regions using the plurality of sets of learning data acquired by the learning data acquisition process, thereby causing the plurality of learning models to learn the correlation between the input data and the output data for each of the object regions; and a learned model storage step of storing the plurality of learned models, which have learned the correlation through the machine learning step, in a learned model storage unit. Machine learning methods.

13. 1. A computer-implemented machine learning method comprising: a learning data acquisition step of acquiring a plurality of sets of learning data, each set including input data including temperature adjustment conditions for adjusting the temperature of the object, region information indicating a specific object region among a plurality of object regions when the object is divided into the plurality of object regions, and output data including the temperature of the specific object region indicated by the region information; a machine learning process of performing machine learning of a single learning model using the plurality of sets of learning data acquired in the learning data acquisition process, thereby causing the single learning model to learn the correlation between the input data and the output data; and a learned model storage step of storing the single learned model, which has learned the correlation through the machine learning step, in a learned model storage unit. Machine learning methods.

14. 1. A computer-implemented machine learning method comprising: a learning data acquisition step of acquiring a plurality of sets of learning data, each set consisting of input data including temperature adjustment conditions for adjusting the temperature of the object and output data including the temperature of each of the object regions; a machine learning process of performing machine learning of a single learning model using the plurality of sets of learning data acquired in the learning data acquisition process, thereby causing the single learning model to learn the correlation between the input data and the output data; and a learned model storage step of storing the single learned model, which has learned the correlation through the machine learning step, in a learned model storage unit. Machine learning methods.

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