Learning apparatus, learning method, and learning program

Abstract

To implement an operation support system of a batch plant using a machine learning model designed for imitation learning.SOLUTION: A processing apparatus 10 includes: a collection unit 1311 which collects histories of production processes, which are combinations of an explanatory variable representing a status in a production process of a product and an objective variable representing operation of a device in the production process; and a learning unit 1315 which optimizes, for each production process, a parameter of a process-specific model configured to output an objective variable from an explanatory variable, based on the histories of production processes. The learning unit 1315 is configured to, after training a model using, as learning data, a history of a first production process and a history of a second production process similar to the first production process, the first production process having the amount of history data equal to or smaller than a predetermined amount, out of the multiple production processes, finely tune a parameter of the model based on the history of the first production process, and construct a process-specific model corresponding to the first production process.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a learning device, a learning method, and a learning program. [Background technology]

[0002] BACKGROUND ART A technique known as imitation learning is known in the past, in which a machine learning model (hereinafter, referred to as a model) learns human behavior and then uses the model to teach behavior to humans, robots, or the like.

[0003] Here, an operation support system for continuous plants using a model that performs imitation learning has been proposed. For example, in a continuous plant such as a chemical plant, imitation learning is performed by having a model learn the operations actually performed by an operator (plant operation imitation learning). Note that a continuous plant, for example, a heat treatment furnace, is a system in which the heat treatment furnace is operated at all times, and products are fed into the heat treatment furnace to perform heat treatment continuously.

[0004] Using a model that performs this type of imitation learning, it is possible not only to assist plant operation with recommended values, but also to autopilot operations. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent No. 7335414 Summary of the Invention [Problem to be solved by the invention]

[0006] In contrast to continuous plants, there is a system called a batch plant, which is a plant in which each production process, such as material input, heating, and cooling, is carried out in sequence in one furnace.

[0007] There are some problems when applying plant operation imitation learning to build a model for a batch plant.

[0008] In batch plants, the operating methods differ depending on the production process. For example, the required operations differ depending on the production process, such as cooling and heating processes, making it difficult to perform consistent control using a single model.

[0009] Furthermore, because batch plants have multiple production processes, creating separate models for each production process in a batch plant would require insufficient data corresponding to each production process, resulting in inaccurate models.

[0010] The present invention has been made in consideration of the above, and aims to provide a learning device, a learning method, and a learning program that realize an operation support system for a batch plant using a machine learning model that performs imitation learning. [Means for solving the problem]

[0011] In order to solve the above-mentioned problems and achieve the objectives, the learning device has a collection unit that collects the history of each production process, which is a combination of explanatory variables that represent the situation in the production process of a product and objective variables that represent the operation of equipment in the production process, and a learning unit that optimizes, for each of the production processes, parameters of a process-specific model that outputs the objective variable from the explanatory variables based on the history of each production process.The learning unit is characterized in that, for a first production process among the multiple production processes, the amount of data in the history is equal to or less than a predetermined amount, the learning unit learns a model using the history of the first production process and the history of a second production process similar to the first production process as learning data, and then fine-tunes the parameters of the model based on the history of the first production process to construct a process-specific model corresponding to the first production process. [Effects of the Invention]

[0012] According to the present invention, an operation support system for a batch plant is realized using a machine learning model that performs imitation learning. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is a diagram illustrating a plant operation system. [Figure 2] FIG. 2 is a diagram illustrating an outline of model learning according to the embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of the configuration of a processing device according to an embodiment. [Figure 4-1] FIG. 4-1 is a diagram showing an example of data registered in the history database (DB) shown in FIG. [Figure 4-2] FIG. 4-2 is a diagram illustrating an example of data registered in the history DB illustrated in FIG. [Figure 4-3] FIG. 4-3 is a diagram illustrating an example of data registered in the history DB illustrated in FIG. [Figure 4-4] FIG. 4-4 is a diagram illustrating an example of data registered in the history DB illustrated in FIG. [Figure 5] FIG. 5 is a diagram illustrating an example of a data configuration of the group information illustrated in FIG. [Figure 6] FIG. 6 is a flowchart of the learning process according to the embodiment. [Figure 7] FIG. 7 is a flowchart of the inference process according to the embodiment. [Figure 8] FIG. 8 is a diagram illustrating an example of a computer that implements a processing device by executing a program. DETAILED DESCRIPTION OF THE INVENTION

[0014] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A learning device, a learning method, and a learning program according to the present invention will be described in detail below with reference to the accompanying drawings. However, the present invention is not limited to the following embodiments.

[0015] [Embodiment Mode] [Configuration of the embodiment] First, the plant operation system will be described with reference to Fig. 1. Fig. 1 is a diagram for explaining the plant operation system.

[0016] The plant operation system 1 is a system for managing and controlling a product production process (hereinafter referred to as a production process) in a plant. The plant includes a chemical plant for producing chemical products. In this embodiment, a plant operation system applied to a batch plant will be described. A batch plant is a plant in which, for example, each production process, such as material input, heating, and cooling, is performed in sequence for one piece of equipment (e.g., a furnace).

[0017] As shown in FIG. 1, the plant operation system 1 includes a processing device 10, a terminal device 20, and a plant system 30.

[0018] The processing device 10 performs processing related to a model (machine learning model) for performing imitation learning. The processing device 10 can function as a learning device.

[0019] The processing device 10 and the plant system 30 are connected to each other via a network so that they can communicate data with each other. For example, the network is the Internet and an intranet. The processing device 10 uses the model to present recommended operation values ​​to an operator of the plant system 30, thereby supporting plant operation. Furthermore, when the processing device 10 satisfies predetermined autopilot conditions set by the operator or the like and receives an autopilot command from the operator, it uses the model to perform autopilot control of the plant system 30.

[0020] The plant system 30 may include equipment used in the production process and a distributed control system (DCS). For example, the equipment is a furnace or a reactor, and each production process, such as material input, heating, and cooling, is performed in sequence for one furnace.

[0021] The terminal device 20 is an information processing device such as a personal computer, a tablet terminal, or a smartphone, or a dedicated terminal for operating equipment in the plant.

[0022] The operator is a user who operates the devices included in the plant system 30 via the terminal device 20. The operator also instructs the processing device 10 to start or stop the autopilot. The models used in the processing device 10 are managed appropriately by a system administrator or the like.

[0023] The processing of each device in the plant operation system 1 will be described with reference to FIG.

[0024] The terminal device 20 operates the equipment of the plant system 30 in response to an operation by an operator (step S1). For example, the terminal device 20 operates to set the temperature inside one piece of equipment (e.g., a furnace) of a batch plant, the pressure inside the equipment, the target value of the production amount in the production process, the amount of raw materials to be input into the equipment, etc.

[0025] The plant system 30 operates in accordance with the operation from the terminal device 20 (step S2). Then, the plant system 30 transmits the operation history to the processing device 10 (step S3).

[0026] For example, the history includes sensor values ​​of various sensors installed in the furnace of the plant system 30 and setting values ​​set by operation from the terminal device 20. The history may also be time-series data in which a time (time stamp) is assigned to each record. The history may also be tagged with process information indicating multiple production processes that were sequentially executed on the equipment, for example.

[0027] The processing device 10 registers the history of each production process collected from the plant system 30 in a history database (DB) for each production process (step S3).

[0028] Next, the processing device 10 acquires learning data to be used for learning (for example, machine learning) of the model (machine learning model) from the history, learns the model, and performs inference using the model (step S4).

[0029] The processing device 10 uses predetermined autopilot conditions to determine, for example, whether or not autopilot is possible by the processing device 10. Details of each process of the processing device 10 will be described later.

[0030] Furthermore, the processing device 10 presents a guidance screen 21 showing the inference result and the autopilot implementation determination result to the operator's terminal device 20 (step S6). When the autopilot conditions are satisfied, the processing device 10 displays on the guidance screen 21 that it is now possible to start the autopilot. When the terminal device 20 instructs the processing device 10 to start the autopilot, the processing device 10 performs autopilot control of the plant system 30 using the model (step S5).

[0031] Furthermore, if the autopilot conditions are not satisfied while the autopilot is in operation, the processing device 10 displays an instruction to stop the autopilot on the guidance screen 21. When an instruction to stop the autopilot is received from the terminal device 20, the processing device 10 stops the autopilot control of the plant system 30. Then, the terminal device 20 operates the equipment of the plant system 30 in response to an operation by the operator (step S1).

[0032] The model learns the operations of the operators through imitation learning, so other operators can imitate the operations by following the operations obtained as inference results from the model.

[0033] [Model Training] In a batch plant to which this embodiment is applied, the operation method differs depending on the production process. In this batch plant, the required operations, such as a cooling process and a heating process, differ depending on the production process. In this embodiment, model learning is performed as described below to enable consistent control of a batch plant with a single model. Figure 2 is a diagram illustrating an overview of model learning in this embodiment.

[0034] In this embodiment, a model (process-specific model) is provided for each production process in a batch plant. As shown in Fig. 2, the processing device learns each process-specific model for each production process using only the history of the corresponding production process.

[0035] For example, in the case of production process A, of the total history n (1≦n≦N) of the production process, only the history of production process A is used as learning data (arrow Y1), and the process A model is constructed by optimizing the parameters of the process A model so that when the operation execution time t and the process variable X, which is an explanatory variable, are input, the operation value Y is output ((1) in Figure 2). Similar learning is performed for other processes.

[0036] As learning data for the model, the operation execution time t n , process variable X n , operation value Y n is used.

[0037] The process variable X has items such as the temperature at one or more locations of an equipment (e.g., a furnace), the flow rate at one or more locations, the pressure at one or more locations, and the concentration of gases generated in the production process. The values ​​of these items are detected by sensors installed in the equipment. The operation execution time t is a timestamp indicating the date and time when the temperature, pressure, flow rate, and gas concentration were acquired.

[0038] The objective variable is the operation to be performed, and is, for example, a set value set by an operation from the terminal device 20. The operations are various operations performed in the production process, such as material input operations, heating operations, cooling operations, valve operations, etc. The set value may be a normalized value of an actually set value.

[0039] However, for a production process whose historical data volume is less than a predetermined volume, a process-specific model is constructed using the history of a production process similar to this production process.

[0040] An example will be described in which the amount of data in the history of production process B is less than a predetermined amount. In this embodiment, learning is performed for production process B using the history of production process B and the history of production process A, which is similar to production process B, as learning data (arrows Y2 and Y3) ((2) in Figure 2). This model is called the process AB model. Next, a process B model corresponding to production process B is constructed by fine-tuning the parameters of the process AB model based on the history of production process B (arrows Y4 and Y5, (3) in Figure 2).

[0041] Here, similar processes are production processes with similar processing contents. For example, in the case of a cooling process, the operating conditions are considered to be similar even if the target temperatures are different. Note that production processes A and B are both cooling processes. Alternatively, production processes A and B may both be heating processes or processes with a common equipment operating method.

[0042] During inference, the processing device 10 uses different process-specific models depending on the production process of the explanatory variable to be inferred.

[0043] [Processing equipment] Next, the processing device 10 will be described in detail. Fig. 3 is a diagram showing an example of the configuration of the processing device 10 according to the embodiment. As shown in Fig. 3, the processing device 10 includes a communication unit 11, a storage unit 12, and a control unit 13.

[0044] The communication unit 11 performs data communication with other devices via a network. For example, the communication unit 11 is a network interface card (NIC).

[0045] The storage unit 12 is a storage device such as a hard disk drive (HDD), a solid state drive (SSD), an optical disk, etc. Note that the storage unit 12 may also be a data-rewritable semiconductor memory such as a random access memory (RAM), a flash memory, or a non-volatile static random access memory (NVSRAM).

[0046] The storage unit 12 stores an OS (Operating System) and various programs executed by the processing device 10. The storage unit 12 includes a history DB 121 and model information 122.

[0047] The history DB 121 is information including history provided from the plant system 30. For example, in the history 1210 of the history DB 121, records are accumulated every minute, divided into production processes.

[0048] In the history 1210, for example, a production process A history 121A, a production process B history 121B, a production process C history 121C, and a production process D history 121D are registered. In the history 1210, the history is registered by dividing it into production processes based on tags or process information indicating the production processes assigned to the history. Alternatively, the processing device 10 may compare the production process schedule of the applicable batch plant with the timestamp of the history, determine the production process that was performed at the stamped date and time, tag the determined production process to the history, and register it in the history DB 121. Alternatively, the processing device 10 may determine which process this history belongs to based on explanatory variables (sensor values) included in the history.

[0049] Figures 4-1 to 4-4 are diagrams showing examples of data registered in the history DB 121 shown in Figure 3. As shown in Figures 4-1 to 4-4, the history DB 121 includes a list of explanatory variables such as time, situation, and operation, and a set value (operation) which is a target variable. Figure 4-1 illustrates a production process A history 121-A, Figure 4-2 illustrates a production process B history 121-B, Figure 4-3 illustrates a production process C history 121-C, and Figure 4-4 illustrates a production process D history 121-D.

[0050] The time indicates the time when the operation is performed. The explanatory variable list includes, for example, a first temperature, a second temperature, and a first flow rate. The explanatory variable list may also include pressure at one or more locations, the concentration of gas generated in the production process, etc.

[0051] The first temperature, the second temperature, the first pressure, the second pressure, and the first flow rate are sensor values ​​of sensors installed at various locations in the plant system 30, respectively.

[0052] The first temperature, the second temperature, the first pressure, the second pressure, and the first flow rate are explanatory variables of the model, and are examples of explanatory variables that represent the status of the production process of the product.

[0053] The time is a timestamp indicating the date and time when the first temperature, the second temperature, the first pressure, the second pressure, the flow rate, and the gas concentration were acquired.

[0054] The operation is, for example, a setting value set by an operation from the terminal device 20. The setting value may be a normalized value of an actually set value. The setting value corresponds to the objective variable of the model.

[0055] The set value is an objective variable of the model, and is an example of an objective variable that represents the operation of equipment in the production process.

[0056] 3. Furthermore, the history DB 121 has group information 1212 that indicates the grouping results of each production process. Fig. 5 is a diagram showing an example of the data configuration of the group information 1212 shown in Fig. 3.

[0057] 5, the group information 1212 associates the identification information of a group with the identification information of a production process. For example, group G1 has production processes A and B, and group G2 has production processes C and E.

[0058] The model information 122 is information such as parameters for constructing each process model. For example, if the process model is a neural network, the model information 122 is the weight and bias of each layer.

[0059] The model information 122 stores model information of a process-specific model 1220. The process-specific model 1220 includes, for example, parameters of a process A model 122A used when inferring data for production process A, a process B model 122B used when inferring data for production process B, a process C model 122C used when inferring data for production process C, and a process D model 122D used when inferring data for production process D.

[0060] The control unit 13 controls the entire processing device 10. The control unit 13 is, for example, an electronic circuit such as a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or a GPU (Graphics Processing Unit), or an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0061] The control unit 13 also has an internal memory for storing programs that define various processing procedures and control data, and executes each process using the internal memory. The control unit 13 also functions as various processing units by running various programs.

[0062] For example, the control unit 13 has a learning unit 131 (learning device) and an inference unit 132 (inference device). The learning unit 131 executes learning of process-specific models. The inference unit 132 uses different process-specific models of explanatory variables of inference targets to infer a target variable from the explanatory variables of the inference target, and applies the target variable to the plant system 30.

[0063] The learning unit 131 includes a collection unit 1311 , a grouping unit 1312 , a learning data reading unit 1313 , a determination unit 1314 , and a learning unit 1315 .

[0064] The collection unit 1311 collects the history of each production process in the plant system 30. The history is a combination of explanatory variables that represent the status of the product in the production process and objective variables that represent the operation of equipment in the production process. The collection unit 1311 registers each history in the history DB 121 for each production process.

[0065] The grouping unit 1312 groups similar production processes among a plurality of production processes for a product, and groups processes having similar processing contents into the same group.

[0066] The grouping unit 1312 groups heating processes, cooling processes, or processes with a common equipment operation method into the same group. For example, if production processes A and B are heating processes, they are grouped into the same group G1. In this case, the grouping unit 1312 refers to tags or process information indicating the production processes attached to the explanatory variables, and groups production processes with similar names into the same group. The grouping unit 1312 may also compare time-series changes in sensor values ​​included in the explanatory variables, and group production processes whose similarity in time-series changes falls within a predetermined range into the same group.

[0067] The learning data reading unit 1313 reads out the history of the production process to be learned from the history DB 121 as learning data.

[0068] The determination unit 1314 determines whether the amount of data in the history of the production process to be learned is equal to or less than a predetermined amount. For a production process (first production process) whose data in the history of the production process is equal to or less than a predetermined amount, the determination unit 1314 causes the learning unit 1315 to learn a model using the history of the first production process and the history of a second production process similar to the first production process as learning data, and then fine-tunes the parameters of this model based on the history of the first production process. The second production process similar to the first production process is a production process that belongs to the group of the first production process.

[0069] The learning unit 1315 optimizes the parameters of a process-specific model that outputs a target variable from an explanatory variable for each production process based on the history of each production process.

[0070] For a first production process among the multiple production processes, whose amount of historical data is equal to or less than a predetermined amount, the learning unit 1315 learns a model using the history of the first production process and the history of a second production process similar to the first production process as learning data.The learning unit 1315 then fine-tunes the parameters of the model based on the history of the first production process and constructs a process-specific model corresponding to the first production process.For the first production process, the learning unit 1315 learns a model using the history of the group to which the first production process belongs as learning data, and then fine-tunes the parameters of the model based on the history of the first production process.

[0071] For a production process whose amount of historical data exceeds a predetermined amount, the learning unit 1315 learns a model using only the history of this production process as learning data, and constructs a process-specific model corresponding to this production process. The learning unit 1315 stores the model parameters of each learned process-specific model (e.g., process A model 122A, process B model 122B, process C model 122C, and process D model 122D) in model information 122.

[0072] The inference unit 132 includes a data acquisition unit 1321 , a process determination unit 1322 (determination unit), a model selection unit 1323 (selection unit), an inference unit 1324 , and a system application unit 1325 .

[0073] The data acquisition unit 1321 acquires explanatory variables of the inference target. The explanatory variables of the inference target are explanatory variables (corresponding to each sensor value) at a predetermined time.

[0074] The process determination unit 1322 determines a production process corresponding to the explanatory variable acquired by the data acquisition unit 1321. The process determination unit 1322 determines the production process, for example, by referring to a tag indicating the production process attached to the explanatory variable. The process determination unit 1322 may determine the production process corresponding to the explanatory variable by comparing the production process schedule of the batch plant to which the explanatory variable is applied with the timestamp of the explanatory variable and determining the production process that was performed at the stamped date and time.

[0075] The model selection unit 1323 selects a production process model corresponding to the production process identified by the process identification unit 1322 from among the multiple process models 1220. For example, if the production process identified by the process identification unit 1322 is production process D, the model selection unit 1323 selects the process D model 122D.

[0076] The inference unit 1324 infers a response variable corresponding to an explanatory variable of an inference target, using the process-specific model selected by the model selection unit 1323. For example, when the model selected by the inference unit 1324 is the process D model 122D, the explanatory variable of the inference target is input to this process D model 122D, and the inferred value output from the process D model 122D is acquired as a response variable corresponding to the explanatory variable of the inference target.

[0077] The system application unit 1325 applies the objective variables inferred by the inference unit 1324 as operation values ​​of the plant system 30. Alternatively, the system application unit 1325 presents the objective variables inferred by the inference unit 1324 to an operator.

[0078] In Figure 3, an example is explained in which the processing device 10 performs both the learning of the process-specific models and the inference using the process-specific models corresponding to each process, but the learning of the process-specific models and the inference using the process-specific models corresponding to each process may also be performed by a different device.

[0079] [Learning process] Next, the learning process according to the embodiment will be described with reference to Fig. 6, which is a flowchart of the learning process according to the embodiment.

[0080] 6, the processing device 10 collects the history of each production process in the plant system 30 (step S11) and registers each history in the history DB 121 for each production process. The processing device 10 groups similar production processes from among multiple production processes for a product (step S12). The processing device 10 reads out the history of the production process to be learned from the history DB 121 as learning data (step S13).

[0081] The processing device 10 determines whether the amount of data on the history of the production process to be learned is equal to or less than a predetermined amount (step S14).

[0082] If the amount of data on the history of the production process to be learned is equal to or less than a predetermined amount (step S14: Yes), the processing device 10 learns a model using the history of the production process to be learned (first production process) and the history of a second production process similar to the first production process as learning data (step S15). Subsequently, the processing device 10 fine-tunes the parameters of this model based only on the history of the first production process (step S16), thereby constructing a process-specific model corresponding to the first production process.

[0083] If the amount of data in the history of the production process to be learned exceeds a predetermined amount (step S14: No), a process-specific model corresponding to this production process is constructed by learning the model using only the history of this production process as learning data (step S17).

[0084] [Inference processing] Next, the inference processing according to the embodiment will be described with reference to Fig. 7, which is a flowchart of the inference processing according to the embodiment.

[0085] 7, the processing device 10 acquires explanatory variables of an inference target (step S21). The processing device 10 determines a production process corresponding to the explanatory variables acquired in step S21 (step S22).

[0086] The processing device 10 performs a model selection process to select a model by process corresponding to the production process identified by the process identification unit 1322 from the plurality of models by process 1220 (step S23).

[0087] The processing device 10 performs an inference process to infer a response variable corresponding to an explanatory variable of an inference target, using the process-specific model selected in step S23 (step S24).

[0088] The processing device 10 applies the response variable inferred in step S24 as an operation value of the plant system 30 or a value presented to an operator (step S25).

[0089] [Effects of the embodiment] As described above, in this embodiment, for a batch plant with multiple production processes, a process-specific model is constructed by performing learning using the history of each production process. Then, in this embodiment, for a first production process whose amount of historical data is less than a predetermined amount, a model is learned using the history of the first production process and the history of a second production process similar to the first production process as learning data, and then the model parameters are fine-tuned based on the history of the first production process. This makes it possible to generate a process-specific model even for production processes with a small amount of learning data.

[0090] Therefore, according to the embodiment, it is possible to generate process-specific models even for production processes with a small amount of training data, and to realize an operation support system for batch plants using a machine learning model that performs imitation learning.

[0091] The second production process may be multiple. The processing device 10 may select, as the second production process, a predetermined number of production processes with a large amount of data from the same group as the first production process, or may select, as the second production process, production processes with a similarity exceeding a predetermined value from the same group as the first production process.

[0092] [System configuration of the embodiment] The processing device 10 is a functional concept and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of the functions of the processing device 10 is not limited to that shown in the figure, and all or part of it can be functionally or physically distributed or integrated in any unit depending on various loads, usage conditions, etc.

[0093] Furthermore, all or any part of the processes performed by the processing device 10 may be realized by a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), and a program analyzed and executed by the CPU and the GPU. Furthermore, each process performed by the processing device 10 may be realized as hardware using wired logic.

[0094] Furthermore, among the processes described in the embodiments, all or part of the processes described as being performed automatically can be performed manually. Alternatively, all or part of the processes described as being performed manually can be performed automatically using a known method. In addition, the processing procedures, control procedures, specific names, and information including various data and parameters described above and illustrated can be changed as appropriate unless otherwise specified.

[0095] [program] 8 is a diagram showing an example of a computer in which a program is executed to realize the processing device 10. The computer 1000 has, for example, a memory 1010 and a CPU 1020. The computer 1000 also has a hard disk drive interface 1030, a disk drive interface 1040, a serial port interface 1050, a video adapter 1060, and a network interface 1070. These components are connected by a bus 1080.

[0096] The memory 1010 includes a ROM 1011 and a RAM 1012. The ROM 1011 stores a boot program such as a BIOS (Basic Input Output System). The hard disk drive interface 1030 is connected to a hard disk drive 1090. The disk drive interface 1040 is connected to a disk drive 1100. A removable storage medium such as a magnetic disk or optical disk is inserted into the disk drive 1100. The serial port interface 1050 is connected to a mouse 1110 and a keyboard 1120, for example. The video adapter 1060 is connected to a display 1130, for example.

[0097] The hard disk drive 1090 stores, for example, an OS (Operating System) 1091, an application program 1092, a program module 1093, and program data 1094. That is, a program that defines each process of the processing device 10 is implemented as a program module 1093 in which code executable by the computer 1000 is written. The program module 1093 is stored, for example, in the hard disk drive 1090. For example, a program module 1093 for executing the same process as the functional configuration of the processing device 10 is stored in the hard disk drive 1090. Note that the hard disk drive 1090 may be replaced with an SSD (Solid State Drive).

[0098] Furthermore, setting data used in the processing of the above-described embodiment is stored as program data 1094, for example, in memory 1010 or hard disk drive 1090. Then, CPU 1020 reads program module 1093 and program data 1094 stored in memory 1010 or hard disk drive 1090 into RAM 1012 as necessary and executes them.

[0099] The program module 1093 and program data 1094 are not limited to being stored in the hard disk drive 1090, but may also be stored in, for example, a removable storage medium and read by the CPU 1020 via the disk drive 1100 or the like. Alternatively, the program module 1093 and program data 1094 may be stored in another computer connected via a network (such as a local area network (LAN) or a wide area network (WAN)). The program module 1093 and program data 1094 may then be read by the CPU 1020 from the other computer via the network interface 1070.

[0100] Although the present invention has been described above as an embodiment, the present invention is not limited to the descriptions and drawings that form part of the disclosure of the present invention. In other words, other embodiments, examples, and operational techniques that can be made by those skilled in the art based on the present invention are all included in the scope of the present invention. [Explanation of symbols]

[0101] 1 Plant operation system 10 Processing equipment 20 Terminal equipment 30 Plant Systems 11 Communications Department 12 Storage section 13 Control Unit 121 History DB 122 Model Information 131 Learning Department 132 Reasoning part 1210 History 1220 Process Model 1311 Collection Department 1312 Grouping Department 1313 Learning data reading unit 1314 Judgment section 1315 Learning Department 1321 Data Acquisition Department 1322 Process discrimination section 1323 Model Selection Section 1324 Reasoning part 1325 System Application Department

Claims

1. a collection unit that collects a history of each production process, which is a combination of explanatory variables that represent the status of the product production process and objective variables that represent the operation of equipment in the production process; a learning unit that optimizes parameters of a process-specific model that outputs the objective variable from the explanatory variables based on a history of each production process for each of the production processes; and The learning device is characterized in that, for a first production process among the multiple production processes, the amount of data for the history is equal to or less than a predetermined amount, the learning unit learns a model using the history of the first production process and the history of a second production process similar to the first production process as learning data, and then fine-tunes parameters of the model based on the history of the first production process to construct a process-specific model corresponding to the first production process.

2. 2. The learning device according to claim 1, wherein the product is manufactured in a batch plant in which a plurality of production processes are executed in sequence using one manufacturing device.

3. further comprising a grouping unit that groups similar production processes among the plurality of production processes, The learning device according to claim 1, characterized in that the learning unit learns a model for the first production process using the history of a group to which the first production process belongs as learning data, and then fine-tunes parameters of the model based on the history of the first production process.

4. 4. The learning device according to claim 3, wherein the grouping unit groups production processes having similar processing contents into the same group.

5. 4. The learning device according to claim 3, wherein the grouping unit groups heating processes, cooling processes, or processes with a common equipment operation method into the same group.

6. A learning method executed by a learning device, a collection step of collecting a history of each production process, which is a combination of explanatory variables representing the status of the product production process and objective variables representing the operation of equipment in the production process; a learning step of optimizing parameters of a process-specific model that outputs the objective variable from the explanatory variables for each of the production processes based on a history of each of the production processes; Including, The learning method is characterized in that the learning step involves learning a model using the history of a first production process among the multiple production processes, for which the amount of history data is equal to or less than a predetermined amount, and the history of the first production process and the history of a second production process similar to the first production process as learning data, and then fine-tuning the parameters of the model based on the history of the first production process to construct a process-specific model corresponding to the first production process.

7. a collection step of collecting a history of each production process, which is a combination of explanatory variables representing a situation in the production process of the product and objective variables representing the operation of equipment in the production process; a learning step of optimizing parameters of a process-specific model that outputs the objective variable from the explanatory variables for each of the production processes based on a history of each of the production processes; A learning program that causes a computer to execute the following: The learning step is a learning program that, for a first production process among the multiple production processes, the amount of historical data is equal to or less than a predetermined amount, learns a model using the history of the first production process and the history of a second production process similar to the first production process as learning data, and then fine-tunes the parameters of the model based on the history of the first production process to construct a process-specific model corresponding to the first production process.

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