Learning apparatus, inference apparatus, learning method, inference method, learning program, and inference program

Abstract

To implement an operation support system of a batch-type plant using a machine learning model for imitation learning.SOLUTION: A processing apparatus 10 includes a collection unit 1311 and a learning unit 1313. The collection unit 1311 collects histories which are combinations of an explanatory variable representing status in a production process of a product, and an objective variable representing operation of a device in the process. The learning unit 1313 calculates a phase variable representing the progress of phase in the process from an explanatory variable, using the histories as learning data, and optimizes a parameter of a model configured to output an objective variable on the basis of the explanatory variable and the phase variable.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a learning device, an inference device, a learning method, an inference method, a learning program, and an inference 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] Furthermore, when building a model based on data such as sensor values ​​obtained in a plant, it may not be possible to observe which process each piece of sensor data belongs to. Also, processes are not always observed as discrete pieces of information, but may be continuous information.

[0011] For example, even if the sensor values ​​obtained in two scenes are the same, the optimal operation will be different if the process in each scene is different. For example, even if the temperature sensor value is the same, if the process at that time is a heating process, an operation for heating will be required, and conversely, if the process at that time is a cooling process, an operation for cooling will be required. For this reason, with conventional technology, it is sometimes difficult to achieve imitation learning because information that identifies the process cannot be obtained.

[0012] The present invention has been made in consideration of the above, and aims to provide a learning device, an inference device, a learning method, an inference method, a learning program, and an inference 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]

[0013] In order to solve the above-mentioned problems and achieve the objectives, the learning device of the present invention is characterized by having a collection unit that collects history, which is a combination of explanatory variables that represent the situation in a product production process and objective variables that represent the operation of equipment in the process, and a learning unit that uses the history as learning data to calculate a phase variable that represents the degree of progress of a phase of the process from the explanatory variables, and optimizes parameters of a model that outputs the objective variable based on the explanatory variables and the phase variables.

[0014] In order to solve the above-mentioned problems and achieve the objectives, the inference device of the present invention is characterized by having an acquisition unit that acquires explanatory variables that represent a situation in a product production process, and an inference unit that uses a trained model to calculate a phase variable that represents the degree of progress of a phase of the process from the explanatory variables, and infers a target variable that represents the operation of equipment in the process based on the explanatory variables and the phase variables. [Effects of the Invention]

[0015] 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]

[0016] [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 first embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of the configuration of the processing device according to the first embodiment. [Figure 4] FIG. 4 is a diagram showing an example of data registered in the history database (DB) shown in FIG. [Figure 5] FIG. 5 is a flowchart of the learning process according to the first embodiment. [Figure 6] FIG. 6 is a flowchart of the inference process according to the first embodiment. [Figure 7]FIG. 7 is a diagram illustrating an example of a computer that implements a processing device by executing a program. DETAILED DESCRIPTION OF THE INVENTION

[0017] Hereinafter, embodiments of a learning device, an inference device, a learning method, an inference program, and an inference program according to the present application will be described in detail with reference to the accompanying drawings. Note that the present invention is not limited to the embodiments described below.

[0018] [Embodiment 1] [Configuration of the First 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.

[0019] 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).

[0020] As shown in FIG. 1, the plant operation system 1 includes a processing device 10, a terminal device 20, and a plant system 30.

[0021] 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 and an inference device.

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] The processing of each device in the plant operation system 1 will be described with reference to FIG.

[0027] 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.

[0028] 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).

[0029] For example, the history includes sensor values ​​of various sensors installed in the furnace of the plant system 30 and setting values ​​set by operations from the terminal device 20. The history may also be time-series data in which a time (time stamp) is added to each record.

[0030] The processing device 10 registers the history collected from the plant system 30 in a history database (DB) (step S3).

[0031] 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).

[0032] 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.

[0033] 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).

[0034] 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).

[0035] 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.

[0036] [Model Training] In a batch plant to which the first 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, the following model learning is performed to enable consistent control of a batch plant with a single model. Figure 2 is a diagram illustrating an overview of model learning in the first embodiment.

[0037] 2, the processing device 10 constructs a model using the history. Specifically, the processing device 10 learns the model using the history as learning data. Furthermore, the processing device 10 infers (predicts, estimates) the objective variable using the learned model based on the explanatory variables included in the history.

[0038] The history is time-series data stored in the history DB 121, which will be described later. The history includes N records. In the example of FIG. 2, the history includes N records from time t n , operation value Y n , process variable X n where n is a subscript to identify the record.

[0039] Here, it is assumed that the process information is unobserved. The process information is information for identifying a process.

[0040] 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 time t is a timestamp indicating the date and time when the temperature, pressure, flow rate, and gas concentration were acquired.

[0041] The model receives input of time t and process variable X, and outputs manipulated value Y. At that time, the model internally calculates process information φ (hereinafter referred to as phase variable), and calculates the final manipulated value Y using the phase variable φ. The model can be said to be a hierarchical model having a layer that calculates the phase variable φ and a layer that calculates the manipulated value Y. The phase variable φ is a latent variable.

[0042] Multiple records are input to the model in batches in ascending order of timestamp. The model processes the records in the order they were input. Here, the batch process is considered to be a continuous phase. For example, to be precise, the batch process is expressed using the progress level, which is a continuous value from 0 to 100. At the start of the batch, the progress level is 0, and then increases monotonically as the batch progresses, reaching 100 at the end of the batch.

[0043] In the model, a continuous phase is divided into K stages, and the phase variable φ takes a discrete value representing each stage. As the phase progresses, the phase variable φ increases.

[0044] For example, if the progress degree ranges from 0 to 100 and K=100, the phase variable φ takes an integer value from 0 to 100 (1, 2, . . . , 100).

[0045] Furthermore, the processing device 10 imposes a constraint on the model that the phase does not regress. For example, if the value of the phase variable φ calculated internally by the model when the first record is input is 1, the value of the phase variable φ calculated internally by the model when the second record is input will be 1 or more due to the constraint.

[0046] In this way, the model calculates the phase variables each time multiple records included in the training data are input in order, and outputs the objective variables based on the explanatory variables and the phase variables. In addition, the model is constrained not to regress each time the phase variables are calculated.

[0047] The model is a state space model such as an input-output HMM (Hidden Markov Model), etc. The processing device 10 imposes constraints (left to right) on the state space model so that state transitions do not regress.

[0048] Furthermore, the phase variable does not have to be one-dimensional. For example, the phase variable φ may be data in One Hot format. Furthermore, the phase variable φ may be a one-dimensional or two- or more-dimensional continuous value. If the phase variable φ is a two- or more-dimensional vector, the absolute value of the vector may be constrained to monotonically increase within a batch.

[0049] The operation value Y, which is the objective variable, is the operation 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.

[0050] [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 embodiment 1. As shown in Fig. 3, the processing device 10 includes a communication unit 11, a storage unit 12, and a control unit 13.

[0051] 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).

[0052] 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).

[0053] 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.

[0054] The history DB 121 is information including history provided from the plant system 30. For example, records are accumulated in the history DB 121 every minute or every second.

[0055] Fig. 4 is a diagram showing an example of data registered in the history DB 121 shown in Fig. 3. As shown in Fig. 4, the history DB 121 includes a time, a first temperature, a second temperature, a first pressure, a second pressure, a flow rate, and a set value.

[0056] The time, the first temperature, the second temperature, the first pressure, the second pressure, and the flow rate are explanatory variables of the model, and are examples of explanatory variables that represent the situation in the production process of the product. The explanatory variables may include the pressure at one or more locations, the concentration of gas generated in the production process, etc.

[0057] The first temperature, the second temperature, the first pressure, the second pressure, and the flow rate are sensor values ​​of sensors installed at various locations in the plant system 30. The time is a timestamp indicating the date and time when the sensor value was acquired or the date and time when an operation was performed in accordance with the set value.

[0058] The set value indicates the content of the operation to be performed. For example, the set value is set by an operation from the terminal device 20, such as the opening degree of a valve. The set value may be a normalized value of the actually set value.

[0059] 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.

[0060] The model information 122 is information such as parameters for constructing a model. For example, the model information 122 is transition probabilities and output probabilities, which are parameters of a hidden Markov model. If the model is a neural network, the model information 122 may be weights and biases of each layer.

[0061] 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).

[0062] 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.

[0063] For example, the control unit 13 includes a learning unit 131 (learning device) and an inference unit 132 (inference device). The learning unit 131 executes model learning. The inference unit 132 performs inference using the learned model. The inference unit 132 also applies the obtained objective variables to the plant system 30.

[0064] The learning unit 131 includes a collection unit 1311, a learning data reading unit 1312, and a learning unit 1313. For example, the learning unit 1313 employs sequential learning using the JIT method.

[0065] 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 production process and objective variables that represent the operation of equipment in the production process. The collection unit 1311 registers the collected history in the history DB 121.

[0066] The learning data reading unit 1312 reads out history as learning data from the history DB 121. The learning data reading unit 1312 outputs the read learning data to the learning unit 1313.

[0067] The learning unit 1313 optimizes the parameters of a model that outputs a target variable from an explanatory variable, and updates the model information 122 with the optimized parameters.

[0068] For example, the learning unit 1313 inputs records in the same batch to the model in ascending order of time from among the records in the history DB 121. When the learning unit 1313 inputs records from different batches, the phase variable of the model is initialized.

[0069] A phase variable that indicates the degree of progress of the process phase is calculated from the explanatory variables, and a response variable is output based on the explanatory variables and the phase variable. For example, the model has a layer for calculating the phase variable and a layer for calculating the operation value (response variable). If each layer is considered to be a separate model, the learning unit 1313 can be said to simultaneously learn a model for estimating the process phase and a model for estimating the operation value using learning data.

[0070] The inference unit 132 includes a data acquisition unit 1321 , an inference unit 1322 , and a system application unit 1323 .

[0071] The data acquisition unit 1321 acquires explanatory variables of the inference target. The explanatory variables of the inference target are the sensor values ​​at a predetermined time. The objective variables corresponding to the explanatory variables of the inference target may be unknown.

[0072] The inference unit 1322 infers a response variable corresponding to an explanatory variable of an inference target using a model constructed from the model information 122. For example, the inference unit 1322 inputs data in the same batch from records in the history DB 121 into the model in ascending order of time. When the inference unit 1322 inputs data from a different batch, the phase variable of the model is initialized.

[0073] The system application unit 1323 applies the objective variables inferred by the inference unit 1322 as operation values ​​of the plant system 30. Alternatively, the system application unit 1323 presents the objective variables inferred by the inference unit 1322 to an operator.

[0074] [Learning process] Next, a description will be given of the learning process according to Embodiment 1. Fig. 5 is a flowchart of the learning process according to Embodiment 1.

[0075] 5, the processing device 10 collects history of the production process in the plant system 30 (step S11) and registers the collected history in the history DB 121. The processing device 10 reads out the history from the history DB 121 as learning data (step S12). For example, the learning data is a record in the history DB 121 in which a setting value has been registered.

[0076] The processing device 10 uses the training data to train the model (step S13) and optimizes the parameters of the model.

[0077] [Inference processing] Next, a description will be given of the inference processing according to embodiment 1. Fig. 6 is a flowchart of the inference processing according to embodiment 1.

[0078] 6, the processing device 10 acquires input data (step S21). The input data is explanatory variables of an inference target. For example, the input data is a record in the history DB 121 in which a setting value is not registered.

[0079] The processing device 10 performs an inference process using the input data (step S22). The processing device 10 inputs explanatory variables included in the input data into a model to obtain a response variable.

[0080] The processing device 10 applies the response variable inferred in step S22 as an operation value of the plant system 30 or a value presented to an operator (step S23).

[0081] [Effects of the First Embodiment] As explained above, the collection unit 1311 collects history data that is a combination of explanatory variables that represent the status of a product production process and objective variables that represent the operation of equipment in the process. The learning unit 1313 uses the history data as learning data to calculate phase variables that represent the degree of progress of the process phase from the explanatory variables, and optimizes the parameters of a model that outputs the objective variable based on the explanatory variables and the phase variables.

[0082] In this way, the model itself calculates the phase variables, so that imitation learning can be realized even when there is no information that identifies the process. As a result, according to the embodiment, imitation learning can be realized with particularly high accuracy in batch plants where the operation methods differ depending on the process.

[0083] The learning unit 1313 optimizes parameters of a model that is constrained not to regress each time a phase variable is calculated, calculates a phase variable each time a plurality of records included in the learning data are input in order, and outputs a target variable based on the explanatory variables and the phase variable. For example, the learning unit 1313 optimizes parameters of a hidden Markov model that is constrained not to regress each time a phase variable is calculated. In this way, by using a state transition model that calculates a phase variable that does not regress, the processing device 10 can represent the progression of a phase.

[0084] [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.

[0085] Furthermore, all or any part of the processes performed by the processing device 10 may be realized by a CPU, 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.

[0086] 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.

[0087] [program] 7 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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]

[0093] 1 Plant operation system 10 Processing equipment 11 Communications Department 12 Storage section 13 Control Unit 20 Terminal equipment 30 Plant Systems 121 History DB 122 Model Information 131 Learning Department 132 Reasoning part 1311 Collection Department 1312 Learning data reading unit 1313 Learning Department 1321 Data Acquisition Department 1322 Reasoning part 1323 System Application Department

Claims

1. a collection unit that collects history data that is a combination of explanatory variables that represent a situation in a production process of a product and objective variables that represent the operation of equipment in the process; a learning unit that uses the history as learning data to calculate a phase variable representing a degree of progress of a phase of the process from the explanatory variables, and optimizes parameters of a model that outputs the objective variable based on the explanatory variables and the phase variables; A learning device comprising:

2. The learning unit is a model that is constrained not to regress each time the phase variable is calculated, and calculates the phase variable each time a plurality of records included in the learning data are input in order, and optimizes parameters of a model that outputs the objective variable based on the explanatory variables and the phase variable.

2. The learning device according to claim 1 .

3. The learning unit optimizes parameters of a hidden Markov model that is constrained not to regress each time the phase variables are calculated.

3. The learning device according to claim 2.

4. an acquisition unit that acquires explanatory variables that represent the status of the product production process; an inference unit that calculates a phase variable representing a degree of progress of a phase of the process from the explanatory variables using a trained model, and infers a target variable representing an operation of a device in the process based on the explanatory variables and the phase variables; An inference device comprising:

5. A learning method executed by a learning device, comprising: a collection procedure for collecting history data that is a combination of explanatory variables that represent the status of a product production process and objective variables that represent the operation of equipment in the process; a learning procedure for calculating a phase variable representing a degree of progress of a phase of the process from the explanatory variables using the history as learning data, and optimizing parameters of a model that outputs the objective variable based on the explanatory variables and the phase variables; A learning method comprising:

6. An inference method executed by an inference device, comprising: An acquisition procedure for acquiring explanatory variables that represent the situation in the product production process; an inference procedure of calculating a phase variable representing a degree of progress of a phase of the process from the explanatory variables using the trained model, and inferring a target variable representing an operation of a device in the process based on the explanatory variables and the phase variables; 10. An inference method comprising:

7. a collection step of collecting history data that is a combination of explanatory variables that represent a situation in a production process of a product and objective variables that represent the operation of equipment in the process; a learning step of calculating a phase variable representing a degree of progress of a phase of the process from the explanatory variables using the history as learning data, and optimizing parameters of a model that outputs the objective variable based on the explanatory variables and the phase variables; A learning program that allows a computer to execute the above.

8. an acquisition step of acquiring explanatory variables representing the status of the product production process; an inference step of calculating a phase variable representing a degree of progress of the phase of the process from the explanatory variables using the trained model, and inferring a target variable representing an operation of a device in the process based on the explanatory variables and the phase variables; An inference program that allows a computer to execute the above.

Images