JP2023151915A - Execution timing determination system, execution timing determination method, and execution timing determination program - Google Patents

Abstract

To provide an execution timing determination technology making it possible to minimize an electric power cost relating to one or plural processes.SOLUTION: An execution timing determination system determines the execution timing of one or plural processes, and includes a first acquisition unit (13) that can refer to an electric power cost prediction model (31) for use in predicting an electric power cost relating to the one or plural processes, and acquires execution period information on an execution period needed to execute an object process out of the one or plural processes, a second acquisition unit (15) that acquires electric power cost information on an electric power cost at a predetermined timing, an electric power cost prediction unit (17) that predicts an electric power cost of the object process on the basis of the execution period information, electric power cost information, and electric power cost prediction model (31), and an execution timing determination unit (19) that determines the execution timing of the object process on the basis of the predicted electric power cost.SELECTED DRAWING: Figure 4

Description

The present invention relates to an execution timing determination device, an execution timing determination method, and an execution timing determination program.

Patent Document 1 describes an operation plan optimization device and the like that optimizes the operation schedule of equipment to be controlled. The operation plan optimization device includes (1) an energy prediction unit that sets a predicted value of consumed energy or supplied energy for a predetermined period in the future for the controlled device based on process data; and (2) a predicted value and a predicted value of the controlled device. (3) a schedule optimization unit that optimizes the operation schedule of the controlled equipment in a predetermined period using a predetermined evaluation index based on the characteristics and process data; It includes an approval request determining section that determines the necessity of schedule approval, and (4) a determination result transmitting section that transmits the determination result of the approval request determining section.

Japanese Patent Application Publication No. 2015-018374

However, the operation plan optimization device described in Patent Document 1 aims at optimizing the operation schedule of a plurality of devices to be controlled. In other words, the operation plan optimization device described in Patent Document 1 does not consider at all the adjustment of the execution timing of the target process based on the predicted power cost for the target process to be executed among one or more manufacturing processes. Not yet. Therefore, in the operation plan optimization device, the target process is executed during a time period when the power cost is high (for example, during the peak time of the power cost), and the power cost related to one or more manufacturing processes may increase.

The present invention has been made in view of such circumstances, and one of the objects is to provide an execution timing determination technique that can reduce power costs related to one or more processes.

An execution timing determination device according to one aspect of the present invention is an execution timing determination device that determines the execution timing of one or more steps, and includes a power cost prediction model for predicting power costs related to the one or more steps. A first acquisition unit that is configured to be referenceable and that acquires execution period information regarding an execution period necessary to execute a target process among one or more processes, and acquires power cost information regarding power cost at a predetermined timing. a second acquisition unit that predicts the power cost of the target process based on the execution period information, power cost information, and the power cost prediction model; and a power cost prediction unit that predicts the power cost of the target process based on the predicted power cost. and an execution timing determination unit that makes a determination.

According to this aspect, the execution timing determination device acquires execution period information regarding the execution period necessary for executing the target process among one or more processes, and acquires power cost information regarding the power cost at a predetermined timing. , the power cost of the target process is predicted based on the execution period information, the power cost information, and the power cost prediction model for predicting the power cost related to one or more processes, and the target process is performed based on the predicted power cost. Determine when to execute a process. Therefore, it is possible to execute the target process (for example, shift the peak power cost) while avoiding time periods when the power cost is high (for example, the peak time of the power cost). Therefore, power costs related to one or more steps can be reduced.

In the above aspect, the execution timing determination unit may determine whether the determination result of the execution timing of the target process based on the predicted power cost satisfies a predetermined condition.

In the above aspect, (i) the power cost prediction unit predicts the power cost of the target process until the judgment result of the execution timing of the target process based on the predicted power cost satisfies a predetermined condition; and (ii) ) The execution timing determination unit may repeatedly determine the execution timing of the target process.

In the above aspect, the execution timing determining unit determines that the power cost predicted by the power cost predicting unit in a predetermined period from the first timing to a second timing after the first timing satisfies a predetermined condition. In this case, the execution timing of the target process may be determined.

In the above aspect, the execution timing determining unit determines that the integral value of the power cost predicted by the power cost predicting unit is the minimum integral value from the first timing to a predetermined period based on a second timing subsequent to the first timing. The first timing when this happens may be determined as the optimal execution timing for the target process.

In the above aspect, when there are multiple target processes, (i) the power cost prediction unit predicts the power cost of the target process, and (ii) the execution timing determination unit predicts the power cost of the target process for each of the multiple target processes. may be performed.

In the above aspect, the execution timing determination device is configured to be able to refer to a battery consumption prediction model that calculates the battery operating period based on the remaining amount of the battery at a predetermined timing, and the first acquisition unit acquires the execution period information. , may be acquired based on information regarding the operating period.

An execution timing determination method according to another aspect of the present invention is an execution timing determination method that is executed by an execution timing determination device that determines the execution timing of one or more steps, and the method includes: Acquisition of execution period information regarding the execution period necessary for executing the process, acquisition of power cost information regarding the power cost at a predetermined timing, execution period information, power cost information, and information regarding one or more steps. The method includes predicting the power cost of the target process based on a power cost prediction model for predicting the power cost, and determining the execution timing of the target process based on the predicted power cost.

According to this aspect, the execution timing determination device acquires execution period information regarding the execution period necessary for executing the target process among one or more processes, and acquires power cost information regarding the power cost at a predetermined timing. , the power cost of the target process is predicted based on the execution period information, the power cost information, and the power cost prediction model for predicting the power cost related to one or more processes, and the target process is performed based on the predicted power cost. Determine when to execute a process. Therefore, it is possible to execute the target process while avoiding time periods when power costs are high. Therefore, power costs related to one or more steps can be reduced.

An execution timing determination program according to another aspect of the present invention includes an execution timing determination device that determines the execution timing of one or more steps. A first acquisition unit that acquires execution period information; a second acquisition unit that acquires power cost information regarding power cost at a predetermined timing; and a second acquisition unit that predicts execution period information, power cost information, and power cost regarding one or more processes. an execution timing determination unit that functions as a power cost prediction unit that predicts the power cost of the target process based on the power cost prediction model; and an execution timing determination unit that determines the execution timing of the target process based on the predicted power cost. program.

According to this aspect, the execution timing determination device acquires execution period information regarding the execution period necessary for executing the target process among one or more processes, and acquires power cost information regarding the power cost at a predetermined timing. , the power cost of the target process is predicted based on the execution period information, the power cost information, and the power cost prediction model for predicting the power cost related to one or more processes, and the target process is performed based on the predicted power cost. Determine when to execute a process. Therefore, it is possible to execute the target process while avoiding time periods when power costs are high. Therefore, power costs related to one or more steps can be reduced.

According to the present invention, power costs related to one or more steps can be reduced.

1 is a diagram showing a block configuration of a process control system according to a first embodiment of the present invention. FIG. 2 is a diagram illustrating an example of a process power cost prediction program according to the first embodiment. It is a figure showing an example of a material input instruction program concerning a 1st embodiment. FIG. 3 is a diagram showing a functional configuration of an MES according to the first embodiment. FIG. 2 is a diagram showing the physical configuration of an MES according to the first embodiment. It is a flowchart of the process control process performed by MES of the process control system based on 1st Embodiment. It is a flowchart of the evaluation and adjustment process of the schedule of the target process performed by MES based on 1st Embodiment. FIG. 3 is a diagram illustrating an example of a power cost acquisition process and a power cost trend prediction process executed by the MES according to the first embodiment. FIG. 3 is a diagram illustrating an example of a power cost acquisition process executed by the MES according to the first embodiment. FIG. 3 is a diagram illustrating an example of a process for deriving the operating time of a target process by a process simulator executed by the MES according to the first embodiment. It is a figure which shows an example of the process of predicting the transition of the electric power cost, and the process of determining the optimal material input time, which are performed by the MES according to the first embodiment. FIG. 2 is a diagram showing a block configuration of a process control system according to a second embodiment. FIG. 3 is a diagram showing a block configuration of a process control system according to a third embodiment. It is a diagram showing a block configuration of a process control system according to a fourth embodiment. It is a figure showing an example of a process electric power cost prediction program concerning a 4th embodiment.

Preferred embodiments of the present invention will be described with reference to the accompanying drawings. In addition, in each figure, those with the same reference numerals have the same or similar configurations.

<First embodiment>
FIG. 1 is a diagram showing a block configuration of a process control system 100 according to an embodiment of the present invention. The process control system 100 is a control system that determines execution timing for each one or more processes and executes the processes at optimal execution timing. A "process" is a process that uses a predetermined material or member, and includes, but is not limited to, a manufacturing process for manufacturing a finished product (e.g., a predetermined module) from a predetermined intermediate product (e.g., a semiconductor chip). I can't do it. The "process" may include a cleaning process for cleaning parts, etc., or a disassembly process for disassembling intermediate products or finished products.

The process control system 100 exemplarily includes a power cost server 1, an ERP (Enterprise Resource Planning) 2, and an MES (Manufacturing Execution System) 10 (execution timing determination device). In this example, the power cost server 1, ERP 2, and MES 10 may be configured by one computer, or may be realized by combining a plurality of computers.

The power cost server 1 measures the power cost Ct at a predetermined time t regarding a manufacturing line L including a plurality of manufacturing processes A, B, and C, and transmits it to the MES 10. In addition to the power cost related to the manufacturing line L, the power cost server 1 may measure the power cost Ct of the entire manufacturing facility including the manufacturing line L and other manufacturing equipment at a predetermined time t.

In the example of FIG. 1, the manufacturing line L includes multiple manufacturing steps A, B, and C. For example, the manufacturing process A is a process of manufacturing an intermediate product Wb based on the intermediate product Wa, the manufacturing process B is a process of manufacturing an intermediate product Wc based on the intermediate product Wb, and the manufacturing process C is a process of manufacturing an intermediate product Wc based on the intermediate product Wb. This is a process of manufacturing a finished product based on the product Wc. In addition, although there is one manufacturing line L in the example of FIG. 1, there may be a plurality of manufacturing lines L. Further, the number of manufacturing steps included in the manufacturing line L is not limited to three, and may be one, two, or four or more.

The ERP 2 sets target quantities and delivery dates for intermediate products or finished products produced in each process A, B, and C of the production line L, and transmits them to the MES 10.

The MES 10 determines the optimal execution timing for each of the plurality of manufacturing processes A, B, and C, and sends an instruction to the manufacturing line L to execute each manufacturing process at the determined execution timing.

The MES 10 exemplarily includes a process power cost prediction program 3, a material input instruction program 4, and a material quantity DB (database) 5.

The process power cost prediction program 3 is a program for predicting the power cost when introducing materials (executing a process) at a specific time in each process. The process power cost prediction program 3 includes, for example, a power cost prediction model 31 and a process simulator 33.

The power cost prediction model 31 is a model for predicting power costs related to one or more processes. The power cost prediction model 31 includes, for example, at least one of a prediction model based on time-by-time power cost trends and a prediction model using external factor information such as the climate. Note that the power cost prediction model 31 only needs to be referenceable, and may be placed outside the MES 10 or the process control system 100.

The process simulator 33 is a program that calculates execution period information regarding the execution period required when executing one or more processes. For example, when material information regarding materials used in the target process is input, the process simulator 33 calculates execution period information regarding the execution period required to execute the target process.

FIG. 2 is a diagram illustrating an example of a process power cost prediction program according to the first embodiment. As shown in FIG. 2, the process power cost prediction program 3 acquires the power cost Ct at time t, the predicted time a, and the quantity of materials used in the manufacturing process, and when introducing the materials at time t+a. Calculate the predicted power cost Ct+a. More specifically, the process simulator 33 calculates the operating time of the target manufacturing process based on the material quantity and outputs it to the power cost prediction model 31. The power cost prediction model 31 calculates the predicted power cost Ct+a based on the power cost Ct at time t, the predicted time a, and the operation time of the target process calculated by the process simulator 33.

Returning to FIG. 1, the material input instruction program 4 is a program that determines the execution timing of a manufacturing process based on the power cost predicted by the process power cost prediction program 3. The material input instruction program 4 is a program that sends an execution instruction to the manufacturing line L so that the manufacturing process is executed at the determined execution timing. The material input instruction program 4 includes, for example, a process execution timing determination model 41. The process execution timing determination model 41 includes, for example, at least one of a local determination model that determines the execution timing for each manufacturing process, and an optimal determination model that determines the optimal execution schedule for the entire manufacturing process. Note that the process execution timing judgment model 41 only needs to be referenceable, and may be placed outside the MES 10 or the process control system 100.

FIG. 3 is a diagram showing an example of a material input instruction program according to the first embodiment. As shown in FIG. 3, the process execution timing judgment model 41 obtains an execution instruction, an execution deadline (for example, the latest execution timing time), and a predicted power cost Ct+a calculated by the power cost prediction model 31. Then, determine the optimal execution timing for the target manufacturing process. The process execution timing determination model 41 transmits an execution start instruction or a standby instruction to the manufacturing line L so that the target manufacturing process is executed at the determined execution timing.

Returning to FIG. 1, the material quantity DB5 is, for example, a database that manages the quantity (material information) of materials or parts used in the manufacturing process. For example, the material quantity DB 5 updates the recorded material information based on the quantity of each intermediate product Wa, Wb, Wc transmitted from the manufacturing line L.

FIG. 4 is a diagram showing the functional configuration of the MES according to the first embodiment. Functionally, the MES 10 includes an information processing unit 11 that executes processing for determining the execution timing of one or more processes, an electric power cost prediction model 31, a process simulator 33, a process execution timing determination model 41, A material quantity DB5 is provided.

The information processing unit 11 exemplarily includes a first acquisition unit 13, a second acquisition unit 15, a power cost prediction unit 17, and an execution timing determination unit 19. Note that each of the above-mentioned parts of the information processing unit 11 uses, for example, a storage area such as a memory or a hard disk (for example, a RAM (Random Access Memory) 10b and a ROM (Read Only Memory) 10c shown in FIG. 5), or is stored in a storage area. This can be realized by a processor (for example, a CPU (Central Processing Unit) 10a shown in FIG. 5) executing a program.

The first acquisition unit 13, the second acquisition unit 15, and the power cost prediction unit 17 correspond to the process power cost prediction program 3 shown in FIGS. 1 and 2. For example, the first acquisition unit 13 acquires execution period information regarding an execution period necessary for executing a target manufacturing process among one or more manufacturing processes based on material information regarding materials used in the target manufacturing process. do. The second acquisition unit 15 acquires power cost information regarding the power cost at a predetermined timing. The power cost measuring unit 17 predicts the power cost of the target manufacturing process based on the execution period information, the power cost information, and the power cost prediction model 31 shown in FIGS. 1 and 2.

The execution timing determination unit 19 corresponds to the material input instruction program 4 shown in FIGS. 1 and 3. The execution timing determination unit 19 determines the execution timing of the target manufacturing process based on the power cost predicted by the power cost prediction unit 17.

FIG. 5 is a diagram illustrating the physical configuration of the MES 10 according to the first embodiment. The MES 10 includes a CPU 10a corresponding to a calculation section, a RAM 10b corresponding to a storage section, a ROM 10c corresponding to a storage section, a communication section 10d, an input section 10e, and a display section 10f. These components are connected to each other via a bus so that they can transmit and receive data. In this example, a case will be described in which the MES 10 is composed of one computer, but the MES 10 may be realized by combining a plurality of computers. Further, the configuration shown in FIG. 5 is an example, and the MES 10 may have configurations other than these, or may not have some of these configurations.

The CPU 10a functions as a control unit that executes programs stored in the RAM 10b or ROM 10c, and performs various controls and data calculations and processing. For example, the CPU 10a executes a program (execution timing determination program) for determining the execution timing of a process. Further, the CPU 10a receives various data from the input section 10e and the communication section 10d, and displays the results of data calculations on the display section 10f or stores them in the RAM 10b.

The RAM 10b is a storage unit in which data can be rewritten, and may be formed of, for example, a semiconductor storage element. The RAM 10b may store, for example, a program executed by the CPU 10a and data used in the program. Note that these are just examples, and data other than these may be stored in the RAM 10b, or some of them may not be stored.

The ROM 10c is a storage section from which data can be read, and may be composed of, for example, a semiconductor storage element. The ROM 10c may store, for example, an execution timing determination program and data that will not be rewritten.

The communication unit 10d is an interface that connects the MES 10 to other devices. The communication unit 10d may be connected to a communication network such as a LAN.

The input unit 10e receives data input from the user, and may include, for example, a keyboard and a touch panel.

The display unit 10f visually displays the calculation results by the CPU 10a, and may be configured by, for example, an LCD (Liquid Crystal Display). The display unit 10f may display control signals and physical quantities in time series, for example.

The execution timing determination program may be provided by being stored in a computer-readable storage medium such as the RAM 10b or ROM 10c, or may be provided via a communication network connected by the communication unit 10d. In the MES 10, the various operations described above are realized by the CPU 10a executing the execution timing determination program. Note that these physical configurations are merely examples, and do not necessarily have to be independent configurations. For example, the MES 10 may include an LSI (Large-Scale Integration) in which a CPU 10a, a RAM 10b, and a ROM 10c are integrated.

<<Process control processing>>
Process control processing executed by the MES of the process control system according to the first embodiment will be described with reference to FIGS. 6 to 11. FIG. 6 is a flowchart of process control processing executed by the MES of the process control system according to the first embodiment. As shown in FIG. 6, the process control system 100 shown in FIG. 1 sets a target manufacturing quantity and delivery date for each one or more manufacturing processes (S1). The process control system 100 evaluates the delivery date (S2).

The process control system 100 determines whether the delivery date is appropriate (S3). If the delivery date is not appropriate (No in S3), the process returns to step S1, and the process control system 100 resets the delivery date. The case where the delivery date is not appropriate includes a case where the delivery date cannot be met even if the process control system 100 executes the manufacturing process immediately (for example, all the manufacturing processes are not completed). If the delivery date is appropriate (Yes in S3), the process advances to step S4.

The process control system 100 disassembles the manufacturing process (S4). The process control system 100 creates a schedule for each disassembled manufacturing process (S5). The schedule for each manufacturing process includes, for example, the latest material input time (execution timing) of each manufacturing process and the quantity of materials to be input for each manufacturing process.

The process control system 100 evaluates the schedule of the target manufacturing process to be executed, and adjusts the schedule based on the evaluation (S6). The process control system 100 calculates, for example, the optimal material input time (execution timing) and the power cost at the predicted end time for each of a plurality of manufacturing processes. That is, the process control system 100 executes step S6 for each one or more manufacturing processes. Details of the process in step S6 will be explained with reference to FIGS. 7 to 11.

The process control system 100 instructs the start of inputting materials to the target manufacturing process (S7). The process control system 100 determines whether all manufacturing processes have been completed (S8). If all the manufacturing steps have not been completed (No in S8), the process returns to step S6. On the other hand, if all the manufacturing steps have been completed (Yes in S8), the process control process is ended.

FIG. 7 is a flowchart of schedule evaluation and adjustment processing for a target process executed by the MES according to the first embodiment. In particular, FIG. 7 is a diagram showing more specifically the processing contents of step S6 shown in FIG. As shown in FIG. 7, the process control system 100 shown in FIG. 1 evaluates the process progress of the pre-manufacturing process (S61). For example, when a plurality of manufacturing processes A, B, and C are executed and manufacturing process B is to be executed, manufacturing process A is the pre-manufacturing process. Furthermore, since the MES 10 manages information such as the order of a plurality of manufacturing processes, for example, the manufacturing processes are evaluated in accordance with the order of execution of the manufacturing processes based on this information.

The process control system 100 determines whether the predicted end time of the manufacturing process A has been determined (S62). If the predicted end time of manufacturing process A has not been determined (in the case of No), the process returns to step S61. On the other hand, if the predicted end time of manufacturing process A has been determined (in the case of Yes), the process advances to step S63.

The process control system 100 evaluates the latest material input time (S63). The process control system 100 determines whether the latest material input time (almost) matches the current time (S64). If the latest material input time (almost) matches the current time (in the case of Yes), the process advances to step S69. On the other hand, if the latest material input time does not (almost) match the current time (in the case of No), the process advances to step S65.

FIG. 8 is a diagram illustrating an example of a power cost acquisition process and a power cost transition prediction process executed by the MES according to the first embodiment. FIG. 9 is a diagram illustrating an example of a power cost acquisition process executed by the MES according to the first embodiment. As shown in FIGS. 7 to 9, the process control system 100 acquires the power cost at the current time, for example, from the power cost server 1 shown in FIG. 1 (S65).

FIG. 10 is a diagram illustrating an example of a process for deriving the operating time of a target process by a process simulator executed by the MES according to the first embodiment. As shown in FIGS. 8 and 10, the process control system 100 calculates the operating time Tp of the target manufacturing process B included in the manufacturing line L using, for example, the process simulator 33 shown in FIG. 1 (S90). Further, as shown in FIG. 8, the process control system 100 calculates the scheduled end time t+Ta of the pre-manufacturing process A (S95).

As shown in FIGS. 7 and 8, the process control system 100 predicts the transition of the power cost from the current time (or the predicted end time of the previous manufacturing process) to the latest input time (S66). More specifically, as shown in FIG. 8, for example, the process control system 100 calculates the transition of the power cost based on the acquisition result of step S65, the calculation result of step S90, and the calculation result of step S95. Predict. Next, the process control system 100 determines and updates the optimal material input time (S67). With reference to FIG. 11, more specifically, the process of predicting the transition of the power cost and the process of determining the optimal material input time will be described.

FIG. 11 is a diagram illustrating an example of a power cost transition prediction process and an optimal material input time determination process executed by the MES according to the first embodiment. As shown in FIGS. 8 and 11, for example, the process control system 100 (power cost prediction unit 17 shown in FIG. 4) also calculates the power cost trend (E[t-n,...,t]) up to the current time. Predict future trends (Te).

Next, the process control system 100 (execution timing determination unit 19 shown in FIG. 4) determines the power cost predicted by the power cost prediction unit 17 shown in FIG. When the power cost (Ep) in a predetermined period based on the latest input time (second timing) from the time) (first timing) satisfies a predetermined condition, the execution timing of the target manufacturing process B is determined. For example, as shown in FIG. 11, the execution timing determination unit 19 searches for the minimum power cost by sliding a time window of width Tp to the end of the prediction interval (t+Te). More specifically, the execution timing determining unit 19 determines the integral value of the power cost predicted by the power cost predicting unit 17, when the integral value in a predetermined period based on the first timing to the second timing becomes the minimum. The first timing is determined as the optimal execution timing for the target manufacturing process B (for example, time "t+Ta+Tq").

According to this configuration, the process control system 100 determines the execution timing of the target manufacturing process when the power cost in the predetermined period satisfies the predetermined condition, so that the optimal execution timing of the target manufacturing process can be determined more appropriately. It is possible.

Returning to FIG. 7, the process control system 100 (execution timing determination unit 19 shown in FIG. 4) determines whether the determination result of the execution timing of the target process based on the predicted power cost satisfies a predetermined condition. (S68). For example, the execution timing determination unit 19 determines whether updating of the optimal material input time has converged. If the updating of the optimal material input time has converged (in the case of Yes), the process advances to step S69. On the other hand, if the update of the optimal material input time has not converged (in the case of No), the process returns to step S63.

As described above, in the process control system 100, until the judgment result of the execution timing of the target manufacturing process B based on the predicted power cost satisfies a predetermined condition (for example, until the update of the optimal material input time is converged) ( i) predicting the power cost of the target manufacturing process by the power cost prediction unit 17; and (ii) determining the execution timing of the target manufacturing process by the execution timing determining unit 19 are repeatedly executed.

Here, for example, in the process control system 100, the optimal material input time is calculated multiple times (for example, about 10 times) using the method described above. For example, when the error in the optimal material loading time calculated multiple times falls within a predetermined period (convergence), the process control system 100 sets the average value of the optimal material loading time calculated multiple times to the "optimal material loading time". It is also possible to ``confirm'' the time as ``material input time''. Note that the "optimum material introduction time" to be "determined" is not limited to the above-mentioned average value, but may include the median value or the mode of the optimum material introduction times calculated a plurality of times.

According to this configuration, in the process control system 100, it is possible to "determine" the "optimum material introduction time" by waiting until the determined optimum material introduction time becomes stable, so that the process control system 100 can more appropriately optimize the "optimum material introduction time". It is possible to introduce material input times.

As shown in FIG. 7, the process control system 100 finally determines the optimal material input time and derives the predicted end time and predicted power cost (S69).

According to the first embodiment, the MES 10 (execution timing determination device) shown in FIGS. 1 and 4 provides execution period information regarding the execution period necessary for executing a target process among one or more processes. Acquire power cost information about the power cost at a predetermined timing based on information about materials used in The power cost of the target process is predicted based on the cost prediction model 31, and the execution timing of the target process is determined based on the predicted power cost. Therefore, it is possible to execute the target process (for example, shift the peak power cost) while avoiding time periods when the power cost is high (for example, the peak time of the power cost). Therefore, power costs related to one or more steps can be reduced.

<Second embodiment>
With reference to FIG. 12, a process control system 100A according to the second embodiment will be described. FIG. 12 is a diagram showing a block configuration of a process control system according to the second embodiment. The process control system 100A according to the second embodiment differs from the process control system 100 according to the first embodiment shown in FIG. 1 in that the power cost prediction model 31 provided in the MES 10 is provided in the power cost server 1. In the process control system 100A according to the second embodiment, the MES 10 transmits a request for predicting the power cost Ct at time t to the power cost server 1. The power cost server 1 calculates the power cost Ct at time t using the power cost prediction model 31, and transmits the calculated power cost Ct to the MES 10.

<Third embodiment>
With reference to FIG. 13, a process control system 100B according to a third embodiment will be described. FIG. 13 is a diagram showing a block configuration of a process control system according to the third embodiment. In the process control system 100 according to the first embodiment, the manufacturing process B is executed on the manufacturing line L based on the manufacturing process A, while in the process control system 100B according to the third embodiment, the manufacturing process Both embodiments differ in that B is performed based on manufacturing process A and manufacturing process D. Below, regarding the third embodiment, points that are different from the first embodiment will be particularly described.

For example, in step S61 shown in FIG. 7, the process control system 100B (MES 10) according to the third embodiment manages information such as the process order of a plurality of manufacturing processes A, D, B, and C. Based on the information, the progress of the manufacturing process is evaluated in the order in which the manufacturing process is performed. Further, for example, when the target manufacturing process is manufacturing process B, the previous manufacturing processes are manufacturing process A and manufacturing process D. It is executed based on the evaluation and the evaluation regarding manufacturing process D. That is, the process control system 100B (MES 10) advances the process to step S62 shown in FIG. 7 when both the evaluation regarding the manufacturing process A and the evaluation regarding the manufacturing process D are good.

According to the third embodiment, in the process control system 100B, the target manufacturing process B is executed on the manufacturing line L based on a plurality of manufacturing processes (for example, manufacturing process A and manufacturing process D). Therefore, even if the target manufacturing process is a process control system that assumes a plurality of manufacturing processes, the same effect as the process control system of the first embodiment can be achieved, that is, during times when electricity costs are high (for example, during peak electricity costs). It becomes possible to execute the target process while avoiding (for example, peak shift of power cost), and it is possible to reduce the power cost related to one or more processes.

<Fourth embodiment>
A process control system 100C according to the fourth embodiment will be described with reference to FIGS. 14 and 15. FIG. 14 is a diagram showing a block configuration of a process control system according to the fourth embodiment. FIG. 15 is a diagram illustrating an example of a process power cost prediction program according to the fourth embodiment.

The differences between the process control system 100C according to the fourth embodiment shown in FIG. 14 and the process control system 100 according to the first embodiment shown in FIG. 1 are as follows.

(1) As shown in FIG. 14, the process control system 100C further includes a battery 8 as external power for the MES 10, which the process control system 100 does not have, and a solar power generation system 9 connected to the battery 8. (2) As shown in FIG. 14, the process control system 100C further includes a battery remaining amount monitor DB7 that manages the remaining amount of the battery 8, which the process control system 100 does not have. (3) As shown in FIG. , the process power cost prediction program 3 of the process control system 100C further includes a battery consumption prediction model 35, which the process control system 100 does not have.

Below, regarding the fourth embodiment, points that are different from the first embodiment will be particularly described. As described above, the process control system 100C can refer to the battery consumption prediction model that calculates the operating period of the battery 8 based on the remaining amount of the battery 8 at a predetermined timing (for example, time t). Note that the battery consumption prediction model 35 only needs to be referenceable, and may be placed outside the MES 10 or the process control system 100C. The battery 8 is supplied to the manufacturing line L to drive the manufacturing line L. The battery remaining amount monitor DB 7 that manages the remaining amount of the battery 8 shown in FIG. 14 inputs the remaining battery amount at time t to the battery consumption prediction model 35.

As shown in FIG. 15, the process control system 100C (first acquisition unit) calculates the operating time based on the material quantity (operating time calculated by the process simulator 33/for example, 10 minutes) and the operating period of the battery 8 (battery consumption Execution period information is acquired as the "external power operation time" (for example, 7 minutes) based on the operation time calculated by the prediction model 35/for example, 3 minutes. The process control system 100C (power cost prediction unit) inputs the power cost Ct input into the power cost prediction model 31, the predicted time a, the external power operation time from the battery consumption prediction model 35, and the battery remaining amount monitor DB7. The power cost of the target process is predicted based on the battery remaining amount at time t from . The process control system 100C (execution timing determination unit) determines the execution timing of the target process based on the predicted power cost.

According to the fourth embodiment, the process control system 100C targets targets based on the power cost Ct, the predicted time a, and the external power operation time from the battery consumption prediction model 35, which are input to the power cost prediction model 31. Predict power costs for your process. Therefore, it is possible to determine the optimal execution timing of the target process based on the predicted power cost, further taking into account the power supply of the battery 8.

The embodiments described above are merely illustrative of the present invention in all respects. It goes without saying that various improvements and modifications can be made without departing from the scope of the invention. That is, in implementing the present invention, specific configurations depending on the embodiments may be adopted as appropriate. Further, each of the embodiments described above is for facilitating understanding of the present invention, and is not for construing the present invention in a limited manner. Each element included in the embodiment, as well as its arrangement, material, conditions, shape, size, etc., are not limited to those illustrated, and can be changed as appropriate.

[Additional notes]
Aspects of this embodiment include the following disclosures.
(Additional note 1)
An execution timing determination device that determines the execution timing of one or more steps,
is configured to be able to refer to a power cost prediction model (31) for predicting power costs related to the one or more processes,
a first acquisition unit (13) that acquires execution period information regarding an execution period necessary for executing a target process among the one or more processes;
a second acquisition unit (15) that acquires power cost information regarding the power cost at a predetermined timing;
a power cost prediction unit (17) that predicts the power cost of the target process based on the execution period information, the power cost information, and the power cost prediction model (31);
an execution timing determination unit (19) that determines the execution timing of the target process based on the predicted power cost;
Execution timing judgment device.
(Additional note 2)
An execution timing determination method executed by an execution timing determination device that determines the execution timing of one or more steps,
Obtaining execution period information regarding an execution period necessary for executing a target step among the one or more steps;
Obtaining power cost information regarding power cost at a predetermined timing;
Predicting the power cost of the target process based on the execution period information, the power cost information, and a power cost prediction model (31) for predicting the power cost regarding the one or more processes;
determining the execution timing of the target process based on the predicted power cost;
How to determine execution timing.
(Additional note 3)
An execution timing determination device that determines the execution timing of one or more steps,
a first acquisition unit (13) that acquires execution period information regarding an execution period necessary for executing a target process among the one or more processes;
a second acquisition unit (15) that acquires power cost information regarding power costs at a predetermined timing;
Power cost prediction that predicts the power cost of the target process based on the execution period information, the power cost information, and a power cost prediction model (31) for predicting the power cost regarding the one or more processes. Part (17),
an execution timing determination unit (19) that determines the execution timing of the target process based on the predicted power cost;
Execution timing judgment program that functions as an execution timing judgment program.

1... Electricity cost server, 2... ERP, 3... Process electricity cost prediction program, 4... Material input instruction program, 5... Material quantity DB, 7... Battery remaining amount monitor DB, 8... Battery, 9... Solar power generation system, 10...MES, 10a...CPU, 10b...RAM, 10c...ROM, 10d...communication section, 10e...input section, 10f...display section, 11...information processing section, 13...first acquisition section, 15...second acquisition section , 17... Power cost prediction unit, 19... Execution timing judgment device, 31... Electricity cost prediction model, 33... Process simulator, 41... Process execution timing judgment model, 100, 100A, 100B, 100C... Process control system

Claims (9)

An execution timing determination device that determines the execution timing of one or more steps,
configured to be able to refer to a power cost prediction model for predicting power costs related to the one or more steps,
a first acquisition unit that acquires execution period information regarding an execution period necessary for executing a target step among the one or more steps;
a second acquisition unit that acquires power cost information regarding the power cost at a predetermined timing;
a power cost prediction unit that predicts the power cost of the target process based on the execution period information, the power cost information, and the power cost prediction model;
an execution timing determination unit that determines the execution timing of the target process based on the predicted power cost;
Execution timing judgment device. The execution timing determination unit determines whether a determination result of the execution timing of the target process based on the predicted power cost satisfies a predetermined condition.
The execution timing determination device according to claim 1. (i) predicting the power cost of the target process in the power cost prediction unit until the judgment result of the execution timing of the target process based on the predicted power cost satisfies the predetermined condition; ii) determining the execution timing of the target process in the execution timing determination unit is repeatedly executed;
The execution timing determination device according to claim 2. The execution timing determination unit is configured to determine whether the power cost predicted by the power cost prediction unit in a predetermined period based on a first timing and a second timing subsequent to the first timing satisfies a predetermined condition. If the condition is satisfied, determining the execution timing of the target process;
The execution timing determination device according to claim 1. The execution timing determining unit determines that the integral value of the power cost predicted by the power cost predicting unit is the minimum in a predetermined period from a first timing to a second timing after the first timing. determining the first timing when
The execution timing determination device according to claim 1. When there are a plurality of target processes, (i) the power cost prediction unit predicts the power cost of the target process for each of the plurality of target processes; and (ii) the execution timing determination unit predicts the power cost of the target process. Determining the execution timing of the target process is executed,
The execution timing determination device according to claim 1. It is configured to be able to refer to a battery consumption prediction model that calculates the operating period of the battery based on the remaining amount of the battery at a predetermined timing,
The first acquisition unit acquires the execution period information based on information regarding the operation period.
The execution timing determination device according to any one of claims 1 to 6. An execution timing determination method executed by an execution timing determination device that determines the execution timing of one or more steps,
Obtaining execution period information regarding an execution period necessary for executing a target step among the one or more steps;
Obtaining power cost information regarding power cost at a predetermined timing;
Predicting the power cost of the target process based on the execution period information, the power cost information, and a power cost prediction model for predicting the power cost regarding the one or more processes;
determining the execution timing of the target process based on the predicted power cost;
How to determine execution timing An execution timing determination device that determines the execution timing of one or more steps,
a first acquisition unit that acquires execution period information regarding an execution period necessary for executing a target process among the one or more processes;
a second acquisition unit that acquires power cost information regarding power costs at a predetermined timing;
a power cost prediction unit that predicts the power cost of the target process based on the execution period information, the power cost information, and a power cost prediction model for predicting the power cost related to the one or more processes;
an execution timing determination unit that determines the execution timing of the target process based on the predicted power cost;
Execution timing judgment program that functions as an execution timing judgment program.

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