Information processing method, information processing device, and program

Abstract

According to the present invention, an information processing unit: displays superimposed data in which a change figure is superimposed on at least one piece of operation data indicating an operation of a device; acquires operation information indicating an operation for changing the display mode of the change figure; and changes, on the basis of the change figure of which the display mode has been changed, a setting related to an evaluation index applied to optimization processing.

Description

Information processing method, information processing apparatus, and program

[0001] The present disclosure relates to a technique that supports management of optimization processing.

[0002] Patent Document 1 discloses a technique of executing control parameter optimization processing (hereinafter referred to as optimization processing) a plurality of times while changing evaluation indicators for the optimization processing.

[0003] However, the technique described in Patent Document 1 does not consider any technique for facilitating changing settings related to evaluation indicators.

[0004] International Publication No. 2023 / 203933

[0005] The present disclosure has been made to solve such problems, and an object of the present disclosure is to provide a technique that facilitates changing settings related to evaluation indicators applied to optimization processing.

[0006] An information processing method according to an aspect of the present disclosure is an information processing method for supporting management of optimization processing, wherein an information processing unit displays superimposed data in which a modified graphic is superimposed on at least one piece of operation data indicating an operation of the apparatus, acquires operation information indicating an operation of changing a display mode of the modified graphic, and changes a setting related to an evaluation indicator applied to the optimization processing based on the modified graphic whose display mode has been changed.

[0007] According to this configuration, it becomes easy to change settings related to evaluation indicators applied to optimization processing.

[0008] Figure 1 is a block diagram showing the configuration of the control parameter generation system. Figure 2 is a perspective view showing an example of a production apparatus. Figure 3 is an example of time-series data showing the change in the position deviation of the driven object relative to the target position. Figure 4 is another example of time-series data showing the change in the position deviation of the driven object relative to the target position. Figure 5 is a diagram showing an example of the configuration of the evaluation index change unit. Figure 6 is a diagram showing an example of the first setting change screen. Figure 7 is a diagram for explaining the processing of the evaluation index change unit related to display example 2. Figure 8 is a diagram showing an example of the second setting change screen. Figure 9 is a diagram showing an example of the second setting change screen after it has been updated by the display control unit. Figure 10 is a diagram showing an example of the third setting change screen. Figure 11 is a diagram showing an example of the fourth setting change screen. Figure 12 is a flowchart showing an example of the processing flow executed by the information processing unit. Figure 13 is a diagram showing the first operation data and the second operation data.

[0009] (Principles underlying this disclosure) In a production apparatus (e.g., a mounting apparatus) equipped with a drive source (e.g., a servo motor) that drives an object to be driven, a technique has been disclosed in which an optimization process is performed multiple times while switching evaluation indicators as a method for generating appropriate control parameters for the drive source (see, for example, Patent Document 1).

[0010] Generally, the number of control parameters for a drive source used in production equipment can exceed 50. Furthermore, the adjustment gradation can exceed 100. For example, if a production device performs 80 operations, has 50 control parameters for the drive source, and has 100 adjustment gradations for the control parameters, the number of combinations is 100^50 × 80. The inventors found that when generating control parameters for such a vast number of combinations, using methods that search for appropriate control parameters using machine learning models, etc., can result in a phenomenon where it takes an enormous amount of time to find appropriate control parameters due to the search range being too wide, or where it is impossible to find appropriate control parameters no matter how much time is spent. Therefore, the inventors diligently conducted experiments and studies to realize a parameter generation method that can efficiently generate appropriate control parameters even when generating control parameters for a vast number of combinations.

[0011] Through the above experiments and studies, the inventors found that instead of searching for appropriate control parameters for all of the vast number of combinations from the beginning, for example, if the production equipment performs 80 operations, the first step is to search for appropriate control parameters for only a portion of these 80 operations, for example, for combinations of just one operation, that is, for combinations of only a portion of the vast number of combinations. In the next step, the number of operations performed by the production equipment is increased, for example, for combinations of two operations, and appropriate control parameters are searched for using the search results from the first step as a starting point. In the next step, the number of operations performed by the production equipment is further increased, for example, for combinations of four operations, and appropriate control parameters are searched using the search results from the previous step as a starting point. By repeating this process and finally searching for appropriate control parameters for all 80 operation combinations, the inventors found that it is possible to generate appropriate control parameters for all 80 operation combinations more efficiently and reliably.

[0012] Based on this knowledge, the inventors conducted further experiments and studies, and arrived at the control parameter generation method and other related inventions described below.

[0013] Furthermore, the technology described in Patent Document 1 makes it difficult to determine the evaluation metrics to be applied to the optimization process. Specifically, when using the technology described in Patent Document 1, it is necessary to set evaluation metrics in order to determine how to improve the control parameters through the optimization process. However, users are often unfamiliar with the process of setting evaluation metrics. For this reason, users may try to change (reset) the settings related to the evaluation metrics during or after the optimization process is completed. Therefore, there is a need for a technology that makes it possible to easily change the settings related to evaluation metrics.

[0014] This disclosure provides the following technologies to solve the above-mentioned problems.

[0015] (1) An information processing method in one aspect of the present disclosure is an information processing method for supporting the management of an optimization process, wherein an information processing unit displays superimposed data in which a modified figure is superimposed on at least one operation data indicating the operation of a device, obtains operation information indicating an operation to change the display manner of the modified figure, and changes the settings relating to evaluation indicators applied to the optimization process based on the modified figure whose display manner has been changed.

[0016] In this configuration, the information processing unit changes the settings related to the evaluation metrics applied to the optimization process based on the modified shape whose display mode has been changed. Therefore, the user can change the settings related to the evaluation metrics by inputting an operation to change the display mode of the modified shape. This makes it possible to easily change the settings related to the evaluation metrics.

[0017] (2) In the information processing method described in (1) above, the information processing unit acquiring the operation information includes acquiring deformation operation information indicating an operation to change the shape of the modified figure, acquiring movement operation information indicating an operation to move the position of the modified figure, and color change operation information indicating a change in the color of the modified figure, and the information processing unit changing the settings relating to the evaluation index may include changing the settings relating to the evaluation index based on at least one of the shape of the modified figure after deformation, the position of the modified figure after movement, and the color of the modified figure after color change.

[0018] In this configuration, the information processing unit changes the settings related to the evaluation indicators based on at least one of the following: the shape of the modified figure after transformation, the position of the modified figure after movement, and the color of the modified figure after color change. Therefore, the user can change the settings related to the evaluation indicators by inputting at least one of the following operations: transforming the modified figure, moving the modified figure, or changing the color of the modified figure. Thus, it is possible to easily change the settings related to the evaluation indicators.

[0019] (3) In the information processing method described in (2) above, the operation of the device is an operation in which the device moves an object to a target position, the evaluation index includes the sum of the accumulated values ​​of the deviation time or position deviation amount in which the position deviation between the position of the object and the target position deviates from the reference range, the modified figure includes a reference range modified figure having a straight line shape or a curved shape, the information processing unit acquiring the deformation operation information includes acquiring deformation operation information indicating an operation to change the shape of the reference range modified figure, and the information processing unit changing the setting of the evaluation index may include changing the reference range based on the shape of the modified reference range modified figure.

[0020] In this configuration, the information processing unit changes the reference range based on the shape of the modified reference range figure after deformation. Therefore, the user can change the reference range settings by inputting an operation to deform the modified reference range figure. Thus, it is possible to easily change the settings related to evaluation indicators.

[0021] (4) In the information processing method described in (3) above, the reference range changing figure has a linear shape, and the acquisition of the operation information by the information processing unit includes acquiring operation information indicating an operation to change at least one of the slope and intercept of the reference range changing figure, and the setting of the evaluation index by the information processing unit may include changing the reference range based on at least one of the slope and intercept of the reference range changing figure.

[0022] In this configuration, the information processing unit changes the reference range based on at least one of the slope and intercept of the reference range change shape. Therefore, the user can change the reference range by inputting an operation to change at least one of the slope and intercept of the reference range change shape. Thus, it is possible to easily change the settings related to the evaluation index.

[0023] (5) In the information processing method described in (3) or (4) above, the reference range changing figure may include an upper limit figure corresponding to the upper limit of the reference range and a lower limit figure corresponding to the lower limit of the reference range.

[0024] With this configuration, the user can change the upper or lower limit of the reference range by inputting an operation to change at least one of the slope and intercept of the upper or lower limit shape. Therefore, it is possible to easily change the settings related to the evaluation index.

[0025] (6) In the information processing method described in any one of (1) to (5) above, the operation of the device is an operation in which the device moves an object to a target position, the evaluation index includes the length of the settling time until the positional deviation between the position of the object and the target position converges to an acceptable range, the modified figure includes a settling time modified figure, the acquisition of the movement operation information by the information processing unit includes acquiring movement operation information indicating an operation to move the position of the settling time modified figure, and the setting of the evaluation index by the information processing unit may include changing the target of the settling time based on the amount of movement of the settling time modified figure.

[0026] In this configuration, the information processing unit changes the target setting for the settling time based on the amount of movement of the settling time change shape. Therefore, the user can change the setting for the target setting for the settling time by inputting an operation to move the settling time change shape. Thus, it is possible to easily change the settings related to the evaluation indicators.

[0027] (7) In the information processing method described in any one of (2) to (6) above, the operation of the device is an operation in which the device moves an object to a target position, the evaluation index includes the length of the settling time until the positional deviation between the position of the object and the target position converges to an acceptable range, the acceptable range includes a first acceptable range which is the acceptable range for a first period before the target settling time, and a second acceptable range which is the acceptable range for a second period after the target settling time, the evaluation index includes the sum of the accumulated values ​​of the deviation time or positional deviation amount for which the positional deviation between the position of the object and the target position deviates from the second acceptable range, the modified figure includes a second acceptable range modified figure, the acquisition of the movement operation information by the information processing unit includes acquiring movement operation information indicating an operation to move the position of the second acceptable range modified figure, and the setting of the evaluation index by the information processing unit may include changing the second acceptable range based on the amount of movement of the second acceptable range modified figure.

[0028] In this configuration, the information processing unit changes the second tolerance range based on the amount of movement of the second tolerance range change shape. Therefore, the user can change the second tolerance range by inputting an operation to move the second tolerance range change shape. Thus, it is possible to easily change the settings related to the evaluation index.

[0029] (8) In the information processing method described in any one of (2) to (7) above, the operation of the device is an operation in which the device moves an object to a target position, the at least one operation data includes a target position figure indicating the target position, the modified figure includes a gradient figure with different coloring patterns depending on the deviation from the target position figure, the coloring patterns correspond to the magnitude of the weight when calculating the evaluation value of the at least one operation data, the acquisition of the deformation operation information by the information processing unit includes acquiring deformation operation information indicating an operation to change the shape of the gradient figure, and the setting of the evaluation index by the information processing unit may include changing the magnitude of the weight based on the shape of the gradient figure after deformation.

[0030] In this configuration, the information processing unit changes the magnitude of the weights used to evaluate the motion data based on the shape of the gradient figure. Therefore, the user can change the magnitude of the weights by inputting an operation that changes the shape of the gradient figure. Thus, it is possible to easily change the settings related to the evaluation index.

[0031] (9) In the information processing method described in any one of (1) to (8) above, the at least one operation data is a plurality of operation data, and the information processing unit changing the setting of the evaluation index includes changing the evaluation criteria for the plurality of operation data, and the information processing unit may further calculate the evaluation value for each of the plurality of operation data based on the changed evaluation criteria, and display the operation data with the highest evaluation value among the plurality of operation data, or the top plurality of operation data with high evaluation values.

[0032] With this configuration, users can change the evaluation criteria by inputting an operation to change the display manner of the modified shape. In other words, they can set new evaluation criteria. Furthermore, with the above configuration, the operation data with the highest evaluation value based on the changed evaluation criteria, or the top multiple operation data with high evaluation values ​​based on the changed evaluation criteria, will be displayed. This allows users to understand whether the changed evaluation criteria are appropriate or not.

[0033] (10) In the information processing method described in any one of (1) to (9) above, the information processing unit may further superimpose the modified figure onto the operation data with the highest evaluation value, or onto the top multiple operation data with high evaluation values.

[0034] This configuration allows for further modification of the evaluation index settings by manipulating the operation data with the highest evaluation value, or a modified shape superimposed on the top multiple operation data with high evaluation values.

[0035] (11) In the information processing method described in (10) above, the information processing unit may further generate an evaluation histogram showing the classification of the plurality of operation data according to the evaluation value of each of the plurality of operation data, and display the evaluation histogram.

[0036] This configuration allows for the display of an evaluation histogram, making it easy to see the impact of changes made to the evaluation metrics.

[0037] (12) In the information processing method described in any one of (1) to (11) above, the at least one operation data is a plurality of operation data, and the information processing unit changing the setting of the evaluation index includes changing the evaluation criteria for the plurality of operation data, and the information processing unit may further calculate the evaluation value for each of the plurality of operation data based on the changed evaluation criteria, select at least two of the plurality of operation data, and display a display screen including the at least two operation data sorted in descending order of evaluation value.

[0038] This configuration displays at least two performance data points sorted in descending order of their evaluation scores. This allows users to easily identify performance data points that receive high evaluation scores when the evaluation criteria are changed.

[0039] (13) In the information processing method according to (12) above, the information processing section further acquires first order information indicating an order of high evaluation from a user for the at least two pieces of motion data selected by the information processing section; and may determine whether or not the order indicated by the first order information matches the order of the magnitude of the evaluation values of the at least two pieces of motion data determined by the information processing section, and output a determination result.

[0040] According to this configuration, the user can grasp whether or not the information processing section is able to evaluate motion data based on the same evaluation criteria as the user's own evaluation criteria.

[0041] (14) An information processing apparatus according to another aspect of the present disclosure is an information processing apparatus for supporting management of optimization processing, comprising a circuit configuration, wherein the circuit configuration displays superimposed data in which a modified graphic is superimposed on motion data indicating an operation of an apparatus, acquires operation information indicating an operation of changing a display mode of the modified graphic, and changes a setting related to an evaluation index applied to the optimization processing based on the modified graphic whose display mode has been changed.

[0042] According to this configuration, it is possible to provide an information processing apparatus that exhibits the same effects as the above information processing method.

[0043] (15) A program according to still another aspect of the present disclosure is a program for causing an information processing apparatus for supporting management of optimization processing to execute processing, wherein the processing comprises: displaying superimposed data in which a modified graphic is superimposed on motion data indicating an operation of an apparatus, acquiring operation information indicating an operation of changing a display mode of the modified graphic, and changing a setting related to an evaluation index applied to the optimization processing based on the modified graphic whose display mode has been changed.

[0044] According to this configuration, it is possible to provide a program that exhibits the same effects as the above information processing method.

[0045] The present disclosure can also be implemented as an information processing system that operates according to such a program. Needless to say, such a program can be distributed via a non-transitory recording medium readable by a computer such as a CD-ROM, or via a communication network such as the Internet.

[0046] (Embodiment of the Present Disclosure) Hereinafter, a specific example of a control parameter generation system according to one aspect of the present disclosure will be described with reference to the drawings. All of the embodiments described herein illustrate one specific example of the present disclosure. Therefore, the numerical values, shapes, components, arrangement and connection forms of components, steps (processes), order of steps, and the like shown in the following embodiments are examples, and are not intended to limit the present disclosure. Furthermore, each drawing is a schematic diagram and is not necessarily strictly illustrated. In each drawing, substantially identical configurations are denoted by the same reference numerals, and overlapping descriptions are omitted or simplified.

[0047] (Embodiment 1) A control parameter generation system 1 according to Embodiment 1 will be described. This control parameter generation system 1 is a system that generates control parameters for use in a production apparatus 20 including a drive source 23 that drives a driven object 24.

[0048] FIG. 1 is a block diagram showing the configuration of the control parameter generation system 1.

[0049] As shown in FIG. 1, the control parameter generation system 1 includes a control parameter generation device 10, a production apparatus 20, and a sensor 30.

[0050] The production apparatus 20 is an apparatus used for producing equipment, and performs mounting, processing, machining, conveyance, and the like of equipment. The production apparatus 20 is installed, for example, on a production line in a factory. Specifically, the production apparatus 20 is, for example, a mounting apparatus, a processing apparatus, a machining apparatus, a conveyance apparatus, or the like.

[0051] The production apparatus 20 performs N (N is an integer of 3 or more) operations. N is 80, for example. The production apparatus 20 includes a memory 21, a control circuit 22, a drive source 23, and a driven object 24.

[0052] The drive source 23 is controlled by the control circuit 22 to drive the object to be driven 24. Specifically, the drive source 23 is, for example, a servo motor, a directional flow control valve for fluids used to control a pneumatic artificial muscle arm, or a directional flow control valve for fluids used to control a hydraulic arm. The servo motor may be, for example, a rotary motor or a linear motor.

[0053] The driven object 24 is an object driven by the drive source 23. For example, if the drive source 23 is a servo motor, the driven object 24 may be a head that transports the workpiece, a nozzle attached to the head for picking up the workpiece, etc. Also, if the drive source 23 is a directional flow control valve, the driven object 24 may be a pneumatic artificial muscle arm, a hydraulic arm, etc.

[0054] Figure 2 is a perspective view of a production apparatus 20 in which, as an example, the drive source 23 is a servo motor and the object to be driven 24 is a nozzle provided on a head. As shown in Figure 2, the production apparatus 20 may, for example, be a mounting apparatus that mounts components onto a substrate 105 placed on a machine base 106.

[0055] The production apparatus 20, as an example, includes a nozzle 81 for picking up parts, a head 80 to which the nozzle 81 is mounted, a servo motor 23A that functions as a drive source 23 for moving the head 80 in the X-axis direction in a plan view of the machine base 106, and a servo motor 23B that functions as a drive source 23 for moving it in the Y-axis direction. Here, the head 80 is connected to the servo motor 23A via an arm 72 and the servo motor 23B.

[0056] Returning to Figure 1, let's continue the explanation of the control parameter generation system 1.

[0057] The control circuit 22 controls the drive source 23 by outputting a command to the drive source 23 to move the drive target 24 to a predetermined target position. The command output by the control circuit 22 to the drive source 23 may be, for example, a position command that commands the position of the drive source 23 or the drive target 24, or it may be, for example, a torque command that commands the torque of the drive source 23.

[0058] The control circuit 22 controls the drive source 23 based on control parameters stored in the memory 21. In other words, the control circuit 22 uses the control parameters stored in the memory 21 when controlling the drive source 23. For example, there are 50 (50 types) of control parameters for one operation performed by the driven object 24.

[0059] Memory 21 stores control parameters used by the control circuit 22 when controlling the drive source 23. The control parameters stored in memory 21 are the control parameters output from the control parameter generation device 10.

[0060] When control parameters are output from the control parameter generation device 10 (an example of an information processing device), the memory 21 acquires the outputted control parameters and updates the stored control parameters with the acquired parameters. In other words, the memory 21 overwrites the stored parameters.

[0061] The sensor 30 measures a position signal indicating the position of the driven object 24 in the production apparatus 20, which performs at least one of N operations, in a time series. It then outputs measurement data indicating the measured position corresponding to each of the at least one operations to the control parameter generation device 10.

[0062] Figure 3 is an example of time-series data showing the change over time in the positional deviation of the driven object 24 relative to the target position when the production device 20 drives the driven object 24 to the target position. This time-series data is generated by the information processing unit 50 based on the measurement data acquired by the sensor 30. In Figure 3, the horizontal axis represents time, and the vertical axis represents the positional deviation of the driven object 24 relative to the target position.

[0063] As shown in Figure 3, in this specification, the tolerance range refers to the range in which the positional deviation from the target position is within the required accuracy. Furthermore, the length from the upper limit LM1 to the lower limit LM2 of the tolerance range is called the settling width.

[0064] Furthermore, as shown in Figure 3, in this specification, the time at which the target position can be evaluated as having been reached (hereinafter referred to as "settlement time") refers to the time at which the driven object 24 last reached the allowable range after it had reached the allowable range and no longer deviated from the allowable range.

[0065] Furthermore, as shown in Figure 3, in this specification, the settling time refers to the time from when the driven object 24 starts to stop based on a command to move the driven object 24 to the target position until the driven object 24 reaches an acceptable position where it can be evaluated that it has reached the target position, that is, the time from the start of stopping to the settling time. Alternatively, the settling time refers to the time from when the driven object 24 starts to move based on a command to move the driven object 24 to the target position until the driven object 24 reaches an acceptable position where it can be evaluated that it has reached the target position, that is, the time from the start of movement to the settling time.

[0066] Furthermore, the time-series data shown in Figure 3 includes multiple intersections P. Each of the multiple intersections P indicates the point in time when the driven object 24 passes (crosses) the target position. Hereafter, the number of times the driven object 24 passes the target position before reaching the settling time will be referred to as the "number of crossings".

[0067] Returning to Figure 1, the control parameter generation device 10 performs optimization processing to generate control parameters. The control parameter generation device 10 is implemented, for example, in a computer device comprising a processor, memory, and an input / output interface, where the processor executes a program stored in memory. Such a computer device is, for example, a personal computer (PC). However, this is just one example. The control parameter generation device 10 may also be composed of a server device (e.g., a cloud server) including one or more computers, each installed in a facility different from the production device 20.

[0068] The control parameter generation device 10 includes an information processing unit 50 such as a processor (an example of a circuit configuration), a storage unit 42 such as a memory, an input unit 44 such as a mouse or keyboard, a display unit 17 such as a liquid crystal display or an organic EL display, and a communication unit 43 such as a communication module.

[0069] As a function realized by the processor executing a program read from a computer-readable non-temporary recording medium (such as ROM), the information processing unit 50 includes an optimization processing unit 60 and an evaluation index changing unit 100. In other words, the above program is a program that causes the information processing unit 50 mounted on the control parameter generation device 10 to function as the optimization processing unit 60 and the evaluation index changing unit 100.

[0070] The optimization processing unit 60 generates updated control parameters by executing optimization processing. The optimization processing unit 60 will be described below using the example of the case where the optimization processing unit 60 performs optimization processing to improve the evaluation index of "settlement time". First, the optimization processing unit 60 selects a representative operation that includes at least one operation from all the operations that the production device 20 can perform. Then, the optimization processing unit 60 causes the production device 20 to execute the selected representative operation. Based on this instruction, the control circuit 22 of the production device 20 outputs a command to the drive source 23 to move the position of the driven object 24 to a predetermined target position. Then, the optimization processing unit 60 acquires measurement data corresponding to each of the one or more operations performed by the production device 20, which are output from the sensor 30. Then, for each of the one or more operations acquired from the sensor 30, the optimization processing unit 60 determines the settlement time based on the measurement data corresponding to that operation.

[0071] The optimization processing unit 60 in this embodiment updates the control parameters through optimization processing to shorten at least the longest settling time among the one or more settling times corresponding to one or more operations, and generates updated control parameters. The optimization processing unit 60 may also perform optimization processing to shorten the average value of the settling times or the average value of the longest P (where P is a value smaller than L) among the L settling times.

[0072] As shown in Figure 1, the optimization processing unit 60 has an optimization algorithm 61 that optimizes control parameters to shorten the settling time. The optimization processing unit 60 then uses the optimization algorithm 61 to perform an optimization process that shortens at least the longest settling time among one or more settling times. The optimization algorithm 61 may be a known algorithm such as a Bayesian optimization algorithm, an evolutionary strategy algorithm (CMA-ES), or a genetic algorithm (GA). Furthermore, the optimization process that shortens the settling time using the optimization algorithm 61 may be a known process performed using the above known algorithms.

[0073] The optimization processing unit 60 may, for example, generate update control parameters by performing the optimization process once, or it may generate update control parameters by repeatedly performing the optimization process until the optimization process termination condition is met.

[0074] The optimization process termination condition is, for example, the period during which the optimization process is repeated. In this case, the optimization processing unit 60 repeatedly performs the optimization process until a predetermined period has elapsed. Alternatively, the optimization process termination condition can be, for example, the number of times the optimization process is repeated. In this case, the optimization processing unit 60 repeatedly performs the optimization process a predetermined number of times. Alternatively, the optimization process termination condition can be, for example, the time that the settling time for each of the one or more operations must satisfy. In this case, the optimization processing unit 60 repeatedly performs the optimization process until the settling time for each of the one or more operations becomes less than or equal to a predetermined time. If the time that the settling time for each of the one or more operations must satisfy is set to 20 milliseconds or less, the optimization processing unit 60 repeatedly performs the optimization process until it generates update control parameters that result in a settling time for each of the one or more operations being 20 milliseconds or less.

[0075] The optimization processing unit 60 outputs the generated update control parameters to the production device 20 for storage in the memory 21. The memory 21 then retrieves the output control parameters and updates the stored control parameters with the retrieved parameters.

[0076] As explained above, the optimization processing unit 60 executes an optimization process for which predetermined evaluation indicators (such as settling time) are set, and outputs update control parameters.

[0077] Incidentally, users may try to change the settings related to evaluation metrics during or after the optimization process. For example, suppose a user sets "setup time" as an evaluation metric and sets a target of, for example, 100 milliseconds or less. Suppose the optimization process is performed under these settings, update control parameters are output, the production device 20 operates based on these update control parameters, and operation data showing the operation of the production device 20 is generated. Specifically, suppose the operation data D10 shown in Figure 4 is output. Figure 4 is another example of time-series data showing the change in position deviation of the driven object 24 relative to the target position over time. In Figure 4, the horizontal axis shows time, and the vertical axis shows the position deviation of the driven object 24 relative to the target position. The operation data D10 shown in Figure 4 has a setup time of 80 milliseconds and 11 crossovers. Therefore, this operation data D10 satisfies the setup time target set by the user. On the other hand, the user may be dissatisfied with the large number of crossovers. In such cases, the user will try to change the settings related to evaluation metrics. In addition, users may attempt to change the settings related to evaluation metrics if the optimization process does not progress (i.e., the operation data D10 that achieves the evaluation metrics is not output) because the user has set evaluation metrics that are difficult to achieve.

[0078] In this embodiment, the evaluation index changing unit 100 (Figure 1) displays superimposed data on the display unit 17, in which a change figure D2 for receiving an operation to change the settings related to the evaluation index is superimposed on the operation data D10. Then, the unit obtains operation information D3 indicating the operation content entered by the user who has confirmed the superimposed data via the change figure D2. Then, the unit changes the settings related to the evaluation index based on the operation information D3.

[0079] Figure 5 shows an example of the configuration of the evaluation index changing unit 100. As shown in Figure 5, the evaluation index changing unit 100 includes an acquisition unit 110, a graphic acquisition unit 120, a display control unit 130, and an evaluation index setting unit 140.

[0080] The acquisition unit 110 acquires the change instruction information D1. The change instruction information D1 is information that includes a change instruction that instructs to change the settings related to the evaluation indicators. The change instruction information D1 is input by a user who wishes to change the settings related to the evaluation indicators via the input unit 44. The change instruction information D1 includes target information that indicates the evaluation indicator to be changed. In other words, the change instruction information D1 includes information that indicates which evaluation indicator's settings will be changed. The acquisition unit 110 inputs the change instruction information D1 to the graphic acquisition unit 120.

[0081] The figure acquisition unit 120 acquires the modified figure D2. The modified figure D2 is a figure for accepting operations to change settings related to evaluation indicators. In this embodiment, the figure acquisition unit 120 acquires the modified figure D2 corresponding to the evaluation indicator to be changed based on the target information. For example, suppose the acquisition unit 110 has acquired target information indicating that the setting related to the evaluation indicator "setup time" is to be changed. In this case, the figure acquisition unit 120 acquires the modified figure D2 corresponding to the evaluation indicator "setup time". As shown in Figure 5, the figure acquisition unit 120 acquires the modified figure D2 from the figure database 421. The figure database 421 stores various modified figures D2 corresponding to various evaluation indicators. The figure acquisition unit 120 inputs the modified figure D2 to the display control unit 130.

[0082] The display control unit 130 generates a setting change image IM (an example of superimposed data) by superimposing a change graphic D2 onto the operation data D10 that indicates the operation of the production device 20. The display control unit 130 displays the setting change image IM on the display unit 17. Hereinafter, the display screen of the display unit 17 on which the setting change image IM is displayed will be called the setting change screen. The setting change screen functions as a user interface. That is, the display control unit 130 can receive various operations from the user via the setting change screen. For example, the display control unit 130 can receive a change operation from the user via the setting change screen to change the settings related to evaluation indicators. The display control unit 130 generates (acquires) operation information D3 indicating the content of the change operation and inputs the operation information D3 to the evaluation indicator setting unit 140.

[0083] The evaluation index setting unit 140 changes the settings related to the evaluation index applied to the optimization process based on the operation information D3.

[0084] The processing of the evaluation index changing unit 100 will be explained below, with reference to specific examples such as Display Example 1, Display Example 2, Display Example 3, and Display Example 4.

[0085] (Display Example 1) Figure 6 is a diagram showing an example of a settings change screen. More specifically, Figure 6 is a diagram showing the first settings change screen 201 where the first settings change image IM1, which is an example of a settings change image IM, is displayed. The first settings change image IM1 is generated by the display control unit 130 by superimposing the settling time change figure F1 and the settling width change figure F2 onto the operation data D10 shown in Figure 4, for example.

[0086] The set time change shape F1 is a shape that accepts an operation to change the set time setting. The set time is an example of an evaluation index. In the example shown in Figure 6, the set time change shape F1 is composed of a solid line extending along the vertical direction of the first setting change screen 201. The position of the set time change shape F1 shown in Figure 6 is called the first initial position. Specifically, when the display control unit 130 displays the first setting change screen 201 on the display unit 17, it places the set time change shape F1 in a predetermined position. This predetermined position is the first initial position. The display control unit 130 detects when the user inputs an operation to move the set time change shape F1 from the first initial position. For example, the display control unit 130 detects when the user moves the set time change shape F1 to the right or left from the first initial position on the first setting change screen 201. At this time, the display control unit 130 obtains the amount of movement of the set time change shape F1. The display control unit 130 generates movement operation information as operation information D3, indicating that the user has input an operation to move the position of the set time change figure F1. The movement operation information includes the amount of movement of the set time change figure F1. The display control unit 130 inputs the movement operation information to the evaluation index setting unit 140.

[0087] The evaluation index setting unit 140 changes the target value of the settling time (an example of a setting related to the evaluation index) based on the amount of movement of the settling time change shape F1. For example, suppose the user inputs an operation to move the position of the settling time change shape F1 to the left of the first initial position. In this case, the evaluation index setting unit 140 makes the target value of the settling time smaller than the initial value of the target value of the settling time (hereinafter referred to as the first initial value) according to the amount of movement of the settling time change shape F1 to the left. The first initial value is not particularly limited, but for example it is 50 milliseconds. That is, if the user inputs an operation to move the position of the settling time change shape F1 to the left of the first initial position, the evaluation index setting unit 140 makes the target value of the settling time shorter than 50 milliseconds. Let's also consider the case where the user inputs an operation to move the position of the settling time change shape F1 to the right of the first initial position. In this case, the evaluation index setting unit 140 increases the target value of the settling time to a value greater than the first initial value (for example, 50 milliseconds) according to the amount of movement of the settling time change figure F1 to the right. If the settling time change figure F1 does not move from the first initial position, the evaluation index setting unit 140 maintains the target value of the settling time at the first initial value.

[0088] When the optimization processing unit 60 detects that the evaluation index setting unit 140 has changed the setting of the settling time, it executes optimization processing based on the changed setting. For example, the optimization processing unit 60 performs optimization processing to search for update control parameters that satisfy the target value of the settling time after the setting change.

[0089] The setting width change figure F2 is a figure that accepts an operation to change the size of the setting width. In other words, the setting width change figure F2 is a figure that accepts an operation to change the size of the tolerance range. In the example shown in Figure 6, the setting width change figure F2 includes two parallel dashed lines that extend along the left-right direction of the first setting change screen 201. Hereinafter, the dashed line located on the upper side of Figure 6 will be called the first line L1, and the dashed line located on the lower side of Figure 6 will be called the second line L2. The first line L1 corresponds to the upper limit of the tolerance range (an example of a setting related to the evaluation index), and the second line L2 corresponds to the lower limit of the tolerance range (an example of a setting related to the evaluation index). The position of the first line L1 shown in Figure 6 will be called the second initial position, and the position of the second line L2 will be called the third initial position. Specifically, when the display control unit 130 displays the first setting change screen 201 on the display unit 17, it positions the first line L1 and the second line L2 in predetermined positions. These predetermined positions are the second initial position and the third initial position. The display control unit 130 detects at least one of the following: that the user has moved the first line L1 upward or downward from the second initial position on the first setting change screen 201, and that the user has moved the second line L2 upward or downward from the third initial position on the first setting change screen 201. At this time, the display control unit 130 acquires the amount of movement of the first line L1 and the second line L2. The display control unit 130 generates movement operation information as operation information D3, indicating that the user has input an operation to move the position of at least one of the first line L1 and the second line L2. The movement operation information includes the amount of movement of the first line L1 and the second line L2. The display control unit 130 inputs the movement operation information to the evaluation index setting unit 140.

[0090] The evaluation index setting unit 140 changes the size of the settling range based on the amount of movement of the first line L1 and the second line L2. In other words, it changes the size of the tolerance range. For example, suppose the display control unit 130 detects that the user has input an operation to move the first line L1 to the upper side of the second initial position. In this case, the evaluation index setting unit 140 increases the upper limit of the tolerance range to the initial value of the upper limit of the tolerance range (hereinafter referred to as the second initial value) according to the amount of upward movement of the first line L1. That is, the evaluation index setting unit 140 expands the tolerance range. Also, suppose the display control unit 130 detects that the user has input an operation to move the first line L1 to the lower side of the second initial position. In this case, the evaluation index setting unit 140 decreases the upper limit of the tolerance range to the second initial value according to the amount of downward movement of the first line L1. That is, the evaluation index setting unit 140 reduces the tolerance range. Furthermore, suppose the display control unit 130 detects that the user has input an operation to move the second line L2 to the upper side of the third initial position. In this case, the evaluation index setting unit 140 increases the lower limit of the allowable range compared to the initial value of the lower limit of the allowable range (hereinafter referred to as the third initial value), according to the amount of upward movement of the second line L2. In other words, the evaluation index setting unit 140 reduces the allowable range. Also, suppose the display control unit 130 detects that the user has input an operation to move the second line L2 to the lower side of the third initial position. In this case, the evaluation index setting unit 140 decreases the lower limit of the allowable range compared to the third initial value, according to the amount of downward movement of the second line L2. In other words, the evaluation index setting unit 140 expands the allowable range.

[0091] When the optimization processing unit 60 detects that the evaluation index setting unit 140 has changed the size of the allowable range (setup width), it executes optimization processing based on the changed size. For example, the optimization processing unit 60 treats the time it takes for the position of the driven object 24 to reach the allowable range after the setting change as the setup time, and performs optimization processing to search for updated control parameters that make the setup time less than or equal to the target value.

[0092] According to the configuration shown in Display Example 1, the evaluation index setting unit 140 changes the target settling time based on the amount of movement of the settling time change shape F1. Therefore, the user can change the target settling time by inputting an operation to move the settling time change shape F1. Thus, it is possible to easily change the settings related to the evaluation index.

[0093] Furthermore, according to the configuration shown in Display Example 1, the evaluation index setting unit 140 changes the size of the settling width based on the positions of the first line L1 and the second line L2 after movement. Therefore, the user can change the size of the settling width by inputting an operation to move the first line L1 and the second line L2. Thus, it is possible to easily change the settings related to the evaluation index.

[0094] (Display Example 2) The user may set a reference range for the operation data. Figure 7 is a diagram illustrating the processing of the evaluation index changing unit 100 related to Display Example 2. More specifically, Figure 7 is time-series data showing yet another example of the change in the position deviation of the driven object 24 relative to the target position. The horizontal axis of Figure 7 shows time, and the vertical axis shows the position deviation of the driven object 24 relative to the target position. As shown in Figure 7, the user defines the reference range by setting a first boundary line B1 and a second boundary line B2 in the operation data. The user may then set the accumulated value of the deviation time or position deviation amount during which the position deviation between the position of the driven object 24 and the target position deviates from the reference range as an evaluation index. In other words, the user may set the sum of the area of ​​the overshoot region A1 (Figure 7) where the position deviation of the driven object 24 deviates from the first boundary line B1, and the area of ​​the undershoot region A2 (Figure 7) where the position deviation of the driven object 24 deviates from the second boundary line B2, as an evaluation index. In this case, the optimization processing unit 60 performs an optimization process to search for update control parameters that reduce the sum of the areas of the overshoot region A1 and the undershoot region A2. Alternatively, the sum of the areas of the overshoot region A1 (Figure 7) or the undershoot region A2 (Figure 7) immediately before settlement may be weighted to penalize the period immediately before settlement more heavily. Or, the longer the elapsed time from the start of movement of the driven object 24, the heavier the penalty may be. Specifically, the evaluation value is the sum of values ​​obtained by multiplying the absolute value of the amount that falls outside the reference range at each time by a coefficient that increases as the elapsed time increases. Alternatively, the evaluation value may be the sum of values ​​obtained by multiplying the area of ​​the overshoot region A1 (Figure 7) or the area of ​​the undershoot region A2 (Figure 7) at each time by a coefficient that increases as the elapsed time increases and as the center time (or start time, end time) of that region increases. In Figure 7, the overshoot region A1 and the undershoot region A2 are shown with hatched lines. At this point, the user may attempt to change the reference range during the optimization process. In other words, the user may attempt to change the position and shape of the first boundary line B1 and the second boundary line B2 (an example of settings related to evaluation indicators).

[0095] Figure 8 shows another example of the settings change screen. More specifically, Figure 8 shows the second settings change screen 202 displaying the second settings change image IM2, which is another example of a settings change image. The second settings change image IM2 is generated by the display control unit 130 superimposing a reference range change figure F3 onto the operation data D10 shown in Figure 4, for example. The reference range change figure F3 includes a first boundary line change figure F31 and a second boundary line change figure F32.

[0096] In the example shown in Figure 8, the first boundary line change figure F31 includes a first control point PO1, a second control point PO2, and a first dashed line L3. The first dashed line L3 is a straight line extending along the left-right direction of the second setting change screen 202. However, the first dashed line L3 may be a curve. The display control unit 130 detects that the user has input an operation to move the first control point PO1 and the second control point PO2.

[0097] The second boundary line change figure F32 includes the third control point PO3, the fourth control point PO4, and the second dashed line L4. The second dashed line L4 is positioned below the first dashed line L3 and is a straight line extending along the left-right direction of the second setting change screen 202. However, the second dashed line L4 may be a curve. The display control unit 130 detects that the user has input an operation to move the third control point PO3 and the fourth control point PO4.

[0098] Furthermore, the display control unit 130 detects when the user selects (for example, clicks) two predetermined points on the second setting change screen 202.

[0099] The display control unit 130 generates an updated image IM3 according to the content of the operation received from the user via the second setting change screen 202, and displays the updated image IM3 on the display unit 17. In other words, the display control unit 130 updates the second setting change screen 202.

[0100] Figure 9 shows an example of the updated second setting change screen 202. For the sake of explanation, the updated second setting change screen 202 will be referred to as the updated screen 203. As shown in Figure 9, the updated screen 203 displays the first boundary line change figure F31, which has been transformed from a straight line to a curve. Specifically, the updated screen 203 displays the first boundary line change figure F31, which has a shape that bulges out in the upper left direction. In other words, the first boundary line change figure F31, which had a curvature of 0%, has been transformed into a curve with a predetermined curvature. Such a first boundary line change figure F31 is formed by connecting the moved first control point PO1 and the moved second control point PO2 using a method such as spline interpolation. The updated screen 203 also displays the second boundary line change figure F32, which has been transformed from a straight line to a curve. Specifically, the updated screen 203 displays the second boundary line change figure F32, which has a shape that bulges out in the lower left direction. Such a second boundary line modification figure F32 is formed by connecting the moved third control point PO3 and the moved fourth control point PO4 using a method such as spline interpolation. In addition, a third boundary line figure F33 is added to the updated screen 203. The third boundary line figure F33 is formed by connecting two points specified by the user using a method such as spline interpolation. If an operation to move the position of the fifth control point PO5 or the sixth control point PO6 of the third boundary line figure F33 is received, the display control unit 130 may change the position and shape of the third boundary line figure F33.

[0101] The display control unit 130 inputs operation information D3 indicating the position and shape of the first boundary line change figure F31, the second boundary line change figure F32, and the third boundary line figure F33 in the updated screen 203 to the evaluation index setting unit 140.

[0102] The evaluation index setting unit 140 changes the reference range based on the operation information D3. For example, it reflects the position and shape of the first boundary line modification figure F31 in Figure 9 to the position and shape of the first boundary line B1 in Figure 7. That is, it moves the position of the first boundary line B1 in Figure 7 based on the position of the first boundary line modification figure F31 in Figure 9, and deforms the shape of the first boundary line B1 in Figure 7 based on the shape of the first boundary line modification figure F31 in Figure 9. It also reflects the position and shape of the second boundary line modification figure F32 in Figure 9 to the position and shape of the second boundary line B2 in Figure 7. That is, it moves the position of the second boundary line B2 in Figure 7 based on the position of the second boundary line modification figure F32 in Figure 9, and deforms the shape of the second boundary line B2 in Figure 7 based on the shape of the second boundary line modification figure F32 in Figure 9.

[0103] When the optimization processing unit 60 detects that the evaluation index setting unit 140 has changed the position and shape of the first boundary line B1 and the second boundary line B2, it executes an optimization process based on the changed first boundary line B1 and second boundary line B2. For example, the optimization processing unit 60 uses the first boundary line B1 after its position and shape have been changed to calculate the sum of the areas of the overshoot region A1. The optimization processing unit 60 also uses the second boundary line B2 after its position and shape have been changed to calculate the sum of the areas of the undershoot region A2. Then, it executes an optimization process to search for update control parameters that can make the sum of the areas less than or equal to the target value.

[0104] The evaluation index setting unit 140 may also reflect the position and shape of the third boundary line figure F33 in Figure 9 in the setting of the position and shape of the first boundary line B1 in Figure 7. In this case, the optimization processing unit 60 may use the first boundary line B1, which reflects the position and shape of the third boundary line figure F33, to calculate the sum of the areas of the overshoot region A1.

[0105] According to the configuration shown in Display Example 2, the evaluation index setting unit 140 changes the reference range based on the position and shape of the modified reference range shape F3. Therefore, the user can change the reference range by inputting an operation to change the position or shape of the modified reference range shape F3. Thus, it is possible to easily change the settings related to the evaluation index.

[0106] Furthermore, according to the configuration shown in Display Example 2, the first boundary line B1 and the second boundary line B2 can be freely changed through intuitive operation.

[0107] Furthermore, according to the configuration shown in Display Example 2, the straight first boundary line B1 and the second boundary line B2 can be transformed into curved shapes. Therefore, it is possible to change the shapes of the first boundary line B1 and the second boundary line B2 so that the reference range decreases over time. Additionally, it is possible to change the shapes of the first boundary line B1 and the second boundary line B2 so that the reference range increases over time.

[0108] (Display Example 3) Figure 10 shows yet another example of the settings change screen. More specifically, Figure 10 shows the third settings change screen 204 where the third settings change image IM4, which is an example of a settings change image, is displayed. The third settings change image IM4 is generated by the display control unit 130 superimposing the settling time change figure F1, the first allowable range change figure F4, and the second allowable range change figure F5 onto the operation data D10 shown in Figure 4, for example. The settling time change figure F1 has already been explained in Display Example 1, so its explanation is omitted here.

[0109] The first tolerance range change figure F4 is a figure that accepts an operation to change the size of the first tolerance range (an example of a setting related to the evaluation index), which is the tolerance range in the first period, which is the period before the target settling time. In other words, it is a figure that accepts an operation to change the size of the settling width in the first period. The first tolerance range change figure F4 includes two parallel dashed lines that extend along the left and right directions of the third setting change screen 204. The dashed line that constitutes the first tolerance range change figure F4 and is located on the upper side of Figure 10 is called the 11th dashed line L11, and the dashed line that is located on the lower side of Figure 10 is called the 12th dashed line L12. The position of the 11th dashed line L11 shown in Figure 10 is called the 11th initial position, and the position of the 12th dashed line L12 is called the 12th initial position. Specifically, when the display control unit 130 displays the third setting change screen 204 on the display unit 17, it positions the 11th dashed line L11 and the 12th dashed line L12 in predetermined positions. These predetermined positions are the 11th initial position and the 12th initial position.

[0110] The display control unit 130 detects at least one of the following: that the user has moved the 11th dashed line L11 upward or downward from its 11th initial position on the third setting change screen 204, and that the user has moved the 12th dashed line L12 upward or downward on the third setting change screen 204 from its 12th initial position. At this time, the display control unit 130 acquires the amount of movement of the 11th dashed line L11 and the 12th dashed line L12. The display control unit 130 generates movement operation information as operation information D3, indicating that the user has input an operation to move the position of at least one of the 11th dashed line L11 and the 12th dashed line L12. The movement operation information includes the amount of movement of the 11th dashed line L11 and the 12th dashed line L12. The display control unit 130 inputs the movement operation information to the evaluation index setting unit 140.

[0111] The evaluation index setting unit 140 changes the size of the first allowable range based on the amount of movement of the 11th dashed line L11 and the 12th dashed line L12. That is, it changes the size of the settling width in the first period. For example, suppose the display control unit 130 detects that the user has input an operation to move the 11th dashed line L11 to the upper side of the 11th initial position. In this case, the evaluation index setting unit 140 increases the upper limit of the first allowable range to be greater than the initial value of the upper limit of the first allowable range (hereinafter referred to as the 11th initial value) according to the amount of upward movement of the 11th dashed line L11. In other words, the evaluation index setting unit 140 expands the first allowable range. Also, suppose the display control unit 130 detects that the user has input an operation to move the 11th dashed line L11 to the lower side of the 11th initial position. In this case, the evaluation index setting unit 140 decreases the upper limit of the first allowable range to be less than the 11th initial value according to the amount of downward movement of the 11th dashed line L11. In other words, the evaluation index setting unit 140 reduces the first allowable range. Also, suppose the display control unit 130 detects that the user has input an operation to move the 12th dashed line L12 to the upper side of the 12th initial position. In this case, the evaluation index setting unit 140 increases the lower limit of the first allowable range to be greater than the initial value of the lower limit of the first allowable range (hereinafter referred to as the 12th initial value) according to the amount of upward movement of the 12th dashed line L12. In other words, the evaluation index setting unit 140 reduces the first allowable range. Also, suppose the display control unit 130 detects that the user has input an operation to move the 12th dashed line L12 to the lower side of the 12th initial position. In this case, the evaluation index setting unit 140 decreases the lower limit of the first allowable range to be less than the 12th initial value according to the amount of downward movement of the 12th dashed line L12. In other words, the evaluation index setting unit 140 expands the first allowable range.

[0112] When the optimization processing unit 60 detects that the evaluation index setting unit 140 has changed the size of the first allowable range, it executes an optimization process based on the changed size of the first allowable range. For example, the optimization processing unit 60 treats the time it takes for the position of the driven object 24 to reach the first allowable range after the setting change as the settling time, and performs an optimization process to search for update control parameters that satisfy a predetermined target value for the settling time.

[0113] The second tolerance range change figure F5 is a figure that accepts an operation to change the size of the second tolerance range (an example of a setting related to the evaluation index), which is the tolerance range in the second period, which is a period after the target settling time. In other words, it is a figure that accepts an operation to change the size of the settling width in the second period. The second tolerance range change figure F5 includes two parallel dashed lines that extend along the left and right directions of the third setting change screen 204. The dashed line that constitutes the second tolerance range change figure F5 and is located on the upper side of Figure 10 is called the 21st dashed line L21, and the dashed line that is located on the lower side of Figure 10 is called the 22nd dashed line L22. The position of the 21st dashed line L21 shown in Figure 10 is called the 21st initial position, and the position of the 22nd dashed line L22 is called the 22nd initial position. Specifically, when the display control unit 130 displays the third setting change screen 204 on the display unit 17, it positions the 21st dashed line L21 and the 22nd dashed line L22 in predetermined positions. These predetermined positions are the 21st initial position and the 22nd initial position.

[0114] The display control unit 130 detects at least one of the following: that the user has moved the 21st dashed line L21 upward or downward from its 21st initial position on the third setting change screen 204, and that the user has moved the 22nd dashed line L22 upward or downward on the third setting change screen 204 from its 22nd initial position. At this time, the display control unit 130 acquires the amount of movement of the 21st dashed line L21 and the 22nd dashed line L22. The display control unit 130 generates movement operation information as operation information D3, indicating that the user has input an operation to move the position of at least one of the 21st dashed line L21 and the 22nd dashed line L22. The movement operation information includes the amount of movement of the 21st dashed line L21 and the 22nd dashed line L22. The display control unit 130 inputs the movement operation information to the evaluation index setting unit 140.

[0115] The evaluation index setting unit 140 changes the size of the second allowable range based on the amount of movement of the 21st dashed line L21 and the 22nd dashed line L22. That is, it changes the size of the settling width in the second period. For example, suppose the display control unit 130 detects that the user has input an operation to move the 21st dashed line L21 to the upper side of the 21st initial position. In this case, the evaluation index setting unit 140 increases the upper limit of the second allowable range to be greater than the initial value of the upper limit of the second allowable range (hereinafter referred to as the 21st initial value) according to the amount of upward movement of the 21st dashed line L21. In other words, the evaluation index setting unit 140 expands the second allowable range. Also, suppose the display control unit 130 detects that the user has input an operation to move the 21st dashed line L21 to the lower side of the 21st initial position. In this case, the evaluation index setting unit 140 decreases the upper limit of the second allowable range to be less than the 21st initial value according to the amount of downward movement of the 21st dashed line L21. In other words, the evaluation index setting unit 140 reduces the second tolerance range. Also, suppose the display control unit 130 detects that the user has input an operation to move the 22nd dashed line L22 to the upper side of the 22nd initial position. In this case, the evaluation index setting unit 140 increases the lower limit of the second tolerance range to the initial value of the lower limit of the second tolerance range (hereinafter referred to as the 22nd initial value) according to the amount of upward movement of the 22nd dashed line L22. In other words, the evaluation index setting unit 140 reduces the second tolerance range. Also, suppose the display control unit 130 detects that the user has input an operation to move the 22nd dashed line L22 to the lower side of the 22nd initial position. In this case, the evaluation index setting unit 140 decreases the lower limit of the second tolerance range to the initial value of the 22nd initial value according to the amount of downward movement of the 22nd dashed line L22. In other words, the evaluation index setting unit 140 expands the second tolerance range.

[0116] When the optimization processing unit 60 detects that the evaluation index setting unit 140 has changed the size of the second tolerance range, it changes the evaluation method of the operation data D10 based on the changed size of the second tolerance range. Specifically, the optimization processing unit 60 relatively highly evaluates operation data D10 in which the position deviation of the driven object 24 with respect to the target position during the second period falls within the second tolerance range. For example, the optimization processing unit 60 calculates the sum of the areas of the overshoot and undershoot regions in which the position deviation of the driven object 24 with respect to the target position during the second period deviates from the second tolerance range, and the smaller the area, the higher the evaluation value of the operation data D10. In other words, the optimization processing unit 60 relatively highly evaluates operation data D10 in which the waveform showing the position deviation of the driven object 24 with respect to the target position converges to the target position after the settling time has elapsed (second period). On the other hand, the optimization processing unit 60 relatively low evaluates operation data D10 in which the position deviation of the driven object 24 with respect to the target position during the second period deviates from the second tolerance range.

[0117] According to the configuration shown in Display Example 3, the evaluation index setting unit 140 changes the size of the second allowable range based on the amount of movement of the second allowable range changing figure F5. Therefore, the user can change the size of the second allowable range by inputting an operation to move the second allowable range changing figure F5. Thus, it is possible to easily change the settings related to the evaluation index.

[0118] Furthermore, according to the configuration shown in Display Example 3, the motion data D10 in which the position deviation of the driven object 24 converges to the target position is relatively highly evaluated, so the search direction of the optimization processing unit 60 can be guided to prioritize the generation of update control parameters that cause the position deviation of the driven object 24 to converge to the target position.

[0119] (Display Example 4) Figure 11 shows another example of the settings change screen. More specifically, Figure 11 shows the fourth settings change screen 205 where the fourth settings change image IM5, which is an example of a settings change image, is displayed. The fourth settings change image IM5 is generated by the display control unit 130 superimposing a settling time change figure F1, a first allowable range change figure F4, and a gradient figure F7 onto the operation data D10 shown in Figure 4, for example. The settling time change figure F1 has already been explained in Display Example 1, and the first allowable range change figure F4 has already been explained in Display Example 3, so their explanations are omitted here. Note that the operation data D10 related to Display Example 4 includes a target position figure TA that indicates the target position.

[0120] The gradient figure F7 is a figure whose coloring pattern changes according to the deviation from the target position figure TA. In the example shown in Figure 11, the fourth setting change screen 205 displays a gradient figure F7 in which the color becomes darker as the vertical deviation (distance) from the target position figure TA increases. Specifically, the gradient figure F7 according to this embodiment has multiple colored layers with different coloring patterns. The multiple colored layers are composed of, for example, a first layer la1, a second layer la2, and a third layer la3. The color density of the first layer la1 is lighter than that of the second layer la2. The color density of the second layer la2 is lighter than that of the third layer la3. The first layer la1 has the smallest deviation from the target position figure TA. The second layer la2 has the second smallest deviation from the target position figure TA. The third layer la3 has the largest deviation from the target position figure TA. The area located outside the third layer la3 is called the outer area. The outer area is not colored.

[0121] The color density in each colored layer of the gradient figure F7 corresponds to the magnitude of the weights used by the optimization processing unit 60 when calculating the evaluation value for the second period of the operation data D10. The gradient figure F7 is a figure that accepts operations to change the magnitude of the weights.

[0122] Hereinafter, the region enclosed by the waveform and time axis drawn above the target position shape TA in the fourth setting change screen 205, and which exists within the second period, will be referred to as the first region AR1. The region enclosed by the waveform and time axis drawn below the target position shape TA in the fourth setting change screen 205, and which exists within the second period, will be referred to as the second region AR2. The regions in the first layer la1, the second layer la2, the third layer la3, and the outer region of the first region AR1 will be referred to as the eleventh region AR11, the twelfth region AR12, the thirteenth region AR13, and the fourteenth region AR14, respectively. Furthermore, the regions located in the first layer la1, the second layer la2, the third layer la3, and the outer region of the second region AR2 are referred to as the 21st region AR21, the 22nd region AR22, the 23rd region AR23, and the 24th region AR24, respectively.

[0123] The display control unit 130 detects that the user has input an operation to stretch the gradient figure F7 vertically on the third setting change screen 204, or an operation to shrink the gradient figure F7 vertically. In other words, the display control unit 130 detects that the user has input an operation to deform the gradient figure F7. At this time, the display control unit 130 detects the expansion or contraction rate of the gradient figure F7. That is, the display control unit 130 detects how much the gradient figure F7 has been stretched or how much it has been contracted. The display control unit 130 generates deformation operation information D3, which indicates the content of the deformation operation performed by the user, and inputs the operation information D3 to the evaluation index setting unit 140.

[0124] The evaluation index setting unit 140 changes the size of each layer based on the expansion or contraction ratio of the gradient figure F7. For example, suppose the display control unit 130 detects that the user has entered an operation to set the expansion ratio of the gradient figure F7 in the vertical direction to 200%. In other words, suppose the user has entered an operation to double the size of the gradient figure F7 by stretching it vertically. In this case, the evaluation index setting unit 140 expands the size of the first layer la1, the second layer la2, and the third layer la3 in the vertical direction according to the magnification ratio entered by the user (2x in this example). Alternatively, suppose the display control unit 130 detects that the user has entered an operation to set the contraction ratio of the gradient figure F7 in the vertical direction to 50%. In other words, suppose the user has entered an operation to set the size of the gradient figure F7 to 0.5 times by shrinking it vertically. In this case, the evaluation index setting unit 140 reduces the vertical size of the first layer la1, the second layer la2, and the third layer la3 according to the magnification factor entered by the user (0.5x in this example).

[0125] When the optimization processing unit 60 detects that the evaluation index setting unit 140 has changed the size of each layer of the gradient figure F7, it evaluates the operation data D10 based on the changed size. The evaluation method of operation data D10 using the gradient figure F7 will be described below.

[0126] First, the optimization processing unit 60 calculates the sum of the areas of the 11th region AR11, the 12th region AR12, the 13th region AR13, and the 14th region AR14. The optimization processing unit 60 also calculates the sum of the areas of the 21st region AR21, the 22nd region AR22, the 23rd region AR23, and the 24th region AR24. Subsequently, the optimization processing unit 60 outputs a first evaluation score by correcting the sum of the areas of the 11th region AR11 and the sum of the areas of the 21st region AR21 using a first weighting coefficient. Subsequently, the optimization processing unit 60 outputs a second evaluation score by correcting the sum of the areas of the 12th region AR12 and the sum of the areas of the 22nd region AR22 using a second weighting coefficient. Next, the optimization processing unit 60 outputs a third evaluation score by correcting the sum of the areas of the 13th region AR13 and the 23rd region AR23 using a third weighting coefficient. Then, the optimization processing unit 60 outputs a fourth evaluation score by correcting the sum of the areas of the 14th region AR14 and the 24th region AR24 using a fourth weighting coefficient. Finally, the optimization processing unit 60 calculates the sum of the first evaluation score, second evaluation score, third evaluation score, and fourth evaluation score as the evaluation value of the operation data D10. The second weighting coefficient is larger than the first weighting coefficient, the third weighting coefficient is larger than the second weighting coefficient, and the fourth weighting coefficient is larger than the third weighting coefficient. In this way, the larger the position deviation of the driven object 24 from the target position during the second period, the lower the evaluation value of the operation data D10 can be calculated, and the smaller the position deviation of the driven object 24 from the target position, the higher the evaluation value of the operation data D10 can be calculated.

[0127] According to the configuration shown in Display Example 4, the magnitude of the weights used when evaluating the operation data D10 (an example of setting the evaluation index) can be easily changed. Specifically, when the gradient figure F7 is stretched, for example, the area of ​​the region that was treated as the 12th region AR12 or the 22nd region AR22 decreases, and the area of ​​the region that is treated as the 11th region AR11 or the 21st region AR21 increases. Also, for example, the area of ​​the region that was treated as the 14th region AR14 or the 24th region AR24 decreases, and the area of ​​the region that is treated as the 13th region AR13 or the 23rd region AR23 increases. As a result, the evaluation value of the operation data D10 is calculated to be higher overall. On the other hand, when the gradient figure F7 is reduced, for example, the area of ​​the region that was treated as the 11th region AR11 or the 21st region AR21 decreases, and the area of ​​the region that is treated as the 12th region AR12 or the 22nd region AR22 increases. Furthermore, for example, the area of ​​the region previously treated as the 13th region AR13 or the 23rd region AR23 decreases, while the area of ​​the region treated as the 14th region AR14 or the 24th region AR24 increases. As a result, the overall evaluation value of the operation data D10 is calculated to be lower. Thus, according to the configuration of display example 4, the magnitude of the weight can be easily changed by inputting an operation to change the size of the gradient figure F7.

[0128] Furthermore, according to the configuration shown in Display Example 4, the magnitude of the weight used when evaluating the operation data D10 is visualized by the color density of the gradient figure F7. This allows the user to intuitively understand the magnitude of the weight.

[0129] Here, we have explained an example where the intensity of the color varies according to the magnitude of the weight, but the type of color may also vary according to the magnitude of the weight. For example, the first layer la1 may be colored blue, the second layer la2 may be colored yellow, and the third layer la3 may be colored red. We have also explained an example where the colored region is composed of the first layer la1, the second layer la2, and the third layer la3, but this is just one example. The colored region may be composed of more layers. In this case, more weight coefficients should be used when calculating the evaluation value of the operation data D10. Alternatively, the colored region may be composed of fewer layers (two layers).

[0130] Furthermore, although this example describes how the display control unit 130 detects that an operation to deform the gradient figure F7 has been input, the display control unit 130 may also detect that a user has input a color change operation to change the type of color or the intensity (shade) of the color of the gradient figure F7. In this case, the display control unit 130 inputs the color change operation information as operation information D3 to the evaluation index setting unit 140. The evaluation index setting unit 140 then changes the magnitude of the weight used when evaluating the operation data D10 according to the color (type of color or intensity) of the gradient figure F7 after the color change.

[0131] Figure 12 is a flowchart showing the processing flow executed by the information processing unit 50 according to this embodiment. The processing shown in Figure 12 is started, for example, when the acquisition unit 110 detects that a user has entered a change instruction. Note that, as a preprocessing step performed before the processing of step S1 in Figure 12, the acquisition of the changed shape D2 by the shape acquisition unit 120 may be performed.

[0132] In step S1, the display control unit 130 displays a setting change image (an example of superimposed data). For example, the display control unit 130 generates a first setting change image IM1 by superimposing a set time change figure F1 and a set width change figure F2 on the operation data D10, and outputs a first setting change screen 201 including the first setting change image IM1 to the display unit 17.

[0133] In step S2, the display control unit 130 acquires operation information D3. Specifically, it acquires operation information D3 that indicates the content of the operation entered by the user on the change shape D2. For example, it generates (acquires) operation information D3 indicating that the user entered an operation such as moving the settling time change shape F1 left or right. Also in step S2, the display control unit 130 inputs the operation information D3 to the evaluation index setting unit 140.

[0134] In step S3, the evaluation indicator setting unit 140 changes the settings related to the evaluation indicator. For example, suppose that operation information D3 indicating that the user has entered an operation to move the settling time change figure F1 to the left on the first setting change screen 201 is entered into the evaluation indicator setting unit 140. In this case, the evaluation indicator setting unit 140 makes the target setting value for the settling time smaller than, for example, the initial value of the target setting time.

[0135] According to the control parameter generation system 1 described above, the information processing unit 50 changes the settings related to the evaluation indicators applied to the optimization process based on the modified figure D2 whose display mode has been changed. Therefore, the user can change the settings related to the evaluation indicators by inputting an operation to change the display mode of the modified figure D2. This makes it possible to easily change the settings related to the evaluation indicators. Thus, for example, it becomes possible to adjust the settings related to the evaluation indicators while checking the waveform data (operation data) during the execution of the optimization process.

[0136] The following modifications can be adopted for this embodiment.

[0137] (1) Figure 13 shows the first operation data D10A and the second operation data D10B related to this modification. The first operation data D10A and the second operation data D10B are waveforms obtained during the optimization process. The settling time of the second operation data D10B is shorter than the settling time of the first operation data D10A. When the optimization process is performed with the settling time set as the evaluation criterion, the evaluation of the second operation data D10B may be higher than that of the first operation data D10A. On the other hand, the amount of deviation from the allowable range of the first operation data D10A is smaller than the amount of deviation from the allowable range of the second operation data D10B. Here, if the user changes the evaluation criterion so that the smaller the area deviating from the allowable range, the higher the evaluation value, the higher the evaluation value may be, and the evaluation of the first operation data D10A may be higher than that of the second operation data D10B.

[0138] The display control unit 130 may detect that the user has input an operation to change the evaluation criteria of the operation data D10. Upon detecting the above operation, the display control unit 130 may input operation information D3 indicating that the evaluation criteria have been changed (updated) to the evaluation index setting unit 140.

[0139] The evaluation index setting unit 140 may calculate the evaluation value of each of the multiple operation data D10 obtained during the optimization process based on the updated evaluation criteria (modified evaluation criteria). The evaluation index setting unit 140 may then determine the operation data D10 with the highest evaluation value based on the modified evaluation criteria, or the top multiple operation data D10 with high evaluation values ​​based on the modified evaluation criteria. The display control unit 130 may output the operation data D10 with the highest evaluation value, or the top multiple operation data D10 with high evaluation values, to the display unit 17.

[0140] According to the configuration of this modified example, the operation data D10 with the highest evaluation value based on the revised evaluation criteria, or the top multiple operation data D10s with high evaluation values ​​based on the revised evaluation criteria, are displayed on the display unit 17. Therefore, the user can easily understand whether or not the revised evaluation criteria are appropriate.

[0141] Furthermore, the display control unit 130 may superimpose the modified figure D2 onto the operation data D10 with the highest evaluation value based on the modified evaluation criteria, or onto the top multiple operation data D10 with high evaluation values ​​based on the modified evaluation criteria.

[0142] In this case, the settings related to the evaluation index can be further modified by manipulating the modified shape D2 superimposed on the operation data D10 with the highest evaluation value, or on the top multiple operation data D10s with high evaluation values.

[0143] (2) While a user can intuitively judge the quality of the operation data D10 by visually inspecting it on the display unit 17, etc., they may not be able to clearly define the criteria for determining that quality. Specifically, a user may make a comprehensive judgment on the quality (level of evaluation) of the operation data D10 based on multiple perspectives (evaluation criteria), such as the length of the settling time, the size of the overshoot area, the magnitude of the vibration, the persistence of the vibration, and the way the vibration converges. It is difficult to express this comprehensive judgment process in numerical values ​​or mathematical formulas.

[0144] Therefore, the display control unit 130 may detect when the user has input an operation to change the evaluation criteria for the operation data D10. For example, the user may be asked to select the operation data D10 that they rate highly from among the multiple operation data D10 obtained during the optimization process. For example, the user may be asked to select (input) the operation data D10 that they rate most highly, the operation data D10 that they rate second most highly, the operation data D10 that they rate third most highly, and so on.

[0145] Upon detecting the above operation, the display control unit 130 may input operation information D3 indicating that the evaluation criteria have been changed (updated) to the evaluation index setting unit 140. Based on the updated evaluation criteria, the evaluation index setting unit 140 may calculate the evaluation value for each of the multiple operation data D10 obtained during the optimization process. Subsequently, the evaluation index setting unit 140 may select at least two of the multiple operation data D10. Then, the evaluation index setting unit 140 may rearrange the at least two operation data D10 in descending order of their evaluation values ​​based on the changed evaluation criteria. The display control unit 130 may display a display screen on the display unit 17 that includes the at least two operation data D10 rearranged in descending order of their evaluation values.

[0146] With the above configuration, even if it is difficult to define evaluation criteria for determining the superiority or inferiority of the operational data D10, the evaluation criteria can be easily changed.

[0147] Furthermore, with the above configuration, a display screen including at least two operational data D10 sorted in descending order of evaluation value is displayed on the display unit 17, so that when the evaluation criteria are changed, the user can easily identify the operational data D10 that will be highly evaluated.

[0148] Furthermore, the display control unit 130 may receive first sequence information from the user. The first sequence information is information indicating the order of the user's evaluation of at least two operation data D10 selected by the evaluation index setting unit 140. The display control unit 130, having acquired the first sequence information, may determine whether the order indicated by the first sequence information matches the order of the evaluation values ​​of at least two operation data D10 determined by the evaluation index setting unit 140. The display control unit 130 may then display the determination result on the display unit 17.

[0149] In this way, the user can easily determine whether the evaluation index setting unit 140 is able to evaluate the operation data D10 based on evaluation criteria similar to those of the user.

[0150] (3) The evaluation index setting unit 140 may evaluate each of the multiple operation data D10 obtained during the optimization process on a scale of 1 to 10, with rank 1 being the lowest evaluation and rank 10 being the highest evaluation. The evaluation index setting unit 140 may generate an evaluation histogram showing the classification according to the evaluation value (evaluation rank) of each of the multiple operation data D10. The display control unit 130 may display the evaluation histogram generated by the evaluation index setting unit 140 on the display unit 17.

[0151] According to this modified version, the evaluation histogram is displayed on the display unit 17, making it easy for the user to confirm the effects of changing the settings related to the evaluation indicators. For example, before changing the settings of the evaluation indicators, the evaluation histogram may show a distribution skewed towards the lowest evaluation, but after changing the settings, the evaluation histogram may change to a distribution skewed towards the highest evaluation. In this case, the user can easily understand that by changing the settings of the evaluation indicators, higher-rated operation data is now being generated.

[0152] (4) In Embodiment 1, an example was described in which the operation data shown in Figure 4 is generated by actually operating the production apparatus 20, but the operation data may be synthesized. For example, synthesized data generated by virtually operating the production apparatus 20 using a predetermined simulator may be used as the operation data.

[0153] (5) In the display example 2 of Embodiment 1, an example was described in which the first boundary line change figure F31 and the second boundary line change figure F32 are transformed from straight lines to curves, but this is just one example. The display control unit 130 may detect when a user inputs an operation to change the inclination of the first boundary line change figure F31. In this case, the display control unit 130 may display the first boundary line change figure F31 on the updated screen 203 (Figure 9) which has been transformed into a shape that slopes downward or upward as it moves to the right. In this case, the evaluation index setting unit 140 may also reflect the position and shape of the first boundary line change figure F31 having an inclined shape in the setting of the position and shape of the first boundary line B1.

[0154] Furthermore, the display control unit 130 may detect, for example, that the user has entered an operation to change the intercept of the first boundary line change figure F31. In other words, it may detect that the user has entered an operation to change the position of the first boundary line change figure F31.

[0155] (6) The first dashed line L3 and the second dashed line L4 may be moved in conjunction. For example, if an operation to move the first dashed line L3 upward by a predetermined distance is detected, the display control unit 130 may move the second dashed line L4 downward by a distance corresponding to the predetermined distance.

[0156] (7) Although detailed illustrations are omitted, a third tolerance range change figure for accepting operations to change the tolerance range (size of the settling range) in the third period, which is the period after the second period, may be displayed on the third setting change screen 204.

[0157] (8) In Embodiment 1, an example was described in which the control parameter generation device 10 (for example, a personal computer) includes the information processing unit 50, but the production device 20 may also include the information processing unit 50. In other words, the processing of the optimization processing unit 60 and the evaluation index changing unit 100 described above may be performed by the information processing unit 50 implemented in the production device 20. In addition, the information processing unit 50 may be implemented in various devices. For example, the information processing unit 50 may be implemented in a portable information terminal carried by a user. In this case, the portable information terminal only needs to have application software installed that is capable of realizing the functions of the information processing unit 50 described in Embodiment 1. Examples of portable information terminals include tablet computers and smartphones.

[0158] (9) The evaluation indicators may include evaluation indicators related to processing quality, evaluation indicators related to productivity, or both. For example, evaluation indicators related to processing quality may include processing accuracy, shape error, defect rate, dimensional error, etc. For example, evaluation indicators related to productivity may include settling time, processing time, cycle time, etc. In this case, the modified figure may be superimposed on the operation data or processing result data displayed on the display unit, and the settings of the evaluation indicators related to processing quality or productivity may be changed based on the user's change of position, shape, or color of the modified figure. For example, the target values ​​of settling time, processing time, or cycle time may be changed based on the amount of movement of the modified figure placed on the time axis, and the tolerance range for processing accuracy, shape error, or dimensional error may be changed based on the change of shape or position of the modified figure that defines the error band. In addition, the weighting of the evaluation indicators related to processing quality and the evaluation indicators related to productivity may be changed based on the change of color or shade of the modified figure.

[0159] (Combinable Configurations) The following describes a group of combinable configurations, including the configurations disclosed herein.

[0160] (First Configuration) The first aspect of the first configuration is an information processing method for supporting the management of an optimization process, wherein an information processing unit acquires schedule information indicating the schedule of the optimization process, generates a figure corresponding to each of at least one evaluation indicators applied to the optimization process, generates a schedule image by arranging the figures along a first direction which is the direction in which the time axis extends based on the schedule information, and outputs the schedule image.

[0161] In a second embodiment of the first configuration, the at least one evaluation index includes a first evaluation index and a second evaluation index different from the first evaluation index, and the information processing unit generates a first figure corresponding to the first evaluation index and a second figure corresponding to the second evaluation index in the generation of the figures, and when the time during which the first evaluation index is applied to the optimization process overlaps with the time during which the second evaluation index is applied, the arrangement of the figures arranges the first figure and the second figure side by side in a second direction intersecting the first direction.

[0162] In a third embodiment of the first configuration, the information processing unit further sets weights for the at least one evaluation index and determines the shape of the figure according to the weights.

[0163] In a fourth embodiment of the first configuration, the information processing unit further sets weights for the at least one evaluation index, changes the weights over time, and outputs a graph showing the changes in the weights.

[0164] In a fifth aspect of the first configuration, the information processing unit further outputs a preset showing candidate weight change functions that define the change in weights over time, and determines the weight change function by accepting an operation to select the preset.

[0165] In a sixth embodiment of the first configuration, the information processing unit further acquires essential condition information indicating essential conditions, sets the essential conditions in the optimization process based on the essential condition information, and the essential conditions include achieving the target of at least one evaluation indicator.

[0166] In the seventh embodiment of the first configuration, the information processing unit further determines whether the essential conditions have been met, and if the essential conditions have not been met, it lowers the evaluation value of the optimization process.

[0167] In the eighth embodiment of the first configuration, the at least one evaluation index includes a preceding evaluation index and a succeeding evaluation index applied to the optimization process after the preceding evaluation index, wherein the information processing unit further determines whether the target of the preceding evaluation index has been achieved in the optimization process to which the preceding evaluation index is applied, and if the target of the preceding evaluation index has been achieved, sets the achievement of the target of the preceding evaluation index as an essential condition for the succeeding optimization process to which the succeeding evaluation index is applied.

[0168] In the ninth embodiment of the first configuration, the at least one evaluation index includes a prior evaluation index and a subsequent evaluation index applied to the optimization process after the prior evaluation index, wherein the information processing unit further determines whether the target of the prior evaluation index has been achieved, and if the target of the prior evaluation index has been achieved, sets the achievement of the target of the prior evaluation index as an essential condition for the subsequent optimization process to which the subsequent evaluation index is applied, and sets the achievement of the target of the subsequent evaluation index as an additional condition for the subsequent optimization process.

[0169] In a tenth embodiment of the first configuration, the at least one evaluation index includes a prior evaluation index and a subsequent evaluation index applied to the optimization process after the prior evaluation index, wherein the information processing unit further sets the condition that the subsequent evaluation index satisfies the limit value if, as a result of the optimization process to which the prior evaluation index is applied, the prior evaluation index does not improve, is output, and the subsequent evaluation index satisfies the limit value.

[0170] In the eleventh embodiment of the first configuration, the information processing unit further assigns thumbnail information to the figure that represents the evaluation index to which the figure corresponds.

[0171] In a twelfth embodiment of the first configuration, the thumbnail information includes information indicating a target set for the at least one evaluation indicator.

[0172] In the thirteenth embodiment of the first configuration, the information processing unit further makes the display mode of the figure corresponding to at least one evaluation index currently applied to the optimization process different from the display mode of the figure corresponding to at least one evaluation index not currently applied to the optimization process.

[0173] In the fourteenth embodiment of the first configuration, the information processing unit outputs a schedule image in which a detail button is superimposed on the figure, and further outputs the details of the evaluation indicator corresponding to the figure, or the settings of the evaluation indicator corresponding to the figure, depending on whether the detail button is selected.

[0174] In the 15th embodiment of the first configuration, the information processing unit further receives an operation to select the figure and outputs details of the evaluation indicators corresponding to the selected figure, or the settings of the evaluation indicators corresponding to the selected figure.

[0175] In the sixteenth embodiment of the first configuration, the information processing unit further determines the size of the figure in the first direction according to the length of time for which the at least one evaluation index is applied to the optimization process.

[0176] In the seventeenth embodiment of the first configuration, the information processing unit further receives a resize instruction to change the size of the figure in the first direction, generates a resized figure by changing the size of the figure based on the resize instruction, and changes the length of time for which the evaluation index corresponding to the resized figure is applied to the optimization process according to the size of the resized figure in the first direction.

[0177] In the eighteenth embodiment of the first configuration, the information processing unit further receives branching condition information indicating branching conditions, and selects from a plurality of options the at least one evaluation index to be applied to the optimization process based on the branching conditions.

[0178] In the 19th embodiment of the first configuration, the plurality of options include a first option and a second option different from the first option, and the information processing unit further arranges a first option figure corresponding to the first option and a second option figure corresponding to the second option along a second direction intersecting the first direction in the schedule image, and displays a branching button on the schedule image to indicate that the schedule is branching.

[0179] In the 20th embodiment of the first configuration, the optimization process is a multi-objective optimization process in which a plurality of evaluation indicators are set, and the information processing unit further acquires the processing result of the multi-objective optimization process, calculates a Pareto solution based on the processing result, plots the processing result including the Pareto solution in a processing result display space, and outputs the processing result display space.

[0180] In a 21st embodiment of the first configuration, the information processing unit further receives a selection operation to select the processing result plotted in the processing result display space, and outputs details of the processing result selected in the selection operation.

[0181] In the 22nd embodiment of the first configuration, the information processing unit further determines a plotting target area when plotting a new processing result in the processing result display space, based on the processing result selected in the selection operation.

[0182] In the 23rd embodiment of the first configuration, the information processing unit further receives an arrangement change operation to change the arrangement of the figures in the first direction, and changes the order in which the optimization process is performed in accordance with the arrangement change operation.

[0183] In the 24th embodiment of the first configuration, the information processing unit further acquires sequence information indicating the order in which the at least one evaluation index is applied to the optimization process, and generates schedule information based on the sequence information in which each of the targets of the at least one evaluation index is satisfied in the order indicated by the sequence information.

[0184] In the 25th embodiment of the first configuration, the information processing unit further displays at least one of the following during the execution of the optimization process: a progress line indicating the progress of the optimization process, the estimated remaining operating time of the optimization process, and the number of operations, which is the number of times the production apparatus has been operated using the control parameters generated by the optimization process.

[0185] A 26th aspect of the first configuration is an information processing device for supporting the management of an optimization process, comprising a circuit configuration, the circuit configuration acquiring schedule information indicating the schedule of the optimization process, generating a figure corresponding to each of at least one evaluation index applied to the optimization process, generating a schedule image by arranging the figures along a first direction which is the direction in which the time axis extends based on the schedule information, and outputting the schedule image.

[0186] The 27th aspect of the first configuration is a program for causing an information processing device to execute a process for supporting the management of an optimization process, wherein the process acquires schedule information indicating the schedule of the optimization process, generates a figure corresponding to each of at least one evaluation index applied to the optimization process, generates a schedule image by arranging the figures along a first direction which is the direction in which the time axis extends based on the schedule information, and outputs the schedule image.

[0187] (Second Configuration) The first aspect of the second configuration is an information processing method for supporting the management of an optimization process, wherein an information processing unit acquires evaluation indicator information including a target for at least one evaluation indicator applied to the optimization process, acquires predicted data that satisfies the target for the at least one evaluation indicator based on the evaluation indicator information, and displays the predicted data.

[0188] In the second embodiment of the second configuration, the information processing unit, in acquiring the prediction data, acquires the original data from the original data storage unit, generates composite data by transforming the original data, and acquires the composite data as the prediction data.

[0189] In the third embodiment of the second configuration, the source data is waveform data showing the progression of a measurement signal over time, and in generating the composite data, the information processing unit performs at least one of the following: stretching the waveform data in the time axis direction, stretching the waveform data in the amplitude direction, and adding random noise.

[0190] In the fourth embodiment of the second configuration, the information processing unit acquires evaluation indicator information that indicates the target of each of the multiple evaluation indicators when acquiring the evaluation indicator information, and acquires prediction data that satisfies the target of each of the multiple evaluation indicators when acquiring the prediction data.

[0191] In a fifth embodiment of the second configuration, the information processing unit acquires evaluation indicator information that shows the respective targets of the multiple evaluation indicators when acquiring the evaluation indicator information, acquires prediction data that satisfies the respective targets of the multiple evaluation indicators and prediction data that satisfies the target of at least one of the multiple evaluation indicators when acquiring the prediction data, and displays the prediction data that satisfies the respective targets of the multiple evaluation indicators and prediction data that satisfies the target of at least one of the multiple evaluation indicators when displaying the prediction data.

[0192] In the sixth embodiment of the second configuration, the prediction data includes a position control waveform showing the change in the position of the object to be driven over time, or a torque control waveform showing the change in torque value over time.

[0193] In the seventh embodiment of the second configuration, the optimization process is a process for optimizing the control parameters in a device that performs an operation based on the control parameters.

[0194] An eighth aspect of the second configuration is an information processing device for supporting the management of an optimization process, comprising a circuit configuration, the circuit configuration acquiring evaluation index information including a target for at least one evaluation index applied to the optimization process, acquiring predicted data that satisfies the target for the at least one evaluation index based on the evaluation index information, and displaying the predicted data.

[0195] A ninth aspect of the second configuration is a program for causing an information processing device to perform a process to support the management of an optimization process, wherein the process acquires evaluation indicator information including a target for at least one evaluation indicator applied to the optimization process, acquires predicted data that satisfies the target for the at least one evaluation indicator based on the evaluation indicator information, and displays the predicted data.

[0196] (Third Configuration) The first aspect of the third configuration is an information processing method for supporting the management of an optimization process, wherein an information processing unit outputs question information indicating a question to the user regarding the optimization process, obtains answer information indicating an answer to the question information, and generates evaluation index information indicating at least one evaluation index to be applied to the optimization process based on the answer information.

[0197] In a second embodiment of the third configuration, the at least one evaluation index is a plurality of evaluation indexes, and the generation of the evaluation index information by the information processing unit includes determining the order in which the plurality of evaluation indexes are applied to the optimization process.

[0198] In a third embodiment of the third configuration, the output of the question information by the information processing unit includes displaying a plurality of waveform data showing the operating waveform of the device, and outputting question information that prompts the user to select a waveform data from the plurality of waveform data to be used as the target operating waveform for waveform improvement by the optimization process.

[0199] In a fourth embodiment of the third configuration, the output of the question information by the information processing unit includes displaying a plurality of waveform data showing the operating waveform of the device and outputting question information that prompts the user to select a waveform data from the plurality of waveform data to be accepted as an operating waveform during the waveform improvement process by the optimization process.

[0200] In a fifth embodiment of the third configuration, the output of the question information by the information processing unit includes displaying a plurality of waveform data showing the operating waveform of the device, and outputting question information that asks about the difficulty of waveform improvement by the optimization process for each of the plurality of waveform data.

[0201] In a sixth embodiment of the third configuration, the output of the question information by the information processing unit includes outputting question information that asks for the ranking of the difficulty levels of waveform improvement by the optimization process for each of the plurality of waveform data.

[0202] In the seventh embodiment of the third configuration, the information processing unit further acquires sequence information indicating the order in which the at least one evaluation index is applied to the optimization process, and generates schedule information based on the sequence information in which each of the targets of the at least one evaluation index is satisfied in the order indicated by the sequence information.

[0203] The eighth aspect of the third configuration is an information processing device for supporting the management of an optimization process, comprising a circuit configuration, the circuit configuration outputting question information indicating a question to the user regarding the optimization process, acquiring answer information indicating an answer to the question information, and generating evaluation index information indicating at least one evaluation index applied to the optimization process based on the answer information.

[0204] A ninth aspect of the third configuration is a program for causing an information processing device to execute a process for supporting the management of an optimization process, wherein the process outputs question information indicating a question to the user regarding the optimization process, obtains answer information indicating an answer to the question information, and generates evaluation index information indicating at least one evaluation index to be applied to the optimization process based on the answer information.

[0205] (Fourth Configuration) The first aspect of the fourth configuration is an information processing method for supporting the management of an optimization process, wherein an information processing unit displays superimposed data in which a modified figure is superimposed on at least one operation data indicating the operation of a device, acquires operation information indicating an operation to change the display manner of the modified figure, and changes the settings relating to the evaluation index applied to the optimization process based on the modified figure whose display manner has been changed.

[0206] In the second embodiment of the fourth configuration, the information processing unit acquiring the operation information includes acquiring deformation operation information indicating an operation to change the shape of the modified figure, acquiring movement operation information indicating an operation to move the position of the modified figure, and color change operation information indicating a change in the color of the modified figure, and the information processing unit changing the settings related to the evaluation index includes changing the settings related to the evaluation index based on at least one of the shape of the modified figure after deformation, the position of the modified figure after movement, and the color of the modified figure after color change.

[0207] In a third embodiment of the fourth configuration, the operation of the device is an operation in which the device moves an object to a target position, the evaluation index includes the sum of the accumulated values ​​of deviation time or position deviation amount for which the position deviation between the position of the object and the target position deviates from a reference range, the modified figure includes a reference range modified figure having a linear or curved shape, the information processing unit acquiring the deformation operation information includes acquiring deformation operation information indicating an operation to change the shape of the reference range modified figure, and the information processing unit changing the setting of the evaluation index includes changing the reference range based on the shape of the modified reference range modified figure.

[0208] In the fourth embodiment of the fourth configuration, the reference range changing figure has a linear shape, the information processing unit acquiring the operation information includes acquiring operation information indicating an operation to change at least one of the slope and intercept of the reference range changing figure, and the information processing unit changing the setting relating to the evaluation index includes changing the reference range based on at least one of the slope and intercept of the reference range changing figure.

[0209] In the fifth embodiment of the fourth configuration, the reference range changing figure includes an upper limit figure corresponding to the upper limit of the reference range and a lower limit figure corresponding to the lower limit of the reference range.

[0210] In a sixth embodiment of the fourth configuration, the operation of the device is an operation in which the device moves an object to a target position, the evaluation index includes the length of the settling time until the positional deviation between the position of the object and the target position converges to an acceptable range, the modified figure includes a settling time modified figure, the acquisition of the movement operation information by the information processing unit includes acquiring movement operation information indicating an operation to move the position of the settling time modified figure, and the setting of the evaluation index by the information processing unit includes changing the target of the settling time based on the amount of movement of the settling time modified figure.

[0211] In a seventh embodiment of the fourth configuration, the operation of the device is an operation in which the device moves an object to a target position, the evaluation index includes the length of the settling time until the positional deviation between the position of the object and the target position converges to an acceptable range, the acceptable range includes a first acceptable range which is the acceptable range for a first period before the target settling time, and a second acceptable range which is the acceptable range for a second period after the target settling time, the evaluation index includes the sum of the accumulated values ​​of the deviation time or positional deviation amount for which the positional deviation between the position of the object and the target position deviates from the second acceptable range, the modified figure includes a second acceptable range modified figure, the acquisition of the movement operation information by the information processing unit includes acquiring movement operation information indicating an operation to move the position of the second acceptable range modified figure, and the setting of the evaluation index by the information processing unit includes changing the second acceptable range based on the amount of movement of the second acceptable range modified figure.

[0212] In the eighth aspect of the fourth configuration, the operation of the device is an operation in which the device moves an object to a target position, the at least one operation data includes a target position figure indicating the target position, the modified figure includes a gradient figure with different coloring patterns depending on the deviation from the target position figure, the coloring patterns correspond to the magnitude of the weight when calculating the evaluation value of the at least one operation data, the information processing unit acquiring the deformation operation information includes acquiring deformation operation information indicating an operation to change the shape of the gradient figure, and the information processing unit changing the setting of the evaluation index includes changing the magnitude of the weight based on the shape of the gradient figure after deformation.

[0213] In the ninth aspect of the fourth configuration, the at least one operation data is a plurality of operation data, and the information processing unit changing the settings relating to the evaluation index includes changing the evaluation criteria for the plurality of operation data, and the information processing unit further calculates the evaluation value for each of the plurality of operation data based on the changed evaluation criteria, and displays the operation data with the highest evaluation value among the plurality of operation data, or the top plurality of operation data with high evaluation values.

[0214] In the tenth embodiment of the fourth configuration, the information processing unit further superimposes the modified figure onto the operation data with the highest evaluation value, or onto the top multiple operation data with high evaluation values.

[0215] In the eleventh aspect of the fourth configuration, the information processing unit further generates an evaluation histogram showing the classification of the plurality of operation data according to the evaluation value of each of the plurality of operation data, and displays the evaluation histogram.

[0216] In the twelfth embodiment of the fourth configuration, the at least one operation data is a plurality of operation data, and the information processing unit changing the settings relating to the evaluation index includes changing the evaluation criteria for the plurality of operation data, and the information processing unit further calculates the evaluation value for each of the plurality of operation data based on the changed evaluation criteria, selects at least two of the plurality of operation data, and displays a display screen including the at least two operation data sorted in descending order of evaluation value.

[0217] In the thirteenth embodiment of the fourth configuration, the information processing unit further acquires first sequence information indicating the order of user evaluations for the at least two operation data selected by the information processing unit, determines whether the order indicated by the first sequence information matches the order of evaluations for the at least two operation data determined by the information processing unit, and outputs the determination result.

[0218] The 14th aspect of the fourth configuration is an information processing device for supporting the management of an optimization process, comprising a circuit configuration, the circuit configuration displays superimposed data in which a modified figure is superimposed on operation data indicating the operation of the device, acquires operation information indicating an operation to change the display manner of the modified figure, and changes the settings relating to evaluation indicators applied to the optimization process based on the modified figure whose display manner has been changed.

[0219] The 15th aspect of the fourth configuration is a program for causing an information processing device to execute a process for supporting the management of an optimization process, wherein the process displays superimposed data in which a modified figure is superimposed on operation data indicating the operation of the device, obtains operation information indicating an operation to change the display manner of the modified figure, and changes the settings relating to the evaluation index applied to the optimization process based on the modified figure whose display manner has been changed.

[0220] (Fifth Configuration) The first aspect of the fifth configuration is an information processing method for optimizing control parameters set in a drive system that performs an operation based on a control signal input from a control device, wherein the information processing device acquires setting information that sets an operation to be evaluated among the operations performed by the drive system, which is to be evaluated in the optimization process for optimizing the control parameters, and generates an optimization program based on the setting information that causes the control device to perform the control process in the optimization process.

[0221] In the second embodiment of the fifth configuration, the setting of the operation to be evaluated includes setting the start and end times of the evaluation period among the operations performed by the drive system.

[0222] In a third embodiment of the fifth configuration, the operation performed by the drive system includes a movement operation that moves the object to be moved from an initial position to a target position, and a return operation that returns the object to be moved from the target position to the initial position, wherein the start time of the evaluation period is set before the start time of the movement operation, and the end time of the evaluation period is set after the completion time of the movement operation and before the start time of the return operation.

[0223] In the fourth embodiment of the fifth configuration, the setting of the operation to be evaluated includes setting a representative operation that the drive system will perform in the optimization process, among the operations that the drive system can perform.

[0224] In a fifth embodiment of the fifth configuration, the control process performed by the control device based on the optimization program includes outputting the control signal for causing the drive system to perform an operation multiple times until the optimization process is completed.

[0225] In the sixth embodiment of the fifth configuration, the control process executed by the control device based on the optimization program includes a process for acquiring operating condition information indicating the execution conditions for an operation to be performed by the drive system.

[0226] In the seventh embodiment of the fifth configuration, the operations that the drive system can perform include representative operations performed by the drive system in the optimization process and non-representative operations that the drive system does not perform in the optimization process, and the operation condition information includes operation specification information indicating whether the target operation to be performed by the drive system is the representative operation or the non-representative operation.

[0227] In the eighth embodiment of the fifth configuration, the control processing performed by the control device based on the optimization program includes: a process of outputting the control signal relating to the target operation when the target operation is the representative operation; and a process of skipping the output of the control signal relating to the target operation when the target operation is the non-representative operation.

[0228] In the ninth embodiment of the fifth configuration, the operation performed by the drive system includes a movement operation that moves the object to be moved from an initial position to a target position, and a return operation that returns the object to be moved from the target position to the initial position, and the control processing performed by the control device based on the optimization program includes, when the object operation is the representative operation, a process that causes the drive system to wait for a predetermined period of time after the completion of the previous return operation and before the start of the movement operation related to the object operation.

[0229] In the tenth embodiment of the fifth configuration, the control process executed by the control device based on the optimization program includes a process of registering a plurality of operations that can be set by the control signal with the drive system.

[0230] In the eleventh embodiment of the fifth configuration, the control process performed by the control device based on the optimization program includes: a process of sequentially outputting a plurality of control signals relating to a plurality of operations performed by the drive system; a process of acquiring timing specification information that specifies the output timing of each of the plurality of control signals; and a process of adjusting the output timing of each of the plurality of control signals based on the timing specification information.

[0231] A twelfth aspect of the fifth configuration is an information processing device for optimizing control parameters set in a drive system that performs an operation based on a control signal input from a control device, comprising a circuit configuration, the circuit configuration acquiring setting information for setting an operation to be evaluated in an optimization process for optimizing the control parameters among the operations performed by the drive system, and generating an optimization program for causing the control device to perform a control process in the optimization process based on the setting information.

[0232] The thirteenth aspect of the fifth configuration is a program for causing an information processing device to execute a process for optimizing control parameters set in a drive system that performs an operation based on a control signal input from a control device, wherein the process acquires setting information that sets an operation to be evaluated in an optimization process for optimizing the control parameters among the operations performed by the drive system, and generates an optimization program for causing the control device to execute a control process in the optimization process based on the setting information.

[0233] The 14th aspect of the fifth configuration is a drive control device for controlling a drive device that drives a moving object, wherein the control device obtains the control parameters from an information processing device that performs an update process for the control parameters in an optimization process for optimizing the control parameters set in the drive control device, obtains a control signal from a control device that performs a control process based on an optimization program generated by the information processing method described in claim 1, and controls the drive device based on the control parameters and the control signal.

[0234] The 15th aspect of the fifth configuration comprises a control device that performs control processing based on an optimization program generated by the information processing method described in the first aspect of the fifth configuration, and a drive control device described in the 14th aspect of the fifth configuration.

[0235] This disclosure is useful in the field of technology that supports the management of optimization processes.

Claims

1. An information processing method for supporting the management of an optimization process, wherein an information processing unit displays superimposed data in which a modified figure is superimposed on at least one operation data indicating the operation of a device, acquires operation information indicating an operation to change the display manner of the modified figure, and changes the settings relating to evaluation indicators applied to the optimization process based on the modified figure whose display manner has been changed.

2. The information processing method according to claim 1, wherein the information processing unit's acquisition of the operation information includes acquiring deformation operation information indicating an operation to change the shape of the modified figure, acquiring movement operation information indicating an operation to move the position of the modified figure, and color change operation information indicating a change in the color of the modified figure, and the information processing unit's change of settings for the evaluation index includes changing the settings for the evaluation index based on at least one of the shape of the modified figure after deformation, the position of the modified figure after movement, and the color of the modified figure after color change.

3. The operation of the device is an operation in which the device moves an object to a target position; the evaluation index includes the sum of the accumulated values ​​of the deviation time or amount of position deviation in which the position deviation between the position of the object and the target position deviates from a reference range; the modified figure includes a reference range modified figure having a linear or curved shape; the acquisition of the deformation operation information by the information processing unit includes acquiring deformation operation information indicating an operation to change the shape of the reference range modified figure; and the setting of the evaluation index by the information processing unit includes changing the reference range based on the shape of the modified reference range modified figure.

4. The information processing method according to claim 3, wherein the reference range changing figure has a linear shape, the information processing unit acquiring the operation information includes acquiring operation information indicating an operation to change at least one of the slope and intercept of the reference range changing figure, and the information processing unit changing the setting relating to the evaluation index includes changing the reference range based on at least one of the slope and intercept of the reference range changing figure.

5. The information processing method according to claim 4, wherein the reference range changing figure includes an upper limit figure corresponding to the upper limit of the reference range and a lower limit figure corresponding to the lower limit of the reference range.

6. The information processing method according to claim 2, wherein the operation of the device is an operation in which the device moves an object to a target position, the evaluation index includes the length of the settling time until the positional deviation between the position of the object and the target position converges to an acceptable range, the modified figure includes a settling time modified figure, the acquisition of the movement operation information by the information processing unit includes acquiring movement operation information indicating an operation to move the position of the settling time modified figure, and the setting of the evaluation index by the information processing unit includes changing the target of the settling time based on the amount of movement of the settling time modified figure.

7. The operation of the apparatus is an operation in which the apparatus moves an object to a target position; the evaluation index includes the length of the settling time until the positional deviation between the position of the object and the target position converges to an acceptable range; the acceptable range includes a first acceptable range which is the acceptable range for a first period before the target settling time, and a second acceptable range which is the acceptable range for a second period after the target settling time; the evaluation index includes the sum of the accumulated values ​​of the deviation time or positional deviation amount for which the positional deviation between the position of the object and the target position deviates from the second acceptable range; the modified figure includes a second acceptable range modified figure; the information processing unit acquiring the movement operation information includes acquiring movement operation information indicating an operation to move the position of the second acceptable range modified figure; and the information processing unit changing the setting of the evaluation index includes changing the second acceptable range based on the amount of movement of the second acceptable range modified figure.

8. The information processing method according to claim 2, wherein the operation of the device is an operation in which the device moves an object to a target position, the at least one operation data includes a target position figure indicating the target position, the modified figure includes a gradient figure with different coloring patterns depending on the deviation from the target position figure, the coloring patterns correspond to the magnitude of the weight when calculating the evaluation value of the at least one operation data, the information processing unit acquiring the deformation operation information includes acquiring deformation operation information indicating an operation to change the shape of the gradient figure, and the information processing unit changing the setting of the evaluation index includes changing the magnitude of the weight based on the shape of the gradient figure after deformation.

9. The information processing method according to claim 1, wherein the at least one operation data is a plurality of operation data, the information processing unit changing the setting of the evaluation index includes changing the evaluation criteria for the plurality of operation data, the information processing unit further calculates an evaluation value for each of the plurality of operation data based on the changed evaluation criteria, and displays the operation data with the highest evaluation value among the plurality of operation data, or the top plurality of operation data with high evaluation values.

10. The information processing method according to claim 9, wherein the information processing unit further superimposes the modified figure onto the operation data with the highest evaluation value, or onto the top multiple operation data with high evaluation values.

11. The information processing unit further generates an evaluation histogram showing the classification of the plurality of operation data according to the evaluation value of each of the plurality of operation data, and displays the evaluation histogram, the information processing method according to claim 9 or 10.

12. The information processing method according to claim 1, wherein the at least one operation data is a plurality of operation data, the information processing unit changing the setting of the evaluation index includes changing the evaluation criteria for the plurality of operation data, the information processing unit further calculates an evaluation value for each of the plurality of operation data based on the changed evaluation criteria, selects at least two operation data from the plurality of operation data, and displays a display screen including the at least two operation data sorted in descending order of evaluation value.

13. The information processing method according to claim 12, further comprising: the information processing unit obtaining first sequence information indicating the order of the user's evaluation of the at least two operation data selected by the information processing unit; determining whether the order indicated by the first sequence information matches the order of the evaluation values ​​of the at least two operation data determined by the information processing unit; and outputting the determination result.

14. An information processing device for supporting the management of an optimization process, comprising a circuit configuration, wherein the circuit configuration displays superimposed data in which a modified figure is superimposed on operation data indicating the operation of the device, acquires operation information indicating an operation to change the display manner of the modified figure, and changes the settings relating to evaluation indicators applied to the optimization process based on the modified figure whose display manner has been changed.

15. A program for causing an information processing device to execute a process for supporting the management of an optimization process, wherein the process includes: displaying superimposed data in which a modified figure is superimposed on operation data indicating the operation of the device; acquiring operation information indicating an operation to change the display manner of the modified figure; and changing the settings relating to evaluation indicators applied to the optimization process based on the modified figure whose display manner has been changed.

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