Information processing method, information processing device, and program

Abstract

In this information processing method for supporting management of optimization processing, an information processing unit: acquires schedule information indicating a schedule of the optimization processing; generates a figure corresponding to each of at least one evaluation index applied to the optimization processing; generates a schedule image by arranging the figures along a first direction, which is the direction in which the time axis extends, on the basis of the schedule information; and outputs the schedule image.

Description

Information processing method, information processing apparatus, and program

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

[0002] Patent Document 1 discloses a technology for executing optimization processing multiple times while changing evaluation criteria for the optimization processing.

[0003] However, the technology described in Patent Document 1 has a problem in that the content of evaluation indices applied to optimization processing is difficult to understand.

[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 clarify the content of evaluation indices applied to optimization processing.

[0006] An information processing method according to one aspect of the present disclosure is an information processing method for supporting management of optimization processing, wherein an information processing unit acquires schedule information indicating a schedule for the optimization processing, generates a graphic corresponding to each of at least one evaluation index applied to the optimization processing, generates a schedule image by arranging the graphics along a first direction that is a direction in which a time axis extends based on the schedule information, and outputs the schedule image.

[0007] According to this configuration, the content of evaluation indices applied to optimization processing can be clarified.

[0008] Figure 1 is a schematic diagram showing an overview of the control parameter generation system according to Embodiment 1. Figure 2 is a block diagram showing the configuration of the control parameter generation system. Figure 3 is a perspective view showing an example of a production apparatus. Figure 4 is a schematic diagram showing an example of the transition of the position deviation of a driven object relative to a target position. Figure 5 is a schematic diagram showing an example of the transition of the position deviation of a driven object relative to a target position. Figure 6 is a schematic diagram showing an example of the transition of the position deviation of a driven object relative to a target position. Figure 7 is a diagram showing an example of the configuration of the schedule control unit. Figure 8A is a diagram showing the first execution status display screen. Figure 8B is a diagram showing the details of the first execution status display screen. Figure 9 is a diagram showing the second execution status display screen. Figure 10 is a diagram showing an example of thumbnail information assigned to the first figure. Figure 11 is a diagram showing an example of thumbnail information assigned to the second figure. Figure 12 is a diagram showing an example of the weight change function selection screen. Figure 13 is a flowchart showing the flow of processing executed by the information processing unit according to Embodiment 1. Figure 14 is a flowchart showing the details of the processing in step S2 of Figure 13. Figure 15 is a diagram showing an example of the configuration of the control parameter generation system according to Embodiment 2. Figure 16 is a diagram showing an example of the configuration of the schedule control unit according to Embodiment 2. Figure 17 is a diagram showing an example of the initial setup input screen according to Embodiment 2. Figure 18 is a diagram showing the third execution status display screen. Figure 19 is a flowchart showing the processing flow executed by the information processing unit according to Embodiment 2. Figure 20 is a diagram showing the fourth execution status display screen. Figure 21 is a flowchart showing the processing flow executed by the information processing unit according to Embodiment 3. Figure 22 is a diagram showing an example of the initial setup input screen according to Embodiment 4. Figure 23 is a diagram showing an example of the Pareto solution display screen showing the evaluation space. Figure 24 is a diagram showing another example of the Pareto solution display screen. Figure 25 is a flowchart showing the processing flow executed by the information processing unit according to Embodiment 4. Figure 26 is a diagram showing an example of the parameter distribution diagram.

[0009] (Principles of this Disclosure) Generally, the number of control parameters for a drive source used in a production device can exceed 50. Furthermore, the number of adjustment levels can exceed 100. For example, if a production device performs 80 operations, the number of control parameters for the drive source is 50, and the number of adjustment levels for the control parameters is 100.50 × 80, the number of combinations is 100^50 × 80. The inventors have found that when generating control parameters for such a vast number of combinations, using a method that searches for appropriate control parameters using a machine learning model or the like 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 reach appropriate control parameters no matter how much time is spent. Therefore, the inventors have 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.

[0010] 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.

[0011] 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.

[0012] Furthermore, a technique has been disclosed for generating appropriate control parameters for a drive source (e.g., a servo motor) in a production device (e.g., a mounting device) that drives an object to be driven, by performing an optimization process multiple times while switching evaluation indicators (see, for example, Patent Document 1).

[0013] The technology described in Patent Document 1 above has the problem that the content of the evaluation metrics applied to the optimization process is difficult to understand. Specifically, in the above technology, multiple evaluation metrics are applied to the optimization process, but the content of these evaluation metrics is not presented to the user, making it difficult for the user to understand the content of the evaluation metrics applied to the optimization process.

[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 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.

[0016] This configuration outputs a schedule image containing a figure corresponding to each of the at least one evaluation metrics applied to the optimization process. This allows the user to clearly understand the content of the evaluation metrics applied to the optimization process.

[0017] Furthermore, with the above configuration, the shapes are arranged along the direction in which the time axis extends, allowing the user to clearly understand the time allocation of the evaluation metrics applied to the optimization process.

[0018] (2) In the information processing method described in (1) above, 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 in which the first evaluation index is applied to the optimization process and the time in which the second evaluation index is applied overlap, the arrangement of the figures arranges the first figure and the second figure side by side in a second direction intersecting the first direction.

[0019] In this configuration, if the time during which the first evaluation metric is applied to the optimization process overlaps with the time during which the second evaluation metric is applied, the first and second shapes are arranged side by side in the second direction. Therefore, it is clearly understood by the user that the time during which the first evaluation metric is applied to the optimization process overlaps with the time during which the second evaluation metric is applied.

[0020] (3) In the information processing method described in (1) or (2) above, the information processing unit may further set weights for the at least one evaluation index and determine the shape of the figure according to the weights.

[0021] With this configuration, the shape of the figure changes according to the weight set for each evaluation metric, allowing users to clearly understand the weight of each evaluation metric.

[0022] (4) In the information processing method described in any one of (1) to (3) above, the information processing unit may further set weights for the at least one evaluation index, change the weights over time, and output a graph showing the change in weights.

[0023] This configuration allows users to clearly understand that the weights set for the evaluation metrics change over time.

[0024] (5) In the information processing method described in (4) above, the information processing unit may further output a preset showing candidates for a weight change function that defines the change in the weights over time, and determine the weight change function by accepting an operation to select the preset.

[0025] In this configuration, the user is presented with a set of preset weight change functions, and the weights of the evaluation metrics change according to the weight change function corresponding to the selected preset. In this way, the user can easily set the manner in which the weights change over time.

[0026] (6) In the information processing method described in any one of (1) to (5) above, the information processing unit may further acquire essential condition information indicating essential conditions, set the essential conditions in the optimization process based on the essential condition information, and the essential conditions may include achieving the target of at least one evaluation indicator.

[0027] In this configuration, achieving the target value of at least one evaluation metric is set as a mandatory condition for the optimization process. This makes it possible to execute an optimization process that satisfies this mandatory condition.

[0028] (7) In the information processing method described in (6) above, the information processing unit may further determine whether the essential conditions have been met, and if the essential conditions have not been met, it may lower the evaluation value of the optimization process.

[0029] With this configuration, if the essential conditions are not met, the evaluation value of the optimization process will decrease, making it possible to more reliably execute an optimization process that satisfies the essential conditions.

[0030] (8) In the information processing method described in (6) or (7) above, 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, and the information processing unit may further determine whether the target of the prior evaluation index has been achieved in the optimization process to which the prior evaluation index is applied, and if the target of the prior evaluation index has been achieved, the achievement of the target of the prior evaluation index may be set as an essential condition for the subsequent optimization process to which the subsequent evaluation index is applied.

[0031] With this configuration, even in optimization processes where subsequent evaluation metrics are applied, the targets of the preceding evaluation metrics are achieved, making it possible to execute optimization processes in which the targets of the preceding evaluation metrics are continuously achieved.

[0032] (9) In the information processing method described in any one of (6) to (8) above, the at least one evaluation indicator includes a prior evaluation indicator and a subsequent evaluation indicator applied to the optimization process after the prior evaluation indicator, and the information processing unit may further determine whether the target of the prior evaluation indicator has been achieved, and if the target of the prior evaluation indicator has been achieved, set the achievement of the target of the prior evaluation indicator as an essential condition for the subsequent optimization process to which the subsequent evaluation indicator is applied, and set the achievement of the target of the subsequent evaluation indicator as an additional condition for the subsequent optimization process.

[0033] This configuration makes it possible to perform optimization processing that achieves the targets of both leading and succeeding evaluation metrics.

[0034] (10) In the information processing method described in any one of (6) to (9) above, 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, and the information processing unit may further set a condition for the subsequent optimization process to which the subsequent evaluation index is applied that if a limit value is output as a result of the optimization process to which the prior evaluation index is applied in which the prior evaluation index is not improved, the subsequent evaluation index satisfies the limit value.

[0035] This configuration makes it possible to perform optimization processing that satisfies the limit values ​​of the leading evaluation indicators.

[0036] (11) In the information processing method described in any one of (1) to (10) above, the information processing unit may further provide the figure with thumbnail information representing the evaluation index to which the figure corresponds.

[0037] This configuration allows users to more clearly understand the content of the evaluation metrics applied to the optimization process, as thumbnail information is attached to the shapes.

[0038] (12) In the information processing method according to (11) above, the thumbnail information may include information indicating a target set for the at least one evaluation index.

[0039] According to this configuration, since information indicating the target set for the evaluation index is included in the thumbnail information, a user can clearly understand the target set for the evaluation index.

[0040] (13) In the information processing method according to any one of (1) to (12) above, the information processing section may further cause a display mode of a graphic corresponding to at least one evaluation index currently applied to the optimization processing to be different from a display mode of a graphic corresponding to at least one evaluation index not currently applied to the optimization processing.

[0041] According to this configuration, since the currently applied evaluation index is displayed in an emphasized manner, a user can clearly recognize the currently applied evaluation index.

[0042] (14) In the information processing method according to any one of (1) to (13) above, in outputting the schedule image, the information processing section outputs a schedule image in which a detail button is superimposed on the graphic, and may further output details of the evaluation index corresponding to the graphic or settings of the evaluation index corresponding to the graphic in response to the detail button being selected.

[0043] According to this configuration, details or settings of the evaluation index are presented to the user in response to the detail button being selected. Therefore, the user can easily check the details or settings of the evaluation index.

[0044] (15) In the information processing method according to any one of (1) to (14) above, the information processing section may further receive an operation of selecting the graphic, and output details of the evaluation index corresponding to the selected graphic or settings of the evaluation index corresponding to the selected graphic.

[0045] According to this configuration, details or settings of the evaluation index are presented to the user in response to selection of a graphic. Accordingly, the user can easily check the details or settings of the evaluation index.

[0046] (16) In the information processing method according to any one of (1) to (15) above, the information processing section may further determine the size of the graphic in the first direction according to a length of time for which the at least one evaluation index is applied to the optimization process.

[0047] According to this configuration, the size of the graphic in the first direction corresponds to the length of time for which the evaluation index is applied, and thus the user can intuitively understand the length of time for which the evaluation index is applied.

[0048] (17) In the information processing method according to (16) above, the information processing section further receives a size change instruction to change the size of the graphic in the first direction, generates a size-changed graphic by changing the size of the graphic based on the size change instruction, and may change a length of time for which an evaluation index corresponding to the size-changed graphic is applied to the optimization process according to the size of the size-changed graphic in the first direction.

[0049] According to this configuration, it becomes possible to change the time for which the evaluation index is applied to the optimization process based on the size change instruction. Accordingly, the user can easily change the time for which the evaluation index is applied to the optimization process.

[0050] (18) In the information processing method according to any one of (1) to (17) above, the information processing section further receives branch condition information indicating a branch condition, and may select the at least one evaluation index to be applied to the optimization process from a plurality of options based on the branch condition.

[0051] According to this configuration, an evaluation index to be applied to the optimization process is selected from a plurality of options based on the branch condition, and therefore an appropriate evaluation index can be applied to the optimization process.

[0052] (19) In the information processing method described in any one of (1) to (18) above, the plurality of options include a first option and a second option different from the first option, and the information processing unit may further arrange 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 display a branching button on the schedule image to indicate that the schedule is branching.

[0053] This configuration allows the user to see a schedule image in which the first option shape, the second option shape, and branching buttons are arranged along the second direction. This allows the user to intuitively understand that the evaluation metric corresponding to the first option shape, or the evaluation metric corresponding to the second option shape, is applied to the optimization process according to the branching conditions.

[0054] (20) In the information processing method described in any one of (1) to (19) above, the optimization process is a multi-objective optimization process in which a plurality of evaluation indicators are set, and the information processing unit may further acquire the processing result of the multi-objective optimization process, calculate a Pareto solution based on the processing result, plot the processing result including the Pareto solution in a processing result display space, and output the processing result display space.

[0055] With this configuration, a processing result display space where the Pareto solution is plotted is output, allowing the user to easily check the Pareto solution of the multi-objective optimization process.

[0056] (21) In the information processing method described in (20) above, the information processing unit may further accept a selection operation to select the processing result plotted in the processing result display space, and output details of the processing result selected in the selection operation.

[0057] With this configuration, the details of the selected processing result are presented to the user, allowing the user to easily check the details of the processing result.

[0058] (22) In the information processing method described in (20) or (21) above, the information processing unit may further determine 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.

[0059] With this configuration, the target plotting area is determined based on the selected processing result. Therefore, the user can easily specify where to plot the processing result. Furthermore, this configuration allows for the exploration of control parameters to suit the user's preferences.

[0060] (23) In the information processing method described in any one of (1) to (22) above, the information processing unit may further accept an arrangement change operation to change the arrangement of the figures in the first direction, and change the order in which the optimization processing is performed in accordance with the arrangement change operation.

[0061] With this configuration, the order in which evaluation metrics are applied changes depending on the layout change operation. Therefore, users can easily change the order in which evaluation metrics are applied.

[0062] (24) In the information processing method described in any one of (1) to (23) above, the information processing unit may further acquire sequence information indicating the order in which the at least one evaluation index is applied to the optimization process, and generate 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.

[0063] This configuration generates schedule information in which each of the goals of at least one evaluation metric is satisfied in the order indicated by the sequential information. Therefore, it becomes possible to perform optimization processing in which each of the goals of at least one evaluation metric is satisfied in the order indicated by the sequential information.

[0064] (25) In the information processing method described in any of (1) to (25) above, the information processing unit may further display 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.

[0065] This configuration allows users to clearly understand the progress of the optimization process, the estimated remaining operating time, and the number of times the production equipment will operate.

[0066] (26) An information processing device according to another aspect of the present disclosure 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.

[0067] This configuration provides an information processing device that achieves the same effects as the information processing method described above.

[0068] (27) A program according to yet another aspect of the present disclosure is a program for causing an information processing device for assisting in the management of an optimization process to execute a process, the process comprising: acquiring schedule information indicating the schedule of the optimization process; generating figures corresponding to each of at least one evaluation indicators 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.

[0069] This configuration allows us to provide a program that achieves the same effect as the information processing method described above.

[0070] This disclosure can also be implemented as an information processing system operated by such a program. Furthermore, it goes without saying that such a program can be distributed via a computer-readable non-temporary recording medium such as a CD-ROM, or via a communication network such as the Internet.

[0071] (Embodiments of the Disclosure) Hereinafter, a specific example of a control parameter generation system according to one aspect of the Disclosure will be described with reference to the drawings. The embodiments shown here are all examples of the Disclosure. Therefore, the numerical values, shapes, components, arrangement and connection configurations of components, as well as the steps (processes) and the order of the steps shown in the following embodiments are examples and are not intended to limit the Disclosure. In addition, each figure is a schematic diagram and is not necessarily a strict illustration. In each figure, substantially identical components are denoted by the same reference numerals, and redundant explanations are omitted or simplified.

[0072] (Embodiment 1) The control parameter generation system according to Embodiment 1 will be described. This control parameter generation system is a system that generates control parameters to be used in a production device equipped with a drive source that drives an object to be driven.

[0073] Figure 1 is a schematic diagram showing an overview of the control parameter generation system 1 according to Embodiment 1. Figure 2 is a block diagram showing the configuration of the control parameter generation system 1.

[0074] As shown in Figure 1, the control parameter generation system 1 comprises a control parameter generation device 10, a production device 20, and a sensor 30.

[0075] The production equipment 20 is a device used to produce equipment, and performs tasks such as mounting, processing, manufacturing, and transporting the equipment. The production equipment 20 is installed, for example, on a factory production line. Specifically, the production equipment 20 includes, for example, mounting equipment, processing equipment, manufacturing equipment, transport equipment, etc.

[0076] The production apparatus 20 performs N operations (where N is an integer greater than or equal to 3). For example, N is 80.

[0077] As shown in Figure 2, the production apparatus 20 includes a memory 21, a control circuit 22, a drive source 23, and a drive target object 24.

[0078] 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.

[0079] 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.

[0080] Figure 3 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 3, the production apparatus 20 may, for example, be a mounting apparatus that mounts components onto a substrate 120 placed on a machine base 105.

[0081] 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 105, 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.

[0082] Returning to Figures 1 and 2, let's continue the explanation of the control parameter generation system 1.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] When control parameters are output from the control parameter generation device 10, memory 21 acquires the outputted control parameters and updates the stored control parameters with the acquired parameters. In other words, memory 21 overwrites the stored parameters.

[0087] The sensor 30 measures the position of the driven object 24 in the production apparatus 20, which performs at least one of N operations, over time. 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.

[0088] Figure 4 is a schematic diagram showing an example of time-series data (hereinafter referred to as waveform data) that shows the change in the position 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. In Figure 4, the horizontal axis represents time, and the vertical axis represents the position deviation of the driven object 24 relative to the target position.

[0089] As shown in Figure 4, in this specification, the tolerance range refers to the range in which the positional deviation from the target position is within the required accuracy.

[0090] Furthermore, as shown in Figure 4, 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.

[0091] Furthermore, as shown in Figure 4, 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 position of 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 time 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 position of 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 time to the settling time.

[0092] Returning to Figure 2, the control parameter generation device 10 performs an optimization process to generate control parameters. The control parameter generation device 10 also outputs an execution status display screen showing the execution status of the optimization process to the display unit 17. 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.

[0093] 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.

[0094] The information processing unit 50, which is implemented by a processor executing a program read from a computer-readable recording medium such as ROM, has an optimization processing unit 60 and a schedule control 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 schedule control unit 100.

[0095] The optimization processing unit 60 generates update 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 metric of "settlement time".

[0096] 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 instructs 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 object to be driven 24 to a predetermined target position. The optimization processing unit 60 then acquires measurement data corresponding to each of the one or more operations performed by the production device 20, which is 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 settling time (an example of an evaluation index) based on the measurement data corresponding to that operation.

[0097] The optimization processing unit 60 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.

[0098] As shown in Figure 2, the optimization processing unit 60 has a first optimization algorithm 61 that optimizes control parameters to shorten the settling time. The optimization processing unit 60 then uses the first optimization algorithm 61 to perform an optimization process that shortens at least the longest settling time among one or more settling times. The first 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 first optimization algorithm 61 may be a known process performed using the above known algorithms.

[0099] The optimization processing unit 60 generates update control parameters by repeatedly performing optimization processing until the optimization processing termination conditions are met. However, the update control parameters may be generated by performing the optimization processing once.

[0100] 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.

[0101] 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.

[0102] Up to this point, we have described an example in which the optimization processing unit 60 performs optimization processing to shorten the settling time, but the optimization processing unit 60 may also perform optimization processing to reduce the degree of deviation.

[0103] The optimization process for reducing the degree of deviation will be described. For each of the one or more operations, the optimization processing unit 60 determines, based on the measurement data corresponding to the operation, the degree to which the object is not within the allowable range from the target position when the position of the driven object 24 is brought to the target position for that operation, i.e., the degree of deviation. In other words, the optimization processing unit 60 may perform an optimization process to reduce the deviation time during which the positional deviation between the position of the driven object 24 and the target position deviates from the allowable range, or the cumulative value of the positional deviation.

[0104] Figures 5 and 6 are schematic diagrams showing an example of time-series data (waveform data) illustrating the change in the position 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. In Figures 5 and 6, the horizontal axis represents time, and the vertical axis represents the position deviation of the driven object 24 relative to the target position. As shown in Figures 5 and 6, when moving the driven object 24 to the target position, the driven object 24 may deviate from the allowable range after initially reaching the allowable range. In such cases, the optimization processing unit 60 determines the degree of deviation by calculating the sum of the time (hereinafter also referred to as the "first sum") during which the driven object 24 is not within the allowable range after it first reaches the allowable range, as shown in Figure 5, for example. Alternatively, in such cases, the optimization processing unit 60 determines the degree of deviation by calculating the sum of the integral values ​​of the position deviations during the time when the driven object 24 is not within the acceptable range after it first reaches the acceptable range, as shown in Figure 6, for example (hereinafter also referred to as the "second sum"). The first sum and the second sum are examples of evaluation indicators.

[0105] The optimization processing unit 60 generates updated control parameters by updating the control parameters through optimization processing to reduce at least the largest deviation degree among the one or more deviation degrees corresponding to one or more operations of the production apparatus 20 determined by the optimization processing unit 60. As shown in Figure 2, the optimization processing unit 60 has a second optimization algorithm 62 that optimizes the control parameters to reduce the deviation time. The optimization processing unit 60 then uses the second optimization algorithm 62 to perform optimization processing to reduce at least the largest deviation degree among the one or more deviation degrees determined by the optimization processing unit 60. The second optimization algorithm 62 may be a known algorithm such as a Bayesian optimization algorithm, an evolutionary strategy algorithm (CMA-ES), or a genetic algorithm (GA), similar to the first optimization algorithm 61 described above. Furthermore, the optimization processing to reduce the deviation degree using the second optimization algorithm 62 may be a known process performed using the known algorithms described above.

[0106] The optimization processing unit 60 repeatedly performs the optimization process until the optimization process termination condition is met, similar to the case where the settling time is shortened. However, it is also possible to generate the update control parameters by performing the optimization process once.

[0107] Here, 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 for a predetermined period. Alternatively, the optimization process termination condition is, for example, the number of times the optimization process is repeated. In this case, the optimization processing unit 60 repeatedly performs the optimization process for a predetermined number of times. Alternatively, the optimization process termination condition is, for example, the degree of deviation of 1 or more that must be satisfied. In this case, the optimization processing unit 60 repeatedly performs the optimization process until the degree of deviation of 1 or more falls below a predetermined level.

[0108] Next, we will describe the schedule control unit 100, which controls the schedule of the optimization process. Figure 7 shows an example of the configuration of the schedule control unit 100. As shown in Figure 7, the schedule control unit 100 includes a schedule acquisition unit 110, a schedule image generation unit 130, and a display control unit 140.

[0109] The schedule acquisition unit 110 acquires schedule information D1 from the schedule holding unit 421, which is a predetermined storage area included in the storage unit 42. The schedule information D1 is information that defines the order of at least one evaluation index applied to the optimization process, the length of time each of the at least one evaluation index is applied in the optimization process, and so on.

[0110] The schedule acquisition unit 110 may also be configured to hold schedule information D1 generated by a tool that generates a schedule based on the user's answers to questions. Alternatively, the schedule acquisition unit 110 may also be configured to hold schedule information D1 written in an editor.

[0111] The schedule acquisition unit 110 inputs the schedule information D1 acquired from the schedule holding unit 421 to the schedule image generation unit 130.

[0112] The schedule image generation unit 130 includes a graphic generation unit 131, an arrangement unit 132, a weight setting unit 133, a shape determination unit 134, a thumbnail determination unit 135, and a drawing unit 136. The schedule image generation unit 130 generates a schedule image IM. The schedule image IM generated by the schedule image generation unit 130 is output to the display screen of the display unit 17 by the display control unit 140, which will be described later. Hereinafter, the display screen on which the schedule image IM is displayed will be called the execution status display screen. Figure 8A is a diagram showing the first execution status display screen 201, which is an example of the execution status display screen. Figure 8A displays the first schedule image IM1, which is an example of the schedule image IM. Figure 9 is a diagram showing the second execution status display screen 202, which is another example of the execution status display screen. Figure 9 displays the second schedule image IM2, which is an example of the schedule image IM. Hereinafter, the graphic generation unit 131 to the drawing unit 136 will be described with reference to Figures 8A and 9.

[0113] The graphic generation unit 131 visualizes at least one evaluation metric set for the optimization process. For example, suppose the schedule acquisition unit 110 acquires schedule information D1 such that the first evaluation metric is applied to the optimization process, then the second evaluation metric is applied, and then the third evaluation metric is applied. In this case, the graphic generation unit 131 generates graphics corresponding to the first evaluation metric, the second evaluation metric, and the third evaluation metric, respectively. Hereinafter, the graphics corresponding to the first evaluation metric, the graphics corresponding to the second evaluation metric, and the graphics corresponding to the third evaluation metric will be referred to as the first graphic F1, the second graphic F2, and the third graphic F3, respectively. In the example shown in Figure 8A, the graphic generation unit 131 generates three rectangles as the first graphic F1, the second graphic F2, and the third graphic F3.

[0114] The placement unit 132 determines the placement of each figure generated by the figure generation unit 131 based on the schedule information D1. The placement unit 132 places each figure along the first direction, which is the direction in which the time axis T extends. In the example shown in Figure 8A, the time axis T extends along the left-right direction in the first schedule image IM1. Here, one end of the time axis T (for example, the left end) represents older time, and the other end (for example, the right end) represents newer time. Furthermore, the schedule acquisition unit 110 acquires schedule information D1 such that the first evaluation metric is applied, followed by the second evaluation metric, and then the third evaluation metric is applied. In this case, as shown in Figure 8A, the placement unit 132 decides to place the first figure F1, the second figure F2, and the third figure F3 in order from left to right in the first schedule image IM1.

[0115] Furthermore, if the time during which a predetermined evaluation index is applied to the optimization process overlaps with the time during which another evaluation index is applied to the optimization process, the arrangement unit 132 decides to arrange the figures in a second direction that intersects the first direction. The schedule acquisition unit 110 may acquire schedule information D1 such that the time during which the first evaluation index is applied overlaps with the time during which the second evaluation index is applied. For example, as shown in Figure 9, the schedule acquisition unit 110 may acquire schedule information D1 indicating that the first evaluation index is applied in the interval from time T1 to T3, and the second evaluation index is applied in the interval from time T2 to time T4. Time T2 is later than time T1, time T3 is later than time T2, and time T4 is later than time T3. In this case, as shown in Figure 9, the arrangement unit 132 arranges the first figure F1, which is a figure corresponding to the first evaluation index, and the second figure F2, which is a figure corresponding to the second evaluation index, along the second direction (for example, the vertical direction in the second schedule image IM2). In this way, the user will clearly understand that there is some overlap between the time when the first evaluation metric is applied and the time when the second evaluation metric is applied. Furthermore, as shown in Figure 9, the schedule image generation unit 130 may highlight the partial overlap between the time when the first evaluation metric is applied and the time when the second evaluation metric is applied by enclosing the time period during which the first and second evaluation metrics are performed in parallel with a dashed line 250.

[0116] The weight setting unit 133 sets the weights for each evaluation index. For example, in the interval from time T1 to time T2, the weight setting unit 133 sets the weight of the first evaluation index to 1 and the weight of the second evaluation index to 0. Also, in the interval from time T2 to time T3, the weight setting unit 133 sets the weights using the weight change function described later, gradually decreasing the weight of the first evaluation index from 1 to 0 and gradually increasing the weight of the second evaluation index from 0 to 1 as time progresses. Also, in the interval from time T3 to time T4, the weight setting unit 133 sets the weight of the first evaluation index to 0 and the weight of the second evaluation index to 1. Here, an example has been described in which the weight setting unit 133 sets the weights so that the sum of the weights for each interval is 1, but the method of setting the weights is not limited to this and can be changed as appropriate.

[0117] Returning to Figure 7, the shape determination unit 134 determines the shape of the figure based on the weights set for the evaluation index. That is, the shape determination unit 134 determines the display manner of the figure. For example, the shape determination unit 134 determines the thickness of the outline of the figure according to the magnitude of the weight set for the evaluation index. That is, if a relatively large weight is set for a certain evaluation index, the shape determination unit 134 decides to make the outline of the figure corresponding to that evaluation index relatively thick. On the other hand, if a relatively small weight is set for a certain evaluation index, the shape determination unit 134 decides to make the outline of the figure corresponding to that evaluation index relatively thin. In addition, the shape determination unit 134 may determine the shape of the figure to be tapered. For example, if the weight set for an evaluation index decreases over time, the shape determination unit 134 may make the shape of the figure corresponding to that evaluation index tapered. Alternatively, the shape determination unit 134 may determine the shape of the figure to be thicker towards the end. For example, if the weight set for an evaluation index increases with the passage of time, the shape determination unit 134 may make the shape of the figure corresponding to that evaluation index a tapered shape. Alternatively, the shape determination unit 134 may determine the shape of each figure based on the type of evaluation index. As an example, the shape determination unit 134 may determine the shape of an optimization process where "settlement time" is set as the evaluation index to be circular, and the shape of an optimization process where "first sum" is set as the evaluation index to be triangular. In addition, the shape determination unit 134 may determine the shape of each figure using various methods.

[0118] Furthermore, the shape determination unit 134 determines the size of the figure in the first direction based on the length of time (hereinafter referred to as the application time) during which a predetermined evaluation index is applied to the optimization process. For example, suppose the time during which the first evaluation index is applied to the optimization process is longer than the time during which the second evaluation index is applied to the optimization process. That is, suppose the application time of the first evaluation index is longer than the application time of the second evaluation index. In this case, the shape determination unit 134 makes the size of the first figure F1 in the first direction longer (larger) than the size of the second figure F2 in the first direction. In this way, since the size of the figure corresponds to the length of the application time of the evaluation index, the user can intuitively understand the length of the application time of each evaluation index.

[0119] The thumbnail determination unit 135 determines the thumbnail information to be attached to a figure based on the content of the evaluation index corresponding to the figure. The following describes the processing of the thumbnail determination unit 135 using the case of attaching thumbnail information to the first figure F1 (Figure 8A) as an example.

[0120] Figure 10 shows an example of thumbnail information assigned to the first figure F1. As described above, the first figure F1 is a figure corresponding to the first evaluation index. Here, the first evaluation index is defined as "the sum of the integral values ​​of the position deviation during the time when the driven object 24 is not within the acceptable range (second sum)." In this case, the thumbnail determination unit 135 decides to assign an image to the first figure F1 as thumbnail information in which the region subject to integration when calculating the second sum is highlighted. Specifically, as shown in Figure 10, the thumbnail information is determined to be an image in which two horizontal lines H1 indicating the acceptable range of position deviation are superimposed on the waveform data, and the region subject to integration is colored with a predetermined color (for example, red). In Figure 10, the region shown with hatched lines represents the region colored with a predetermined color (for example, red).

[0121] Incidentally, evaluation metrics sometimes have target values ​​set. For example, the first evaluation metric may have a target value of "22". In this case, the optimization process to which the first evaluation metric is applied searches for update control parameters that can make the second sum 22 or less. When a target value is set for the first evaluation metric, the thumbnail determination unit 135 determines thumbnail information based on this target value. For example, as shown in Figure 10, the thumbnail determination unit 135 decides to assign the number "22" to the first figure F1, and to assign the text (string) "The goal is for the red area to be 22 or less" to the first figure F1. By assigning such thumbnail information to the first figure F1, a user who views the first figure F1 can intuitively understand the content of the optimization process that will be executed when the first evaluation metric is applied.

[0122] Figure 11 shows an example of thumbnail information assigned to the second figure F2. Referring to Figure 11, the processing of the thumbnail determination unit 135 when assigning thumbnail information to the second figure F2 (Figure 8A) will be explained. As described above, the second figure F2 is a figure corresponding to the second evaluation index. Here, the second evaluation index is assumed to be "setup time". Also, the target value of the second optimization process is assumed to be "20 milliseconds". That is, while the second evaluation index is applied, the optimization process searches for update control parameters that can make the setup time 20 milliseconds or less. In this case, the thumbnail determination unit 135 assigns an image in which the target setup time (20 milliseconds) is highlighted as thumbnail information to the second figure F2. Specifically, as shown in Figure 11, the thumbnail determination unit 135 decides to assign an image to the second figure F2 in which a vertical line V1 indicating the position of the target setup time is drawn on the waveform data. Furthermore, as shown in Figure 11, the thumbnail determination unit 135 decides to assign thumbnail information to the second figure F2, including the number "20" and the text (string) "The goal is for the settling time to be 20 milliseconds or less." In this way, the user who views the second figure F2 can intuitively understand the content of the optimization process performed while the second evaluation index is applied.

[0123] The materials (thumbnail materials) such as images, numbers, strings of characters, patterns, and shapes that the thumbnail determination unit 135 uses to generate thumbnail information are pre-stored in the thumbnail holding unit 422 (Figure 7), which is a predetermined storage area included in the storage unit 42. The thumbnail determination unit 135 generates thumbnail information using the materials stored in the thumbnail holding unit 422.

[0124] Returning to Figure 7, the drawing unit 136 generates a schedule image IM by drawing shapes. Specifically, the drawing unit 136 places each shape generated by the shape generation unit 131 at the position determined by the placement unit 132, deforms these shapes according to the shape settings determined by the shape determination unit 134, and adds thumbnail information generated by the thumbnail determination unit 135 to these shapes. In this way, the drawing unit 136 generates a schedule image IM.

[0125] Furthermore, if the weight setting unit 133 has set weights for each evaluation index, the drawing unit 136 draws graphs on the schedule image IM that represent the change in weights over time. For example, as shown in Figure 9, the drawing unit 136 draws nonlinear graphs G1 and G2 on the schedule image IM that represent the change in the weight of the fourth evaluation index from 1 to 0 and the change in the weight of the fifth evaluation index from 0 to 1 over time.

[0126] When the drawing unit 136 generates a schedule image IM, it inputs the schedule image IM to the display control unit 140.

[0127] The display control unit 140 outputs the schedule image IM to the display unit 17. This presents the user with a display screen (execution status display screen) showing the schedule image IM. Specifically, the display control unit 140 outputs various execution status display screens to the display unit 17 (display), such as the first execution status display screen 201 shown in Figure 8A and the second execution status display screen 202 shown in Figure 9. The execution status display screen functions as a user interface. In other words, the information processing unit 50 according to this embodiment can receive various operations from the user through the execution status display screen.

[0128] The execution status display screen is displayed on the display unit 17, for example, when the optimization processing unit 60 is executing the optimization process. The display control unit 140 in this embodiment highlights the figure corresponding to the evaluation metric currently applied to the optimization process. For example, consider the case where the second evaluation metric is currently applied to the optimization process, and the first and third evaluation metric are not applied. In this case, the display control unit 140 makes the border color of the second figure F2 different from the border colors of the first figure F1 and the third figure F3. In addition, the display control unit 140 may make the size of the second figure F2 larger than the sizes of the first figure F1 and the third figure F3. Alternatively, the border of the second figure F2 may be made thicker than the borders of the first figure F1 and the third figure F3. Alternatively, the second figure F2 may be made to blink. In this way, by making the figure corresponding to the evaluation metric currently applied to the optimization process stand out, the display control unit 140 can clearly make it clear to the user which evaluation metric is being applied to the optimization process.

[0129] The drawing unit 136 in this embodiment draws setting buttons on a graphic for displaying the details of the evaluation indicator and the settings of the evaluation indicator. For example, as shown in Figure 8A, the drawing unit 136 draws a first setting button B1 on a first graphic F1, a second setting button B2 on a second graphic F2, and a third setting button B3 on a third graphic F3. The display control unit 140 accepts an operation to select one of these setting buttons. For example, if the user selects the first setting button B1, the display control unit 140 draws the details and settings of the first evaluation indicator in the detailed setting / confirmation screen display area 400 shown in Figure 8A. The detailed setting / confirmation screen display area 400 is the area where the details of the evaluation indicator and the settings of the evaluation indicator are displayed. The same applies when the selection of the second setting button B2 or the third setting button B3 is accepted. In this way, the user can easily confirm the information showing the details and settings of the evaluation indicator.

[0130] It is not mandatory for the drawing unit 136 to draw setting buttons on the graphic. When the display control unit 140 receives an operation from the user to select any part of the graphic, it may draw the details and settings of the evaluation indicator corresponding to that graphic on the detailed settings / confirmation screen display area 400.

[0131] Furthermore, the drawing unit 136 in this embodiment draws a scale conversion button 300 (Figure 8A) for converting the time scale on the schedule image IM. The scale conversion button 300 includes an enlargement button 301 and a reduction button 302. As shown in Figure 8A, the enlargement button 301 is represented by an icon resembling a "+" symbol. The reduction button 302 is represented by an icon resembling a "-" symbol. When the display control unit 140 receives an operation from the user to select the enlargement button 301, it enlarges the time scale and narrows the drawing range of the schedule image IM. For example, instead of the schedule image IM in which the first figure F1, the second figure F2, and the third figure F3 are drawn, the display unit 17 displays a schedule image IM in which the first figure F1 and the second figure F2 are drawn. On the other hand, when the display control unit 140 receives an operation to select the reduction button 302, it reduces the time scale and displays a schedule image IM in which a wider range of schedules are displayed. For example, the display control unit 140 causes the display unit 17 to display a schedule image IM on which the first figure F1, second figure F2, third figure F3, the 101st figure (not shown), and the 102nd figure (not shown) are drawn, instead of the schedule image IM on which the first figure F1, second figure F2, and third figure F3 are drawn. The 101st figure is a graphic representation of the 101st evaluation index that is applied to the optimization process after the third evaluation index, and the 102nd figure is a graphic representation of the 102nd evaluation index that is applied to the optimization process after the 101st evaluation index.

[0132] Furthermore, the drawing unit 136 in this embodiment draws weight setting buttons on the graphic for setting the weights of each evaluation index. For example, as shown in Figure 9, the drawing unit 136 draws a first weight setting button B11 on the first graphic F1 and a second weight setting button B21 on the second graphic F2. When the display control unit 140 detects that the first weight setting button B11 has been selected by the user, it displays a weight setting screen for setting the weight of the first evaluation index on the display unit 17. The user can directly set the weight of the first evaluation index through this weight setting screen. Similarly, when the second weight setting button B21 is selected, the display control unit 140 displays a weight setting screen for directly setting the weight of the second evaluation index on the display unit 17.

[0133] Furthermore, the drawing unit 136 draws the weight switching buttons on the schedule image IM. For example, as shown in Figure 9, the drawing unit 136 draws the first weight switching button B31 near the first figure F1. When the display control unit 140 detects that the user has entered an operation to select the first weight switching button B31, it displays the weight change function selection screen 260 on the display unit 17. The weight change function selection screen 260 is a screen that displays a list of presets showing candidate weight change functions, which are functions that determine the change in weights over time. Figure 12 is a diagram showing an example of the weight change function selection screen 260. In the example shown in Figure 12, the weight change function selection screen 260 displays the first function FU1, the second function FU2, and the third function FU3. The first function FU1 is a function that changes the weights curvilinearly. The second function FU2 is a function that changes the weights linearly. The third function FU3 is a function in which the weights remain constant (the weights do not change). When the display control unit 140 detects that the user has selected one of the weight change functions, it changes the weight of the first evaluation index using the selected weight change function. Also, as shown in Figure 9, the drawing unit 136 draws the second weight switching button B32 near the second figure F2. When the display control unit 140 detects that the user has entered an operation to select the second weight switching button B32, it displays the weight change function selection screen 260 on the display unit 17 and prompts the user to select a weight change function to determine the weight of the second evaluation index.

[0134] If the first function FU1 is selected, the display control unit 140 may display a message on the display unit 17 prompting the user to specify two points on the execution status display screen. For example, in the example shown in Figure 9, the display control unit 140 may display a message on the display unit 17 prompting the user to specify two points on the execution status display screen within the range from "weight 0" to "weight 1" in the second direction (here, the up and down direction), and within the range in which the time axis T extends in the first direction (here, the left and right direction). When the operation to specify two points on the execution status display screen is received, the display control unit 140 may automatically generate a function that curvilinearly connects the two specified points by spline interpolation, and change the weight of the evaluation index by this function. The display control unit 140 may also receive an operation to adjust the positions of the two points on the time axis T. In this case, the display control unit 140 changes the curvature of the function generated by spline interpolation according to the adjusted positions of the two points.

[0135] Furthermore, when the display control unit 140 detects that the user has entered an operation to change the arrangement of the figures, it changes the order of the evaluation indicators applied to the optimization process. For example, suppose the user enters an operation to swap the positions of the first figure F1 and the second figure F2 shown in Figure 8A (an example of an arrangement change operation). In this case, the display control unit 140 displays the schedule image IM with the positions of the first figure F1 and the second figure F2 swapped on the display unit 17. At this time, the display control unit 140 also changes (updates) the contents of the schedule information D1. Specifically, it generates new schedule information D1 that specifies an order in which the optimization process to which the second evaluation indicator is applied is executed first, followed by the optimization process to which the first evaluation indicator is applied. When the schedule information D1 is updated, the optimization processing unit 60 executes the optimization process according to the updated schedule information D1. That is, the optimization processing unit 60 executes the optimization process to which the first evaluation indicator is applied after the second evaluation indicator is applied. In this way, the user can easily change the order of the evaluation indicators applied to the optimization process. Furthermore, users can intuitively change the order in which evaluation metrics are applied.

[0136] Furthermore, the display control unit 140 may change the shape of the figure based on an operation received from the user. For example, suppose the user inputs a resize operation to change the size of a predetermined figure in a first direction. One example of a resize operation is to click the right end of the first figure F1 with the mouse cursor and then move the mouse cursor to the right in the first schedule image IM1. When the display control unit 140 detects that this resize operation has been input, it displays the first figure F1 with its size extended in the first direction, specifically the first figure F1 that has been lengthened to the right (an example of a resized figure), on the first execution status display screen 201. In this case, the display control unit 140 also updates the schedule information D1 to extend the time during which the first evaluation index is applied to the optimization process (the application time of the first evaluation index). Another example of a resize operation is to click the right end of the first figure F1 with the mouse cursor and then move the mouse cursor to the left in the first schedule image IM1. When the display control unit 140 detects that this resizing operation has been input, it displays the first figure F1, whose size in the first direction has been shortened, on the first execution status display screen 201. In this case, the display control unit 140 also updates the schedule information D1 to shorten the application time of the first evaluation metric. In this way, the user can easily change the time (application time) that the evaluation metric is applied to the optimization process. Furthermore, the user can change the application time through intuitive operation.

[0137] Furthermore, if the system has received an operation to select multiple evaluation indicators and then receives an operation to change the application time of one of the evaluation indicators, the display control unit 140 may shorten or increase the application time by maintaining the ratio of the application times of the selected indicators.

[0138] Furthermore, the display control unit 140 may accept the insertion of a figure based on an operation from the user. For example, suppose a first figure F1 and a second figure F2 are arranged side by side in a first direction. Suppose the display control unit 140 detects that an operation has been input to insert a tenth figure (not shown) between the first figure F1 and the second figure F2 in the first direction. In this case, the display control unit 140 may update the schedule information D1 to specify that the first evaluation metric be applied to the optimization process, then the tenth evaluation metric be applied to the optimization process, and then the second evaluation metric be applied to the optimization process. Also, if the display control unit 140 detects an operation to delete the tenth figure, for example, it may update the schedule information D1 to specify that the first evaluation metric be applied, and then the second evaluation metric be applied. In this way, the user can easily add or delete evaluation metrics applied to the optimization process.

[0139] Figure 8B shows details of the first execution status display screen 201. As shown in Figure 8B, the first execution status display screen 201 includes a progress line L100 that indicates the progress of the optimization process. When the display control unit 140 detects, for example, that a user has entered an operation to select the progress line L100, it displays the progress information display field R1. The progress information display field R1 displays progress information indicating the progress of the optimization process to which a predetermined evaluation metric is applied. In the example shown in Figure 8B, the progress information display field R1 displays progress information indicating the progress of the optimization process to which a second evaluation metric is applied. The progress information includes optimization time, number of operations, estimated remaining operation time, and remaining number of operations.

[0140] The optimization time indicates the time elapsed since the start of the optimization process to which a predetermined evaluation metric is applied. In the example shown in Figure 8B, it is displayed that "30 hours" have elapsed since the start of the optimization process to which the second evaluation metric is applied (referred to as the second optimization process for convenience of explanation). The display control unit 140 may also display a first progress rate in the progress information display field R1, which indicates the ratio of the optimization time to the estimated total optimization time, as described later. In Figure 8B, the progress information display field R1 shows that the first progress rate is "60%".

[0141] The number of operations indicates the number of times the production device 20 has operated since the start of the optimization process to which a predetermined evaluation index is applied. In the example shown in Figure 8B, it is shown that the production device 20 has operated "30,000 times" since the start of the second optimization process. The display control unit 140 may also display a second progress rate in the progress information display field R1, which shows the ratio of the number of operations to the expected total number of operations, as described later. In Figure 8B, the progress information display field R1 shows that the second progress rate is "50%".

[0142] The estimated remaining operating time is the estimated total optimization time minus the optimization time. Figure 8B shows that the estimated remaining operating time is "20 hours".

[0143] The remaining number of operations is shown as the number of operations performed minus the expected total number of operations. Figure 8B shows that the remaining number of operations is "20,000".

[0144] Furthermore, as shown in Figure 8B, the first execution status display screen 201 includes a prediction display area R2. The prediction display area R2 displays prediction information regarding the optimization process to which predetermined evaluation indicators are applied. The prediction information includes the predicted total optimization time, the predicted completion time, and the predicted total number of operations.

[0145] The estimated total optimization time represents the estimated total time required from the start to the end of the optimization process to which the predetermined evaluation metrics are applied. In the example shown in Figure 8B, the second optimization process is expected to take "50 hours" from start to finish.

[0146] The estimated completion time indicates the time when the optimization process to which the predetermined evaluation indicator is applied is expected to finish. In Figure 8B, the second optimization process is expected to finish at "MM / DD / HH".

[0147] The estimated total number of operations indicates the estimated total number of times the production device 20 will operate from the start to the end of the optimization process to which a predetermined evaluation metric is applied. In Figure 8B, it is estimated that the production device 20 will operate "50,000 times" from the start to the end of the second optimization process.

[0148] As described above, the optimization processing unit according to this embodiment repeatedly executes the optimization process until the optimization process termination condition is met. Therefore, if the schedule acquisition unit 110 acquires schedule information D1 such that the first evaluation indicator is applied to the optimization process, then the second evaluation indicator is applied to the optimization process, and then the third evaluation indicator is applied, then the optimization process performed with the first evaluation indicator applied, the optimization process performed with the second evaluation indicator applied, and the optimization process performed with the third evaluation indicator applied will each be repeatedly executed until the optimization process termination condition is met. During the execution of this optimization process based on the schedule information D1, which loops until the optimization process termination condition is met, the position of the progress line L100 on the first execution status display screen 201, the optimization time, the estimated remaining operation time, and the number of operations are constantly updated and displayed on the display unit 17.

[0149] The display control unit 140 may recalculate the optimization time, number of operations, estimated total optimization time, estimated end time, estimated total number of operations, etc., during or after the user's editing of the first execution status display screen 201, and display the results of the recalculation on the first execution status display screen 201. For example, suppose the user inputs an operation to extend the size of the second figure F2 shown in Figure 8B in the first direction. That is, suppose the user inputs an operation to extend the time during which the second evaluation index is applied to the optimization process. In this case, the display control unit 140 may recalculate the estimated total optimization time, estimated end time, and estimated total number of operations, and display the estimated total optimization time, estimated end time, and estimated total number of operations that reflect the extended time during which the second evaluation index is applied on the first execution status display screen 201.

[0150] Furthermore, if the display control unit 140 detects that an operation has been input to change any of the values ​​of "optimization time," "number of operations," "expected completion time," and "expected number of operations" displayed in the progress information display field R1 (Figure 8B) or the forecast display field R2 (Figure 8B), it may change the application time of each evaluation indicator while maintaining the ratio of the application times of multiple evaluation indicators that are not yet completed (or have not yet been executed).

[0151] Figure 13 is a flowchart showing the processing flow executed by the information processing unit 50 according to this embodiment. The processing shown in Figure 13 is started when a user using the control parameter generation system 1 inputs an operation to the input unit 44 indicating that the scheduled image IM generation process should be started.

[0152] In step S1, the schedule acquisition unit 110 acquires schedule information D1 from the schedule holding unit 421.

[0153] In step S2, the schedule image generation unit 130 creates a schedule image IM. Specifically, in step S2, the graphic generation unit 131 of the schedule image generation unit 130 visualizes each of the at least one evaluation indicators specified in the schedule information D1 to be applied to the optimization process. Also in step S2, the arrangement unit 132 of the schedule image generation unit 130 determines the arrangement of each of the visualized evaluation indicators based on the schedule information D1.

[0154] In step S3, the display control unit 140 outputs the schedule image IM generated by the schedule image generation unit 130 to the display unit 17.

[0155] Figure 14 is a flowchart showing the details of the process in step S2 of Figure 13.

[0156] In step S21, the figure generation unit 131 generates a figure corresponding to the evaluation index.

[0157] In step S22, the arrangement unit 132 determines the arrangement of the figures. Specifically, the arrangement unit 132 determines the arrangement of the figures generated by the figure generation unit 131 in a first direction so that the figures are arranged in the order in which the optimization process is executed.

[0158] In step S23, the weight setting unit 133 determines the weight of each evaluation index.

[0159] In step S24, the shape determination unit 134 determines the shape of the figure based on the weights of each evaluation index.

[0160] In step S25, the thumbnail determination unit 135 determines the content of the thumbnail information to be assigned to the figure.

[0161] In step S26, the drawing unit 136 draws the schedule image IM. Specifically, the drawing unit 136 deforms the figure generated by the figure generation unit 131 according to the shape determined by the shape determination unit 134, places it at the position determined by the placement unit 132, and adds thumbnail information determined by the thumbnail determination unit 135 to the figure. This generates the schedule image IM.

[0162] According to the control parameter generation system 1 described above, a schedule image IM containing a figure corresponding to each of the at least one evaluation indicators applied to the optimization process is output to the display unit 17. Therefore, the user can clearly understand the content of the evaluation indicators applied to the optimization process.

[0163] Furthermore, according to the control parameter generation system 1, since the shapes are arranged along the direction in which the time axis T extends, the user can clearly understand the time allocation of the evaluation indicators applied to the optimization process.

[0164] Furthermore, according to the control parameter generation system 1, the shape of the figure changes according to the weight set for the evaluation index, so that the weight of each evaluation index can be clearly understood by the user.

[0165] Furthermore, with the control parameter generation system 1, nonlinear graphs G1 and G2 (Figure 9) are drawn on the schedule image IM, making it clear to the user that the weights set for the evaluation index change over time.

[0166] Furthermore, the control parameter generation system 1 provides thumbnail information to the figures, allowing the user to more clearly understand the content of the evaluation metrics applied to the optimization process. Moreover, in this embodiment, at least one of the color and numerical value representing the target value set for the evaluation metric is included in the thumbnail information, allowing the user to clearly understand the target value set for the evaluation metric.

[0167] The following modifications can be adopted for this embodiment.

[0168] (1-1) The thumbnail determination unit 135 may include a color in the thumbnail information that indicates the target value of the evaluation index. For example, the thumbnail information may be determined such that if the target value of the settling time is 30 milliseconds or less, at least a part of the figure is displayed in red; if the target value of the settling time is 25 milliseconds or less, at least a part of the figure is displayed in yellow; and if the target value of the settling time is 20 milliseconds or less, at least a part of the figure is displayed in green. Alternatively, the thumbnail determination unit 135 may represent the target value of the evaluation index by the intensity of the color.

[0169] (1-2) In Embodiment 1, the settling time, the first sum, and the second sum were described as examples of evaluation indicators, but the types of evaluation indicators are not limited to these. Various evaluation indicators may be applied to the optimization process as needed.

[0170] (1-3) Embodiment 1 described an example in which an optimization process is performed to search for control parameters used by the control circuit 22 when controlling the drive source 23, but this is just one example. The control parameter generation system 1 can also be applied to optimize multiple items that are in a trade-off relationship with each other, or to other items that the user is unaware of. For example, it can be used to optimize component design.

[0171] When optimizing component design, optimization is sometimes used to design lightweight and strong components. For example, optimization may be used to design a shape that maintains the shape of the outer frame while removing excess material from the inside. However, there are cases where the shape obtained as a result of optimization satisfies the strength requirements but is inappropriate from other perspectives. For example, if the shape is prone to causing injury, it is inappropriate from a safety standpoint. In this case, the control parameter generation system 1 may be used to perform optimization that satisfies both strength and safety requirements. In addition, the control parameter generation system 1 according to this embodiment is applicable to various optimization processes.

[0172] (1-4) In Embodiment 1, an example was described in which the control parameter generation device 10 (for example, a personal computer) includes an information processing unit 50, but the production device 20 may also include an information processing unit 50. In other words, the processing of the optimization processing unit 60 and the schedule control unit 100 described above may be executed by an 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.

[0173] (1-5) In Embodiment 1, the evaluation indicators may include evaluation indicators related to processing quality, evaluation indicators related to productivity, or both. Examples of evaluation indicators related to processing quality may include processing accuracy, shape error, defect rate, dimensional error, etc. Examples of evaluation indicators related to productivity may include settling time, processing time, cycle time, etc. These evaluation indicators may be arranged in the schedule image along the time axis as corresponding figures, so that the user can visually understand which evaluation indicator is applied at each point in time. Furthermore, if the application times of at least part of the evaluation indicators related to processing quality and the evaluation indicators related to productivity overlap, the corresponding figures may be arranged in a direction that intersects the time axis. This makes it possible to clearly display periods in which quality improvement is emphasized, periods in which productivity improvement is emphasized, or periods in which both are considered simultaneously.

[0174] (Embodiment 2) The control parameter generation system 1A according to Embodiment 2 branches the schedule information D1 based on a condition that utilizes one of a plurality of evaluation indicators. The control parameter generation system 1A outputs a display screen to the display unit 17 in which a plurality of figures are arranged in a direction (second direction) that intersects the time axis T.

[0175] The following describes a control parameter generation system 1A, which is configured with some changes to the control parameter generation system 1 according to Embodiment 1.

[0176] Regarding the control parameter generation system 1A, components similar to those in the control parameter generation system 1 according to Embodiment 1 will be given the same reference numerals as they have already been described, and their detailed explanations will be omitted. In other words, the following explanation will focus on the differences from the control parameter generation system 1.

[0177] Figure 15 shows an example of the configuration of the control parameter generation system 1A. As shown in Figure 15, the control parameter generation system 1A is configured by changing the information processing unit 50 to an information processing unit 50A from the control parameter generation system 1 according to Embodiment 1. The information processing unit 50A includes a schedule control unit 100A. Figure 16 shows an example of the configuration of the schedule control unit 100A. As shown in Figure 16, the schedule control unit 100A includes an evaluation unit 160 and a schedule generation unit 150.

[0178] The evaluation unit 160 calculates an evaluation score (an example of an evaluation value) by evaluating the optimization process based on predetermined evaluation criteria.

[0179] The schedule generation unit 150 generates schedule information D1 based on user operations and stores the generated schedule information D1 in the schedule holding unit 421 (Figure 16).

[0180] Figure 17 shows an example of an initial setup input screen 210 that the schedule generation unit 150 displays on the display unit 17. The operation of the schedule generation unit 150 will be described below with reference to Figure 17. The schedule generation unit 150 displays an editing start button B101 and an editing completion button B102 on the initial setup input screen 210. When the editing start button B101 is detected to have been selected by the user, the schedule generation unit 150 displays a material selection screen on the display unit 17, which displays a list of materials for generating schedule information D1. The materials displayed on the material selection screen include graphical evaluation indicators and a branch button B50, which will be described later. The user edits the initial setup input screen 210 using these materials. For example, as shown in Figure 17, the user places the branch button B50 in the center of the initial setup input screen 210, places the 11th graphic F11 and the 12th graphic F12 to its left, and inputs the operation to connect them. Furthermore, the user inputs an operation to place the 11th shape F11 and the 12th shape F12 to the right of the branching button B50, along the second direction (in this case, the up and down direction).

[0181] Figure 11 F11 is a figure corresponding to the 11th evaluation metric applied to the optimization process. Figure 12 F12 is a figure corresponding to the 12th evaluation metric applied to the optimization process after the 11th evaluation metric.

[0182] The branch button B50 is a button that accepts the setting of branch conditions. When the branch button B50 is selected (for example, clicked), the schedule generation unit 150 displays the branch condition setting screen. The schedule generation unit 150 accepts input of branch conditions through the branch condition setting screen. For example, the schedule generation unit 150 accepts a branch condition indicating "apply the evaluation metric with the higher evaluation rating after the 12th evaluation metric." However, this is just an example, and the content of the branch conditions accepted by the schedule generation unit 150 can be changed as appropriate. In addition, when the schedule generation unit 150 detects that the branch button B50 has been selected, it may display the currently set branch conditions on the display unit 17.

[0183] When the schedule generation unit 150 detects that the editing completion button B102 has been selected by the user, it generates schedule information D1 based on the editing content entered by the user. For example, the schedule generation unit 150 generates schedule information D1 based on the initial setting input screen 210 shown in Figure 17. Specifically, it generates schedule information D1 by first executing an optimization process to which the 11th evaluation index is applied, then executing an optimization process to which the 12th evaluation index is applied, then having the evaluation unit 160 calculate the 11th evaluation score, which is the evaluation score of the optimization process executed with the 11th evaluation index applied, and the 12th evaluation score, which is the evaluation score of the optimization process executed with the 12th evaluation index applied, then determining which of the 11th evaluation score and the 12th evaluation score is larger, and then executing an optimization process to which the evaluation index for which the larger evaluation score was calculated is applied.

[0184] The schedule information D1 generated by the schedule generation unit 150 is stored in the schedule holding unit 421. Based on this schedule information D1, the schedule image generation unit 130 generates a schedule image IM.

[0185] Figure 18 shows a third execution status display screen 203, which is an example of an execution status display screen. The third execution status display screen 203 displays a third schedule image IM3 generated based on schedule information D1 generated by the schedule generation unit 150. In the example shown in Figure 18, the third execution status display screen 203 includes an eleventh figure F11, a twelfth figure F12, and a branch button B50 arranged along the first direction (here, the vertical direction) in which the time axis T extends. The third execution status display screen 203 also includes a first choice figure C1 and a second choice figure C2 arranged below the branch button B50, side by side in a second direction (here, the left-right direction) that intersects the time axis T. The first choice figure C1 corresponds to the eleventh evaluation index. The second choice figure C2 corresponds to the second evaluation index.

[0186] The third execution status display screen 203 is displayed on the display unit 17 when the optimization processing by the optimization processing unit 60 is being performed. The optimization processing by the optimization processing unit 60 is performed according to the schedule information D1.

[0187] Referring to Figure 18, the operation of the display control unit 140 and the evaluation unit 160 according to Embodiment 2 will be described. First, the optimization processing unit 60 performs optimization processing with the 11th evaluation index applied, and then performs optimization processing with the 12th evaluation index applied. The display control unit 140 monitors the processing content of the optimization processing while it is being executed. The display control unit 140 may highlight the currently applied evaluation index by blinking the 11th figure F11 while the optimization processing with the 11th evaluation index applied is being performed, and blinking the 12th figure F12 while the optimization processing with the 12th evaluation index applied is being performed.

[0188] Here, we assume that the 11th evaluation metric is "setup time when using the first filtering process," and the 12th evaluation metric is "setup time when using the second filtering process." The first and second filtering processes are existing processes performed on waveform data, etc., for purposes such as noise reduction and signal smoothing. The first and second filtering processes are different processes from each other. Furthermore, we assume that "20 milliseconds" is set as the target value for the setup time of the 11th and 12th evaluation metrics, respectively. That is, while the 11th evaluation metric is applied, and while the 12th evaluation metric is applied, the optimization processing unit 60 will execute a process to search for update control parameters that can set the setup time to 20 milliseconds or less. Furthermore, the evaluation unit 160 will assign a higher evaluation score to the evaluation metric that finds update control parameters that set the setup time to 20 milliseconds or less in a shorter time.

[0189] When both the optimization process to which the 11th evaluation index is applied (referred to as the 11th optimization process) and the optimization process to which the 12th evaluation index is applied (referred to as the 12th optimization process) are completed, the display control unit 140 inputs the processing results of the 11th optimization process and the 12th optimization process to the evaluation unit 160. Based on the processing results received from the display control unit 140, the evaluation unit 160 calculates the evaluation scores for the 11th optimization process and the 12th optimization process. As described above, the evaluation unit 160 assigns a higher evaluation score to the evaluation index that finds the update control parameter that sets the settling time to 20 milliseconds or less in a shorter time. The evaluation unit 160 inputs the evaluation scores for the 11th optimization process and the 12th optimization process to the display control unit 140.

[0190] If the evaluation score of the 11th optimization process (11th evaluation score) is higher than the evaluation score of the 12th optimization process (12th evaluation score), the display control unit 140 commands the optimization processing unit 60 to start the optimization process to which the 11th evaluation index is applied. At this time, the display control unit 140 blinks the first choice figure C1 shown in Figure 18. In this way, the user can be made aware that the evaluation score of the 11th optimization process was higher than that of the 12th optimization process, and that the optimization process to which the 11th evaluation index is applied has started.

[0191] Figure 19 is a flowchart showing the processing flow executed by the information processing unit 50A according to Embodiment 2.

[0192] In step S31, the schedule generation unit 150 of the information processing unit 50A receives branching condition information. For example, the schedule generation unit 150 receives branching condition information from the user on the initial setting input screen 210.

[0193] In step S32, the figure generation unit 131 generates a schedule image IM (for example, a third schedule image IM3) in which a first choice figure C1 corresponding to one of the evaluation index options applied to the optimization process and a second choice figure C2 corresponding to an option other than the first choice are arranged side by side in a second direction. Also in step S32, the schedule image generation unit 130 outputs a branch button B50 on the schedule image IM to indicate that the schedule has branched.

[0194] In step S33, the display control unit 140 extracts evaluation indicators that satisfy the branching conditions.

[0195] In step S34, the display control unit 140 decides to apply the evaluation index that satisfies the branching condition to the optimization process.

[0196] According to the control parameter generation system 1A described above, an evaluation index to be applied to the optimization process is selected from multiple options based on the branching conditions. Specifically, from two options, such as the 11th evaluation index and the 12th evaluation index, the evaluation index that satisfies the branching conditions is selected as the evaluation index to be applied to the optimization process. Therefore, an appropriate evaluation index can be applied to the optimization process. This configuration is particularly useful when the user is unfamiliar with setting evaluation indicators. For example, it is particularly useful when it is difficult for the user to determine whether to apply the 11th evaluation index or the 12th evaluation index to the optimization process.

[0197] Furthermore, according to the control parameter generation system 1A of this embodiment, a schedule image IM including a first choice figure C1, a second choice figure C2, and a branch button B50 is presented to the user. This allows the user to intuitively understand that the evaluation index corresponding to the first choice figure C1, or the evaluation index corresponding to the second choice figure C2, is applied to the optimization process according to the branch conditions.

[0198] The following modifications can be adopted for this embodiment.

[0199] (2-1) In Embodiment 2, an example was described in which schedule information D1 is constructed based on user input on the initial setting input screen 210, and the third schedule image IM3 generated based on this schedule information D1 is displayed on the display unit 17. However, this is just one example. The schedule information D1 may be automatically updated during or after the optimization process.

[0200] As an example, we will describe a case in which, while the optimization process is in progress, the display control unit 140 receives input of a branching condition from the user, and the schedule generation unit 150 automatically updates the schedule information D1 based on this branching condition. If the display control unit 140 detects, for example, that the user has selected a pause button (not shown) while the optimization process to which the 11th evaluation index is applied, it sends a predetermined signal to the optimization processing unit 60 to temporarily pause the optimization process. If the display control unit 140 receives an operation from the user requesting the display of the branching button B50, it displays the branching button B50 on the execution status display screen. Then, in response to receiving an operation from the user to select the branching button B50, the display control unit 140 displays the branching condition setting screen and accepts input of the branching condition through this screen. For example, suppose the display control unit 140 receives a branching condition indicating that "between the case where the 11th evaluation index is applied and the case where the 12th evaluation index is applied, the evaluation index that results in a higher evaluation score for the optimization process will be applied after the 12th evaluation index." In this case, the schedule generation unit 150 updates the schedule information D1 and specifies that the optimization process to which the 11th evaluation index is applied and the optimization process to which the 12th evaluation index is applied should be executed, that a decision should be made based on the branching conditions, and that the evaluation index with the higher evaluation score should be applied to the optimization process. The display control unit 140 detects that the user has selected a resume button (not shown) and causes the optimization processing unit 60 to execute the 11th and 12th optimization processes, and causes the evaluation unit 160 to calculate the evaluation scores for the 11th and 12th optimization processes. Next, the evaluation index that was set for the optimization process with the higher evaluation score is automatically applied to the optimization process that is performed after the decision.

[0201] Thus, the schedule information D1 does not need to be generated solely based on the information (initial settings) entered through the initial setting input screen 210, but may be updated based on the content of additional operations entered during the execution of the optimization process. Furthermore, if the schedule information D1 is updated, the schedule image generation unit 130 may generate a schedule image IM based on the updated schedule information D1. The display control unit 140 may display the updated schedule image IM on the display unit 17.

[0202] (Embodiment 3) The schedule generation unit 150 according to Embodiment 3 receives essential condition information indicating essential conditions to be set for a predetermined optimization process. Based on the essential condition information, the schedule generation unit 150 sets essential conditions for the optimization process.

[0203] In Embodiment 3, components similar to those in Embodiment 2 are denoted by the same reference numerals, and their descriptions are omitted. Furthermore, in Embodiment 3, the block diagram shown in Figure 15 is used with reference.

[0204] The schedule generation unit 150 displays an edit start button B101 and an edit completion button B102 on the initial settings input screen 210. When the schedule generation unit 150 detects that the edit start button B101 has been selected by the user, it accepts an operation from the user to edit the initial settings input screen 210.

[0205] For example, the schedule generation unit 150 receives an operation from the user via the initial setup input screen 210 to instruct the system to first apply the 21st evaluation metric to the optimization process, then the 22nd evaluation metric, and then the 23rd evaluation metric. In other words, the schedule generation unit 150 obtains sequence information indicating the order in which at least one evaluation metric is applied to the optimization process.

[0206] Furthermore, the schedule generation unit 150 accepts input of essential condition information from the user. Essential condition information is information indicating essential conditions. For example, the schedule generation unit 150 acquires first essential condition information indicating that "outputting update control parameters that satisfy the target value of the 21st evaluation indicator" is the first essential condition. The schedule generation unit 150 also acquires second essential condition information indicating that "in the optimization process to which the 22nd evaluation indicator is applied, outputting update control parameters that satisfy the target value of the 21st evaluation indicator" is the second essential condition. The contents of the first and second essential conditions can be changed as appropriate.

[0207] When the schedule generation unit 150 detects that the editing complete button B102 has been selected, it generates schedule information D1 based on the information entered by the user on the initial setup input screen 210. For example, the schedule generation unit 150 generates schedule information D1 in which the target values ​​of the 21st evaluation indicator, the 22nd evaluation indicator, and the 23rd evaluation indicator are satisfied in the order indicated by the sequence information. Based on this schedule information D1, the schedule image generation unit 130 generates a fourth schedule image IM4, which is an example of a schedule image IM. The fourth schedule image IM4 is input to the display control unit 140, and the display control unit 140 outputs the fourth schedule image IM4 to the display unit 17, thereby presenting the user with a fourth execution status display screen 204, which is an example of an execution status display screen.

[0208] Figure 20 shows a fourth execution status display screen 204, which is an example of an execution status display screen. The fourth schedule image IM4 is displayed on the fourth execution status display screen 204. As shown in Figure 20, the fourth schedule image IM4 includes the first section AR1, the second section AR2, the third section AR3, the fourth section AR4, and the fifth section AR5. These sections correspond to the order of processing executed by the optimization processing unit 60. That is, in this embodiment, the processing shown in the first section AR1 is performed, followed by the processing shown in the second section AR2, then the processing shown in the third section AR3, then the processing shown in the fourth section AR4, and then the processing shown in the fifth section AR5.

[0209] In the first section AR1, an optimization process is performed to apply the 21st evaluation index. The 21st figure F21 is displayed in the first section AR1. The 21st figure F21 is a figure corresponding to the 21st evaluation index. The 21st evaluation index is, for example, "settlement time". The target value of the 21st evaluation index is "20 milliseconds". In other words, in the first section AR1, an optimization process is performed to search for update control parameters that can make the settlement time 20 milliseconds or less. The display control unit 140 may blink the 21st figure F21 displayed on the fourth execution status display screen 204 while the 21st evaluation index is applied.

[0210] In the second section AR2, the first determination is performed. In the example shown in Figure 20, the first determination button B51 is displayed in the second section AR2. In this embodiment, the schedule image generation unit 130 draws a determination button (first determination button B51 in Figure 20) on the schedule image IM to make it clear that the first determination is performed when the schedule generation unit 150 has acquired the first essential condition.

[0211] In the first determination, it is determined whether the first essential condition received from the user on the initial setup input screen 210 has been met. As described above, in this embodiment, first essential condition information is obtained that specifies "outputting an update control parameter that satisfies the target value of the 21st evaluation index" as the first essential condition. Therefore, in the first determination, it is determined whether an update control parameter that satisfies the target value of the 21st evaluation index has been output by the optimization process in the first section AR1. That is, it is determined whether an update control parameter that sets the settling time to 20 milliseconds or less has been output as a result of the optimization process in the first section AR1. The main unit that performs the first determination is, for example, the display control unit 140. However, a functional unit other than the display control unit 140 (such as the optimization processing unit 60) may be the main unit that performs the first determination. If the first essential condition has been met, the display control unit 140 communicates with the optimization processing unit 60 and causes the optimization processing unit 60 to start the optimization process to which the 22nd evaluation index is applied (the optimization process shown in the third section AR3). On the other hand, if the first essential condition is not met, the display control unit 140 communicates with the optimization processing unit 60 and causes the optimization processing unit 60 to execute the optimization process to which the 21st evaluation index is applied (the optimization process shown in the first section AR1) again. In other words, the display control unit 140 repeatedly executes the optimization process in the first section AR1 until the first essential condition is met (until an update control parameter that sets the settling time to 20 milliseconds or less is output).

[0212] The third section AR3 indicates the section in which the optimization process to which the 22nd evaluation index and the 2nd essential condition are applied is performed. As described above, the schedule generation unit 150 has acquired information indicating that the 2nd essential condition is "to output update control parameters that satisfy the target value of the 21st evaluation index in the optimization process to which the 22nd evaluation index is applied." Also, as described above, the schedule generation unit 150 has received an operation from the user to instruct the optimization process to which the 22nd evaluation index is applied to be executed after the optimization process to which the 21st evaluation index is applied (the optimization process in the first section AR1). Based on this information, the schedule image generation unit 130 has generated the fourth schedule image IM4, and as shown in Figure 20, the 21st figure F21 corresponding to the 21st evaluation index and the 22nd figure F22 corresponding to the 22nd evaluation index are displayed in the third section AR3. Also, as shown in Figure 20, the string "essential condition" is displayed near the 21st figure F21. This allows the user to intuitively understand that outputting update control parameters that satisfy the target value of the 21st evaluation index is an essential condition for the optimization process in the third interval, AR3.

[0213] Furthermore, as shown in Figure 20, the string "Evaluation Index" is displayed near the 22nd figure F22. This allows the user to intuitively understand that in the third interval AR3, an optimization process to which the 22nd evaluation index is applied is executed. In other words, in the third interval AR3, the user can intuitively understand that an optimization process is executed to search for update control parameters that satisfy the target value of the 22nd evaluation index while satisfying the target value of the 21st evaluation index. Hereinafter, in a predetermined optimization process, a target set separately from the essential conditions will be referred to as the second target (an example of an additional condition). In the third interval AR3, satisfying the target value of the 21st evaluation index is an essential condition, and the target value of the 22nd evaluation index is the second target.

[0214] Furthermore, the display control unit 140 may make the user understand that outputting control parameters that satisfy the target value of the 21st evaluation index is an essential condition for the optimization process in the third section AR3 by highlighting the 21st figure F21 in the third section AR3, for example, by making the frame color of the 21st figure F21 in the third section AR3 different from that of the 22nd figure F22.

[0215] In the fourth section AR4, a second determination is made. In the example shown in Figure 20, the second determination button B52 is displayed in the fourth section AR4. In this embodiment, the schedule image generation unit 130 draws a determination button (second determination button B52 in Figure 20) on the schedule image IM to make it clear that a second determination is made when the schedule generation unit 150 has acquired the second essential condition.

[0216] The second determination checks whether the second essential condition has been met. Specifically, the second determination checks whether the update control parameters that achieve the target value of the 21st evaluation index are output during the optimization process in the third interval AR3. In other words, it checks whether the update control parameters that reduce the settling time to 20 milliseconds or less are output.

[0217] In this example, the display control unit 140, having completed the second determination, inputs the determination result of whether or not the second essential condition is met to the evaluation unit 160. Based on the input determination result, the evaluation unit 160 calculates the evaluation score of the optimization process performed in the third section AR3. If the second essential condition is not met, the evaluation unit 160 corrects the evaluation score of the optimization process performed in the third section AR3 by a penalty value. The penalty value is, for example, a multiplier that reduces the evaluation score. In other words, if the update control parameters that do not satisfy the second essential condition are output as a result of the optimization process performed in the third section AR3, the evaluation unit 160 lowers the evaluation of the optimization process performed in the third section AR3. On the other hand, if the essential condition is met, the evaluation unit 160 does not correct the evaluation score by a penalty value.

[0218] Furthermore, if the evaluation score of the optimization process performed in the third section AR3 is below a predetermined threshold, for example, if the evaluation score of the optimization process becomes below the predetermined threshold as a result of correction by the penalty value, the optimization processing unit 60 may re-execute the optimization process in the third section AR3. Specifically, the optimization processing unit 60 may repeatedly execute the optimization process in the third section AR3 until it outputs an update control parameter that exceeds the predetermined threshold in evaluation score.

[0219] Furthermore, the evaluation unit 160 may calculate the evaluation score after all optimization processes have been completed. That is, the evaluation unit 160 may calculate the evaluation score after the processing from the first section AR1 to the fifth section AR5 has been completed.

[0220] Furthermore, in the optimization process to which the prior evaluation index is applied, if the target value of the prior evaluation index is achieved, the schedule generation unit 150 of this embodiment may set the achievement of the target value of the prior evaluation index as an essential condition for the optimization process to which the subsequent evaluation index is applied (referred to as the subsequent optimization process).

[0221] Focusing on the third section AR3 and the fifth section AR5 in Figure 20, the 21st and 22nd evaluation indicators are applied to the optimization process before the 23rd evaluation indicator. Therefore, the 21st and 22nd evaluation indicators are considered leading evaluation indicators. On the other hand, the 23rd evaluation indicator is considered a succeeding evaluation indicator. Now, suppose that in the optimization process performed in the third section AR3, update control parameters that achieve the target values ​​of the 21st and 22nd evaluation indicators are output. In this case, the schedule generation unit 150 may set achieving the target values ​​of the 21st and 22nd evaluation indicators as a mandatory condition for the optimization process performed in the fifth section AR5.

[0222] The fifth section, AR5, indicates the section in which the optimization process to which the 23rd evaluation index is applied is performed. The 21st figure F21, the 22nd figure F22, and the 23rd figure F23 are displayed in the fifth section, AR5. As shown in Figure 20, the text "Required Condition" is displayed near the 21st figure F21 and the 22nd figure F22. This allows the user to intuitively understand that satisfying the target values ​​of the 21st and 22nd evaluation indexes is a required condition for the optimization process performed in the fifth section, AR5. Also, as shown in Figure 20, the text "Evaluation Index" is displayed near the 23rd figure F23. This allows the user to intuitively understand that satisfying the target value of the 23rd evaluation index is the second objective of the optimization process performed in the fifth section, AR5. Furthermore, the display control unit 140 may make the frame color of the 23rd figure F23 different from the frame colors of the 21st figure F21 and the 22nd figure F22. This may help users understand that achieving the target value of the 23rd evaluation indicator is the second objective.

[0223] Figure 21 is a flowchart showing the processing flow executed by the information processing unit 50A according to Embodiment 3.

[0224] In step S41, the schedule generation unit 150 obtains essential condition information from the user. For example, the schedule generation unit 150 receives essential condition information when it receives information from the user on the initial setup input screen 210. Based on the essential condition information, the schedule generation unit 150 sets achieving the target value of a predetermined evaluation indicator (for example, the 21st evaluation indicator) as an essential condition for the optimization process.

[0225] In step S42, the schedule generation unit 150 generates schedule information D1. For example, the schedule generation unit 150 generates schedule information D1 based on the information received from the user on the initial setting input screen 210. For example, if two indicators set for the 21st figure F21 and the 22nd figure F22 are specified for the period of the third interval AR3 in Figure 20, and the 21st figure F21 is specified as a mandatory condition, the optimization indicator for the period of the third interval AR3 will be the specified evaluation value (for example, the worst evaluation value specified in advance or infinity) if the indicator for the 21st figure F21 is not satisfied, and the evaluation value for the 22nd figure F22 will be used if the indicator for the 21st figure F21 is satisfied, and this will be the schedule information D1 for the period of the third interval AR3.

[0226] In step S43, the schedule image generation unit 130 generates a schedule image IM based on the schedule information D1.

[0227] In step S44, the display control unit 140 outputs the schedule image IM to the display unit 17. The user sets conditions for the first judgment button B51 and the second judgment button B52. For example, the first judgment button B51 can be set to have the same required conditions as the 21st figure F21, and the second judgment button B52 can be set to have the condition that both the 21st figure F21 and the 22nd figure F22 are satisfied. The first judgment button B51 may also be set to have a condition for the maximum time for optimizing the first section AR1. If a maximum time is set, the schedule information will automatically move to optimizing the third section AR3 if the required conditions are not met.

[0228] If the user adjusts the width of the 21st shape F21, the width of the 22nd shape F22 will be adjusted accordingly. Adjusting the width will also adjust the maximum optimization time.

[0229] When a user switches between required conditions / evaluation metrics, the schedule information will be updated accordingly.

[0230] In step S45, the optimization processing unit 60 starts an optimization process to which essential conditions are applied (set). For example, it starts an optimization process to which achieving the target value of the 21st evaluation index is applied as an essential condition.

[0231] In step S46, the display control unit 140 detects that the optimization process for which the essential conditions have been applied (set) has been completed.

[0232] In step S47, the display control unit 140 determines whether the essential conditions have been met. That is, it determines whether the target value of a predetermined evaluation index (for example, the 21st evaluation index) applied to the optimization process has been achieved.

[0233] In step S48, the display control unit 140 inputs the result of determining whether or not the essential conditions have been met to the evaluation unit 160.

[0234] In step S49, the evaluation unit 160 calculates an evaluation score for a specific optimization process based on the determination result obtained from the display control unit 140.

[0235] In step S50, the optimization processing unit 60 terminates the optimization process.

[0236] According to the control parameter generation system 1A described above, achieving the target values ​​of at least some of the evaluation indicators is set as an essential condition for the optimization process. In this way, it becomes possible to search for updated control parameters that satisfy the essential condition.

[0237] Furthermore, in the control parameter generation system 1A according to this embodiment, the achievement of the target value of the prior evaluation index is set as an essential condition for the subsequent optimization process. This makes it possible to execute an optimization process in which the target of the prior evaluation index is continuously achieved. Moreover, with the above configuration, it becomes possible to execute the optimization process while adding essential conditions in stages. That is, as the optimization process progresses, it becomes possible to gradually add essential conditions to the optimization process. This makes it possible to proceed with the optimization process while narrowing down the update control parameters that can achieve the target value. As a result, it is possible to efficiently search for update control parameters.

[0238] Furthermore, in the control parameter generation system 1A according to this embodiment, achieving the target value of the prior evaluation index and achieving the target value of the subsequent evaluation index (second target) are set as additional conditions for the subsequent optimization process. In this way, it is possible to perform an optimization process that can achieve both the target value of the prior evaluation index and the target value of the subsequent evaluation index. Specifically, it becomes possible to search for control parameters that can achieve both the target value of the prior evaluation index and the target value of the subsequent evaluation index through the optimization process.

[0239] The following modifications can be adopted for this embodiment.

[0240] (3-1) In Embodiment 3, an example was described in which the 21st optimization process is repeatedly executed until the target value of the 21st evaluation index is achieved. However, when the 21st evaluation index no longer improves, that evaluation index may be made a mandatory condition.

[0241] For example, suppose an optimization process to which the 21st evaluation index is applied is executed with the aim of searching for an update control parameter that can set the settling time to 20 milliseconds or less. As a result, an update control parameter that sets the settling time to 25 milliseconds is output, but even if the optimization process is repeated more than a predetermined number of times, no update control parameter that can shorten the settling time to less than 25 milliseconds is output. In this case, the schedule generation unit 150 acquires 25 milliseconds as the limit value. The "setting the settling time to 25 milliseconds or less" may then be set as a mandatory condition for the 22nd optimization process, which is performed after the 21st optimization process.

[0242] (3-2) Essential condition information does not necessarily have to be entered on the initial setup input screen 210, and may be entered during the optimization process. For example, suppose that based on the user's edits on the initial setup input screen 210, schedule information D1 is generated indicating that the optimization process to which the 21st evaluation metric is applied will be executed for a predetermined time, and then the optimization process to which the 22nd evaluation metric is applied will be executed. Suppose that the optimization processing unit 60 starts the optimization process to which the 21st evaluation metric is applied based on this schedule information D1. Then, the essential condition information may be accepted during the optimization process to which the 21st evaluation metric is applied. As an example, essential condition information indicating that "achieving the target value of the 21st evaluation metric" is an essential condition may be accepted. In this case, even if a predetermined time has elapsed, the optimization processing unit 60 will continue the optimization process to which the 21st evaluation metric is applied until it outputs an update control parameter that satisfies the target value of the 21st evaluation metric.

[0243] (Embodiment 4) Multiple evaluation metrics may be set for a single optimization process. Hereinafter, an optimization process with multiple evaluation metrics set will be referred to as a multi-objective optimization process. When the optimization processing unit 60 executes a multi-objective optimization process, update control parameters are searched for that improve multiple evaluation metrics simultaneously. One method for searching for update control parameters that improve multiple evaluation metrics simultaneously is to set weights for each evaluation metric, but the appropriate weights for the metrics may be unknown. Therefore, in Embodiment 4, when a multi-objective optimization process is performed, the user is shown the intermediate results of the process, and the user calculates the balance of the multiple metrics that the user is targeting.

[0244] In Embodiment 4, components similar to those in Embodiment 2 are denoted by the same reference numerals, and their descriptions are omitted. Furthermore, in Embodiment 4, the block diagram shown in Figure 15 is used with reference.

[0245] Figure 22 shows an example of the initial setup input screen 210 according to this embodiment. In the example shown in Figure 22, the user places the first figure F1 and the second figure F2 along the second direction (here, the vertical direction), places a branch button B53 to the right of them, and places the 31st figure F31 to the right of that. The 31st figure F31 shown in Figure 22 is a figure corresponding to a composite index. The composite index will be described later. The schedule generation unit 150 generates schedule information D1 based on the contents entered in the initial setup input screen 210. The processing performed according to this schedule information D1 will be described below.

[0246] First, the optimization processing unit 60 performs a multi-objective optimization process to search for update control parameters that can improve both the first evaluation index corresponding to the first figure F1 and the second evaluation index corresponding to the second figure F2.

[0247] After the start of the multi-objective optimization process, the display control unit 140 periodically determines whether or not the branching conditions have been met. The branching conditions are, for example, that a certain amount of time has elapsed since the start of the multi-objective optimization process, or that at least one of the first evaluation indicator and the second evaluation indicator has improved. The branching conditions are input by the user through the branching condition setting screen displayed on the initial setting input screen 210 when the display control unit 140 or the schedule generation unit 150 detects that the user has selected the branching button B53. When the branching conditions are met, the display control unit 140 acquires the update control parameters generated by the multi-objective optimization process and inputs these update control parameters to the evaluation unit 160.

[0248] The evaluation unit 160 evaluates the update control parameters input from the display control unit 140 according to predetermined evaluation criteria. For example, the evaluation unit 160 evaluates the degree of achievement of the target values ​​of the first evaluation indicator and the second evaluation indicator. The evaluation results from the evaluation unit 160 are input to the display control unit 140.

[0249] When the display control unit 140 acquires an evaluation result, it plots the evaluation result (an example of a processing result) in the evaluation space SP (an example of a processing result display space). Figure 23 is a diagram showing an example of a Pareto solution display screen where the evaluation space SP is displayed. The horizontal axis a1 of the evaluation space SP corresponds to the first evaluation index, and the vertical axis a2 corresponds to the second evaluation index. If the evaluation of the update control parameter with respect to the first evaluation index is high, the point P indicating the evaluation result is plotted on the right side of the evaluation space SP (positive direction of the horizontal axis a1). If the evaluation of the second evaluation index is high, the point P indicating the evaluation result is plotted on the upper side of the evaluation space SP (positive direction of the vertical axis a2).

[0250] The display control unit 140 calculates the Pareto solution each time a new evaluation result (new point P) is plotted in the evaluation space SP. Existing methods can be used to calculate the Pareto solution. In the example shown in Figure 23, points P1, P2, P3, P4, and P5 included in the Pareto frontier 801 are calculated as Pareto solutions. Note that the multiple points P10 shown in Figure 23 are subsolutions. The display control unit 140, having calculated the Pareto solution, outputs the evaluation space SP shown in Figure 23 to the display unit 17. If there are newly plotted points P in the evaluation space SP, the display control unit 140 may highlight the newly plotted points P by blinking or other means.

[0251] The Pareto solution display screen shown in Figure 23 functions as a user interface. The display control unit 140 in this embodiment accepts user input through the Pareto solution display screen.

[0252] For example, the display control unit 140 accepts an operation from the user to select an arbitrary point P in the evaluation space SP (selection operation). In this case, the display control unit 140 displays the details of the point P (evaluation result) selected by the user on the display unit 17. Specifically, it displays waveform data when the production device 20 is operated using the update control parameter corresponding to point P. The display control unit 140 may also display the update control parameter itself corresponding to point P on the display unit 17.

[0253] Although detailed illustrations are omitted, the Pareto solution display screen includes a weight setting mode start button. When the display control unit 140 detects that the weight setting mode start button has been selected by the user, it starts the weight setting process. In the weight setting process, the display control unit 140 first accepts an operation from the user to select an arbitrary point P. For example, the display control unit 140 accepts an operation to select point P2 as shown in Figure 23. Upon detecting this operation, the display control unit 140 calculates the slope of the first line L1 connecting the origin O of the evaluation space SP and point P2. For example, assume that for every 1 increase in the value of the horizontal axis a1 (corresponding to the first evaluation index), the value of the vertical axis a2 (corresponding to the second evaluation index) increases by 3. That is, assume that the slope of the first line L1 is "3". Based on this slope, the display control unit 140 sets the weights of each evaluation index when performing multi-objective optimization processing or single-objective optimization processing. For example, the display control unit 140. The weights of each evaluation index are set so that a new Pareto solution is plotted along the direction in which the first line L1 extends. Specifically, the display control unit 140 sets the ratio of the weights of the first evaluation index and the second evaluation index to "1:3". A single-objective optimization process refers to an optimization process in which one evaluation index is set for each optimization process.

[0254] As another example of weight setting processing, the display control unit 140 may accept an operation from the user to select multiple points in the evaluation space SP. For example, the display control unit 140 may accept an operation to select points P2 and P3 as shown in Figure 23. In this case, the display control unit 140 calculates the slope of the first line L1 connecting the origin O of the evaluation space SP and point P2, and the slope of the second line L2 connecting the origin O and point P3. The slope of the first line L1 is "3" as described above. The slope of the second line L2 is assumed to be "1". Based on these slopes, the display control unit 140 sets the weights of each evaluation index when performing multi-objective optimization processing so that a new Pareto solution is plotted in the plot target area 802 surrounded by the first line L1, the second line L2, and the Pareto frontier 801. For example, the display control unit 140 sets the ratio of the weights of the first evaluation index and the second evaluation index to "1 to 3:1". In Figure 23, the plot target area 802 is indicated by dot hatching.

[0255] As another example of the weight setting process, the display control unit 140 may accept range specification information from the user. Figure 24 shows another example of a Pareto solution display screen. Range specification information is an operation that specifies multiple coordinates in the evaluation space SP. The display control unit 140 acquires range specification information by accepting, for example, a drag operation from the user. Based on the range specification information, the display control unit 140 may set a target area. For example, suppose the user has specified a specified range 821, which is shown by dot hatching in Figure 24. In this case, the display control unit 140 sets the area corresponding to the specified range 821 as the plot target area. That is, it sets the weights of each evaluation index when performing the multi-objective optimization process so that a new Pareto solution is plotted within the area corresponding to the specified range 821.

[0256] Once the display control unit 140 completes the weight setting process, the schedule generation unit 150 generates a combined index by combining the first evaluation index and the second evaluation index using the weights described above. The schedule generation unit 150 then updates the schedule information D1 to specify that an optimization process to which the combined index is applied should be executed. Based on this schedule information D1, the optimization processing unit 60 executes a second multi-objective optimization process to which the combined index has been set.

[0257] Figure 25 is a flowchart showing an example of the processing performed by the information processing unit 50A according to this embodiment.

[0258] In step S61, the display control unit 140 obtains the processing result of the multi-objective optimization process.

[0259] In step S62, the display control unit 140 calculates a Pareto solution based on the multiple processing results.

[0260] In step S63, the display control unit 140 plots a plurality of processing results, including the Pareto solution, in the evaluation space SP.

[0261] In step S64, the display control unit 140 displays the evaluation space SP on the display unit 17.

[0262] In step S65, the display control unit 140 starts the weight setting process. For example, the display control unit 140 starts the weight setting process when it detects that the weight setting mode start button has been selected by the user.

[0263] In step S66, the display control unit 140 accepts a selection operation to select at least one of the evaluation results (points P) plotted in the evaluation space SP.

[0264] In step S67, the display control unit 140 outputs details of the evaluation result selected in the selection operation. For example, it displays waveform data corresponding to the selected evaluation result.

[0265] In step S68, the display control unit 140 determines the plot target area 802 for plotting the evaluation result in the evaluation space SP, based on the evaluation result selected in the selection operation.

[0266] According to the control parameter generation system 1A of this embodiment, an evaluation space SP on which the Pareto solution is plotted is output, so the user can easily check the Pareto solution of the multi-objective optimization process.

[0267] Furthermore, in this embodiment, when the user selects a point P (evaluation result), details of the selected point P (e.g., waveform data) are presented to the user. Therefore, the user can easily confirm the details of the processing results.

[0268] Furthermore, in this embodiment, the plot target area 802 is determined based on the selected point P (evaluation result). In other words, the search direction for the update control parameters is automatically determined by the intuitive operation of selecting point P. Therefore, even if the user is unfamiliar with setting evaluation indicators, the optimization processing unit 60 can easily perform the optimization process to search for the update control parameters desired by the user.

[0269] The following modifications can be adopted for this embodiment.

[0270] (4-1) In Embodiment 4, an example was described in which update control parameters generated by the multi-objective optimization process are acquired and the evaluation results of the evaluation unit 160 for these update control parameters are plotted on the Pareto solution display screen, but this is just one example. The display control unit 140 may also display the distribution of update control parameters generated by the multi-objective optimization process in a diagram.

[0271] Figure 26 shows an example of a parameter distribution diagram 900. The parameter distribution diagram 900 shows the distribution of update control parameters generated by the optimization processing unit 60 performing multi-objective optimization processing. For example, suppose that a first parameter and a second parameter are generated as update control parameters by performing multi-objective optimization processing. The first parameter and the second parameter are different types of parameters. The vertical axis of Figure 26 corresponds to the first parameter (labeled "Parameter 1" in Figure 26), and the horizontal axis corresponds to the second parameter (labeled "Parameter 2" in Figure 26). Each of the points PO1 to PO5 shown in Figure 26 corresponds to an update control parameter generated by the multi-objective optimization processing.

[0272] According to the configuration of this modified example, the distribution of update control parameters generated by the multi-objective optimization process is visualized by the parameter distribution diagram 900, allowing the user to intuitively understand the results of the multi-objective optimization process.

[0273] Furthermore, in Embodiment 4, an example was described in which the user is allowed to select an arbitrary point P displayed on the Pareto solution display screen during the weight setting process, but this is just one example. The display control unit 140 may also accept an operation to select an arbitrary point PO included in the parameter distribution diagram 900 during the weight setting process. In this case, the display control unit 140 adjusts the settings of the optimization process so that an update control parameter with a value close to the value of the update control parameter selected by the user is searched for. For example, if point PO1 shown in Figure 26 is selected by the user, the display control unit 140 adjusts the settings of the optimization process so that in the next and subsequent optimization processes, an update control parameter with a value close to the value of the update control parameter corresponding to point PO1 is output. In other words, the display control unit 140 adjusts the settings of the optimization process so that a value close to the user's preferred parameter value is searched for.

[0274] (Combinable Configurations) The following describes a group of combinable configurations, including the configurations disclosed herein.

[0275] (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.

[0276] 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.

[0277] 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.

[0278] 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.

[0279] 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.

[0280] 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.

[0281] 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.

[0282] 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.

[0283] 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.

[0284] 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.

[0285] 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.

[0286] In a twelfth embodiment of the first configuration, the thumbnail information includes information indicating a target set for the at least one evaluation indicator.

[0287] 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.

[0288] 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.

[0289] 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.

[0290] 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.

[0291] 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.

[0292] In the eighteenth embodiment of the first configuration, the information processing unit further receives branching condition information indicating branching conditions, and selects at least one evaluation index to be applied to the optimization process from a plurality of options based on the branching conditions.

[0293] 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.

[0294] 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.

[0295] 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.

[0296] 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.

[0297] 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.

[0298] 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.

[0299] 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.

[0300] 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.

[0301] 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.

[0302] (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.

[0303] 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.

[0304] 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.

[0305] 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.

[0306] 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.

[0307] 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.

[0308] 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.

[0309] 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.

[0310] 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.

[0311] (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.

[0312] 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.

[0313] 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.

[0314] 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.

[0315] 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.

[0316] 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.

[0317] 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.

[0318] 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.

[0319] 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.

[0320] (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.

[0321] 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.

[0322] 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.

[0323] 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.

[0324] 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.

[0325] 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.

[0326] 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.

[0327] 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.

[0328] 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.

[0329] 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.

[0330] 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.

[0331] 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.

[0332] 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.

[0333] 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.

[0334] 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.

[0335] (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.

[0336] 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.

[0337] 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.

[0338] 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.

[0339] 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.

[0340] 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 that indicates the execution conditions for an operation to be performed by the drive system.

[0341] 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.

[0342] 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.

[0343] 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.

[0344] 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.

[0345] 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.

[0346] 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.

[0347] 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.

[0348] 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.

[0349] 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.

[0350] 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, comprising: an information processing unit acquiring schedule information indicating the schedule of the optimization process; generating figures corresponding to each of at least one evaluation indicators 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.

2. The information processing method according to claim 1, wherein the at least one evaluation index includes a first evaluation index and a second evaluation index different from the first evaluation index, 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.

3. The information processing method according to claim 1, wherein 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.

4. The information processing method according to claim 2, further comprising: the information processing unit setting weights for at least one evaluation index; changing the weights over time; and outputting a graph showing the changes in the weights.

5. The information processing unit further outputs a preset showing candidates for a weight change function that defines the change in the weights over time, and determines the weight change function by accepting an operation to select the preset, as described in claim 4.

6. The information processing method according to claim 1, wherein 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.

7. 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, according to claim 6.

8. The information processing method according to claim 6, wherein 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, and the information processing unit further determines whether the target of the prior evaluation index has been achieved in the optimization process to which the prior evaluation index is applied, 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.

9. The information processing method according to claim 6, wherein 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, and 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.

10. The information processing method according to claim 6, wherein 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, and 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 as a limit value, and the subsequent evaluation index satisfies the limit value.

11. The information processing method according to claim 1, wherein the information processing unit further provides the figure with thumbnail information representing the evaluation index to which the figure corresponds.

12. The information processing method according to claim 11, wherein the thumbnail information includes information indicating a target set for the at least one evaluation indicator.

13. The information processing method according to claim 1, wherein 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.

14. The information processing unit outputs a schedule image in which a detail button is superimposed on the figure when outputting the schedule image, 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, according to the information processing unit according to claim 1.

15. 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, according to claim 1.

16. The information processing method according to claim 1, wherein 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.

17. The information processing unit further receives a resizing 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 resizing 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.

18. The information processing method according to claim 1, wherein the information processing unit further receives branching condition information indicating branching conditions, and selects the at least one evaluation index to be applied to the optimization process from a plurality of options based on the branching conditions.

19. The information processing method according to claim 18, wherein the plurality of options include a first option and a second option different from the first option, 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 indicating that the schedule branches.

20. The information processing method according to claim 1, wherein the optimization process is a multi-objective optimization process in which a plurality of evaluation indicators are set, and the information processing unit further obtains 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.

21. 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, according to claim 20.

22. The information processing method according to claim 21, wherein the information processing unit further determines a target plotting area when plotting a new processing result in the processing result display space, based on the processing result selected in the selection operation.

23. 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, the information processing method according to claim 1.

24. The information processing method according to claim 1, further comprising: the information processing unit acquiring sequence information indicating the order in which the at least one evaluation index is applied to the optimization process; and generating 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.

25. The information processing method according to claim 1, wherein 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.

26. 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.

27. A program for causing an information processing device to execute a process for supporting the management of an optimization process, wherein the process includes: acquiring schedule information indicating the schedule of the optimization process; generating a figure corresponding to each of at least one evaluation indicator 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.

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