Information processing method, information processing device, and program

Abstract

Provided is an information processing method for assisting in management of an optimization process, wherein an information processing unit outputs question information indicating an optimization-process-related question to a user, acquires answer information indicating an answer to the question information, and, on the basis of the answer information, generates evaluation index information indicating at least one evaluation index applied to the optimization process.

Description

Information processing method, information processing apparatus, and program

[0001] The present disclosure relates to a technique for generating control parameters for controlling a production apparatus.

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

[0003] However, the technique described in Patent Document 1 has a problem in that it is difficult to determine an evaluation index to be applied to the optimization processing.

[0004] International Publication No. 2023 / 203933

[0005] The present disclosure has been made to solve such problems, and an object thereof is to provide a technique that facilitates determining an evaluation index 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 outputs question information indicating questions to a user regarding the optimization processing, acquires answer information indicating answers to the question information, and generates evaluation index information indicating at least one evaluation index to be applied to the optimization processing based on the answer information.

[0007] According to this configuration, it becomes easy to determine the evaluation index to be applied to the optimization processing.

[0008] FIG. 1 is a block diagram showing a configuration of a control parameter generation system. FIG. 2 is a perspective view showing an example of a production apparatus. FIG. 3 is a schematic diagram showing an example of time-series data indicating a transition of a positional deviation of a driven object with respect to a target position over time. FIG. 4 is a diagram showing an example of a configuration of an evaluation index control unit. FIG. 5 is a diagram showing a first question screen. FIG. 6 is a diagram showing a second question screen. FIG. 7A is a diagram showing an example of an evaluation index display screen. FIG. 7B is a diagram showing details of the evaluation index display screen. FIG. 8 is a flowchart showing an example of a flow of processing executed by an information processing unit. FIG. 9 is a flowchart showing an example of a detailed flow of processing executed by the information processing unit.

[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] In the technology described in Patent Document 1, it is difficult to determine the evaluation metrics to be applied to the optimization process. Specifically, when using the technology described in Patent Document 1, it is necessary to set evaluation metrics in order to determine how to improve the control parameters through the optimization process. However, users are often unfamiliar with the process of setting evaluation metrics. Therefore, there is a need for a technology that allows for easy setting of evaluation metrics.

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

[0015] (1) An information processing method in one aspect of the present disclosure is an information processing method for supporting the management of an optimization process, wherein an information processing unit 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.

[0016] In this configuration, the information processing unit outputs question information to the user, obtains answer information for the question information, and generates evaluation metric information based on the answer information. The user can use this evaluation metric information to determine the evaluation metrics to be applied to the optimization process. As a result, it becomes easier to determine the evaluation metrics to be applied to the optimization process.

[0017] (2) In the information processing method described in (1) above, 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 may include determining the order in which the plurality of evaluation indexes are applied to the optimization process.

[0018] This configuration generates evaluation metric information that indicates the order in which multiple evaluation metrics are applied to the optimization process. In other words, the information processing unit determines, based on the response information, the order in which multiple evaluation metrics are applied to the optimization process. This makes it even easier to determine the evaluation metrics to be applied to the optimization process.

[0019] (3) In the information processing method described in (1) or (2) above, the output of the question information by the information processing unit may include displaying a plurality of waveform data showing the operating waveform of the device and outputting question information that causes the user to select from the plurality of waveform data a waveform to be accepted as the target operating waveform for waveform improvement by the optimization process.

[0020] With this configuration, information indicating the acceptable waveform data (referred to as target acceptable data) for the waveform improvement target through optimization processing is obtained as response information. Therefore, it becomes possible to have the information processing unit determine the evaluation index to be applied to the optimization process based on the target acceptable data.

[0021] Furthermore, in the above configuration, question information is output that prompts the user to select target acceptable data from multiple waveform data sets. Therefore, the user can input answer information by selecting any data from among the multiple waveform data sets. In this way, even if it is difficult for the user to verbalize and input the answer information, the user can easily input the answer information.

[0022] (4) In the information processing method described in any one of (1) to (3) above, the output of the question information by the information processing unit may include displaying a plurality of waveform data showing the operating waveform of the device and outputting question information that causes the user to select from the plurality of waveform data waveforms an acceptable operating waveform in the process of waveform improvement by the optimization process.

[0023] With this configuration, information indicating waveform data that is acceptable as an intermediate operational waveform during waveform improvement by optimization processing (referred to as intermediate acceptable data) and waveform data that is not acceptable as an intermediate operational waveform during waveform improvement by optimization processing (referred to as intermediate unacceptable data) is obtained as response information. Therefore, it becomes possible to have the information processing unit determine the evaluation index to be applied to the optimization processing based on the intermediate acceptable data and intermediate unacceptable data. In this way, it becomes possible to perform optimization processing that generates intermediate acceptable data while not generating intermediate unacceptable data.

[0024] (5) In the information processing method described in any one of (1) to (4) above, the output of the question information by the information processing unit may include displaying a plurality of waveform data showing the operating waveform of the device and outputting question information asking about the difficulty of waveform improvement by the optimization process for each of the plurality of waveform data.

[0025] This configuration allows for obtaining information indicating the difficulty level of waveform improvement through optimization processing for each of multiple waveform candidate data (referred to as difficulty ranking information) as response information. Therefore, it becomes possible to have the information processing unit determine the order of evaluation indicators applied to the optimization process based on the difficulty ranking information.

[0026] (6) In the information processing method described in any one of (1) to (5) above, the output of the question information by the information processing unit may include outputting question information that asks for the ranking of the difficulty level of waveform improvement by the optimization process for each of the plurality of waveform data.

[0027] This configuration allows for obtaining information as response data that ranks the difficulty of waveform improvement through optimization processing. By generating evaluation index information based on this response data, it becomes possible to execute optimization processing in order from the easiest to the most difficult waveform improvement.

[0028] (7) In the information processing method described in any one of (1) to (6) 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.

[0029] 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.

[0030] (8) 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 which 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.

[0031] This configuration provides an information processing device that achieves the same effects as the information processing method described above.

[0032] (9) A program according to yet another aspect of the present disclosure is a program for causing an information processing device to perform a process for assisting in the management of an optimization process, the process outputting question information indicating a question to the user regarding the optimization process, obtaining answer information indicating an answer to the question information, and generating evaluation index information indicating at least one evaluation index to be applied to the optimization process based on the answer information.

[0033] This configuration allows us to provide a program that achieves the same effect as the information processing method described above.

[0034] 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.

[0035] (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.

[0036] (Embodiment 1) The control parameter generation system 1 according to Embodiment 1 will be described below. This control parameter generation system 1 is a system that generates control parameters to be used in a production apparatus 20 equipped with a drive source 23 that drives an object to be driven 24.

[0037] Figure 1 is a block diagram showing the configuration of the control parameter generation system 1.

[0038] 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.

[0039] 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.

[0040] The production device 20 performs N (where N is an integer greater than or equal to 3) operations. For example, N is 80. The production device 20 includes a memory 21, a control circuit 22, a drive source 23, and a drive target object 24.

[0041] The operation of the drive source 23 is controlled by the control circuit 22, and the drive source 23 drives a driven object 24. Specifically, the drive source 23 is, for example, a servo motor, a directional flow control valve for fluid used in controlling a pneumatic artificial muscle arm, or a directional flow control valve for fluid used in controlling a hydraulic arm. The servo motor may be, for example, a rotary motor or a linear motor.

[0042] The driven object 24 is an object driven by the drive source 23. For example, when the drive source 23 is a servo motor, the driven object 24 is a head that conveys a workpiece to be machined, a nozzle provided on the head for sucking the workpiece to be machined, or the like. Further, when the drive source 23 is a directional flow control valve, the driven object 24 is, for example, a pneumatic artificial muscle arm, a hydraulic arm, or the like.

[0043] FIG. 2 is a perspective view of a production apparatus 20 having a configuration in which, as an example, the drive source 23 is a servo motor and the driven object 24 is a nozzle provided in a head. As shown in FIG. 2, as an example, the production apparatus 20 may be a mounting apparatus that mounts components onto a substrate 105 placed on a machine base 106.

[0044] As an example, the production apparatus 20 includes a nozzle 81 that sucks components, a head 80 provided with the nozzle 81, a servo motor 23A that functions as the drive source 23 for moving the head 80 in the X-axis direction in a plan view of the machine base 106, and a servo motor 23B that functions as the drive source 23 for moving the head 80 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.

[0045] Returning to FIG. 1 again, the description of the control parameter generation system 1 will be continued.

[0046] The control circuit 22 controls the drive source 23 by outputting a command to the drive source 23 for setting the position of the driven object 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 driven object 24, or may be, for example, a torque command that commands the torque of the drive source 23.

[0047] 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.

[0048] The memory 21 stores control parameters used when the control circuit 22 controls the drive source 23. The control parameters stored in the memory 21 are control parameters output from the control parameter generation device 10.

[0049] When a control parameter is output from the control parameter generation device 10 (an example of an information processing device), the memory 21 acquires the output control parameter, and updates the stored control parameter with the acquired control parameter. That is, the memory 21 overwrites and stores the control parameter.

[0050] The sensor 30 measures, in time series, a position signal indicating the position of the driven object 24 in the production apparatus 20 that performs at least one operation among N operations. Then, the sensor 30 outputs measurement data indicating the measured position, which corresponds to each of the at least one operation, to the control parameter generation device 10.

[0051] FIG. 3 is an example of time-series data (waveform data) showing a transition over time of the positional deviation of the driven object 24 relative to a target position when the production apparatus 20 drives the driven object 24 to the target position. This waveform data is generated by the information processing unit 50 based on measurement data acquired by the sensor 30. In FIG. 3, the horizontal axis represents time, and the vertical axis represents the positional deviation of the driven object 24 relative to the target position.

[0052] As shown in FIG. 3, in the present specification, the allowable range refers to a range in which the positional deviation from the target position is within the required accuracy.

[0053] Furthermore, as shown in Figure 3, in this specification, the time at which the target position can be evaluated as having been reached (hereinafter referred to as "settlement time") refers to the time at which the driven object 24 last reached the allowable range after it had reached the allowable range and no longer deviated from the allowable range.

[0054] Furthermore, as shown in Figure 3, in this specification, the settling time refers to the time from when the driven object 24 starts to stop based on a command to move the driven object 24 to the target position until the driven object 24 reaches an acceptable position where it can be evaluated that it has reached the target position, that is, the time from the start of stopping to the settling time. Alternatively, the settling time refers to the time from when the driven object 24 starts to move based on a command to move the driven object 24 to the target position until the driven object 24 reaches an acceptable position where it can be evaluated that it has reached the target position, that is, the time from the start of movement to the settling time.

[0055] Furthermore, the waveform data shown in Figure 3 includes multiple intersections P. Each of the multiple intersections P indicates the point in time when the driven object 24 passes (crosses) the target position. Hereafter, the number of times the driven object 24 passes the target position before reaching the settling time will be referred to as the "number of crossings".

[0056] Returning to Figure 1, the control parameter generation device 10 performs optimization processing to generate control parameters. The control parameter generation device 10 is implemented, for example, in a computer device comprising a processor, memory, and an input / output interface, where the processor executes a program stored in memory. Such a computer device is, for example, a personal computer (PC). However, this is just one example. The control parameter generation device 10 may also be composed of a server device (e.g., a cloud server) including one or more computers, each installed in a facility different from the production device 20.

[0057] 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.

[0058] The information processing unit 50, which has an optimization processing unit 60 and an evaluation index control unit 100, is a function realized by the processor executing a program read from a computer-readable non-temporary recording medium (such as ROM). In other words, the above program is a program that causes the information processing unit 50 mounted on the control parameter generation device 10 to function as the optimization processing unit 60 and the evaluation index control unit 100.

[0059] The optimization processing unit 60 generates updated control parameters by executing optimization processing. The optimization processing unit 60 will be described below using the example of the case where the optimization processing unit 60 performs optimization processing to improve the evaluation index of "settlement time". First, the optimization processing unit 60 selects a representative operation that includes at least one operation from all the operations that the production device 20 can perform. Then, the optimization processing unit 60 causes the production device 20 to execute the selected representative operation. Based on this instruction, the control circuit 22 of the production device 20 outputs a command to the drive source 23 to move the position of the driven object 24 to a predetermined target position. Then, the optimization processing unit 60 acquires measurement data corresponding to each of the one or more operations performed by the production device 20, which are output from the sensor 30. Then, for each of the one or more operations acquired from the sensor 30, the optimization processing unit 60 determines the settlement time based on the measurement data corresponding to that operation.

[0060] The optimization processing unit 60 in this embodiment updates the control parameters through optimization processing to shorten at least the longest settling time among the one or more settling times corresponding to one or more operations, and generates updated control parameters. The optimization processing unit 60 may also perform optimization processing to shorten the average value of the settling times or the average value of the longest P (where P is a value smaller than L) among the L settling times.

[0061] As shown in Figure 1, the optimization processing unit 60 has an optimization algorithm 61 that optimizes control parameters to shorten the settling time. The optimization processing unit 60 then uses the optimization algorithm 61 to perform an optimization process that shortens at least the longest settling time among one or more settling times. The optimization algorithm 61 may be a known algorithm such as a Bayesian optimization algorithm, an evolutionary strategy algorithm (CMA-ES), or a genetic algorithm (GA). Furthermore, the optimization process that shortens the settling time using the optimization algorithm 61 may be a known process performed using the above known algorithms.

[0062] The optimization processing unit 60 may, for example, generate update control parameters by performing the optimization process once, or it may generate update control parameters by repeatedly performing the optimization process until the optimization process termination condition is met.

[0063] 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 evaluation index information generation unit 150 repeatedly performs the optimization process a predetermined number of times. Another possible termination condition is, 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.

[0064] The optimization processing unit 60 outputs the generated updated control parameters to the production device 20 for storage in the memory 21. The memory 21 then acquires the outputted control parameters and updates the stored control parameters with the acquired control parameters.

[0065] As explained above, the optimization processing unit 60 executes an optimization process for which predetermined evaluation indicators (such as settling time) are set, and outputs update control parameters. However, users may be unfamiliar with setting evaluation indicators. Therefore, the evaluation indicator control unit 100 (Figure 1) according to this embodiment displays a question to the user and accepts a response from the user. Based on the user's response, the evaluation indicator control unit 100 determines the evaluation indicators to be applied to the optimization process. In this way, even if the user is unfamiliar with setting evaluation indicators, it becomes possible to set appropriate evaluation indicators for the optimization process.

[0066] Figure 4 shows an example of the configuration of the evaluation index control unit 100. As shown in Figure 4, the evaluation index control unit 100 includes a question acquisition unit 110, a waveform acquisition unit 120, a display control unit 130, an answer acquisition unit 140, and an evaluation index information generation unit 150.

[0067] The question acquisition unit 110 acquires question information D1, which indicates a question for the user, from the question database 421. The question database 421 stores multiple pieces of question information D1. The question acquisition unit 110 sequentially acquires question information D1 from among the multiple pieces of question information D1 stored in the question database 421. Hereinafter, the first piece of question information D1 acquired by the question acquisition unit 110 will be called the first question information, the second piece of question information D1 acquired will be called the second question information, and the third piece of question information D1 acquired will be called the third question information. The question database 421 also stores relational information that shows the relationship between the question information D1 and the evaluation index. For example, relational information that shows that the first question information corresponds to the evaluation index "setup time" and the third question information corresponds to the evaluation index "crossover count" is stored in the question database 421. The question acquisition unit 110 inputs the acquired question information D1 to the display control unit 130.

[0068] Furthermore, the question acquisition unit 110 analyzes the question information D1 when it acquires it. Specifically, the question acquisition unit 110 analyzes whether or not a waveform display flag is associated with the question information D1. If a waveform display flag is associated with the question information D1, the question acquisition unit 110 transmits a predetermined signal to the waveform acquisition unit 120, causing the waveform acquisition unit 120 to acquire waveform data D3.

[0069] The waveform acquisition unit 120 acquires waveform data D3 from the waveform holding unit 422 in accordance with the instructions of the question acquisition unit 110. The waveform acquisition unit 120 inputs the acquired waveform data D3 to the display control unit 130.

[0070] The waveform holding unit 422 stores various waveform data D3. For example, the waveform holding unit 422 stores multiple waveform data D3 that show the operating waveforms of the production device 20. More specifically, the waveform holding unit 422 stores various time-series data (waveform data D3) that show the change over time in the position deviation of the driven object 24 relative to the target position, and various waveform data D3 generated by operating the driven object 24 with various control parameters. The waveform data D3 may be stored in the form of a mathematical formula or in the form of image data.

[0071] Furthermore, the waveform holding unit 422 stores attribute information that shows the relationship between the waveform data D3 and the target value of the evaluation index that the waveform data D3 satisfies. For example, suppose the waveform holding unit 422 holds a first waveform data W1 (Figure 5) as an example of waveform data D3. Suppose the settling time of this first waveform data W1 is 50 milliseconds or less. In this case, the first waveform data W1 is stored in the waveform holding unit 422 with attribute information indicating that "the settling time is 50 milliseconds or less" associated with it.

[0072] The type, content, and format of the data stored in the waveform holding unit 422 can be changed as appropriate without departing from the spirit of this disclosure.

[0073] The display control unit 130 generates a question display image D4 based on the question information D1 acquired by the question acquisition unit 110 and the waveform data D3 acquired by the waveform acquisition unit 120. The display control unit 130 inputs the question display image D4 to the display unit 17. Hereinafter, the display screen of the display unit 17 on which the question display image D4 is displayed will be referred to as the question screen. The question screen functions as a user interface. That is, the information processing unit 50 can receive various types of information from the user through the question screen.

[0074] The response acquisition unit 140 acquires response information D5, which represents the response obtained from the user through the question screen. The response acquisition unit 140 inputs the response information D5 to the question acquisition unit 110 and the evaluation index information generation unit 150.

[0075] Figure 5 shows a first question screen 201, which is an example of a question screen. As shown in Figure 5, the first question screen 201 includes a first area A1, a second area A2, and a third area A3.

[0076] The first area A1 includes a first question field 301 and a first answer field 401. The first question field 301 displays the first question information acquired by the question acquisition unit 110. The first question information consists of a string of characters (text). However, this is just an example, and the first question information may also consist of voice data. That is, the display control unit 130 may output the question as text or as voice. In the example shown in Figure 5, the first question information "Q1: What is the target setting time?" is displayed in the first question field 301. The first answer field 401 consists of an input field that accepts text input from the user. The user enters an arbitrary answer into the first answer field 401 by operating the input unit 44 (for example, a keyboard). However, this is just an example, and if the input unit 44 includes a sound pickup device (such as a microphone), the user may input the answer by voice. In the example shown in Figure 5, the answer "50 msec" is entered into the first answer field 401. The response acquisition unit 140 acquires the response entered in the first response field 401 as response information D5. Hereinafter, the response information D5 entered in the first response field 401 will be referred to as the first response information.

[0077] In this embodiment, the question acquisition unit 110 acquires the next question information D1 when the answer acquisition unit 140 acquires the answer information D5. In this example, the question acquisition unit 110 acquires the second question information in response to the answer acquisition unit 140 acquiring the first answer information and inputs it to the display control unit 130. The display control unit 130 updates the first question screen 201 in response to receiving the second question information. That is, the display control unit 130 outputs the first question screen 201, in which the second question information is displayed in the second area A2, to the display unit 17.

[0078] The second area A2 includes a second question field 302 and a second answer field 402. The second question field 302 displays the second question information acquired by the question acquisition unit 110. In the example shown in Figure 5, the second question information "What is the target leveling width?" is displayed in the second question field 302. The second answer field 402, like the first answer field 401, is composed of an input field that accepts text input from the user. In the example shown in Figure 5, the answer "0.1 mm" is entered in the second answer field 402. The answer acquisition unit 140 acquires the answer entered in the second answer field 402 as answer information D5. Hereinafter, the answer information D5 entered in the second answer field 402 will be referred to as the second answer information. The question acquisition unit 110 generates third question information in response to the answer acquisition unit 140 acquiring the second answer information and inputs it to the display control unit 130. The display control unit 130 updates the first question screen 201 and outputs the first question screen 201, with the third question information displayed in the third area A3, to the display unit 17.

[0079] The third area A3 includes the third question field 303 and the first waveform display field 501. The third question field 303 displays the third question information acquired by the question acquisition unit 110. The third question information includes text indicating instructions to the user. In the example shown in Figure 5, the third question field 303 displays the instruction, "Please select all waveforms that you will accept as the final target." In other words, question information is output that prompts the user to select a waveform data D3 from multiple waveform data D3 that will be accepted as the target operating waveform for waveform improvement through optimization processing.

[0080] Multiple waveform data D3 are displayed in the first waveform display area 501. That is, multiple waveform data D3 representing the operating waveforms of the production device 20 are displayed. Each of the multiple waveform data D3 is data acquired by the waveform acquisition unit 120 based on a command from the question acquisition unit 110 and input by the waveform acquisition unit 120 to the display control unit 130. In the example shown in Figure 5, three waveform data D3 are displayed in the first waveform display area 501. For the sake of explanation, these waveform data D3 are referred to as the first waveform data W1, the second waveform data W2, and the third waveform data W3.

[0081] In this embodiment, the question acquisition unit 110 determines the waveform data D3 to be acquired by the waveform acquisition unit 120 based on the first answer information and the second answer information. Specifically, the question acquisition unit 110 causes the waveform acquisition unit 120 to acquire waveform data D3 that is associated with attribute information indicating that "the settling time is 50 milliseconds or less" and attribute information indicating that "the target settling width is 0.1 mm or less". Therefore, the first waveform display field 501 displays waveform data D3 where "the settling time is 50 milliseconds or less" and "the target settling width is 0.1 mm or less". However, this is just an example, and the question acquisition unit 110 may cause the waveform acquisition unit 120 to acquire random waveform data D3.

[0082] The user operates the input unit 44 (for example, a mouse) to select a waveform data D3 from among multiple waveform data D3 displayed in the first waveform display field 501 that is acceptable as the target operating waveform for waveform improvement through optimization processing. If no waveform data D3 that is acceptable as the target operating waveform for waveform improvement is displayed in the first waveform display field 501, the user selects "None". The user's selection result is acquired by the response acquisition unit 140 as response information D5 (third response information). The third response information includes attribute information linked to the waveform data D3 selected by the user. The evaluation index information generation unit 150, which will be described later, generates evaluation index information D6 based on this attribute information. Details will be described later.

[0083] Figure 6 shows a second question screen 202, which is another example of a question screen. When the display control unit 130 detects that answer information D5 has been entered for all questions displayed on the first question screen 201, it switches the question screen from the first question screen 201 to the second question screen 202. As shown in Figure 6, the second question screen 202 includes a fourth area A4 and a fifth area A5.

[0084] The fourth area A4 includes the fourth question field 304 and the second waveform display field 502. The fourth question field 304 displays the fourth question information acquired by the question acquisition unit 110. The fourth question information includes text indicating instructions to the user. In the example shown in Figure 6, the fourth question field 304 displays the instruction, "Please select all waveforms that you will accept as intermediate results." In other words, question information is output that prompts the user to select waveform data D3 from multiple waveform data D3 to be accepted as operating waveforms during the waveform improvement process by optimization.

[0085] The second waveform display area 502 displays multiple waveform data D3 that the question acquisition unit 110 has had the waveform acquisition unit 120 acquire. The question acquisition unit 110 may determine the type of waveform data D3 to have the waveform acquisition unit 120 acquire based on the first to third answer information, or it may have the waveform acquisition unit 120 acquire random waveform data D3. Hereinafter, the three waveform data D3 displayed in the second waveform display area 502 will be referred to as the fourth waveform data W4, the fifth waveform data W5, and the sixth waveform data W6. The user operates the input unit 44 to select a waveform data D3 from among the multiple waveform data D3 displayed in the second waveform display area 502 that is acceptable as an operating waveform during the waveform improvement process by optimization. The user's selection result is acquired by the answer acquisition unit 140 as answer information D5 (fourth answer information).

[0086] The fifth area A5 includes the fifth question field 305 and the third waveform display field 503. The fifth question field 305 displays the fifth question information acquired by the question acquisition unit 110. In the example shown in Figure 6, the fifth question field 305 displays the instruction, "Please assign a priority order (easiest order). If you are unsure, assign the same rank to all of them." In other words, question information is output that asks about the difficulty level of waveform improvement through optimization processing for each of the multiple waveform data. More specifically, question information is output that asks about the ranking of the difficulty levels of waveform improvement through optimization processing for each of the multiple waveform data.

[0087] The third waveform display area 503 displays multiple waveform data D3 acquired by the waveform acquisition area 120 from the question acquisition area 110. The question acquisition area 110 may determine the type of waveform data D3 to be acquired by the waveform acquisition area 120 based on the first to fourth answer information, or it may have the waveform acquisition area 120 acquire random waveform data D3. In the example shown in Figure 6, three waveform data D3 are displayed in the third waveform display area 503. Hereinafter, these waveform data D3 will be referred to as the seventh waveform data W7, the eighth waveform data W8, and the ninth waveform data W9. The user, by operating the input area 44, ranks the seventh waveform data W7 to the ninth waveform data W9 in order from those that are estimated to be easy to improve through optimization processing. The user's input results are acquired by the answer acquisition area 140 as answer information D5 (fifth answer information).

[0088] The screen configuration of the first question screen 201 shown in Figure 5 and the screen configuration of the second question screen 202 shown in Figure 6 are merely examples. In addition to the question information D1 described above, various other types of question information D1 may be displayed on the first question screen 201 or the second question screen 202. Furthermore, the display control unit 130 may display further question information D1 and obtain further answer information D5 by displaying a third question screen (not shown) on the display unit 17. For example, the display control unit 130 may display question information D1 on the third question screen that includes a question such as "Is it alright to move to a position beyond the target position?"

[0089] Returning to Figure 4, the evaluation index information generation unit 150 generates evaluation index information D6 based on the response information D5. The evaluation index information D6 is information that defines the evaluation index to be applied to the optimization process. More specifically, the evaluation index information D6 is information that defines at least one of the types, number, and order of the evaluation index to be applied to the optimization process.

[0090] The evaluation index information generation unit 150 outputs ordered evaluation indices based on the user's answers to questions. The output of evaluation indices is realized by a database, a rule-based program, or a program using deep learning. As an example, the evaluation index information generation unit 150 maintains multiple ordered evaluation indices. These evaluation indices are called the first evaluation indices, the second evaluation indices, and the third evaluation indices. Based on the answers to questions in the question database 421, the evaluation index information generation unit 150 selects at least one of the first evaluation indices, the second evaluation indices, and the third evaluation indices. The first evaluation indices, the second evaluation indices, and the third evaluation indices are, for example, ordered evaluation indices used when a skilled technician performed past adjustments. The evaluation index information generation unit 150 decides which of the first, second, and third evaluation indices to select based on the similarity between the answers from past adjustments and the user's answers to questions, as well as attribute information associated with waveforms.

[0091] The evaluation index information generation unit 150 calculates and outputs evaluation indexes that are ordered based on the user's answers to the questions, as well as the adjustment time required for each index, or the number of operations required to operate the device for adjustment.

[0092] The evaluation index information generation unit 150 according to this embodiment generates evaluation index information D6 based on the first answer information, the second answer information, and the third answer information. As an example, let's assume that the user has selected the second waveform data W2 in response to the third question information ("Please select all waveforms that are acceptable as the final target."). Here, let's assume that the second waveform data W2 is associated with attribute information indicating that "the settling time is 50 milliseconds or less", attribute information indicating that "the number of crossovers is 4 or less", and attribute information indicating that "the target settling width is 0.1 mm or less". Based on this attribute information, the evaluation index information generation unit 150 selects evaluation indices to be applied to the optimization process. At this time, the evaluation index information generation unit 150 obtains target values ​​associated with the evaluation indices. In this example, the evaluation index information generation unit 150 decides to apply the first evaluation indices, the second evaluation indices, and the third evaluation indices to the optimization process. For example, the evaluation index information generation unit 150 decides to apply "setup time when the target setup width is set to 0.1 mm or less" as the first evaluation index, and to apply the target value "100 milliseconds or less" which is associated with the first evaluation index. The evaluation index information generation unit 150 also decides to apply "setup time when the target setup width is set to 0.1 mm or less" as the second evaluation index, and to apply the target value "50 milliseconds or less" which is associated with the second evaluation index. The evaluation index information generation unit 150 also decides to apply "number of crossovers" as the third evaluation index, and to apply the target value "4 times or less" which is associated with the third evaluation index.

[0093] Next, the evaluation index information generation unit 150 refers to the fourth response information. The fourth response information is used to determine the search direction for the optimization process. For example, the fourth response information is used to suppress optimization processes that are inconvenient for the user. An optimization process that is inconvenient for the user is an optimization process that may cause the production device 20 to make an error. As an example, suppose the user has selected the fourth waveform data W4 as their answer to the fourth question information ("Please select all waveforms that you will accept as intermediate results."). In other words, suppose the user has entered fourth response information indicating that they will accept the fourth waveform data W4, but will not accept the fifth waveform data W5 and the sixth waveform data W6. In this case, the evaluation index information generation unit 150 refrains from generating evaluation index information D6 that would result in an optimization process that generates waveform data identical or similar to the fifth waveform data W5 and the sixth waveform data W6. In other words, the evaluation index information generation unit 150 refrains from applying evaluation indices to the optimization process that would result in an optimization process that generates waveform data identical or similar to the fifth waveform data W5 and the sixth waveform data W6. This helps to prevent optimization processes that are inconvenient for the user.

[0094] Next, the evaluation index information generation unit 150 refers to the fifth response information. The fifth response information is used to determine the order of the evaluation indicators (for example, the first evaluation indicator, the second evaluation indicator, and the third evaluation indicator) applied to the optimization process. As an example, suppose the user selects the eighth waveform data W8 first, the seventh waveform data W7 second, and the ninth waveform data W9 third in response to the fifth question information ("Please assign priority order (simple order). If you are unsure, assign the same rank to all."). In other words, suppose the user has entered fifth response information indicating that it is easiest to improve the eighth waveform data W8 through the optimization process, the second easiest to improve the seventh waveform data W7 through the optimization process, and the third easiest to improve the ninth waveform data W9 through the optimization process. The eighth waveform data W8 is waveform data that can be generated when the optimization process to which the first evaluation indicator and its target value are applied is executed. The seventh waveform data W7 is waveform data that may be generated when an optimization process is performed to which the above-mentioned second evaluation index and its target value are applied. The ninth waveform data W9 is waveform data that may be generated when an optimization process is performed to which the above-mentioned third evaluation index and its target value are applied. In other words, the eighth waveform data W8 is waveform data corresponding to the first evaluation index, the seventh waveform data W7 is waveform data corresponding to the second evaluation index, and the ninth waveform data W9 is waveform data corresponding to the third evaluation index.

[0095] Based on the fifth response information, the evaluation indicator information generation unit 150 determines the order of each evaluation indicator. In this example, the evaluation indicator information generation unit 150 decides to first apply the first evaluation indicator and its target value to the optimization process, then apply the second evaluation indicator and its target value to the optimization process, and then apply the third evaluation indicator and its target value to the optimization process.

[0096] Furthermore, the evaluation index information generation unit 150 generates (acquires) sequence information indicating the order in which the first evaluation index, the second evaluation index, and the third evaluation index are applied to the optimization process, based on the response information D5 received from the user. Then, the evaluation index information generation unit 150 generates schedule information based on the sequence information, in which the targets of each of these evaluation indexes are met in the order indicated by the sequence information.

[0097] The processing of the evaluation index information generation unit 150 described above is just one example. The evaluation index information generation unit 150 can generate evaluation index information D6 by various methods. For example, the evaluation index information generation unit 150 may generate evaluation index information D6 based on the third response information without referring to the fourth response information and the fifth response information. Alternatively, it may generate evaluation index information D6 based only on the first response information. In addition, the evaluation index information generation unit 150 can generate evaluation index information D6 in various other ways.

[0098] When the evaluation index information generation unit 150 generates evaluation index information D6, it inputs the evaluation index information D6 to the display control unit 130. When the display control unit 130 receives the evaluation index information D6, it displays the evaluation index information D6 on the display unit 17. Hereinafter, the display screen of the display unit 17 on which the evaluation index information D6 is displayed will be referred to as the evaluation index display screen 250.

[0099] Figure 7A shows an example of the evaluation index display screen 250. The evaluation index display screen 250 includes the sixth domain A6 and the seventh domain A7.

[0100] Area 6A6 is the area where evaluation index information D6 is displayed. In other words, it is the area where information indicating at least one of the types, target values, and order of evaluation indicators applied to the optimization process is displayed. In the example shown in Figure 7A, Area 6A6 contains the first figure F1, the second figure F2, and the third figure F3. The first figure F1 is a figure corresponding to the first evaluation index. The second figure F2 is a figure corresponding to the second evaluation index. The third figure F3 is a figure corresponding to the third evaluation index. Thumbnail information is attached to each figure. Thumbnail information is information indicating the evaluation index and its target value. Thumbnail information consists of, for example, characters, figures, numbers, graphs, etc. The first figure F1 is attached to the first thumbnail information TH1 indicating that the first evaluation index is the settling time, the target value of the settling time is 100 milliseconds or less, and the target settling width is 0.1 mm or less. The second figure F2 is assigned the second thumbnail information TH2, which indicates that the second evaluation metric is settling time, the target value of the settling time is 50 milliseconds or less, and the target settling width is 0.1 mm or less. The third figure F3 is assigned the third thumbnail information TH3, which indicates that the third evaluation metric is the number of crossings, and the target value of the number of crossings is 4 or less.

[0101] The arrangement of each figure corresponds to the order in which each figure is applied to the optimization process. In the example shown in Figure 7A, the first figure F1 and the second figure F2 are arranged from left to right. This indicates that the optimization process to which the first evaluation metric is applied is performed first, followed by the optimization process to which the second evaluation metric is applied. Also in the example shown in Figure 7A, the third figure F3 is placed below the center of the second figure F2. This indicates that after the optimization process to which the first evaluation metric is applied is completed, the second evaluation metric is applied to the optimization process up to a certain point, and from that point onward, both the second and third evaluation metric are applied to the optimization process. In this way, the ordered evaluation metric is displayed in the sixth area A6. The arrangement of the first figure F1, the second figure F2, and the third figure F3 is determined based on the schedule information.

[0102] The size of each shape (for example, its size in the left-right direction) corresponds to the length of time the evaluation metric is applied to the optimization process (hereinafter referred to as the adjustment time). In other words, the longer the adjustment time, the larger the size of the shape displayed on the evaluation metric display screen 250. The length of the adjustment time for each evaluation metric may be determined by presenting the user with question information D1 regarding the adjustment time and obtaining answer information D5 to this question information D1. However, this is just one example, and the user may be allowed to specify the adjustment time directly.

[0103] The seventh region A7 is a region where waveform data generated by operating the production device 20 using the updated control parameters output as a result of the optimization process is displayed. In the example shown in Figure 7A, the seventh region A7 includes the eleventh region A11, the twelfth region A12, and the thirteenth region A13.

[0104] The first optimized waveform W11 is displayed in the 11th region A11. The first optimized waveform W11 is waveform data generated by operating the production apparatus 20 using updated control parameters output as a result of performing an optimization process (referred to as the first optimization process) to which the first evaluation index and its target value are applied. The display control unit 130 detects that the first optimized waveform W11 has been generated by monitoring the first optimization process performed by the optimization processing unit 60, acquires the first optimized waveform W11, and displays the first optimized waveform W11 in the 11th region A11.

[0105] The second optimized waveform W12 is displayed in the 12th region A12. The second optimized waveform W12 is waveform data generated by operating the production device 20 using updated control parameters output as a result of performing an optimization process (referred to as the second optimization process) to which the second evaluation index and its target value are applied. The display control unit 130 monitors the second optimization process performed by the optimization processing unit 60, as in the case of the first optimized waveform W11, acquires the second optimized waveform W12, and displays the second optimized waveform W12 in the 12th region A12.

[0106] The third optimized waveform W13 is displayed in the 13th region A13. The third optimized waveform W13 is waveform data generated by operating the production apparatus 20 using updated control parameters output as a result of performing an optimization process (referred to as multi-objective optimization processing) to which the second evaluation index and its target value, and the third evaluation index and its target value, are applied. The display control unit 130 monitors the multi-objective optimization processing by the optimization processing unit 60, as in the case of the first optimized waveform W11, acquires the third optimized waveform W13, and displays the third optimized waveform W13 in the 13th region A13.

[0107] Furthermore, predicted data may be displayed in the 11th region A11 to the 13th region A13. For example, the display control unit 130 may display predicted data in the 11th region A11 that is expected to be generated when the first optimization process is executed. Similarly, the display control unit 130 may display predicted data in the 12th region A12 that is expected to be generated when the second optimization process is executed. Similarly, the display control unit 130 may display predicted data in the 13th region A13 that is expected to be generated when the multi-objective optimization process is executed.

[0108] When displaying prediction data in the 11th region A11 to the 13th region A13, the display control unit 130 may display high-rating and low-rating buttons around the prediction data. If the user inputs an operation to select the high-rating button, the display control unit 130 inputs a first signal to the optimization processing unit 60 instructing it to adjust the search direction of the optimization process. For example, suppose the user inputs an operation to select the high-rating button corresponding to the prediction data displayed in the 11th region A11. In this case, the first signal is sent to the optimization processing unit 60. The optimization processing unit 60 then executes an optimization process that prioritizes searching for update control parameters that generate waveform data with similar characteristics to the 11th region A11. On the other hand, if the low-rating button is selected, the display control unit 130 sends a second signal to the optimization processing unit 60 to adjust the search direction of the optimization process. For example, suppose the user inputs an operation to select the low-rating button corresponding to the prediction data displayed in the 12th region A12. In this case, the second signal is transmitted to the optimization processing unit 60, and the optimization processing unit 60 refrains from searching for update control parameters during the optimization process that generate waveform data having similar characteristics to the predicted data displayed in the 12th region A12.

[0109] The display control unit 130 does not necessarily need to display high-rating and low-rating buttons. When the display control unit 130 receives an operation to select the 11th area A11, it may send a first signal to the optimization processing unit 60 to search for waveform data with similar characteristics to the predicted data displayed in the 11th area A11. Alternatively, suppose the display control unit 130 detects an operation to select the 11th area A11 (e.g., click) but does not detect an operation to select the 12th area A12. In this case, the display control unit 130 may send a second signal to the optimization processing unit 60 to refrain from searching for update control parameters that generate waveform data with similar characteristics to the predicted data displayed in the 12th area A12.

[0110] Furthermore, user ratings are not limited to two levels. For example, users may assign an acceptable order. Specifically, multiple prediction data can be ordered by rearranging multiple waveforms.

[0111] Figure 7B shows details of the evaluation index display screen 250. As shown in Figure 7B, the evaluation index display screen 250 includes a progress line L100 that indicates the progress of the optimization process. When the display control unit 130 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 that indicates the overall progress of the optimization process. The progress information includes optimization time, number of operations, estimated remaining operation time, and remaining operation times. The progress status here refers to the overall progress of a series of optimization processes to which the first evaluation index, second evaluation index, and third evaluation index selected by the evaluation index information generation unit 150 are applied. In addition to the progress information for the entire series of optimization processes, or instead, the progress information display field R1 may also display progress information for each section divided by evaluation index. For example, the optimization time, number of operations, estimated remaining operation time, and remaining operation times may be displayed for each section to which the first evaluation index is applied, the section to which the second evaluation index is applied, and the section to which the third evaluation index is applied.

[0112] The optimization time indicates the time elapsed since the optimization process began. In the example shown in Figure 7B, it is displayed that the time elapsed since the optimization process began is "30 hours". The display control unit 130 may also display a first progress rate in the progress information display field R1, which indicates the progress rate of the optimization time relative to the estimated total optimization time, as described later. In Figure 7B, the progress information display field R1 shows that the first progress rate is "60%".

[0113] The number of operations indicates the number of times the production device 20 has operated since the optimization process began. In the example shown in Figure 7B, it is shown that the production device 20 has operated "30,000 times" since the optimization process began. The display control unit 130 may also display a second progress rate in the progress information display field R1, which shows the ratio of the number of times the production device 20 has operated so far to the estimated total number of operations, which will be described later. In Figure 7B, the progress information display field R1 shows that the second progress rate is "50%".

[0114] The estimated remaining operating time is the estimated total optimization time minus the optimization time. Figure 7B shows that the estimated remaining operating time is "20 hours".

[0115] The remaining number of operations is shown as the number of operations performed minus the expected total number of operations. Figure 7B shows that the remaining number of operations is "20,000".

[0116] Furthermore, as shown in Figure 7B, the evaluation index display screen 250 includes a prediction display area R2. The prediction display area R2 displays prediction information regarding the optimization process. The prediction information includes the predicted total optimization time, predicted completion time, and predicted total number of operations, which are predicted by the evaluation index information generation unit 150 based on the response information D5 received from the user.

[0117] The estimated total optimization time indicates the estimated total time required from the start to the end of the optimization process. In the example shown in Figure 7B, it is estimated that the optimization process will take "50 hours" from start to finish.

[0118] The estimated completion time indicates the time when the optimization process is expected to finish. In Figure 7B, the optimization process is expected to finish at "MM / DD / HH".

[0119] 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. In Figure 7B, it is estimated that the production device 20 will operate "50,000 times" from the start to the end of the optimization process.

[0120] As described above, the optimization processing unit 60 according to this embodiment repeatedly executes the optimization process until the optimization process termination condition is met. Therefore, if the optimization process is performed based on a schedule in which the first evaluation indicator is applied to the optimization process, then the second evaluation indicator is applied, and then the third evaluation indicator is applied, then the optimization process performed when the first evaluation indicator is applied, the optimization process performed when the second evaluation indicator is applied, and the optimization process performed when the third evaluation indicator is applied will each be repeatedly executed until the optimization process termination condition is met. During the execution of this optimization process based on predetermined schedule information, which loops until the optimization process termination condition is met, the position of the progress line L100 on the evaluation indicator display screen 250, the optimization time, the estimated remaining operation time, and the number of operations are constantly updated and displayed on the display unit 17.

[0121] Furthermore, the display control unit 130 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 evaluation indicator display screen 250, and display the recalculated results on the evaluation indicator display screen 250. For example, suppose the user inputs an operation to extend the execution time of the optimization process. In this case, the display control unit 130 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 extension of the optimization process execution time on the evaluation indicator display screen 250.

[0122] Figure 8 shows an example of the processing flow executed by the information processing unit 50 according to this embodiment. The processing shown in Figure 8 is started when the information processing unit 50 detects that the user has entered an operation to command the display unit 17 to display the question screen.

[0123] In step S1, the display control unit 130 displays the question information D1 on the display unit 17. For example, the display control unit 130 displays the first question information, the second question information, and the third question information on the display unit 17.

[0124] In step S2, the response acquisition unit 140 acquires response information D5. For example, the response acquisition unit 140 acquires first response information to third response information indicating the answers to first question information to third question information.

[0125] In step S3, the evaluation index information generation unit 150 generates evaluation index information D6 based on the response information D5. For example, the evaluation index information generation unit 150 generates evaluation index information D6 based on the first to third response information.

[0126] Specifically, the evaluation index information generation unit 150 obtains the target settling time and target settling width from the user. The evaluation index information generation unit 150 then presents the user with candidate waveforms that are ultimately acceptable and obtains the waveform that is ultimately acceptable. Similarly, the evaluation index information generation unit 150 obtains the user with candidate waveforms that are intermediate results that are acceptable. Furthermore, the evaluation index information generation unit 150 obtains waveforms in a simple order from the user for the intermediate results that are acceptable and uses this as response information D5.

[0127] The evaluation index information generation unit 150 uses the user-set target set time and target set width as the evaluation index for the simplest waveform (for example, the set width index) and optimizes it accordingly as the evaluation index in the first stage.

[0128] Similarly, the evaluation index information generation unit 150 sets evaluation indexes for the second stage transition for evaluation indexes assigned to the second and subsequent simpler waveforms.

[0129] Figure 9 is a flowchart showing an example of a detailed flow of processing performed by the information processing unit 50.

[0130] In step S11, the question acquisition unit 110 acquires question information D1 from the question database 421.

[0131] In step S12, the question acquisition unit 110 determines whether the question requires waveform data D3. Specifically, the question acquisition unit 110 analyzes the question information D1 acquired in step S11 to determine whether it is necessary to have the waveform acquisition unit 120 acquire waveform data D3. If the question requires waveform data D3 (YES in step S12), the process proceeds to step S13. If the question does not require waveform data D3 (NO in step S12), the process proceeds to step S14.

[0132] In step S13, the waveform acquisition unit 120 acquires waveform data D3 according to the instructions of the question acquisition unit 110.

[0133] In step S14, the display control unit 130 displays a question screen (for example, the first question screen 201). The display control unit 130 displays question information D1 on the question screen. If the waveform acquisition unit 120 has acquired waveform data D3, the display control unit 130 displays the waveform data D3 on the question screen along with the question information D1. If the waveform acquisition unit 120 has not acquired waveform data D3, the display control unit 130 displays the question information D1 on the question screen. The user enters an answer to the question information D1 displayed on the question screen.

[0134] In step S15, the response acquisition unit 140 acquires the response information D5.

[0135] In step S16, the display control unit 130 determines whether the question has ended. For example, the display control unit 130 determines that the question has ended if the answer acquisition unit 140 has acquired more than a certain number of answer information D5. The certain number can be set in advance by the user. If the question has ended (YES in step S16), the process proceeds to step S17. If the question has not ended (NO in step S16), the process returns to step S11.

[0136] In step S17, the evaluation index information generation unit 150 generates evaluation index information D6. Then the process ends. The evaluation index information D6 generated by the evaluation index information generation unit 150 may be input to the display control unit 130. The display control unit 130 may display the evaluation index information D6 on the display unit 17. For example, the display control unit 130 may display the evaluation index display screen 250 shown in Figure 7A on the display unit 17.

[0137] According to the control parameter generation system 1 of this embodiment, the information processing unit 50 outputs question information D1 regarding the optimization process to the user, obtains answer information D5 for the question information D1, and generates evaluation index information D6 based on the answer information D5. The user can use this evaluation index information D6 to determine the evaluation index to be applied to the optimization process. As a result, it becomes easier to determine the evaluation index to be applied to the optimization process.

[0138] Furthermore, according to the control parameter generation system 1 of this embodiment, evaluation index information D6 is generated, which indicates the order in which multiple evaluation indices are applied to the optimization process. In other words, the information processing unit 50 determines, based on the response information D5, the order in which the multiple evaluation indices are applied to the optimization process. This makes it even easier to determine the evaluation indices to be applied to the optimization process.

[0139] Furthermore, according to the control parameter generation system 1 of this embodiment, since the third question information is output, information indicating the waveform data that is acceptable as the target operating waveform for waveform improvement by the optimization process (target acceptable data) is obtained as answer information D5. Therefore, it becomes possible to have the information processing unit 50 determine the evaluation index to be applied to the optimization process based on the target acceptable data.

[0140] Furthermore, the control parameter generation system 1 outputs question information D1 that prompts the user to select predetermined data from multiple waveform data. Therefore, the user can input answer information D5 by selecting any data from the multiple waveform data. In this way, even if it is difficult for the user to verbalize and input the answer information D5, the user can easily input the answer information D5.

[0141] Furthermore, according to the control parameter generation system 1 of this embodiment, since the fourth question information is output, information indicating waveform data that is acceptable as an intermediate operating waveform during waveform improvement by the optimization process (intermediate acceptable data) and waveform data that is not acceptable as an intermediate operating waveform during waveform improvement by the optimization process (intermediate unacceptable data) is obtained as answer information D5. Therefore, it becomes possible to have the information processing unit 50 determine the evaluation index to be applied to the optimization process based on the intermediate acceptable data and intermediate unacceptable data. In this way, it becomes possible to perform an optimization process that generates intermediate acceptable data while not generating intermediate unacceptable data.

[0142] Furthermore, according to the control parameter generation system 1 of this embodiment, since fifth question information is output, information indicating the difficulty level of waveform improvement through optimization processing for each of the multiple waveform candidate data (referred to as difficulty ranking information) can be obtained as answer information D5. Therefore, it becomes possible to have the information processing unit 50 determine the order of evaluation indicators applied to the optimization process based on the difficulty ranking information. In particular, in this embodiment, fifth question information is output that asks for the ranking of the difficulty level of waveform improvement through optimization processing for each of the multiple waveform data. As a result, information indicating the ranking of the difficulty level of waveform improvement through optimization processing can be obtained as answer information D5. By having the information processing unit 50 generate evaluation indicator information D6 based on this answer information D5, it becomes possible to execute the optimization process in order from the easiest to the most difficult waveform to improve.

[0143] Qualitatively, the overall processing time (total time from start to finish) of the optimization process is often shortened when an optimization process with a lower difficulty for waveform improvement is performed first, followed by an optimization process with a higher difficulty for waveform improvement, rather than performing an optimization process with a higher difficulty for waveform improvement first. In other words, it is often more efficient to first perform an optimization process with a lower difficulty for waveform improvement to narrow down the control parameters to some extent, and then perform an optimization process with a higher difficulty for waveform improvement. According to this embodiment, since evaluation index information D6 is generated so that an optimization process with a higher difficulty for waveform improvement is performed after an optimization process with a lower difficulty for waveform improvement is performed, the overall processing time of the optimization process can be shortened.

[0144] The following modifications can be adopted for this embodiment.

[0145] (1) Questions do not need to be prepared in advance. The information processing unit 50 may generate question information D1 using a machine learning-trained generative AI. The machine learning-trained generative AI is, for example, a large-scale language model. In this case, the question information D1 does not need to be stored in the question database 421 or the like in advance.

[0146] (2) The question acquisition unit 110 may acquire multiple question information D1 at once. For example, it may acquire the first to third question information together and input the first to third question information together to the display control unit 130. In this case, the display control unit 130 may display the first to third question information together, rather than sequentially displaying the first to third question information on the first question screen 201.

[0147] (3) Question information D1 for determining the weight of each evaluation metric may be displayed on the screen. For example, the user may be presented with questions to determine the weights of the second and third evaluation metrics when the multi-objective optimization process is executed. However, this is just an example, and the user may be directly required to specify (input) the weights of each evaluation metric when the multi-objective optimization process is executed.

[0148] (4) In Embodiment 1, an example was described in which waveform data is used as time-series data indicating the position deviation of the driven object 24, but this is just one example. For example, various time-series data other than waveforms may be used, such as a numerical sequence that records the position deviation of the driven object 24 in time series, or log data that sequentially records the fluctuations in the position deviation of the driven object 24.

[0149] (5) The system may also be equipped with a function to update question information and evaluation index information. For example, the evaluation index control unit 100 may display a question screen on the display unit 17 and present additional question information D1 to the user while the optimization processing by the optimization processing unit 60 is being executed. Based on the answer information D5 to this question information D1, the evaluation index control unit 100 may update the evaluation index information D6. In other words, the content and target values ​​of the evaluation index applied to the optimization processing may be updated.

[0150] (6) The evaluation index information generation unit 150 may obtain time information, which includes any of the following: the time during which the evaluation index is applied to the optimization process (adjustment time), the time during which the user can respond to emergencies (arrival time), and the time during which the user can manually operate the optimization process, from another system (for example, an attendance system built in the company where the user works) that is configured to communicate with the control parameter generation system 1. The evaluation index information generation unit 150 may generate evaluation index information based on the above time information. For example, the evaluation index information generation unit 150 may generate evaluation index information that specifies that a relatively difficult optimization process is executed during the time when the user can respond to emergencies, and a relatively easy optimization process is executed during the time when it is difficult for the user to respond to emergencies. With this configuration, if the production equipment 20 performs an unexpected operation as a result of a difficult optimization process, the user can make an emergency stop of the equipment. Also, since a difficult optimization process is not executed during the time when it is difficult for the user to respond to emergencies, the production equipment 20 is less likely to perform an unexpected operation. As a result, the optimization process can be executed safely.

[0151] (7) In Embodiment 1, an example was described in which the control parameter generation device 10 (for example, a personal computer) includes the information processing unit 50, but the production device 20 may also include the information processing unit 50. In other words, the processing of the optimization processing unit 60 and the evaluation index control unit 100 described above may be executed by the information processing unit 50 implemented in the production device 20. In addition, the information processing unit 50 may be implemented in various devices. For example, the information processing unit 50 may be implemented in a personal information terminal carried by a user. In this case, the personal 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 personal information terminals include tablet computers and smartphones.

[0152] (8) 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. The information processing unit may output question information to the user asking which indicators related to quality or productivity to prioritize and to what extent, or which of quality and productivity to give more importance, and select evaluation indicators related to processing quality or evaluation indicators related to productivity based on the answer information. If multiple evaluation indicators are selected, the information processing unit may determine the application order or weighting of each evaluation indicator based on the answer information. This makes it possible to easily set evaluation indicators according to the desired processing results and production plan, even if the user does not have sufficient expertise in optimization processing.

[0153] (Combinable Configurations) The following describes a group of combinable configurations, including the configurations disclosed herein.

[0154] (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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] In a twelfth embodiment of the first configuration, the thumbnail information includes information indicating a target set for the at least one evaluation indicator.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] (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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] (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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] In the sixth embodiment of the fifth configuration, the control process executed by the control device based on the optimization program includes a process for acquiring operating condition information indicating the execution conditions for an operation to be performed by the drive system.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] This disclosure is useful in the field of technology for searching for optimal control parameters.

Claims

1. 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.

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

3. The information processing method according to claim 1, wherein the information processing unit outputs the question information, which includes displaying a plurality of waveform data showing the operating waveform of the device, and outputting question information that causes the user to select an acceptable waveform data from the plurality of waveform data as the target operating waveform for waveform improvement by the optimization process.

4. The information processing method according to claim 1, wherein the information processing unit outputs the question information, which includes displaying a plurality of waveform data showing the operating waveform of the device, and outputting question information that causes the user to select a waveform data from the plurality of waveform data to be acceptable as an operating waveform in the process of waveform improvement by the optimization process.

5. The information processing method according to claim 1, wherein the information processing unit outputs the question information, which includes displaying a plurality of waveform data showing the operating waveform of the device, and outputting question information asking about the difficulty of waveform improvement by the optimization process for each of the plurality of waveform data.

6. The information processing method according to claim 5, wherein the information processing unit outputting the question information 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.

7. 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.

8. 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 to be applied to the optimization process based on the answer information.

9. 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.

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