JP6528461B2 - Information processing apparatus, measurement data acquisition program, measurement data acquisition method, and model generation method - Google Patents

Description

  The present invention relates to an information processing apparatus, a model generation program, and a model generation method.

  Conventionally, control design is performed to design how to control a control target using a model of the control target. In this control design, the set values of parameters used for control of the control target are changed, and the state of the control target is measured after waiting until the state of the control target becomes a steady state. Then, in the control design, a model of the control target is generated based on the measured data measured.

  However, when measurement is performed while waiting for the state of the control target to be in a steady state for each measurement condition, it takes time to collect measurement data. Therefore, techniques for performing quasi-stationary measurement have been proposed. In quasi-stationary measurement, the set values of parameters are changed at such a speed that it can be regarded as almost stationary, and time series data that can be acquired is used as steady-state data.

Shihogi Kouko et al., "Soot Modeling of Gasoline Engines Using Quasi-Stationary Measurement", 1st Multi-Symposium of the Control Society of Japan Society of Control and Automatic Control, 2014

  By the way, quasi-stationary measurement is used when there is one parameter. However, the control target may use a plurality of parameters for control of the control target as the number of devices increases. For this reason, when using a plurality of parameters, it is difficult to generate a control target model by quasi-stationary measurement.

  In one aspect, it is an object of the present invention to provide an information processing apparatus capable of collecting quasi-stationary data capable of generating a model of a control target even when a plurality of parameters are used for control of the control target, a model generation program and a model generation method. Do.

  In the first proposal, the information processing apparatus includes a generation unit, an output unit, and an acquisition unit. The generation unit generates a plurality of measurement conditions by changing only one of the plurality of parameters from the previous measurement condition in the space according to the changeable range of each of the plurality of parameters regarding control of the control target . The output unit sequentially outputs operation instructions of the plurality of measurement conditions generated by the generation unit. The acquisition unit acquires measurement data indicating a state of the control target whose parameter has been changed to measurement conditions according to the operation instruction output from the output unit.

  According to one embodiment of the present invention, even when a plurality of parameters are used for control of a controlled object, a model of the controlled object can be generated.

FIG. 1 is an explanatory diagram for explaining an example of a system configuration. FIG. 2 is a diagram showing an example of a functional configuration of the information processing apparatus. FIG. 3 is a diagram showing an example of the normalized space. FIG. 4 is a diagram showing an example of a three-dimensional Hilbert curve. FIG. 5 is a view showing an example of a two-dimensional Hilbert curve of first to third places. FIG. 6 is a diagram for explaining correspondence between measurement data and measurement conditions. FIG. 7 is a diagram for explaining the change of the change parameter. FIG. 8 is a diagram schematically showing the flow of generating a model. FIG. 9 is a flowchart illustrating an example of a procedure of model generation processing. FIG. 10 is an explanatory diagram of an example of the configuration of a computer that executes a model generation program.

  Hereinafter, embodiments of an information processing apparatus, a model generation program, and a model generation method disclosed in the present application will be described in detail based on the drawings. The disclosed technology is not limited by the present embodiment. Moreover, you may combine suitably the Example shown below in the range which does not produce contradiction.

[System configuration]
In control design, a model of a control target is used to design how to control the control target. The system 1 is a system that generates a model to be controlled. In the present embodiment, the case where the control target is an engine and control design of the engine is described as an example. In the control design of an engine, in order to determine numerical values used for control of the engine, the engine operation is measured to determine how the state of the engine changes, and the engine is modeled based on the measured data. For example, in engine control design, a model is generated by measuring how exhaust gas and fuel consumption change due to changes in valve opening and fuel injection amount. Then, in the control design of the engine, the generated model is used to design how to control the engine. For example, in the control design of an engine, it is designed what valve opening degree and fuel injection amount are good to suppress exhaust gas and fuel consumption while obtaining the required output from the generated model.

  FIG. 1 is an explanatory diagram for explaining an example of a system configuration. As shown in FIG. 1, the system 1 includes an information processing device 10 and an engine 11.

  The engine 11 is an object to be controlled in control design.

  The information processing apparatus 10 is an apparatus that generates a model of the engine 11 to be controlled. The information processing apparatus 10 is, for example, a computer such as a personal computer or a server computer. The information processing apparatus 10 may be implemented as a single computer or may be implemented by a plurality of computers. In the present embodiment, the case where the information processing apparatus 10 is one computer will be described as an example.

  The information processing apparatus 10 operates the engine 11 under various measurement conditions. For example, the information processing apparatus 10 changes the measurement conditions set in the parameters for controlling the engine 11 and outputs an operation instruction to the engine 11. This parameter is input information to the model in control design. Examples of parameters for controlling the engine 11 include valve opening and fuel injection amount. The parameters are not limited to this.

The information processing apparatus 10 also acquires measurement data indicating the state of the engine 11 under various measurement conditions. For example, the information processing apparatus 10 acquires measurement data indicating the state of the engine 11 under various measurement conditions in which only one of the plurality of parameters is changed from the previous measurement condition. At this time, the information processing apparatus 10 changes the value of any one parameter from the immediately preceding measurement condition at a slow speed at which it can be regarded as substantially steady, for each measurement condition. The information processing apparatus 10 uses measurement data sequentially acquired while changing any one parameter at a slow speed as data in a steady state. For example, the information processing device 10 changes the valve opening degree and the fuel injection amount one by one at a slow speed that can be regarded as substantially steady, and acquires measurement data indicating the state of the engine 11. As measurement data, for example, NO _x (nitrogen oxides) contained in exhaust gas, PM (particulate matter), and the concentration of CO ₂ (carbon dioxide), to obtain data of the fuel consumption.

[Configuration of information processing apparatus]
Next, the information processing apparatus 10 according to the present embodiment will be described. FIG. 2 is a diagram showing an example of a functional configuration of the information processing apparatus. As shown in FIG. 2, the information processing apparatus 10 includes an external I / F (interface) unit 20, an input unit 21, a display unit 22, a storage unit 23, and a control unit 24. Note that the information processing apparatus 10 may have other devices other than the above-described devices.

  The external I / F unit 20 is an interface that inputs and outputs information with another device. As the external I / F unit 20, various input / output ports such as Universal Serial Bus (USB), and network interface cards such as a LAN card can be adopted.

The external I / F unit 20 transmits and receives various information to and from other devices via a communication cable (not shown). For example, the external I / F unit 20 outputs an operation instruction specifying measurement conditions to a control device (not shown) that controls the engine 11. The controller operates the engine 11 under the designated measurement conditions. The control device measures the state of the engine 11 at a predetermined cycle. For example, the control device measures the concentrations of NO _x , PM, and CO ₂ contained in the exhaust gas of the engine 11 and the fuel efficiency in a cycle of 16 ms. Then, the control device outputs, as measurement data, data indicating the measured state of the engine 11 in association with the measurement time at a predetermined cycle. That is, each time measurement is performed, the control device outputs measurement data in chronological order. The external I / F unit 20 receives the measurement data output from the control device.

  The input unit 21 is an input device for inputting various types of information. The input unit 21 may be an input device that receives an input of an operation such as a mouse or a keyboard. The input unit 21 receives input of various information related to control design. For example, the input unit 21 accepts, for each parameter that controls the engine 11, an input of a changeable range in which the parameter can be changed. Further, the input unit 21 receives, for each parameter that controls the engine 11, an input of a change speed at which the parameter is changed. In addition, the input unit 21 receives, for each parameter that controls the engine 11, an input of a period until measurement data corresponding to the measurement condition is acquired. Hereinafter, a period until measurement data corresponding to the measurement condition is acquired is also referred to as “dead time”. The input unit 21 receives an operation input from the user, and inputs operation information indicating the received operation content to the control unit 24.

  The display unit 22 is a display device that displays various information. Examples of the display unit 22 include display devices such as a liquid crystal display (LCD) and a cathode ray tube (CRT). The display unit 22 displays various information. For example, the display unit 22 displays various screens such as an operation screen.

  The storage unit 23 is a storage device that stores various data. For example, the storage unit 23 is a storage device such as a hard disk, a solid state drive (SSD), or an optical disk. The storage unit 23 may be a semiconductor memory capable of rewriting data such as a random access memory (RAM), a flash memory, and a non volatile static random access memory (NV SRAM).

  The storage unit 23 stores an operating system (OS) executed by the control unit 24 and various programs. For example, the storage unit 23 stores various programs including a program for executing model generation processing described later. Furthermore, the storage unit 23 stores various data used in a program executed by the control unit 24. For example, the storage unit 23 stores measurement path information 30 and measurement data 31.

  The measurement path information 30 is data storing how to change the measurement conditions set in the parameters for controlling the engine 11. For example, the measurement path information 30 is stored in the order in which a plurality of measurement conditions for the parameters are changed.

  The measurement data 31 is data in which measurement data of the engine 11 is stored. The measurement data 31 stores measurement data in association with the measurement conditions for each measurement condition.

  The control unit 24 is a device that controls the information processing apparatus 10. As the control unit 24, an electronic circuit such as a central processing unit (CPU) or a micro processing unit (MPU) or an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA) can be adopted. The control unit 24 has an internal memory for storing programs and control data that define various processing procedures, and executes various processing by these. The control unit 24 functions as various processing units by operating various programs. For example, the control unit 24 includes a reception unit 40, a measurement condition generation unit 41, an output unit 42, an acquisition unit 43, a storage unit 44, and a model generation unit 45.

  The reception unit 40 performs various receptions. For example, the receiving unit 40 receives various operation instructions related to control design. For example, the receiving unit 40 causes the display unit 22 to display an operation screen related to control design, and receives an input of an operation instruction instructing start of model generation from the operation screen. Further, the receiving unit 40 receives an input of a changeable range in which the parameter can be changed, for each of the parameters for controlling the engine 11 from the operation screen. The receiving unit 40 also receives, for each parameter that controls the engine 11, an input of a change speed at which the parameter is changed. In addition, the receiving unit 40 receives an input of dead time for each parameter that controls the engine 11. The changeable range of each parameter is determined by the user according to, for example, the range of parameters for modeling the engine 11. The change speed of each parameter is, for example, a change speed at which the change of the state of the engine 11 can be regarded as substantially steady by the user observing the change of the state of the engine 11 in advance by changing each parameter individually. Determined by The dead time of each parameter is determined by the user, for example, by changing each parameter individually and observing changes in the state of the engine 11 in advance.

  The measurement condition generation unit 41 generates a plurality of measurement conditions for a plurality of parameters for controlling the control object in association with a predetermined order. For example, the measurement condition generation unit 41 changes only one of the plurality of parameters from the previous measurement condition in the space according to the changeable range of each of the plurality of parameters for controlling the control target. Generate measurement conditions. For example, the measurement condition generation unit 41 is a measurement condition in which only one of the plurality of parameters changes from the measurement condition immediately before, and the space within the changeable range of the plurality of parameters is comprehensively Generate multiple changing measurement conditions. For example, the measurement condition generation unit 41 generates a plurality of measurement conditions using a space filling curve disposed in a normalized space in which the changeable ranges of the plurality of parameters are normalized. The space-filling curve is a curve that changes in an exhaustive manner in space by sequentially changing the value of any one parameter in the space of a plurality of parameters. For example, Hilbert curve is used as this space filling curve.

  Here, the flow of generating measurement conditions will be described using a specific example. The measurement condition generation unit 41 normalizes the changeable ranges of the valve opening degree for controlling the engine 11 and the fuel injection amount to the range of 0 to 1, respectively. FIG. 3 is a diagram showing an example of the normalized space. A normalized space in which the valve opening degree and the changeable range of the fuel injection amount are normalized becomes a two-dimensional space in which the valve opening degree and the fuel injection amount are in the range of 0-1. The measurement condition generation unit 41 arranges the Hilbert curve in the normalized space. An example of a two-dimensional Hilbert curve is shown in the example of FIG. The Hilbert curve is one of the fractal figures and is a curve obtained by recursively repeating the U-shaped locus, and is one of space-filling curves in which the limit of the repetition matches the region. The Hilbert curve is a one-stroke curve composed of a combination of changes of one parameter. Since the Hilbert curve is a space-filling curve, the curve formed by repeating to some extent exhausts the area exhaustively. In addition, the Hilbert curve has a portion that changes in the opposite direction in almost all parts. Hilbert curves can be created in any dimensional space. FIG. 4 is a diagram showing an example of a three-dimensional Hilbert curve.

  An example of a method of constructing a two-dimensional Hilbert curve will be described. The two-dimensional Hilbert curve has four ways (RUL (n); DLU (n); LDR (n); URD (n)) from the difference in the starting conditions. n is the number of repetitions (order, order). The Hilbert curve of each order is drawn according to the rules shown in the following equations (1)-(4).

RUL (n) = URD (n-1) → RUL (n-1) ↑ RUL (n-1) ← DLU (n-1) (1)
DLU (n) = LDR (n-1) ↓ DLU (n-1) ← DLU (n-1) ↑ RUL (n-1) (2)
LDR (n) = DLU (n-1) ← LDR (n-1) ↓ LDR (n-1) → URD (n-1) (3)
URD (n) = RUL (n-1) ↑ URD (n-1) → URD (n-1) ↓ LDR (n-1) (4)

  However, RUL (0) = DLU (0) = LDR (0) = URD (0) = “”. "" Means no operation.

  Describe RUL (n) specifically. The Hilbert curve RUL (n) divides each side of the square area into 2 n equally and divides it into small square areas, and from the center of the small square area at the lower left, follows the arrow in order toward the center of the next square area It is constituted by tying up. FIG. 5 is a view showing an example of a two-dimensional Hilbert curve of first to third places.

  The Hilbert curve RUL (1) having the order n of 1 is as shown below from the equation (1).

RUL (1) = URD (0) → RUL (0) ↑ RUL (0) ← DLU (0)
= → ← ←

  The Hilbert curve RUL (1) is obtained by connecting the centers of four small square areas formed by dividing a square area into two equal parts in the order of lower left, lower right, upper right, and upper left.

  Similarly, the Hilbert curve RUL (2) of order n 2 is as shown below.

RUL (2) = URD (1) → RUL (1) ↑ RUL (1) ← DLU (1)
= (↑ →)) → (→ ←)) ↑ (→ ←)) ((↓ ↑))
= → → → → → ↑ → ↑ ↑ ← ← ← ↑

  The Hilbert curve RUL (2) is a curve formed by connecting the centers of 16 small square areas formed by dividing the square area into four equal parts in the order of the arrows from the lower left.

  The measurement condition generation unit 41 normalizes the changeable ranges of the valve opening degree and the fuel injection amount into the range of 0 to 1, respectively. In the measurement condition generation unit 41, the center of the lower leftmost small square of the normalized two-dimensional normalized space is (0; 0), and the center of the uppermost right small square is (1; 1). Arrange the Hilbert curve in correspondence.

  The measurement condition generation unit 41 returns the normalized space in which the Hilbert curve is arranged to the original space of the variable range of each of the valve opening degree and the fuel injection amount, and makes the Hilbert curve correspond to the original space. The measurement condition generation unit 41 generates measurement conditions along a curve corresponding to the original space. Even in the original space, the curve corresponding to the Hilbert curve changes only one of the parameters. The measurement condition generation unit 41 generates a measurement condition in which the value of the changing parameter is changed at the changing speed of the parameter along the curve. For example, when the change rate of the parameter is one per second, the measurement condition generation unit 41 generates a measurement condition that changes the value of the parameter per second by one. As a result, only any one parameter changes from the previous measurement condition, and a measurement condition in which each parameter is comprehensively changed in the space according to the changeable range of each parameter is generated.

  The measurement condition generation unit 41 stores the generated measurement conditions in the measurement path information 30 in the order along the curve corresponding to the original space. That is, the measurement condition generation unit 41 stores a plurality of measurement conditions in the measurement path information 30 in association with the order of change.

  The output unit 42 causes the external I / F unit 20 to output an operation instruction specifying a measurement condition to a control device (not shown) that controls the engine 11. For example, the output unit 42 outputs an operation instruction to operate the engine 11 under measurement conditions in the order of the order stored in the measurement path information 30. The engine 11 is changed to the measurement conditions and operates under the measurement conditions. The engine 11 changes any parameter at a slow speed that can be regarded as steady according to the operation instruction. The external I / F unit 20 receives measurement data indicating the state of the engine 11 whose parameters have been changed to measurement conditions in time series. The measurement data received in this time series can be regarded as steady state data.

  The acquisition unit 43 acquires measurement data indicating the state of the engine 11 that is received by the external I / F unit 20.

  The storage unit 44 stores the measurement data acquired by the acquisition unit 43 in the measurement data 31 in association with the measurement condition. Here, even if the measurement condition is changed, the engine 11 takes a long time to react, and there is an idle time until measurement data corresponding to the measurement condition is acquired.

  Therefore, the storage unit 44 specifies a change parameter changing under the measurement condition of the operation instruction output from the output unit 42. Then, the storage unit 44 stores the measurement data acquired by the acquisition unit 43 in the measurement data 31 in association with the measurement condition before the dead time of the specified change parameter. FIG. 6 is a diagram for explaining correspondence between measurement data and measurement conditions. In the example of FIG. 6, the measurement conditions are changed to U (1) to U (6) at times 1 to 6. Moreover, measurement data X (1)-X (6) are acquired in time 1-6. In the example of FIG. 6, it is assumed that the dead time is three. In this case, the storage unit 44 stores the measurement data in association with the measurement condition of three hours before (three steps before). In the example of FIG. 6, U (1) to U (6) and X (4) to X (9) are stored in the measurement data 31 in association with each other. As a result, measurement data that can be regarded as a steady state is stored in the measurement data 31 for each measurement condition.

  By the way, in the measurement of the present embodiment, the change parameter of the measurement condition may change during the dead time of one parameter. FIG. 7 is a diagram for explaining the change of the change parameter. The example of FIG. 7 shows the timing at which the change parameter changes from the first parameter to the second parameter. The measurement data of the timing 50 changing the first parameter is acquired at the timing 51 after the dead time. In the example of FIG. 7, at timing 51, the second parameter is changed. As described above, when the change parameter of the measurement condition changes during the dead time, the measurement data may be influenced by the change of the change parameter. For this reason, the storage unit 44 discards the measurement data for the dead time after the parameter changes, when the parameter in which the measurement condition changes changes. In the example of FIG. 7, the storage unit 44 discards the measurement data for five measurement cycles, which are dead time of the first parameter from the timing when the change parameter changes, without storing the measurement data 31 in the measurement data 31. This makes it possible to generate a model excluding measurement data that may have been affected by changes in multiple parameters. Thus, when discarding measurement data, it is preferable to make the order n of a Hilbert curve into about 3-5. This is because the Hilbert curve has more change points at which the change parameter changes as the order is higher, and a large amount of measurement data is discarded.

The model generation unit 45 generates a model of the engine 11. For example, the model generation unit 45 generates a model by performing machine learning using the measurement condition stored in the measurement data 31 and the measurement data corresponding to the measurement condition for each type of measurement data. For example, the model generation unit 45 weights peripheral data by regression analysis using LOLIMOT (Local Linear Model Tree) or Gaussian process (Gaussian Process) for each concentration of NO _x , PM, and CO ₂ , and fuel consumption. Generate a model using the method of outputting the average. Thereby, the concentration of NO _x , PM, and CO ₂ under various measurement conditions and the fuel consumption can be predicted from the generated model.

  Here, it demonstrates using a specific example. FIG. 8 is a diagram schematically showing the flow of generating a model. In the information processing apparatus 10, the receiving unit 40 receives, for each parameter that controls the engine 11, the input of the parameter changeable range, the change rate of the parameter, and the dead time of the parameter.

  The measurement condition generation unit 41 generates a plurality of measurement conditions for a plurality of parameters for controlling the engine 11 in association with a predetermined order. For example, the measurement condition generation unit 41 arranges a Hilbert curve in a normalized space in which the changeable ranges of each of the plurality of parameters are normalized, and generates a measurement condition along a curve corresponding to the Hilbert curve. . The measurement condition generating unit 41 stores the measurement conditions generated along the curve in the measurement path information 30 by associating the order along the curve.

  The output unit 42 outputs an operation instruction to operate the engine 11 under the measurement conditions in the order of the order stored in the measurement path information 30.

  The acquisition unit 43 acquires measurement data indicating the state of the engine 11.

  The storage unit 44 stores the measurement data acquired by the acquisition unit 43 in the measurement data 31 in association with the measurement condition. For example, the storage unit 44 specifies a change parameter changing under the measurement condition of the operation instruction output from the output unit 42. Then, the storage unit 44 stores the measurement data acquired by the acquisition unit 43 in the measurement data 31 in association with the measurement condition before the dead time of the specified change parameter. In addition, when the change parameter of the measurement condition changes, the storage unit 44 discards the measurement data for the dead time after the change parameter changes.

  The model generation unit 45 generates a model by performing machine learning using the measurement conditions stored in the measurement data 31 and the measurement data corresponding to the measurement conditions for each type of measurement data. As a result, measurement data under various measurement conditions can be predicted for each type of measurement data from the generated model.

  Here, in the conventional quasi-stationary measurement, measurement data can be obtained for only one parameter. The control target may have a plurality of parameters to be controlled as the number of devices increases. For this reason, the correlation with other parameters can not be expressed only by using only one parameter quasi-stationary. For example, with multiple parameters, the slow speed of the quasi-stationary condition depends on the direction of change. For this reason, when changing a plurality of parameters simultaneously, infinite speed setting is required, which is virtually impossible. Moreover, when changing several parameters simultaneously, since dead time changes with directions of a change, the matching which correct | amended dead time is difficult. For this reason, in conventional quasi-stationary measurement, it is difficult to collect quasi-stationary data for a plurality of parameters. There are cases where parameters with strong nonlinearity exist, and it is not possible to generate an accurate model only by measurement data of one parameter.

  On the other hand, the information processing apparatus 10 according to the present embodiment generates a measurement condition in which any one of the plurality of parameters for controlling the control target is changed from the immediately preceding measurement condition. Therefore, the information processing apparatus 10 can easily set the change speed of the parameter and the dead time of the parameter. Further, the information processing apparatus 10 according to the present embodiment generates measurement conditions that comprehensively change in the space according to the changeable ranges of the plurality of parameters that control the control target. For this reason, the information processing apparatus 10 can sufficiently collect measurement data even when there is a parameter with strong nonlinearity, and can generate a model with high accuracy.

[Flow of processing]
Next, a flow of model generation processing in which the information processing apparatus 10 according to the present embodiment generates a model will be described. FIG. 9 is a flowchart illustrating an example of a procedure of model generation processing. The model generation processing is executed at a predetermined timing, for example, a timing when an input of an operation instruction for instructing start of generation of a model from an operation screen is received.

  As shown in FIG. 9, the receiving unit 40 receives an input of a parameter changeable range, a change rate of the parameter, and a dead time of the parameter for each of the parameters for controlling the engine 11 from the operation screen (S10).

  The measurement condition generation unit 41 generates a plurality of measurement conditions for a plurality of parameters for controlling the engine 11 in association with a predetermined order (S11). For example, the measurement condition generation unit 41 arranges a Hilbert curve in a normalized space in which the changeable ranges of each of the plurality of parameters are normalized, and generates a measurement condition along a curve corresponding to the Hilbert curve. . The measurement condition generation unit 41 associates the order along the curve with the measurement condition generated along the curve, and stores the measurement condition in the measurement path information 30 (S12).

  The output unit 42 outputs an operation instruction to operate the engine 11 under the measurement conditions in accordance with the order of the order stored in the measurement path information 30 (S13). The acquisition unit 43 acquires measurement data (S14). The acquisition unit 43 associates the acquired measurement data with the measurement condition and stores the acquired measurement data in the measurement data 31 (S15). For example, the storage unit 44 stores the acquired measurement data in the measurement data 31 in association with the measurement condition before the dead time of the change parameter. In addition, when the change parameter of the measurement condition changes, the storage unit 44 discards the measurement data for the dead time after the change parameter changes.

  The model generation unit 45 performs machine learning using the measurement condition stored in the measurement data 31 and the measurement data corresponding to the measurement condition for each type of measurement data to generate a model (S16), and the process ends Do.

[effect]
As described above, the information processing apparatus 10 according to the present embodiment is configured such that only one of the plurality of parameters in the space by the changeable range of each of the plurality of parameters related to control of the control target is one before. A plurality of measurement conditions are generated by changing the measurement conditions. The information processing apparatus 10 sequentially outputs the generated operation instructions of the plurality of measurement conditions. The information processing apparatus 10 acquires measurement data indicating the state of the control target whose parameter has been changed to the measurement condition by the output operation instruction. Thereby, the information processing apparatus 10 can collect quasi-stationary data of a plurality of parameters. Thereby, the information processing apparatus 10 can generate a model of the control target even when using a plurality of parameters for control of the control target.

  In addition, the information processing apparatus 10 according to the present embodiment generates a plurality of measurement conditions using a space filling curve disposed in a normalized space in which changeable ranges of the plurality of parameters are normalized. As a result, the information processing apparatus 10 can generate the measurement conditions that change in the space according to the changeable ranges of the plurality of parameters in an exhaustive manner, with only one of the parameters changing from the previous measurement condition.

  Further, the information processing apparatus 10 according to the present embodiment receives a dead time until measurement data corresponding to the measurement condition is acquired. The information processing apparatus 10 stores the acquired measurement data in association with measurement conditions before the dead time. Thereby, the information processing apparatus 10 can associate the measurement condition with the quasi-stationary measurement data corresponding to the measurement condition.

  Further, the information processing apparatus 10 according to the present embodiment discards the measurement data for the dead time when the parameter in which the measurement condition changes changes. Thus, the information processing apparatus 10 can exclude measurement data that may have been affected by changes in a plurality of parameters.

  In addition, the information processing apparatus 10 according to the present embodiment moves a point on the space filling curve, which has a plurality of parameters related to control of the control object, built on the computer, along the space filling curve. For each movement of the point, measurement data indicating the state of the control object when the control object is controlled based on the respective values of the plurality of parameters corresponding to the point after movement is acquired. The information processing apparatus 10 generates a control model to be controlled based on the acquired plurality of measurement data. Thereby, the information processing apparatus 10 can generate a model of the control target even when using a plurality of parameters for control of the control target.

  Although the embodiments of the disclosed apparatus have been described above, the disclosed technology may be implemented in various different forms other than the above-described embodiments. Therefore, another embodiment included in the present invention will be described below.

  For example, in the said Example, the case where the control object was made into the engine 11 was illustrated. However, it is not limited to these. For example, any control target may be used as long as it takes time until the state of the control target stabilizes in the steady state after changing the measurement conditions. For example, the control target may be an actuator, a plant that performs various types of production, or a large machine.

  Moreover, in the said Example, the case where the model for every classification of measurement data was produced | generated was illustrated. However, it is not limited to these. For example, using a measurement condition stored in the measurement data 31 and measurement data in a steady state corresponding to the measurement condition, machine learning may be performed to generate a model that predicts measurement data of a plurality of types. For example, the model generation unit 45 may generate one model that predicts measurement data of all types.

  Further, each component of each device illustrated is functionally conceptual, and does not necessarily have to be physically configured as illustrated. That is, the specific state of dispersion and integration of each device is not limited to that shown in the drawings, and all or part thereof is functionally or physically dispersed in any unit depending on various loads, usage conditions, etc. It can be integrated and configured. For example, each processing unit of the reception unit 40, the measurement condition generation unit 41, the output unit 42, the acquisition unit 43, the storage unit 44, and the model generation unit 45 may be appropriately integrated. Furthermore, all or any part of each processing function performed in each processing unit may be realized by a CPU and a program analyzed and executed by the CPU, or may be realized as hardware by wired logic. .

[Model Generator]
The various processes described in the above embodiments can also be realized by executing a prepared program on a computer system such as a personal computer or a workstation. So, below, an example of a computer system which runs a program which has the same function as the above-mentioned example is explained. First, a model generation program for controlling alerting to a driver will be described. FIG. 10 is an explanatory diagram of an example of the configuration of a computer that executes a model generation program.

  As shown in FIG. 10, the computer 400 includes a central processing unit (CPU) 410, a hard disk drive (HDD) 420, and a random access memory (RAM) 440. The respective units 400 to 440 are connected via a bus 500.

  The HDD 420 stores in advance a model generation program 420 a that exhibits the same functions as the reception unit 40, the measurement condition generation unit 41, the output unit 42, the acquisition unit 43, the storage unit 44, and the model generation unit 45. The model generation program 420a may be separated as appropriate.

  The HDD 420 also stores various information. For example, the HDD 420 stores various data used to determine the OS and the order quantity.

  Then, the CPU 410 reads out the model generation program 420 a from the HDD 420 and executes the same to execute the same operation as each processing unit of the embodiment. That is, the model generation program 420 a executes the same operation as the reception unit 40, the measurement condition generation unit 41, the output unit 42, the acquisition unit 43, the storage unit 44, and the model generation unit 45.

  The model generation program 420a described above does not have to be stored in the HDD 420 from the beginning.

  Also, for example, the model generation program 420a may be stored in a “portable physical medium” such as a flexible disk (FD), a CD-ROM, a DVD disk, a magneto-optical disk, an IC card or the like inserted into the computer 400. . Then, the computer 400 may read and execute the program from these.

  Furthermore, the program is stored in “another computer (or server)” connected to the computer 400 via a public line, the Internet, a LAN, a WAN or the like. Then, the computer 400 may read and execute the program from these.

Reference Signs List 1 system 10 information processing apparatus 11 engine 20 external I / F unit 21 input unit 22 display unit 23 storage unit 24 control unit 30 measurement route information 31 measurement data 40 reception unit 41 measurement condition generation unit 42 output unit 43 acquisition unit 44 storage unit 45 Model Generator

Claims (7)

A measurement condition in which only one of a plurality of parameters related to control of a control target is changed from a previous measurement condition, and a plurality of parameters in which the plurality of parameters are respectively changed in respective changeable ranges A generation unit that generates measurement conditions;
An output unit that sequentially outputs operation instructions of a plurality of measurement conditions generated by the generation unit;
An acquisition unit that acquires measurement data indicating a state of the control target in which the parameter is changed to measurement conditions according to an operation instruction output from the output unit;
An information processing apparatus comprising: The generation unit generates a plurality of measurement conditions using a space-filling curve disposed in a normalized space obtained by normalizing changeable ranges of the plurality of parameters. Information processing device. A reception unit that receives a period until measurement data corresponding to the measurement condition is acquired;
A storage unit that stores the measurement data acquired by the acquisition unit in association with the measurement condition of the previous period received by the reception unit;
The information processing apparatus according to claim 1, further comprising: The information processing apparatus according to claim 3, wherein the storage unit discards the measurement data for the period after the parameter changes when the parameter in which the measurement condition changes changes. On the computer
A measurement condition in which only one of a plurality of parameters related to control of a control target is changed from a previous measurement condition, and a plurality of parameters in which the plurality of parameters are respectively changed in respective changeable ranges Generate measurement conditions,
Output the operation instructions of multiple generated measurement conditions in order,
A measurement data acquisition program that executes processing of acquiring measurement data that indicates the state of the control target in which the parameter has been changed to measurement conditions according to the output operation instruction. The computer is
A measurement condition in which only one of a plurality of parameters related to control of a control target is changed from a previous measurement condition, and a plurality of parameters in which the plurality of parameters are respectively changed in respective changeable ranges Generate measurement conditions,
Output the operation instructions of multiple generated measurement conditions in order,
A measurement data acquisition method comprising: executing measurement data indicating a state of the control target in which the parameter has been changed to a measurement condition according to an output operation instruction. A measurement condition in which only one of a plurality of control-related control parameters is changed from a previous measurement condition built on a computer , wherein each of the plurality of parameters can be changed Operation instructions of a plurality of measurement conditions which are respectively changed are sequentially output to acquire measurement data indicating a state of the control object when the control object is controlled,
A model generation method characterized by a computer executing a process of generating a control model of the control target based on a plurality of acquired measurement data.