JPWO2020178938A1 - Information processing equipment, information processing methods and information processing programs - Google Patents

Abstract

The impact analysis unit (103) analyzes the impact on the device when the parameter value is changed for each of the plurality of parameters. The parameter selection unit (104) selects as a selection parameter a parameter having an influence degree that matches the selection condition from a plurality of parameters. The similarity calculation unit (106) calculates the similarity between the parameter corresponding to the selection parameter and the selection parameter among the plurality of parameters set in each operation model for the plurality of operation models of the device for each operation model. .. The initial probability calculation unit (107) calculates the initial probability, which is the probability set as the initial value in each operation model, for each operation model based on the similarity of each operation model.

Description

The present invention relates to a technique for estimating the state of an apparatus using an operation model.

As a technique related to the present invention, there is a technique described in Patent Document 1.
In Patent Document 1, a plurality of operation models for estimating the state of the device are prepared. Then, in Patent Document 1, the model likelihood of each operation model is calculated using the state estimation value set in each operation model, the sensor value measured in the device, and the like. Then, in Patent Document 1, the state estimation accuracy is improved by updating the model probability of each motion model using the model likelihood.

Japanese Unexamined Patent Publication No. 2009-031096

In the technique of Patent Document 1, a method of setting an initial value (hereinafter, referred to as an initial probability) of a model probability of each motion model is not disclosed.
Therefore, when the technique of Patent Document 1 is used, it is assumed that the same initial probability is uniformly set for all the operation models, or the system user sets the initial probability of each operation model by guessing.

In view of such circumstances, it is a main object of the present invention to obtain an initial probability of a highly valid operation model without relying on the competence or experience of the system user.

The information processing device according to the present invention is
For multiple parameters, the impact analysis unit that analyzes the impact on the equipment when the parameter value is changed for each parameter,
A parameter selection unit that selects a parameter having an influence degree that matches the selection conditions as a selection parameter from the plurality of parameters.
With respect to a plurality of operation models of the device, a similarity calculation unit that calculates the similarity between the parameter corresponding to the selection parameter and the selection parameter among the plurality of parameters set in each operation model for each operation model, and
Each motion model has an initial probability calculation unit that calculates an initial probability, which is a probability set as an initial value in each motion model, based on the similarity of each motion model.

According to the present invention, it is possible to obtain a highly valid initial probability of an operation model without relying on the competence or experience of the system user.

The figure which shows the hardware configuration example of the information processing apparatus which concerns on Embodiment 1. FIG. The figure which shows the functional structure example of the information processing apparatus which concerns on Embodiment 1. FIG. The flowchart which shows the operation example of the information processing apparatus in Embodiment 1. FIG. The figure which shows the physical model selection process which concerns on Embodiment 1. The figure which shows the influence degree analysis process which concerns on Embodiment 1. FIG. The figure which shows the example of the parameter selection process which concerns on Embodiment 1. FIG. The figure which shows the calculation example of the similarity degree and the calculation example of a model probability which concerns on Embodiment 1. FIG. The figure which shows the example of the similarity and the initial probability of each operation model which concerns on Embodiment 1. FIG. The figure which shows the outline of the state estimation system which concerns on Embodiment 1. FIG.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the following description and drawings of the embodiments, those having the same reference numerals indicate the same parts or corresponding parts.

Embodiment 1.
*** Explanation of state estimation system ***
First, the state estimation system that is the premise of this embodiment will be described.

FIG. 9 shows an outline of a state estimation system that estimates the state of the device 300 using a plurality of operation models. The device 300 is, for example, a manufacturing device, a control device, or the like arranged in a factory. The device 300 is not limited to these.
In FIG. 9, the motion model A, the motion model B, and the motion model C are used. The operation model A, the operation model B, and the operation model C simulate the operation of the device 300. The motion model A simulates, for example, the motion when the device 300 is operating normally. The operation model B simulates, for example, an operation when a minor defect occurs in the device 300. The operation model C simulates, for example, an operation when a serious malfunction occurs in the device 300.
The sensor 400 periodically monitors the portion 301 of the device 300. For example, the sensor 400 periodically measures the current value of the portion 301.
Then, each time the sensor value (for example, the current value) is measured by the sensor 400, the operation model A, the operation model B, and the operation model B and the operation model B are used by using the state estimation value set in each operation model, the measured sensor value, and the like. Each model probability of the motion model B is updated. Further, each time the model probability is updated, the operation model having the highest model probability is selected, and the state of the device 300 is estimated using the selected operation model.
In the example of FIG. 9, since the model probability of the motion model C is the highest, the motion model C is selected.
As described above, since Patent Document 1 does not disclose a method of setting the initial probability which is the initial value of the model probability, for example, the same initial probability is uniformly set in each operation model, or the system user It is conceivable that the initial probability of each motion model is set by guessing.
In the example of FIG. 9, the same initial probability (0.25) is uniformly set for each operation model.
In the present embodiment, a configuration for obtaining the initial probability of a highly valid operation model without relying on the competence or experience of the system user will be described.

*** Explanation of configuration ***
FIG. 1 shows a hardware configuration example of the information processing apparatus 100 according to the present embodiment.
Further, FIG. 2 shows an example of a functional configuration of the information processing apparatus 100 according to the present embodiment.

The information processing device 100 is a computer.
As shown in FIG. 1, the information processing device 100 includes a processor 901, a main storage device 902, an auxiliary storage device 903, and a communication device 904 as hardware.
The auxiliary storage device 903 stores a program that realizes the functions of the physical model selection unit 1021, the influence degree analysis unit 103, the parameter selection unit 104, the similarity calculation unit 106, and the initial probability calculation unit 107 shown in FIG. ..
These programs are loaded from the auxiliary storage device 903 into the main storage device 902. Then, the processor 901 executes these programs to operate the physical model selection unit 1021, the influence degree analysis unit 103, the parameter selection unit 104, the similarity calculation unit 106, and the initial probability calculation unit 107, which will be described later.
FIG. 1 schematically shows a state in which the processor 901 is executing a program that realizes the functions of the physical model selection unit 1021, the influence degree analysis unit 103, the parameter selection unit 104, the similarity calculation unit 106, and the initial probability calculation unit 107. It is represented in.
Further, the physical model storage unit 101 and the operation model storage unit 105 shown in FIG. 2 are realized by, for example, the main storage device 902 or the auxiliary storage device 903.

As shown in FIG. 2, the information processing apparatus 100 includes a physical model storage unit 101, a physical model selection unit 1021, an influence degree analysis unit 103, a parameter selection unit 104, an operation model storage unit 105, a similarity calculation unit 106, and an initial probability. A calculation unit 107 is provided.

The physical model storage unit 101 stores the physical models of the plurality of devices 300. A physical model is provided for each device 300.
The physical model simulates the operation of the device 300. In addition, a plurality of parameters are provided in the physical model.

Based on the device information 201, the physical model selection unit 1021 selects the physical model of the device 300 whose initial probability is to be calculated from the plurality of physical models stored in the physical model storage unit 101. In the following, the device 300 for which the initial probability is calculated is referred to as the target device 300.
The device information 201 is, for example, ID (Identifier) information of the target device 300.

The influence degree analysis unit 103 analyzes the influence degree on the target device 300 when the parameter values of a plurality of parameters are changed for each parameter.
The parameter sensor information 202 indicates a corresponding sensor 400 for each parameter.
The influence degree analysis unit 103 analyzes the influence degree on the target device 300 when the parameter value is changed by using the sensor value of the sensor 400 shown in the parameter sensor information 202 for each parameter. The sensor value of the sensor 400 when the parameter value is changed is obtained by a simulation using the physical model selected by the physical model selection unit 1021.
The degree of influence is the degree of change (sensitivity) of the sensor value with respect to the change of the parameter. For example, the influence degree analysis unit 103 calculates the influence degree by "(parameter value after change-parameter value before change) ÷ (sensor value after change-sensor value before change)". When this formula is used, the higher the value obtained by this formula, the higher the degree of influence.
The process performed by the impact analysis unit 103 corresponds to the impact analysis process.

The parameter selection unit 104 selects, as a selection parameter, a parameter having an influence degree that matches the selection condition from a plurality of parameters.
Further, the parameter selection unit 104 acquires the parameter corresponding to the selection parameter from the operation model storage unit 105 among the plurality of parameters set in the plurality of operation models stored in the operation model storage unit 105.
Then, the parameter selection unit 104 outputs the value of the selection parameter and the value of the parameter acquired from the operation model storage unit 105 to the similarity calculation unit 106.
The process performed by the parameter selection unit 104 corresponds to the parameter selection process.

The motion model storage unit 105 stores a plurality of motion models.
The motion model simulates the motion of the device 300 as described above. The operation model storage unit 105 corresponds to various aspects of the device 300 (normal operation, minor defects, serious defects, etc.) as in the operation model A, the operation model B, and the operation model C shown in FIG. Multiple operation models are stored.
In addition, the same parameters as those set in the physical model are set in each operation model.

The similarity calculation unit 106 calculates the similarity between the parameter corresponding to the selection parameter and the selection parameter among the plurality of parameters set in each operation model for each operation model.
That is, the similarity calculation unit 106 calculates the similarity between the parameters of each operation model acquired by the parameter selection unit 104 and the selection parameters for each operation model.
The process performed by the similarity calculation unit 106 corresponds to the similarity calculation process.

The initial probability calculation unit 107 calculates the initial probability, which is the model probability set as the initial value in each operation model, for each operation model based on the similarity of each operation model.
That is, the initial probability calculation unit 107 calculates the initial probability of each operation model by using the similarity of each operation model calculated by the similarity calculation unit 106.
The process performed by the initial probability calculation unit 107 corresponds to the initial probability calculation unit.

*** Explanation of operation ***
Next, an operation example of the information processing apparatus 100 according to the present embodiment will be described with reference to FIG.

First, in step S11, the physical model selection unit 1021 selects a physical model (physical model selection process).

FIG. 4 shows an outline of step S11.
As shown in FIG. 4, the physical model selection unit 1021 acquires the device information 201, and acquires the physical model 203 corresponding to the ID described in the device information 201 from the physical model storage unit 101.
In the example of FIG. 4, the device A is the target device. Then, in the device information 201, "100-001" is described as the ID of the device A. The physical model selection unit 1021 acquires the physical model 203 corresponding to the ID “100-001” from the physical model storage unit 101.

Next, in step S12, the impact analysis unit 103 analyzes the impact (impact analysis process).

FIG. 5 shows an outline of step S12.
As shown in FIG. 5, the influence degree analysis unit 103 analyzes the influence degree of each parameter on the target device 300 by changing each of the plurality of parameters of the physical model 203 one by one.
As described above, the parameter sensor information 202 indicates the sensor 400 corresponding to each parameter. In the example of FIG. 5, in the parameter sensor information 202, the sensor 1 and the ID (1-1) of the sensor 1 are shown as the sensor 400 corresponding to the element a, and the sensor 2 and the sensor are shown as the sensor 400 corresponding to the element b. ID (1-2) of 2 is shown. That is, for example, when the value of the dimension of the element a is changed, the sensor value of the sensor 1 changes. Further, for example, when the value of the dimension of the element is changed, the value of the sensor 2 changes. The sensor value of the sensor 1 when the value of the element a is changed and the sensor value of the sensor 2 when the value of the element b is changed can be obtained by a simulation using a physical model.
The parameter sensor information 202 calculates the degree of influence for each parameter using, for example, the above-mentioned mathematical formula.

Next, in step S13, the parameter selection unit 104 selects a parameter having an influence degree that matches the selection condition from a plurality of parameters as a selection parameter (parameter selection process).

For example, the parameter selection unit 104 selects a parameter having an influence degree equal to or higher than the threshold value as a selection parameter. The selection condition in this case is a condition that the degree of influence is equal to or higher than the threshold value.
Further, for example, the parameter selection unit 104 may select a parameter having a higher degree of influence within the nth order (n is an integer of 1 or more) as a selection parameter. The selection condition in this case is that the degree of influence is within the top nth.
Further, the parameter selection unit 104 acquires the parameter corresponding to the selection parameter from the operation model storage unit 105 among the plurality of parameters set in the plurality of operation models stored in the operation model storage unit 105.
For example, when the element a and the element b shown in FIG. 5 are selected as the selection parameters, the parameter selection unit 104 acquires the value of the element a and the value of the element b of each operation model from the operation model storage unit 105.
Then, the parameter selection unit 104 includes the value before the change of the element a of the physical model (“10” in the example of FIG. 5), the value before the change of the element b (“20” in the example of FIG. 5), and the operation model. The value of the element a and the value of the element b of each operation model acquired from the storage unit 105 are output to the similarity calculation unit 106.

Next, in step S14, the similarity calculation unit 106 calculates the similarity (similarity calculation process).

FIG. 6 shows an outline of step S14.
In the example of FIG. 6, the similarity calculation unit 106 has elements a, b, c. .. .. The degree of similarity between the value of and the values of the elements a, b, c ... Of the operation model A is obtained. Further, the similarity calculation unit 106 may use elements a, b, c. .. .. The degree of similarity between the value of and the values of the elements a, b, c ... Of the operation model B is obtained. Note that "a, b, c" in FIG. 6 indicates selection parameters, "aA, bA, cA" indicates the corresponding parameters of the operation model A, and "aB, bB, cB" indicates the correspondence of the operation model B. Indicates the parameters to be used.
The similarity calculation unit 106 calculates, for example, the Euclidean distance d shown in FIG. 7 for each motion model. Then, the similarity calculation unit 106 applies the calculated Euclidean distance d to the mathematical formula of <Pattern 1> or <Pattern 2> shown in FIG. 7 to calculate the similarity of each motion model.

Next, in step S15, the initial probability calculation unit 107 calculates the initial probability of each operation model (initial probability calculation process).

For example, the initial probability calculation unit 107 calculates the initial probability by the method shown in FIG. 7. Specifically, the initial probability calculation unit 107 calculates the initial probability of the motion model A by "(similarity of motion model A) / (sum of similarity of all motion models)".
FIG. 8 shows an example of the similarity and initial probability of each motion model.
The initial probability calculation unit 107 outputs the initial probability calculated in this way to a specified output destination. As a result, an initial probability consistent with the actual behavior of the target device 300 can be obtained.

*** Explanation of the effect of the embodiment ***
As described above, according to the present embodiment, it is possible to obtain a highly valid initial probability of the operation model without relying on the competence or experience of the system user.

In the above description, an example in which the attribute (for example, dimension) of the element of the device 300 is used as a parameter has been described. However, the parameters are not limited to the attributes of the elements of the device 300. For example, physical quantities (temperature, humidity, air volume, length, height, weight, etc.) related to the installation environment of the device 300, years of use of the device 300, and the like can be used as parameters.

Further, the method of calculating the degree of influence, the method of calculating the degree of similarity, and the method of calculating the initial probability are not limited to those described above, and other calculation methods can be used.

*** Explanation of hardware configuration ***
Finally, a supplementary explanation of the hardware configuration of the information processing apparatus 100 will be given.
The processor 901 shown in FIG. 3 is an IC (Integrated Circuit) that performs processing.
The processor 901 is a CPU (Central Processing Unit), a DSP (Digital Signal Processor), or the like.
The main storage device 902 shown in FIG. 3 is a RAM (Random Access Memory).
The auxiliary storage device 903 shown in FIG. 3 is a ROM (Read Only Memory), a flash memory, an HDD (Hard Disk Drive), or the like.
The communication device 904 shown in FIG. 3 is an electronic circuit that executes data communication processing.
The communication device 904 is, for example, a communication chip or a NIC (Network Interface Card).

In addition, the OS (Operating System) is also stored in the auxiliary storage device 903.
Then, at least a part of the OS is executed by the processor 901.
The processor 901 executes a program that realizes the functions of the physical model selection unit 1021, the influence degree analysis unit 103, the parameter selection unit 104, the similarity calculation unit 106, and the initial probability calculation unit 107 while executing at least a part of the OS. ..
When the processor 901 executes the OS, task management, memory management, file management, communication control, and the like are performed.
Further, at least one of the information, data, signal value, and variable value indicating the processing results of the physical model selection unit 1021, the influence degree analysis unit 103, the parameter selection unit 104, the similarity calculation unit 106, and the initial probability calculation unit 107 , Main storage device 902, auxiliary storage device 903, and storage in at least one of the registers and cache memory in the processor 901.
The programs that realize the functions of the physical model selection unit 1021, the influence degree analysis unit 103, the parameter selection unit 104, the similarity calculation unit 106, and the initial probability calculation unit 107 are magnetic disks, flexible disks, optical disks, compact disks, and Blu-ray discs. (Registered trademark) It may be stored in a portable recording medium such as a disc or a DVD. Then, a portable recording medium containing a program that realizes the functions of the physical model selection unit 1021, the influence degree analysis unit 103, the parameter selection unit 104, the similarity calculation unit 106, and the initial probability calculation unit 107 is commercially distributed. You may.

Further, the "parts" of the physical model selection unit 1021, the influence degree analysis unit 103, the parameter selection unit 104, the similarity calculation unit 106, and the initial probability calculation unit 107 are "circuits" or "processes" or "procedures" or "processes". May be read as.
Further, the information processing device 100 may be realized by a processing circuit. The processing circuit is, for example, a logic IC (Integrated Circuit), a GA (Gate Array), an ASIC (Application Specific Integrated Circuit), or an FPGA (Field-Programmable Gate Array).
In this case, the physical model selection unit 1021, the influence degree analysis unit 103, the parameter selection unit 104, the similarity calculation unit 106, and the initial probability calculation unit 107 are each realized as a part of the processing circuit.
In this specification, the superordinate concept of the processor and the processing circuit is referred to as "processing circuit Lee".
That is, the processor and the processing circuit are specific examples of the "processing circuit Lee", respectively.

100 Information processing device, 101 Physical model storage unit, 1021 Physical model selection unit, 103 Impact analysis unit, 104 Parameter selection unit, 105 Operation model storage unit, 106 Similarity calculation unit, 107 Initial probability calculation unit, 201 Equipment information, 202 parameter sensor information, 203 physical model, 300 equipment, 400 sensor, 901 processor, 902 main storage, 903 auxiliary storage, 904 communication device.

The figure which shows the hardware configuration example of the information processing apparatus which concerns on Embodiment 1. FIG. The figure which shows the functional structure example of the information processing apparatus which concerns on Embodiment 1. FIG. The flowchart which shows the operation example of the information processing apparatus which concerns on Embodiment 1. The figure which shows the physical model selection process which concerns on Embodiment 1. The figure which shows the influence degree analysis process which concerns on Embodiment 1. FIG. The figure which shows the example of the parameter selection process which concerns on Embodiment 1. FIG. The figure which shows the calculation example of the similarity degree and the calculation example of a model probability which concerns on Embodiment 1. FIG. The figure which shows the example of the similarity and the initial probability of each operation model which concerns on Embodiment 1. FIG. The figure which shows the outline of the state estimation system which concerns on Embodiment 1. FIG.

FIG. 9 shows an outline of a state estimation system that estimates the state of the device 300 using a plurality of operation models. The device 300 is, for example, a manufacturing device, a control device, or the like arranged in a factory. The device 300 is not limited to these.
In FIG. 9, the motion model A, the motion model B, and the motion model C are used. The operation model A, the operation model B, and the operation model C simulate the operation of the device 300. The motion model A simulates, for example, the motion when the device 300 is operating normally. The operation model B simulates, for example, an operation when a minor defect occurs in the device 300. The operation model C simulates, for example, an operation when a serious malfunction occurs in the device 300.
The sensor 400 periodically monitors the portion 301 of the device 300. For example, the sensor 400 periodically measures the current value of the portion 301.
Then, each time the sensor value (for example, the current value) is measured by the sensor 400, the operation model A, the operation model B, and the operation model B and the operation model B are used by using the state estimation value set in each operation model, the measured sensor value, and the like. Each model probability of the motion model C is updated. Further, each time the model probability is updated, the operation model having the highest model probability is selected, and the state of the device 300 is estimated using the selected operation model.
In the example of FIG. 9, since the model probability of the motion model C is the highest, the motion model C is selected.
As described above, since Patent Document 1 does not disclose a method of setting the initial probability which is the initial value of the model probability, for example, the same initial probability is uniformly set in each operation model, or the system user It is conceivable that the initial probability of each motion model is set by guessing.
In the example of FIG. 9, the same initial probability (0.25) is uniformly set for each operation model.
In the present embodiment, a configuration for obtaining the initial probability of a highly valid operation model without relying on the competence or experience of the system user will be described.

The information processing device 100 is a computer.
As shown in FIG. 1, the information processing device 100 includes a processor 901, a main storage device 902, an auxiliary storage device 903, and a communication device 904 as hardware.
The auxiliary storage device 903, a physical model selection unit 10 2 shown in FIG. 2, impact analysis unit 103, the parameter selection unit 104, a program for realizing the functions of the similarity calculating unit 106 and the initial probability calculation unit 107 is stored There is.
These programs are loaded from the auxiliary storage device 903 into the main storage device 902. Then, the processor 901 by executing these programs, the physical model selection unit 10 2 to be described later, impact analysis unit 103, the parameter selection unit 104, the operation of the similarity calculating unit 106 and the initial probability calculation unit 107.
FIG. 1 illustrates a state in which the processor 901 is executing a program that realizes the functions of the physical model selection unit 102, the influence degree analysis unit 103, the parameter selection unit 104, the similarity calculation unit 106, and the initial probability calculation unit 107. It is represented as a target.
Further, the physical model storage unit 101 and the operation model storage unit 105 shown in FIG. 2 are realized by, for example, the main storage device 902 or the auxiliary storage device 903.

As shown in FIG. 2, the information processing apparatus 100 includes a physical model storage unit 101, a physical model selection unit 102, an influence degree analysis unit 103, a parameter selection unit 104, an operation model storage unit 105, a similarity calculation unit 106, and an initial stage. A probability calculation unit 107 is provided.

Physical model selection unit 10 2, based on the device information 201 from among a plurality of physical models stored in the physical model storage unit 101, selects a physical model of the calculation target device 300 of the initial probability. In the following, the device 300 for which the initial probability is calculated is referred to as the target device 300.
The device information 201 is, for example, ID (Identifier) information of the target device 300.

The influence degree analysis unit 103 analyzes the influence degree on the target device 300 when the parameter values of a plurality of parameters are changed for each parameter.
The parameter sensor information 202 indicates a corresponding sensor 400 for each parameter.
The influence degree analysis unit 103 analyzes the influence degree on the target device 300 when the parameter value is changed by using the sensor value of the sensor 400 shown in the parameter sensor information 202 for each parameter. Sensor value of the sensor 400 in the case of changing the parameter value is obtained by simulation using more selected physical model to the physical model selection unit 10 2.
The degree of influence is the degree of change (sensitivity) of the sensor value with respect to the change of the parameter. For example, the influence degree analysis unit 103 calculates the influence degree by "(parameter value after change-parameter value before change) ÷ (sensor value after change-sensor value before change)". When this formula is used, the higher the value obtained by this formula, the higher the degree of influence.
The process performed by the impact analysis unit 103 corresponds to the impact analysis process.

The initial probability calculation unit 107 calculates the initial probability, which is the model probability set as the initial value in each operation model, for each operation model based on the similarity of each operation model.
That is, the initial probability calculation unit 107 calculates the initial probability of each operation model by using the similarity of each operation model calculated by the similarity calculation unit 106.
The process performed by the initial probability calculation unit 107 corresponds to the initial probability calculation process.

First, in step S11, the physical model selector 10 2 selects a physical model (physical model selection process).

FIG. 4 shows an outline of step S11.
Physical model selection unit 10 2, as shown in FIG. 4, and acquires the device information 201, acquires the physical model 203 corresponding to the ID described in the equipment information 201 from the physical model storage unit 101.
In the example of FIG. 4, the device A is the target device. Then, in the device information 201, "100-001" is described as the ID of the device A. Physical model selection unit 10 2 obtains a physical model 203 corresponding to the ID "100-001" from the physical model storage unit 101.

In addition, the OS (Operating System) is also stored in the auxiliary storage device 903.
Then, at least a part of the OS is executed by the processor 901.
The processor 901 while executing at least part of the OS, execute the physical model selection unit 10 2, impact analysis unit 103, the parameter selection unit 104, a program for realizing the functions of the similarity calculating unit 106 and the initial probability calculation unit 107 do.
When the processor 901 executes the OS, task management, memory management, file management, communication control, and the like are performed.
Further, at least one of information, data, a signal value, and a variable value indicating the processing results of the physical model selection unit 102, the influence degree analysis unit 103, the parameter selection unit 104, the similarity calculation unit 106, and the initial probability calculation unit 107. Is stored in at least one of a register and a cache memory in the main storage device 902, the auxiliary storage device 903, and the processor 901.
Further, programs that realize the functions of the physical model selection unit 102, the influence degree analysis unit 103, the parameter selection unit 104, the similarity calculation unit 106, and the initial probability calculation unit 107 are magnetic disks, flexible disks, optical disks, compact disks, and the like. It may be stored in a portable recording medium such as a Blu-ray (registered trademark) disc or a DVD. Then, a portable recording medium containing a program that realizes the functions of the physical model selection unit 102, the influence degree analysis unit 103, the parameter selection unit 104, the similarity calculation unit 106, and the initial probability calculation unit 107 is commercially distributed. You may let me.

Further, the "parts" of the physical model selection unit 102, the influence degree analysis unit 103, the parameter selection unit 104, the similarity calculation unit 106, and the initial probability calculation unit 107 are referred to as "circuits" or "processes" or "procedures" or "procedures". It may be read as "processing".
Further, the information processing device 100 may be realized by a processing circuit. The processing circuit is, for example, a logic IC (Integrated Circuit), a GA (Gate Array), an ASIC (Application Specific Integrated Circuit), or an FPGA (Field-Programmable Gate Array).
In this case, the physical model selection unit 102, the influence degree analysis unit 103, the parameter selection unit 104, the similarity calculation unit 106, and the initial probability calculation unit 107 are each realized as a part of the processing circuit.
In this specification, the superordinate concept of the processor and the processing circuit is referred to as "processing circuit Lee".
That is, the processor and the processing circuit are specific examples of the "processing circuit Lee", respectively.

100 an information processing apparatus, 101 a physical model storage unit, 10 second physical model selection unit, 103 impact analysis unit, 104 parameter selection unit, 105 operation model storage unit, 106 similarity calculating unit, 107 an initial probability calculation unit, 201 device information , 202 parameter sensor information, 203 physical model, 300 equipment, 400 sensor, 901 processor, 902 main storage, 903 auxiliary storage, 904 communication device.

Claims (6)

For multiple parameters, the impact analysis unit that analyzes the impact on the equipment when the parameter value is changed for each parameter,
A parameter selection unit that selects a parameter having an influence degree that matches the selection conditions as a selection parameter from the plurality of parameters.
With respect to a plurality of operation models of the device, a similarity calculation unit that calculates the similarity between the parameter corresponding to the selection parameter and the selection parameter among the plurality of parameters set in each operation model for each operation model, and
An information processing device having an initial probability calculation unit that calculates an initial probability, which is a probability set as an initial value in each operation model, based on the similarity of each operation model for each operation model. The impact analysis unit
The information processing apparatus according to claim 1, wherein the degree of influence on the device when the parameter value is changed is analyzed for each parameter by simulation. The impact analysis unit
The information processing apparatus according to claim 1, wherein as the degree of influence on the device, the degree of influence on the sensor value of a sensor provided in the device when the parameter value is changed is analyzed for each parameter. The impact analysis unit
The information processing device according to claim 1, wherein any of the attributes of the elements of the device, the physical quantity related to the installation environment of the device, and the number of years of use of the device are used as the plurality of parameters. For each parameter, the computer analyzes the degree of influence on the device when the parameter value is changed for multiple parameters, and analyzes it for each parameter.
The computer selects, as a selection parameter, a parameter having an influence degree that matches the selection conditions from the plurality of parameters.
The computer calculates the similarity between the parameter corresponding to the selection parameter and the selection parameter among the plurality of parameters set in each operation model for the plurality of operation models of the device for each operation model.
An information processing method that calculates an initial probability, which is a probability that the computer sets as an initial value in each operation model, for each operation model based on the similarity of each operation model. For multiple parameters, the impact analysis unit that analyzes the impact on the equipment when the parameter value is changed for each parameter,
A parameter selection unit that selects a parameter having an influence degree that matches the selection conditions as a selection parameter from the plurality of parameters.
With respect to a plurality of operation models of the device, a similarity calculation unit that calculates the similarity between the parameter corresponding to the selection parameter and the selection parameter among the plurality of parameters set in each operation model for each operation model, and
An information processing program that causes a computer to execute an initial probability calculation unit that calculates an initial probability, which is a probability set as an initial value in each operation model, based on the similarity of each operation model for each operation model.

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