# US 12,761,612 B2 · Analysis method, non-transitory computer-readable recording medium and information processing device for assisting operator in quick decision making

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- Number: US 12,761,612 B2
- Document ID: US12761612B2
- Status: Granted
- Kind: B2
- Application number: 18/272,293
- Filed: 2021-12-06
- Published: 2024-05-02
- Granted: 2026-10-13
- Earliest claimed: 2021-01-28
- Assignees: Yokogawa Electric Corporation
- Applicants: Yokogawa Electric Corporation
- Inventors: Soichiro Torai; Shinichi Chiyoda; Kenichi Ohara
- Web page: https://patentgrep.com/patent/US12761612B2

## CPC

- G05B 13/048: {using a predictor}
- G05B 13/027: {using neural networks only}
- G05B 13/041: {in which a variable is automatically adjusted to optimise the performance}
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)

## Abstract

An information processing device acquires a prediction result obtained when a premise is applied to a causal model having a plurality of variables related to operation of a plant. Based on the prediction result, the information processing device specifies a relevant variable dependent on the premise from the plurality of variables. Thereafter, with respect to the relevant variable, the information processing device displays information on a state of the relevant variable obtained according to the prediction result and a statistic of plant data corresponding to the relevant variable in plant data that is generated in the plant.

## Claims

### Claim 1

A computer-implemented analysis method comprising:
acquiring a prediction result obtained when a premise is applied to a causal model having a plurality of variables related to operation of a plant;
based on the prediction result, specifying a relevant variable dependent on the premise from the plurality of variables;
with respect to the relevant variable, displaying information on a state of the relevant variable obtained according to the prediction result and a statistic of plant data corresponding to the relevant variable in plant data that is generated in the plant; and
extracting a factor that has an effect on a production management index by machine learning from multidimensional operation data containing environmental changes for products in the plant, and converting a result of the machine learning into a form that is easy for an operator to consider and understand and representing the form to the operator so as to assist the operator in quick decision making during the operation,
wherein the computer-implemented analysis method further comprises:
collecting a plurality of sets of process data that are output from the plant and that contain the plurality of variables;
executing clustering of classifying the sets of process data by an operation state of the plant; and
using training data including the process data and a result of the clustering, executing a structure training on the causal model, and
wherein the executing clustering includes clustering elements of time and elements of tags of the process data, and calculating respective membership probabilities.

### Claim 2 (depends on 1)

The analysis method according to claim 1, wherein the displaying includes displaying, as the information on the state of the relevant variable, a condition and a probability value that are obtained according to the prediction result and degree information quantitatively representing a degree at which the condition is complied with in the operation of the plant.

### Claim 3 (depends on 1)

The analysis method according to claim 1, wherein the executing includes
based on relevance of constituent devices that constitute the plant, specifying a parent-child relationship of the constituent devices; and
using the training data including the process data, the result of clustering, and the parent-child relationship, executing the structure training on the causal model.

### Claim 4 (depends on 3)

The analysis method according to claim 3, wherein
the executing includes, using the training data and an objective variable representing a state of the plant, executing structure training on a Bayesian network,
the acquiring includes acquiring the prediction result by prediction performed by inputting the premise in which the variable serving as an object and the state of the plant are specified to a trained Bayesian network,
the specifying includes specifying, in each cluster to which each node belongs in the Bayesian network, a node with the highest probability value obtained by the prediction as the relevant variable, and
the displaying includes, with respect to the relevant variable, displaying the condition and the probability value that are obtained according to the prediction result, the degree information, and the statistic in a comparable manner.

### Claim 5 (depends on 1)

The analysis method according to claim 1, wherein
the information on the state of the relevant variable is a management criterion which represents a state in which a probability value is the highest as a result of the prediction.

### Claim 6 (depends on 1)

The analysis method according to claim 1, wherein
the statistic is information including an average of all data, a mode of all data, a maximum of all data, a minimum of all data, and a standard deviation of all data.

### Claim 7

A non-transitory computer-readable recording medium having stored therein an analysis program that causes a computer to execute a process comprising:
acquiring a prediction result obtained when a premise is applied to a causal model having a plurality of variables related to operation of a plant;
based on the prediction result, specifying a relevant variable dependent on the premise from the plurality of variables;
with respect to the relevant variable, displaying information on a state of the relevant variable obtained according to the prediction result and a statistic of plant data corresponding to the relevant variable in plant data that is generated in the plant; and
extracting a factor that has an effect on a production management index by machine learning from multidimensional operation data containing environmental changes for products in the plant, and converting a result of the machine learning into a form that is easy for an operator to consider and understand and representing the form to the operator so as to assist the operator in quick decision making during the operation,
wherein the process further comprises:
collecting a plurality of sets of process data that are output from the plant and that contain the plurality of variables;
executing clustering of classifying the sets of process data by an operation state of the plant; and
using training data including the process data and a result of the clustering, executing a structure training on the causal model, and
wherein the executing clustering includes clustering elements of time and elements of tags of the process data, and calculating respective membership probabilities.

### Claim 8

An information processing device comprising:
a processor configured to:
acquire a prediction result obtained when a premise is applied to a causal model having a plurality of variables related to operation of a plant;
based on the prediction result, specify a relevant variable dependent on the premise from the plurality of variables;
with respect to the relevant variable, display information on a state of the relevant variable obtained according to the prediction result and a statistic of plant data corresponding to the relevant variable in plant data that is generated in the plant; and
extract a factor that has an effect on a production management index by machine learning from multidimensional operation data containing environmental changes for products in the plant, and convert a result of the machine learning into a form that is easy for an operator to consider and understand and representing the form to the operator so as to assist the operator in quick decision making during the operation,
wherein the processor is further configured to:
collect a plurality of sets of process data that are output from the plant and that contain the plurality of variables;
execute clustering of classifying the sets of process data by an operation state of the plant; and
using training data including the process data and a result of the clustering, execute a structure training on the causal model, and
wherein executing clustering includes clustering elements of time and elements of tags of the process data, and calculating respective membership probabilities.