Analysis method, non-transitory computer-readable recording medium and information processing device for assisting operator in quick decision making

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.

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.