Method and system for artificial intelligence (ai) based alarm management

A system comprising:

• at least one memory storing programmed instructions;
• one or more Input/Output (I/O) interfaces;
• and one or more hardware processors (102) operatively coupled to the at least one memory, wherein the one or more hardware processors are configured by the programmed instructions to:
• receive a user query associated with an automation system, wherein the user query is in Natural Language (NL);
• classify the user query into one of a) data request query b) an anomaly detection and diagnosis query c) a log request query and d) a feedback for previous response using a query classifier;
• simultaneously convert the user query into an associated timeseries query representation using a sequence-to-sequence model;
• generate a plurality of database queries based on the timeseries query representation using query generator, wherein the plurality of database queries comprises a multivariate timeseries database query and a relational database query;
• retrieve a data pertaining to the user query from an associated database from a plurality of databases based on an associated plurality of database queries and a classification information, wherein the retrieved data is in one of a) structured data format and b) an unstructured data format, wherein the retrieved data comprises one of a) a general information b) an anomaly information and c) a log information, wherein the plurality of databases comprises a timeseries databases and a relational database, and wherein the anomaly information is used to identify a critical alarm and to take appropriate actions;
• and
• display the retrieved data to the user by converting into a user readable format using an associated data conversion technique, wherein the unstructured data format is converted into user readable format using a Natural Language Processing (NLP) technique and the structured timeseries data is converted in the form of text, tables and graphs.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• receiving a user query associated with an automation system, wherein the user query is in Natural Language (NL);
• classifying the user query into one of a) data request query b) an anomaly detection and diagnosis query c) a log request query and d) a feedback for previous response using a query classifier;
• simultaneously converting the user query into an associated timeseries query representation using a sequence-to-sequence model;
• generating a plurality of database queries based on the timeseries query representation using query generator, wherein the plurality of database queries comprises a multivariate timeseries database query and a relational database query;
• retrieving a data pertaining to the user query from an associated database from a plurality of databases based on an associated plurality of database queries and a classification information, wherein the retrieved data is in one of a) structured data format and b) an unstructured data format, wherein the retrieved data comprises one of a) a general information b) an anomaly information and c) a log information, wherein the plurality of databases comprises a timeseries databases and a relational database, and wherein the anomaly information is used to identify a critical alarm and to take appropriate actions;
• and
• displaying the retrieved data to the user by converting into a user readable format using an associated data conversion technique, wherein the unstructured data format is converted into user readable format using a Natural Language Processing (NLP) technique and the structured timeseries data is converted in the form of text, tables and graphs.