Methods for self-organizing data collection and storage
AssigneeStrong Force IOT Portfolio 2016, LLC
InventorsCharles Howard Cella, Gerald William Duffy, JR., Jeffrey P. McGuckin, Mehul Desai
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Abstract
Methods for data collection in an industrial environment having self-organization functionality are disclosed. A method may include analyzing a plurality of sensor inputs and sampling data received from the plurality of sensor inputs. The method may also include self-organizing at least one of: (i) a storage operation of the data; (ii) a collection operation of sensors that provide the plurality of sensor inputs, and (iii) a selection operation of the plurality of sensor inputs. The storage operation includes storing the data in a local database, and summarizing the data over a given time period to reduce a size of the data.
Claims14
A method for data collection in an industrial environment having self-organization functionality, the method comprising:
- analyzing a plurality of sensor inputs;
- sampling data received from the plurality of sensor inputs;
- and
- self-organizing at least one of:
- (i) a storage operation of the data;
- (ii) a collection operation of sensors that provide the plurality of sensor inputs, and (iii) a selection operation of the plurality of sensor inputs, wherein the storage operation includes storing the data in a local database, and summarizing the data over a given time period to reduce a size of the data.
A method for data collection in an industrial environment having self-organization functionality, the method comprising:
- analyzing a plurality of sensor inputs;
- sampling data received from the plurality of sensor inputs;
- and
- self-organizing at least one of:
- (i) a storage operation of the data;
- (ii) a collection operation of sensors that provide the plurality of sensor inputs, and (iii) a selection operation of the plurality of sensor inputs, wherein the storage operation comprises:
- storing the data in a local database;
- summarizing the data over a given time period to reduce a size of the data;
- committing the summarized data to a local ledger;
- identifying one or more other accessible signal acquisition instruments on an accessible network;
- and
- synchronizing the summarized data at the local ledger with at least one of the other accessible signal acquisition instruments.
A method for data collection in an industrial environment having self-organization functionality, the method comprising:
- analyzing a plurality of sensor inputs;
- sampling data received from the sensor inputs;
- self-organizing at least one of:
- (i) a storage operation of the data (ii) a collection operation of sensors that provide the plurality of sensor inputs, and (iii) a selection operation of the plurality of sensor inputs, wherein the storage operation comprises:
- storing the data in a local database;
- summarizing the data over a given time period to reduce a size of the data;
- identifying one or more other accessible signal acquisition instruments on an accessible network;
- nominating at least one of the one or more other accessible signal acquisition instruments as a logical communication hub;
- and
- providing the logical communication hub with a list of available data and their associated sources.
A method for data collection in an industrial environment having self-organization functionality, the method comprising:
- analyzing a plurality of sensor inputs;
- sampling data received from the sensor inputs;
- and
- self-organizing at least one of (i) a storage operation of the data, (ii) a collection operation of sensors that provide the plurality of sensor inputs, and (iii) a selection operation of the plurality of sensor inputs, wherein the storage operation comprises:
- storing the data in a local database;
- summarizing the data over a given time period to reduce a size of the data;
- storing the data in a local database;
- and
- automatically organizing at least one parameter of the local database utilizing machine learning, wherein organizing is based at least in part on receiving information regarding at least one of an accuracy of classification and an accuracy of prediction of an external machine learning system that uses data from the database.
Dependencies are inferred from the claim text.