System and method for corrective action to achieve baseline condition
A system, method, and medium for corrective action to achieve baseline condition including a communication component and a processor. The communication component receives input data associated with one or more operating conditions of an equipment utilized for baseline activity and captures baseline data relating to a system baseline associated with a corrective action recommendation. The processor predicts an optimal operating condition based on at least one of a statistical model, a mathematical model, or a machine learning model of the equipment and determine the corrective action recommendation based on the optimal operating condition. The corrective action recommendation is associated with the system baseline. The processor also detects one or more anomalies from the baseline data deviating from the system baseline beyond a predetermined range, and re-evaluates the optimal operating condition based on the anomaly or anomalies.
1 . A postal mail handling system for corrective action to achieve baseline condition, comprising:
a communication component configured to receive input data associated with one or more operating conditions of postal mail handling equipment utilized for baseline activity and capture baseline data relating to a system baseline associated with a corrective action recommendation, wherein the input data includes seasonality data pertaining to postal mail handling; and
a processor configured to predict an optimal operating condition based on at least one of a statistical model, a mathematical model, or a machine learning model of the postal mail handling equipment, determine the corrective action recommendation based on the optimal operating condition, execute the determined corrective action to achieve baseline condition, detect at least one anomaly from the baseline data deviating from the system baseline beyond a predetermined range, and re-evaluate the optimal operating condition based on the at least one anomaly;
wherein the corrective action recommendation is associated with the system baseline.
2 . The postal mail handling system as described in claim 1 , wherein:
the processor is configured to determine a dynamic relationship with an equipment operating condition of an entire system and the optimal operating condition based on the at least one of the statistical model, the mathematical model, and the machine learning model of the postal mail handling equipment; and
the processor is configured to determine the corrective action recommendation based on the dynamic relationship.
3 . The postal mail handling system as described in claim 1 , wherein the input data further includes at least one of structured equipment time series data, unstructured equipment time series data, maintenance data, equipment specifications, equipment documentation, system metadata, equipment metadata, and operational data.
4 . The postal mail handling system as described in claim 1 , wherein the communication component is configured to capture the baseline data in response to detecting an acceptance signal associated with the corrective action recommendation.
5 . The postal mail handling system as described in claim 1 , wherein the processor is configured to determine whether the at least one anomaly of the baseline data deviates from the system baseline beyond the predetermined range based on the system baseline of the optimal operating condition.
6 . The post mail handling system as described in claim 1 , wherein the processor is configured to report the system baseline and the at least one anomaly to at least one of a user interface module or an external system interface.
7 . The postal mail handling system as described in claim 1 , wherein the processor is configured to re-evaluate the optimal operating condition in response to information received from a work order management system.
8 . A postal mail handling method for corrective action to achieve baseline condition, the method comprising:
receiving input data associated with one or more operating conditions of postal mail handling equipment utilized for baseline activity, wherein the input data includes seasonality data pertaining to postal mail handling;
predicting an optimal operating condition based on at least one of a statistical model, a mathematical model, or a machine learning model of the postal mail handling equipment;
determining a corrective action recommendation based on the optimal operating condition, the corrective action recommendation being associated with a system baseline;
executing the determined corrective action to achieve baseline condition;
capturing baseline data relating to the system baseline associated with the corrective action recommendation;
detecting at least one anomaly of the baseline data deviating from the system baseline beyond a predetermined range; and
re-evaluating the optimal operating condition based on the at least one anomaly.
9 . The postal mail handling method as described in claim 8 , further comprising:
determining a dynamic relationship with an equipment operating condition of an entire system and the optimal operating condition based on the at least one of the statistical model, the mathematical model, and the machine learning model of the postal mail handling equipment;
wherein determining the corrective action recommendation includes determining the corrective action recommendation based on the dynamic relationship.
10 . The postal mail handling method as described in claim 8 , wherein the input data further includes at least one of structured equipment time series data, unstructured equipment time series data, maintenance data, equipment specifications, equipment documentation, system metadata, equipment metadata, and operational data.
11 . The postal mail handling method as described in claim 8 , wherein capturing the baseline data includes capturing the baseline data in response to detecting an acceptance signal associated with the corrective action recommendation.
12 . The postal mail handling method as described in claim 8 , wherein detecting the at least one anomaly of the baseline data deviating from the system baseline beyond the predetermined range includes determining that the at least one anomaly of the baseline data deviates from the system baseline beyond the predetermined range based on the system baseline of the optimal operating condition.
13 . The postal mail handling method as described in claim 8 , further comprising reporting the system baseline and the at least one anomaly to at least one of a user interface module or an external system interface.
14 . The postal mail handling method as described in claim 8 , wherein re-evaluating the optimal operating condition includes re-evaluating the optimal operating condition in response to information received from a work order management system.
15 . A non-transitory computer readable medium including executable instructions which, when executed, causes at least one processor to execute corrective action to achieve baseline condition for a postal mail handling system by:
receiving input data associated with one or more operating conditions of postal mail handling equipment utilized for baseline activity, wherein the input data includes seasonality data pertaining to postal mail handling;
predicting an optimal operating condition based on at least one of a statistical model, a mathematical model, or a machine learning model of the postal mail handling equipment;
determining a corrective action recommendation based on the optimal operating condition, the corrective action recommendation being associated with a system baseline;
executing the determined corrective action to achieve baseline condition;
capturing baseline data relating to the system baseline associated with the corrective action recommendation;
detecting at least one anomaly of the baseline data deviating from the system baseline beyond a predetermined range; and
re-evaluating the optimal operating condition based on the at least one anomaly.
16 . The non-transitory computer readable medium as described in claim 15 , further comprising:
determining a dynamic relationship with an equipment operating condition of an entire system and the optimal operating condition based on the at least one of the statistical model, the mathematical model, and the machine learning model of the postal mail handling equipment;
wherein determining the corrective action recommendation includes determining the corrective action recommendation based on the dynamic relationship.
17 . The non-transitory computer readable medium as described in claim 15 , wherein the input data further includes at least one of structured equipment time series data, unstructured equipment time series data, maintenance data, equipment specifications, equipment documentation, system metadata, equipment metadata, and operational data.
18 . The non-transitory computer readable medium as described in claim 15 , wherein capturing the baseline data includes capturing the baseline data in response to detecting an acceptance signal associated with the corrective action recommendation.
19 . The non-transitory computer readable medium as described in claim 15 , wherein detecting the at least one anomaly of the baseline data deviating from the system baseline beyond the predetermined range includes determining that the at least one anomaly of the baseline data deviates from the system baseline beyond the predetermined range based on the system baseline of the optimal operating condition.
20 . The non-transitory computer readable medium as described in claim 15 , wherein re-evaluating the optimal operating condition includes re-evaluating the optimal operating condition in response to information received from a work order management system.