Automated positive train control event data extraction and analysis engine and method therefor
A system and method for automating workflow and performing root cause analysis for enforcement events is presented. The system can enable accurate detection of an enforcement event and identifies the root cause of such events. The system can provide a user with an interface to monitor the enforcement event by collecting a list of data characterizing the enforcement event, as well as analyze the data to evaluate what is the root cause of the enforcement event. The system can extract critical information from train system logs of the train using an extraction model to generate a window of activity providing an analysis model with a comprehensive scope to analyze the enforcement event. The system can give the user robust and accurate information of the root cause of the enforcement event.
1. A system for railroad brake event handling, comprising:
a memory having a database with train system logs; and
a processor operably coupled to the memory and capable of executing machine-readable instructions to perform program steps, the program steps comprising:
receiving a railroad policy enforcement event message corresponding to an enforcement event;
extracting data indicating characteristics of the enforcement event from the railroad enforcement event message;
transforming the extracted data into one or more dictionaries having a predetermined format;
analyzing the one or more dictionaries to determine a cause of the enforcement event;
categorizing the enforcement event as one of a plurality of events corresponding to the cause of the enforcement event; and
transmitting a notification having a detailed synopsis of the enforcement event to event subscribers.
2. The system of claim 1 , wherein the enforcement event is a PTC brake event.
3. The system of claim 1 , the program steps further comprising:
transmitting an outgoing message to request at least one train system log corresponding to the enforcement event.
4. The system of claim 1 , wherein the data corresponds to the at least one train system log indicating the characteristics of the enforcement event.
5. The system of claim 4 , the program steps further comprising: generating a time window based on the data surrounding a time of the enforcement event.
6. The system of claim 5 , wherein the time window can include time measurements before the enforcement event, after the enforcement event, or both before and after the enforcement event.
7. The system of claim 1 , the program steps further comprising:
comparing a time of the enforcement with the data to verify the enforcement event actually occurred.
8. The system of claim 1 , the program steps further comprising:
determining whether the data is structured or unstructured.
9. The system of claim 8 , the program steps further comprising:
identifying a pattern to the unstructured data and transforming the unstructured data into structured data.
10. The system of claim 9 , wherein the dictionaries are analyzed using a training data model having user-based parameters, live data, or historical data.
11. A method of railroad enforcement event handling, comprising:
receiving a railroad policy enforcement event message corresponding to an enforcement event;
extracting data indicating characteristics of the enforcement event from the railroad enforcement event message;
transforming the extracted data into one or more dictionaries having a predetermined format;
analyzing the one or more dictionaries to determine a cause of the enforcement event;
categorizing the enforcement event as one of a plurality of events corresponding to the cause of the enforcement event; and
transmitting a notification having a detailed synopsis of the enforcement event to event subscribers.
12. The method of claim 11 , wherein the enforcement event is a PTC brake event.
13. The method of claim 11 , further comprising:
transmitting an outgoing message to request at least one train system log corresponding to the enforcement event.
14. The method of claim 11 , wherein the data corresponds to the at least one train system log indicating the characteristics of the enforcement event.
15. The method of claim 14 , further comprising:
generating a time window based on the data surrounding a time of the enforcement event.
16. The method of claim 15 , wherein the time window can include time measurements before the enforcement event, after the enforcement event, or both before and after the enforcement event.
17. The method of claim 11 , further comprising:
comparing a time of the enforcement with the data to verify the enforcement event actually occurred.
18. The method of claim 11 , further comprising:
determining whether the data is structured or unstructured.
19. The method of claim 18 , further comprising:
identifying a pattern to the unstructured data and transforming the unstructured data into structured data.
20. The method of claim 19 , wherein the dictionaries are analyzed using a training data model having user-based parameters, live data, or historical data.