Rescue 1122 Artificialy Intelligent Command and Control Center (C3 System)

This project targeted the current rescue 1122 system with a classical form filling style. We figure out the causes of delay in response and then agile the decision making with our NLP-based Artificially intelligent C3 system. Out system is based on semantics and machine learning with GIS-based routing. The system has the capability to detect the problem from a plain natural language (English) statement/ set of statements. Then parse contact, location, and accident details like which sort of emergency, how many persons are in it, and What actions to be taken for maximum rescue with minimum possible use of assets. This not only speeds up the decision-making but also formulate the over situation awareness by creating a Common Operational Picture. Major Functionalists are: 1- Replaced main emergency logging interface with simple single entry interface. 2- Included GIS-based location mapping of assets. 3- Intelligently parses basic information like contact and address from the input string. 4- Uses NLP to accommodate less arithmetical reporting. 5- Understand the type of emergencies like a person in emergency etc. 6- Learns the actions taken and What actions to be taken. 7- Maps the shorts routs between the place of emergency and rescue Vehicle. 8- Leads the Rescue team to point of emergency. 9- then Maps route to hospitals if required. 10- Paint all information on COP and logs on Server.