Abstract: A system for managing electronic data representing emergency alerts comprises a computing device associated with an asset and a server. The server receives threshold data from the computing device and one or more data records representing reports of an event. The server computes an anomaly metric based on a comparison of the event to recent events and, if the anomaly metric does not exceed a threshold, computes a trend metric based on the frequency of similar events over time. If either metric exceeds its respective threshold, the server transmits a control signal comprising an alert to the computing device. The system may apply natural language processing, machine learning, and knowledge graphs to evaluate event characteristics and generate contextual summaries. By filtering alerts based on relevance and context, the system conserves computing resources and enables early detection of emerging threats, improving the responsiveness and efficiency of emergency alert infrastructure.