Systems and methods for real-time translation of aquatic sensor measurements to probable causes via combination of cause-and-effect queries and language model based semantic similarity

Abstract

An aquatic environmental monitoring, analysis, and reporting system comprising a mobile waterborne craft equipped with a sensor subsystem for acquiring water-quality measurements. A first computing unit converts raw sensor signals into numerical measurement values, while a second computing unit executes program instructions implementing an environment analysis module that detects anomalies relative to expected parameter ranges. Upon anomaly detection, an inquiry module automatically generates natural-language queries and submits them to external search services, receiving textual results describing potential causes. An assessment module parses the results to extract cause-related terms, represents them in a numerical similarity space, and groups them into semantically similar cause clusters ranked as probable causes. A report generation module synthesizes the measurements, environmental conditions, and probable cause clusters into a human-readable diagnostic report transmitted in real time to a user device while the craft remains deployed, enabling end-to-end cause identification without laboratory sample processing. An aquatic environmental monitoring, analysis, and reporting system comprising a mobile waterborne craft equipped with a sensor subsystem for acquiring water-quality measurements. A first computing unit converts raw sensor signals into numerical measurement values, while a second computing unit executes program instructions implementing an environment analysis module that detects anomalies relative to expected parameter ranges. Upon anomaly detection, an inquiry module automatically generates natural-language queries and submits them to external search services, receiving textual results describing potential causes. An assessment module parses the results to extract cause-related terms, represents them in a numerical similarity space, and groups them into semantically similar cause clusters ranked as probable causes. A report generation module synthesizes the measurements, environmental conditions, and probable cause clusters into a human-readable diagnostic report transmitted in real time to a user device while the craft remains deployed, enabling end-to-end cause identification without laboratory sample processing.

Dependencies are inferred from the claim text.