Fluid Density Measurement Correction via Weather Data

The patent introduces a digital model trained with machine learning as an intermediary between the sensor unit and the final measurement output. This digital model processes raw sensor data and weather data, separating the measurement function from environmental influences. The model acts as a mediator that filters out weather-related measurement deviations while preserving actual fluid density information, thereby improving measurement precision without requiring complex hardware modifications.

The patent replaces traditional mechanical or physical correction methods with a machine learning-based digital model. Instead of using complex physical compensation mechanisms or multiple sensors to counteract weather effects, the system uses software-based machine learning algorithms to automatically correct measurement deviations. This substitution of mechanical/physical systems with intelligent algorithms improves measurement accuracy while keeping the overall system complexity manageable.

The patent applies preliminary action by pre-training the machine learning model with historical measurement data and weather data before actual operation. During the training phase, the model learns the relationship between weather conditions and measurement deviations from long-term historical data. Once trained, the model can immediately apply learned corrections to new measurements without requiring long observation periods, thus achieving both high reliability (from extensive training data) and fast detection (from immediate application).

The patent makes the measurement system dynamic by implementing a machine learning model that can adapt and learn from new data continuously. The model is not static but can be retrained and updated, allowing it to adjust to changing weather patterns and measurement conditions over time. This dynamic approach enables the system to maintain high reliability while providing rapid detection capabilities, as the model can process new measurements in real-time while continuously improving its accuracy.

The patent applies universality by designing a machine learning model that can handle multiple functions: it processes different types of sensor data, incorporates various weather parameters, and outputs corrected measurements. The same digital model framework can be applied to different fluid density measurement scenarios and even other types of environmental compensation. This multi-functionality justifies the increased complexity by providing a versatile solution that improves measurement precision across multiple applications and conditions.

The patent changes parameters by transforming raw sensor measurements and weather data into corrected fluid density values through the machine learning model. The model processes multiple input parameters (raw density measurements, temperature, humidity, pressure, etc.) and transforms them into a single corrected output parameter. This parameter transformation approach improves measurement precision by considering multiple influencing factors simultaneously, while the standardized parameter handling keeps the system architecture manageable.