A Multi-Source Signal-Conditioned Fiber Optic Sensing System for Microwave Temperature Acquisition and Prediction

Due to the existence of electromagnetic standing waves, uniform heating and accurate temperature data collection during microwave cooking have always been a challenge. This paper presents a multi-source temperature data acquisition system for microwave heating environments and introduces a brain-inspired prediction algorithm based on data structure to enhance temperature control logic. First, a temperature monitoring system for microwave heating environments, termed FOTS (Fiber Optic Temperature Sensing System), is developed. This system consists of an 8-probe fiber optic sensor array and an infrared temperature sensor. Then, a brain-inspired audio-visual (BIAV) predictive algorithm is proposed to simulate the brain's integration mechanism. The core of this algorithm extracts key time-series data, calculates trend features to output the related key moment labels, and puts forward a vision-enhanced LSTM for prediction. Finally, experiments demonstrate the accuracy of BIAV in predicting food temperatures using sensor data, and existing control algorithms are optimized and validated. The proposed FOTS and BIAV algorithm in microwave fields provide new ideas and methods for multi-sensor data processing. Additionally, existing control algorithms are optimized using predicted accurate temperature values and two-dimensional temperature fields, making microwave heating more uniform and safer, which is of great significance for industrial control.