In this paper, the key technology of interferometric microwave thermometer is studied, the research can be applied to the temperature measurement of human body and subcutaneous tissue. This paper proposes a hardware architecture of interferometric microwave thermometer with 2 GHz bandwidth, in which the phase shifter is used to correct phase error and the quadrature demodulator is used to realize autocorrelation detection function. The results show that when input power is 7 dBm, the detection sensitivity can reach 176.54 mV/dBm and the temperature resolution of the microwave radiometer can reach 0.4 K. Correction algorithm is designed to improve the accuracy of temperature measurement. After correction, the phase error is reduced from 40° to 1.4° and when temperature changes 0.1 °C, the voltage value changes obviously. Step-by-step calibration and overall calibration are used to calibrate the device. Inversion algorithm can determine the relationship between physical temperature and output voltage. The mean square error of water temperature inversion by multiple linear regression algorithm is 0.607 and that of BP neural network algorithm is 0.334. The inversion accuracy can be improved by reducing the temperature range. Our work provides a promising realization of accurate, rapid and non-contact detection device of human body temperature.

To reduce the high pre-weaning mortality rate of new-born piglets crushed by sows, a kind of recognition and evaluation method for sows' behavior based on the scheme of time-sharing and multiplexing by adopting triaxial acceleration and video sensors at day and night is proposed in this paper. For darker scene at night, random forest classifier with optimal 43-dimensional feature vector subset proposed in this paper is adopted to recognize four kinds of macro behaviors of sows roughly by adopting triaxial acceleration sensor MPU6050. The recognition rate can reach 89.4%. For brighter light scene during the day, an improved bilinear convolutional neural network method based on CBAM module is proposed in this paper to recognize seven kinds of micro behaviors of sows by video sensor. The recognition rate can reach 84.4%. The methods proposed in this paper can meet the requirement of real-time to recognize the behavior of sows during 24 hours on the premise of ensuring accuracy. Finally, an evaluation model of sows' maternal behavior level is set up in this paper. The achivement of the study can not only help the farm to select sows with higher maternal ability for breeding piglets, but also avoid the large-scale economic losses caused by the high mortality rate of piglets before weaning of the farm.