Method for improving temperature measurement accuracy of low-power bluetooth ear tags in sheep breeding scenarios

Body temperature, as one of the important physiological indicators of sheep, is closely related to their health status. Therefore, the accuracy and service life of temperature measurement devices directly affect the reliability of monitoring data and the efficiency of breeding. In response to the problems of insufficient temperature measurement accuracy and short service life of existing ear tags, a method for improving the temperature measurement accuracy of low-power Bluetooth ear tags in the sheep breeding scenario is proposed. In terms of hardware design, a low-power Bluetooth system and chip are introduced as the microcontroller of the ear tag, which improves the power consumption and performance compared to traditional Bluetooth; based on the traditional constant current source-driven dual AD circuit, considering the size of the ear tag and computing capacity, it is improved to a voltage-driven dual AD circuit, and an AD1 reference source detection circuit is added on this basis to achieve a dual AD synchronous sampling circuit architecture, avoiding errors caused by circuit heating, environmental temperature changes, and power supply voltage fluctuations; at the same time, to solve the errors caused by circuit signal noise and common-mode voltage, a signal conditioning unit is introduced to effectively suppress power supply fluctuations and common-mode interference; for the ear tag printed circuit board (PCB), a crescent-shaped design is adopted, which saves 33% of space compared to the traditional square or circular design. In terms of the thermal conduction structure, it breaks through the traditional probe design and introduces 0.5-millimeter high-purity electronic-grade copper as the thermal conduction base of the ear ring label, designs the contact surface according to the internal shape of the sheep's ear, and places it on the side where the ear ring label contacts the ear. This greatly increases the contact area and significantly improves the thermal conduction efficiency, while reducing the contact thermal resistance; in terms of the working mode, an intermittent working mode is designed based on the temperature change characteristics of sheep, with a temperature measurement frequency of once per minute and a transmission frequency of once every ten minutes, significantly reducing the energy consumption of the equipment; in terms of temperature calibration, based on the temperature change characteristics of sheep, the temperature segmentation strategy is optimized, setting the upper and lower limits of temperature measurement to 35 ℃ to 45 ℃, and using uniform sampling points for measurement selection. The selected Murata NCP18WF104 thermistor is calibrated according to the selected temperature range, using a constant temperature water bath and ammonia heating tube to provide a stable constant temperature environment, using a Fluke 1595 and 6015T to measure the thermistor and unqualified platinum resistors, and then comparing the fitting results of the Hogg and Stan-Hart equation to select the best-fitting fourth-order Stan-Hart equation to establish a high-precision thermistor-temperature conversion model, performing linear fitting on the measurement results, and the fitting error under experimental conditions is less than 0.1 ℃. Through field testing, the ear tags have been verified. In the Feimenyuan Farm in Hefei City, Anhui Province, actual tests were conducted in 4 sheep sheds, selecting 126 sheep for temperature testing and ear tag stability testing, lasting for three days. The experimental results show that the optimized ear tag temperature measurement error is controlled within ±0.2 ℃, the effective data transmission rate exceeds 95.3%, and the equipment service life is extended by approximately 1-1.5 years. Through the collaborative optimization of hardware, structure, working mode, and temperature calibration, the performance indicators of wearable body temperature monitoring devices have been improved, providing reliable technical support for precise health monitoring in the intelligent agriculture field.