Animal blood sugar monitoring system and blood sugar state recognition method based on machine learning

Abstract

The invention discloses an animal blood sugar monitoring system and a blood sugar state recognition method based on machine learning. The animal blood sugar monitoring system comprises an acquisitiondevice and an intelligent terminal application, and the acquisition device acquires contact pressure, heat intake and acceleration information of monitored animals and judges whether effective contactoccurs or not according to the magnitude of the contact pressure; the intelligent terminal application receives effective animal data of the acquisition device and recognizes the effective animal data into the animal blood sugar state. By means of the recognition method, a normal blood sugar value of monitored animals in different states can be obtained through learning, and based on the learnt information, the blood sugar state of the animals can be recognized. By means of the system, the blood sugar can be automatically detected and remotely monitored in real time, manual detection or recording is not needed, and a lot of manpower can be saved; by means of the method, the difference like the type, the age, the individual and the motion state of the animals can be eliminated, all monitored objects are truly customized, and the purpose of precisely and effectively detecting blood sugar is achieved.

Description

technical field [0001] The invention relates to the field of animal health monitoring, in particular to an animal blood sugar monitoring system and a machine learning-based blood sugar state recognition method. Background technique [0002] Blood glucose is an important health indicator for animals. Blood glucose monitoring is one of the important ways to evaluate the health status of animals. The timeliness, accuracy and reliability of blood glucose monitoring directly affect the prevention, diagnosis, treatment and postoperative recovery of diseases. [0003] Traditional animal blood glucose detection uses a single blood glucose sample point, which is very inconvenient to track changes in animal blood glucose. At present, the measurement of animal blood glucose is mainly done manually. The owner needs to manually measure the animal blood glucose every day, record the blood glucose value, and draw the blood glucose curve. A lot of work. Moreover, some animals, such as free...

AI Technical Summary

Problems solved by technology

[0003] Traditional animal blood glucose detection uses a single blood glucose sample point, which is very inconvenient to track changes in animal blood glucose. At present, the measurement of animal blood glucose is mainly done manually. The owner needs to manually measure the animal blood glucose every day, record the blood glucose value, and draw the blood glucose curve. heavy workload

Moreover, some animals, such as free-ranging cattle, cannot cooperate with blood sugar testing, and often require anesthesia to achieve it. For such animals, traditional blood sugar testing methods cannot implement high-frequency testing

[0004] In addition, different kinds of animals, different age groups of the same kind of animals, different individuals of the same kind of animals at the same age, and different exercise states of the same individual animals often have different healthy / normal blood sugar levels, such as: cattle with high blood sugar in the postprandial state Because of its blood sugar before meals or under starvation; another example: the normal blood sugar of dogs is 3.9--6.2mmol / L, while the normal blood sugar of mice is 3.5--8.9mmol / L. Therefore, the blood sugar of animals The monitoring of animals should take into account differences in animal species, ages, individuals, etc., but traditional methods are difficult to fully achieve

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