Cattle illness data analysis method and device

Abstract

The embodiment of the invention provides a cattle illness data analysis method and device. The method concretely comprises the following steps: obtaining state index data representing cattle health states; selecting sample data, performing sample training of selected sample data according to an initialized cost matrix based on illness classification in advance and a multi-classification Bayesian process classifier based on cost sensibility, and obtaining the illness classification model of training samples; and according to the obtained state index data, the cost matrix and the training model, calculating the illness classification probability of the cattle to be detected and outputting the illness classification probability of the cattle to be detected. The method provided by the embodiment of the invention provides a cost-sensitive multi-classification Bayesian and Gaussian process to process the calculation of the illness classification probability of cattle, perform calculation of the state index states of the cattle and output a plurality of illness classification probabilities, and misjudgement cost factors are added to overcome the defects that a current discriminating method only can obtain general low misjudgement rate and only can output single classification results.

Description

technical field [0001] The present invention relates to the medical field, in particular to a cow disease data analysis method and device for monitoring the health status of farmed cows, especially dairy cows. Background technique [0002] The current combination and application of big data, Internet of Things technology and biological wearable devices provides an important basis and support for the informatization and scientificization of dairy farming. Algorithms such as classification and prediction are commonly used technical means in the identification of cattle diseases, and have a wide range of applications. [0003] Existing dairy cow health discrimination schemes usually use fixed thresholds or parameters for calculation. Adjusting parameters or thresholds requires retraining the model, which cannot be adjusted dynamically during operation. In the calculation process of the current existing algorithms, only the misdiagnosis rate of generalization is considered, whi...

AI Technical Summary

Problems solved by technology

[0003] Existing dairy cow health discrimination schemes usually use fixed thresholds or parameters for calculation. Adjusting parameters or thresholds requires retraining the model, which cannot be adjusted dynamically during operation

In the calculation process of the current existing algorithms, only the misdiagnosis rate of generalization is considered, which may cause a certain indicator deviation is not too large, but in fact, the indicators with high risk are not classified and exposed in time, resulting in relatively high risk. big loss

In addition, the threshold parameters or weights of existing methods are mostly set by veterinarians and industry experts at one time or manually adjusted item by item, and the accuracy rate is low

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