Animal disease prediction algorithm

A technology for predicting algorithms and diseases, applied in computing, computer components, neural learning methods, etc., can solve problems such as incomplete data, large algorithm calculation, neural network overfitting, etc., to achieve strong recognition ability, high precision, The effect of high predictive power and robustness

CN111543965AInactive Publication Date: 2020-08-18HUNAN NORMAL UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2020-08-18
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses an animal disease prediction algorithm, and relates to the technical field of animal disease identification. The algorithm is composed of a two-layer neural network structure,a sub-layer neural network is composed of a convolutional neural network or a recurrent neural network and is responsible for sound recognition, and a main layer is composed of a recurrent neural network and is responsible for disease prediction according to physical sign parameters such as heart rate, blood oxygen, body temperature and the sound recognition result of the sub-layer. Sound and conventional sign data are separately processed, the data volume of data processing is reduced while data processing details are kept, the difficulty of data training of the two network structures is reduced, and a technical means is provided for predicting animal diseases through heart rate, blood oxygen, body temperature and sound.
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Description

technical field

[0001] The invention relates to the technical field of animal disease prediction, in particular to an animal disease prediction algorithm. Background technique

[0002] Signs of animals, such as body temperature, blood oxygen, heart rate, movement, cry, etc., are closely related to animal diseases, reproduction and production efficiency, and are of great significance to the study of improving animal growth efficiency and preventing diseases. For example, through changes in body temperature and heart rate, it is possible to analyze whether there are problems with the feed and growth environment of pigs during the breeding process; through changes in body temperature, blood oxygen, and heart rate, it is possible to predict diseases of pigs. At present, conventional physiological sign detection mainly adopts contact methods, such as measuring body temperature with a thermometer and measuring blood oxygen with an oximeter. The efficiency is low, and long-term un...

Examples

Embodiment Construction

[0044] In order to make the purpose, technical solution and advantages of the present invention clearer, the following in conjunction with the attached Figures 1 to 3 The given examples illustrate the present invention in further detail.

[0045] The invention provides an animal disease prediction algorithm. The method adopts a double-layer neural network structure, and the main layer network structure is composed of a cyclic neural network, which is responsible for disease prediction according to four parameters of body temperature, heart rate, blood oxygen and voice recognition results; The layered neural network structure consists of a convolutional neural network or a recurrent neural network, which is responsible for sound recognition.

[0046] Specifically, such as figure 1 As shown, the key steps of the main layer network are described as follows:

[0047] Step 1: Divide the continuously sampled sound into a longer time period T (such as 5 minutes), and then divide it ...