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1. WO2019216647 - METHOD FOR PREDICTING MILK YIELDS, TMR NUTRIENT COMPOSITION TO ACHIEVE TARGET MILK YIELDS, AND TMR NUTRIENT COMPOSITION TO ACHIEVE TARGET COST, ON BASIS OF DEEP LEARNING-BASED PREDICTION MODELS

Publication Number WO/2019/216647
Publication Date 14.11.2019
International Application No. PCT/KR2019/005509
International Filing Date 08.05.2019
IPC
A23N 17/00 2006.1
AHUMAN NECESSITIES
23FOODS OR FOODSTUFFS; THEIR TREATMENT, NOT COVERED BY OTHER CLASSES
NMACHINES OR APPARATUS FOR TREATING HARVESTED FRUIT, VEGETABLES, OR FLOWER BULBS IN BULK, NOT OTHERWISE PROVIDED FOR; PEELING VEGETABLES OR FRUIT IN BULK; APPARATUS FOR PREPARING ANIMAL FEEDING-STUFFS
17Apparatus specially adapted for preparing animal feeding-stuffs
G06N 3/08 2006.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
G06N 20/00 2019.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
CPC
A01K 29/005
AHUMAN NECESSITIES
01AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
KANIMAL HUSBANDRY; CARE OF BIRDS, FISHES, INSECTS; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
29Other apparatus for animal husbandry
005Monitoring or measuring activity, e.g. detecting heat or mating
A23N 17/00
AHUMAN NECESSITIES
23FOODS OR FOODSTUFFS; TREATMENT THEREOF, NOT COVERED BY OTHER CLASSES
NMACHINES OR APPARATUS FOR TREATING HARVESTED FRUIT, VEGETABLES OR FLOWER BULBS IN BULK, NOT OTHERWISE PROVIDED FOR; PEELING VEGETABLES OR FRUIT IN BULK; APPARATUS FOR PREPARING ANIMAL FEEDING- STUFFS
17Apparatus specially adapted for preparing animal feeding-stuffs
A23N 17/007
AHUMAN NECESSITIES
23FOODS OR FOODSTUFFS; TREATMENT THEREOF, NOT COVERED BY OTHER CLASSES
NMACHINES OR APPARATUS FOR TREATING HARVESTED FRUIT, VEGETABLES OR FLOWER BULBS IN BULK, NOT OTHERWISE PROVIDED FOR; PEELING VEGETABLES OR FRUIT IN BULK; APPARATUS FOR PREPARING ANIMAL FEEDING- STUFFS
17Apparatus specially adapted for preparing animal feeding-stuffs
007for mixing feeding-stuff components
G06N 20/00
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
G06N 3/0454
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0454using a combination of multiple neural nets
G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
Applicants
  • (주)씽크포비엘 THINKFORBL CO.,LTD [KR]/[KR]
Inventors
  • 박지환 PARK, Ji Hwan
  • 천선일 CHON, Sunil
Agents
  • 특허법인 신우 SHINWOO PATENT AND LAW&FIRM
Priority Data
10-2018-005229708.05.2018KR
Publication Language Korean (ko)
Filing Language Korean (KO)
Designated States
Title
(EN) METHOD FOR PREDICTING MILK YIELDS, TMR NUTRIENT COMPOSITION TO ACHIEVE TARGET MILK YIELDS, AND TMR NUTRIENT COMPOSITION TO ACHIEVE TARGET COST, ON BASIS OF DEEP LEARNING-BASED PREDICTION MODELS
(FR) PROCÉDÉ DE PRÉDICTION DE RENDEMENTS LAITIERS, COMPOSITION NUTRITIVE DE RATION COMPLÈTE POUR OBTENIR DES RENDEMENTS LAITIERS CIBLES, ET COMPOSITION NUTRITIVE DE RATION COMPLÈTE POUR OBTENIR UN COÛT CIBLE, SUR LA BASE DE MODÈLES DE PRÉDICTION BASÉS SUR UN APPRENTISSAGE PROFOND
(KO) 딥러닝 기반 예측 모델에 기초한, 산유량, 목표 산유량 달성을 위한 TMR 영양 조성, 목표 비용 달성을 위한 TMR 영양 조성 예측 방법
Abstract
(EN) Provided is a method for predicting milk yields performed by a livestock farm management server that communicates with the outside through a communication network. The method for predicting milk yields of the present disclosure comprises the steps of: receiving, through the communication network, n data sets (n is an integer of 2 or more) retroactively from a reference date, accumulated from a past p-month prior date (p is an integer of 2 or more) to the reference date, wherein each data set of the n data sets includes state information data of a target cow to be managed, nutrient intake data of the target cow to be managed, and ambient state data on the basis of each specific date between the past p-month prior date and the reference date; and applying the received data sets and predicting milk yields that may be expected for the target cow to be managed for q months (q is an integer of 1 or more) from the reference date on the basis of a first prediction model. The state information data of the target cow to be managed includes the date of birth or the age in months of the target cow to be managed and the number of postpartum weeks on the specific date, the nutrient intake data of the target cow to be managed includes daily dry matter intake, water intake, metabolic energy intake, metabolic protein intake, MET intake, LYS intake, calcium intake, and phosphorus intake on the specific date, and the ambient state data includes average temperature and average humidity information of the specific date.
(FR) Cette invention concerne un procédé de prédiction de rendements laitiers effectués par un serveur de gestion d'exploitation d'élevage qui communique avec l'extérieur par l'intermédiaire d'un réseau de communication. Le procédé de prédiction de rendements laitiers selon l'invention comprend les étapes consistant à : recevoir, par l'intermédiaire du réseau de communication, n ensembles de données (n étant un entier supérieur ou égal à 2) rétroactivement à partir d'une date de référence, accumulés à partir d'une date antérieure des p mois précédents (p étant un entier supérieur ou égal à 2) par rapport à la date de référence, chaque ensemble de données des n ensembles de données comprenant des données d'informations d'état d'une vache cible à gérer, des données d'apports nutritionnels de la vache cible à gérer, et des données d'état ambiant sur la base de chaque date spécifique entre la date antérieure des p mois précédents et la date de référence ; et appliquer les ensembles de données reçus et prédire des rendements laitiers qui peuvent être attendus pour la vache cible à gérer pour q mois (q étant un entier supérieur ou égal à 1) à partir de la date de référence sur la base d'un premier modèle de prédiction. Les données d'informations d'état de la vache cible à gérer comprennent la date de naissance ou l'âge en mois de la vache cible à gérer et le nombre de semaines post-partum à la date spécifique, les données d'apports nutritionnels de la vache cible à gérer comprennent l'apport quotidien de matières sèches, l'apport d'eau, l'apport énergétique métabolique, l'apport protéique métaboliques, l'apport de Met, l'apport de Lys, l'apport de calcium et l'apport de phosphore à la date spécifique, et les données d'état ambiant comprennent des informations de température moyenne et d'humidité moyenne à la date spécifique.
(KO) 통신망을 통해 외부와 통신하는 축산 농가 관리 서버에 의해 수행되는 산유량 예측 방법이 제공된다. 본 개시의 산유량 예측 방법은, 상기 통신망을 통하여, 기준 일자에서 소급하여 과거 p개월(p는 2 이상의 정수) 이전 일자부터 상기 기준 일자까지 누적된 n개(n은 2 이상의 정수)의 데이터 세트를 수신- 상기 n개의 데이터 세트의 각 데이터 세트는, 상기 과거 p개월 이전 일자와 상기 기준 일자 중간의 각 특정 일자를 기준으로 한, 관리 대상 소의 상태 정보 데이터, 상기 관리 대상 소의 영양 섭취 데이터, 및 주변 상태 데이터를 포함함 -하는 단계, 및 제1 예측 모델을 기초로, 상기 수신된 데이터 세트를 적용하여, 상기 기준 일자부터 이후 q개월(q는 1 이상의 정수) 동안 상기 관리 대상 소에 대해 기대할 수 있는 우유 생산량을 예측하는 단계를 포함한다. 상기 관리 대상 소의 상태 정보 데이터는, 상기 관리 대상 소의 생년월일 또는 월령과, 상기 특정 일자에서의 산후 주차를 포함하고, 상기 관리 대상 소의 영양 섭취 데이터는, 상기 특정 일자에서의 일일 건물섭취량, 수분섭취량, 대사에너지 섭취량, 대사단백질 섭취량, MET 섭취량, LYS 섭취량, 칼슘 섭취량, 및 인 섭취량을 포함하며, 상기 주변 상태 데이터는, 상기 특정 일자의 평균 기온 및 평균 습도 정보를 포함한다.
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