CN113344101A - Intelligent identification method and identification system for livestock abnormal behavior judgment - Google Patents
Intelligent identification method and identification system for livestock abnormal behavior judgment Download PDFInfo
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- 244000144972 livestock Species 0.000 title claims abstract description 156
- 206010000117 Abnormal behaviour Diseases 0.000 title claims abstract description 47
- 238000000034 method Methods 0.000 title claims abstract description 33
- 230000006399 behavior Effects 0.000 claims abstract description 108
- 238000012544 monitoring process Methods 0.000 claims abstract description 25
- 238000012545 processing Methods 0.000 claims abstract description 20
- 230000002159 abnormal effect Effects 0.000 claims abstract description 16
- 241001465754 Metazoa Species 0.000 claims description 21
- 238000012549 training Methods 0.000 claims description 17
- 230000035790 physiological processes and functions Effects 0.000 claims description 15
- 230000009471 action Effects 0.000 claims description 13
- 230000001488 breeding effect Effects 0.000 claims description 12
- 238000009395 breeding Methods 0.000 claims description 11
- 238000013135 deep learning Methods 0.000 claims description 9
- 230000036760 body temperature Effects 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 3
- 238000013136 deep learning model Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 3
- 230000012173 estrus Effects 0.000 claims description 3
- 230000002431 foraging effect Effects 0.000 claims description 3
- 230000006870 function Effects 0.000 claims description 3
- 230000013011 mating Effects 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 230000001850 reproductive effect Effects 0.000 claims description 3
- 239000000523 sample Substances 0.000 claims description 3
- 238000012806 monitoring device Methods 0.000 claims 1
- 201000010099 disease Diseases 0.000 description 5
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 235000012631 food intake Nutrition 0.000 description 2
- 230000033764 rhythmic process Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
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- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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Abstract
The invention discloses an intelligent identification method and an identification system for judging abnormal behaviors of livestock, wherein the intelligent identification method for judging the abnormal behaviors of the livestock comprises the following steps of S1: the system comprises a behavior data acquisition and processing module and is used for acquiring and processing the audio, images and environment monitoring of the livestock. The method mainly comprises the steps of comparing real-time livestock states through model learning, simulating livestock behaviors by utilizing model fitting, and judging whether the behaviors of the livestock are abnormal or not through behavior conformity, so that the data are judged to be accurate.
Description
Technical Field
The invention relates to the technical field of livestock breeding, in particular to an intelligent identification method and an intelligent identification system for judging abnormal behaviors of livestock.
Background
The behavior of the livestock is the external manifestation of the physiological health condition of the mind and mind of the livestock. The livestock behavior monitoring and analysis is beneficial to establishing a behavior model and discovering abnormal behaviors, and the economic loss is reduced. At present, domestic livestock behaviors are mainly monitored by means of artificial observation of culturists, and the method is strong in subjectivity and low in precision. With the continuous development of modern information technology, the intelligent behavior monitoring method is also rapidly developed and continuously updated.
Therefore, a method for identifying whether the behavior of the livestock is abnormal or not based on livestock environment monitoring, action state identification based on video and emotion expression based on voice is provided.
The method aims to establish a livestock behavior data acquisition database and a behavior analysis system, excavate deep meanings of livestock behaviors, ensure that early prevention and timely early warning are carried out on abnormal behaviors of livestock in the livestock breeding industry, and reduce the influence of factors such as diseases, livestock psychology and the like on the livestock breeding effect in the breeding industry.
Disclosure of Invention
Based on the technical problems in the background art, the invention provides an intelligent identification method and an identification system thereof for judging abnormal behaviors of livestock.
The invention provides an intelligent identification method for livestock abnormal behavior judgment, which comprises the following steps:
s1: data acquisition is carried out on the real-time behavior audio frequency of the livestock, the real-time behavior image of the livestock and the real-time environment monitoring aspects of the livestock, and in the acquisition, the differentiated parts in all data are extracted and characteristic data capable of reflecting the behavior of the livestock are integrated and put in storage;
s2: modeling is carried out according to the livestock behavior characteristic database, in the modeling process, classification modeling is carried out according to the types and physiological states of livestock, a livestock abnormal behavior identification model library for identifying whether the livestock behaviors are abnormal can be formed, models in the model library can be combined and arranged according to different types of livestock, and the physiological states are classified in a second stage;
s3: the real-time behavior monitoring system is applied to the livestock breeding scene, and can utilize all equipment in the breeding environment to monitor and collect the real-time behavior audio frequency of livestock, the real-time behavior image of the livestock and the real-time environment where the livestock is located, compare the real-time behavior image with the livestock behavior characteristic database, judge the percentage of abnormal behaviors and give an early warning.
S2-1: constructing an abnormal behavior recognition model of the livestock based on a Caffe deep learning framework by utilizing three items of large data in the livestock behavior feature database in the S1;
s2-2: fitting a model on a livestock behavior characteristic database, defining the number of training periods, input data and output data by means of a fitting function, and training an abnormal behavior recognition model of the livestock by using the livestock behavior characteristic database to obtain an abnormal behavior recognition model database of the livestock, wherein the abnormal behavior recognition model database can accurately recognize whether the behavior of the livestock is abnormal;
s2-3: the method comprises the steps of performing data independent training on different physiological states in a fitting definition period, wherein the special physiological states comprise a livestock oestrus period, a mating period and a reproductive period, and other necessary physiological states comprise a rest period, a feeding period, a foraging period and a group fighting period.
S3-1: collecting livestock behaviors by using equipment in a livestock raising place, inputting the livestock behaviors into a behavior abnormity judgment server, comparing a deeply learned abnormal behavior identification model of the livestock with the livestock behaviors collected in real time in the server, and outputting the behavior conformity degree of each identification model in a livestock and model library;
s3-2: early warning is carried out to the domestic animal according to the degree of conformity, and through the manual work individual of carrying out the spot check domestic animal again, instrument detection is carried out to individual sign of domestic animal and psychology, puts forward the accuracy of behavior judgement to the domestic animal action judgement in the action judgement is unusual, inputs this time domestic animal action judgement characteristic and judged result to the database in, realizes deep learning once more, enriches the data bulk of unusual action in the database.
An intelligent identification system for livestock abnormal behavior judgment, comprising:
the behavior data acquisition and processing module is used for acquiring and processing the audio and image of the livestock and monitoring the environment;
the model building and training module is used for building and training a livestock abnormal behavior recognition model library which can accurately recognize whether the livestock behavior is abnormal or not based on a deep learning method;
the real-time livestock behavior judgment module is positioned in a real-time environment where the livestock is positioned and used for quickly and accurately judging whether the livestock behaviors are abnormal or not;
the result output and display module is used for displaying data acquisition and outputting the coincidence percentage data of each model in the livestock behavior and deep learning model library;
the early warning processing module is used for judging based on the percentage data of the abnormal behaviors given by the server, adjusting the upper limit and the lower limit of the percentage data and judging the limit of the percentage data;
and the database module is used for storing various data in the server and the database.
As a further scheme of the invention, the behavior data acquisition and processing module comprises a portable microphone and a sound acquisition chamber for audio acquisition equipment, the behavior data acquisition and processing module comprises an infrared monitoring probe, a wide-angle camera and a ball camera group for audio and video acquisition equipment, and the behavior data acquisition and processing module comprises an online automatic continuous monitoring instrument and a sensor group for environment monitoring equipment.
As a further scheme of the invention, the sensor group is also arranged on the body surface of the livestock and used for monitoring the physical signs of the livestock, and the physical sign data of the livestock are body temperature and heart rate.
The beneficial effects of the invention are as follows: the intelligent identification method mainly comprises the steps of comparing real-time livestock states through model learning, simulating livestock behaviors by using a model, judging whether the behaviors of the livestock are abnormal or not according to behavior conformity, and judging data to be accurate; the livestock behavior model construction is that a mapping relation is established between information such as livestock voice audio information, livestock activity video information and sensor monitoring data and the classification of livestock behaviors, the accuracy of nondestructive monitoring and classification of the livestock behaviors based on technologies such as audio analysis and machine vision is further improved, a system for early warning diseases of the livestock behaviors is designed based on the method, when the individual food consumption of animals monitored in real time and the deviation degree of the behavior information and the model exceed a set threshold value of the system, disease early warning is sent to breeding personnel, the workload of the breeding personnel is reduced, and meanwhile, the economic loss caused by diseases is reduced.
Drawings
Fig. 1 is a flow chart of an intelligent identification method for livestock abnormal behavior judgment and an identification method of an identification system thereof according to the present invention;
fig. 2 is a composition diagram of an intelligent recognition method for determining abnormal behavior of livestock and a recognition system thereof according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1, an intelligent identification method for livestock abnormal behavior judgment includes the following steps:
s1: data acquisition is carried out on the real-time behavior audio frequency of the livestock, the real-time behavior image of the livestock and the real-time environment monitoring aspects of the livestock, and in the acquisition, the differentiated parts in all data are extracted and characteristic data capable of reflecting the behavior of the livestock are integrated and put in storage;
s2: modeling is carried out according to the livestock behavior characteristic database, in the modeling process, classification modeling is carried out according to the types and physiological states of livestock, a livestock abnormal behavior identification model library for identifying whether the livestock behaviors are abnormal can be formed, models in the model library can be combined and arranged according to different types of livestock, and the physiological states are classified in a second stage;
s3: the real-time behavior monitoring system is applied to the livestock breeding scene, and can utilize all equipment in the breeding environment to monitor and collect the real-time behavior audio frequency of livestock, the real-time behavior image of the livestock and the real-time environment where the livestock is located, compare the real-time behavior image with the livestock behavior characteristic database, judge the percentage of abnormal behaviors and give an early warning.
The construction and training of each model in the model library in S2 specifically comprises the following steps:
s2-1: constructing an abnormal behavior recognition model of the livestock based on a Caffe deep learning framework by utilizing three items of large data in the livestock behavior feature database in the S1;
s2-2: fitting a model on a livestock behavior characteristic database, defining the number of training periods, input data and output data by means of a fitting function, and training an abnormal behavior recognition model of the livestock by using the livestock behavior characteristic database to obtain an abnormal behavior recognition model database of the livestock, wherein the abnormal behavior recognition model database can accurately recognize whether the behavior of the livestock is abnormal;
s2-3: the method comprises the steps of performing data independent training on different physiological states in a fitting definition period, wherein the special physiological states comprise a livestock oestrus period, a mating period and a reproductive period, and other necessary physiological states comprise a rest period, a feeding period, a foraging period and a group fighting period.
The judgment of the abnormal behavior of the livestock in the S3 specifically comprises the following steps:
s3-1: collecting livestock behaviors by using equipment in a livestock raising place, inputting the livestock behaviors into a behavior abnormity judgment server, comparing a deeply learned abnormal behavior identification model of the livestock with the livestock behaviors collected in real time in the server, and outputting the behavior conformity degree of each identification model in a livestock and model library;
s3-2: early warning is carried out to the domestic animal according to the degree of conformity, and through the manual work individual of carrying out the spot check domestic animal again, instrument detection is carried out to individual sign of domestic animal and psychology, puts forward the accuracy of behavior judgement to the domestic animal action judgement in the action judgement is unusual, inputs this time domestic animal action judgement characteristic and judged result to the database in, realizes deep learning once more, enriches the data bulk of unusual action in the database.
When the deviation degree of the individual animal food consumption and behavior information monitored in real time and the model exceeds a threshold value set by a system, disease early warning is sent to culturists, wherein the threshold value is the coincidence degree of the output livestock entity behaviors and each recognition model behavior in the model base, the threshold value has an upper limit, and the upper limit is a judgment standard for judging whether the animal is abnormal or not.
Referring to fig. 2, an intelligent recognition system for livestock abnormal behavior judgment includes:
the behavior data acquisition and processing module is used for acquiring and processing the audio and image of the livestock and monitoring the environment;
the model building and training module is used for building and training a livestock abnormal behavior recognition model library which can accurately recognize whether the livestock behavior is abnormal or not based on a deep learning method;
the real-time livestock behavior judgment module is positioned in a real-time environment where the livestock is positioned and used for quickly and accurately judging whether the livestock behaviors are abnormal or not;
the result output and display module is used for displaying data acquisition and outputting the coincidence percentage data of each model in the livestock behavior and deep learning model library;
the early warning processing module is used for judging based on the percentage data of the abnormal behaviors given by the server, adjusting the upper limit and the lower limit of the percentage data and judging the limit of the percentage data;
and the database module is used for storing various data in the server and the database.
The behavior data acquisition and processing module is provided with a portable microphone and a sound acquisition chamber for audio acquisition equipment, the behavior data acquisition and processing module is provided with an infrared monitoring probe, a wide-angle camera and a ball camera group for audio and video acquisition equipment, and the behavior data acquisition and processing module is mainly provided with an online automatic continuous monitoring instrument and a sensor group for environment monitoring equipment.
The body surface that the domestic animal was still located to the sensor group is used for monitoring the sign of domestic animal, and the sign data of domestic animal are body temperature and rhythm of the heart, detect respectively through body temperature sensor and rhythm of the heart sensor.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (6)
1. An intelligent identification method aiming at livestock abnormal behavior judgment is characterized by comprising the following steps:
s1: data acquisition is carried out on the real-time behavior audio frequency of the livestock, the real-time behavior image of the livestock and the real-time environment monitoring aspects of the livestock, and in the acquisition, the differentiated parts in all data are extracted and characteristic data capable of reflecting the behavior of the livestock are integrated and put in storage;
s2: modeling is carried out according to the livestock behavior characteristic database, in the modeling process, classification modeling is carried out according to the types and physiological states of livestock, a livestock abnormal behavior identification model library for identifying whether the livestock behaviors are abnormal can be formed, models in the model library can be combined and arranged according to different types of livestock, and the physiological states are classified in a second stage;
s3: the real-time behavior monitoring system is applied to the livestock breeding scene, and can utilize all equipment in the breeding environment to monitor and collect the real-time behavior audio frequency of livestock, the real-time behavior image of the livestock and the real-time environment where the livestock is located, compare the real-time behavior image with the livestock behavior characteristic database, judge the percentage of abnormal behaviors and give an early warning.
2. The intelligent identification method for livestock abnormal behavior judgment according to claim 1, wherein the construction and training of each model in the model library in the S2 specifically comprises the following steps:
s2-1: constructing an abnormal behavior recognition model of the livestock based on a Caffe deep learning framework by utilizing three items of large data in the livestock behavior feature database in the S1;
s2-2: fitting a model on a livestock behavior characteristic database, defining the number of training periods, input data and output data by means of a fitting function, and training an abnormal behavior recognition model of the livestock by using the livestock behavior characteristic database to obtain an abnormal behavior recognition model database of the livestock, wherein the abnormal behavior recognition model database can accurately recognize whether the behavior of the livestock is abnormal;
s2-3: the method comprises the steps of performing data independent training on different physiological states in a fitting definition period, wherein the special physiological states comprise a livestock oestrus period, a mating period and a reproductive period, and other necessary physiological states comprise a rest period, a feeding period, a foraging period and a group fighting period.
3. The intelligent identification method for livestock abnormal behavior judgment according to claim 1, wherein the judgment of the livestock abnormal behavior in the S3 specifically comprises the following steps:
s3-1: collecting livestock behaviors by using equipment in a livestock raising place, inputting the livestock behaviors into a behavior abnormity judgment server, comparing a deeply learned abnormal behavior identification model of the livestock with the livestock behaviors collected in real time in the server, and outputting the behavior conformity degree of each identification model in a livestock and model library;
s3-2: early warning is carried out to the domestic animal according to the degree of conformity, and through the manual work individual of carrying out the spot check domestic animal again, instrument detection is carried out to individual sign of domestic animal and psychology, puts forward the accuracy of behavior judgement to the domestic animal action judgement in the action judgement is unusual, inputs this time domestic animal action judgement characteristic and judged result to the database in, realizes deep learning once more, enriches the data bulk of unusual action in the database.
4. A recognition system for use in an intelligent recognition method for livestock abnormal behavior judgment as claimed in claim 1, comprising:
the behavior data acquisition and processing module is used for acquiring and processing the audio and image of the livestock and monitoring the environment;
the model building and training module is used for building and training a livestock abnormal behavior recognition model library which can accurately recognize whether the livestock behavior is abnormal or not based on a deep learning method;
the real-time livestock behavior judgment module is positioned in a real-time environment where the livestock is positioned and used for quickly and accurately judging whether the livestock behaviors are abnormal or not;
the result output and display module is used for displaying data acquisition and outputting the coincidence percentage data of each model in the livestock behavior and deep learning model library;
the early warning processing module is used for judging based on the percentage data of the abnormal behaviors given by the server, adjusting the upper limit and the lower limit of the percentage data and judging the limit of the percentage data;
and the database module is used for storing various data in the server and the database.
5. The method as claimed in claim 4, wherein the behavior data acquisition and processing module comprises a portable microphone and a sound acquisition chamber for audio acquisition devices, the behavior data acquisition and processing module comprises an infrared monitor probe, a wide-angle camera and a ball camera group for audio and video acquisition devices, and the behavior data acquisition and processing module comprises an environment monitoring device mainly comprising an online automatic continuous monitoring instrument and a sensor group.
6. The method as claimed in claim 5, wherein the sensor group is further disposed on a body surface of the livestock for monitoring physical signs of the livestock, and the physical sign data of the livestock is body temperature and heart rate.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113924994A (en) * | 2021-10-27 | 2022-01-14 | 新疆农垦科学院 | Livestock health index monitoring, managing and analyzing system |
| CN115359050A (en) * | 2022-10-19 | 2022-11-18 | 正大农业科学研究有限公司 | Method and device for detecting abnormal feed intake of livestock |
| CN116934088A (en) * | 2023-07-24 | 2023-10-24 | 瑞安市致富鸽业有限公司 | Intelligent pigeon breeding management method and system based on analysis model |
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2021
- 2021-06-23 CN CN202110695388.1A patent/CN113344101A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113924994A (en) * | 2021-10-27 | 2022-01-14 | 新疆农垦科学院 | Livestock health index monitoring, managing and analyzing system |
| CN115359050A (en) * | 2022-10-19 | 2022-11-18 | 正大农业科学研究有限公司 | Method and device for detecting abnormal feed intake of livestock |
| CN115359050B (en) * | 2022-10-19 | 2023-02-28 | 正大农业科学研究有限公司 | Method and device for detecting abnormal feed intake of livestock |
| CN116934088A (en) * | 2023-07-24 | 2023-10-24 | 瑞安市致富鸽业有限公司 | Intelligent pigeon breeding management method and system based on analysis model |
| CN116934088B (en) * | 2023-07-24 | 2024-03-08 | 瑞安市致富鸽业有限公司 | Intelligent pigeon breeding management method and system based on analysis model |
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Application publication date: 20210903 |