# Research on the Intelligent Cloud Animal Husbandry Platform

Authors: Buyu Wang; Jingwei Qi
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Aiming at the problem that the traditional talent training mode is difficult to cultivate the intelligent animal husbandry compound talents needed by the implementation of the rural revitalization strategy, a cloud animal husbandry platform is designed to cultivate “Understand Agriculture, devote to Rural Community and Be Sincere to Farmers” compound talents. The platform adopts the two-wing multilevel architecture design of the concept of the cloud side, which is composed of three parts: the data center platform, resource sharing platform, and business cloud service platform. The data center platform is responsible for the collection, processing, integration, and asset management of the four-dimensional real data and provides computing, storage, and data asset services for the cloud livestock platform. The resource sharing platform provides sharing services from resources and development kits to carriers. Livestock cloud service platform is to carry out the integration of industry, education, and science and education, the design and implementation of cloud teaching, cloud scientific research, and cloud breeding business cloud services. Through its practical application in many universities and enterprises, the platform is verified to be safe, effective, and reliable in teaching, scientific research, and social services and has both operational and promotional value.


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## Body

## 1. Introduction

In promoting the rural revitalization strategy, there is a growing demand for agricultural talents [1, 2]. Rural revitalization needs to promote the comprehensive integration and development of the primary, secondary, and tertiary industries of the rural economy and needs to attract compound agricultural talents who understand agriculture, love rural areas and farmers, and are good at operations [3, 4]. But, at present, our country’s higher agricultural college graduates in these aspects are relatively lacking. Even if they choose to enter the agricultural sector, in the development of modern agriculture, the power "of the play is not very big. There is no farming. The disconnect between theory and practice of professional teaching, especially animal husbandry talents, to practice ability requirements is very high. However, to solve the problem of cultivating “Understand Agriculture, devote to Rural Community and Be Sincere to Farmers” compound talents for agricultural colleges must face and solve the problem [5, 6].Cultivating compound talents of “Understand Agriculture, devote to Rural Community, and Be Sincere to Farmers” is an important topic in the construction of new agricultural sciences in Agriculture and forestry colleges. Many scholars have done a lot of fruitful research on the use of advanced information technology methods such as Internet of things, cloud computing, big data, and artificial intelligence to achieve the upgrading of talent training [7, 8]. They have put forward a series of effective methods, especially when combined with science and education and the integration of production and education concepts. It points out a new direction for the cultivation of compound talents in animal husbandry [9–11]. However, there is no relevant report on how to systematically build a set of closed-loop linkage information platforms for talent cultivation that can realize the mutual empowerment of “teaching, scientific research, and social service.”Use digital technology to complete themapping portrait of animal husbandry enterprises. In the process of teaching, experiment, and practice, college students can learn animal production technology and breeding equipment operation and management technology through enterprise digital portrait anytime and anywhere. In terms of multimedia teaching technology, real-scene teaching, virtual simulation teaching, smart farming technology practice teaching, and other contents are added to realize animal husbandry teaching from multimedia teaching to smart animal husbandry teaching.This paper studies the design of a cloud livestock platform based on cloud edge architecture [12]. The cloud livestock platform consists of three business platforms: a data center, a resource sharing platform, and a business cloud service. Using intelligent Internet of things, big data, and artificial intelligence technology, the data center business system is developed to automatically collect the data from the farm. According to the rules of the animal husbandry business, the data from the farm are preprocessed and integrated, and data assets are established, and the digital portrait of the farm is completed. The resource-sharing service system provides the resource-sharing mechanism and means for users. Research on intelligent teaching service for users of agricultural and forestry colleges and realize the task of cultivating livestock talents with “Understand Agriculture, Devote to Rural Community, and Be Sincere to Farmers. ” Develop intelligent aquaculture management services for agricultural and animal husbandry enterprise users, realize the full coverage of the aquaculture farm from biosafety, production safety, food safety to intelligent decision-making management and control business, and build a smart aquaculture production management system for the aquaculture farm. To provide farmers and herdsmen with the most advanced intelligent farming technology training services. Finally, the SaaS animal husbandry cloud service platform for agricultural and forestry university users, agricultural and animal husbandry enterprise users, and farmers and herdsmen users is studied.


## 2. Overall Framework of the Cloud Livestock Platform

The overall framework of the platform adopts two multilevel models. The two wings are the standard specification system and the security protection system, and the multilayer model layer is composed of the basic platform layer, data resource layer, business support layer, application system layer, and user layer. The basic platform layer is the necessary system hardware and software platform to support the system’s operation. The data resource layer contains all the basic databases of the system. The business support layer is the necessary support middleware to support system operation. The application system layer realizes business functions and provides services for users. The standard system and information security system are the necessary guarantees for the construction and operation of the system. The standard specification consists of an intelligent farming business standard specification and a livestock data standard specification. Among them, the intelligent breeding business standard specification stipulates the intelligent pig, sheep, and cattle production business process and production standards, and the data standard specification stipulates the fusion and exchange standards, sharing standards, traceability standards, and breeding data dictionary standards of real data. The foundation layer, data layer, business layer, application layer, and user layer are the multilayer architecture of the cloud livestock platform. The overall architecture of the cloud livestock platform is a two-wing and multilevel cloud edge–end architecture, as shown in Figure1.Figure 1
Overall framework diagram of the cloud livestock platform.


## 3. Network Topology of the Cloud Livestock Platform

The network platform mainly includes the server, network and communication systems, storage and backup systems, network security systems, data forwarding systems, and data acquisition systems corresponding to the livestock cloud data center, virtual simulation center, and real-scene data acquisition center. The overall design diagram is shown in Figure2.Figure 2
Network topology diagram.


## 4. Key Application Agent Architecture

The application support system includes remote monitoring, multitenant isolation, offline maintenance, machine application, approval, automatic allocation, and recycling. The cloud storage system provides persistent cloud storage based on distributed storage systems or third-party independent storage systems. The cloud storage system uses technologies such as multicopy, real-time backup, and multilink aggregation to ensure data security and reliability.Fully utilize servers and multiple hypervisors, such as KVM, ESXi, and Xen. Based on service requirements, cloud hosts can be rapidly deployed and maintained online, network adapters and hard disks can be deleted, and CPU and memory can be changed in real time.The cloud host service monitors all cloud host status, including various running indicators of physical servers and VMS, and automatically detects and recovers faults to minimize service interruption.Through the optimization and management upgrade of block storage, object storage, and file system storage, the real data, business data, and log data corresponding to the cloud animal husbandry system can be stored and managed in the most suitable way. The architecture of the application supporting the animal husbandry cloud system is shown in Figure3.Figure 3
Key application agent architecture diagram.


## 5. Resource Planning and Data Manager System Design

According to the analysis of data sources and status quo, the core data of the system are real data, including environmental data, scene data, process data, and individual physical sign data. Among them, environmental real data and individual physical sign data are structured data, while process real data are semistructured data (production data are object-relational data composed of attribute data, image, audio, and video), and scene data are audio and video object data. The resources are summarized in Table1.Table 1
Resource summary.
Animal typeNumber of animalsReal typeData typeProduction cycleData storageHorses1000Live environmentStructured data90 Days5 GBScene imagingUnstructured data7.5 TBProcess of liveSemistructured data500 GBReal scene of signsSemistructured data350 GBCows5000Live environmentStructured data300 Days75 GBScene imagingUnstructured data76 TBProcess of liveSemistructured data2 TBReal scene of signsSemistructured data1.5 TBSheep100000Live environmentStructured data225 Days7 GBScene imagingUnstructured data38 TBProcess of liveSemistructured data1 TBReal scene of signsSemistructured data750 GBPigs1000000Live environmentStructured data270 Days135 GBScene imagingUnstructured data180 TBProcess of liveSemistructured data7.5 TBReal scene of signsSemistructured data2.8 TBLaying hens100000Live environmentStructured data30 Days8 GBScene imagingUnstructured data12 TBStructured data are the data in a relational database; unstructured data are the file data such as images, videos, and audio; and semistructured data are composed of relational data and file data.


### 5.1. Resource Planning

#### 5.1.1. Real Data of Farm Environment

The real environment data include the real environment data of five types of breeding farms, including horse farms, cattle farms, sheep farms, pig farms, and chicken farms. The characteristics of environmental real data are large amount of data, short sampling period (120S), and the environmental data of multiple breeding units need to be shared synchronously. The storage capacity of each unit is 1KB. The amount of data is about 100 m or so. The annual data volume is about 35 G.


#### 5.1.2. Real Data of Livestock Individual

The amount of real data of individual physical signs is relatively small. In the whole life cycle, the update frequency is low, and it is mainly composed of static data, which generates about 1 G of data every year.


#### 5.1.3. Real Data of Smart Breeding

Process real data are semistructured production data generated by the intelligent breeding process of pigs and cattle. The updating frequency is very high, and there are many farms. If 1,500 processes are completed every day on average and each process generates 2 M of related data (including photos, videos, and audio clips), the data generated every day are about 3 G. The annual data volume is about 1 TB.


#### 5.1.4. Real Data of Farm Video

Video data of production scenes are collected from horse farms, cattle farms, sheep farms, and pig farms, and video data corresponding to key behavioral characteristics of animals is extracted to form a series of scene real scene datasets. The live data of the horse scene includes video data of a horse feeding cycle (90 days, 2 cameras). The live data of the cattle scene contain video data of a cattle feeding and production cycle (300 days, 6 cameras). The live data of the sheep scene includes a video data of the sheep production cycle (225 days, 4 cameras). The live data of the pig farm is consisted of a video data of the production cycle of finishing pigs (175 days,4 cameras), a video data of the production cycle of boars (90 days,4 cameras), and a video data of the production cycle of breeding pigs (270 days, 12 cameras). The video data were collected at a bit rate of 4 Mbps and a resolution of 1080 P (the minimum resolution required for animal behavior analysis).Total data storage capacity: 296 TB. Data of finishing pigs: 28.84 TB; data of boars: 14.83 TB; data of sows: 133.48 TB; data of cows: 74.16 TB; data of horses: 7.42 TB; data of sheep: 37.08 TB.


#### 5.1.5. Other Data

In addition to the real data, there are about 10 TB of master data and model data corresponding to system software such as livestock cloud systems, operating systems, database systems, virtual simulations, and other platforms. Teaching, research, and shared services generate about 5 TB of process data per year.


### 5.2. Livestock Data Processing

The four-dimensional real-scene acquisition sensor may fail when working in the complex environment of the breeding farm, leading to abnormal measurement data and carrying large errors, which seriously affects the accuracy of data. Therefore, it is necessary to preprocess the initial data and eliminate the gross errors [13].The commonly used methods to distinguish gross errors include Letts criterion, Chauvenet criterion, Romanovsky criterion, Grubbs criterion, and Dixon criterion. In the real-scene acquisition system of the cloud livestock platform, the coarse error self-learning engine is established to select different methods for different types of sensors to distinguish the coarse error. The Romanovsky criterion was used to collect live data of individual physical signs.There will be gross errors in the real data collected, and different data errors need to be processed. This paper uses the gross error engine to complete it. As an example, the collected animal body temperature is treated by equations (1)–(3). Formula (1) represents the body temperature of animals, formula (2) represents the average body temperature of animals, and formula (3) calculates the error of certain sampling data. According to formula (1)–(3), each group of sampled data is judged to be the data with gross errors and then eliminated according to the Romanovsky criterion.Multiple independent equal-precision measurements were made for the body temperature, and the measured results were shown in the following formula:(1)t1,t2,…,tn.If the collected values are considered suspicious, they will be removed, that is, they will not participate in the calculation. The average value of the other collected values is shown in the following formula:(2)t¯=1n−1∑j=1ntj,j≠k.Given the error of the I collection result, the standard deviation is shown in the following formula:(3)σ=∑j=1nVj2n−2,j≠k.The temperature error of individual animals should not exceed 0.1. The values of test coefficientsK (n, α) are shown in Table 2 according to the collection times n (5≤n≤13) and the selected α (0.01, 0.05).Table 2
Test coefficient K value table.
αn56789101112130.016.535.044.363.963.713.543.413.312.290.053.563.042.782.622.512.432.372.33Iftk meets tk−t¯>Kσ, then the k collection result tk contains a large error and should be removed; otherwise, it is valid data. Test each physical sign data set one by one until the collection data set no longer contains a large error.After data pretreatment, it is necessary to deal with the measurement errors of sensors with different media to reduce the redundant and noisy data collected by the acquisition node. This platform uses the optimized recursive digital processing algorithm Kalman filter to deal with the measurement error of real data. Animal body temperature is taken as an example.The equation of state is shown in the following formula:(4)Xk=Xk−1+Wk−1.The observation equation is shown in the following formula:(5)Yk=Xk+Vk.The core equations of the Kalman filter are shown in formulas (6)–(10).(6)X^k|k−1=X^k−1|k−1,(7)Pk|k−1=Pk−1|k−1+R,(8)Kk=Pk|k−1Pk|k−1+R−1,(9)X^k|k=X^k|k−1+KkYk−X^k|k−1,(10)Pk|k=I−KkPk|k−1.When the application times are more than 10 times, the error is controlled within 0.1. When the cloud livestock platform collects real-scene data, the single effective data fusion and superposition are more than 10 times, ensuring the effectiveness of the system data.


### 5.3. Data Manager System Design

Based on requirements, the storage of structured real data, semistructured real data, and unstructured real data is planned in a unified manner. Structured data are managed by the MySQL relational database and the MongoDB NoSQL database, while semistructured data and unstructured data are stored by HDFS and Ceph, respectively, and metadata is established. Data are stored by MySQL to record the access path, access mode, and basic information about unstructured data.This is to establish a unified data management system for the four-dimensional real data (environment, scene, process, and individual physical signs) of horses, cattle, sheep, pigs, and chickens, and realize the unified management of structured data, unstructured data, and semistructured data. Master data management is established for all data. Data distribution and sharing are completed through data authorization and authentication. Original data does not need to be dumped, ensuring data consistency and reliability. The data manager system structure is shown in Figure4.Figure 4
Data manager structure diagram.


## 5.1. Resource Planning

### 5.1.1. Real Data of Farm Environment

The real environment data include the real environment data of five types of breeding farms, including horse farms, cattle farms, sheep farms, pig farms, and chicken farms. The characteristics of environmental real data are large amount of data, short sampling period (120S), and the environmental data of multiple breeding units need to be shared synchronously. The storage capacity of each unit is 1KB. The amount of data is about 100 m or so. The annual data volume is about 35 G.


### 5.1.2. Real Data of Livestock Individual

The amount of real data of individual physical signs is relatively small. In the whole life cycle, the update frequency is low, and it is mainly composed of static data, which generates about 1 G of data every year.


### 5.1.3. Real Data of Smart Breeding

Process real data are semistructured production data generated by the intelligent breeding process of pigs and cattle. The updating frequency is very high, and there are many farms. If 1,500 processes are completed every day on average and each process generates 2 M of related data (including photos, videos, and audio clips), the data generated every day are about 3 G. The annual data volume is about 1 TB.


### 5.1.4. Real Data of Farm Video

Video data of production scenes are collected from horse farms, cattle farms, sheep farms, and pig farms, and video data corresponding to key behavioral characteristics of animals is extracted to form a series of scene real scene datasets. The live data of the horse scene includes video data of a horse feeding cycle (90 days, 2 cameras). The live data of the cattle scene contain video data of a cattle feeding and production cycle (300 days, 6 cameras). The live data of the sheep scene includes a video data of the sheep production cycle (225 days, 4 cameras). The live data of the pig farm is consisted of a video data of the production cycle of finishing pigs (175 days,4 cameras), a video data of the production cycle of boars (90 days,4 cameras), and a video data of the production cycle of breeding pigs (270 days, 12 cameras). The video data were collected at a bit rate of 4 Mbps and a resolution of 1080 P (the minimum resolution required for animal behavior analysis).Total data storage capacity: 296 TB. Data of finishing pigs: 28.84 TB; data of boars: 14.83 TB; data of sows: 133.48 TB; data of cows: 74.16 TB; data of horses: 7.42 TB; data of sheep: 37.08 TB.


### 5.1.5. Other Data

In addition to the real data, there are about 10 TB of master data and model data corresponding to system software such as livestock cloud systems, operating systems, database systems, virtual simulations, and other platforms. Teaching, research, and shared services generate about 5 TB of process data per year.


## 5.1.1. Real Data of Farm Environment

The real environment data include the real environment data of five types of breeding farms, including horse farms, cattle farms, sheep farms, pig farms, and chicken farms. The characteristics of environmental real data are large amount of data, short sampling period (120S), and the environmental data of multiple breeding units need to be shared synchronously. The storage capacity of each unit is 1KB. The amount of data is about 100 m or so. The annual data volume is about 35 G.


## 5.1.2. Real Data of Livestock Individual

The amount of real data of individual physical signs is relatively small. In the whole life cycle, the update frequency is low, and it is mainly composed of static data, which generates about 1 G of data every year.


## 5.1.3. Real Data of Smart Breeding

Process real data are semistructured production data generated by the intelligent breeding process of pigs and cattle. The updating frequency is very high, and there are many farms. If 1,500 processes are completed every day on average and each process generates 2 M of related data (including photos, videos, and audio clips), the data generated every day are about 3 G. The annual data volume is about 1 TB.


## 5.1.4. Real Data of Farm Video

Video data of production scenes are collected from horse farms, cattle farms, sheep farms, and pig farms, and video data corresponding to key behavioral characteristics of animals is extracted to form a series of scene real scene datasets. The live data of the horse scene includes video data of a horse feeding cycle (90 days, 2 cameras). The live data of the cattle scene contain video data of a cattle feeding and production cycle (300 days, 6 cameras). The live data of the sheep scene includes a video data of the sheep production cycle (225 days, 4 cameras). The live data of the pig farm is consisted of a video data of the production cycle of finishing pigs (175 days,4 cameras), a video data of the production cycle of boars (90 days,4 cameras), and a video data of the production cycle of breeding pigs (270 days, 12 cameras). The video data were collected at a bit rate of 4 Mbps and a resolution of 1080 P (the minimum resolution required for animal behavior analysis).Total data storage capacity: 296 TB. Data of finishing pigs: 28.84 TB; data of boars: 14.83 TB; data of sows: 133.48 TB; data of cows: 74.16 TB; data of horses: 7.42 TB; data of sheep: 37.08 TB.


## 5.1.5. Other Data

In addition to the real data, there are about 10 TB of master data and model data corresponding to system software such as livestock cloud systems, operating systems, database systems, virtual simulations, and other platforms. Teaching, research, and shared services generate about 5 TB of process data per year.


## 5.2. Livestock Data Processing

The four-dimensional real-scene acquisition sensor may fail when working in the complex environment of the breeding farm, leading to abnormal measurement data and carrying large errors, which seriously affects the accuracy of data. Therefore, it is necessary to preprocess the initial data and eliminate the gross errors [13].The commonly used methods to distinguish gross errors include Letts criterion, Chauvenet criterion, Romanovsky criterion, Grubbs criterion, and Dixon criterion. In the real-scene acquisition system of the cloud livestock platform, the coarse error self-learning engine is established to select different methods for different types of sensors to distinguish the coarse error. The Romanovsky criterion was used to collect live data of individual physical signs.There will be gross errors in the real data collected, and different data errors need to be processed. This paper uses the gross error engine to complete it. As an example, the collected animal body temperature is treated by equations (1)–(3). Formula (1) represents the body temperature of animals, formula (2) represents the average body temperature of animals, and formula (3) calculates the error of certain sampling data. According to formula (1)–(3), each group of sampled data is judged to be the data with gross errors and then eliminated according to the Romanovsky criterion.Multiple independent equal-precision measurements were made for the body temperature, and the measured results were shown in the following formula:(1)t1,t2,…,tn.If the collected values are considered suspicious, they will be removed, that is, they will not participate in the calculation. The average value of the other collected values is shown in the following formula:(2)t¯=1n−1∑j=1ntj,j≠k.Given the error of the I collection result, the standard deviation is shown in the following formula:(3)σ=∑j=1nVj2n−2,j≠k.The temperature error of individual animals should not exceed 0.1. The values of test coefficientsK (n, α) are shown in Table 2 according to the collection times n (5≤n≤13) and the selected α (0.01, 0.05).Table 2
Test coefficient K value table.
αn56789101112130.016.535.044.363.963.713.543.413.312.290.053.563.042.782.622.512.432.372.33Iftk meets tk−t¯>Kσ, then the k collection result tk contains a large error and should be removed; otherwise, it is valid data. Test each physical sign data set one by one until the collection data set no longer contains a large error.After data pretreatment, it is necessary to deal with the measurement errors of sensors with different media to reduce the redundant and noisy data collected by the acquisition node. This platform uses the optimized recursive digital processing algorithm Kalman filter to deal with the measurement error of real data. Animal body temperature is taken as an example.The equation of state is shown in the following formula:(4)Xk=Xk−1+Wk−1.The observation equation is shown in the following formula:(5)Yk=Xk+Vk.The core equations of the Kalman filter are shown in formulas (6)–(10).(6)X^k|k−1=X^k−1|k−1,(7)Pk|k−1=Pk−1|k−1+R,(8)Kk=Pk|k−1Pk|k−1+R−1,(9)X^k|k=X^k|k−1+KkYk−X^k|k−1,(10)Pk|k=I−KkPk|k−1.When the application times are more than 10 times, the error is controlled within 0.1. When the cloud livestock platform collects real-scene data, the single effective data fusion and superposition are more than 10 times, ensuring the effectiveness of the system data.


## 5.3. Data Manager System Design

Based on requirements, the storage of structured real data, semistructured real data, and unstructured real data is planned in a unified manner. Structured data are managed by the MySQL relational database and the MongoDB NoSQL database, while semistructured data and unstructured data are stored by HDFS and Ceph, respectively, and metadata is established. Data are stored by MySQL to record the access path, access mode, and basic information about unstructured data.This is to establish a unified data management system for the four-dimensional real data (environment, scene, process, and individual physical signs) of horses, cattle, sheep, pigs, and chickens, and realize the unified management of structured data, unstructured data, and semistructured data. Master data management is established for all data. Data distribution and sharing are completed through data authorization and authentication. Original data does not need to be dumped, ensuring data consistency and reliability. The data manager system structure is shown in Figure4.Figure 4
Data manager structure diagram.


## 6. Animal Husbandry Data Center Platform Design and Implementation

The platform of the animal husbandry data center mainly includes the animal husbandry cloud system, animal husbandry real data resource management system, animal husbandry real data fusion system, real data asset management system and corresponding server, network and communication system, storage and backup system, data forwarding and data acquisition system, and network security system. The system adopts a three-layer architecture to carry the business, namely, the cloud layer, the basic cloud platform, and the cloud service. Among them, the cloud layer mainly includes data acquisition terminals, data forwarding nodes, production handheld terminals, and virtual simulation development terminals to complete the data acquisition function. The basic cloud platform mainly includes cloud storage, cloud computing, and IP cloud switching. Cloud services mainly include cloud collaboration services, cloud diagnosis and treatment services, cloud identification services, cloud mall services, pig raising cloud services, cattle raising cloud services, sheep raising cloud services, and education and training cloud services, providing computing, storage, data services, and business support for cloud livestock platforms. The overall architecture is shown in Figure5.Figure 5
Data center platform architecture diagram.


### 6.1. Animal Husbandry Cloud Management System

A set is designed for users in the livestock industry, and the resources of the platform can be managed through the graphical user interface, which solves the problem of self-built cloud operation and promotion in the livestock industry. The animal husbandry cloud management system is deeply customized for animal husbandry users based on the open-source OpenStack architecture and supports computing resources, storage resources, network resources, infrastructure, user projects, policy monitoring and approval, and global management functions with the business language of a graphical user interface.Therefore, the principles of animal husbandry cloud pipe design are openness, self-service and self-operation, and maintenance. The cloud system uses open platforms and standards and fully supports infrastructure, middleware, and service cloud standards, as well as distributed object storage open standards. The cloud system is not bound to any hardware manufacturer, and the service interfaces adopt standard interfaces. Animal husbandry cloud management system designs a series of convenient personalized service system for all kinds of users. Enterprise information administrators can manage and maintain the cloud platform using simple graphical operation and maintenance interface. Through the online web resource management interface, college teachers and students can easily obtain Iaas, Paas, and Saas cloud services from the platform to carry out cloud teaching and cloud scientific research activities. Establish automatic monitoring and early warning services for data collection, transmission, storage, and application. It automatically records the running status, remaining data capacity, and data application requirements of the system, and provides visual cloud access and early warning services for different users.


### 6.2. Livestock Breeding Real Scene Acquisition System

The image of the farm was carried out from the four dimensions of the environment, scene, process, individual signs, and the data from the four dimensions, were collected to establish the digital image of the farm. The whole business chain and the whole process chain of breeding are moved back to the data center with four-dimensional reality, providing data support for users to carry out teaching, scientific research, and social services. The acquisition system architecture diagram is shown in Figure6.Figure 6
Four-dimensional real scene acquisition architecture diagram.


### 6.3. Animal Husbandry Big Data Fusion System

The data standard specification is used as the basis of data fusion, and the data security mechanism is used as the security guarantee of data fusion and exchange process to study the livestock big data fusion system. Multidimensional association rules of time series are used to complete the task of data association and exchange, and the aggregation and fusion of heterogeneous data established by association is carried out to form animal husbandry big data. Data is published and subscribed by switching nodes and switching bridge services. The cross-domain exchange service is used to realize the data exchange service between shared domains.. The data exchange engine is the core of the data fusion exchange platform. The data exchange engine mainly includes service processing and data processing and performs multiple tasks such as data collection, cleaning, conversion, and distribution among business data acquisition units. It provides functions and services such as data collection, data distribution, control center, policy configuration, task scheduling management, switching data management, data desensitization, and resumable transmission.


## 6.1. Animal Husbandry Cloud Management System

A set is designed for users in the livestock industry, and the resources of the platform can be managed through the graphical user interface, which solves the problem of self-built cloud operation and promotion in the livestock industry. The animal husbandry cloud management system is deeply customized for animal husbandry users based on the open-source OpenStack architecture and supports computing resources, storage resources, network resources, infrastructure, user projects, policy monitoring and approval, and global management functions with the business language of a graphical user interface.Therefore, the principles of animal husbandry cloud pipe design are openness, self-service and self-operation, and maintenance. The cloud system uses open platforms and standards and fully supports infrastructure, middleware, and service cloud standards, as well as distributed object storage open standards. The cloud system is not bound to any hardware manufacturer, and the service interfaces adopt standard interfaces. Animal husbandry cloud management system designs a series of convenient personalized service system for all kinds of users. Enterprise information administrators can manage and maintain the cloud platform using simple graphical operation and maintenance interface. Through the online web resource management interface, college teachers and students can easily obtain Iaas, Paas, and Saas cloud services from the platform to carry out cloud teaching and cloud scientific research activities. Establish automatic monitoring and early warning services for data collection, transmission, storage, and application. It automatically records the running status, remaining data capacity, and data application requirements of the system, and provides visual cloud access and early warning services for different users.


## 6.2. Livestock Breeding Real Scene Acquisition System

The image of the farm was carried out from the four dimensions of the environment, scene, process, individual signs, and the data from the four dimensions, were collected to establish the digital image of the farm. The whole business chain and the whole process chain of breeding are moved back to the data center with four-dimensional reality, providing data support for users to carry out teaching, scientific research, and social services. The acquisition system architecture diagram is shown in Figure6.Figure 6
Four-dimensional real scene acquisition architecture diagram.


## 6.3. Animal Husbandry Big Data Fusion System

The data standard specification is used as the basis of data fusion, and the data security mechanism is used as the security guarantee of data fusion and exchange process to study the livestock big data fusion system. Multidimensional association rules of time series are used to complete the task of data association and exchange, and the aggregation and fusion of heterogeneous data established by association is carried out to form animal husbandry big data. Data is published and subscribed by switching nodes and switching bridge services. The cross-domain exchange service is used to realize the data exchange service between shared domains.. The data exchange engine is the core of the data fusion exchange platform. The data exchange engine mainly includes service processing and data processing and performs multiple tasks such as data collection, cleaning, conversion, and distribution among business data acquisition units. It provides functions and services such as data collection, data distribution, control center, policy configuration, task scheduling management, switching data management, data desensitization, and resumable transmission.


## 7. Animal Husbandry Resource Sharing Platform Design

The animal husbandry resource sharing platform mainly includes resource sharing and carrier sharing. Resource sharing mainly includes multitenant sharing, API data sharing, and exchange space sharing, and carrier sharing mainly includes the sharing management of teacher LABS and equipment.


### 7.1. Data Resource Sharing System

The animal husbandry data asset sharing system mainly shares the data in the real data resource catalog after fusion and adopts three modes of sharing: multitenant sharing, exchange space sharing, and API sharing.Multitenant sharing is mainly used for data exchange between multitenants on the big data platform. In this mode, the exchanged data do not move physically. In this mode, the data are available, and the exchange does not move.Swap space sharing is mainly for large data development suite-sharing system users. When the user needs to get a real data set for big data analysis and calculation, the issuer of the detailed data contains sensitive information and should not be open to the public to share, but the subscriber data need to be calculated using the data as a data source reasoning out a certain result, which requires security exchange. Secure exchanges store subscription publishing data in secure swap space for transparent calculations and send the results to subscribers.API sharing, the most common data sharing mode, uses RestFul API interfaces to provide external data access services and provides users with authentication mechanisms and data acquisition mechanisms.


### 7.2. Teaching Resource Sharing System

The teaching resource sharing system provides real-scene, virtual simulation, development kit, and carrier sharing services. Real-scene resource sharing provides four-dimensional real-scene material library management, virtual simulation resource management, and animal husbandry course resource management functions. The real-scene material library includes horses, cattle, sheep, pigs, chickens, feed factories, and store processing plants as four-dimensional real-scene resources. Virtual simulation resource management has to provide pig, cattle, sheep, and biological safety throughout the whole process of virtual simulation resources and animal husbandry experiments and models. The animal husbandry course resources sharing uses real-scene, virtual simulation, and intelligent breeding resources to build new animal husbandry courses and provide sharing services.


## 7.1. Data Resource Sharing System

The animal husbandry data asset sharing system mainly shares the data in the real data resource catalog after fusion and adopts three modes of sharing: multitenant sharing, exchange space sharing, and API sharing.Multitenant sharing is mainly used for data exchange between multitenants on the big data platform. In this mode, the exchanged data do not move physically. In this mode, the data are available, and the exchange does not move.Swap space sharing is mainly for large data development suite-sharing system users. When the user needs to get a real data set for big data analysis and calculation, the issuer of the detailed data contains sensitive information and should not be open to the public to share, but the subscriber data need to be calculated using the data as a data source reasoning out a certain result, which requires security exchange. Secure exchanges store subscription publishing data in secure swap space for transparent calculations and send the results to subscribers.API sharing, the most common data sharing mode, uses RestFul API interfaces to provide external data access services and provides users with authentication mechanisms and data acquisition mechanisms.


## 7.2. Teaching Resource Sharing System

The teaching resource sharing system provides real-scene, virtual simulation, development kit, and carrier sharing services. Real-scene resource sharing provides four-dimensional real-scene material library management, virtual simulation resource management, and animal husbandry course resource management functions. The real-scene material library includes horses, cattle, sheep, pigs, chickens, feed factories, and store processing plants as four-dimensional real-scene resources. Virtual simulation resource management has to provide pig, cattle, sheep, and biological safety throughout the whole process of virtual simulation resources and animal husbandry experiments and models. The animal husbandry course resources sharing uses real-scene, virtual simulation, and intelligent breeding resources to build new animal husbandry courses and provide sharing services.


## 8. Business Cloud Service Platform Design

Relying on the animal husbandry data center platform and animal husbandry resource sharing platform, the cloud service platform for the animal husbandry business has been developed, including cloud teaching, cloud scientific research, and cloud breeding system.The cloud service platform mainly includes cloud teaching, cloud scientific research, and cloud breeding systems, which provides cloud collaboration services, cloud diagnosis and treatment services, cloud identification services, cloud mall services, pig cloud services, cattle cloud services, sheep cloud services, and education and training cloud services for users. The cloud service platform has been developed based on the OpenStack framework and provides different services and management in IaaS, PaaS, and SaaS.Cloud teaching is composed of four-dimensional creation, immersive display, teaching and training, AI teaching and research, teaching management, and teaching evaluation system functions. It designs immersive teaching, live interactive teaching, and panoramic interactive live teaching for users, helps practice teaching, supports virtual simulation experiment teaching, virtual practice training, and other applications. Carry out comprehensive disruptive innovation, ranging from the smart open classroom to cloud teaching and research, AI teaching and management, and intelligent evaluation.Cloud scientific research provides a multitenant Hadoop big data sharing and analysis environment supported by IaaS for scientific research users. Data sets are obtained in the secure sharing mode, and various analysis tools provided by system integration are used to carry out big data scientific research. The system supports a variety of mainstream relational databases, such as Oracle, MySQL, DB2, Teradata, SQL Server, PostgreSQL, Sybase, DB2, and others. In addition, it also supports Hive of the Hadoop platform and Ali’s Open Data Processing Service (ODPS), analysis database service (ADS), component and RDS for MySQL, etc. The big data development suite sharing system includes a query analyzer, task scheduler, data integration, metadata management, machine learning, microservice API, rights management, BI analysis, and other functions. Users can use the development suite to complete big data research work without engineers in the field of big data.The cloud farming system is composed of daily production performance analysis, production performance index analysis, production comparative analysis, and behavior analysis. Production index analysis includes 16 analyses and applications that can directly reflect the production performance index and guide the production process. Behavior analysis includes visual-based individual identification, piglet count, estrus behavior identification, rumination behavior, abnormal behavior, crawling behavior, hoof disease, and childbirth behavior identification. Support access to RTMP real-time video streaming, RTSP real-time video streaming, USB real-time video streaming, and MP4 video files, compatible with mainstream real-time video streaming protocols, physical hardware devices, and video files. The system can access real-time analysis of real-time video streaming and video files. After real-time video stream connection analysis, the screen delay is less than 10 seconds compared with the main screen, which provides monitoring decision support for the intelligent breeding management service.


## 9. Conclusions

With the continuous integration and boost of intelligent technology into the education industry, intelligent education is imperative. In order to solve the problem of training compound talents in agriculture and forestry universities and the implementation of “new agricultural science,” this paper studies the cloud animal husbandry platform and raises the education for talents in the field of animal husbandry with intelligent technology from the theoretical level to the application level. The cloud livestock platform adopts a cloud two-wing multilevel architecture design, which is composed of a data center platform, a resource sharing platform, and a business cloud service platform. In particular, the data quality control in the process of data collection is studied, and the error data are eliminated by the method of gross error estimation. Through the portrait of breeding enterprises, the real breeding environment is moved to the classrooms of colleges and universities so that students can enter the enterprises to carry out online practice activities with virtual avatars. The platform has upgraded traditional multimedia teaching, developed new real-scene teaching, virtual simulation teaching, and digital online teaching of smart farming and provided smart farming technology services for enterprises. The platform has been applied to relevant universities and enterprises, which verifies the availability, stability, and reliability of the platform and has good research value. The cloud livestock platform still has some shortcomings, which need further research and improvement in the future. For example, the number of access farms is limited, the construction of teaching resources is not rich enough, and the services provided for agricultural and animal husbandry enterprises are not diverse enough.



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*Source: 4940511-2022-09-06.xml*