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Intelligent monitoring and control system for pig intensive breeding environment- 【鑫 芯 物 联】

来自: 成都鑫芯电子科技有限公司 浏览次数:2157 2017-08-15From : Chengdu Xinxin Electronic Technology Co., Ltd.

The intelligent monitoring system for pig intensive breeding environment is designed to meet the needs of pig intensive healthy breeding. It develops a new type of integrated integrated sensing layer products, intelligent data acquisition devices and control devices that are multifunctional and integrated, and builds a life cycle quality and safety system for pig healthy breeding. Through the application of the Internet of Things technology , the automation, informatization, and intelligentization of pig intensive breeding will be significantly improved, the production and quality of pigs will be improved, and the development of modern agriculture will be promoted; at the same time, the related Internet of Things industry will achieve large-scale development.

The intelligent monitoring system for pig intensive breeding environment includes intelligent monitoring system for breeding environment, intelligent monitoring, early warning and diagnosis system for pig disease, digital pig fine feeding system, pig breeding and breeding system, pig life cycle quality traceability system, pig intensive healthy breeding Networked application software platform.

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1. Intelligent monitoring system for breeding environment:

The pig house environment intelligent monitoring system is based on the Internet of Things technology . It monitors the pig's growth environment information online, including air temperature and humidity , NH3, H2S, CO2, and illumination. The intelligent wireless control equipment automatically regulates the growth environment of the pig house. In order to achieve healthy growth and reproduction of pigs, sow productivity is improved, and high-quality pork, pig hair and other products are provided, thereby improving economic benefits.

2. Intelligent monitoring, early warning and diagnosis system for swine disease:

Based on the Internet of Things technology, an intelligent monitoring, early warning and diagnosis system for swine diseases is established. Through remote sensing, image and video monitoring systems for individual pigs, real-time monitoring of pig growth is used to detect epidemic diseases in time, and early warning is provided through system models and experts. Information and diagnostics.

Systematic analysis and collection of domain knowledge, establishment of pig disease diagnosis and treatment model, pig disease early warning model and expert consultation algorithm; development of epidemic disease diagnosis system, using modern information technology such as 3G, M2M, call center, etc. The pig disease diagnosis in multiple modes, such as remote consultation, supports a variety of communication methods such as the Internet, telephone, mobile phone, and short message; the pig epidemic warning system is developed to realize early detection and early warning of the epidemic situation to control the spread of various infectious diseases.

3. Digital hog fine feeding system:

Developed a digital hog fine feeding system. Based on the production status of the farm, the nutritional requirements based on breeds, hybrid types, production characteristics, physiological stages, diet structure, climate, environmental temperature and humidity as variables were automatically matched and compared. Perform digital simulation of pig breeding process and production test verification on the digital model of similar pigs in the central database, in order to affect the demand and production performance of breeding pig breeding, and use different environmental factors as variables to simulate changes in pig performance and physiological indicators. To achieve digital fine feeding.

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4. Pig breeding system:

In order to improve the breeding efficiency of breeding pigs and sows, shorten the slaughter cycle, reduce the number of breeding pigs, and thus reduce production costs and feed consumption, use pig network combined breeding technology, sensor technology, predictive optimization model technology, radio frequency identification technology, and genetic optimization Principles: In pig breeding, scientific selection, optimized breeding, and scientific monitoring of the estrus cycle of female animals are carried out.

Pig life cycle quality traceability system

The whole life cycle quality and safety system of pig health breeding has been constructed. The front-end data collection end is based on RFID technology to record the whole process of pig breeding, slaughter, circulation and other links. The back-end data management software end stores the data collected by the front-end. , Statistics, analysis, distribution, etc., while docking with the management system of relevant government regulatory departments, to achieve the positive tracking of the entire life cycle information from pig farming to meat retail terminals, and also to achieve the relevance of meat retail terminals to pig farming. Reverse traceability of information.

6. IoT application software platform for pig intensive healthy breeding:

Through the platform, real-time and high-precision breeding environments can be used to collect environmental parameters, receive alarms for abnormal information, intelligent automatic control, and notification of linkage operation information.

Managers can also access the Internet at any time and place through the platform. As long as they can access the Internet and have a browser or client, they can access the environmental data in the pig house and implement remote control. Monitoring terminals include computers, mobile phones, and touch screens.

The intelligent monitoring and control system for the pig intensive breeding environment can not only realize the 24-hour monitoring of the pig breeding environment, but also control the environment without being on duty to keep the environmental parameters in a certain suitable range. It is a good choice for pig farm customers.


August 15, 2017

Chengdu Xinxin Electronic Technology Co., Ltd. Editorial Department

Editor: Little Devil in Gourd


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