为便于养殖户实时了解规模化牲畜养殖舍环境质量,提高牲畜养殖自动化水平,文章基于C#Winform设计一款牲畜养殖环境智能监控软件。首先,根据养殖需求给出监控软件整体设计方案;其次,对数据库系统进行设计;再次,对监控软件核心功能进行详...为便于养殖户实时了解规模化牲畜养殖舍环境质量,提高牲畜养殖自动化水平,文章基于C#Winform设计一款牲畜养殖环境智能监控软件。首先,根据养殖需求给出监控软件整体设计方案;其次,对数据库系统进行设计;再次,对监控软件核心功能进行详细设计;最后,对监控软件的功能与可靠性进行验证。试验结果表明:牲畜养殖环境智能监控软件能够通过通用串行总线(Universal Serial Bus,USB)稳定接收下位机上传的数据,让养殖户随时查看各养殖舍温度、湿度、光照强度、氨气浓度、调控设备运行状态以及数据变化趋势等信息,有效实现环境数据可视化操作与科学管理,满足规模化牲畜智能化养殖需求。展开更多
The capacity of livestock breeding in China has increased rapidly since 1949, and the total output of meat, poultry and eggs maintains the world's top first in recent 20 years. Livestock emissions and pollution is...The capacity of livestock breeding in China has increased rapidly since 1949, and the total output of meat, poultry and eggs maintains the world's top first in recent 20 years. Livestock emissions and pollution is closely associated with its population and spatial distribution. This paper aims to investigate the spatial patterns of livestock and poultry breeding in China. Using statistical yearbook and agricultural survey in 2007, the county-level populations of livestock and poultry are estimated as equivalent standardized pig index (ESP), per cultivated land pig index (PCLP) and per capita pig index (PCP). With the help of spatial data analysis (ESDA) tools in Geoda and ArcGIS software, especially the Moran's I and LISA statistics, the nationwide global and local clustering trends of the three indicators are examined respectively. The Moran's I and LISA analysis shows that ESP and PCP are significantly clustering both globally and locally. However, PCLP is clustering locally but not significant globally. Furthermore, the thematic map series (TMS) and related gravity centers curve (GCC) are introduced to explore the spatial patterns of livestock and poultry in China. The indicators are classified into 16 levels, and the GCCs for the three indicators from level 1 to 16 are discussed in detail. For districting purpose, each interval between gravity centers of near levels for all the three indicators is calculated, and the districting types of each indicator are obtained by merging adjacent levels. The districting analysis for the three indicators shows that there exists a potential uniform districting scheme for China's livestock and poultry breeding. As a result, the China's livestock and poultry breeding would be classified into eight types: extremely sparse region, sparse region, relatively sparse region, normally sparse region, normal region, relatively concentrated region, concentrated region and highly concentrated region. It is also found that there exists a clear demarcation line between the concentrated and the sparse regions. The line starts from the county boundary between Xin Barag Left Banner and Xin Barag Right Banner, Inner Mongolia Autonomous Region to the west coast of Dongfang County, Hainan Province.展开更多
文摘为便于养殖户实时了解规模化牲畜养殖舍环境质量,提高牲畜养殖自动化水平,文章基于C#Winform设计一款牲畜养殖环境智能监控软件。首先,根据养殖需求给出监控软件整体设计方案;其次,对数据库系统进行设计;再次,对监控软件核心功能进行详细设计;最后,对监控软件的功能与可靠性进行验证。试验结果表明:牲畜养殖环境智能监控软件能够通过通用串行总线(Universal Serial Bus,USB)稳定接收下位机上传的数据,让养殖户随时查看各养殖舍温度、湿度、光照强度、氨气浓度、调控设备运行状态以及数据变化趋势等信息,有效实现环境数据可视化操作与科学管理,满足规模化牲畜智能化养殖需求。
基金Key Program of Special Science Research in Environmental Protection Public Welfare Industry, No.201009017Research Plan of LREIS, No.088RA900KAKey Project for the Strategic Plan in IGSNRR, CAS, No.2012ZD010
文摘The capacity of livestock breeding in China has increased rapidly since 1949, and the total output of meat, poultry and eggs maintains the world's top first in recent 20 years. Livestock emissions and pollution is closely associated with its population and spatial distribution. This paper aims to investigate the spatial patterns of livestock and poultry breeding in China. Using statistical yearbook and agricultural survey in 2007, the county-level populations of livestock and poultry are estimated as equivalent standardized pig index (ESP), per cultivated land pig index (PCLP) and per capita pig index (PCP). With the help of spatial data analysis (ESDA) tools in Geoda and ArcGIS software, especially the Moran's I and LISA statistics, the nationwide global and local clustering trends of the three indicators are examined respectively. The Moran's I and LISA analysis shows that ESP and PCP are significantly clustering both globally and locally. However, PCLP is clustering locally but not significant globally. Furthermore, the thematic map series (TMS) and related gravity centers curve (GCC) are introduced to explore the spatial patterns of livestock and poultry in China. The indicators are classified into 16 levels, and the GCCs for the three indicators from level 1 to 16 are discussed in detail. For districting purpose, each interval between gravity centers of near levels for all the three indicators is calculated, and the districting types of each indicator are obtained by merging adjacent levels. The districting analysis for the three indicators shows that there exists a potential uniform districting scheme for China's livestock and poultry breeding. As a result, the China's livestock and poultry breeding would be classified into eight types: extremely sparse region, sparse region, relatively sparse region, normally sparse region, normal region, relatively concentrated region, concentrated region and highly concentrated region. It is also found that there exists a clear demarcation line between the concentrated and the sparse regions. The line starts from the county boundary between Xin Barag Left Banner and Xin Barag Right Banner, Inner Mongolia Autonomous Region to the west coast of Dongfang County, Hainan Province.
