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Among many types of big data , non-spatial big data is the most connected industry and can span time and space and geographical restrictions. It is precisely because "spatial big data" is integrated into all walks of life like water and electricity. In the future, the construction of " digital China " will rely on the thorough connection of the underlying data, thereby creating a service-oriented government that benefits the people and improves the people's livelihood system. The expression of agricultural resources and industrial development on the spatial map allows agriculture to sense environmental impact factors in real time and realize disaster early warning.
Poverty situation data "above", spatial big data to achieve precise poverty alleviation:
Utilizing the latest and most comprehensive data of poor households, combined with geographical location for visual display, unified display of population, public services, tourism and other data on the poverty map, pinpointing the causes and industrial opportunities of poverty, establishing a "basic data support platform, poverty alleviation command The four major platforms of dispatching platform, project fund management platform, and poverty alleviation work inspection platform. Spatial big data makes poverty alleviation more tailored to local conditions, suits local needs, and improves practicality.
Industrial big data involves multiple types of rich data, and structured and unstructured data doping. This requires accurate classification and labeling of source data in order to make the data "top image" into a spatial location language.
You can also use Postgres technology, in addition to storing general structured data, you can also store JSON-type data (for processing unstructured data), and provide a large number of methods for this type of data, making additions, deletions, changes, and investigations become much easier.
Big data of ride-hailing space in the whole agricultural process
Agricultural big data mainly refers to big data from fields such as crop planting area and field distribution, water and land resources status, fertilizer use status, agricultural machinery operation status, total grain production and yield, grain logistics processing and consumption, etc., which can be related to spatial location information. contact. After showing the farmland area, water resources, agricultural product output and distribution on a "one map", you can accurately understand the development status and outstanding problems of the agricultural industry in a certain area. Combined with other industry distribution and one map, you can use other Industry drives the growth of agriculture, develops the rural economy as a whole, and provides overall solutions .
According to the relevant person in charge of Beijing Jiage Tiandi Technology Co., Ltd., in terms of "lot management", which is the most basic and core part of smart agriculture and agricultural big data, high-resolution remote sensing images can be used according to the characteristics of the texture and type of cultivated land. Determine the location of the plot in real time, identify the boundaries of the plot, calculate the plot and planting area, and store it in the cloud.
On this basis, the terrain, slope, comprehensive soil properties, and crop growth and yield data of each year can be combined. From the joint development of land management and crop management, instead of " feeding by the sky ", but "feeding by the sky", re-approach agricultural resource management with spatial big data thinking, closely integrate with agricultural resources, and accurately position each inch of land as the most suitable Farming methods.
With regard to the problems of small subsidies and low revenues faced by the agricultural industry, how to reduce user costs has become one of the topics of smart agriculture. The relevant person in charge said that the overall solution provides more than just data, but allows customers to obtain valuable data in the shortest time through various visual tools.
In the future, with the help of remote sensing imagery, satellite data, and artificial intelligence deep learning capabilities, environmental factors can also be collected from multiple sources. Relevant persons in charge take pest management as an example, bringing together existing types of pests, targeted plant protection programs, special pesticides, etc. The database can be combined with crop growth and change in different lands and different periods to formulate the best plant protection plan and early warning plan for plant diseases and insect pests, to achieve intensive and intelligent production in China's agriculture .
March 23, 2018